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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.10858">arXiv:2409.10858</a> <span> [<a href="https://arxiv.org/pdf/2409.10858">pdf</a>, <a href="https://arxiv.org/format/2409.10858">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Speech Recognition for Analysis of Police Radio Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Srivastava%2C+T">Tejes Srivastava</a>, <a href="/search/eess?searchtype=author&query=Chou%2C+J">Ju-Chieh Chou</a>, <a href="/search/eess?searchtype=author&query=Shroff%2C+P">Priyank Shroff</a>, <a href="/search/eess?searchtype=author&query=Livescu%2C+K">Karen Livescu</a>, <a href="/search/eess?searchtype=author&query=Graziul%2C+C">Christopher Graziul</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.10858v1-abstract-short" style="display: inline;"> Police departments around the world use two-way radio for coordination. These broadcast police communications (BPC) are a unique source of information about everyday police activity and emergency response. Yet BPC are not transcribed, and their naturalistic audio properties make automatic transcription challenging. We collect a corpus of roughly 62,000 manually transcribed radio transmissions (~46… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10858v1-abstract-full').style.display = 'inline'; document.getElementById('2409.10858v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10858v1-abstract-full" style="display: none;"> Police departments around the world use two-way radio for coordination. These broadcast police communications (BPC) are a unique source of information about everyday police activity and emergency response. Yet BPC are not transcribed, and their naturalistic audio properties make automatic transcription challenging. We collect a corpus of roughly 62,000 manually transcribed radio transmissions (~46 hours of audio) to evaluate the feasibility of automatic speech recognition (ASR) using modern recognition models. We evaluate the performance of off-the-shelf speech recognizers, models fine-tuned on BPC data, and customized end-to-end models. We find that both human and machine transcription is challenging in this domain. Large off-the-shelf ASR models perform poorly, but fine-tuned models can reach the approximate range of human performance. Our work suggests directions for future work, including analysis of short utterances and potential miscommunication in police radio interactions. We make our corpus and data annotation pipeline available to other researchers, to enable further research on recognition and analysis of police communication. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10858v1-abstract-full').style.display = 'none'; document.getElementById('2409.10858v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by SLT 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.14861">arXiv:2406.14861</a> <span> [<a href="https://arxiv.org/pdf/2406.14861">pdf</a>, <a href="https://arxiv.org/format/2406.14861">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> </div> </div> <p class="title is-5 mathjax"> Resilience of the Electric Grid through Trustable IoT-Coordinated Assets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Nair%2C+V+J">Vineet J. Nair</a>, <a href="/search/eess?searchtype=author&query=Venkataramanan%2C+V">Venkatesh Venkataramanan</a>, <a href="/search/eess?searchtype=author&query=Srivastava%2C+P">Priyank Srivastava</a>, <a href="/search/eess?searchtype=author&query=Sarker%2C+P+S">Partha S. Sarker</a>, <a href="/search/eess?searchtype=author&query=Srivastava%2C+A">Anurag Srivastava</a>, <a href="/search/eess?searchtype=author&query=Marinovici%2C+L+D">Laurentiu D. Marinovici</a>, <a href="/search/eess?searchtype=author&query=Zha%2C+J">Jun Zha</a>, <a href="/search/eess?searchtype=author&query=Irwin%2C+C">Christopher Irwin</a>, <a href="/search/eess?searchtype=author&query=Mittal%2C+P">Prateek Mittal</a>, <a href="/search/eess?searchtype=author&query=Williams%2C+J">John Williams</a>, <a href="/search/eess?searchtype=author&query=Kumar%2C+J">Jayant Kumar</a>, <a href="/search/eess?searchtype=author&query=Poor%2C+H+V">H. Vincent Poor</a>, <a href="/search/eess?searchtype=author&query=Annaswamy%2C+A+M">Anuradha M. Annaswamy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.14861v2-abstract-short" style="display: inline;"> The electricity grid has evolved from a physical system to a cyber-physical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. Howev… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14861v2-abstract-full').style.display = 'inline'; document.getElementById('2406.14861v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.14861v2-abstract-full" style="display: none;"> The electricity grid has evolved from a physical system to a cyber-physical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things (IoT) devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity co-simulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14861v2-abstract-full').style.display = 'none'; document.getElementById('2406.14861v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to the Proceedings of the National Academy of Sciences (PNAS) 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.05145">arXiv:2311.05145</a> <span> [<a href="https://arxiv.org/pdf/2311.05145">pdf</a>, <a href="https://arxiv.org/format/2311.05145">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ICCPS61052.2024.00012">10.1109/ICCPS61052.2024.00012 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Enhancing power grid resilience to cyber-physical attacks using distributed retail electricity markets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Nair%2C+V+J">Vineet Jagadeesan Nair</a>, <a href="/search/eess?searchtype=author&query=Srivastava%2C+P">Priyank