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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Louay Karadsheh Faten Hamad Hussam Fakhouri Faculty of Information Technology University of Petra Amman Jordan Sultan Qaboos University Sultanate of Oman The University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Timely security patches are crucial in reducing vulnerabilities across computing infrastructures, yet excessive downtime or violations of concurrency constraints can adversely affect service availability and overall performance. In this work, we optimize the problem of constrained patch scheduling using a multi-optimizer metaheuristic approach. We first model patch scheduling as an optimization problem aiming to minimize both total downtime and vulnerability exposure while also respecting concurrency limits, which restrict the number of machines simultaneously offline. The objective function incorporates a penalty factor to discourage over-concurrent patching. We compare eight modern metaheuristic algorithms with variable patch durations. Extensive experiments are conducted, analyzing best objective scores, downtime, exposure, concurrency penalty, computational time, and convergence trends. **Our findings indicate that Equilibrium Optimizer (EO) achieves the lowest final cost (6.5351), followed closely by Multi-Verse Optimizer (MVO) (6.8295). Whereas Grey Wolf Optimizer (GWO) and Moth Flame Optimization (MFO), yield moderate results, while Whale Optimization Algorithm (WOA) exhibits the highest cost (10.3944). These outcomes underscore the impact of concurrency-limited patching constraints on performance and emphasize the benefits of selecting optimizers that effectively balance exploration and exploitation for scheduling in security-critical environments.

关键词： Concurrent computing Costs Processor scheduling Metaheuristics Fires Market research linear programming Whale optimization algorithms Security Convergence

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A Novel Optimized Li-ion Battery Charging Protocol for EV Onboard Chargers

A Novel Optimized Li-ion Battery Charging Protocol for EV On...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Mohammad Reza Gholipour Mehdi Saradarzadeh Ehsan Asadi Shahrokh Farhangi School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Faculty of Electrical and Computer Engineering Jundi-Shapur University of Technology Dezful Iran

ISBN: (数字)9798331533946

ISBN: (纸本)9798331533953

An optimal charging profile for Li-ion batteries is proposed in this paper. The objective of the charging process is to minimize the charging time of a Li-ion battery while concurrently minimizing its energy losses. To address this challenge, a multi-objective evolutionary optimization algorithm, SPEA2, is employed. For this purpose, the extraction of electrical parameters for the equivalent circuit model of the Li-ion battery is addressed. Thereafter, the polarization voltages of the Li-ion battery are utilized as a key criterion in the SPEA2 optimization algorithm. Additionally, a power loss analysis of the onboard charger converter is conducted, and simulation results are presented to validate the theoretical analysis. The results demonstrate that the proposed charging method has reduced the charging time by $\mathbf{1 6. 1 7 \%}$ and the power losses of the charger system by 6.08% compared to the Constant Current-Constant Voltage (CC-CV) charging profile.

关键词： Lithium-ion batteries Energy loss Protocols Simulation linear programming Power electronics Integrated circuit modeling Optimization Standards Equivalent circuits

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Needles in the Haystack-Rapid Error Detection for Cell Tracking Solutions

Needles in the Haystack-Rapid Error Detection for Cell Track...

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IEEE International Symposium on Biomedical Imaging

作者： Draga Doncila Pop Pierre Le Bodic Juan Nunez-Iglesias Department of Data Science & AI Monash University Australia eResearch Monash University Australia

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Extracting complete cell lineages or trajectories from time-lapse microscopy images, or cell tracking, is a necessary pre-cursor for many bioimaging analyses. Powered in part by extremely accurate segmentations produced by deep learning methods, automated tracking tools make fewer mistakes than ever. Still, no method is perfect, and remaining errors must be corrected for biologists to draw meaningful conclusions from the data. While some interfaces exist for exploring and editing tracks, this is an entirely unguided process, with users having to skim through thousands of mostly correct tracks to find the rare error. In this paper, we present an edge-ranking method for proofreading auto-generated ground truth or finding remaining errors in a set of tracks. Using the Cell Tracking Challenge benchmark datasets and their ground truth annotations to simulate human proofreading, we show that through our method, users can correct 50% of remaining errors in a tracking solution after viewing a median of just 2.6% of edges.

