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IEEE Symposium on Intelligent Vehicle

作者： Haoran Zhu Haoze He Anna Choromanska Satish Ravindran Binbin Shi Lihui Chen Department of Electrical and Computer Engineering Learning Systems Laboratory New York University NY USA NXP Semiconductors San Jose CA USA

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Automotive radar has been extensively utilized in cars for many years as an essential sensor, primarily due to its robustness in extreme weather conditions, its capacity to measure Doppler information in the surrounding environment, and its cost-effectiveness. Recently, developments in radar technologies and the availability of open-source radar data sets have attracted more attention to radars and using them for perception tasks in deep learning based autonomous driving. However, annotating radar data for large-scale autonomous driving perception tasks is challenging, i.e., it is difficult for humans to label this data and often requires a semi-automatic approach that involves projecting labels from other sensors, such as cameras and LiDARs. The lack of high-quality labeled data has limited the performance of radar perception models. In this paper, we propose MVRAE, a Multi-View Radar AutoEncoder, which employs self-supervised learning to learn meaningful representations from multi-view radar data without any labels. Our approach is based on the intuition that a good representation for multi-view radar data, which includes range-angle, range-Doppler, and angle-Doppler views, should enable the reconstruction of one view solely from the representations of the other two views. Experimental results demonstrate that our proposed self-supervised method, that can be used as a pre-training step for autonomous driving task, allows the model to learn meaningful representations from unlabeled radar data and achieves enhanced label efficiency for downstream tasks, such as radar semantic segmentation. To the best of our knowledge, MVRAE is the first work that employs self-supervised learning and conducts systematic experiments with multi-view radar data.

关键词： Representation learning Systematics Semantic segmentation Radar Self-supervised learning Data models Doppler radar

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Impact of High-Frequency Repetitive Bipolar Square Wave Voltages on the Lifetime of Turn-to-Turn Insulation

Impact of High-Frequency Repetitive Bipolar Square Wave Volt...

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Conference on electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Md Asifur Rahman Easir Arafat Farzana Islam Mona Ghassemi Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA

ISBN: (数字)9798350366679

ISBN: (纸本)9798350366686

Emerging power electronic drives and converters are planned to operate at high voltages with high switching frequencies (up to a few hundred kHz), which will affect the reliability of insulation systems of windings of electrical machines. To evaluate the insulation quality under such high-frequency pulses, lifetime test is a prominent way. For simplification of test procedure, turn-to-turn insulation samples are approved by IEC 60034-18-42. In past studies, the impact of switching frequencies has been investigated under lower frequencies (up to 20 kHz), which cannot address the challenges of next-generation wide bandgap (WBG)-based power converters. In this study, the lifetimes of turn-to-turn insulations under high switching frequencies (50 kHz) are tested at four different rise times (50 ns, 100 ns, 150 ns, 200 ns), and the impact of switching frequency on the lifetime of the insulation is analyzed.

关键词： Insulation Time-frequency analysis Power system measurements Photonic band gap Switching frequency Windings High-voltage techniques Reliability engineering Power electronics Next generation networking

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Symbiotic Control of Uncertain Dynamical systems: Harnessing Synergy Between Fixed-Gain Control and Adaptive Learning Architectures

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IEEE Transactions on Automatic Control 2025年

作者： Yucelen, Tansel Sarsilmaz, Selahattin Burak Yildirim, Emre University of South Florida Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems TampaFL33620 United States Utah State University Department of Electrical and Computer Engineering LoganUT84322 United States

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that offers the strengths of fixed-gain control and adaptive learning architectures. Specifically, this framework synergistically integrates these architectures to mitigate the effects of uncertainties in a more predictable manner as compared to adaptive learning alone and it does not require any knowledge on such uncertainties. Both parametric and nonparametric uncertainties are considered, where we utilize neural networks to approximate the unknown uncertainty basis for the latter case. Counterintuitively, the proposed framework has the ability to achieve a desired level of closed-loop system behavior even with an insufficient number of neurons (e.g., when the neural network approximation error is large) or in the face of injudiciously selected adaptive learning parameters (e.g., high leakage term parameters). © 1963-2012 IEEE.

关键词： Closed loop control systems

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Bipolar MVDC Cable Under Different Ambient Conditions for All-Electric Aircraft

Bipolar MVDC Cable Under Different Ambient Conditions for Al...

