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X-ORIGINAL-URL:https://ee.iisc.ac.in
X-WR-CALDESC:Events for EE
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TZID:Asia/Kolkata
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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231222T100000
DTEND;TZID=Asia/Kolkata:20231222T113000
DTSTAMP:20260404T030553
CREATED:20231212T083929Z
LAST-MODIFIED:20231212T083929Z
UID:241198-1703239200-1703244600@ee.iisc.ac.in
SUMMARY: Ph.D. Thesis Defence: Resource-Aware State-Triggered Networked Control Systems
DESCRIPTION: Ph.D. Thesis Defence.Speaker: Anusree RajanSupervisor: Pavankumar TallapragadaDate and Time: Friday\, 22 December 2023\, at 10 amVenue (Hybrid): MMCR (C241)\, EE DepartmentMS Teams LinkTitle: Resource-Aware State-Triggered Networked Control SystemsAbstract:Networked control systems are very popular nowadays\, with different fields of applications such as environmental monitoring\, industrial automation\, military surveillance\, and disaster management. State-triggered control is a commonly used control method in the field of networked control systems owing to its advantage of efficient utilization of resources while simultaneously achieving control objectives. In this control method\, the communication times are opportunistic and implicitly determined by a triggering rule. In addition\, state-triggered control can be designed with provable guarantees for a variety of systems\, including nonlinear systems\, distributed systems\, and multi-agent systems\, and for a variety of control objectives\, such as stabilization\, filtering\, trajectory tracking\, distributed optimization\, multi-agent consensus\, and model predictive control.However\, the question of how to theoretically analyze the resource usage by a state-triggered control system is not well understood even in the simplest settings. Understanding inter-event times generated by a triggering rule is necessary for higher level planning and scheduling for control over shared or constrained resources as well as for the analytical quantification of the usage of communication or other resources compared to a time-triggered controller. This motivates the first part of the thesis\, in which\, we provide a systematic way to analyze the evolution of inter-event times in planar linear systems\, under a general class of scale-invariant event triggering rules. We provide a sufficient condition for the convergence or non-convergence of inter-event times to a steady state value. We also provide a sufficient condition for the asymptotic average inter-event time to be a constant for all non-zero initial states of the system. Then\, under a special case\, we comment on the asymptotic behaviour of the inter-event times\, including on whether the inter-event times converge to a periodic sequence. Later\, we extend our analysis of inter-event times to linear systems under region-based self-triggered control. In this control method\, the state space is partitioned into a finite number of conic regions and each region is associated with a fixed inter-event time. We provide several necessary conditions and sufficient conditions for the local convergence of inter-event times to a constant or to a given periodic sequence.In the second part of this thesis\, we consider a design problem. Most of the existing event- or self-triggered controllers are designed using sampled-data zero-order-hold (ZOH) control input. However\, many communication protocols used in networked control systems\, such as TCP and UDP\, have a minimum packet size. So\, ZOH control may lead to under-utilization of each packet while also increasing the number of communication instances. On the other hand\, use of non-ZOH control leads to better utilization of the minimum payload of each packet while also reducing the overall number of communication instances. With these motivations\, we propose a new control method called event-triggered parametrized control (ETPC). In this control method\, between two consecutive events\, each control input to the plant is a linear combination of a set of linearly independent scalar functions. At each event\, the coefficients of the parameterized control input are chosen to minimize the error in approximating a continuous time control signal and then they are communicated to the actuator. We\, first\, showcase this method by focusing on the specific problem of stabilization of linear systems. We design two event-triggering rules that guarantee global asymptotic stability of the origin of the closed loop system under some conditions on the model uncertainty. Later\, we use a similar idea to propose an event-triggered polynomial control method for trajectory tracking of unicycle robots. We design an event-triggered parametrized controller for trajectory tracking by a unicycle robot and provide guarantees for uniform ultimate boundedness of the tracking error.Due to time limitations\, the defence will focus on the second part of the thesis.——————————- All are Welcome —————————-
URL:https://ee.iisc.ac.in/event/ph-d-thesis-defence-resource-aware-state-triggered-networked-control-systems/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231221T110000
DTEND;TZID=Asia/Kolkata:20231221T130000
DTSTAMP:20260404T030553
CREATED:20231218T044331Z
LAST-MODIFIED:20231218T044331Z
UID:241280-1703156400-1703163600@ee.iisc.ac.in
SUMMARY:[EE/CPS Seminar] Apurv Shukla - Differentially Private Online Resource Allocation
DESCRIPTION:EE/CPS SeminarSpeaker: Apurv ShuklaPostdoctoral Associate at Texas A&M UniversityTitle: Differentially Private Online Resource AllocationDate: Thursday\, 21 December 2023Time: 11amVenue: EE MMCR (C241)Abstract:We consider an online resource allocation problem when thedecision-maker wishes to preserve the privacy of incoming arrivals andrewards. We present a family of algorithms based on private meanestimation and primal-dual schemes that achieve optimal utilityguarantees. We establish utility and privacy bounds on the behavior ofalgorithms in this family under minimal assumptions on the arrivalprocess. These results are further corroborated by a set of numericalexperiments that demonstrate the tightness of our bounds.Biography of the speaker:Apurv is a Postdoctoral Associate at Texas A&M working with Prof. Le Xieand Prof. PR Kumar. His research interest lies in learning and controlwith applications in power systems. He obtained his PhD from ColumbiaUniversity and his bachelor’s degree from IIT Kharagpur.
URL:https://ee.iisc.ac.in/event/ee-cps-seminar-apurv-shukla-differentially-private-online-resource-allocation/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231211T110000
DTEND;TZID=Asia/Kolkata:20231211T130000
DTSTAMP:20260404T030553
CREATED:20231211T040647Z
LAST-MODIFIED:20231211T040647Z
UID:241170-1702292400-1702299600@ee.iisc.ac.in
SUMMARY:[Talk] TCE Lecture Series on Power Systems
DESCRIPTION:Title:  Control and Management of Electric Grid with Inverter-Based Resources (IBRs)  \nSpeaker: \nProf Sukumar Kamalasadan \nProfessor and Distinguished Scholar \nDepartment of Electrical & Computer Engineering \nThe University of North Carolina at Charlotte \n Abstract: \nIn this talk\, the management and control of the electric grid with renewables is discussed. Functions that include renewable energy resources\, energy storage\, advanced management systems\, and the operational framework is illustrated. Further\, operational methods including newer management and control tools are presented with a special emphasis on supply-side management functions. Finally\, evolving methods and pathways of electric grid management that integrate data sets generated from sensors and meters are discussed with a special emphasis on the overall reliability and resiliency of the electric grid with renewable energy resources. \n \n \nShort Biography: \nSukumar Kamalasadan is a Professor and Distinguished Scholar of electric power engineering at the University of North Carolina at Charlotte (UNCC) and the Director of power energy and intelligent systems lab (PEISL) within the Energy Production and Infrastructure Center (EPIC) at UNCC. He received his Ph.D. degree in electrical engineering from the University of Toledo\, OH in 2004. His research interests include inverter-based resources modeling and integration\, data-driven approaches to power grid modernization\, smart grid\, microgrid\, power system operation and optimization\, and power system dynamics\, stability\, and control. Prof. Kamalasadan’s research for the last 20 years has resulted in tools and methods that have a high-level impact on electric utility modernization with a fleet-wide deployment of his tools that enabled modern grid management and control towards 100% integration of renewable energy. His research work has secured more than $12M in grants and contracts notably from the US Department of Energy\, National Science Foundation (NSF)\, Siemens Energy\, Duke Energy Corporation\, Schweitzer Engineering Lab\, and several other industries. He is the chief architect of Duke Energy Smart Grid Laboratory at UNCC\, a $5M facility. Prof. Kamalasadan has co-authored more than 250 refereed journal and conference articles and has received several awards from IEEE and NSF including the National Science Foundation CAREER award. He has delivered more than 100 talks in the form of tutorials\, keynotes\, panels\, and webinars/workshops at various international IEEE conferences.
URL:https://ee.iisc.ac.in/event/talk-tce-lecture-series-on-power-systems/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231201T150000
DTEND;TZID=Asia/Kolkata:20231201T170000
DTSTAMP:20260404T030553
CREATED:20231128T085732Z
LAST-MODIFIED:20231128T085732Z
UID:241165-1701442800-1701450000@ee.iisc.ac.in
SUMMARY:[EE Ph. D. Colloq.] - Dual Mode Operation of Grid-tied Inverters: Modeling\, Islanding Detection\, and Transfer of Control
DESCRIPTION:Ph. D. Thesis ColloquiumStudent: Sugoto MaulikAdvisor: Prof. Vinod JohnDegree: PhDDate and Time: 03:00 PM\, 1st December 2023Place: MMCR EE\, IISc.=========================================Title: Dual Mode Operation of Grid-tied Inverters: Modeling\, Islanding Detection\, and Transfer of ControlAbstract: Increased penetration of renewable energy sources like solar PVs and wind is fundamentally altering the power flow dynamics in distribution networks. These localized forms of generation add redundancy to the power system and increase its load-handling capacity. However\, these advantages come at the cost of reduced stability and altered protection requirements. These distributed forms of generation (DGs) are interfaced with the power grid via power electronic converters operating at high bandwidths compared to conventional sources. While these offer higher performance\, but consequently lower the stability margins. An analytical framework is thus necessary for modeling and stability analysis of such systems. The dynamics involved in modeling a grid-tied DG system span a wide spectrum of frequencies. While simplified modeling can lead to inaccuracies\, an all-inclusive model leads to complex and unintuitive models. This work proposes a systematic approach to model the behavior of 3-phase AC grid-tied DG systems using dynamic phasors. Dynamic phasors allow for a state-space representation of the relevant dynamics.The developed state space model is used for the following:1.      Islanding detectionIslands are formed in 3-phase distribution networks when an active DG is disconnected from the grid. If undetected\, the DG continues to energize its local loads\, leading to safety concerns. In this work\, a state-feedback approach is developed for islanding detection\, which places a system pole in the right half plane (RHP). This ensures the destabilization of the islanded network and a zero non-detection zone. Methods for tuning of the control parameters to meet the system islanding detection requirements are proposed. The scheme is designed and implemented experimentally.2.      Transfer of ControlPost-island detection\, the DG is required to disconnect from the grid while ensuring uninterrupted power flow to its local loads. A control scheme involving a voltage control loop and grid current feed-forward is developed to achieve a fast transfer from grid-following to grid-forming mode of operation. The introduced voltage control loop ensures that rated voltage is maintained across the loads\, and the grid current feed-forward is used to minimize the transients during the transfer process. The method is designed and implemented in conjunction with the islanding detection scheme and verified experimentally with local loads.3.      Stability analysis of grid-tied DG systemsOwing to the formation of microgrids and weak grids in the distribution network\, the stability assessment of such networks becomes essential. This assessment is performed by extending the dynamic phasor-based model for islanded systems to model grid-tied systems as well. The developed model includes the dynamics of the PLL\, grid\, DG current levels\, and load. In addition to passive loads\, considered in the relevant literature\, the proposed model also incorporates the effect of constant power and constant current type power electronic loads. It is demonstrated\, analytically and experimentally\, that the presence of local loads has a stabilizing impact on the synchronization stability of a DG. Additionally\, an upper limit on the bandwidth of power-electronic type constant power loads is derived\, affirming the observation that high bandwidth loads lead to reduced system stability.All the proposed methods are validated on hardware prototypes that have been developed as a part of the work. ——————           ALL ARE WELCOME            —————
