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Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

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ieee Transactions on automation science and engineering 2025年 22卷 14953-14966页

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong Xi’an Jiaotong University School of Automation Science and Engineering Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi’an710049 China University of Pittsburgh Department of Industrial Engineering Department of Electrical and Computer Engineering PittsburghPA15106 United States Tsinghua University Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Xi’an Jiaotong University School of Electrical Engineering Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitude. © 2004-2012 ieee.

关键词： Stochastic programming

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Supervisory Control of Discrete Event Systems Modeled With Labeled Petri Nets for Diagnosability Enforcement

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ieee Transactions on automation science and engineering 2025年 22卷 13855-13870页

作者： Li, Chengzong Chen, Yufeng Mostafa, Almetwally M. Khalgui, Mohamed Qinghai University School of Energy and Electrical Engineering Xining810000 China Macau University of Science and Technology Institute of Systems Engineering Taipa China King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia German International University Faculty of Engineering Berlin13507 Germany

This paper proposes an active fault diagnosis method to enforce the diagnosability of discrete event systems using labeled Petri nets by constructing a diagnostic supervisor. For a non-diagnosable net model, its diagnosability is viewed as a control specification and addressed by using supervisory control techniques. First, an event-based monitor and a Petri net structure, referred to as data isolation arcs, are introduced to apply control specifications for labeled Petri nets. Then, a diagnostic supervisor reachability graph is generated to estimate the current state of the diagnoser. By analyzing the diagnostic supervisor reachability graph, an integer linear programming model is formulated to design a diagnostic supervisor, which can prevent the system from entering into any indeterminate cycle only by observing the occurrence of events. Finally, by the obtained diagnostic supervisor, the resulting net model is shown to be diagnosable. Some examples are presented to demonstrate the proposed method. © 2004-2012 ieee.

关键词： Supervisory personnel

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When Crowdsensing Meets Smart Cities: A Comprehensive Survey and New Perspectives

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ieee Communications Surveys and Tutorials 2025年第2期27卷 1101-1151页

作者： Wang, Zhenning Cao, Yue Jiang, Kai Zhou, Huan Kang, Jiawen Zhuang, Yuan Tian, Daxin Leung, Victor C. M. Wuhan University School of Cyber Science and Engineering Wuhan430072 China Northwestern Polytechnical University College of Computer Xi’an710129 China Guangdong University of Technology School of Automation Guangzhou510006 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Beihang University School of Transportation Science and Engineering Beijing100191 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518061 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Crowdsensing has received widespread attention in recent years. It is extensively employed in smart cities and intelligent transportation systems. This paper comprehensively surveys the latest research advancements in crowdsensing for smart cities from a novel perspective. Specifically, this paper is categorized according to sensing entities in smart cities, including human-oriented sensing, vehicle-oriented sensing, and infrastructure-oriented sensing. Meanwhile, the development of Unmanned Aerial Vehicle (UAV)-assisted sensing in recent years is also summarized, accompanied by a timeline of related research. To facilitate easy comprehension, we have positioned the reading flow into the corresponding architectures, resolved problems, existing technical solutions, and specific application scenarios for different sensing entities. In particular, the problems to be solved are further analyzed from four technical perspectives, namely mathematics and operational research, artificial intelligence and machine learning, incentive mechanisms, security and privacy protection. Based on the proposed taxonomy, recent studies are thoroughly investigated to illustrate the current state of research in crowdsensing. Furthermore, this paper highlights the emerging applications of human-oriented and vehicle-oriented sensing in smart cities, as well as the frameworks, platforms, simulators, and datasets involved in crowdsensing. Finally, this paper discusses research directions related to crowdsensing in smart cities, such as digital twins, metaverses, and artificial intelligence-generated content. The primary goal of this survey is to review and synthesize prior research, identify potential avenues for future research, and explore opportunities for collaboration with other relevant research domains. © 1998-2012 ieee.

关键词： Smart city

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HG2former: HSV-Gamma Guided Transformers for Efficient Underwater Image Enhancement

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ieee Journal of Oceanic engineering 2025年第2期50卷 866-878页

作者： Qing, Yuhao Shen, Liquan Fang, Zhijun Wang, Yueying Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai200444 China Donghua University School of Computer Science and Technology Shanghai200051 China

Due to optical phenomena, such as the absorption and scattering of light in underwater environments, underwater images often suffer from degradation in color, contrast, clarity, and noise. Existing deep learning-based methods for underwater image enhancement typically learn a direct mapping from low-quality to high-quality underwater images, without fully considering the mapping of local luminance, chrominance, and contrast features. In this article, we propose a transformer model guided hue, saturation, value (HSV) and gamma correction for underwater image enhancement. The HG2former combines the HSV color model and gamma correction techniques to isolate the three fundamental characteristics of color, providing rich, differentiated enhancement for both color and contrast in underwater images. In addition, nonlinear gamma correction adaptively adjusts the brightness and contrast of images, addressing issues of visibility reduction and color distortion in underwater imaging. Furthermore, we introduce a meticulously designed encoder–decoder structure, along with an improved multihead self-attention module, to capture the spatial distribution patterns of underwater images while modeling both local and long-range dependencies. Extensive experimental results on multiple data sets demonstrate that the proposed HG2former outperforms other state-of-the-art methods. © 1976-2012 ieee.

