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2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020

作者： Zhmud, Vadim Roth, Hubert Hardt, Wolfram Novosibirsk State Technical University Department of Automation Novosibirsk Russia Control Technologies University of Siegen Department of Regulatory Siegen Germany Technical University of Chemnitz Department of Computer Science Chemnitz Germany

ISBN: (纸本)9781728169514

Many tasks of control of technical objects require the design of regulators. Correct calculation of the regulator provides high static and dynamic accuracy. Among the many methods for solving this problem, the most successful methods are based on numerical optimization. The reasons for this are that almost any complexity of the mathematical model of an object is not an obstacle for these methods;many specialized and universal mathematical software products such as MATLAB, Simulink, Mathcad, VisSim, SimInTech, and others offer algorithmic support for modeling and optimization of such systems. These software products have built-in algorithms for numerical optimization of many parameters, such as Powell, Polak-Ribiere, Fletcher-Reeves, Monte-Carlo. If the object model is known with sufficient accuracy and the objective (cost) function together with the test tasks for the system are justified, then such software most often quite successfully solve the problem of numerical calculation of regulator coefficients. However, in some cases, the desired extremum of the objective function is located in such a range of the values of the desired coefficients that do not allow the problem to be considered successfully solved, since theory and practice indicate that these coefficients in this case should be positive, and the optimization result gives negative values for some of such coefficients. In particular, the coefficient of the derivative link is sometimes negative. This paper provides a theoretical justification and model confirmation of a method that allows to exclude such a result that provides a more successful solution of the problem, and the resulting regulator provides the best dynamic accuracy along with the best static accuracy. The method is the use of Gaussian noise generator as a disturbing effect in the system when modeling its operation for the purpose of numerical optimization of the regulator. With all this, the noise level should be chosen sufficiently small;t

关键词： Optimization

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Object Detection at Sea Using Ensemble Methods Across Spectral Ranges

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IFAC-PapersOnLine 2021年第16期54卷 1-6页

作者： Frederik E.T. Schöller Martin K. Plenge-Feidenhans’l Jonathan D. Stets Mogens Blanke Automation and Control Group Department of Electrical Engineering Technical University of Denmark DK-2800 Kgs. Lyngby Denmark Section of Image Analysis and Computger Graphics Department of Applied Mathematics and Computer Science DTU

Having the option of a temporally unmanned bridge when conditions allow, while maintaining or even enhancing navigational safety, is a long term aim in the maritime industry. Such a system requires excellent perception of the environment using an array of sensors. This paper investigates performance of object detection at sea using electro-optical sensors in relevant spectral ranges and discusses how missed detection risk is minimised for objects within navigation range. Using a combination of cameras in visible, near- and far infrared ranges, convolutional neural networks are employed for object detection. Ensemble techniques are suggested to minimise the amount of missed detections and it is shown how optimisation of confidence thresholds can be used to increase performance. The results are based on image data from vessels in near-coast operation in Danish waters.

关键词： Multi-modal sensor fusion Deep learning Detection performance Object detection at sea computer vision Autonomous Marine Vehicles

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Structural Robustness of Complex Networks: A Survey of A Posteriori Measures

arXiv

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arXiv 2023年

作者： Lou, Yang Wang, Lin Chen, Guanrong The Graduate School of Information Science and Technology Osaka University Osaka Suita565-0871 Japan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various industrial and social networks against malicious attacks, which has various meanings in different research contexts and here it refers to the ability of a network to sustain its functionality when a fraction of the network fail to work due to attacks. The rapid development of complex networks research indicates special interest and great concern about the network robustness, which is essential for further analyzing and optimizing network structures towards engineering applications. This comprehensive survey distills the important findings and developments of network robustness research, focusing on the a posteriori structural robustness measures for single-layer static networks. Specifically, the a posteriori robustness measures are reviewed from four perspectives: 1) network functionality, including connectivity, controllability and communication ability, as well as their extensions;2) malicious attacks, including conventional and computation-based attack strategies;3) robustness estimation methods using either analytical approximation or machine learning-based prediction;4) network robustness optimization. Based on the existing measures, a practical threshold of network destruction is introduced, with the suggestion that network robustness should be measured only before reaching the threshold of destruction. Then, a posteriori and a priori measures are compared experimentally, revealing the advantages of the a posteriori measures. Finally, prospective research directions with respect to a posteriori robustness measures are recommended. © 2023, CC BY.