URL:https://ee.iisc.ac.in/event/ee-ph-d-colloq-dual-mode-operation-of-grid-tied-inverters-modeling-islanding-detection-and-transfer-of-control/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231129T160000
DTEND;TZID=Asia/Kolkata:20231129T173000
DTSTAMP:20260404T030553
CREATED:20231129T050107Z
LAST-MODIFIED:20231129T050107Z
UID:241167-1701273600-1701279000@ee.iisc.ac.in
SUMMARY:Talk: India Semiconductor Mission by Prof Tummala Rao
DESCRIPTION:Title: Next Gen\, Global- level and Large-scale Device\, Packaging and Systems R&D and Workforce development in India. \nSpeaker: Professor Tummala Rao is a distinguished IISc alumnus and an ISM\, India advisor. Former IBM Fellow and Founding Director of Georgia Tech 3D Systems Packaging Research Center. \nDate and Time: Wednesday\, November 29\, 2023. 4:00 PM \nVenue: MMCR Department of Electrical Engineering IISc \nTeams Link \nAbstract: India has not been a global player in semiconductors and packaging. But it can be with the new ISM initiative in manufacturing and R&D. Prof. Rao Tummala believes that India can transform its electronics by transforming its academics into industry-centric R&D culture\, based on the Georgia Tech model\, by performing massive global level R&D that India is capable of. But this R&D must be consistent with global industry needs in integrated systems packaging by highly- innovative\, young\, and energetic faculty and great executive Directors that India already has at its IITs and IISc. The proposal describes the strategic\, next- gen global level R&D\, education and skill development programs\, infrastructure needed for these and the resulting IP creation and exponential growth in startups to attract many global companies to make India globally competitive. Indian Government is committed to invest in next gen R&D at its academic institutions with the state-of-the-art facilities\, as well as state of the art facilities for technology development at ISRC thereby attracting companies both for R&D and manufacturing in both semiconductors and packages. With these investments\, India\, for the first time\, has all the programs necessary for it to be a global player in the short term and global leader in the long term in integrated systems. If such an investment by the GOI is matched by the industry in a consortium mode\, India can transform itself from its current design-centric to system centric with expertise and resources spanning from system design and architectures to system integration\, assembly and test to offer system foundry for the world. \nA proposal was developed by more than 50 academic faculty from India’s top 13 academic institution and 20  colleges and universities under the advice and leadership of Prof. Rao Tummala\, as a champion for vision\, strategy\, and programs\, based on a highly successful Georgia Tech model for leading-edge R&D from concept to commercialization\, education of large number cross-disciplinary students\, and industry partnership with about 100 global companies—all simultaneously. The Indian proposal is expected to result in 12 India-wide Centers of Excellence in 12 different strategic technologies funded by GOI and potentially another 12 COEs funded by the private industry. The proposal also involves partnership with more than 50 global semiconductor and packaging companies from around the globe and 24 global academic experts from Purdue\, Georgia Tech\, Penn State\, USC\, University of Arkansas\, Maryland\, Florida International and Illinois in the proposed 12 strategic research areas (SRAs). \nThe proposed R&D and workforce development programs are the best examples of how government\, universities and global companies can work together for mutual benefit. For the academic community\, the Indian universities will begin to perform global R&D and educate the needed workforce for the emerging semiconductor\, packaging\, and systems industry – the two most important strategic needs for the global industry. In so doing\, it will develop global academic faculty leaders and provide jobs for their students in India. For global academic collaborators from US\, Indian R&D provides additional opportunities\, in addition to educated students from India as their Ph. D students for the global industry\, India will develop most comprehensive\, large scale global- level R&D\, unlike most\, if not all countries\, and attract large number of companies to come to India\, just for R&D in a decade. For its investment\, Indian government can claim to have set up the complete ecosystem from research to technology development to manufacturing\, just like in advanced countries\, in semiconductors and packaging to grow its economy to capture a significant portion of $2T market\, to become 3rd largest economy by 2030. \n This program requires large number of faculty and students from electrical\, mechanical\, materials and chemical engineering disciplines \nBiography: Education: B.E: Indian Institute of Science. Ph.D.: U of Illinois \n\nIndustry: 25 years at IBM \n\n\n\nIBM Fellow and Director of Advanced Packaging Lab\nPioneered Industry 1st Plasma Display\nPioneered industry’s first LTCC for all RF applications.\nDeveloped Industry’s 1st 100-144 integrated chip package\, very much like today’s chip let package.\n\n\n\nAcademia: 28 Years at Georgia Tech as Distinguished & Chair Professor and Founding Director \n\n\n\n Founding Director of first and only   NSF Eng. Research Center in packaging in US pioneering System on Package vision\nCreated a model at Georgia Tech for a very large and successful global industry consortium.\nEducated 10\,000 engineers in more than 20 different packaging courses.\nGraduated 900 Ph. D & MS packaging engineers to supply to almost all electronic companies in US.\n\n\n\nProfessional Awards \n\n\n\nIEEE named him Father of Modern Packaging and created IEEE Rao Tummala Electronic Packaging Award\, a technical field award.\n Wrote > 800 papers\, 7 Textbooks and > 100 US patents.\nMember of National Academy of Eng.in US and India\, Fellow of IEEE\, &IMAPS\nPresident of IEEE CPMT 2000-2004 and President of IMAPS 1998-2000\n Distinguished Alumni of IISc\, U of Illinois and Distinguished faculty of Georgia Tech\n\n\n\nIndia; Now Advisor to Government of India and Championing large scale R&D and industry consortium in India. \n\nAll are welcome.
URL:https://ee.iisc.ac.in/event/talk-india-semiconductor-mission-by-prof-tummala-rao/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231129T100000
DTEND;TZID=Asia/Kolkata:20231129T110000
DTSTAMP:20260404T030553
CREATED:20231128T053238Z
LAST-MODIFIED:20231128T053238Z
UID:241163-1701252000-1701255600@ee.iisc.ac.in
SUMMARY:[Talk] Technology trends and innovation in HV transmission industry\,
DESCRIPTION:Talk \nTitle: Technology trends and innovation in HV transmission industry \nSpeaker: Dr. Manoj Pradhan\, Global R & D Manager HVDC\, Hitachi Energy\, Sweden \nDate: WEDNESDAY\, 29 November 2023 \nTime: 10:00-11:00 AM \nVenue: Seminar Hall\, HVE BUILDING \nCoffee will be served \nABSTRACT \nIn this presentation\, Dr. Pradhan\, R&D manager from Hitachi Energy will share technology trends in HV transmission industry and especially role of HVDC technology in enabling energy transition towards integration of renewable resources. He will provide a comprehensive view of how Hitachi Energy is playing a leading role in advancing the world’s energy system to be more sustainable\, flexible and secure. ​As the pioneering technology leader\, we collaborate with customers and partners to enable a sustainable energy future. \nBiography \n  \nManoj Pradhan joined ABB in 2008\, is now global R&D portfolio manager for HVDC at Hitachi Energy (formerly ABB)\, located in Sweden. He is responsible for technology\, product and solution readiness for both offshore and onshore HVDC projects. \nMr. Pradhan has represented Hitachi Energy in several international multiparty joint industrial initiatives (JIP). He is actively involved and leading discussions in several clean energy forum viz.\, European commission working group on offshore renewable\, EU Horizon InterOPERA and OceanGrid initiatives to enable EU’s renewable energy ambition. \nHe is a great promoter of sustainability and digitalization of power infrastructure. He has also led new business development projects in EV charging sector to align players in the ecosystem to make offerings to accelerate EV adoption. He has led cross business and customer engagement initiatives in decarbonization of steel industry. He has been board member of large research consortium on digitalization\, digital cellulose center. \nHe holds MSc. and PhD degrees in Electrical Sciences from Indian Institute of Science Bangalore\, India (2000-2004). He was senior researcher and postdoctoral research fellow at IIT Madras\, ETH Zurich\, Switzerland\, University of Queensland\, Australia. He is author of > 25 article in IEEE journals and international conferences and Inventor of > 25 granted patents and several high impact publications in CIGRE and IEEE. Mr. Pradhan holds management diploma from Wharton business school\, USA and diploma in global innovation management from Chalmers University of Technology\, Sweden. \nALL ARE WELCOME
URL:https://ee.iisc.ac.in/event/talk-technology-trends-and-innovation-in-hv-transmission-industry/
LOCATION:HV seminar Hall
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231124T150000
DTEND;TZID=Asia/Kolkata:20231124T170000
DTSTAMP:20260404T030553
CREATED:20231124T054903Z
LAST-MODIFIED:20231124T054903Z
UID:241160-1700838000-1700845200@ee.iisc.ac.in
SUMMARY:Physics-Aware AI for Enabling Grid Resiliency
DESCRIPTION:Title:  Physics-Aware AI for Enabling Grid Resiliency\n  \nSpeaker:  \nDr. Anurag K Srivastava \nRaymond J. Lane Professor and Chairperson\, \nLane Department of Computer Science and Electrical Engineering \nDirector\, Smart Grid REsearch and Analytics Lab (SG-REAL) \nWest Virginia University\, Morgantown\, WV\, USA \n\nDate: 24th November 2023\, 3 pm \n  \nVenue: MMCR\, EE Dept\, IISc \n  \n  \nAbstract: \nAvailability of data from massive sensors deployment in the cyber-physical electric grid enables new monitoring and control applications.  Advancement in artificial intelligence (AI) provides an opportunity to develop data-driven technique utilizing these datasets. Some possible applications include\, early alarm and diagnosis\, predicative analysis\, distributed and decentralized control. New applications need to consider physics-induced limits and high-performance requirement in a dynamic environment. Availability of additional sensor data brings its own challenges including data anomalies\, real time processing\, data fusion\, data management and cyber-security management.  Differentiating between data anomalies\, cyber events and physical system events can be very challenging due to the similar signatures. This talk will focus on limits of AI for dynamic power grid applications\, example of Physics-Aware Machine Learning (PAAI) applications to enhance situational awareness\, and associated challenges and opportunities for enabling power grid resiliency. \n  \n \n  \nSpeaker Bio: Anurag K. Srivastava is a Raymond J. Lane Professor and Chairperson of the Computer Science and Electrical Engineering Department at the West Virginia University. He is also an adjunct professor at the Washington State University and senior scientist at the Pacific Northwest National Lab. He received his Ph.D. degree in electrical engineering from the Illinois Institute of Technology in 2005. His research interest includes data-driven algorithms for power system operation and control including cyber-resiliency analysis. Dr. Srivastava high impact research projects resulted in tools installed at the utility control center supported for more than $50M by US Department of Energy\, National Science Foundation\, Siemens Corporate Research\, Electric Power Research Institute\, Schweitzer Engineering Lab\, Power System Engineering Research Center\, Office of Naval Research and several National Labs. He is an IEEE Fellow and the author of more than 360 technical publications including three books on power system security and resiliency as well as 3 patents.
