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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Hui Liu Lucheng Hong Ying Lu Xingrui Zhang School of eletrical engineering Southeast University Nanjing China Technology R&D Center ASWEI Technology Co. Ltd. Suzhou China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

With the increasing integration of distributed renewable energy, distribution networks are facing increasingly severe issues such as voltage violations and increased network losses. Although there has been considerable research on reactive power voltage control using reinforcement learning, few studies have successfully maintained good performance when distribution network topology changes or data is missing. In this paper, a reactive voltage control strategy for distribution networks is proposed, which integrates Graph Neural Networks (GNNs) and the Twin delayed deep deterministic Policy Gradient (Td3) algorithm. This method effectively addresses the shortcomings mentioned above. First, the reactive voltage control problem in the distribution network is modeled as a Markov decision Process (MdP). The GNN is used to extract topological features of the distribution network, thereby enhancing the agent's perception of the system state and robustness, enabling the optimization of reactive power output for photovoltaic inverters and Static Var Compensators (SVCs). Finally, simulations are conducted on the improved IEEE 33-node system, and the results demonstrate that the proposed method performs excellently in reactive voltage control for distribution networks.

关键词： Training reactive power Adaptation models Markov decision processes distribution networks Feature extraction Graph neural networks Topology Smart grids Voltage control

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Coded Modulation at Low-SNr – Comparison with 4d-BPSK Constellation Modulation

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Journal of Lightwave technology 2025年

作者： Blatter, Tobias Kulmer, Laurenz Matsuda, Keisuke Theo, rodde Horst, Yannik Leuthold, Juerg ETH Zürich Department of Information Technology and Electrical Engineering Zürich8092 Switzerland Mitsubishi Electric Corporation Information Technology R&D Center Kamakura247-8501 Japan

Transmitting high capacity under low SNr conditions is increasingly a challenge in many wireless and free-space communications applications. E.g. in free space optical satellite links signals have to overcome a large loss budget due to free space path losses and atmospheric turbulence while maintaining a highcapacity link. In this context, polarization switched (PS) QPSK or coded modulation using block code were proposed as powerefficient modulation formats to serve low SNr conditions. recently, the four-dimensional BPSK (4d-BPSK) modulation format, which is a four-dimensional constellation modulation of BPSK has been proposed and demonstrated to achieve a higher power efficiency when compared with PS-QPSK. In this paper 4dBPSK and other power-efficient modulation formats, including coded modulation with extended Hamming, Golay or reed-Muller code as well as probabilistically shaped 16QAM, are compared in simulation using generalized mutual information as a metric. We also discuss the complexities of the log-likelihood ratio calculations. The simulation results were validated in experiments. The comparison results provide guidelines for selecting a power efficient modulation format under constraints of bandwidth and complexity, and show that 4d-BPSK is an attractive option. © 1983-2012 IEEE.

关键词： Quadrature phase shift keying

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The characteristics of temperature-responsive ionic liquids on the integrated operational effectiveness of water reclamation from semiconductor wastewater using forward osmosis

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Chemosphere 2025年 371卷 144059页

作者： Shih, Cheng-En Kuan, Wen-Hui Department of Safety Health and Environmental Engineering Ming Chi University of Technology New Taipei City243303 Taiwan Center for Sustainability and Energy Technologies Chang Gung University Taoyuan33323 Taiwan Biochemical Technology R&D Center Ming Chi University of Technology New Taipei City243303 Taiwan Chronic Diseases and Health Promotion Research Center Chang Gung University of Science and Technology Chiayi61363 Taiwan

Large amounts of wastewater are produced from semiconductor manufacturing, and the production energy consumption has skyrocketed with its global demand in recent years. Forward osmosis (FO) provides unique merits in reclaiming the wastewater if suitable draw solutes with high water flux, low leakage, and limited energy requirement in regeneration are available. Two lower critical solution temperature-ionic liquids (LCST-ILs), tetrabutylphosphonium trimethylbenzensulfonate ([P4444][TMBS]) and tetrabutylphosphonium maleate ([P4444][Mal]) were synthesized and systematically assessed as recycled draw solutes in FO for the water reclamation from the wastewater of Si-ingot sawing. The water flux of [P4444][TMBS] and [P4444][Mal] was 4.12 and 2.67 LMH in the FO for the authentic sawing wastewater, respectively. The characteristics of relatively higher hydrophobic and the structure configuration ensure the lower thermal-stimulus separation energy (Ets) of the spent [P4444][Mal] solution and its relative ease in recycling because of the higher regeneration ratio. The more hydrophilic [P4444][TMBS] based draw solution (dS) exhibits higher osmotic pressure, which is beneficial for water filtration but leads to higher Ets. The estimated energy required for the integrated processes, including FO filtration for the sawing wastewater, thermal separation of draw solute, and the reclaimed water polishing, for the system with [P4444][Mal] as draw solutes is 14.22 kWh m−3. The value significantly reduced to 1.33 kWh m−3 if low-grade waste heat ( © 2024

关键词： Water filtration

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Energy-Efficient Cloud Computing Through reinforcement Learning-Based Workload Scheduling

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 645-656页

作者： Malipatil, Ashwini r Paramasivam, M.E. Gulyamova, dilfuza Saravanan, Aanandha ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Ghodhbani, refka Department of Computer Science and Engineering BNM Institute of Technology Bangalore India Department of ECE Sona College of Technology Salem India Computer Engineering Department University of Information Technologies in Tashkent Tashkent Uzbekistan Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (drL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes deep Q-Networks (dQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as round robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures. © (2025), (Science and Information Organization). All rights reserved.