关键词： Complex networks

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Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Feasible Regions Identification based on Historical Solutions for Constrained Optimization Problems

Feasible Regions Identification based on Historical Solution...

引用

Congress on Evolutionary Computation

作者： Mengli Shan Changhe Li Mai Peng Xiaobo Liu Michalis Mavrovouniotis Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Artificial Intelligence Anhui University of Science & Technology Hefei China State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining Anhui University of Science & Technology Huainan China ERATOSTHENES Centre of Excellence Limassol Cyprus Department of Civil Engineering and Geomatics Cyprus University of Technology Limassol Cyprus School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (数字)9798331534318

ISBN: (纸本)9798331534325

The presence of constraints often leads to the formation of narrow and fragmented feasible regions within the search region, presenting significant challenges for optimization problem-solving. This paper introduces a novel approach, Feasible Regions Identification based on Historical Solutions (FRIHS), designed to address these challenges. FRIHS leverages previously evaluated solutions to partition the search region into ε-feasible and ε-infeasible regions. Additionally, by analyzing the correlations among constraints, they are reformulated as auxiliary objectives, effectively transforming the constrained optimization problem into a constrained multi-objective optimization problem. The method employs the classical evolutionary algorithm Differential Evolution and the multi-objective method NSGA-III to search the most promising feasible regions. The effectiveness of FRIHS is evaluated through a comparative analysis with five advanced constraint-handling algorithms across a benchmark test suite. Experimental results indicate that the proposed approach demonstrates competitive performance on the test problems.

关键词： Correlation Evolutionary computation Benchmark testing Search problems Rendering (computer graphics) Problem-solving Optimization Faces

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Prediction of Drug-Disease Associations Based on Long Short-Term Memory Network and Gaussian Interaction Profile Kernel 15th

Prediction of Drug-Disease Associations Based on Long Short-...

引用

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Jiang, Han-Jing Wang, Yan-Bin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Computer Science and Technology Zhejiang University Hangzhou315000 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Drug-disease association depicts the accessibility landscape of the association at the molecular level, thereby revealing the intermolecular reaction in the process of drug reposition. However, the scarcity of drug-disease association data often complicates the drug reposition. In this work, we present a method for predicting drug-disease association, which is called drug-disease association prediction via Gaussian interaction profile kernel and long short-term memory network (G-LSTM). G-LSTM combines a recurrent network framework with a Gaussian interaction profile kernel to learn underlying features that accurately characterize drug-disease-association data. We have verified G-LSTM models on the two benchmark datasets Fdataset and Cdataset, respectively. The experimental results show that the predictive performance was significantly improved, with AUC of 0.9527 and 0.9705, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Multi-Player Pursuit-Evasion Game With Interaction Constraints: A Cooperative Game Theoretic Approach Based on Coalition Structure

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IEEE Transactions on automation science and Engineering 2025年 22卷 16925-16937页

作者： Xiwen Ma Maolong Lv Kairong Duan Wei Xie Jingsong Yang Weidong Zhang School of Oceanography Shanghai Jiao Tong University Shanghai China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Ministry of Natural Resources Hangzhou China College of Air Traffic Control and Navigation Air Force Engineering University Xi’an China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Automation Shanghai Jiao Tong University Shanghai China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai China School of Information and Communication Engineering Hainan University Haikou Hainan China

