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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhijian Wu Kaiyi Feng Dingjiang Huang School of Data Science and Engineering East China Normal University Shanghai China School of Control and Computer Engineering North China Electric Power University Hebei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, Transformer-based methods have achieved impressive performance in many computer vision tasks (e.g., image super-resolution (SR)) due to the advantages of long-range modeling. However, the computational cost requirement renders these methods unsuitable on resource-constrain devices, especially for image SR tasks involving high-resolution images. In this paper, we propose a concise and effective Gated Convolutional Attention Unit (GCAU) that uses cheap convolutional operations. Specifically, GCAU consists of Convolutional Transposed Attention (CTA) and Locally-enhanced Gating (LeG) in parallel. The former allows for efficient modeling of the global relational interactions by calculating cross-covariance across channels dimension, while the latter controls the information flow from the former directing the network to focus on more refined image attributes. Without bells and whistles, we present a simple SR Transformer GCAT by cascading the GCAUs. Extensive experimental results demonstrate that our GCAT achieves state-of-the-art performance among the existing efficient SR methods with significantly less complexity. Especially, GCAT is on average 5× faster than SwinIR-light with comparable performance.

关键词： Legged locomotion Performance evaluation computer vision Convolution Computational modeling Superresolution Logic gates Transformers Speech processing Signal resolution

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State of Charge Management for Grid-Connected PV with Supercapacitor System Considering LVRT Operation

State of Charge Management for Grid-Connected PV with Superc...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Kritprajun, Paychuda Tolbert, Leon M. Sun, Jingjing Wang, Jingxin Praisuwanna, Nattapat Liu, Yunting Ferrari, Maximiliano The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States The Pennsylvania State University Department of Electrical Engineering University ParkPA United States Oak Ridge National Laboratory Grid Components and Control Oak RidgeTN United States

ISBN: (纸本)9798350316445

This paper presents a state of charge (SOC) management strategy to ensure low-voltage ride-through (LVRT) services of a grid-connected photovoltaic (PV) with a supercapacitor (SC) energy storage system (PVSS). A concept of SOC reservation for LVRT operations is introduced to guarantee that the PV grid-connected system can provide LVRT services complying with grid code requirements. SOC management is derived by considering SC limitations, the current rating of the converters, and grid conditions. Therefore, safe operation of the power system and PVSS can be achieved. The study also adopts an electrothermal model of the SC to adjust the current set points during SOC management by considering the temperature-dependent SC parameters;thus, both safety and full utilization of the SC can be achieved. Consequently, SOC management can rapidly be accomplished, thereby increasing the certainty of providing LVRT services to the system. © 2023 IEEE.

关键词： Grid-connected photovoltaic system low-voltage ride-through state of charge management supercapacitor

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Interior Scene Intelligent Generated for Smart Homes Based on GAN-ID Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1046-1055页

作者： Zhang, Fu-Quan Ma, Lin-Juan Zhou, Jun-Xian Wang, Run-Hong Zhang, Guang-Zhen College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University Fuzhou China Fujian Province University Fuzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Minjiang University Fuzhou China Information Media School of Tianjin Technician Institute of Mechanical & Electrical Technology Tianjin China

The purpose of this paper is to present a new GAN-ID algorithm to gener-ate interior scene intelligently based on Artificial Intelligence of Things (AIoT). Although great progress has been made when GAN algorithm applied in smart homes and smart cities, the issue of the trade-off between the accuracy and the efficiency in interior scene has been overlooked. A number of experiments were performed to check the performance and it is indicated that the results of the proposed GAN-ID has great advantages in model sample generation than other common methods when generating interior scene intelligently, which is similar to state-of-the-art. It is meaningful when it can be applied in smart cities and smart homes filed based on AIoT in the future. © 2024, J. Network Intell. All rights reserved.