URL:https://ee.iisc.ac.in/event/physics-aware-ai-for-enabling-grid-resiliency/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231114T143000
DTEND;TZID=Asia/Kolkata:20231114T163000
DTSTAMP:20260404T030553
CREATED:20231114T084042Z
LAST-MODIFIED:20231114T084042Z
UID:241155-1699972200-1699979400@ee.iisc.ac.in
SUMMARY:Ph.D. Thesis Colloquium
DESCRIPTION:Thesis Title              :     Developmental Studies on a Multistage Induction Coilgun-Based Electromagnetic  \n                                        Launcher \nStudent Name          :     Ranashree Ram \nAbstract \nThe archetypal chemical propellant-based launchers (e.g.\, guns\, missiles\, spacecraft launchers\, etc.) with their hot trailing plume has been widely deployed over the decades for various applications. However\, because of certain disadvantages of these systems and the physical limitations associated with their designs\, electromagnetic launchers (EMLs) seem to offer an alternative way forward as the next-generation hypervelocity (>3 km/s) launchers. The multistage induction coilgun is one such futuristic class of EMLs that works on the principle of electromagnetic induction between an array of coils (or drive coils)\, which are wound on a long barrel of appropriate length\, and an electrically conducting projectile (or armature) placed inside the barrel. Previously charged high-voltage capacitor banks are sequentially discharged into the coils through solid-state switches leading to the generation and flow of very high pulsed currents (kA) through the coils. Time-varying magnetic flux thus produced by the pulsed currents through the coils interact with the projectile inside and induce a resultant current on it. The propulsive electromagnetic force exerted on the projectile is a product of the current through the coil\, the induced current on the projectile\, and the mutual inductance. The “turn on” and “turn off” of the coils of the various stages must be precisely and appropriately synchronized during the multistage operation to achieve a higher projectile velocity and this makes its successful design and operation a challenge. Owing to its high confidentiality in defense and space applications\, not much can be known from the published works. In the present work a four-stage induction coilgun has been designed and developed in the laboratory. The research work presented in the thesis aims to understand the factors contributing to achieving a higher muzzle velocity for a projectile of a given mass while launching a payload with the coilgun. The projectile of a coilgun can be either sleeve-type (ring-shaped projectile) or solenoid-type (multi-turn projectile). \nThe author also designed and fabricated a high-speed infrared transmitter-receiver-based sensor (with 25 ns rise and fall time) to quickly sense the moving projectile (or armature) inside the barrel. The triggering instant of the subsequent stage coils of a multistage coilgun critically depends on the projectile’s position inside the barrel. The projectile will fail to achieve the highest muzzle velocity if the subsequent stage coils are not optimally triggered in a sequence. The fast-moving projectile through the barrel necessitates the fast sensing of its position inside the barrel. In addition\, the author has also designed\, developed\, and fabricated a high-speed gate driver circuit with a peak 25 kV DC isolation for the signal circuit from the high voltage power circuit within a compact space of the printed circuit board (PCB) to trigger the high-voltage SCRs used for triggering the pulsed power source of each stage of the coilgun.. \nThe large current flowing through each stage coil creates EMI problems in the coilgun. The EMI issues corrupt the sensor data\, which prevents successful sensing of the projectile’s position. Also\, EMI causes the SCRs to trigger the coils spuriously even when the projectile has not reached its optimal triggering position inside the coil. Synchronizing the triggering of stages by preventing the EMI issues is a significant challenge and is very important in successfully operating a multistage induction coilgun. The author could successfully synchronize the stages of the coilgun by preventing spurious triggering of the SCRs using appropriate EMI mitigation techniques. \n The influence of the capacitance of the capacitor bank used in the high voltage pulsed power supply on the optimum triggering position of the projectile inside the drive coil of the coilgun has been analyzed. An empirical relationship between the projectile velocity and the charging voltage of the capacitor bank has been formulated for the first time in this thesis. The subject of the study presented in this thesis also focuses on analyzing the parameters on which the efficiency of an induction coilgun depends and how it can be optimized. Study has been performed to optimize the shape and dimensions of the projectile to achieve the highest muzzle velocity. The dependency of the projectile motion on the flow of induced current in the subsequent stages has been analyzed. The study also focuses on establishing an approach to choosing a proper distance between the stages in a multistage induction coilgun. \nA comprehensive and explicit analysis has been performed to study and explain the reasons behind the differences in the optimum triggering positions of the projectile inside each stage coil and the achieved muzzle velocities for different arrangements of the drive coil current directions in a multistage induction coilgun. \n  \nMS Teams Link: 
URL:https://ee.iisc.ac.in/event/ph-d-thesis-colloquium-2/
LOCATION:HV seminar Hall
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231109T160000
DTEND;TZID=Asia/Kolkata:20231109T170000
DTSTAMP:20260404T030553
CREATED:20231109T040748Z
LAST-MODIFIED:20231109T040827Z
UID:241152-1699545600-1699549200@ee.iisc.ac.in
SUMMARY:EE Talk: The Role of Privacy-Preserving Computation Frameworks Enabling Cooperation in Business and Legal Contracts
DESCRIPTION:Title: The Role of Privacy-Preserving Computation Frameworks Enabling Cooperation in Business and Legal Contracts \nAbstract:  Collaborative data analytics and the usage of advanced software controls are core elements for transformation of the industrial systems and automotive systems of today to future ‘smart’ manufacturing and mobility. In many scenarios within Industry 4.0 systems\, innovation is limited due to data being siloed between competing business organizations or entities in an organization. Similarly\, the concern for lack of sufficient protection mechanisms limit several transformations within the mobility/automotive domain. Effective protection mechanisms can enable the usage of data sources without the loss of competitive edge and software controls without the fear of misuse. While modification of traditional IT security methods have been actively proposed for these domains\, we argue these are typically insufficient. In this talk\, we discuss the role of privacy-preserving computation frameworks as a technical supplement to business and legal contracts to enable cooperation. Additionally\, we highlight constraints for these systems (with a focus on automotive) that make the use of traditional methods challenging.  \nSpeaker: Shalabh Jain is a Senior Research Scientist with the Security and Privacy group at the Bosch Research and Technology Center in Pittsburgh. Prior to joining Bosch Research he worked as a hardware engineer for Texas Instruments. He has been actively working in the domain of security and privacy for automotive systems\, cloud systems\, Industry 4.0 and IoT networks. He is an associate at the Carnegie Bosch Institute and responsible for mentoring postdoctoral fellows in the domain security and privacy. He is a privacy evangelist within Bosch\, co-founding the Privacy Engineering Guild and organizing the annual Bosch Privacy Engineering Summit. Additionally\, he is the North America representative for Open Bosch Research\, working on establishing collaborations between startups and Bosch Corporate Research. He leads the Pittsburgh chapter of Automotive Security Research Group (ASRG)\, a non-profit organization for building the next generation of security experts in the automotive domain. He is He received his PhD and masters from University of Maryland\, College Park and his Bachelors degree from IIT Roorkee. \nVenue and Time: EE 1st floor\, MMCR\, 9th November\, Thursday 4 PM – 5 PM
URL:https://ee.iisc.ac.in/event/the-role-of-privacy-preserving-computation-frameworks-enabling-cooperation-in-business-and-legal-contracts/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20231018T100000
DTEND;TZID=Asia/Kolkata:20231018T110000
DTSTAMP:20260404T030553
CREATED:20231005T040158Z
LAST-MODIFIED:20231005T040230Z
UID:241106-1697623200-1697626800@ee.iisc.ac.in
SUMMARY:EE Seminar: Charging of Electric vehicles: Direct DC\, vehicle to grid & heavy duty
DESCRIPTION:Date & Time: Wednesday\, 18/10/2023\, 10 am – 11 am\nVenue: MMCR\, Dept. of Electrical Engineering (Hybrid Mode)\nFor online participants: Meeting Link\n\nTitle of the Talk: Charging of Electric vehicles: Direct DC\, vehicle to grid & heavy duty \n\n\nAbstract: In this talk\, we will investigate three critical technologies for electric vehicle charging\, namely\, direct DC charging with solar and battery\, vehicle-to-grid (V2G) technology and charging of heavy-duty vehicles. First\, we will look at (bidirectional) power converter topologies for solar-powered EV chargers and how to increase power density and efficiency. Then\, we will explore how EV batteries can be used as energy storage for the grid and how charging algorithms can help facilitate the mass EV-grid integration and reduce battery capacity fade. Finally\, we will study electrification of heavy-duty vehicles such as (trolley) busses and modular design of chargers for megawatt level charging of trucks. To conclude\, we will look at examples of current research at TU Delft in EV charging space.\n \nSpeaker Bio: Gautham Ram is an Assistant Professor of Electric Mobility in the Department of Electrical Sustainable Energy\, Faculty of EEMCS at TU Delft. His current research focuses on electric vehicles\, EV charging\, batteries and power electronics. He is currently involved in many research projects at national and EU level concerning electric mobility such as TULIPS\, Drive2X\, FLOW\, FlexEC\, FLEXInet. He is the coordinator and lecturer for the Massive Open Online Course (MOOC) on Electric cars on edX.org with ~215\,000 learners from 175 countries. He received his PhD from TU Delft in 2018 for the development of a solar-powered V2G electric vehicle charger and was later a postdoctoral researcher from 2017-19. Google scholar: https://scholar.google.co.in/citations?user=ri-Hp7cAAAAJ&hl=en
URL:https://ee.iisc.ac.in/event/ee-seminar-charging-of-electric-vehicles-direct-dc-vehicle-to-grid-heavy-duty/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230929T160000
DTEND;TZID=Asia/Kolkata:20230929T173000
DTSTAMP:20260404T030553
CREATED:20230913T053937Z
LAST-MODIFIED:20230918T040821Z
UID:241060-1696003200-1696008600@ee.iisc.ac.in
SUMMARY:[EE Faculty Colloquium] Overview of Research on Pulse Width Modulation\, Motor Control and Electrical Machines
DESCRIPTION:Title: Overview of Research on Pulse Width Modulation\, Motor Control and Electrical Machines at EE\, IISc \nSpeaker: Prof. G. Narayanan\, Professor\, Electrical Engineering\, IISc \nVenue: MMCR\, EE & Online Teams link \nTime: 4pm\, 29 September 2023 \nAbstract:\nThe talk will provide a brief overview of research on pulse width modulation (PWM)\, motor control and electrical machines\, which the speaker has been a part of\, in the department of electrical engineering over the past 2-3 decades. Research highlights will be presented on the following aspects: \n\nSpace vector based PWM for motor drives and four-quadrant voltage-source converters\nOffline optimized PWM for induction motor drives\nHigh power converters for motor drives and active front-end converters\nPower-electronic control of induction motor and its variants\nEffect of inverter dead-time and its compensation\nInduction motor drive-based emulation of wind turbine\nPower electronic based emulation of electrical machines\n\nRecent and on-going activities on the following with be discussed briefly: \n\nPermanent-magnet-free high-performance motors based on reluctance principle\nHigh-speed switched reluctance motors and contact-free electromagnetic bearings\nMulti-disciplinary initiatives involving multiple departments\, multiple institutions / organizations across technical domains\n\nSpeaker’s Bio:\nG. Narayanan received his BE from College of Engineering Guindy\, Anna University\, Chennai\, in 1992; MTech from IIT Kharagpur in 1994; and PhD from IISc in 2000. He has been on the faculty of EE\, IISc\, since August 2003. He researches in the areas of power electronics\, motor drives and electrical machines. He has taught / has been teaching multiple courses in these broad domains.\n\n* All are welcome * \n—————————————-
URL:https://ee.iisc.ac.in/event/ee-faculty-colloquium-overview-of-research-on-pulse-width-modulation-motor-control-and-electrical-machines/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230929T100000
DTEND;TZID=Asia/Kolkata:20230929T110000
DTSTAMP:20260404T030553
CREATED:20230925T092359Z
LAST-MODIFIED:20230925T092359Z
UID:241089-1695981600-1695985200@ee.iisc.ac.in
SUMMARY:[EE Thesis Defense] - "Speech Dereverberation Using Autoregressive Models of Sub-band Envelopes"\, Anurenjan\,
DESCRIPTION:Title Dereverberation of Speech Using Autoregressive Models of Sub-band Envelopes. \nSpeaker Anurenjan P. R.  \nFaculty Advisor: Dr. Sriram Ganapathy \nExaminer : Prof. Umesh S. (IITM) \nAbstract \nThe speech-based technologies are radically changing the way we interact with systems and how we access information. In many of these applications\, the users prefer to interact with the system through a far-field microphone without the nuance of a handheld or body-worn device. Examples of such applications are automated meeting analysis\, speech-based dictation systems\, hands-free interfaces for controlling consumer-products\, IoT\, virtual assistants in mobile phones and smart speakers. The major challenge in capturing speech from the far-field is the degradation of the signal quality due to reverberation. Reverberation refers to the delayed and weighted summation of the direct component of the speech signal with the reflected versions. This talk is focused on developing methods for speech dereverberation\, i.e.\, restoring the functional quality of reverberated speech\, using autoregressive models of sub-band envelopes. signal analysis  \nThe technique of frequency domain linear prediction (FDLP) is used for finding the autoregressive model parameters.  The FDLP is the frequency domain dual of the conventional Time Domain Linear Prediction (TDLP). Just as the TDLP estimates the spectrum of a signal\, the FDLP estimates the temporal envelopes of the signal using an autoregressive model. We apply the FDLP approach to the sub-bands of speech signal that are distributed in the mel-scale. \nThis talk will describe two broad directions for addressing issues in the far-field speech using the FDLP approach. In the first part of the talk\, we explore a front-end design for automatic speech recognition (ASR) applications that suppresses the reverberation artifacts in the FDLP envelope. In the second part of the thesis\, we develop a speech enhancement model using the envelope and carrier decomposition given by the FDLP technique. \nIn the design of the ASR front end\, I will discuss a novel approach for 3-D acoustic modeling framework\, where the spatio-spectral features from all the sub-band channels are extracted. The features that are input to the 3-D CNN are extracted by modeling the signal peaks in the spatio-spectral domain using a multi-variate autoregressive modeling approach. In the subsequent part of this section\, I will describe a neural model for speech dereverberation using the long-term sub-band envelopes of speech. The neural dereverberation model estimates the envelope gain\, which when applied to reverberant signals\, allows the suppression of the late reflection components. The de-reverberated envelopes are used for feature extraction in speech recognition. The key novelty in this model is the joint learning of the reverberation and the ASR system. In these ASR experiments using the proposed framework\, we illustrate significant performance gains over previously proposed front ends.  \nThe second part of the thesis deals with the FDLP based speech dereverberation for enhancement applications\, where the goal is to restore the audible quality of the speech signal. For this task\, we decompose the sub-band speech signal into the constituent envelope and carrier part. A dereverberation neural model is designed that attempts to enhance the envelope and carrier signals jointly. Further\, joint learning of the speech enhancement model with the end-to-end ASR model is proposed with a single neural framework. The proposed model therefore can generate improved audio quality and provide robust representations for far-field ASR. Finally\, I will illustrate the subjective quality improvement of the audio signal as well as the improvement in ASR performance obtained by the proposed envelope-carrier model.     \nAcknowledgement This work was partly supported by project grants from Samsung Research India\, Bangalore and the College of Engineering\, Trivandrum\, Kerala.  \nBio Mr. Anurenjan is a PhD student at the LEAP lab\, Electrical Engineering\, IISc. He is also working as Assistant Professor in Government Engineering College\, Idukki. Mr. Anurenjan completed his Bachelors in Technology from Government Engineering College\, Barton Hill\, Trivandrum\, Kerala in 2006 and his Masters in Technology from College of Engineering\, Trivandrum\, Kerala in 2008. He joined the LEAP lab as a PhD candidate under AICTE-QIP program in the year 2017. He hails from Trivandrum district of Kerala. He is interested in signal processing\, machine learning and speech processing. Mr. Anurenjan is a member of IEEE SPS and the ISCA. During his free hours\, Mr. Anurenjan likes to play badminton and goes for swimming.  \n——- \nAll are invited. Coffee/Tea will be served before the talk. 