关键词： deep reinforcement learning

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Segmentation Of Neutrophil Nucleus in White Blood Cell Images Using Kapur’s entropy based Improved Grey Wolf Optimization

Segmentation Of Neutrophil Nucleus in White Blood Cell Image...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： r Saranya A Bakiya Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Center for Biomedical Signal Processing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

Qualitative analysis of blood disorders is the most challenging job in current medical image. diagnosing disorders through qualitative analysis relies heavily on approximation. disorders related to white blood cells (WBC) are notably prevalent in medical practice. Identifying blood disorders leads to classification of various blood related conditions. Hence, the automated identification of blood related disorder allows to bypass the existing complex environment and focus on the complex image insight provided by the images. In this study, the automated segmentation of neutrophil nucleus in white blood cells microscopic through Kapur’s entropy multilevel thresholding technique. In the process of an efficient segmentation through Kapur’s entropy, the threshold optimal values are required to change the class variance or entropy criteria. To achieve these optimizations, Improved Grey Wolf (IGWO) nature-inspired metaheuristic algorithms are employed for this study. The experimental test has conducted on standard images using various levels of threshold (2, 3, 4, 5 and 6). The efficacy of Kapur’s entropy based improved grey wolf optimization approach has been evaluated and performed with established optimization methods contains Genetic Algorithm(GA), Moth Fly Optimization (MFO), Firefly Optimization, Fire Hawk Optimization and Particle Swarm Optimization (PSO). results demonstrated that Kapur’s entropy based IGWO outperformed for neutrophil nucleus segmentation in microscopic images when compared to other optimization techniques in both quality and consistency.

关键词： White blood cells Image segmentation Microscopy Optimization methods Entropy Particle swarm optimization Optimization Standards Medical diagnostic imaging Genetic algorithms

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Control, Modeling and Stability Analysis of drU-based Grid-forming Converter

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Chinese Journal of Electrical engineering 2025年第1期11卷 83-92页

作者： Wencong Wu Minzhi Wang Liang Tu Haiqing Cai Guanglei Yan Xiaohui Qu Haohan Gu Wei Chen State Key Laboratory of HVDC China Southern Power GridGuangzhou 510663China National Energy Power Grid Technology R&D Center Guangzhou 510663China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power Systems China Southern Power GridGuangzhou 510663China CSG Key Laboratory for Power System Simulation China Southern Power GridGuangzhou 510663China School of Electrical Engineering Southeast UniversityNanjing 210096China

diode rectifier unit(drU)-based high-voltage direct current(HVdC)transmissionsystems areeffectivein achieving the stableandeconomical operation of offshore *** theuncontrollable characteristicsof drUs,a grid-forming(GFM)strategy forwind-turbine converters isnecessary to support offshore AC voltageand ***,the active power-synchronization control in traditional GFM converters is unsuitable for drU-based GFM ***,the stability issue for drU-based HVdC systems involving drU-based GFM and grid-following(GFL)converters has not yet been *** solve these issues,this study begins with the characteristics of a drU-based HVdC system and presents a control scheme for drU-based GFM converters for power ***,the dq-frame impedance model of the drU-based GFM converteris proposed for the stability analysis of the entire HVdC ***,a simulation platform is built to verify the model accuracy and system stability.

关键词： diode rectifier unit offshore wind-powergeneration grid-forming converter impedance model stabilityanalysis

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Experimental Verification of Circuit-Losses Analysis-Model of dC-Output Converter developed using Approximated Equations from Measurement data and datasheet data

Experimental Verification of Circuit-Losses Analysis-Model o...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： ryota Kondo Tsuyoshi Funaki Advanced Technology R&D Center Mitsubishi Electric Corporation Hyogo Japan Division of Electric Electronics and Information Engineering Osaka Univercity Osaka Japan

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

This paper proposes a losses-analysis model of a dC-output converter based on the approximated equations from measurements and a datasheet. Target verification circuit consists of a dC-voltage source, a smooth capacitor, two half bridge legs, and a load inductor connected to the midpoints of the half bridge legs, which controls the continuous average dC current of the load inductor. The use of self-measured data, compared with the use of datasheet values, enables expanding the ranges of the considerable target parameters. Hence, lesser order approximated equations can deal with more target parameters with less calculation period than that in a conventional time domain simulation. In this study, calculation flow of losses from power devices losses and inductor losses to circuit losses was developed. Subsequently, a power losses model and inductor losses model were developed via the linear least square method. Finally, a 400 V experimental system was developed to verify the accuracy of the circuit losses at 16 specified operating conditions by varying three verification parameters, switching frequency, gate resistance, and inductance. The result shows that the losses calculated using the proposed model has good accuracy with an error of 1.9 % to 10.6 % to the measured result.