关键词： Deep learning Image segmentation Microscopy Image edge detection Needles linear programming Biology Trajectory Optimization Biomedical imaging

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Distribution System State Estimation Using Automatic Differentiation with JAX

Distribution System State Estimation Using Automatic Differe...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Rochisnu Dutta Ravi Teja Alla Amarsagar Reddy Ramapuram Matavalam K. Shanti Swarup Dept. of Electrical Engineering IIT Madras Chennai India School of ECEE Arizona State University Tempe USA Arizona State University Tempe USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

With the growing integration of active control de-vices in distribution systems, Distribution System State Estimation (DSSE) is increasingly critical for ensuring effective operational control and preventing outages. This paper presents a novel DSSE approach that combines OpenDSS as a power flow solver with Python for state estimation, utilizing smart meter data accessed via the py-dss-interface. A significant contribution of this work is the application of JAX (Just After eXecution), a machine learning framework, to accelerate the gradient descent solution of the Weighted Least Squares (WLS) objective function, removing the need for explicitly computing the Jacobian matrix. The proposed method is validated on unbalanced IEEE 13, 37, and 123 radial bus distribution systems, showing substantial improvements in computational efficiency and accuracy.

关键词： Jacobian matrices Accuracy Machine learning linear programming Smart meters Computational efficiency State estimation Load flow

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Data Privacy Protection Algorithm for Carrier Communication Nodes in Power Internet of Things Based on Frequent Itemsets

Data Privacy Protection Algorithm for Carrier Communication ...

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

作者： Hua Li Zhengrong Wu Wenqin Chen Jialin Luo Xun Min CHINA SOUTHERN POWER GRID Guangzhou Guangdong China Digital Power Grid Technology (Guangdong) Co. Ltd China Southern Power Grid. Guangzhou Guangdong China Guizhou Grid Company Limited Guiyang Guizhou China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

Because the original data of the carrier communication node of power Internet of Things is non-stationary and nonlinear, its discreteness is large, which is not conducive to feature extraction, which leads to the possibility of data privacy protection and low data security. Therefore, this paper proposes a data privacy protection algorithm based on frequent itemsets for power Internet of Things carrier communication nodes. Through the set rules, the network resources of the power Internet of Things are analyzed, the corresponding itemsets are connected, the residual components are calculated, and the correlation between two adjacent data is extracted by using frequent itemsets. The comprehensive attributes of the data set are obtained, the local information entropy of the communication node data privacy is described, and the data sequence characteristics of the privacy data are extracted, to construct the data privacy protection objective function and realize the data privacy protection algorithm design of the power Internet of Things carrier communication node. The experimental results show that the algorithm in this paper shows a low data information loss rate on multiple data sets, which can effectively reduce data information loss and protect the privacy and security of data. Under both internal and external attacks, the anti-attack performance is over 98%, after privacy processing by this algorithm, privacy leakage can be limited to the maximum extent, and privacy protection performance is excellent.

关键词： Data privacy Privacy Itemsets Data security Power system stability linear programming Stability analysis Smart grids Internet of Things Protection

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Spectrum and Power Allocation Using DRL in Cooperative Spectrum Sharing

Spectrum and Power Allocation Using DRL in Cooperative Spect...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Yang Yu Benjun Liu Chang Zhang Liangliang Wang Yuqi Xia Boyang Zhang Electronic Information School Hubei Three Gorges Polytechnic Yichang China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

This paper delves into optimizing joint spectrum and power allocation between a matched primary and secondary link pair in cooperative spectrum sharing. Weighted sum energy efficiency (WSEE) is adopted as the objective function to address the challenges of green communication. We propose a scheme based on deep reinforcement learning (DRL) to address this nonconvex spectrum and power allocation problem. Leveraging DRL, the spectrum and power allocation problem is autonomously optimized by only utilizing local information. Simulation results reveal that it achieves near-optimal performance and significantly enhances the network convergence speed with low computational overheads.

关键词： Simulation Green communications Quality of service Computer applications linear programming Deep reinforcement learning Energy efficiency Resource management Information technology Convergence

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A Policy Reuse Reinforcement Learning Framework for Hard Latency Constrained Resource Scheduling

A Policy Reuse Reinforcement Learning Framework for Hard Lat...

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IEEE Conference on Wireless Communications and Networking

作者： Luyuan Zhang An Liu College of Information Science and Electronic Engineering Zhejiang University

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the forthcoming 6G era, extend reality (XR) has been regarded as an emerging application for ultra-reliable and low latency communications (URLLC) with new traffic characteristics and more stringent requirements. In addition to the quasi-periodical traffic in XR, burst traffic with both large frame size and random arrivals in some real world low latency communication scenarios has become the leading cause of network congestion or even collapse, and there still lacks an efficient algorithm for the resource scheduling problem under burst traffic with hard latency constraints. We propose a policy reuse reinforcement learning framework for resource scheduling with hard latency constraints (PRRL-RSHLC), which maximizes the hard-latency constrained effective throughout (HLC-ET) of users. The proposed algorithm reuses polices from both old policies learned under other similar environments and domain-knowledge-based (DK) policies constructed using expert knowledge to improve the performance. Simulations show that PRRL-RSHLC can achieve superior performance with faster convergence speed compared to baseline aIgorithms.