URL:https://ee.iisc.ac.in/event/ee-thesis-defense-speech-dereverberation-using-autoregressive-models-of-sub-band-envelopes-anurenjan/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230927T160000
DTEND;TZID=Asia/Kolkata:20230927T170000
DTSTAMP:20260404T030553
CREATED:20230926T041251Z
LAST-MODIFIED:20230926T041251Z
UID:241091-1695830400-1695834000@ee.iisc.ac.in
SUMMARY:PhD Thesis Colloquium - Subhas Chandra Das\, EE (ERP)
DESCRIPTION:Title: Experimental Investigations on Switching Behaviour of Traction-Grade IGBTs over Wide Operating Conditions\n\nSubhas Chandra Das (PhD -ERP) \n\nSupervisor: Prof. G. Narayanan\nDate and Time: 27 Sep 2023 (Wednesday)\, 4 pm – 5 pm\nVenue: MMCR\, EE Department (Hybrid mode)\n\nTeams meeting link:\n\nAbstract\n\n\nInsulated gate bipolar transistors (IGBTs) are the dominant power semiconductor devices in high power applications\, such as\, locomotive traction and megawatt-level renewable energy systems. Power electronic converters in such applications are expected to have a long-life span of about 20-30 years. Hence\, efficiency and reliability of these converters are very important. IGBT switching behavior has a direct influence on both power conversion efficiency and system reliability. \nThe various switching characteristics parameters of IGBTs\, which are available in the respective device datasheets\, are limited to certain operating conditions. For an example\, the switching characteristic parameters are available for only one or two DC link voltages; however\, in applications such as diesel-electric locomotives\, IGBTs have to operate over a wide range of the DC link voltages. Similarly\, the characteristic parameters are available at only one or two junction temperatures (e.g. 25 oC and 125 oC); but\, the IGBTs in traction and wind energy systems have to operate over wide range of temperatures including sub-zero ambient temperatures. \nIn this work\, switching behavior of IGBTs of four different makes are studied experimentally over a wide range of operating conditions. The load current is considered upto 1.667p.u.\, where 1 p.u corresponds to the rated current of the IGBTs. The range of DC link voltage considered is from 0.571 p.u. to 1.321p.u.\, where 1.0 p.u. is the nominal voltage of the application. The junction temperature range is considered from -35 oC to +125 oC. The following are the major highlights of the research work: \n1. Generation of experimental data on switching behavior of IGBTs over wide range of operating conditions as mentioned above. \n2. The experimental data\, which are generated\, complement the technical information available in device datasheets. \n3. The experimental investigation are carried out on four traction-grade IGBTs of different makes and of comparable ratings to ensure that the findings of the study are applicable to reasonable cross-section of the available commercial devices. \n4. Experimental study on the switching behavior of an IGBT converter leg\, having top and bottom devices of two different makes\, and its comparison with the switching behavior of a converter leg\, having complementary devices of the same make. \n5. Experimental study of the rise and fall times of the device switching voltages and currents\, both during turn-on and turn-off\, over the complete range of operating conditions. \n6. Evaluation of turn-on and turn-off switching energy losses as functions of load current\, DC link voltages and junction temperatures\, which are valid over the complete operating range. \n7. Experimental study of reverse recovery characteristics of anti-parallel diode of IGBTs with varying DC link voltage\, load current and junction temperatures. \n8. Experimental investigation on the effect of variations in DC link voltage\, load current and junction temperatures on device peak stress parameters\, namely\, peak device voltage\, peak device current\, peak rate of change of device voltage\, and peak rate of change of device current. \n9. Experimental study of sub-intervals of the turn-on switching delay\, turn-off switching delays and parameters related to the switching delay intervals over the complete operating range. \n10. Correlation of the various turn-on and turn-off switching parameters with junction temperatures based on the experimental data generated. \n11. Study of the consistency of the above correlations across different traction-grade devices of comparable ratings and different makes. \n12. Critical review of various thermo-sensitive electrical parameters (TSEPs) already reported in literature. \n13. Identification of new TSEPs that can be obtained from the measured gate-emitter voltage during switching delay times. \n\n\nALL ARE WELCOME
URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-subhas-chandra-das-ee-erp/
LOCATION:Multi-Media Class Room (MMCR)\, EE Department (Hybrid mode)
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230915T093000
DTEND;TZID=Asia/Kolkata:20230915T110000
DTSTAMP:20260404T030553
CREATED:20230906T053622Z
LAST-MODIFIED:20230908T103500Z
UID:241024-1694770200-1694775600@ee.iisc.ac.in
SUMMARY:PhD Thesis Defense of Mr. Siddarth Asokan
DESCRIPTION:Name of the Candidate: Mr. Siddarth Asokan\nPh.D. Supervisor: Prof. Chandra Sekhar Seelamantula (EE)\n \nExternal Examiner: Prof. Santanu Chaudhury (Director\, IIT Jodhpur; Professor\, IIT Delhi)\n\nTitle of the Thesis: On the Optimality of Generative Adversarial Networks — A Variational Perspective\n\nDate & Time: September 15\, 2023; 9.30 AM (Coffee will be served during the defense)\nVenue: Multimedia Classroom (MMCR)\, Department of Electrical Engineering\, IIScAbstract:Generative adversarial networks are a popular generative modeling framework\, where the task is to learn the underlying distribution of data. GANs comprise a min-max game between two neural networks\, the generator and the discriminator. The generator transforms noise\, typically Gaussian distributed\, into a desired output\, typically images. The discriminator learns to distinguish between the target samples and the generator output. The objective is to learn the optimal generator —  one that can generate samples that perfectly confuse the discriminator. GANs are trained to either minimize a divergence function or an integral probability metric (IPM) between the data and generator distributions. Common divergences include the Jensen-Shannon divergence in the standard GAN (SGAN)\, the chi-squared divergence in least-squares GAN (LSGAN) and f-divergences in f-GANs. Popular IPMs include the Wasserstein-2 metric or the Sobolev metric. The choice of the IPM results in a constraint class over which the discriminator is optimized\, such as Lipschitz-1 functions in Wasserstein GAN (WGAN) or functions with bounded energy in their gradients as in the case of Sobolev GAN. While GANs excel at generating realistic images\, their optimization is not well understood. This thesis focuses on understanding the optimality of GANs\, viewed from the perspective of Variational Calculus. The thesis is organized into three parts.In Part-I\, we consider the functional analysis of the discriminator in various GAN formulations. In f-GANs\, the functional optimization of the loss coincides with pointwise optimization as reported in the literature. We extend the analysis to novel GAN losses via a new contrastive-learning framework called Rumi-GAN\, in which the target data is split into positive and negative classes. We design novel GAN losses that allow for the generator to learn the positive class while the discriminator is trained on both classes. For the WGAN IPM\, we propose a novel variant of the gradient-norm penalty\, and show by means of Euler-Lagrange analysis\, that the optimal discriminator solves the Poisson partial differential equation (PDE). We solve the PDE via Fourier-series approximations and involving radial basis function (RBF) expansions. We extend the approach to image generation by means of latent-space matching in Wasserstein autoencoders (WAE). We also present generalizations to higher-order gradient penalties for the LSGAN and WGAN losses\, and show that the optimal discriminator can be implemented by means of a polyharmonic spline interpolator\, giving rise to the name PolyGANs. PolyGANs\, implemented by means of an RBF discriminator whose weights and centers are evaluated in closed-form\, results in superior convergence of the generator.In Part-II\, we tackle the issue of choosing the input distribution of the generator. We introduce Spider GANs\, a generalization of image-to-image translation GANs\, wherein providing the generator with data coming from a closely related/“friendly neighborhood” source dataset accelerates and stabilizes training\, even in scenarios where there is no visual similarity between the source and target datasets. Spider GANs can be cascaded\, resulting in state-of-the-art performance when trained with StyleGAN architectures on small\, high-resolution datasets\, in merely one-fifth of the training time. To identify “friendly neighbors” of a target dataset\, we propose the “signed Inception distance” (SID)\, which employs the PolyGAN discriminator to quantify the proximity between datasets.In Part-III\, we extend the analysis performed in Part-I to GAN generators. In divergence-minimizing GANs\, the optimal generator matches the gradient of its push-forward distribution with the gradient of the data distribution (known as the score)\, linking GANs to score-based Langevin diffusion. In IPM-GANs\, the optimal generator performs flow-matching on the gradient-field of the discriminator\, thereby deriving an equivalence between the score-matching and flow-matching frameworks. We present implementations of flow-matching GANs\, and develop an active-contour-based technique to train the generator in SnakeGANs. Finally\, we leverage the gradient field of the discriminator to evolve particles in a Langevin-flow setting\, and show that the proposed discriminator-guided Langevin diffusion accelerates baseline score-matching diffusion without the need for noise conditioning.\nBiography of the Candidate: Siddarth Asokan received a Bachelor of Engineering (B.E.) degree in 2017 with a specialization in Electronics and Communication Engineering from M.S. Ramaiah Institute of Technology\, Bangalore.  During 2016–2017\, he worked in Robert Bosch Centre for Cyber-Physical Systems (RBCCPS) as a Project Intern on the Smart Cities Project. Subsequently\, he joined RBCCPS as a direct PhD student in 2017 working under the guidance of Prof. Chandra Sekhar Seelamantula\, and has since been with the Spectrum Lab\, Department of Electrical Engineering. He received the Microsoft Research Fellowship in 2018\, the Qualcomm Innovation Fellowship in 2019\, 2021\, 2022\, and 2023 and the RBCCPS PhD Fellowship in 2020 and 2021. He is also a recipient of the Best Presenter Award at the AI/ML track of the IISc EECS Symposium 2023\, and has been selected to present his PhD research at the Doctoral Consortium at the British Machine Vision Conference\, 2023. His research interests are in signal processing\, image processing and machine learning\, focusing on building mathematical foundations of generative learning frameworks.\nAll are invited.
URL:https://ee.iisc.ac.in/event/phd-thesis-defense-of-mr-siddarth-asokan/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230825T150000
DTEND;TZID=Asia/Kolkata:20230825T170000
DTSTAMP:20260404T030553
CREATED:20230822T095358Z
LAST-MODIFIED:20230825T073226Z
UID:240999-1692975600-1692982800@ee.iisc.ac.in
SUMMARY:Cancelled [Talk] Prof Neetesh Saxena Cardiff University UK
DESCRIPTION:Cancelled * \n\n\nDue to some unavoidable circumstances\, we are cancelling the talk by Prof Neetesh Saxena of Cardiff University scheduled today. Sorry for any inconvenience this may cause.\n\n\nTitle:  Cyber-physical Smart Grid Situational Awareness\n\nSpeaker:\n\n\nProf Neetesh Saxena \nSenior Lecturer\nSchool of Computer Science and Informatics\nCardiff University\, UK\n\n\nDate: 25th August 2023\, 3 pm\n\n\nVenue: MMCR\, EE Dept\, IISc\n\n\nAbstract: \nIn recent years\, the impact evaluation of cyber-physical security of the smart grid has become highly notable and extremely important and critical research direction due to several recent cyber-attacks attempts in different countries. The smart grid is vulnerable to cyber-attacks due to its integration with communication and control technologies. The cyber-attacks can affect operations and decision-making at the energy management system or effectively destroy the critical components\, even shut down the power operations and can disrupt service for its customers. Inaccurate information leads to triggering inappropriate actions by the operators. The cyber-attacks either can directly target a power component of the smart grid system or can be triggered through the communication network to the power system. In this talk\, I will discuss cyber security issues with a case study to explore the cyber-physical situational awareness.\n\nBiography:\nProf Neetesh is a Senior Lecturer with the School of Computer Science and Informatics at Cardiff University\, UK having 16+ years of professional experience. Previously\, he had affiliations with the Bournemouth University (UK)\, Georgia Institute of Technology (USA)\, Stony Brook University (USA) and SUNY Korea. He was a DAAD Scholar at Bonn-Aachen International Center for Information Technology (B-IT)\, Rheinische-Friedrich-Wilhelms Universität\, Bonn (Germany) and was also a TCS Research Scholar. His current research interests include cyber security and critical infrastructure security\, including cyber-physical system security: smart grid\, V2G and cellular communication networks.
