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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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TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20241216T160000
DTEND;TZID=Asia/Kolkata:20241216T170000
DTSTAMP:20260526T163947
CREATED:20241216T042200Z
LAST-MODIFIED:20241216T042200Z
UID:241859-1734364800-1734368400@ee.iisc.ac.in
SUMMARY:EE Talk: The Role of Distribution System Operators (DSOs) in Enabling Integration and Orchestrating Coordinated Operation of DERs
DESCRIPTION:Title: The Role of Distribution System Operators (DSOs) in Enabling Integration and Orchestrating Coordinated Operation of DERs \nTime and Date: 4 PM to 5 PM\, Monday 16 December 2024 \nMode: Hybrid Mode \nJoin the meeting now \nVenue: MMCR\, 1st Floor\, EE\, IISc \nAbstract: The electricity landscape is undergoing significant changes due to the proliferation of distributed energy resources (DERs)\, and increasingly smart consumers (prosumers)\, proactively managing their local consumption and generation – through intelligent devices like smart thermostats\, solar panels\, and batteries energy storage systems. Recent advances in information & communication technologies\, and smart metering\, provide strategic opportunities for prosumers to reform their conventional energy practices towards more consumer-centric economies. From an operational perspective\, managing power distribution networks is becoming more difficult with such active grid-edge systems providing limited to no visibility or control. Towards addressing these challenges\, distribution network operators are broadening the scope of their roles and deepening their operational reach to become Distribution System Operators (DSOs) to accommodate a high penetration of DERs\, coordinate the DER flexibility and ensure reliable and quality supply to end consumers. In this context\, this seminar will discuss some DSO coordination strategies for enabling DERs to actively participate in local as well as system-wide management tasks along with some modelling and simulation capabilities towards analyzing the system-level impacts of implementing such coordination mechanisms. \nA person wearing glasses and a pink shirt \nDescription automatically generatedBio: Dr. Monish Mukherjee (M’ 21) received his B.E. degree from the Department of Electrical Engineering\, Jadavpur University\, Kolkata\, India in 2016 and his Ph.D. degree in Electrical and Computer Engineering from Washington State University\, Pullman\, WA\, in 2021. He is currently a research scientist & engineer at Pacific Northwest National Laboratory (PNNL)\, USA. He also holds an adjunct faculty appointment at Washington State University in Pullman. In PNNL\, he leads the development of the Resilience Applications for Transactive Energy Systems. He also leads an effort for developing distribution resource planning and DER coordination mechanisms for the state of Vermont\, USA along with some ongoing ADMS-related efforts in PNNL.  His research interests include transactive energy systems\, distribution system modelling and simulation\, grid resiliency and condition monitoring of high voltage power equipment. \n________________________________________________________________________________ \nJoin the meeting now \nMeeting ID: 485 337 297 291 \nPasscode: yD3h3v2y
URL:https://ee.iisc.ac.in/event/ee-talk-the-role-of-distribution-system-operators-dsos-in-enabling-integration-and-orchestrating-coordinated-operation-of-ders/
LOCATION:Multi-Media Class Room (MMCR)\, EE Department (Hybrid mode)
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DTSTART;TZID=Asia/Kolkata:20241219T120000
DTEND;TZID=Asia/Kolkata:20241219T130000
DTSTAMP:20260526T163947
CREATED:20241209T061300Z
LAST-MODIFIED:20241209T061300Z
UID:241856-1734609600-1734613200@ee.iisc.ac.in
SUMMARY:Colloquium on Low-Complexity Classification of Patients with Amyotrophic Lateral Sclerosis from Healthy Controls: Exploring the Role of Hypernasality
DESCRIPTION:NAME OF THE STUDENT         :  Anjali Jayakumar \nDEGREE REGISTERED             :     M. Tech. (Research) \nDATE AND DAY                  :     19th December\, 2024\, THURSDAY \nTIME                          :     12:00 PM \nVENUE                         :     EE\, MMCR \nTeams meeting link      :     https://tinyurl.com/2zckabj2 \nT I T L E\nLow-Complexity Classification of Patients with Amyotrophic Lateral Sclerosis from Healthy Controls: Exploring the Role of Hypernasality \nAbstract:\nAmyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder characterized by motor neuron degeneration\, leading to muscle weakness\, atrophy\, and speech impairments. Dysarthria\, a motor speech disorder\, is an early symptom in approximately 30% of ALS patients\, with hypernasality—excessive nasal resonance due to velopharyngeal dysfunction—observed in around 73.88% of individuals with bulbar-onset ALS. These speech impairments significantly hinder communication and affect patients’ quality of life. Current ALS monitoring methods\, including clinical assessments\, genetic testing\, electromyography (EMG)\, and magnetic resonance imaging (MRI) can be time-consuming and invasive\, whereas speech-based approaches provide a non-invasive and efficient alternative for continuous monitoring. However\, the lack of large ALS-specific speech datasets hinders the development of reliable models. This study aims to develop a simplified\, low-complexity model to distinguish ALS speech from healthy control (HC) speech\, exploring the role of hypernasality for effective classification. By leveraging hypernasality as an indicator of ALS\, the study seeks to develop machine learning models that train on healthy speech data\, avoiding the need for large amounts of ALS speech data. Ultimately\, the study aims to develop a low-complexity classification method for classifying ALS patients from HC subjects using their speech.