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IEEE Conference on Decision and control

作者： Shuhua Gao Cheng Xiang Ming Yu Kuan Tak Tan Tong Heng Lee School of Control Science and Engineering Shandong University Jinan China Department of Electrical and Computer Engineering National University of Singapore Singapore Power Automation Pte Ltd Singapore Engineering Cluster Singapore Institute of Technology Singapore

The literature presents a vast array of advanced metaheuristic methods for photovoltaic parameter estimation. However, the focus of this study is not to introduce another new method into this already crowded field. Instead, we examine two important but often overlooked questions: (i) are existing results globally optimal, and (ii) can a simpler method achieve comparable performance. We conduct case studies using two widely used I-V curve datasets. To address the first issue, we develop a branch and bound algorithm that, despite its sluggishness, either certifies the global minimum or provides a tight upper bound. These values serve as useful benchmarks for fair metaheuristic evaluation and further development. To answer the second question, our extensive examination and comparison surprisingly reveal that a basic differential evolution (DE) algorithm can achieve the certified global minimum or obtain the best known result. Additionally, the DE algorithm's runtime is much shorter than that of current state-of-the-art metaheuristic methods, making it a great choice for time-sensitive applications. This remarkable finding suggests that using increasingly complex metaheuristics in ordinary PV parameter estimation problems might be unnecessary. Finally, we discuss the implications of these outcomes and propose the simple DE method as the premier choice for industrial applications.

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2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

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Consistent and Asymptotically Efficient Localization from Range-Difference Measurements

arXiv

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arXiv 2023年

作者： Zeng, Guangyang Mu, Biqiang Shi, Ling Chen, Jiming Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen China The Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source's position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased (thus consistent) estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Stealthy Attacks on Sampled-Data control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

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Mediterranean Conference on control and automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation

Model predictive control for tracking using artificial refer...

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IEEE Conference on Decision and control

作者： P. Krupa J. Köhler A. Ferramosca I. Alvarado M.N. Zeilinger T. Alamo D. Limon Computer Science Group Gran Sasso Science Institute (GSSI) L’Aquila Italy ETH Zürich Institute for Dynamic Systems and Control Zürich Switzerland Department of Management Information and Production Engineering University of Bergamo Via Marconi 5 Dalmine (BG) Italy Department of Systems Engineering and Automation Universidad de Sevilla Seville Spain

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper provides a comprehensive tutorial on a family of Model Predictive control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation.

关键词： Surveys Asymptotic stability Costs Tutorials Optimization Predictive control

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Syntax-based data augmentation for Hungarian-English machine translation

arXiv

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arXiv 2022年

作者： Nagy, Attila Nanys, Patrick Konrád, Balázs Frey Bial, Bence Ács, Judit Department Of Automation And Applied Informatics Budapest University Of Technology And Economics Hungary Institute For Computer Science And Control Eötvös Loránd Research Network

We train Transformer-based neural machine translation models for Hungarian-English and English-Hungarian using the Hunglish2 corpus. Our best models achieve a BLEU score of 40.0 on Hungarian-English and 33.4 on English-Hungarian. Furthermore, we present results on an ongoing work about syntax-based augmentation for neural machine translation. Both our code and models are publicly available. © 2022, CC BY.

关键词： Syntactics

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OA-ECBVC: A Cooperative Collision-free Encirclement and Capture Approach in Cluttered Environments

arXiv

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arXiv 2023年

作者： Wang, Xinyi Ding, Yulong Chen, Yizhou Han, Ruihua Xi, Lele Chen, Ben M. The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong The Department of Control Science and Engineering Tongji University Shanghai China The Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China Department of Computer Science The University of Hong Kong Hong Kong Hebei University of Science and Technology Shijiazhuang China

This article investigates the practical scenarios of chasing an adversarial evader in an unbounded environment with cluttered obstacles. We propose a Voronoi-based decentralized algorithm for multiple pursuers to encircle and capture the evader by reacting to collisions. An efficient approach is presented for constructing an obstacle-aware evader-centered bounded Voronoi cell (OA-ECBVC), which strictly ensures collision avoidance in various obstacle scenarios when pursuing the evader. The evader can be efficiently enclosed in a convex hull given random initial configurations. Furthermore, to cooperatively capture the evader, each pursuer continually compresses the boundary of its OA-ECBVC to quickly reduce the movement space of the evader while maintaining encirclement. Our OA-ECBVC algorithm is validated in various simulated environments with different dynamic systems of robots. Real-time performance of resisting uncertainties shows the superior reliability of our method for deployment on multiple robot platforms. Copyright © 2023, The Authors. All rights reserved.

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Online Scheduling in Virtualized TSN Networks via Joint Admission control and VNF Embedding

Online Scheduling in Virtualized TSN Networks via Joint Admi...

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IEEE International Conference on Communications in China (ICCC)

作者： Yajing Zhang Cailian Chen Chaoqun You Mingyan Li Xinping Guan Tony Q. S. Quek Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China School of Computer Science Fudan University College of Computer Science Chongqing University Chongqing P. R. China Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Introducing virtualization enhances the flexibility of time-sensitive networking (TSN), wherein applications manifest as service function chains comprising a series of virtual network functions (VNFs). However, such virtualized TSN realizes determinacy through global configuration, being too complicated to serve dynamic applications in time. To address this issue, we innovatively propose to achieve TSN scheduling by distributively executing admission control (AC), whereby the scheduling complexity is radically reduced. Specifically, we first build a two-way AC model that captures TSN multi-queue characteristics. Then, we define admissible regions of nodes and links, working as metrics for AC decision-making and enabling feasible TSN scheduling. Built upon this, we propose a joint AC and VNF embedding mechanism, Rapid Admission control (RapidAC), which consists of two algorithms. The first algorithm responds to dynamic applications rapidly and derives node-mapping solutions by judging nodes’ admissible regions. Based on this, the second algorithm augments the detailed VNF embedding solution according to admissible regions of links. Simulation results show that RapidAC reduces runtime by 90% compared with existing TSN scheduling algorithms.

关键词： Measurement Runtime Scheduling algorithms Service function chaining Heuristic algorithms Simulation Admission control

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An Analysis about Capital and Maintenance Costs related to Electrical Flexibility

An Analysis about Capital and Maintenance Costs related to E...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Eduardo Perez Liberato Matheus Bawden Caio Mateus Cardozo Rego Yan Tavares De Oliveira José Oniram de Aquino Limaverde Filho Thiago Felicio De Souza Ronan Gustavo Carvalho Furtado Eugênio Libório Feitosa Fortaleza Automation and Control Group (GRACO) University of Brasília - UnB Brasília Brazil Computer Science Department University of Brasília - UnB Brasília Brazil Electrical Engineering Department University of Brasília - UnB Brasília Brazil Furnas Eletrobras Rio de Janeiro Brazil

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The investment in non-dispatchable renewable energy sources (such as solar and wind) increased over the last decade in response to growing concerns about global warming. However, this phenomenon also increases the flexibility gap for the electrical grid regarding dispatchable energy sources, commonly combustion power plants. Considering such background, this study aims to evaluate the amount of resources it would cost only to have a ready-to-use power plant when needed, by evaluating the average and peak of the power consumption. The findings express a final amount of resources to serve as a basis to energy companies.

关键词： Hydraulic turbines Wind Costs Power demand Fitting Hydroelectric power generation Global warming Maintenance Fuels Investment

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