URL:https://ee.iisc.ac.in/event/talk-prof-neetesh-saxena-cardiff-university-uk/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230823T110000
DTEND;TZID=Asia/Kolkata:20230823T130000
DTSTAMP:20260404T030553
CREATED:20230818T102510Z
LAST-MODIFIED:20230818T102510Z
UID:240997-1692788400-1692795600@ee.iisc.ac.in
SUMMARY:EE PhD Colloquium on Imaging Inverse problems
DESCRIPTION:Title : Improved Derivative based Regularizations for Imaging Inverse problems \nStudent : Manu GhulyaniAdvisor : Prof. Muthuvel Arigovindan \nDate and Time:   23.08.2023 (Wednesday)\,  11 am. \nVenue :  MMCR\, Department of Electrical Engineering \nAbstract: \nImages undergo degradation during the capturing process due to physical limitations inherent to the capturing devices. Addressing this degradation and recovering high-quality images constitute the image recovery problem\, a crucial concern with diverse applications across various fields such as biology\, astronomy\, and medicine. The enhancement of captured image resolution significantly influences these disciplines. Examples of this problem include tasks like reconstructing computed tomography images\, magnetic resonance imaging\, image deconvolution\, and microscopic image reconstruction. \nImage recovery is frequently approached using regularization techniques\, with derivative-based regularizations being popular due to their ability to exploit image smoothness\, yielding interpretable results devoid of introduced artifacts. Total Variation regularization (TV)\, proposed by Rudin\, Osher\, and Fatemi\, is a seminal approach for image recovery. TV involves the norm of the image’s gradient\, aggregated over all pixel locations. As TV encourages minimal values in the derivative norm\, it leads to piece-wise constant solutions\, resulting in what is known as the “staircase effect.” To mitigate this effect\, the Hessian Schatten norm regularization (HSN) employs second-order derivatives\, represented by the pth norm of eigenvalues in the image hessian vector\, summed across all pixels. HSN demonstrates superior structure-preserving properties compared to TV. However\, HSN solutions tend to be overly smoothed. To address this\, we introduce a non-convex shrinkage penalty applied to the Hessian’s eigenvalues\, deviating from the convex lp norm. While the analytical form of this penalty was unknown\, we derived the algorithm using proximal operations. We established that the proposed regularization adhered to restricted proximal regularity\, ensuring the algorithm’s convergence. The images recovered by this regularization were sharper than the convex counterparts. \nIn the subsequent work\, we extend the concept of the Hessian-Schatten norm. By encompassing Schatten norms of the Hessian and introducing a smoothness constraint\, we broaden the scope of Hessian Schatten norm. The resulting regularization can be derived as a Lagrange dual of the Hessian Schatten norm\, akin to the total generalized variation. Furthermore\, we present an efficient variable splitting scheme for solving image restoration challenges. \nTotal Generalized Variation (TGV) represents an important generalization of Total Variation. TGV involves multiple orders of derivatives\, with higher-order TGV leading to improved recovered image quality. This enhancement has been validated through numerical experiments in image denoising. Consequently\, a demand arises for an algorithm capable of solving TGV for any order. While various methods address TGV regularization\, many are confined to second-order TGV\, and only a few explore orders greater than three for image recovery with TGV regularization. To our knowledge\, no algorithm resolves image recovery challenges employing TGV regularization for orders exceeding three under a general forward model. This challenge arises from the intricate nature of TGV representation. We surmount this obstacle by presenting two simple matrix based representations of TGV: the direct and compact forms. We prove the equivalence of both forms with the original TGV definition. Leveraging the compact representation\, we propose a generalized ADMM-based algorithm to solve TGV regularization for any order. \nALL ARE WECOME.
URL:https://ee.iisc.ac.in/event/ee-phd-colloquium-on-imaging-inverse-problems/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230818T160000
DTEND;TZID=Asia/Kolkata:20230818T173000
DTSTAMP:20260404T030553
CREATED:20230810T060333Z
LAST-MODIFIED:20230810T060446Z
UID:240982-1692374400-1692379800@ee.iisc.ac.in
SUMMARY:[Talk]: Prof Ramakrishna Gokaraju University of Saskatchewan\, Canada
DESCRIPTION:Talk Title:  Future Clean Power and Energy Systems: Co-generation with New Nuclear Based Small Modular Reactors (SMRs) and Renewable Energy for Electricity and Energy Applications\n\nSpeaker:\n\nProf Ramakrishna Gokaraju\, PhD\, PEng \nProfessor\, Department of Electrical & Computer Engineering\nAssociate Dean\, Graduate Studies & Special Projects\, College of Engineering\nUniversity of Saskatchewan\, Canada\n\n\nDate: 18th August 2023\, 4 pm\n\n\nVenue: MMCR\, EE Dept\, IISc\n\n\nAbstract: \nSmall modular reactors (SMRs)—a fast-emerging nuclear power plant technology—and renewables hold significant promise for the development of future clean energy systems. They are suitable for large grids as well as remote load centers and offer load following and frequency response capabilities. This talk will first provide a background of this technology and recent developments related to this technology. Followed by this it will describe a dynamic model of an integral pressurized water reactor (iPWR)-type SMR and studies assessing the contribution of the reactor to the electrical side dynamics. SMRs with their faster response rates\, along with intermittent renewable energy sources (RESs) could be effectively used to develop sustainable clean energy systems. The talk will also describe a dynamic simulation model developed in our lab showing how SMRs along with renewable energy could be used for electricity and district heating.\n\nBiography:\nProf. Ramakrishna Gokaraju received his Bachelor of Engineering degree (with Distinction) in Electrical and Electronics Engineering from the National Institute of Technology\, Trichy\, India in April 1992. He received the M.Sc. and Ph.D. degrees in electrical and computer engineering from the University of Calgary\, Canada\, in 1996 and 2000\, respectively. He worked with IBM Toronto Lab from 2000-‘22. Prof. Gokaraju joined the Electrical & Computer Engineering Department\, University of Saskatchewan in 2003 and is a professor in the department of electrical & computer engineering. He is also currently the Associate Dean of Graduate Studies and Special Projects in the College of Engineering. His current research works are on power system protection\, smart grids and nuclear-based “SMRs” for power and energy applications. He currently serves the Natural Sciences and Engineering Council of Canada (NSERC) Evaluation Group in Electrical and Computer Engineering. His research is funded by the NSERC Discovery Grant and the Canadian Nuclear Safety Commission (CNSC).
URL:https://ee.iisc.ac.in/event/talk-prof-ramakrishna-gokaraju-university-of-saskatchewan-canada/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230817T160000
DTEND;TZID=Asia/Kolkata:20230817T173000
DTSTAMP:20260404T030553
CREATED:20230814T111017Z
LAST-MODIFIED:20230814T111017Z
UID:240995-1692288000-1692293400@ee.iisc.ac.in
SUMMARY:[Talk] Prof Nando Ochoa of University of Melbourne 17th Aug 4 PM MMCR EE
DESCRIPTION:Title: “Smart Meter-Driven Approaches for PV-Rich Low Voltage Network Modelling\, Operation and Planning”\n \nSpeaker: Prof Nando Ochoa Pizzali\n \nAffiliation: University of Melbourne\, Australia\n \nVenue: MMCR\, EE Dept\n\nDate: 17th Aug 2023\n\nTime: 4:00 PM\n \nAbstract: Residential solar PV is installed behind the meter of mainly single-phase customers connected to three-phase low voltage (LV) feeders (e.g.\, 400V line-to-line). This means that for distribution companies to adequately quantify the impacts from reverse power flows due to excess solar PV generation\, the corresponding electrical models are required. These models are critical when calculating voltages given the non-linear and unbalance nature of LV feeders. However\, the task of producing electrical models of thousands of LV feeders is already a significant challenge for distribution companies around the world\, which\, in turn\, makes the operation and planning of PV-rich LV networks even more challenging. It is in this context that the exploitation of historical smart meter data can not only help distribution companies with their modelling tasks but also provide radical alternatives to how they operate and plan future PV-rich LV networks.\n\nThis talk presents and discusses the findings of three advanced smart meter-driven approaches using realistic case studies from Victoria\, Australia. The first enhances LV models. Using simplified three-phase voltage drop equations and multiple linear regression\, it is able to estimate three-phase and single-phase line impedances which\, in turn\, allows for the quick and accurate calculation of customer voltages for operational purposes. The second\, a more radical approach\, goes model-free. It demonstrates that neural networks can be trained to capture the physics of three-phase LV feeders with dozens of single-phase customers; making it possible to have fast and accurate voltage calculations. The last one\, from a planning perspective\, also demonstrates that regression techniques and data from early solar PV penetrations can be used to quickly estimate the hosting capacity of LV networks without the need for complex and detailed network studies.\n\n\nBio:  Prof Nando is a Professor of Smart Grids and Power Systems at The University of Melbourne\, Australia\, and Chief Scientist & Co-Founder at VoltMind. He is an IEEE PES Distinguished Lecturer\, an Editorial Board Member of the IEEE Power and Energy Magazine\, and an IEEE Senior Member. Previously\, from 2011 to 2021\, he was full and part-time with The University of Manchester\, UK. From 2007 to 2010 he was a Research Fellow in Energy Systems at the University of Edinburgh\, UK. In 2010\, he also undertook an industrial secondment with the Edinburgh-based company Psymetrix Ltd (part of GE). He hold a Bachelor’s degree in Mechanical and Electrical Engineering received from UNI (Peru)\, and a Research MSc and a PhD in Electrical Power Engineering\, both received from UNESP Ilha Solteira (Brazil).