关键词： Legged locomotion Analytical models Accuracy Measurement uncertainty Loss measurement Mathematical models data models Electrical resistance measurement Integrated circuit modeling Inductors

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Network View and Cognitive Mechanism for Virtual Network resource Management Based Intelligent

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Chinese Journal of Electronics 2025年第3期23卷 574-578页

作者： Xiaochun Wu Chunming Wu Bin Wang Yaguan Qian Julong Lan Computer Science College Zhejiang University Hangzhou China Institute of Network and Communication Engineering Zhejiang Gongshang University Hangzhou China National Digital Switching System Engineering & Technology R&D Center Zhengzhou China

Network virtualization has been a particularly important driving force behind the development of NGN. Finding effective ways to control self-adapting virtual networks has become a research hot spot. This paper tries to find an efficient way in combining dynamic resource sensing with traffic character and requirements in virtual network, based on that an intelligent cognition based virtual network resources management model has been put forward. We design a network topology view by coloring network topology matrix with current resources model. We present an intelligent virtual resource management mechanism based on the proposed network topology view. Experiment results illustrate that the proposed mechanism can improve VN capacity by automatic resource adjust-ment, which can obtain high utilization of VN resources without increasing computational expenses.

关键词： Technological innovation Network topology Computational modeling Force dynamics Network resource management dynamic scheduling Sensors Virtualization Next generation networking

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A Hardware-Friendly AVS3 Entropy decoder Architecture for 8K Ultra-High-definition Video

A Hardware-Friendly AVS3 Entropy Decoder Architecture for 8K...

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data Compression Conference (dCC)

作者： Jiaqi Zhang Xu Han Chuanmin Jia Shanshe Wang Siwei Ma National Engineering Research Center of Visual Technology School of Computer Science Peking University China Wangxuan Institute of Computer Technology Peking University China Information Technology R&D Innovation Center of Peking University China

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

Logarithmic binary arithmetic coding (LBAC) is a entropy coding method first introduced in AVS2 and now used in the third generation of audio video coding standard (AVS3). While LBAC provides high coding efficiency, the data dependencies in AVS3 make it challenging to parallelize parsing syntax element. To enhance the throughput of AVS3 LBAC entropy decoding engine, a sophisticated hardware implementation architecture is meticulously designed in the paper. The proposed method decouples the bitstream decoding process into four distinct modules. To ensure the decoding efficiency and performance, an advanced parallel pipeline is carefully designed. Furthermore, we develop sub-branch state prediction mechanism and grouped decoding method to further improve the decoding efficiency. The proposed AVS3 entropy decoder has been implemented on the S10 FPGA System Board, and experimental results show that the proposed method could achieve real-time and seamless decoding efficiency for 8K AVS3 bitstreams, even at bitrates as high as 207.1Mbps.

关键词： Video coding Syntactics Streaming media Throughput Entropy real-time systems decoding Field programmable gate arrays Standards Binary sequences

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Assessment of the Blood Separation Performances of Asymmetric Cellulose Acetate Membranes Prepared through Combined Vapor-Induced Phase Separation and Electrospinning

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ACS Applied Materials and Interfaces 2025年第22期17卷 2025 May 20页

作者： dizon, Gian Vincent Huang, Yu-Jen Lin, Fang-Cheng Maggay, Irish Valerie Chang, Yung Venault, Antoine R&D Center for Membrane Technology Department of Chemical Engineering Chung Yuan Christian University Chung-Li32023 Taiwan

This study introduces the formation of a composite cellulose acetate (CA) membrane for the removal of blood cells from blood-derived solutions. A vapor-induced phase separation (VIPS) CA membrane formed the bottom layer of the composite membrane. The exposure time to vapors was adjusted with the aim to open the pores of the surface exposed to the nonsolvent and obtain a membrane with a mean pore size of less than 0.4 μm. A 20 min exposure led to few pores in the microfiltration range decorating the top surface, while much more numerous pores were observed on the bottom surface in contact with the substrate during the phase-inversion process. differences between the top and bottom surface porosity in turn influenced wetting by water. The membranes with fastest top and bottom surfaces wettability were selected. Then, nanofibers with a mean pore size of 2.8 ± 0.3 μm were formed by electrospinning a CA solution on the bottom surface of the VIPS membrane. It resulted in the formation of a composite membrane with an asymmetric structure (large top surface pores and smaller bulk and bottom surface pores). The membranes were proven to be nonhemolytic (hemolysis rate © 2025 The Authors. Published by American Chemical Society.

关键词： Composite membranes

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