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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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Determining the Reaction Time for Triggering Supportive Control Actions to Guarantee Adequate Frequency Response in Smart Grids

Determining the Reaction Time for Triggering Supportive Cont...

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2020 Spring Simulation Conference, SpringSim 2020

作者： Luo, Jiecai Djouadi, Seddik M. Olama, Mohammed M. Zhang, Yichen Southern University and A and M College Electrical Engineering Department 801 Harding Blvd. Baton RougeLA70813 United States University of Tennessee Min H. Kao Department of Electrical Engineering and Computer Science 1520 Middle Drive KnoxvilleTN37996 United States Oak Ridge National Laboratory Computational Sciences and Engineering Division 1 Bethel Valley Rd. Oak RidgeTN37831 United States Argonne National Laboratory Energy Systems Division 9700 S Cass Ave. LemontIL60439 United States

ISBN: (纸本)9781565553705

The progressive integration of renewable energy resources in the modern power grid can result in unsatisfactory frequency responses. To address this problem, wind turbine generators (WTGs), in particular, can be employed to support the power grid. Most works, however, only consider a simple step disturbance. In this paper, a new class of worst-case disturbances is introduced and their nefarious impact on renewable-penetrated power systems is investigated. The worst-case disturbances are derived using optimal control theory. More importantly, the proposed mechanism allows to determine the reaction time to trigger supportive control actions of WTGs to ensure satisfactory frequency response. Numerical results are provided and the effects of the worst-case disturbance on the reaction time are presented. © 2020 SCS.

关键词： Wind Turbine Generators

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TAROGE-M: Antarctic High-Mountain Radio Antenna Array for Detecting Ultra-High Energy ANITA Anomalous Events 38

TAROGE-M: Antarctic High-Mountain Radio Antenna Array for De...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Wang, Shih-Hao Chen, Pisin Chen, Yaocheng Chen, Ying-Chih Choi, Taejin Ham, Young-Bae Hsu, Shih-Ying Huang, Jian-Jung Huang, MingHuey A. Jee, Geonhwa Jung, Jongil Kim, Jieun Kuo, Chung-Yun Kwon, Hyuck-Jin Lee, Changsup Leung, Chung-Hei Liu, Tsung-Che Nam, Jiwoo Shiao, Yu-Shao J. Shin, Bok-Kyun Su, Shih-Chieh Wang, Min-Zu Wang, Shih-Hao Wang, Yu-Hsin Anker, Astrid Barwick, Steven W. Besson, Dave Z. Bouma, Sjoerd Cataldo, Maddalena Gaswint, Geoffrey Glaser, Christian Hallmann, Steffen Hanson, Jordan C. Henrichs, Jakob Kleinfelder, Stuart A. Lahmann, Robert Meyers, Zachary S. Nelles, Anna Novikov, Alexander Paul, Manuel P. Pyras, Lilly Persichilli, Christopher Plaisier, Ilse Rice-Smith, Ryan Seikh, Mohammad F. H. Tatar, Joulien Welling, Christoph Zhao, Leshan Department of Physics National Taiwan University Taipei10617 Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei10617 Taiwan Kavli Institute for Particle Astrophysics and Cosmology SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Division of Atmospheric Sciences Korea Polar Research Institute Incheon Korea Republic of Department of Polar Science Korea University of Science and Technology Daejeon Korea Republic of Department of Energy Engineering National United University Miaoli Taiwan Department of Astronomy Space Science and Geology Chungnam National University Daejeon34134 Korea Republic of Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of applied physics National Pingtung university Pingtung900 Taiwan Chung Cheng Institute of Technology National Defense University Taoyuan335 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Nano Device Laboratories Hsinchu300 Taiwan Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Department of Physics and Astronomy University of California IrvineCA92697 United States Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States DESY Zeuthen15738 Germany ECAP Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden Whittier College Department of Physics WhittierCA90602 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Resea

TAROGE-M is a radio antenna array atop ∼ 2.7 km-high Mt. Melbourne in Antarctica for detecting ultra-high energy (UHE, E > 1017 eV) air showers in near-horizontal directions. Besides the detection of cosmic rays and Earth-skimming tau neutrinos, its primary goal is to reproduce the discovery and verify the origin of so-called ANITA anomalous events, having feature of upward-going UHE air showers but cannot be explained by tau neutrinos. The detection concept takes advantages of a high altitude for a broad view toward the horizon;strong and near-vertical geomagnetic field for enhancing the radio signal;and quiet radio environment in Antarctica. Its relatively simple design and high duty cycle make it easily to be extended and thus to achieve an exposure competitive with ANITA experiments within a few years. The first TAROGE-M station operating at 180-450 MHz frequencies deployed in 2020 detected seven UHE cosmic ray events within 25.3-day livetime. The events have a mean energy of ∼ 1 EeV and an estimated flux consistent with other experiments, and validate the station as an UHE particle detector. In 2022-2023 season, the system was upgraded for a robust long-term operation, with a more detailed calibration with a drone-borne pulser. We also discuss the planned upgrade, including the implementation of interferometric trigger, which is expected to increase the cosmic ray and tau neutrino acceptances by a factor of 10 at 0.1 EeV, corresponding to a lower energy threshold by a factor of 3. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

