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METHOD:PUBLISH
X-WR-CALNAME:EE
X-ORIGINAL-URL:https://ee.iisc.ac.in
X-WR-CALDESC:Events for EE
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
TZOFFSETFROM:+0530
TZOFFSETTO:+0530
TZNAME:IST
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230620T080000
DTEND;TZID=Asia/Kolkata:20230707T170000
DTSTAMP:20260528T105850
CREATED:20230703T083659Z
LAST-MODIFIED:20230703T084023Z
UID:240793-1687248000-1688749200@ee.iisc.ac.in
SUMMARY:Summer School 2023
DESCRIPTION:Summer School 2023 Website Link \nsummer school | EE (iisc.ac.in)
URL:https://ee.iisc.ac.in/event/summer-school-2023/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230621T163000
DTEND;TZID=Asia/Kolkata:20230706T223000
DTSTAMP:20260528T105850
CREATED:20230612T003521Z
LAST-MODIFIED:20230718T050519Z
UID:240769-1687365000-1688682600@ee.iisc.ac.in
SUMMARY:Provisional Research Admission Results 2023
DESCRIPTION:Provisional research admission results 2023 \nProvisinal Result reserch 2023 \n 
URL:https://ee.iisc.ac.in/event/provisional-research-admission-results-2023/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230710T150000
DTEND;TZID=Asia/Kolkata:20230710T170000
DTSTAMP:20260528T105850
CREATED:20230710T034621Z
LAST-MODIFIED:20230710T034621Z
UID:240823-1689001200-1689008400@ee.iisc.ac.in
SUMMARY:IEEE PES Talk: Prof. Satyajayant Misra\, 10-07-2023\, Monday 3PM - 4PM\, MMCR\, EE
DESCRIPTION:Title: An Information-Centric Network Architecture for DDoS Protection in the Smart Grid \n\n\n\nTime and Time: 3 PM – 4 PM\, Monday\, 10 July 2023 \n\nVenue:  MMCR\, EE\, IISc \n\n\n\n\n\nAbstract: With the proliferation of differently-abled and heterogeneous devices in the smart grid Denial of Service (DoS) is becoming an even more potent attack vector than it was before. We demonstrate the ease with which an adversary can orchestrate DoS and distributed DoS (DDoS) attacks on the grid. In this talk\, we will discuss our proposed architecture iCAD–an information-centric network architecture\, and our prior architecture iCAAP\, on which iCAD is built. We discuss our architecture in detail and demonstrate the architecture and the mitigation technique’s effectiveness in mitigating significant DoS/DDoS attacks. \n\n\n\n\n \nBio: Dr. Satyajayant Misra (Jay) is a professor of computer science and electrical and computer engineering at New Mexico State University (NMSU). He is also the associate dean of research for the College of Engineering. His research expertise is in cybersecurity and computer networking and his recent research interests are in edge computing\, future Internet\, the smart grid\, cryptocurrencies\, and decentralized finance. He has over 100 peer-reviewed publications in several prestigious venues\, such as ACM CCS\, IEEE/ACM Transactions on Networking and Mobile Computing\, IEEE/ACM Supercomputing Conference\, and IEEE Transactions on Intelligent Transportation Systems. His research has garnered over 7700 international citations and he has an h-index of 26 and an i-10 index of 60. His research has been supported by Intel Labs\, US NSF\, DoD\, DoE\, DoEd\, and the FAA\, and national labs such as Sandia National Lab\, LANL\, and Idaho National Lab.