关键词： Color

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Overview of 2024 CAD Contest at ICCAD 24

Overview of 2024 CAD Contest at ICCAD

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43rd international conference on computer-Aided Design, ICCAD 2024

作者： Fang, Shao-Yun Liu, Yi-Yu Cheng, Chung-Kuan Tseng, Tsun-Ming Department of EE National Taiwan University of Science and Technology Taipei Taiwan Department of CS National Taiwan University of Science and Technology Taipei Taiwan Department of CSE University of California San Diego San Diego United States EDA Technical University of Munich Munich Germany

ISBN: (纸本)9798400710773

The "CAD Contest at ICCAD" is a challenging, multi-month, research and development competition, focusing on advanced, real-world problems in the field of electronic design automation (EDA). Since 2012, the contest has been publishing many sophisticated circuit design problems, from system-level design to physical design, together with industrial benchmarks and solution evaluators. Contestants can participate in one or more problems provided by EDA/IC industry. The winners will be awarded at an ICCAD special session dedicated to this contest. Every year, the contest attracts more than a hundred teams, fosters productive industry-academia collaborations, and leads to hundreds of publications in top-tier conferences and journals. The 2024 CAD Contest has 221 teams from all over the world, which generates the highest participation record. Moreover, the problems of this year cover state-of-the-art EDA research trends such as logic optimization, multibit flip-flop, and Machine Learning (ML) for EDA from well-known EDA/IC companies. We believe the contest keeps enhancing impact and boosting EDA researches. © 2024 Copyright is held by the owner/author(s).

关键词： Logic Synthesis

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Classification of Colorectal Cancer Tissues Using Stacking Ensemble Learning 7th

Classification of Colorectal Cancer Tissues Using Stacking E...

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7th international conference on Communication, Device and Networking, ICCDN 2024

作者： Das, Abhrodeep Hazra, Animesh Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India

ISBN: (纸本)9789819764648

Advancement in digital pathology has enabled deep learning-based computer vision techniques for automated diagnosis and prognosis of diseases. The essentiality of early detection and prognosis of any cancer category lies in the fact that it can speed up the subsequent medical treatment procedures of patients. About 10% of all cancer cases worldwide are related to colorectal cancer (CRC), and it is also the third most common category of cancer (Egeblad et al. in Dev Cell 18:884–901, 2010). So, it is clinically important to classify and make an objective evaluation of colorectal cancer histological images. The classification performance of current methodologies primarily relies on the use of various combinations of texture-based features and classifiers or transfer learning to classify different organisational kinds. As a result of the diversity of tissue types and characteristics present in histological images, classification is still a challenging task. In this study, we have put forth a novel and effective stacking (Wolpert in Neural Netw 5:241–59, 1992) ensemble (Zhang and Yunqian (eds) Ensemble machine learning: methods and applications. Springer science;Business Media, 2012) technique for classification on the histopathological image analysis benchmark dataset Kather-5 K (Kather et al. in Sci Rep 6:27988, 2016). The ensemble consists of two cutting-edge deep learning architectures, ResNet18 (He et al. in Proceedings of the ieee conference on computer vision and pattern recognition 2016, pp 770–778) and EfficientNetB0 (Tan and Le Efficientnet: rethinking model scaling for convolutional neural networks. international conference on machine learning 2019 May 24. PMLR, pp 6105–6114), acting as weak learners, and an ANN acting as the meta-learner. The proposed approach obtained a remarkable accuracy of 97.20% in CRC classification on the said dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Colorectal cancer Convolutional neural network Ensemble learning Residual network Stacking

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Consideration of Durability and Storability at the Overall Dependability Assessment of Industrial Control Systems 1

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7th international Russian automation conference, RusAutoCon 2024

作者： Solovyev, A.V. Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences 44/2 Vavilova str. Moscow119333 Russia

ISBN: (数字)9783031824944

ISBN: (纸本)9783031824937

The article presents the indicators for the quantitative assessment of durability and storability developed in this research and mathematical models for their calculation and use in the assessment of the overall dependability of industrial control systems. The relevance of the research is underpinned by the fact that data in digital form during the digital transformation of the economy and society is becoming a key factor in digital manufacturing in general and when used in industrial control systems in particular. Mathematical models are developed for the following indicators: storage life, probability of storability, mean storage life, and gamma-percentile storage life. The proposed system of indicators is new and was developed on the basis of international standards for dependability assessment, such as GOST 27.102-2021, IEC 60319, ISO 2394, GOST R 54989-2012, and ISO/TR 18492:2005. The advantage of the proposed system of indicators is their simplicity and harmonization with the existing calculations of standardized dependability indicators. In further research, the author plans to improve the presented mathematical models of durability and storability assessment and develop a methodology for their consideration in the overall dependability assessment of industrial control systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital control systems

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Distributed Denial of Service Attack Detection by Machine Learning Techniques 22

Distributed Denial of Service Attack Detection by Machine Le...