关键词： Law enforcement Packet loss Reinforcement learning Ultra reliable low latency communication Throughput linear programming Scheduling Delays Optimization Convergence

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Optimal Placement of Battery Energy Storage System for Voltage Profile Improvement and Reduction of Power Losses

Optimal Placement of Battery Energy Storage System for Volta...

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Power Engineering Conference (SAUPEC), Southern African Universities

作者： Nkululeko Skunana Komla A. Folly Department of Electrical Engineering University of Cape Town Cape Town South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

This paper investigates the strategic placement of Battery Energy Storage Systems (BESS) in a modified 16-bus Witzenberg distribution network with a renewable energy source, such as a photovoltaic (PV) system to improve voltage profile regulation and reduce power losses. The integration of renewable energy sources (RES) into distribution networks has introduced challenges in maintaining voltage stability and minimizing power losses. To address these issues, the optimal placement of BESS is achieved by minimizing the costs associated with voltage profile deviations and power losses in the distribution system, thereby improving the performance of the 16-bus Witzenberg distribution network. The methodology adopted involves the use of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to solve the optimization problem, and the results obtained from both algorithms are compared. The optimization objective function considers both voltage deviation and power loss reduction costs. The results demonstrate that after optimal placement of BESS, both achieved a 58.75% reduction in active power losses (from 8 MW to 3.3 MW) and improved the voltage deviation index by 56.26% (from 11.98% to 5.24%). These results suggest that strategic BESS placement can significantly enhance distribution network performance and support increased RES integration.

关键词： Photovoltaic systems Renewable energy sources Costs Battery energy storage system Voltage Distribution networks linear programming Indexes Particle swarm optimization Genetic algorithms

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Animal welfare and economic optimisation of farrowing systems

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Animal Welfare 2023年第1期20卷 57-67页

In many countries, including the UK, the majority of domestic sows are housed in farrowing crates during the farrowing and lactation periods. Such systems raise welfare problems due to the close confinement of the sow. Despite the fact that many alternative housing systems have been developed, no commercially viable/feasible option has emerged for large scale units. Current scientific and practical knowledge of farrowing systems were reviewed in this study to identify alternative systems, their welfare and production potential. The aim was to establish acceptable trade-offs between profit and welfare within alternative farrowing systems. linear programming (LP) was used to examine possible trade-offs and to support the design of welfare-friendly yet commercially viable alternatives. The objective of the LP was to optimise the economic performance of conventional crates, simple pens and designed pens subject to both managerial and animal welfare constraints. Quantitative values for constraints were derived from the literature. The potential effects of each welfare component on productivity were assessed by a group of animal welfare scientists and used in the model. The modelled welfare components (inputs) were extra space, substrate and temperature. Results showed that, when using piglet survival rate in the LP based on data drawn from the literature and incorporating costs of extra inputs in the model, the crates obtained the highest annual net margin and the designed pens and the pens were in second and third place, respectively. The designed pens and the pens were able to improve their annual net margin once alternative reference points, following expert-derived production functions, were used to adjust piglet survival rates in response to extra space, extra substrate and modified pen heating. The non-crate systems then provided higher welfare and higher net margin for sows and piglets than crates, implying the possibility of a win-win situation.

关键词： alternative housing systems animal welfare economic optimisation farrowing systems linear programming pig

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Research on Optimized Operation of Regional Integrated Energy System Based on SAPSO Algorithm

Research on Optimized Operation of Regional Integrated Energ...

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

作者： Yinhu Guo Yufang Wang School of Business Shanghai Dianji University Shanghai China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

Achieving efficient use of energy by optimizing the regional integrated energy system is conducive to further attaining the goal of carbon neutrality and carbon peaking. This paper takes a foreign park as the research object and its annual operating cost as the optimization target, establishes an optimization model, and uses the SAPSO algorithm to solve the model. The results show that the annual operating cost can be effectively reduced, and the economic optimization of the park's integrated energy system can be realized.

关键词： Costs Optimization models Energy conservation Simulated annealing Carbon neutral linear programming Smart grids Protection Carbon Convergence

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