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Preface

Synthesis Lectures on Computer Vision

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Synthesis Lectures on computer Vision 2023年 Part F825卷 v-vi页

作者： Wan, Jun Escalera, Sergio Li, Stan Z. Guo, Guodong Escalante, Hugo Jair State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Mathematics and Informatics University of Barcelona and Computer Vision Center Barcelona Spain AI Lab Westlake University Hangzhou China Department of Computer Science and Electrical Engineering West Virginia University Morgantown China Department of Computer Science Instituto Nacional de Astrofísica Optica y Electrónica Puebla Mexico

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Cryogenic in-memory computing using tunable chiral edge states

arXiv

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arXiv 2022年

作者： Liu, Yuting Lee, Albert Qian, Kun Zhang, Peng He, Haoran Ren, Zheyu Cheung, Shun Kong Li, Yaoyin Zhang, Xu Ma, Zichao Xiao, Zhihua Yu, Guoqiang Wang, Xin Liu, Junwei Wang, Zhongrui Wang, Kang L. Shao, Qiming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong School of Material Science and Engineering Harbin Institute of Technology Shenzhen518055 China Device Research Laboratory Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States IAS Center for Quantum Technologies The Hong Kong University of Science and Technology Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam Road 999077 Hong Kong Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong Beijing National Laboratory for Condensed Matter Physics Institute of Physics University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100190 China Department of Physics The City University of Hong Kong 999077 Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology The Hong Kong University of Science and Technology Hong Kong ACCESS AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong

Cryogenic electronics have become essential in reducing the number of input/output ports to the quantum chips and generating multiplexed reading and control pulses for scalable quantum computation 1-5. A demanding request for cryogenic electronics is to support efficient machine learning algorithms, which have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control 6,7. However, current digital cryogenic electronics suffer from the von Neumann bottleneck, where the physical separation of computation and memory units causes a huge amount of energy and delay when processing data-intensive programs. Energy-efficient hardware implementation of machine learning algorithms for quantum computation requires nonvolatile and electrically-programmable devices, memristors, working at cryogenic temperatures that enable in-memory computing 8,9. Magnetic topological insulators are promising candidates due to their tunable magnetic order by electrical currents with high energy efficiency 10-15. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a chiral edge state-based cryogenic in-memory computing scheme. On the one hand, the chiral edge state can be tuned from left-handed to right-handed chirality through spin-momentum locked topological surface current injection. On the other hand, the chiral edge state exhibits giant and bipolar anomalous Hall resistance, which facilitates the electrical readout. The memristive switching and reading of the chiral edge state exhibit high energy efficiency, high stability, and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks based on magnetic topological memristors demonstrate a software-level accuracy and lower energy consumption for image recogniti

关键词： Topology

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Output containment control of discrete-time multi-agent systems with application to multiple ground vehicles

arXiv

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arXiv 2020年

作者： Wang, Shimin Jiang, Simin Zhan, Zhi Wu, Yuanqing Zhong, Renxin Yat-Sen, Sun Department of Electrical and Computer Engineering University of Alberta AB Canada Guangdong Key Laboratory of Intelligent Transportation Systems School of Intelligent Systems Engineering University Guangzhou China School of Automation Guangdong University of Technology Guangzhou China

This paper investigates the output containment control problem for heterogeneous discrete-time multi-agent systems with multiple leaders. The objective is to develop a distributed control law such that the output of the followers can converge to the convex hull spanned by the output of the leaders exponentially. The convex hull formed by the output of the leaders and the system matrix of leaders are estimated via an adaptive distributed containment observer. Based on this observer, a distributed dynamic output feedback control protocol is first devised for heterogeneous discrete-time multi-agent systems using only neighboring relative output information. The proof is depicted by showing certain output containment errors converge to zero exponentially, which indicates the containment control objective is well achieved. A distributed dynamic state-feedback control law is deduced as a special case of the output feedback control. For the application to control multiple ground vehicles, a prescribed offset for maintaining the minimum safety gap between adjacent agents is considered. Finally, numerical simulations with application to ground vehicles validate the effectiveness and the computational feasibility of the proposed control protocols. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