URL:https://ee.iisc.ac.in/event/ieee-pes-talk-prof-satyajayant-misra-10-07-2023-monday-3pm-4pm-mmcr-ee/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230711T150000
DTEND;TZID=Asia/Kolkata:20230711T170000
DTSTAMP:20260528T105851
CREATED:20230704T112914Z
LAST-MODIFIED:20230704T112914Z
UID:240799-1689087600-1689094800@ee.iisc.ac.in
SUMMARY:M.Tech.(Res.) Thesis Defense of Vishwabandhu Uttam
DESCRIPTION:Title: A Unified Modeling Approach for Design and Performance Improvement of Triple Active Bridge Converter\nName of the Student: VISHWABANDHU UTTAM\,  M.Tech (Res) in Electrical Engineering\n\nResearch Supervisor: Vishnu Mahadeva Iyer\nExternal Examiner: Anirudh Guha\, Assistant Professor\, IIT Palakkad\n\nDate/Time: 11th July 2023\, Tuesday at 3:00 PM\nLocation: MMCR (Electrical Engineering)\n\nAbstract:Triple Active Bridge (TAB) converter is a multi-port DC-DC converter. This converter is an extension of the popular Dual Active Bridge converter. It features desirable traits of the DAB converter\, such as high-power density\, galvanic isolation\, and bi-directional power flow between any of the ports. As in other multi-port converters\, redundant power conversion is minimized through component sharing among the ports in a TAB converter. All the switches in a TAB converter can undergo soft switching over a wide range of operating points\, reducing switching losses and the size of auxiliary components. The multiple degrees of freedom in modulating a TAB converter offer several design and operational flexibilities.However\, this converter has yet to come into the limelight despite these advantages. One of the reasons is the lack of a unified analytical framework for the design and operation of this converter. The existing models for the TAB converter are limited in scope and cannot be easily used for the design and operational optimization of the converter. This work focuses on developing simple\, unified models for analyzing the TAB converter.\nThe popular First Harmonic Approximated (FHA) large-signal and small-signal models are evaluated to understand their limitations. Improved large-signal and small-signal Generalised Harmonic Models (GHM) are developed by incorporating the impact of higher-order harmonics. While the GHM is shown to be superior for small-signal analysis of the converter\, it is not suitable to analyze the soft-switching bounds of the TAB converter. To overcome the limitations of GHM\, a Unified Model that incorporates the impact of the magnetising inductance of the three-winding transformer is proposed. The Unified Model can accurately predict the AC port currents at the switching instants and is used to study the soft-switching bounds of the TAB converter. The GHM and Unified Model are validated through extensive switching circuit simulations and experimental results from a 1 kW hardware prototype developed in the laboratory. Further\, a new design algorithm for the TAB converter is proposed. The proposed algorithm leverages the FHA model’s simplicity and the Unified Model’s accuracy. Finally\, a new modulation scheme based on Penta Phase Shift with five degrees of freedom is proposed to achieve soft switching across the operational range of the TAB converter.\n\nWe request your presence at the thesis defense.\nAll are welcome.
URL:https://ee.iisc.ac.in/event/m-tech-res-thesis-defense-of-vishwabandhu-uttam/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230712T110000
DTEND;TZID=Asia/Kolkata:20230712T130000
DTSTAMP:20260528T105851
CREATED:20230711T065544Z
LAST-MODIFIED:20230711T065729Z
UID:240829-1689159600-1689166800@ee.iisc.ac.in
SUMMARY:[Oral Examination Talk] - A Learnable Distillation Approach For Model-agnostic Explainability With Multimodal Applications\, {Debarpan\, EE} [MMCR\, EE\, 11:00AM\, July 12]
DESCRIPTION:Title: A Learnable Distillation Approach For Model-agnostic Explainability With Multimodal Applications \nVenue: MMCR\, C241\, EE\, IISc\, and also in the Teams Link\n\n\nDate and Time: July 12\, 11:00AM.\n\n\n\nSpeaker: ​ Debarpan Bhattacharya\, MTech (Res) EE\, IISc\n\n\nAbstract: \nDeep neural networks are the most widely used examples of sophisticated mapping functions from feature space to class labels. In the recent years\, several high impact decisions in domains such as finance\, healthcare\, law and autonomous driving\, are made with deep models. In these tasks\, the model decisions lack interpretability\, and pose difficulties in making the models accountable. Hence\, there is a strong demand for developing explainable approaches which can elicit how the deep neural architecture generates the output decisions. \nThe current frameworks for explainability of model learning are based on gradients (eg. GradCAM\, guided-gradCAM\, Integrated gradients etc) or based on locally linear assumptions (eg. LIME). Some of these approaches require the knowledge of the deep model architecture\, which may be restrictive in many applications. Further\, most of the prior works in the literature highlight the results on a set of small number of examples to illustrate the performance of these XAI methods\, often lacking statistical evaluation. This talk proposes a new approach for explainability based on mask estimation approaches\, called the Distillation Approach for Model-agnostic Explainability (DAME). The DAME is a saliency-based explainability model that is post-hoc\, model-agnostic\, and applicable to any architecture/domain. The DAME is a student-teacher modeling approach\, where the teacher model is the original model for which the explainability is sought\, while the student model is the mask estimation model. The input sample is augmented with various data augmentation techniques to produce numerous samples in the immediate vicinity of the input. Using these samples\, the mask estimation model is learned to learn the saliency map of the input sample for predicting the labels. A distillation loss is used to train the DAME model\, and the student model tries to locally approximate the original model. Once the DAME model is trained\, the DAME generates a region of the input (either in space or in time-domain for images and audio samples\, respectively) that best explains the model predictions.  \nWe also propose an evaluation framework\, for both image and audio tasks\, where the XAI models are evaluated in a statistical framework on a set of held-out of examples with the Intersection-over-Union (IoU) metric. We have validated the DAME model for vision\, audio and biomedical tasks. Firstly\, we deploy the DAME for explaining a ResNet-50/ViT classifier pre-trained on ImageNet dataset for the object recognition task. Secondly\, we explain the predictions made by ResNet-50 classifier fine-tuned on Environmental Sound Classification (ESC-10) dataset for the audio event classification task. Finally\, we validate the DAME model on the COVID-19 classification task using cough audio recordings. In these tasks\, the DAME model is shown to outperform existing benchmarks for explainable modeling.  \n\n\n  \n​\n—————\n\nAll are welcome