Srivastava</a>, <a href="/search/eess?searchtype=author&query=Annaswamy%2C+A">Anuradha Annaswamy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.05145v3-abstract-short" style="display: inline;"> We propose using a hierarchical retail market structure to alert and dispatch resources to mitigate cyber-physical attacks on a distribution grid. We simulate attacks where a number of generation nodes in a distribution grid are attacked. We show that the market is able to successfully meet the shortfall between demand and supply by utilizing the flexibility of remaining resources while minimizing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.05145v3-abstract-full').style.display = 'inline'; document.getElementById('2311.05145v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.05145v3-abstract-full" style="display: none;"> We propose using a hierarchical retail market structure to alert and dispatch resources to mitigate cyber-physical attacks on a distribution grid. We simulate attacks where a number of generation nodes in a distribution grid are attacked. We show that the market is able to successfully meet the shortfall between demand and supply by utilizing the flexibility of remaining resources while minimizing any extra power that needs to be imported from the main transmission grid. This includes utilizing upward flexibility or reserves of remaining online generators and some curtailment or shifting of flexible loads, which results in higher costs. Using price signals and market-based coordination, the grid operator can achieve its objectives without direct control over distributed energy resources and is able to accurately compensate prosumers for the grid support they provide. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.05145v3-abstract-full').style.display = 'none'; document.getElementById('2311.05145v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to the 15th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) and as part of the CPS-IoT Week 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.00583">arXiv:2205.00583</a> <span> [<a href="https://arxiv.org/pdf/2205.00583">pdf</a>, <a href="https://arxiv.org/ps/2205.00583">ps</a>, <a href="https://arxiv.org/format/2205.00583">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Accelerated Algorithms for a Class of Optimization Problems with Constraints </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Parashar%2C+A">Anjali Parashar</a>, <a href="/search/eess?searchtype=author&query=Srivastava%2C+P">Priyank Srivastava</a>, <a href="/search/eess?searchtype=author&query=Annaswamy%2C+A+M">Anuradha M. Annaswamy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.00583v2-abstract-short" style="display: inline;"> This paper presents a framework to solve constrained optimization problems in an accelerated manner based on High-Order Tuners (HT). Our approach is based on reformulating the original constrained problem as the unconstrained optimization of a loss function. We start with convex optimization problems and identify the conditions under which the loss function is convex. Building on the insight that… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.00583v2-abstract-full').style.display = 'inline'; document.getElementById('2205.00583v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.00583v2-abstract-full" style="display: none;"> This paper presents a framework to solve constrained optimization problems in an accelerated manner based on High-Order Tuners (HT). Our approach is based on reformulating the original constrained problem as the unconstrained optimization of a loss function. We start with convex optimization problems and identify the conditions under which the loss function is convex. Building on the insight that the loss function could be convex even if the original optimization problem is not, we extend our approach to a class of nonconvex optimization problems. The use of a HT together with this approach enables us to achieve a convergence rate better than state-of-the-art gradient-based methods. Moreover, for equality-constrained optimization problems, the proposed method ensures that the state remains feasible throughout the evolution, regardless of the convexity of the original problem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.00583v2-abstract-full').style.display = 'none'; document.getElementById('2205.00583v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.00977">arXiv:2204.00977</a> <span> [<a href="https://arxiv.org/pdf/2204.00977">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Deep Speech Based End-to-End Automated Speech Recognition (ASR) for Indian-English Accents </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Dubey%2C+P">Priyank Dubey</a>, <a href="/search/eess?searchtype=author&query=Shah%2C+B">Bilal Shah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.00977v1-abstract-short" style="display: inline;"> Automated Speech Recognition (ASR) is an interdisciplinary application of computer science and linguistics that enable us to derive the transcription from the uttered speech waveform. It finds several applications in Military like High-performance fighter aircraft, helicopters, air-traffic controller. Other than military speech recognition is used in healthcare, persons with disabilities and many… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.00977v1-abstract-full').style.display = 'inline'; document.getElementById('2204.00977v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.00977v1-abstract-full" style="display: none;"> Automated Speech Recognition (ASR) is an interdisciplinary application of computer science and linguistics that enable us to derive the transcription from the uttered speech waveform. It finds several applications in Military like High-performance