arXiv

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arXiv 2022年

作者： Ye, Jiarong Yeh, Yin-Ting Xue, Yuan Wang, Ziyang Zhang, Na Liu, He Zhang, Kunyan Ricker, Ryeanne Yu, Zhuohang Roder, Allison Lopez, Nestor Perea Organtini, Lindsey Greene, Wallace Hafenstein, Susan Lu, Huaguang Ghedin, Elodie Terrones, Mauricio Huang, Shengxi Huang, Sharon Xiaolei College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Physics The Pennsylvania State University University ParkPA United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering The Pennsylvania State University University ParkPA United States Department of Biomedical Engineering George Washington University WashingtonDC United States Systems Genomics Section Laboratory of Parasitic Diseases National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA United States Department of Pathology and Laboratory Medicine Division of Clinical Pathology The Pennsylvania State University College of Medicine HersheyPA United States Department of Veterinary and Biomedical Sciences The Pennsylvania State University University ParkPA United States 413G Eric. J. Barron Innovation Hub 123 S. Burrowes Street State CollegePA16801 United States

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such a machine learning approach for analyzing Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups-for example, amide, amino acid, carboxylic acid-we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses. © 2022, CC BY.

关键词： Raman spectroscopy

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SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

arXiv

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arXiv 2023年

作者： Luo, Xiangde Fu, Jia Zhong, Yunxin Liu, Shuolin Han, Bing Astaraki, Mehdi Bendazzoli, Simone Toma-Dasu, Iuliana Ye, Yiwen Chen, Ziyang Xia, Yong Su, Yanzhou Ye, Jin He, Junjun Xing, Zhaohu Wang, Hongqiu Zhu, Lei Yang, Kaixiang Fang, Xin Wang, Zhiwei Lee, Chan Woong Park, Sang Joon Chun, Jaehee Ulrich, Constantin Maier-Hein, Klaus H. Ndipenoch, Nchongmaje Miron, Alina Li, Yongmin Zhang, Yimeng Chen, Yu Bai, Lu Huang, Jinlong An, Chengyang Wang, Lisheng Huang, Kaiwen Gu, Yunqi Zhou, Tao Zhou, Mu Zhang, Shichuan Liao, Wenjun Wang, Guotai Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Department of Radiation Oncology Sichuan Cancer Hospital & Institute Chengdu China Shanghai AI Lab Shanghai China Co. Ltd. Beijing China Department of Medical Radiation Physics Stockholm University Solna Sweden Department of Biomedical Engineering and Health Systems KTH Huddinge Sweden Northwestern Polytechnical University Xi’an China Guangzhou China Wuhan National Laboratory for Optoelectronics with MoE Key Laboratory for Biomedical Photonics China Yonsei University College of Medicine Seoul Korea Republic of Oncosoft Inc. Seoul Korea Republic of Heidelberg Germany Department of Computer Science Brunel University London Uxbridge United Kingdom MedMind Technology Co. Ltd. Beijing China Shanghai Jiao Tong University Shanghai China Nanjing University of Science and Technology Nanjing China

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep learning has achieved promising results in many medical image segmentation tasks. However, for NPC OARs and GTVs segmentation, few public datasets are available for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge’s goal was to segment 45 OARs and 2 GTVs from the paired CT scans. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68% to 86.70%, and 70.42% to 73.44% for OARs and GTVs, respectively. We conclude that the segmentation of large-size OARs is well-addressed, and more efforts are needed for GTVs and small-size or thin-structure OARs. The benchmark will remain publicly available here: https://***. © 2023, CC BY-NC-ND.

关键词： Radiotherapy

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A non-intrusive approach for interfacing dynamic models with static distribution power flow analysis tools 3

A non-intrusive approach for interfacing dynamic models with...

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3rd IEEE Conference on Energy Internet and Energy System Integration, EI2 2019

作者： Li, Qinmiao Abhyankar, Shrirang Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Energy Systems Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9781728131375

Maintaining the transient stability of distribution systems will be important as the grid transitions from a passive distribution network to an active one with low-inertia distributed energy sources. Although tools are available for distribution network steady-state analysis, there is currently a lack of distribution system dynamics analysis tools. In this paper, we present a nonintrusive approach for interfacing dynamic models, which have differential-algebraic equations (DAEs), with a general purpose distribution power flow solver. We present the numerical details of this interface and demonstrate them via interfacing a dynamic generator and a PV-DER model with OpenDSS. © 2019 IEEE.

关键词： Dynamic models

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