\nThe study begins by simplifying deep learning models\, transitioning from complex Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) architectures to simpler Deep Neural Networks (DNNs) of varying complexity. These models are trained using Mel Frequency Cepstral Coefficients (MFCCs)\, along with their deltas and double-deltas. Additionally\, various temporal statistics of the MFCCs and their derivatives are explored to reduce feature dimensionality\, thereby decreasing model complexity in terms of the number of model parameters and Floating-Point Operations (FLOPs)\, resulting in reduced computational cost. The study then investigates the presence of hypernasality in ALS speech of varying dysarthria severity\, as well as the HC speech\, using HuBERT representations and a DNN model trained on healthy speech for nasal vs. non-nasal phoneme classification. Finally\, the study integrates hypernasality in ALS speech into the ALS vs. HC classification by training a model for nasal vs. non-nasal phoneme classification using only healthy speech data. The model then classifies ALS vs. HC speech\, with ALS treated as the nasal class and HC as the non-nasal class\, demonstrating its effectiveness in distinguishing ALS speech from HC speech\, while also validating the potential of simplified DNN models for the classification.\nThe results show that reduced-complexity DNN models can outperform CNN-BiLSTM models\, achieving up to 5.67% and 6.59% higher classification accuracies for Spontaneous Speech (SPON) and Diadochokinetic Rate (DIDK) tasks\, respectively\, with a significant reduction in the number of model parameters by 99.99% and FLOPs by 99.60%. Dimensionality reduction minimizes complexity\, with a further reduction of 94.59% in the number of model parameters and 94.61% in FLOPs\, resulting in minimal accuracy loss of 1.76% for SPON and 5.17% for DIDK. Analysis of hypernasality across varying ALS severity levels reveals that individuals with severe dysarthria exhibit the highest levels of nasalized speech\, followed by those with mild dysarthria\, with normal ALS speech and healthy controls showing the lowest levels. This finding is validated with manually annotated nasality scores. Hypernasality proves to be an effective indicator for distinguishing ALS from HC\, achieving up to 66.48% and 81.46% accuracy for SPON and DIDK tasks\, respectively\, with low-complexity models.
URL:https://ee.iisc.ac.in/event/colloquium-on-low-complexity-classification-of-patients-with-amyotrophic-lateral-sclerosis-from-healthy-controls-exploring-the-role-of-hypernasality/
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DTSTART;TZID=Asia/Kolkata:20241231T030000
DTEND;TZID=Asia/Kolkata:20241231T160000
DTSTAMP:20260526T163947
CREATED:20241230T040210Z
LAST-MODIFIED:20241230T040210Z
UID:241869-1735614000-1735660800@ee.iisc.ac.in
SUMMARY:Colloquium on Design and Performance Optimization of Power Converters for Energy Storage Systems
DESCRIPTION:PhD Thesis Colloquium\nTitle: Design and Performance Optimization of Power Converters for Energy Storage Systems \nSpeaker: P. Roja\nDate: Tuesday\, Dec 31\, 2024\nTime: 3.00pm-4.00pm\nVenue: MMCR – EE \nAbstract:\nEnergy shortages and power outages have emerged as critical concerns in the contemporary energy landscape\, exacerbated by escalating energy demands and the global imperative towards clean energy and decarbonization. Addressing these challenges necessitates the deployment of energy storage systems (ESS) to mitigate both long- and short-duration outages\, coupled with the integration of renewable energy sources through power converter interfaces. While battery-based ESS are conventionally employed for short-term blackouts\, this work focuses on developing ultracapacitor (UC)-based ESS tailored for pulsed power applications\, chosen for their inherent high-power density and superior lifecycle characteristics. The research also investigates isolated DC-DC converters\, specifically phase-shifted full-bridge (PSFB) topology\, opted due to its constant frequency operation and inherent soft-switching features. \nThis research encompasses the optimization of UC stack sizing and power converter design for specific contingency requirements. The inherent non-linear behavior of UCs is analyzed\, leading to the development of a framework for accurately characterizing the effective UC stack capacitance. This framework is utilized to propose a systematic design procedure that optimizes the discharge ratio and iteratively selects stack parameters\, minimizing the overall system cost.\nFurthermore\, the research investigates PSFB converter for both low and high-power applications. A comprehensive analysis of the PSFB topology is conducted\, examining the influence of various circuit parameters\, including transformer parasitics and device capacitances\, on converter operation and the design trade-offs. This analysis culminates in the development of a two-level loss-optimal iterative design algorithm that determines a unique set of design parameters across a wide range of specifications. \nFor high-power applications\, the research explores a modular system of PSFB converters configured in an input parallel output parallel (IPOP) topology. Recognizing the limitations of traditional equal power-sharing schemes\, this work proposes an asymmetrical module design coupled with a Lagrangian loss-optimal load-sharing control technique to enhance system efficiency. This approach enables the system to operate with high efficiency across the entire load range\, effectively managing both fixed and dynamic loads. \nThe efficacy of modeling\, analysis and the proposed design algorithms for the UC stack and the PSFB converter\, including its modular configurations\, is validated through experimental verification on 1-3kW hardware prototypes.
URL:https://ee.iisc.ac.in/event/colloquium-on-design-and-performance-optimization-of-power-converters-for-energy-storage-systems/
LOCATION:MMCR\, Hall C 241\, 1st floor\, EE department
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