fighter aircraft, helicopters, air-traffic controller. Other than military speech recognition is used in healthcare, persons with disabilities and many more. ASR has been an active research area. Several models and algorithms for speech to text (STT) have been proposed. One of the most recent is Mozilla Deep Speech, it is based on the Deep Speech research paper by Baidu. Deep Speech is a state-of-art speech recognition system is developed using end-to-end deep learning, it is trained using well-optimized Recurrent Neural Network (RNN) training system utilizing multiple Graphical Processing Units (GPUs). This training is mostly done using American-English accent datasets, which results in poor generalizability to other English accents. India is a land of vast diversity. This can even be seen in the speech, there are several English accents which vary from state to state. In this work, we have used transfer learning approach using most recent Deep Speech model i.e., deepspeech-0.9.3 to develop an end-to-end speech recognition system for Indian-English accents. This work utilizes fine-tuning and data argumentation to further optimize and improve the Deep Speech ASR system. Indic TTS data of Indian-English accents is used for transfer learning and fine-tuning the pre-trained Deep Speech model. A general comparison is made among the untrained model, our trained model and other available speech recognition services for Indian-English Accents. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.00977v1-abstract-full').style.display = 'none'; document.getElementById('2204.00977v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.04502">arXiv:2203.04502</a> <span> [<a href="https://arxiv.org/pdf/2203.04502">pdf</a>, <a href="https://arxiv.org/format/2203.04502">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Learning Invariant Stabilizing Controllers for Frequency Regulation under Variable Inertia </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Srivastava%2C+P">Priyank Srivastava</a>, <a href="/search/eess?searchtype=author&query=Hidalgo-Gonzalez%2C+P">Patricia Hidalgo-Gonzalez</a>, <a href="/search/eess?searchtype=author&query=Cortes%2C+J">Jorge Cortes</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.04502v1-abstract-short" style="display: inline;"> Declines in cost and concerns about the environmental impact of traditional generation have boosted the penetration of renewables and non-conventional distributed energy resources into the power grid. The intermittent availability of these resources causes the inertia of the power system to vary over time. As a result, there is a need to go beyond traditional controllers designed to regulate frequ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.04502v1-abstract-full').style.display = 'inline'; document.getElementById('2203.04502v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.04502v1-abstract-full" style="display: none;"> Declines in cost and concerns about the environmental impact of traditional generation have boosted the penetration of renewables and non-conventional distributed energy resources into the power grid. The intermittent availability of these resources causes the inertia of the power system to vary over time. As a result, there is a need to go beyond traditional controllers designed to regulate frequency under the assumption of invariant dynamics. This paper presents a learning-based framework for the design of stable controllers based on imitating datasets obtained from linear-quadratic regulator (LQR) formulations for different switching sequences of inertia modes. The proposed controller is linear and invariant, thereby interpretable, does not require the knowledge of the current operating mode, and is guaranteed to stabilize the switching power dynamics. We also show that it is always possible to stabilize the switched system using a communication-free local controller, whose implementation only requires each node to use its own state. Simulations on a 12-bus 3-region network illustrate our results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.04502v1-abstract-full').style.display = 'none'; document.getElementById('2203.04502v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.00698">arXiv:2112.00698</a> <span> [<a href="https://arxiv.org/pdf/2112.00698">pdf</a>, <a href="https://arxiv.org/ps/2112.00698">ps</a>, <a href="https://arxiv.org/format/2112.00698">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/CCWC51732.2021.9375950">10.1109/CCWC51732.2021.9375950 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kalgaonkar%2C+P">Priyank Kalgaonkar</a>, <a href="/search/eess?searchtype=author&query=El-Sharkawy%2C+M">Mohamed El-Sharkawy</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.00698v1-abstract-short" style="display: inline;"> Due to the advent of modern embedded systems and mobile devices with constrained resources, there is a great demand for incredibly efficient deep neural networks for machine learning purposes. There is also a growing concern of privacy and confidentiality of user data within the general public when their data is processed and stored in an external server which has further fueled the need for devel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.00698v1-abstract-full').style.display = 'inline'; document.getElementById('2112.00698v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.00698v1-abstract-full" style="display: none;"> Due to the advent of modern embedded systems and mobile devices with constrained resources, there is a great demand for incredibly efficient deep neural networks for machine learning purposes. There is also a growing concern of privacy and confidentiality of user data within the general public when their data is processed and stored in an external server