URL:https://ee.iisc.ac.in/event/oral-examination-talk-a-learnable-distillation-approach-for-model-agnostic-explainability-with-multimodal-applications-debarpan-ee-mmcr-ee-1100am-july-12/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230714T110000
DTEND;TZID=Asia/Kolkata:20230714T130000
DTSTAMP:20260528T105851
CREATED:20230710T034816Z
LAST-MODIFIED:20230710T034816Z
UID:240825-1689332400-1689339600@ee.iisc.ac.in
SUMMARY:[PhD Colloquium of Akshara Soman\, EE on 14/7\, 11AM] {Investigating Neural Encoding of Word Learning and Speech Perception}
DESCRIPTION:Dear All\,\nInviting you to the PhD Thesis Colloquium talk with the following details. \n \n—-\nSpeaker: Ms. Akshara Soman\n\n\n\n\nTitle: Investigating Neural Encoding of Word Learning and Speech Perception\n\nDate & Time : 14-7-2023\, 11:00AM\nVenue : MMCR (C241)\, EE\, IISc\n\nResearch Supervisor: Prof. Sriram Ganapathy\, EE.\n \n=================================================================ABSTRACT\nLanguage learning and speech perception are remarkable feats performed by the human brain\, involving complex neural mechanisms that allow us to understand and communicate with one another. Unravelling the mysteries of these mechanisms has far-reaching implications\, from theories of human cognition to developing effective language learning strategies and advancing speech technology. By employing a multidisciplinary approach encompassing neural investigations using EEG signals\, behavioral analyses\, and machine learning perspectives\, this talk sheds light on the underlying processes involved in word learning and speech perception.\n\nThe talk is divided into three parts. The first part begins by examining how an imitation based learning of foreign sounds is captured in the EEG signals. In this listen and reproduce setting\, subjects were introduced to words from a foreign language (Japanese)\, and English. The subjects were also asked to articulate the words. The results show that time-frequency features and phase in the EEG signal contain information for language discrimination. Further analysis showed that speech production improved over time\, and the frontal brain regions were involved in language learning. These findings suggest the potential of EEG for personalized language exercises and for assessing learners’ abilities.\n\nThe next part of the talk investigates how learning patterns change when semantics are introduced and presented in a sentence context. The participants listen to Japanese words in an English sentence\, once before understanding the semantics of these words and later with the semantic exposure. We quantify the learning patterns in the EEG signal. Notably\, a delayed P600 component emerges for Japanese words\, suggesting short-term memory processing unlike the N400 typically seen for incongruent words in the known language. The brain regions associated with semantic learning are also identified in this study using the EEG data.\n\nIn the final part of the talk\, we analyze the neural mechanisms of human speech comprehension using a match-mismatch classification of the continuous speech stimulus and the neural response (EEG). We make three major contributions on this front –  i) Illustrate the role of word-boundaries in continuous speech comprehension for the first time\, ii) Elicit the encoding of speech data (acoustics) as well as the text data (semantics) in the EEG signal\, and\, iii) Increased signature of semantic content (text) in the EEG data in acoustically challenging environments of dichotic listening.  The findings have potential applications for understanding speech recognition in noise\, brain-computer interfaces\, and attention studies.\n\nIn summary\, the talk will attempt to enhance our understanding of language learning\, speech comprehension\, and the neural mechanisms involved.\n===========================================================\n\n\nALL ARE WELCOME\n\n\n\n\n\n\n—-
URL:https://ee.iisc.ac.in/event/phd-colloquium-of-akshara-soman-ee-on-14-7-11am-investigating-neural-encoding-of-word-learning-and-speech-perception/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230719T143000
DTEND;TZID=Asia/Kolkata:20230719T170000
DTSTAMP:20260528T105851
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230720T090000
DTEND;TZID=Asia/Kolkata:20230722T173000
DTSTAMP:20260528T105851
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230721T110000
DTEND;TZID=Asia/Kolkata:20230721T130000
DTSTAMP:20260528T105851
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230726T090000
DTEND;TZID=Asia/Kolkata:20230726T230000
DTSTAMP:20260528T105851
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230727T103000
DTEND;TZID=Asia/Kolkata:20230727T123000
DTSTAMP:20260528T105851
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/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20230728T160000
DTEND;TZID=Asia/Kolkata:20230728T173000
DTSTAMP:20260528T105851
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)
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DTSTART;TZID=Asia/Kolkata:20230728T163000
DTEND;TZID=Asia/Kolkata:20230728T173000
DTSTAMP:20260528T105851
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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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DTSTART;TZID=Asia/Kolkata:20230731T160000
DTEND;TZID=Asia/Kolkata:20230731T173000
DTSTAMP:20260528T105851
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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