URL:https://ee.iisc.ac.in/event/talk-prof-nando-ochoa-of-university-of-melbourne-17th-aug-4-pm-mmcr-ee/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230811T150000
DTEND;TZID=Asia/Kolkata:20230811T170000
DTSTAMP:20260404T030553
CREATED:20230809T114701Z
LAST-MODIFIED:20230814T084627Z
UID:240977-1691766000-1691773200@ee.iisc.ac.in
SUMMARY:Ph.D. Thesis Oral Defense of Mr. Anoop C. S.: Automatic speech recognition for low-resource Indian languages
DESCRIPTION:Name of the student:  ANOOP C. S.\n\nAdvisor: Prof. A. G. Ramakrishnan & Dr. G. N. Rathna\n\nExternal examiner: Prof. Umesh S\, Dept of EE\, IIT Madras\n\nDate and Time: 11 August 2023 (Friday) 3:00 PM\n\nVenue (hybrid): MMCR\, C 241\, First Floor\, Dept. of EE\n                                                                AND\n\nMicrosoft Teams meeting link: \n\n\n\n\n\n\n\n\n\nJoin conversation\nteams.microsoft.com\n\n\n\n\n\n\n\n\n\nTITLE: Automatic speech recognition for low-resource Indian languages\n\nBuilding good models for automatic speech recognition (ASR) requires large amounts of annotated speech data. Most Indian languages are low-resourced and lack enough training data to build robust and efficient ASR systems. However\, many have an overlapping phoneme set and a strong correspondence between their character sets and pronunciations. This thesis exploits such similarities among the Indian languages to improve speech recognition in low-resource settings.\n\nSignificant contributions of the thesis:\n\nExploiting the pronunciation similarities across multiple Indian languages through shared label sets:\n\nThe use of a common set of tokens is proposed across multiple Indian languages and their performance analyzed in mono and multilingual settings.\n\n\nIt is found that the Sanskrit Library Phonetic Encoding (SLP1) tokens\, which exploit the pronunciation-based structuring of character Unicodes in Indian languages\, perform better than other grapheme-to-phoneme (G2P) based tokens in monolingual ASR settings.\nSyllable-based sub-words perform better than the character-based token units in monolingual speech recognition. However\, character-based SLP1 tokens perform better in cross-lingual transfer.\n\n\nStrategies for improving the performance of ASR systems in low-resource scenarios (target languages) exploiting the annotated data from high-resource languages (source languages):\n\nThree different low-resource settings have been studied:\n\nA) Labelled audio data is not available in the target language. Only a limited amount of unlabeled data is available. Unsupervised domain adaptation (UDA) schemes popular in image classification problems have been adopted to tackle this case.\n\n\nThe adversarial training with gradient reversal layers (GRL) and domain separation networks (DSN) provide word error rate (WER) improvements of 6.71% and 7.32%\, respectively\, on Sanskrit compared to a baseline hybrid DNN-HMM system trained on Hindi.\nThe UDA models outperform multi-task training with language recognition as the auxiliary task.\nSelection of the source language is critical in UDA systems.\n\n\nB) Target language has only a small amount of labeled speech data and has some amount of text data to build language models. In this case\, available data in high-resource languages is used through shared label sets to build unified acoustic (AM) and language models (LM).\n\n\nUnified language-agnostic AM + LM performs better than monolingual AM + LM in cases where (a) only limited speech data is available for training the acoustic models and (b) the test speech data is from domains different from that used in training.\nIn general\, multilingual AM + monolingual LM performs the best.\n\n\nC) There are N target languages with limited training data and several source languages with large training sets. In this case\, the usefulness of model-agnostic meta-learning (MAML) pre-training is established for Indian languages and improvements are proposed with text-similarity-based loss-weightings.\n\n\nMAML beats joint multilingual pretraining by an average of 5.4% in CER and 20.3% in WER.\nWith just 25% of the data\, MAML performance matches joint multilingual models trained on the whole target data.\nSimilarity with the source languages impacts the target language’s ASR performance.\nText-similarity measured through cosine and Mahalanobis distances is used to weigh the losses during MAML pretraining. It yields a mean absolute improvement of 1% in WER.\n\n\n\n                                                                                                              ALL ARE WELCOME ONLINE!\n Meeting Recording\n\n\n\nhttps://ee.iisc.ac.in/wp-content/uploads/2023/08/Anoop-PhD-Viva-20230811_150431-Meeting-Recording_Cut.mp4
URL:https://ee.iisc.ac.in/event/ph-d-thesis-oral-defense-of-mr-anoop-c-s-automatic-speech-recognition-for-low-resource-indian-languages/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230808T110000
DTEND;TZID=Asia/Kolkata:20230808T120000
DTSTAMP:20260404T030553
CREATED:20230804T091124Z
LAST-MODIFIED:20230804T091231Z
UID:240974-1691492400-1691496000@ee.iisc.ac.in
SUMMARY:PhD Colloquium - 8 August: Resource-Aware State-Triggered Control of Networked Control Systems
DESCRIPTION:Speaker: Anusree RajanSupervisor: Pavankumar TallapragadaDate and Time: Tuesday\, 8 August 2023\, at 11amVenue: MMCR (C241)\, EE DepartmentTitle: Resource-Aware State-Triggered Control of Networked Control SystemsAbstract:Networked control systems are very popular nowadays\, with different fields of applications such as environmental monitoring\, industrial automation\, military surveillance\, and disaster management. Event- or self-triggered control is a commonly used control method in the field of networked control systems owing to its advantage of efficient utilization of resources while simultaneously achieving control objectives. In these control methods\, the communication times are opportunistic and implicitly determined by a triggering rule. Thus\, understanding inter-event times generated by a triggering rule is necessary for higher level planning and scheduling for control over shared or constrained resources as well as for analytical quantification of the usage of communication or other resources compared to a time-triggered controller.In the first part of this thesis\, we provide a systematic way to analyze the evolution of inter-event times in planar linear systems\, under a general class of scale-invariant event triggering rules. We provide a sufficient condition for the convergence or non-convergence of inter-event times to a steady state value. We also provide a sufficient condition for the asymptotic average inter-event time to be a constant for all non-zero initial states of the system. Then\, under a special case\, we comment on the asymptotic behaviour of the inter-event times\, including on whether the inter-event times converge to a periodic sequence. Later\, we extend our analysis of inter-event times to linear systems under region-based self-triggered control. In this control method\, the state space is partitioned into a finite number of conic regions and each region is associated with a fixed inter-event time. We provide several necessary conditions and sufficient conditions for the local convergence of inter-event times to a constant or to a given periodic sequence.In the second part of this thesis\, we consider a design problem. Most of the existing event- or self-triggered controllers are designed using sampled-data zero-order-hold (ZOH) control input. However\, many communication protocols used in networked control systems\, such as TCP and UDP\, have a minimum packet size. So\, ZOH control may lead to under-utilization of each packet while also increasing the number of communication instances. On the other hand\, use of non-ZOH control leads to better utilization of the minimum payload of each packet while also reducing the overall number of communication instances. With these motivations\, we propose a new control method called event-triggered parametrized control (ETPC). In this control method\, between two consecutive events\, each control input to the plant is a linear combination of a set of linearly independent scalar functions. At each event\, the coefficients of the parameterized control input are chosen to minimize the error in approximating a continuous time control signal and then they are communicated to the actuator. We\, first\, showcase this method by focusing on the specific problem of stabilization of linear systems. We design two event-triggering rules that guarantee global asymptotic stability of the origin of the closed loop system under some conditions on the model uncertainty. Later\, we use a similar idea to propose an event-triggered polynomial control method for trajectory tracking of unicycle robots. We design an event-triggered parametrized controller for trajectory tracking by a unicycle robot and provide guarantees for uniform ultimate boundedness of the tracking error.Due to time limitations\, the colloquium will focus on the first part of the thesis.——————————- All are Welcome —————————-
URL:https://ee.iisc.ac.in/event/phd-colloquium-8-august-resource-aware-state-triggered-control-of-networked-control-systems/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230803T153000
DTEND;TZID=Asia/Kolkata:20230803T173000
DTSTAMP:20260404T030553
CREATED:20230802T112318Z
LAST-MODIFIED:20230802T112938Z
UID:240963-1691076600-1691083800@ee.iisc.ac.in
SUMMARY:Orientation Program 2023
DESCRIPTION:Dear All \n  A warm welcome to all the new Masters and Research students! \nThe department of EE is flourishing with a broad spectrum of research activities and multiple master’s programs.  Being a proud member of the department\, you may be eager to know more about the department and the courses. \nFor this\, a Welcome/Orientation program is planned for the 3rd (Thursday) at 3:30 pm. The venue for the program is classroom 308 on the second floor. It will start with a brief history and current activities. Followed by a presentation by the DCC Chair about the course requirement for the masters and research students.  Then we wish to know about you!   \nCoffee & Tea will be served at the end. \n Please attend the event without fail.
URL:https://ee.iisc.ac.in/event/orientation-program-for-new-students/
LOCATION:B308\,2nd floor\, EE Dept.
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230803T150000
DTEND;TZID=Asia/Kolkata:20230803T170000
DTSTAMP:20260404T030553
CREATED:20230802T100042Z
LAST-MODIFIED:20230802T100130Z
UID:240956-1691074800-1691082000@ee.iisc.ac.in
SUMMARY:Technical Talk by Dr. Amristanshu Pandey\, University of Vermont\, USA
DESCRIPTION:IEEE PES Student Branch Chapter\, IISc has organized a technical talk on 3rd August 2023 at 3 PM IST. Please find the talk details as below. \n\n\n\n\n\nTitle: Homotopy-based Circuit-theoretic Optimizations for Power Grids \n\n\n\n\n\nSpeaker: Dr. Amritanshu Pandey\, University of Vermont\, USA \n\n\n\n\n\nDate and Time: 3rd August 2023\, 3 PM ISTVenue: MMCR Electrical Engineering Department\, IISc\, Bengaluru.  \n\n\n\n\n\nAbstract: Optimizations are at the center of many power grid analyses. With rapid decarbonization and electrification\, the scale and complexity of these optimizations are increasing. Current state-of-the-art tools for nonconvex optimization are not always robust at solving these emerging power grid problems. In this talk\, I will discuss a novel domain-focused circuit-theoretic optimization method for power grids. In this approach\, the first-order optimality conditions for an optimization problem are mapped into a set of equivalent circuits\, the response for which represents a problem’s stationary point. As these equivalent circuits are large and highly nonlinear\, we developed and used a novel homotopy method: Incremental Model Building (IMB). I will cover this homotopy in this talk and show that this method can also be extended to optimize for discrete control variables in the grid. I will conclude the talk by showing pertinent results and comparisons against a state-of-the-art method that won the recent ARPA-E grid optimization challenge. \n\n\n\n\n\nSpeaker Bio: Amrit Pandey is an Assistant Professor in the Electrical and Biomedical department at the University of Vermont with a courtesy appointment in the Electric and Computer Engineering and Engineering and Public Policy departments at Carnegie Mellon University. His overarching research goal is to develop electric energy system technologies to help combat climate change while modernizing the underlying system. In the past\, he and his team developed a novel circuit-theoretic simulation and optimization framework for power grids. The project culminated in a new grid analytics tool: Simulation of Unified Grid Analysis and Renewables (SUGAR)\, which Pearl Street Technologies\, Inc. has since commercialized. This work has won several best paper awards\, including two best-of-the-best paper awards at IEEE PES General Meeting in 2017 and 2021. His current research focuses on developing computational methods that address problems in the space of large-scale grid simulation and optimization\, grid cybersecurity\, and energy inequity.