关键词： Automation

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Transcriptomic insights unveil the crucial roles of cytochromes, NADH, and pili in Ag(I) reduction by Geobacter sulfurreducens

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Chemosphere 2024年 358卷 142174页

作者： Liu, Chunmao Guo, Dongchao Wen, Su Dang, Yan Sun, Dezhi Li, Pengsong Beijing Key Lab for Source Control Technology of Water Pollution College of Environmental Science and Engineering Beijing Forestry University Beijing100083 China Engineering Research Center for Water Pollution Source Control & Eco-remediation College of Environmental Science and Engineering Beijing Forestry University Beijing100083 China School of Computer Science Beijing Information Science and Technology University Beijing100101 China

Silver (Ag) is a pivotal transition metal with applications in multiple industries, necessitating efficient recovery techniques. Despite various proposed methods for silver recovery from wastewaters, challenges persist especially for low concentrations. In this context, bioreduction by bacteria like Geobacter sulfurreducens, offers a promising approach by converting Ag(I) to Ag nanoparticles. To reveal the mechanisms driving microbial Ag(I) reduction, we conducted transcriptional profiling of G. sulfurreducens under Ag(I)-reducing condition. Integrated transcriptomic and protein-protein interaction network analyses identified significant transcriptional shifts, predominantly linked to c-type cytochromes, NADH, and pili. When compared to a pilus-deficient strain, the wild-type strain exhibited distinct cytochrome gene expressions, implying specialized functional roles. Additionally, despite a down-regulation in NADH dehydrogenase genes, we observed up-regulation of specific downstream cytochrome genes, highlighting NADH's potential role as an electron donor in the Ag(I) reduction process. Intriguingly, our findings also highlight the significant influence of pili on the morphology of the resulting Ag nanoparticles. The presence of pili led to the formation of smaller and more crystallized Ag nanoparticles. Overall, our findings underscore the intricate interplay of cytochromes, NADH, and pili in Ag(I) reduction. Such insights suggest potential strategies for further enhancing microbial Ag(I) reduction. © 2024 Elsevier Ltd

关键词： Proteins

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Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

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IFAC-PapersOnLine 2023年第2期56卷 6124-6129页

作者： Tushar Desai Federico Oliva Riccardo M.G. Ferrari Daniele Carnevale Delft Center for Systems and Control Delft University of Technology Delft Netherlands Department of Civil Engineering and Computer Science Engineering University of Rome "Tor Vergata" Rome Italy

For reliable and safe battery operations, accurate and robust State of Charge (SOC) and model parameters estimation is vital. However, the nonlinear dependency of the model parameters on battery states makes the problem challenging. We propose a Moving-Horizon Estimation (MHE)-based robust approach for joint state and parameters estimation. Dut to all the time scales involved in the model dynamics, a multi-rate MHE is designed to improve the estimation performance. Moreover, a parallelized structure for the observer is exploited to reduce the computational burden, combining both multi-rate and a reduced-order MHEs. Results show that the battery SOC and parameters can be effectively estimated. The proposed MHE observers are verified on a Simulink-based battery equivalent circuit model.

关键词： Energy systems Observer Design Battery Management Robust Estimation Moving Horizon Estimation Parameter-varying systems

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Dynamic Optimization of Electric Vehicle Charging for Sustainable Power Grids using Sine Hunter-Prey Optimization with GRUs 3

Dynamic Optimization of Electric Vehicle Charging for Sustai...

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3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Syed, Mohd Khizar Chandra Behera, Prakash Josephson, P Joel Khan, Shamim Ahmad Kholmukhamedov, Timur Ponni Valavan, M. Kohinoor college Department of Physics Maharashtra India St. Claret College Department of Science Bangalore India Malla Reddy Engineering College Department of ECE Telangana Medchal Malkajgiri India Glocal School of Science & Technology Glocal University Uttar Pradesh India Turin Polytechnic University Automatic Control and Computer Engineering Department Tashkent Uzbekistan K.Ramakrishnan College of Engineering Department of AI & DS Tamil Nadu Trichy India

ISBN: (纸本)9798350390728

The integration of electric vehicles (EVs) into power grids presents a significant challenge for sustainability due to their dynamic charging demands and the need to accommodate renewable energy sources. Existing optimization methods often fail to adequately address the complexity of this problem. In this study, propose a novel approach that combines Sine Hunter-Prey Optimization (SHPO) with Gated Recurrent Units (GRUs) to dynamically optimize EV charging for sustainable power grid operation. Traditional optimization methods struggle to capture the nonlinear and time-varying nature of EV charging demands and renewable energy availability. To overcome this limitation, SHPO's ability to efficiently explore complex solution spaces and GRUs' capability to model temporal dependencies in sequential data. This proposed method optimizes EV charging schedules by considering grid stability, renewable energy utilization, and cost minimization. Validating the approach using historical data on EV charging patterns, renewable energy generation, and grid conditions. Performance metrics include grid stability indices, renewable energy integration rates, and cost savings compared to baseline strategies. Experimental results demonstrate that the method outperforms traditional optimization techniques, leading to more sustainable and resilient power grid operation with 92% stability. Overall, this study contributes to advancing the modern methods in dynamic optimization of EV charging, paving the way for more sustainable transportation and energy systems. © 2024 IEEE.

