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Optimal Scheduling for Networked Microgrids Considering Back-to-Back MVDC Systems

IEEE Transactions on Smart Grid 2025年第3期16卷 2000-2011页

作者： Zhou, Anping Wang, Jianhui Khodayar, Mohammad E. Southern Methodist University Department of Electrical and Computer Engineering DallasTX75205 United States

Networked microgrids (MGs) have emerged as a favored and promising scheme to facilitate the integration of distributed energy resources and enhance the resiliency of power support. To advance the flexible operation of distribution systems, this paper proposes an optimal scheduling model for networked MGs, embedding three innovative medium-voltage direct current (MVDC) technologies with voltage source converters, namely two-terminal back-to-back MVDC systems (BMSs), three-terminal BMSs, and multi-terminal BMSs. Compared to traditional MVDC systems, these newly introduced back-to-back MVDC technologies exhibit strengthened operational flexibility. We first formulate their explicit models based on real-world applications, incorporating constraints related to normal operation and power supply modes. Then, we present a day-ahead scheduling model for networked MGs equipped with these BMSs. Subsequently, we develop tractable reformulations for nonlinear constraints considered in the scheduling model, leading to a mixed-integer linear programming problem. Finally, numerical simulations on the IEEE 123-bus test system are conducted to demonstrate the effectiveness and superiority of the proposed scheduling model. © 2010-2012 IEEE.

关键词： Microgrids

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Multi-View Test-Time Adaptation for Semantic Segmentation in Clinical Cataract Surgery

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2307-2318页

作者： Li, Heng Ou, Mingyang Li, Haojin Qiu, Zhongxi Niu, Ke Fu, Huazhu Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Beijing Information Science and Technology University Computer School Beijing100192 China Institute of High Performance Computing Agency for Science Technology and Research Fusionopolis Way 138632 Singapore Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen518055 China

Cataract surgery, a widely performed operation worldwide, is incorporating semantic segmentation to advance computer-assisted intervention. However, the tissue appearance and illumination in cataract surgery often differ among clinical centers, intensifying the issue of domain shifts. While domain adaptation offers remedies to the shifts, the necessity for data centralization raises additional privacy concerns. To overcome these challenges, we propose a Multi-view Test-time Adaptation algorithm (MUTA) to segment cataract surgical scenes, which leverages multi-view learning to enhance model training within the source domain and model adaptation within the target domain. In the training phase, the segmentation model is equipped with multi-view decoders to boost its robustness against variations in cataract surgery. During the inference phase, test-time adaptation is implemented using multi-view knowledge distillation, enabling model updates in clinics without data centralization or privacy concerns. We conducted experiments in a simulated cross-center scenario using several cataract surgery datasets to evaluate the effectiveness of MUTA. Through comparisons and investigations, we have validated that MUTA effectively learns a robust source model and adapts the model to target data during the practical inference phase. © 1982-2012 IEEE.

关键词： Semantic Segmentation

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Geometric Interpretation of Sensitivity to Structured Uncertainties in Spintronic Networks

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IEEE Control Systems Letters 2025年 9卷 192-197页

作者： O'Neil, S.P. Jonckheere, E.A. Schirmer, S. United States Military Academy Department of Electrical Engineering and Computer Science NY United States University of Southern California Dept of Electrical and Computer Engineering CA United States Swansea University Faculty of Science and Engineering Physics United Kingdom

We present a geometric model of the differential sensitivity of the fidelity error for state transfer in a spintronic network based on the relationship between a set of matrix operators. We show an explicit dependence of the differential sensitivity on the fidelity (error), and we further demonstrate that this dependence does not require a trade-off between the fidelity and sensitivity. We prove that for closed systems, ideal performance in the sense of perfect state transfer is both necessary and sufficient for optimal robustness in terms of vanishing sensitivity. We demonstrate the utility of this geometric interpretation of the sensitivity by applying the model to explain the sensitivity versus fidelity error data in two examples. © 2017 IEEE.

关键词： Sensitivity analysis

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Few-Shot Referring Video Single- and Multi-Object Segmentation Via Cross-Modal Affinity with Instance Sequence Matching

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International Journal of computer Vision 2025年 1-19页

作者： Liu, Heng Li, Guanghui Gao, Mingqi Zhen, Xiantong Zheng, Feng Wang, Yang School of Computer Science and Technology Anhui University of Technology Maxiang Road Ma’anshan243032 China Department of Computer Science and Engineering Southern University of Science and Technology Xueyuan Avenue Shenzhen518055 China School of Computer Science and Information Engineering Hefei University of Technology Feicui Road Hefei230601 China

Referring Video Object Segmentation (RVOS) aims to segment specific objects in videos based on the provided natural language descriptions. As a new supervised visual learning task, achieving RVOS for a given scene requires a substantial amount of annotated data. However, only minimal annotations are usually available for new scenes in realistic scenarios. Another practical problem is that, apart from a single object, multiple objects of the same category coexist in the same scene. Both of these issues may significantly reduce the performance of existing RVOS methods in handling real-world applications. In this paper, we propose a simple yet effective model to address these issues by incorporating a newly designed cross-modal affinity (CMA) module based on a Transformer architecture. The CMA module facilitates the establishment of multi-modal affinity over a limited number of samples, allowing the rapid acquisition of new semantic information while fostering the model’s adaptability to diverse scenarios. Furthermore, we extend our FS-RVOS approach to multiple objects through a new instance sequence matching module over CMA, which filters out all object trajectories with similarity to language features that exceed a matching threshold, thereby achieving few-shot referring multi-object segmentation (FS-RVMOS). To foster research in this field, we establish a new dataset based on currently available datasets, which covers many scenarios in terms of single-object and multi-object data, hence effectively simulating real-world scenes. Extensive experiments and comparative analyses underscore the exceptional performance of our proposed FS-RVOS and FS-RVMOS methods. Our method consistently outperforms existing related approaches through practical performance evaluations and robustness studies, achieving optimal performance on metrics across diverse benchmark tests. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Modal analysis