which has further fueled the need for developing such efficient neural networks for real-time inference on local embedded systems. The scope of our work presented in this paper is limited to image classification using a convolutional neural network. A Convolutional Neural Network (CNN) is a class of Deep Neural Network (DNN) widely used in the analysis of visual images captured by an image sensor, designed to extract information and convert it into meaningful representations for real-time inference of the input data. In this paper, we propose a neoteric variant of deep convolutional neural network architecture to ameliorate the performance of existing CNN architectures for real-time inference on embedded systems. We show that this architecture, dubbed CondenseNeXt, is remarkably efficient in comparison to the baseline neural network architecture, CondenseNet, by reducing trainable parameters and FLOPs required to train the network whilst maintaining a balance between the trained model size of less than 3.0 MB and accuracy trade-off resulting in an unprecedented computational efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.00698v1-abstract-full').style.display = 'none'; document.getElementById('2112.00698v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 3 figures, published in an IEEE Conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.05356">arXiv:2109.05356</a> <span> [<a href="https://arxiv.org/pdf/2109.05356">pdf</a>, <a href="https://arxiv.org/format/2109.05356">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/LCSYS.2021.3135510">10.1109/LCSYS.2021.3135510 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Agent-Supervisor Coordination for Decentralized Event-Triggered Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Srivastava%2C+P">Priyank Srivastava</a>, <a href="/search/eess?searchtype=author&query=Cavraro%2C+G">Guido Cavraro</a>, <a href="/search/eess?searchtype=author&query=Cortes%2C+J">Jorge Cortes</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.05356v2-abstract-short" style="display: inline;"> This paper proposes decentralized resource-aware coordination schemes for solving network optimization problems defined by objective functions which combine locally evaluable costs with network-wide coupling components. These methods are well suited for a group of supervised agents trying to solve an optimization problem under mild coordination requirements. Each agent has information on its local… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.05356v2-abstract-full').style.display = 'inline'; document.getElementById('2109.05356v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.05356v2-abstract-full" style="display: none;"> This paper proposes decentralized resource-aware coordination schemes for solving network optimization problems defined by objective functions which combine locally evaluable costs with network-wide coupling components. These methods are well suited for a group of supervised agents trying to solve an optimization problem under mild coordination requirements. Each agent has information on its local cost and coordinates with the network supervisor for information about the coupling term of the cost. The proposed approach is feedback-based and asynchronous by design, guarantees anytime feasibility, and ensures the asymptotic convergence of the network state to the desired optimizer. Numerical simulations on a power system example illustrate our results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.05356v2-abstract-full').style.display = 'none'; document.getElementById('2109.05356v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 1 figure</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> in IEEE Control Systems Letters, vol. 6, pp. 1970-1975 (2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.06494">arXiv:2103.06494</a> <span> [<a href="https://arxiv.org/pdf/2103.06494">pdf</a>, <a href="https://arxiv.org/format/2103.06494">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/LCSYS.2020.3002687">10.1109/LCSYS.2020.3002687 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Nesterov Acceleration for Equality-Constrained Convex Optimization via Continuously Differentiable Penalty Functions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Srivastava%2C+P">Priyank Srivastava</a>, <a href="/search/eess?searchtype=author&query=Cortes%2C+J">Jorge Cortes</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.06494v1-abstract-short" style="display: inline;"> We propose a framework to use Nesterov's accelerated method for constrained convex optimization problems. Our approach consists of first reformulating the original problem as an unconstrained optimization problem using a continuously differentiable exact penalty function. This reformulation is based on replacing the Lagrange multipliers in the augmented Lagrangian of the original problem by Lagran… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.06494v1-abstract-full').style.display = 'inline'; document.getElementById('2103.06494v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.06494v1-abstract-full" style="display: none;"> We propose a framework to use Nesterov's accelerated method for constrained convex optimization problems. Our approach consists of first reformulating the original problem as an unconstrained optimization problem using a continuously differentiable exact penalty function. This reformulation is based on replacing the Lagrange multipliers in the augmented Lagrangian of the original problem by Lagrange multiplier functions. The expressions of these Lagrange multiplier functions, which depend upon the gradients of the objective function and the constraints, can make the unconstrained penalty function non-convex in general even if the original problem is convex. We establish