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4th Annual IEEE Kansas Power and Energy Conference, KPEC 2023

作者： Huang, Jianqiao Fang, Xin Zhou, Xinyang Tan, Jin Dong, Shuan Hoke, Andy National Renewable Energy Laboratory Power System Engineering Center GoldenCO United States Illinois Institute of Technology Electrical and Computer Engineering Chicago United States Mississippi State University Electrical and Computer Engineering Department StarkvilleMS United States

ISBN: (纸本)9798350335408

As hybrid power plants (HPPs), such as photovoltaic (PV) and battery combinations, become increasingly important in power systems with high renewable energy penetration to address PV variability and ensure grid stability. This paper focuses on the urgent need to model the coordination between PV and battery systems in HPPs while accounting for battery degradation. We present a generation scheduling model that explicitly incorporates PV-battery hybridization in the unit commitment problem. Moreover, the cost function of the HPP scheduling problem endogenously considers battery degradation with adjustable weights to strike a balance between minimizing production costs and prolonging battery life, particularly when providing energy arbitrage and ancillary services. Using a realistic island system simulation, we demonstrate that accounting for battery degradation in the scheduling problem can significantly extend battery life with only minor additional production costs. © 2023 IEEE.

关键词： Cost functions

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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computer Modeling in engineering & Sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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A Holistic State Estimation Framework for Active Distribution Network with Battery Energy Storage system

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Journal of Modern Power systems and Clean Energy 2022年第3期10卷 627-636页

作者： Shaojian Song Huangjiao Wei Yuzhang Lin Cheng Wang Antonio Gómez-Expósito IEEE School of Electrical Engineering Guangxi UniversityNanning 530004China Department of Electrical and Computer Engineering University of MassachusettsLowellMA 01852USA State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Laboratory of Engineering for Energy and Environmental Sustainability University of SevilleSpain

Battery energy storage systems(BESSs)are expected to play a crucial role in the operation and control of active distribution networks(ADNs).In this paper,a holistic state estimation framework is developed for ADNs with BESSs integrated.A dynamic equivalent model of BESS is developed,and the state transition and measurement equations are *** on the equivalence between the correction stage of the iterated extended Kalman filter(IEKF)and the weighted least squares(WLS)regression,a holistic state estimation framework is proposed to capture the static state variables of ADNs and the dynamic state variables of BESSs,especially the state of charge(SOC).A bad data processing method is also *** simulation results show that the proposed holistic state estimation framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs,providing comprehensive situational awareness for the whole system.

关键词： Active distribution network(ADN) anomaly detection battery energy storage system(BESS) Kalman filtering situational awareness state estimation state of charge(SOC)

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Deep Learning Approach for High Recall Pneumonia Classification with Swin Transformer and L2 Regularization 23

Deep Learning Approach for High Recall Pneumonia Classificat...

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8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Pinasthika, Krisna Sofyanda, Erika Yussi Ulumiyah, Silfiatul Muflikhah, Lailil Computer Vision Research Group Brawijaya University Indonesia Technology Education Learning Research Group Brawijaya University Indonesia Information System Research Group Brawijaya University Indonesia Department of Informatics Engineering Brawijaya University Indonesia

ISBN: (纸本)9798400708503

Pneumonia is one of the lung infections that can be lethal to people. Pneumonia can be diagnosed by conducting an X-ray scan of the human chest. Even though X-ray scanning is straightforward, the process of identifying pneumonia in X-ray data is still difficult to do even by radiologists. Therefore, a computational algorithm is needed that can be used to analyze X-ray image data. The use of Deep Learning (DL) methods for analyzing images has been proven to work well in various cases, especially X-ray data. Based on previous research, the transformer architecture has the advantage of analyzing with a larger receptive area if compared to Convolutional Neural Networks (CNN) which have limited kernel size. In this study, we used the Swin transformer in classifying pneumonia diseases through X-ray images. Through the study conducted, the Swin Transformer Architecture is able to provide a fairly good evaluation metric when compared to the use of convolutional neural networks. The Tiny variant of the Swin Transformer architecture is able to provide evaluation metrics with a precision value of 0.870, recall of 0.995, and F1-Score of 0.928. On the other hand, the use of L2 regularization in stage 3 and stage 4 of the Swin Tiny architecture is able to provide a high recall value of 0.997, but the use of L2 regularization does not provide an increase in precision and F1-Score. In biomedical cases that are only concerned with recall values, the use of L2 regularization on Swin Tiny can provide advantages. © 2023 ACM.