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22nd international Learning and Technology conference, L and T 2025

作者： Jewani, Varkha K. P.E., Ajmire M., Atique B., Sharma I.B., Dhaou S., Awadhesh Chaurasia Sant Gadge Baba Amravati University Amravati India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia RTM Nagpur University Department of Computer Science Nagpur India

ISBN: (纸本)9798331532215

The exponential expansion of computer networks and the internet makes it clear that there is a chance of being attacked and harmed. In the meantime, one of the most crucial defensive measures against the more complex and frequent network attacks is the intrusion detection and prevention systems (IDSs and IPSs). Due to insufficient datasets, anomaly-based research in intrusion detection systems is plagued by imprecise deployment, analysis, and evaluation. The researchers analyzed a variety of datasets, including DARPA98, KDD99, ISC2012, and ADFA13, to assess how well their suggested intrusion detection and intrusion prevention techniques performed. There are several issues: lack of adequate attack coverage, unrepresentative payloads, and anonymised packet information, insufficient traffic volume and diversity, or a deficiency in feature set and metadata. This study is focused on CICIDS-2017 which is the recently updated IDS dataset that meets real-world requirements and is accessible publicly. In addition to benign network flows and distributed denial of service (DDoS) attacks, it contains seven common attack network flows as well. To provide the optimal combination of features for the identification of the attack category, it also assesses the efficacy of several machine learning (ML) algorithms and network traffic feature sets. Even though various ML techniques like Linear Regression, Random Forests, and Decision Tree can be used, and performed very well, Hybrid Algorithm is designed to get better analysis on dataset. © 2025 ieee.

关键词： Network intrusion

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Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification

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ieee Transactions on Neural Networks and Learning Systems 2025年第5期36卷 8166-8177页

作者： Zhang, Zhijun He, Yu Mai, Weijian Luo, Yamei Li, Xiaoli Cheng, Yuanxiong Huang, Xiaoming Lin, Run South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Guangdong Second Provincial General Hospital Department of Pulmonary and Critical Care Medicine Guangzhou518025 China Sun Yat-sen Memorial Hospital Otolaryngology-Head and Neck Surgery Guangzhou510120 China Sun Yat-sen University The First Affiliated Hospital Department of Radiology Guangdong Guangzhou510080 China

The brain signal classification is the basis for the implementation of brain–computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manual intervention, such as channel selection and dimensionality reduction, and often struggle to achieve satisfactory classification accuracy. To achieve high classification accuracy and as little manual intervention as possible, a convolutional dynamically convergent differential neural network (ConvDCDNN) is proposed for solving the electroencephalography (EEG) signal classification problem. First, a single-layer convolutional neural network is used to replace the preprocessing steps in previous work. Then, focal loss is used to overcome the imbalance in the dataset. After that, a novel automatic dynamic convergence learning (ADCL) algorithm is proposed and proved for training neural networks. Experimental results on the BCI Competition 2003, BCI Competition III A, and BCI Competition III B datasets demonstrate that the proposed ConvDCDNN framework achieved state-of-the-art performance with accuracies of 100%, 99%, and 98%, respectively. In addition, the proposed algorithm exhibits a higher information transfer rate (ITR) compared with current algorithms. © 2012 ieee.

关键词： Convolutional neural networks

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Distribution Locational Marginal Emission for Carbon Alleviation in Distribution Networks: Formulation, Calculation, and Implication

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ieee Transactions on automation science and engineering 2025年 22卷 15006-15017页

作者： Sang, Linwei Xu, Yinliang Sun, Hongbin Wu, Zaijun Wu, Qiuwei Wu, Wenchuan Southeast University School of Electrical Engineering Nanjing Shenzhen China Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen China Tsinghua University Department of Electrical Engineering Beijing China Southeast University Southeast University Shenzhen Research Institute School of Electrical Engineering China Queen’s University School of Electronics Electrical Engineering and Computer Science BelfastBT8 5BN United Kingdom

Regulating the proper carbon-aware intervention policy is one of the keys to emission alleviation in the distribution network, whose basis lies in effectively attributing the emission responsibility using emission factors. This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the marginal change of both active and reactive load demand for incentivizing carbon alleviation. It first formulates the day-head distribution network scheduling model based on the second-order cone program (SOCP). The emission propagation and responsibility are analyzed from demand to supply to system emission. Considering the complex and implicit mapping of the SOCP-based scheduling model, the implicit theorem is leveraged to exploit the optimal condition of SOCP. The corresponding SOCP-based implicit derivation approach is proposed to calculate the DLMEs effectively in a model-based way. Comprehensive numerical studies are conducted to verify the superiority of the proposed method by comparing its calculation efficacy to the conventional marginal estimation approach, assessing its effectiveness in carbon alleviation with comparison to the average emission factors, and evaluating its carbon alleviation ability of reactive DLME. © 2004-2012 ieee.

关键词： Numerical methods

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