关键词： State of charge

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Probabilistic Load Flow Calculation of Power System Integrated with Wind Farm Based on Kriging Model

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Energy engineering 2021年第3期118卷 565-580页

作者： Lu Li Yuzhen Fan Xinglang Su Gefei Qiu Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming650500China School of Control and Computer Engineering North China Electric Power UniversityBeijing102206China

Because of the randomness and uncertainty,integration of large-scale wind farms in a power system will exert significant influences on the distribution of power *** paper uses polynomial normal transformation method to deal with non-normal random variable correlation,and solves probabilistic load flow based on Kriging *** method is a kind of smallest unbiased variance estimation method which estimates unknown information via employing a point within the confidence scope of weighted linear *** with traditional approaches which need a greater number of calculation times,long simulation time,and large memory space,Kriging method can rapidly estimate node state variables and branch current power distribution *** one of the generator nodes in the western Yunnan power grid,a certain wind farm is chosen for empirical *** are used to compare with those by Monte Carlo-based accurate solution,which proves the validity and veracity of the model in wind farm power modeling as output of the actual turbine through PSD-BPA.

关键词： Probabilistic load flow Kriging model wind turbine clusters polynomial normal transformation correlation

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Application of deep learning in pothole road recognition: a YOLOv5 method combining ASFF and SimAm 3

Application of deep learning in pothole road recognition: a ...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Lu, Weiquan Zhang, Jiarong Ju, Jinli Liu, Ziyu Zhao, Yuxiang Qin, Rong Lu, Danni Zhou, Xiaolu School of Information Engineering Xi'an Mingde Institute of Technology Xi'an710124 China School of Intelligent Manufacturing and Control Technology Xi'an Mingde Institute of Technology Xi'an710124 China School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan430200 China

ISBN: (纸本)9781510680449

In the application of computer vision recognition, the recognition objects of some tissue-like structures present highly complex and variable characteristics. For this practical usage scenario, traditional classification algorithms usually cannot achieve the desired recognition effect. To achieve better recognition performance in this direction, this study integrates ASFF into the YOLOv5 model and introduces the SimAm attention mechanism module. The improved network structure is more lightweight, has fewer network parameters, and the attention mechanism is more effective. We trained the modified YOLO model on datasets, and experimental results show a significant increase in mAP value and a 20% increase in accuracy. This indicates that improvements to the model can significantly enhance performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep learning

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Many-Objective Optimization Evolutionary Algorithm Based on Dynamic Region Partitioning 4

Many-Objective Optimization Evolutionary Algorithm Based on ...

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4th International Conference on Consumer Electronics and computer engineering, ICCECE 2024

作者： Li, Wenlong Chen, Hanning Wang, Guopeng Song, Lizhe Xia, Yelin Wang, Zhixue Jia, Kun School of Artificial Intelligence Tiangong University Tianjin China School of Computer Science and Technology Tiangong University Tianjin China The Open University of China Beijing China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China School of Control Science and Engineering Tiangong University Tianjin China Jingwei Textile Machinery Co. Ltd. Beijing China

ISBN: (纸本)9798350384062

Multi objective optimization evolutionary algorithms (MOEAs) play a crucial role in addressing multi-objective optimization problems (MOPs) in the field of artificial intelligence. However, MOEAs often struggle to simultaneously maintain good convergence and diversity in Many-objective optimization problems (MaOPs). To address this problem, this paper proposes a Many-objective optimization evolutionary algorithm based on dynamic region partitioning approach (MaOEA-DRP). After non-dominated sorting, the solutions are initially uniformly partitioned into regions based on Euclidean distance. Within each region, at most one solution closest to the ideal point is selected. Subsequently, dynamic region partitioning is applied to the remaining individuals. In each iteration, a solution closest to the ideal point is chosen within the obtained region until the population size is satisfied. Experimental results indicate that MaOEA-DRP outperforms other algorithms significantly in terms of performance on the test problems. © 2024 IEEE.

关键词： Evolutionary algorithms

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Resilient Cooperative control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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