URL:https://ee.iisc.ac.in/event/technical-talk-by-dr-amristanshu-pandey-university-of-vermont-usa/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230731T160000
DTEND;TZID=Asia/Kolkata:20230731T173000
DTSTAMP:20260404T030553
CREATED:20230728T172440Z
LAST-MODIFIED:20230801T100131Z
UID:240929-1690819200-1690824600@ee.iisc.ac.in
SUMMARY:Time Forecasting of COVID-19 Signals: Challenges and Model Development
DESCRIPTION:Title: Real-Time Forecasting of COVID-19 Signals: Challenges and Model Development \nSpeaker: Dr. Aniruddha Adiga\,  Research Scientist\, Biocomplexity Institute at the University of Virginia \nHost Faculty: Prof. Chandra Sekhar Seelamantula\, EE\, IISc \nVenue: Multimedia Classroom (MMCR)\, Department of Electrical Engineering\, Indian Institute of Science \nDate & Time: July 31\, 2023; 4 PM onward (Coffee will be served during the talk) \nAbstract:  \nCOVID-19 is the largest pandemic the world has seen with approximately 700 million confirmed cases\, 8 million confirmed deaths pandemic to date\, and unprecedented social\, economic\, and political impact. During the pandemic\, we also observed an extensive development of computational and mathematical models to aid policymakers and response efforts. An essential use of such models is in early warning systems and forecasting of COVID-19 signals. Real-time forecasting of COVID-19 signals is a challenging problem due to data quality issues\, nonstationarity of time series\, evolving targets\, behavioral adaptations\, etc. It has been observed that under such circumstances\, ensemble models consisting of a diverse set of model classes are a better choice than individual models.  \nIn this talk\, I will discuss our efforts toward the development of an ensemble model consisting of statistical\, deep learning\, and compartmental models and our participation in national-level collaborative forecasting efforts. Through these efforts we have observed that all classes of models are important\, however\, different model classes performed differently during various phases of the pandemic. Armed with this understanding\, I will present a modification to the ensembling method to employ this phase information and use different weighting schemes for different phases to produce improved forecasts. However\, predicting the phases of the time series is another challenge\, especially when behavioral and immunological adaptations govern the evolution of the time series. I will discuss a phase prediction algorithm that employs auxiliary datasets and transfer entropy techniques. We evaluate our model’s performance with other models in the collaborative effort. \nBiography of the speaker:  \n\nAniruddha Adiga is a research scientist at the Biocomplexity Institute at the University of Virginia. His interests are in signal processing and machine learning with a current focus on the development of forecasting models. From May 2018 to May 2019\, he was a postdoctoral associate at North Carolina State University. He received his PhD from the Department of Electrical Engineering at the Indian Institute of Science. Aniruddha has published in top venues such as KDD\, AAAI\, IJCAI\, BigData\, etc. His paper in IEEE BigData 22 received the “Best Paper” award. His work also supports public health agencies such as the US CDC\, EU CDC\, and the Virginia Department of Health.\n\n\nTechnically co-sponsored by IEEE Signal Processing Society\, Bangalore Chapter
URL:https://ee.iisc.ac.in/event/time-forecasting-of-covid-19-signals-challenges-and-model-development/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230728T163000
DTEND;TZID=Asia/Kolkata:20230728T173000
DTSTAMP:20260404T030553
CREATED:20230725T041910Z
LAST-MODIFIED:20230725T041950Z
UID:240904-1690561800-1690565400@ee.iisc.ac.in
SUMMARY:[Thesis Defense Talk - Shreyas Ramoji\, 28/7 @430pm\, MMCR\, EE] - "Supervised Learning Approaches for Language and Speaker Recognition"
DESCRIPTION:Thesis Defense Talk\n \nVenue: MMCR\, EE\nDate: 28/7/2023\nTime: 4:30pm [High Tea at 4:15pm]\nSpeaker: Shreyas Ramoji\n\nTitle: Supervised Learning Approaches for Language and Speaker Recognition\n\n\nAbstract:\nIn the age of artificial intelligence\, one of the important goals of the speech processing research community is to enable machines to automatically recognize who is speaking and in what language.\n\nIn the first part of this talk\, I will discuss our efforts towards a supervised version of the generative model based embedding extractor for speaker and language recognition. We call the embeddings from this supervised approach as s-vectors. In this approach\, a database of speech recordings (in the form of a sequence of short-term feature vectors) is modeled with a Gaussian Mixture Model\, called the Universal Background Model (GMM-UBM). The deviation in the mean components is captured in a lower dimensional latent space\, called the i-vector space\, using a factor analysis framework. In our research\, we propose a fully supervised version of the i-vector model\, where each label class is associated with a Gaussian prior with a class-specific mean parameter. The joint prior (marginalized over the sample space of classes) on the latent variable becomes a GMM. With detailed data analysis and visualization\, we show that the s-vector features yield representations that succinctly capture the language (accent) label information and also perform significantly improved the recognition of various accents of the same language.\n\nIn the second part of the talk\, I will discuss our efforts for the problem of fully supervised end-to-end speaker verification\, where a binary decision has to be made whether a pair of recordings belong to the same speaker or not. We proposed a neural network approach for back-end modeling\, where the likelihood ratio score of the generative probabilistic discriminative analysis (PLDA) model is posed as a discriminative similarity function\, and the learnable parameters of the score function are optimized using a verification cost. The proposed model\, termed as neural PLDA (NPLDA)\, is initialized using the generative PLDA model parameters. The loss function for the NPLDA model is an approximation of the minimum detection cost function (DCF) used as one of the evaluation metrics in various speaker verification challenges. The speaker recognition experiments using the NPLDA model are performed on the speaker verification task in the VOiCES datasets as well as the SITW challenge dataset. Further\, we explore a fully neural approach where the neural model outputs the verification score directly\, given the acoustic feature inputs. This Siamese neural network (E2E-NPLDA) model combines embedding extraction and back-end modeling into a single processing pipeline. Several speaker recognition experiments were performed on benchmark datasets where the proposed N  E2E-NPLDA models are shown to improve significantly over the then state-of-art system.\n\nI will conclude the talk by highlighting some of the noteworthy approaches that were published during the course of this research work\, and identifying some important research directions related to this thesis that can be pursued in the future.\n\nBio:\nShreyas Ramoji is a Research Associate at the Learning and Extraction of Acoustic Patterns (LEAP) Laboratory\, Department of Electrical Engineering\, Indian Institute of Science\, Bengaluru. He obtained his Bachelor of Engineering degree from the Department of Electronics and Communication Engineering\, PES Institute of Technology\, Bangalore South Campus. His research interests include speaker and language recognition\, diarization\, representation learning for multilingual and conversational speech\, ML/AI applied to healthcare and the environment\, natural language processing\, explainability and interpretability of neural networks\, and neuro-symbolic AI.\n\n\n\n\n\n—
URL:https://ee.iisc.ac.in/event/thesis-defense-talk-shreyas-ramoji-28-7-430pm-mmcr-ee-supervised-learning-approaches-for-language-and-speaker-recognition/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230728T160000
DTEND;TZID=Asia/Kolkata:20230728T173000
DTSTAMP:20260404T030553
CREATED:20230727T063239Z
LAST-MODIFIED:20230727T063239Z
UID:240923-1690560000-1690565400@ee.iisc.ac.in
SUMMARY:EE PhD Thesis Colloquium -- Francis C Joseph -- 28th July\, 4 PM
DESCRIPTION:Title: Parallel Algorithms for Efficient Utilization of Multiprocessor Architectures in Power System ApplicationsSpeaker: FRANCIS C JOSEPH . of Ph.D. (Engg) in Electrical Engineering under Electrical EngineeringDate/Time: Jul 28 / 16:00:00Location: Room 303\, 2nd Floor\, EEResearch Supervisor: Dr. Gurunath GurralaAbstract:Computer hardware capabilities have been enormously increasing over the years. Multi-core processors\, graphic processing units (GPUs)\, and field programmable gate array (FPGA) accelerators have seen significant growth in recent years. They have opened new computational paradigms such as edge computing\, fog computing\, grid computing\, distributed computing\, cloud computing\, and exascale supercomputing. However\, efficient utilization of most of these computational paradigms in traditional engineering disciplines such as power engineering is very challenging. In this thesis\, we develop efficient algorithms for multiprocessor-based high-performance computing and edge computing platforms for two power system applications\, power system stability assessment and power quality measurements respectively.Faster than real-time transient stability assessment of large power grids using time-domain simulations with detailed models is comp utationally challenging. Today the commercial tools being used for this application in Energy Management Systems (EMS) across the world rely on parallel batch processing methods which don’t utilize the architecture of the computational paradigms efficiently. In this thesis for transient stability simulations\, we explore a time parallel algorithm\, Parareal in Time\, which belongs to a class of temporal decomposition methods for time parallel solutions of differential equations. Two effective implementation approaches\, Master Worker and Distributed\, are analysed for large systems\, and scaling tests are performed using a state space model with a Message Passing Interface (MPI) in a multiprocessor environment. One of the findings was that the performance of the Parareal depends on the accuracy and the computational cost of the coarse solver used for initialization and subsequent correction steps. A potential coarse solver\, Modified Euler (ME)\, a well-known solver for transient stability simulations even in commercial packages\, has been explored to adapt its step size by controlling the Local Truncation Error (LTE) to achieve the desired accuracy. An LTE estimator using a Multistage Homotopy Analysis Method (MHAM)\, which gives an approximate solution to a set of non-linear equations in the form of a power series\, is proposed to control the LTE at each integration step to enable adaptation of the ME step size. The proposed MHAM-assisted adaptive ME solver is found to be faster with comparable accuracy when compared to the conventional fixed and adaptive Modified Euler solver for large systems transient stability simulations. Since MHAM is lighter than the ME solver and LTE estimate is sufficient for step size adaptation\, an adaptive MHAM coarse solver is proposed for the Parareal. However\, MHAM provides a non-zero auxiliary parameter `c’ to select a family of solutions. Hence\, an optimisation framework is also proposed to select this parameter based on the system’s dynamics automatically. Based on many case studies on test systems of different sizes\, it is found that maintaining the LTE lower than the Parareal convergence tolerance improves the speedup of the Master-Worker paradigm\, however for the distributed implementation maintaining LTE higher than the convergence tolerance gives improved speedup. An approach to include unscheduled events which arise in power system operation due to the operation of protective relays is also proposed for Parareal.In Parareal implementation\, each coarse time segment is assigned to one processor in the MPI environment. In order to improve speedup\, multiple processors in a node is to be assigned to a coarse time segment. Therefore\, a shared memory-based space parallel transient stability solver is also considered for further performance enhancement. Space parallelisation of transient stability simulation involves breaking the network into subnetworks and solving each part independently while ensuring the original network’s convergence. Therefore\, a Multi Area Thevenin Equivalent (MATE) based parallel solver implementation on a shared memory platform is proposed in which both the space parallelisation and task parallelisation are explored. It is shown that the ideal speedup can be closely matched by the space parallelism and can be exceeded by space + task parallelism while the network is well-partitioned and it can be further improved when combined with time parallelism. The current state-of-the-art chips provide multicore architectures for edge computing applications also. One such low-cost\, open-source\, heterogeneous\, resource-constrained hardware platform is called “Parallella”. The unique hardware architecture of the Parallella provides many edge computing resources in the form of a Zynq SoC (dual-core ARM + FPGA) and a 16-core co-processor called Epiphany. This Parallella device was used as a measurement device for edge computing applications research in smart grids which could sample 3 voltages and 4 currents at 32 kHz sampling rate. One application of such a device to measure the harmonics and compute various Power Quality (PQ) indices is explored in the thesis. In this regard\, we have developed a parallel implementation of multichannel FFT on Epiphany for the streaming data. Epiphany 16-core architecture has very limited memory resources and the order in which the cores are to be accessed has a significant impact on the execution. Proper decomposition of the FFT algorithm tasks and scheduling of the tasks for efficient core and memory usage are crucial which requires a good understanding of the Epiphany architecture. The obtained PQ measurements from the proposed implementation are found to be comparable to commercial power analyser measurements.Acknowledgements: This work is funded by the • SERB Science and Technology Award for Research (SERB-STAR) grant\, File No: STR/2020/000019 titled Hybrid Parallel Solvers for Faster than Real-time Transient Stability Analysis of Large Power Grids. • Bosch Research and Technology Centre\, Bangalore\, India and by the Robert Bosch Centre for Cyber-Physical Systems\, Indian Institute of Science\, Bangalore\, India (under Project E-Sense: Sensing and Analytics for Energy Aware Smart Campus) • DST Young Scientist Grant DST-YSS/2015/001371\, IndiaMeeting Link : \n\n\n\n\n\n\n\n\nJoin conversation\nteams.microsoft.com
URL:https://ee.iisc.ac.in/event/ee-phd-thesis-colloquium-francis-c-joseph-28th-july-4-pm/
LOCATION:B 303 (Old 311)\, Dept. of Electrical Engineering (Hybrid Mode)
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230727T103000
DTEND;TZID=Asia/Kolkata:20230727T123000
DTSTAMP:20260404T030553
CREATED:20230726T071146Z
LAST-MODIFIED:20230726T071146Z
UID:240921-1690453800-1690461000@ee.iisc.ac.in
SUMMARY:[Colloquium EE 27 Jul 2023] Dual Mode Operation of Grid-tied Inverters: Modeling\, Stability Analysis\, and Islanding Detection