This paper presents a comprehensive mathematical approach to address the multi-player and multi-objective pursuit-evasion games problem, incorporating coalition structure constraints from a cooperation-competition perspective. Social interaction networks are developed to approximate priority communication alliances based on individual pReferences, establishing a multi-connected topology and decision space for the games. An N-player variable-sum differential game model, featuring autonomous obstacle avoidance, is formulated by integrating kinematic constraints and the social forces method. Rigorous proofs are provided for the uniqueness of payoff distribution, the stability of alliance structures, and the convergence of many-to-many differential games to Nash Equilibrium. Simulation and experimental results are presented to validate the effectiveness and performance of the proposed method. Note to Practitioners—A structured stable alliance is essential for fostering collective intelligence, especially in scenarios marked by intense conflict and the necessity for cooperation. While traditional methods (e.g., optimal control, differential games) are widely studied, many rely on rigid, random, or preassigned large-scale alliances that miss dynamic group interactions and cooperative alliance dynamics, leading to inefficient resource allocation. Drawing inspiration from social force models, we propose a novel coalition-structure-based cooperative game theory framework. By integrating multi-agent interactions via a coupled differential game on interconnected topologies, it offers new perspectives for multi-agent pursuit-evasion with obstacles and coalition constraints, advancing collective intelligence research in military defense, law enforcement, and maritime surveillance—domains where group dynamics are critical.

关键词： Games Differential games Resource management automation Dynamics Topology Oceanography Collision avoidance Force Finite element analysis

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Ensemble Multi-Label Classification Via Closed Frequent Labelsets and Label Taxonomies

SSRN

引用

SSRN 2024年

作者： Ferrandin, Mauri Cerri, Ricardo Department of Control Automation and Computer Science Federal University of Santa Catarina Rua João Pessoa 2750 SC Blumenau89036-256 Brazil Instituto de Ciências Matemáticas e de Computação University of São Paulo Avenida Trabalhador São-carlense 400 - Centro SP São Carlos13566-590 Brazil

Ensembles are computational models that combine the strengths of multiple algorithms or models to enhance predictive accuracy, robustness, and generalization across various applications in machine learning and data analysis. By reducing the impact of individual algorithmic biases, ensembles can mitigate overfitting risk and improve model stability. Ensembles are valuable tools for achieving superior performance in complex and dynamic real-world scenarios. This paper proposes three new ensemble algorithms, called EF2H, that were created using the F2H multi-label classification model. The F2H algorithm transforms the multi-label problem into a hierarchical multi-label problem to obtain the predictions. The experiments were conducted with 32 multi-label datasets, and the results were compared with state-of-the-art algorithms in the field. The results showed that the EF2H versions were very competitive algorithms, outperforming the well-known ECC algorithm and achieving equivalent predictive performance to the algorithms RFPCT and RFDTBR. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor

A Model-based Approach for Indoor Localization Leveraging Si...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yaoxin Duan Rongbin Hu Zexing Liu Yuanyi Zhang Wendi Nie Kam-Yiu Lam Chun Jason Xue Yongli Song Guan Gui Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education China School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Institute of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Computer Science Department City University of Hong Kong Hong Kong PRC Beijing Institute of Computer Technology and Applications China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

As widely deployed WiFi sensors in indoor scenarios, e.g., WiFi access points and WiFi monitors, WiFi signal-based indoor localization has attracted increasing attention from research communities in the past decade. Among various WiFi-based localization techniques, received signal strength (RSS) fingerprinting based on multiple sensors reveals its superiority and effectiveness in complex indoor environments. Existing multi-sensor-based techniques mainly focus on designing efficient algorithms to improve localization performance. However, the limitations of 1) densely pre-deployed WiFi sensors and, 2) sensitivity to changing sensors are not considered appropriately. In this paper, we propose a novel technique called Single Mobile Sensor (SMS) localization, which leverages a single mobile sensor for indoor localization. The SMS localization technique employs a fingerprinting technique with a custom-designed model, named SMS Fingerprint Matching (SMSFM) model, which is responsible for matching fingerprints constructed by a single mobile sensor to estimate targets' location. Numerous experiments conducted on a practical testbed have revealed that the SMSFM model surpasses conventional models, leading to SMS localization delivering competitive localization accuracy compared to previous multi-sensor-based technologies, despite relying solely on a single sensor.

关键词： Location awareness Accuracy Sensitivity Fingerprint recognition Sensors Trajectory Indoor environment Wireless fidelity Monitoring

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