关键词： computer vision

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Development of High-Power Bidirectional DC Solid-State Power Controller for Aircraft Applications

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2022年第5期10卷 5498-5508页

作者： Dong, Zhou Ren, Ren Wang, Fei Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA Monolithic Power System Inc. San Jose CA USA Oak Ridge National Laboratory Oak Ridge TN USA

The high-power solid-state power controller (SSPC) will be a critical component for the future electrified aircraft propulsion system. This article presents the development of a 1 kV 500 A bidirectional dc SSPC using SiC power modules and transient voltage suppression (TVS) diodes. The design procedure and implementation of the SSPC are presented in detail and the system level influence is discussed. Due to the limited power of a single switch, parallel and/or series connections of switches are necessary for high-power SSPCs, and the corresponding challenges are investigated with possible solutions. In addition, the high peak-to-average clamping voltage ratio caused by Ldi/dt of the turn-off current commutation and foldback characteristics of high-power TVS diodes is addressed. Finally, the testing setups for the high-power SSPC are presented, and all key functions are tested for the SSPC. The power rating and the specific power of the developed SSPC are superior to state-of-the-art counterparts to show the advantage of the developed one.

关键词： Switches Diodes Voltage Transient analysis Absorption Multichip modules Cable TV Electrified aircraft SiC power module solid-state circuit breaker solid-state power controller TVS diode

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On Learning Deep O(n)-Equivariant Hyperspheres

arXiv

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arXiv 2023年

作者： Melnyk, Pavlo Felsberg, Michael Wadenbäck, Mårten Robinson, Andreas Le, Cuong Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

In this paper, we utilize hyperspheres and regular n-simplexes and propose an approach to learning deep features equivariant under the transformations of nD reflections and rotations, encompassed by the powerful group of O(n). Namely, we propose O(n)-equivariant neurons with spherical decision surfaces that generalize to any dimension n, which we call Deep Equivariant Hyperspheres. We demonstrate how to combine them in a network that directly operates on the basis of the input points and propose an invariant operator based on the relation between two points and a sphere, which as we show, turns out to be a Gram matrix. Using synthetic and real-world data in nD, we experimentally verify our theoretical contributions and find that our approach is superior to the competing methods for O(n)-equivariant benchmark datasets (classification and regression), demonstrating a favorable speed/performance trade-off. The code is available on GitHub. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features 13

Evolutionary Multiobjective Feature Selection Assisted by Un...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Duan, Xuan Liu, Songbai Ji, Junkai Li, Lingjie Lin, Qiuzhen Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets. © 2024 IEEE.

关键词： Feature Selection

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Comprehensive Overview of Multi-agent systems for Controlling Smart Grids

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CSEE Journal of Power and Energy systems 2022年第1期8卷 115-131页

作者： Om Prakash Mahela Mahdi Khosravy Neeraj Gupta Baseem Khan Hassan Haes Alhelou Rajendra Mahla Nilesh Patel Pierluigi Siano Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd.Jaipur-302005India Media Integrated Communication Laboratory Graduate School of EngineeringOsaka UniversityOsakaJapan Computer Science and Engineering Department Oakland UniversityRochesterUSA Department of Electrical and Computer Engineering Hawassa UniversityHawassa 1530Ethiopia Faculty of Mechanical and Electrical Engineering Tishreen UniversityLattakiaSyria Department of Electrical Engineering National Institute of Technology Kurukshetra Department of Management&Innovation Systems University of Salerno84084 SalernoItaly

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power *** controlling smart grids,various control systems with different architectures have already been ***-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant *** systems provide an alternative for fast and accurate power network *** paper provides a comprehensive overview of MASs used for the control of smart *** paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart *** of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically *** than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.

关键词： Coordinated control multi-agent systems renewable energy sources smart energy infrastructure smart grid

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Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception 27

Advswap: Covert Adversarial Perturbation with High Frequency...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Huang, Yuanhao Zhang, Qinfan Xing, Jiandong Cheng, Mengyue Yu, Haiyang Ren, Yilong Xiong, Xiao School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing100191 China Zhongguancun Laboratory Beijing100094 China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798331505929

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based highfrequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability. © 2024 IEEE.

关键词：

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Monocular Depth Estimation with Boundary Attention Mechanism and Shifted Window Adaptive Bins

SSRN

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SSRN 2024年

作者： Liang, Mengnan Wang, Congcong Zhao, Meng Shi, Fan Zhang, Chao Han, Yilin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Laboratory of Computer Vision System of Ministry of Education Tianjin University of Technology Tianjin300384 China

Monocular depth estimation is a classic research topic in computer vision. In recent years, development of Convolutional Neural Networks (CNNs) has facilitated significant breakthroughs in this field. However, there still are two challenges: 1) The network struggles to effectively fuse edge features in the feature fusion stage, which ultimately results in the loss of structure or distorted boundaries of objects in the scene. 2) Classification based studies typically depend on Transformers for global modeling, a process that often introduces substantial performance overhead. In this paper, we propose two modules to address the aforementioned issues. The first module is the Boundary Attention Module (BAM), which leverages the attention mechanism to enhance the ability of the network to perceive object boundaries during the feature fusion stage. In addition, to mitigate the performance overhead resulting from predicting adaptive bins, we propose a Shift Window Adaptive Bins (SWAB) module to reduce the amount of calculation in global modeling. The proposed method is evaluated on two public datasets, NYU Depth V2 and KITTI, and achieves state-of-the-art (SOTA) performance. © 2024, The Authors. All rights reserved.

关键词： computer vision

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