DESCRIPTION:Dual Mode Operation of Grid-tied Inverters: Modeling\, Stability Analysis\, and Islanding DetectionSpeaker: SUGOTO MAULIK . of Ph.D. (Engg) in Electrical EngineeringDate/Time: Jul 27 / 10:30:00 amLocation: MMCR EE\, IIScResearch Supervisor: Vinod JohnTeams link.Abstract:Increased penetration of renewable energy sources like solar PVs and wind is fundamentally altering the power flow dynamics in distribution networks. These localized forms of generation add redundancy to the power system and increase its load-handling capacity. However\, these advantages come at the cost of reduced stability and altered protection requirements. These distributed forms of generation (DGs) are interfaced with the power grid via power electronic converters operating at high bandwidths compared to conventional sources. While these offer higher performance\, but consequently lower the stability margins. An analytical framework is thus necessary for modeling and stability analysis of such systems. The dynamics involved in modeling a grid-tied DG system span a wide spectrum of frequencies. While simplified modeling can lead to inaccuracies\, an all-inclusive model leads to an over-complicated and unintuitive model. This work proposes a systematic approach to model the behavior of 3-phase grid-tied DG systems using dynamic phasors. Dynamic phasors allow for a state-space representation of the relevant dynamics. The developed state space model is then used for the following:1. Islanding detection: Islands are formed in 3-phase distribution networks when an active distributed generation (DG) is disconnected from the grid. If undetected\, the DG continues to energize its local loads leading to safety concerns. In this work\, a state-feedback approach is developed for islanding detection\, which places a system pole in the right half plane (RHP). This ensures the destabilization of the islanded network and a zero non-detection zone. The scheme is designed and implemented experimentally.2. Transfer of Control: Post-island detection\, the DG is required to disconnect from the grid while ensuring uninterrupted power flow to its local loads. A control scheme involving a voltage control loop and grid current feed-forward is developed to achieve a fast transfer from grid-following to grid-forming mode of operation. The introduced voltage control loop ensures that rated voltage is maintained across the loads\, and the grid current feed-forward is used to minimize the transients during the transfer process. The method is designed and implemented in conjunction with the islanding detection scheme and verified experimentally with local loads.3. Stability analysis of grid-tied DG systems: Owing to the formation of microgrids and weak grids in the distribution network\, the stability assessment of such networks becomes essential. This assessment is performed by extending the dynamic phasor-based model for islanded systems to model grid-tied systems as well. The developed model includes the dynamics of the PLL\, grid\, DG current levels\, and load. In addition to passive loads\, considered in the relevant literature\, the proposed model also incorporates the effect of constant power and constant current type power electronic loads. It is demonstrated\, analytically and experimentally\, that the presence of local loads has a stabilizing impact on the synchronization stability of a DG. Additionally\, an upper limit on the bandwidth of power-electronic type constant power loads is derived\, affirming the observation that high bandwidth loads lead to reduced system stability.All the proposed methods are validated on hardware prototypes that have been developed as a part of the work.Meeting Link : \n\n\n\n\n\n\n\n\nJoin conversation\nteams.microsoft.com\n\n\n\n\n\n—
URL:https://ee.iisc.ac.in/event/colloquium-ee-27-jul-2023-dual-mode-operation-of-grid-tied-inverters-modeling-stability-analysis-and-islanding-detection/
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DTSTART;TZID=Asia/Kolkata:20230726T090000
DTEND;TZID=Asia/Kolkata:20230726T230000
DTSTAMP:20260404T030553
CREATED:20230724T115812Z
LAST-MODIFIED:20230726T052436Z
UID:240902-1690362000-1690412400@ee.iisc.ac.in
SUMMARY:PhD Thesis Colloquium of student\, Kamisetti Prasad
DESCRIPTION:PhD Thesis Colloquium \nTitle: “Modeling\, Design and Control of Power-Electronic-Actuated Electromagnetic Bearings” \nSpeaker: Kamisetti N V Prasad\, \nDepartment: Electrical Engineering \nSupervisor: Prof. G. Narayanan \nDate and Time: 26 July 2023 (Wednesday)\, 9 am – 10 am \nVenue: MMCR\, EE Department \n========================================= \nAbstract \nMany practical electrical machines\, turbines\, and compressors operate in speeds\, ranging from tens of thousands of rpm to hundreds of thousands of rpm\, and also\, handling a significant amount of power. While high-speed operation reduces the machine dimensions for a given power rating\, its challenges include high bearing loss\, reduced bearing life\, and high viscous drag. Contactless bearings\, such as gas\, oil\, or electromagnetic bearings (EMB)\, offer longer life than conventional bearings in high-speed applications. In addition to being contactless\, an electromagnetic bearing (EMB) is lubrication-free; hence this is suited for both clean conditions (e.g.\, food and pharmaceutical industry) and hazardous applications (e.g.\, petroleum and chemical industry). This thesis presents the modelling\, analysis\, design and control of power-electronic-actuated EMBs. The scope of thesis includes both radial EMB and axial (or thrust) EMB\, which handle the radial and axial forces\, respectively\, acting on the rotor assembly. \nDrawing from the switched reluctance machine (SRM) literature\, a flux linkages-based modelling approach is proposed for radial and axial EMB. The flux-linkage characteristics can be obtained through either numerical simulation or measurement\, and can be used to generate the force vs current vs displacement characteristics of the bearing. Such modelling includes the effects of magnetic saturation\, leakage flux and fringing. An improved design procedure is proposed\, which guarantees linear force characteristics along with the desired maximum force. A radial EMB and an axial EMB are designed for load capacities of 180 N and 1600 N\, respectively\, using the improved design procedure and are validated using finite element analysis tools. Further\, a modified geometry of the thrust bearing is proposed to reduce the thrust disc diameter (and thereby\, to cater to higher rotational speeds)\, while maintaining the same load capacity. A systematic PID design procedure is presented for the position control of the EMB\, guaranteeing the required stability margins. The performance of this controller is validated through simulations using detailed models of EMB. \nPosition control of the EMB\, which is an unstable system\, require high-bandwidth control of the EMB coil currents. This\, in turn\, requires high-switching-frequency power amplifiers to feed the coils. An SiC device-based asymmetric H-bridge converter of 300V\, 10 A\, with a switching frequency of 50 kHz\, is designed and tested. Further\, the current controller is designed\, and its reference tracking capability is validated experimentally for different types of current references that are expected during the EMB operation. Further\, this thesis proposes a novel test rig for thrust-bearing characterization. This test rig can characterize the given thrust bearing under static and dynamic conditions (under rotation and varying loading). \n—————— ALL ARE WELCOME —————
URL:https://ee.iisc.ac.in/event/phd-thesis-colloquium-of-my-student-kamisetti-prasad/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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DTSTART;TZID=Asia/Kolkata:20230721T110000
DTEND;TZID=Asia/Kolkata:20230721T130000
DTSTAMP:20260404T030553
CREATED:20230714T112921Z
LAST-MODIFIED:20230721T031651Z
UID:240869-1689937200-1689944400@ee.iisc.ac.in
SUMMARY:[PhD Colloquium Talk by Prachi Singh] - 21-7 in MMCR\, EE @ 330-430pm {Graph Clustering Approaches for Speaker Diarization of Conversational Speech}
DESCRIPTION:Dear All\,\n\n\nWe are pleased to invite you to the following PhD colloquium talk\,\n\n\n========================== \n \nWho: Ms. Prachi Singh\, PhD candidate\, EE.\n\nWhen: 21/7/2023 at 11AM [Note the updated the time]. High Tea at 1045am\n\nWhere: MMCR\, EE\, IISc and in the Teams Link\n\n\nWhat: Graph Clustering Approaches for Speaker Diarization of Conversational Speech \n\n\nAbstract\nIn this era of advanced machine intelligence\, real-world speech applications need to be equipped to deal with conversations involving multiple speakers. An essential first step in speech information extraction from conversational speech is the task of finding “who spoke when”\, also referred to as speaker diarization. The focus of this talk is to describe our efforts in investigating graph clustering techniques for this problem. While graph models have been used in several other domains\, its application to temporal segmentation of speech is the first of its kind.\n\nThe talk is divided into three main parts. In the first part of this talk\, I will describe a novel proposal on self-supervised learning to perform joint representation learning and clustering\, called self-supervised clustering (SSC) for diarization. On the learned representations\, we explore path integral clustering (PIC)\, a graph-based clustering algorithm. The PIC is an agglomerative graph clustering method that performs clustering based on the edge connections of a node\, called path integral. The proposed SSC with path integral clustering (SSC-PIC) is shown to achieve state-of-the-art performance for benchmark datasets.\n\nThe second part of the talk is an extension of SSC-PIC to incorporate metric learning. We design a neural version of the probabilistic linear discriminant analysis (PLDA) approach with learnable parameters to compute a log-likelihood score between embeddings from two segments of the recording.  We propose a joint self-supervised representation learning and metric learning approach called Selfsup-PLDA-PIC.\n\nIn the third part of the talk\, we introduce an end-to-end supervised graph clustering approach. We develop a supervised learning setup using labeled conversational data for training this model. In this setting\, we propose a supervised clustering approach called Supervised HierArchical gRaph Clustering (SHARC) for speaker diarization. This approach uses Graph Neural Networks (GNN) to capture the similarity between the speaker embeddings and perform hierarchical clustering. An extension of this work is the joint training of the speaker embedding extractor along with the GNN module\, referred to as end-to-end SHARC (E-SHARC). To incorporate overlapped speech detection\, I will illustrate how to extend the E-SHARC model for diarization of overlapped speech recordings.\n\nThe talk will conclude with a summary of our key contributions\, while highlighting the pros and cons of using graph-based models for speaker diarization. \n\n\n==========================\n\n\n\n\n\nAll are welcome
URL:https://ee.iisc.ac.in/event/phd-colloquium-talk-by-prachi-singh-21-7-in-mmcr-ee-330-430pm-graph-clustering-approaches-for-speaker-diarization-of-conversational-speech/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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DTSTART;TZID=Asia/Kolkata:20230720T090000
DTEND;TZID=Asia/Kolkata:20230722T173000
DTSTAMP:20260404T030553
CREATED:20230630T011505Z
LAST-MODIFIED:20230714T113104Z
UID:240785-1689843600-1690047000@ee.iisc.ac.in
SUMMARY:A 3 Day workshop on electric vehicle power train design on 20 21 22 July 2023
DESCRIPTION:A 3 Day workshop on electric vehicle power train design on 20 21 22 July 2023 at Dept of EE IISc. \nThe link for workshop : \nhttp://www.nwevtech.com \nPoster:  IISc EE EV workshop poster
URL:https://ee.iisc.ac.in/event/a-3-day-workshop-on-electric-vehicle-power-train-design-on-20-21-22-july-2023/
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DTSTART;TZID=Asia/Kolkata:20230719T143000
DTEND;TZID=Asia/Kolkata:20230719T170000
DTSTAMP:20260404T030553
CREATED:20230718T050416Z
LAST-MODIFIED:20230718T050416Z
UID:240871-1689777000-1689786000@ee.iisc.ac.in
SUMMARY:Ph.D. Thesis Colloquium
DESCRIPTION:Colloquium Announcement \n\nCandidate’s Name       :  BABY SINDHU A V \nDegree Registered      :  Ph.D. \nDate  & Time                :   19th July 2023 @ 2.30 PM \nVenue                            :   Seminar Hall\, High Voltage Lab and in the Teams Link \nTitle                               :  Development of Polymeric Nano/Micro Composite Insulation with \n                                     Better Performance for Various High Voltage  Power Applications \n\nAbstract \n  \nThe  demand for electrical  power is increasing  day by day  necessitating a higher voltage level for power transmission and the development of high speed rails \, electric vehicles\, more electric aircrafts and all electric ships demand for improvement in electric motor capacity in those vehicles. Also the use of cast resin type dry transformers in high moisture area and confined area is increasing since it is more reliable in extreme conditions and also they require less maintenance. All these applications demand for  better insulating materials which can address all the above issues cost effectively. In  recent years\,  the use of  polymeric insulating material  in HV power apparatus is increasing. Hence this study focuses on the development of polymeric  composite insulating  material  for various electrical power applications. \nSilicone rubber is a  preferred  material for use as weathershed material in outdoor polymeric insulators used in high voltage power transmission lines.   The tracking & erosion on the insulator surface due to the electrical discharges  and corona cutting  of the insulator surface  are the main issues related to outdoor polymeric insulators and these are  addressed in this study.   Tracking and erosion performance of silicone rubber filled with nano/micro fillers of different loadings is  analysed using Inclined Plane Test (IPT) as per IEC 60587.  A computational study on the behavior of the samples subjected to  tracking  is also done and the same is verified with the experimental results obtained in this work. Corona ageing studies are done by ageing the samples in a corona chamber for 25 hours. Hydrophobicity changes\, crack width formation and erosion performance after corona ageing are evaluated. An effort is made to correlate the value of leakage current to the eroded mass and a reliable online condition monitoring tool is also developed as a part of the thesis work. \n   Again\, epoxy is extensively used in  many  electrical  power apparatus such as ground wall insulation of the high voltage rotating machines\, as spacer material  in Gas Insulated Substations (GIS)\, as solid insulation in dry type transformers etc. Heat dissipation is an important area of concern when using  epoxy as ground wall insulation in rotating machines and as an insulation in  cast resin dry type transformer. The performance of epoxy filled with nano/ micron sized fillers are  investigated in this study in terms of their heat removal capacity and at the same time  retaining their dielectric properties. The improvement in thermal conductivity is correlated with the performance of various composites developed. The formation of track in the ground wall insulation and the failure of the machine is a major issue as far as rotating machines are considered. Hence the tracking time of various epoxy composites are observed and compared. The initiation of a faint track on the surface of the insulator is monitored with the help of a ratio of third harmonic component to the fundamental component. This ratio can be used as an efficient condition monitoring tool for rotating machines by measuring the leakage current online. \n    In summary polymeric composite insulating  materials based on silicone rubber and epoxy with different fillers and loadings and having   better electrical and thermal performance than the conventional materials  are developed in this study. \n  \n                                                                                                   All are welcome \n  \nMeeting link
URL:https://ee.iisc.ac.in/event/ph-d-thesis-colloquium/
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