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Distributed Population-Based Simultaneous Perturbation Stochastic Approximation for Fine-Tuning Large Language Models 21st

Distributed Population-Based Simultaneous Perturbation Stoch...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Tan, Yajing Huang, Yuwei Duan, Qiqi Yang, Yijun Shi, Yuhui Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9789819601219

Very recently, a memory-efficient version (called MeZO) of simultaneous perturbation stochastic approximation (SPSA), one well-established zeroth-order optimizer from the automatic control community, has shown competitive fine-tuning performance on large language models (LLMs) with billions of weights under the limited memory of a single GPU, which often hinders the direct usage of the standard backpropagation (BP) update. Furthermore, population-based evolutionary methods (such as evolution strategies from OpenAI and population-based training from DeepMind) have shown a promising direction to improving and scaling up various large-scale AI systems via efficiently utilizing increasingly available distributed computing resources, owing to their relatively simple yet efficient data and control flows. In this paper, we propose a population-based SPSA for fine-tuning LLMs with non-differentiable objective functions. Specifically, our proposed method combines the desirable exploration ability of distributed population-based evolutionary methods with the powerful exploitation ability of SPSA in the modern distributed computing environment wherein the efficient implementation of BP is still challenging because of its complex dependencies in forward-pass and backward-pass information flows. Simulation experiments on a set of challenging (non-differentiable) natural language process tasks empirically validate the potential of our method on fine-tuning even in a relatively small-scale distributed computing scenario. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stochastic systems

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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

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Journal of Electronic science and Technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Performance Analysis of Constrained Evolutionary Multi-objective Optimization Algorithms on Artificial and Real-World Problems 13th

Performance Analysis of Constrained Evolutionary Multi-obje...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Nan, Yang Ishibuchi, Hisao Pang, Lie Meng Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635375

Most real-world optimization problems have multiple objectives and constraints. To address constrained multi-objective optimization problems (CMOPs), researchers have proposed many constrained evolutionary multi-objective optimization (EMO) algorithms. They usually evaluate the proposed constrained EMO algorithms on artificial CMOPs in the literature. As a result, some newly specialized constrained EMO algorithms are designed for those artificial problems. Our previous work demonstrated that an old EMO algorithm NSGA-II with a simple constraint handling mechanism can outperform some newly proposed algorithms on real-world CMOPs. In this paper, we further examine the performance of constrained EMO algorithms on artificial and real-world CMOPs. We also analyze the relationship between algorithms’ performance and problems’ properties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Numerical Analysis of Pareto Set Modeling 13th

Numerical Analysis of Pareto Set Modeling

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Shu, Tianye Ishibuchi, Hisao Nan, Yang Pang, Lie Meng Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635375

Recently, neural network-based inverse models have been used for multi-objective optimization. The basic idea is to approximate the mapping from the Pareto front to the Pareto set. In general, inverse modeling from a low-dimensional objective space to a high-dimensional decision space is difficult. However, good results have been reported in the literature. In this paper, we numerically examine the performance of inverse modeling. For visual examination, we use 2- and 3-objective distance minimization problems with 2 to 1,000 decision variables. Our experimental results show that inverse modeling improves the quality of the final population, which is used as the training data. One interesting observation is that improving fitting accuracy by increasing the neural network complexity does not improve the quality of the model outputs. We also demonstrate that inverse modeling works well even for large-scale multi-objective problems with 1,000 decision variables when high-quality training data are available. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neural network models

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Small Population Size is Enough in Many Cases with External Archives 13th

Small Population Size is Enough in Many Cases with Externa...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Nan, Yang Ishibuchi, Hisao Pang, Lie Meng Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635375

In the evolutionary multi-objective optimization (EMO) field, evolutionary algorithms have a population to store a well-converged and well-distributed solution set. In the literature, the population size is usually specified in the range of 100–300 to approximate the entire Pareto front. Recently, a new framework with an external archive has been actively studied where a solution set selected from the archive is used to approximate the Pareto front. In this framework, a small population size can be better than a standard population size since a smaller population size means a larger number of generations under the termination condition specified by the same number of evaluated solutions. In this paper, we numerically examine whether better results are obtained from a smaller population size specification than a standard specification when the algorithm performance is evaluated using a solution set selected from an external archive. In our experiments, we examine the performance of four representative EMO algorithms (NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA) with four specifications of the population size (36, 66, 120, and 210) on two frequently-used artificial test suites (DTLZ and WFG) and three real-world test suites (RWA, DDMOP, and RE). Our experimental results show that better results are obtained from a smaller population size specification in many cases when we cannot evaluate many solutions (i.e., when solution evaluation is expensive). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Evolutionary multi-objective optimization Performance comparison Small population size

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