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IEEE Kansas Power and Energy Conference

作者： Farzana Islam Anoy Saha Md Asifur Rahman Mona Ghassemi Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA

ISBN: (数字)9798350372403

ISBN: (纸本)9798350372410

To realize net zero emissions in aviation sector, all electric aircraft (AEA) are an optimistic technology. To make commercial wide body AEA a reality, one of the main challenges is to design their electric power system (EPS) having high power density and the lowest possible system mass. The power needed and pressure level during take-off and cruising time create opposing conditions, so designing one cable that meets all the requirements is crucial. In low-pressure conditions at cruising altitude (12.2 km), cable design faces significant thermal challenges because of limited heat transfer. A comprehensive multiphysics model is necessary to compute the distribution of temperature across the cable under various environmental conditions to find the optimal cable configuration. This study focuses on a cylindrical bipolar 5 kV DC cable system in a duct with multilayer insulation at atmospheric and low (18.8k Pa) pressure based on the maximum current that flows in the introduced EPS of N3-X (NASA) AEA system under the worst-case of single contingency conditions so that the designed cable can handle any single contingency situations maintaining thermal limits.

关键词： Temperature distribution Power system measurements Power cables Atmospheric modeling Simulation Contingency management Power cable insulation

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Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

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IEEE Transactions on Power systems 2025年

作者： Zhou, Yuqi Zhu, Hao National Renewable Energy Laboratory Power Systems Engineering Center GoldenCO80401 United States The University of Texas Chandra Family Department of Electrical & Computer Engineering AustinTX78712 United States

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations. © 1969-2012 IEEE.

关键词： Decentralized control

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Contrast enhancement of optical videoscope images

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Iran Journal of computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Benjamin Poole Rajan Ratnakumar Dulip Tharaka Madurasinghe Christian Kümmerle Ganesh Kumar Venayagamoorthy Minwoo Lee Department of Computer Science University of North Carolina Charlotte USA Real-Time Power and Intelligent Systems Laboratory Clemson University USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases.

关键词： Robust control Network topology Data models Real-time systems Phasor measurement units Power systems Topology Power system reliability Reliability Load flow

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Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1199-1209页

作者： Ziyan Zhao Shixin Liu MengChu Zhou Abdullah Abusorrah State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern UniversityShenyang 110819China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

Group scheduling problems have attracted much attention owing to their many practical *** work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due *** is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production *** objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the *** obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy *** computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its *** high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

关键词： Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling

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Optimizing Foundation Model Inference on a Many-Tiny-Core Open-Source RISC-V Platform

IEEE Transactions on Circuits and Systems for Artificial Int...

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IEEE Transactions on Circuits and systems for Artificial Intelligence 2024年第1期1卷 37-52页

作者： Viviane Potocnik Luca Colagrande Tim Fischer Luca Bertaccini Daniele Jahier Pagliari Alessio Burrello Luca Benini Integrated Systems Laboratory (IIS) of ETH Zürich ETZ Züurich Switzerland Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

Transformer-based foundation models have become crucial for various domains, most notably natural language processing (NLP) or computer vision (CV). These models are predominantly deployed on high-performance GPUs or hardwired accelerators with highly customized, proprietary instruction sets. Until now, limited attention has been given to RISC-V-based general-purpose platforms. In our work, we present the first inference results of transformer models on an open-source many-tiny-core RISC-V platform implementing distributed Softmax primitives and leveraging ISA extensions for SIMD floating-point operand streaming and instruction repetition, as well as specialized DMA engines to minimize costly main memory accesses and to tolerate their latency. We focus on two foundational transformer topologies, encoder-only and decoder-only models. For encoder-only models, we demonstrate a speedup of up to 12.8 $\times$ between the most optimized implementation and the baseline version. We reach over 79% FPU utilization and 294 GFLOPS/W, outperforming State-of-the-Art (SoA) accelerators by more than 2 $\times$ utilizing the HW platform while achieving comparable throughput per computational unit. For decoder-only topologies, we achieve 16.1 $\times$ speedup in the Non-Autoregressive (NAR) mode and up to 35.6 $\times$ speedup in the Autoregressive (AR) mode compared to the baseline implementation. Compared to the best SoA dedicated accelerator, we achieve 2.04 $\times$ higher FPU utilization.

关键词： Frequency modulation Transformers computer architecture Computational modeling Kernel Artificial intelligence Hardware

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