sufficient conditions on the objective function and the constraints of the original problem under which the unconstrained penalty function is convex. This enables us to use Nesterov's accelerated gradient method for unconstrained convex optimization and achieve a guaranteed rate of convergence which is better than the state-of-the-art first-order algorithms for constrained convex optimization. Simulations illustrate our results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.06494v1-abstract-full').style.display = 'none'; document.getElementById('2103.06494v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 1 figure</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Control Systems Letters, vol. 5, no. 2, pp. 415-420, April 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.03507">arXiv:2103.03507</a> <span> [<a href="https://arxiv.org/pdf/2103.03507">pdf</a>, <a href="https://arxiv.org/format/2103.03507">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/LCSYS.2021.3084555">10.1109/LCSYS.2021.3084555 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Solving Linear Equations with Separable Problem Data over Directed Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Srivastava%2C+P">Priyank Srivastava</a>, <a href="/search/eess?searchtype=author&query=Cortes%2C+J">Jorge Cortes</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.03507v2-abstract-short" style="display: inline;"> This paper deals with linear algebraic equations where the global coefficient matrix and constant vector are given respectively, by the summation of the coefficient matrices and constant vectors of the individual agents. Our approach is based on reformulating the original problem as an unconstrained optimization. Based on this exact reformulation, we first provide a gradient-based, centralized alg… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.03507v2-abstract-full').style.display = 'inline'; document.getElementById('2103.03507v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.03507v2-abstract-full" style="display: none;"> This paper deals with linear algebraic equations where the global coefficient matrix and constant vector are given respectively, by the summation of the coefficient matrices and constant vectors of the individual agents. Our approach is based on reformulating the original problem as an unconstrained optimization. Based on this exact reformulation, we first provide a gradient-based, centralized algorithm which serves as a reference for the ensuing design of distributed algorithms. We propose two sets of exponentially stable continuous-time distributed algorithms that do not require the individual agent matrices to be invertible, and are based on estimating non-distributed terms in the centralized algorithm using dynamic average consensus. The first algorithm works for time-varying weight-balanced directed networks, and the second algorithm works for general directed networks for which the communication graphs might not be balanced. Numerical simulations illustrate our results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.03507v2-abstract-full').style.display = 'none'; document.getElementById('2103.03507v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.04101">arXiv:2011.04101</a> <span> [<a href="https://arxiv.org/pdf/2011.04101">pdf</a>, <a href="https://arxiv.org/format/2011.04101">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Enabling DER Participation in Frequency Regulation Markets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Srivastava%2C+P">Priyank Srivastava</a>, <a href="/search/eess?searchtype=author&query=Chang%2C+C">Chin-Yao Chang</a>, <a href="/search/eess?searchtype=author&query=Cortes%2C+J">Jorge Cortes</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.04101v2-abstract-short" style="display: inline;"> Distributed energy resources (DERs) are playing an increasing role in ancillary services for the bulk grid, particularly in frequency regulation. In this paper, we propose a framework for collections of DERs, combined to form microgrids and controlled by aggregators, to participate in frequency regulation markets. Our approach covers both the identification of bids for the market clearing stage an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.04101v2-abstract-full').style.display = 'inline'; document.getElementById('2011.04101v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.04101v2-abstract-full" style="display: none;"> Distributed energy resources (DERs) are playing an increasing role in ancillary services for the bulk grid, particularly in frequency regulation. In this paper, we propose a framework for collections of DERs, combined to form microgrids and controlled by aggregators, to participate in frequency regulation markets. Our approach covers both the identification of bids for the market clearing stage and the mechanisms for the real-time allocation of the regulation signal. The proposed framework is hierarchical, consisting of a top layer and a bottom layer. The top layer consists of the aggregators communicating in a distributed fashion to optimally disaggregate the regulation signal requested by the system operator. The bottom layer consists of the DERs inside each microgrid whose power levels are adjusted so that the tie line power matches the output of the corresponding aggregator in the top layer. The coordination at the top layer requires the knowledge of cost functions, ramp rates and capacity bounds of the aggregators. We develop meaningful abstractions for these quantities respecting the power flow constraints and taking into account the load uncertainties, and propose a provably correct distributed algorithm for optimal disaggregation of regulation signal amongst the microgrids. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.04101v2-abstract-full').style.display = 'none'; document.getElementById('2011.04101v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.07971">arXiv:2007.07971</a> <span> [<a href="https://arxiv.org/pdf/2007.07971">pdf</a>, <a href="https://arxiv.org/format/2007.07971">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Frequency Regulation with Heterogeneous Energy Resources: A Realization using Distributed Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Anderson%2C+T">Tor Anderson</a>, <a href="/search/eess?searchtype=author&query=Muralidharan%2C+M">Manasa Muralidharan</a>, <a href="/search/eess?searchtype=author&query=Srivastava%2C+P">Priyank Srivastava</a>, <a href="/search/eess?searchtype=author&query=Haghi%2C+H+V">Hamed Valizadeh Haghi</a>, <a href="/search/eess?searchtype=author&query=Cortes%2C+J">Jorge Cortes</a>, <a href="/search/eess?searchtype=author&query=Kleissl%2C+J">Jan Kleissl</a>, <a href="/search/eess?searchtype=author&query=Martinez%2C+S">Sonia Martinez</a>, <a href="/search/eess?searchtype=author&query=Washom%2C+B">Byron Washom</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.07971v4-abstract-short" style="display: inline;"> This paper presents one of the first real-life demonstrations of coordinated and distributed resource control for secondary frequency response in a power distribution grid. We conduct a series of tests with up to 69 heterogeneous active devices consisting of air handling units, unidirectional and bidirectional electric vehicle charging stations, a battery energy storage system, and 107 passive dev… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.07971v4-abstract-full').style.display = 'inline'; document.getElementById('2007.07971v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.07971v4-abstract-full" style="display: none;"> This paper presents one of the first real-life demonstrations of coordinated and distributed resource control for secondary frequency response in a power distribution grid. We conduct a series of tests with up to 69 heterogeneous active devices consisting of air handling units, unidirectional and bidirectional electric vehicle charging stations, a battery energy storage system, and 107 passive devices consisting of building loads and photovoltaic generators. Actuation commands for the test devices are obtained by solving an economic dispatch problem at every regulation instant using distributed ratio-consensus, primal-dual, and Newton-like algorithms. The distributed control setup consists of a set of Raspberry Pi end-points exchanging messages via an ethernet switch. The problem formulation minimizes the sum of device costs while tracking the setpoints provided by the system operator. We demonstrate accurate and fast real-time distributed computation of the optimization solution and effective tracking of the regulation signal by measuring physical device outputs over 40-minute time horizons. We also perform an economic benefit analysis which confirms eligibility to participate in an ancillary services market and demonstrates up to $53K of potential annual revenue for the selected population of devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.07971v4-abstract-full').style.display = 'none'; document.getElementById('2007.07971v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.08759">arXiv:1901.08759</a> <span> [<a href="https://arxiv.org/pdf/1901.08759">pdf</a>, <a href="https://arxiv.org/format/1901.08759">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Misleading Metadata Detection on YouTube </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Palod%2C+P">Priyank Palod</a>, <a href="/search/eess?searchtype=author&query=Patwari%2C+A">Ayush Patwari</a>, <a href="/search/eess?searchtype=author&query=Bahety%2C+S">Sudhanshu Bahety</a>, <a href="/search/eess?searchtype=author&query=Bagchi%2C+S">Saurabh Bagchi</a>, <a href="/search/eess?searchtype=author&query=Goyal%2C+P">Pawan Goyal</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1901.08759v1-abstract-short" style="display: inline;"> YouTube is the leading social media platform for sharing videos. As a result, it is plagued with misleading content that includes staged videos presented as real footages from an incident, videos with misrepresented context and videos where audio/video content is morphed. We tackle the problem of detecting such misleading videos as a supervised classification task. We develop UCNet - a deep networ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.08759v1-abstract-full').style.display = 'inline'; document.getElementById('1901.08759v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.08759v1-abstract-full" style="display: none;"> YouTube is the leading social media platform for sharing videos. As a result, it is plagued with misleading content that includes staged videos presented as real footages from an incident, videos with misrepresented context and videos where audio/video content is morphed. We tackle the problem of detecting such misleading videos as a supervised classification task. We develop UCNet - a deep network to detect fake videos and perform our experiments on two datasets - VAVD created by us and publicly available FVC [8]. We achieve a macro averaged F-score of 0.82 while training and testing on a 70:30 split of FVC, while the baseline model scores 0.36. We find that the proposed model generalizes well when trained on one dataset and tested on the other. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.08759v1-abstract-full').style.display = 'none'; document.getElementById('1901.08759v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at European Conference on Information Retrieval(ECIR) 2019. 7 Pages</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> 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