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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Distributed Nash equilibrium for pursuit-evasion game with one evader and multiple pursuers

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science China(Information sciences) 2025年

作者： Libing WANG Zhixin LIU Shuo YUAN Zhiqiang PU Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering Wayne State University Institute of Automation Chinese Academy of Sciences

This paper investigates the pursuit-evasion problem involving one evader and multiple pursuers with limited sensing capability, where the evader tries to maximize the distance with the pursuers, while the pursuers have different objectives based on whether they can receive the information of the evader. The subgroup of pursuers who can observe the evader(called leaders) tries to be close to the evader, and the other subgroup of pursuers(called followers) tries to synchronize with their neighbors. When the subgraph formed by all leaders is complete, sufficient conditions are given to guarantee that the pursuers capture the evader and the pursuit-evasion game composed of the evader and leaders reaches Nash equilibrium. Furthermore, for the incomplete subgraph case, the distributed observers are proposed to estimate the relative positions between the evader and all leaders. It is shown that the distributed control strategy based on the observers converges exponentially to the Nash equilibrium solution, and makes the pursuers capture the evader. Finally, simulation examples are provided to verify the effectiveness of the proposed strategies.

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EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin Cancer Classification 27th

EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ray, Amartya Gayen, Soumyajit Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031781032

Skin cancer poses a serious global health challenge, where timely and precise diagnosis is essential to improve patient outcomes. Recently, neural networks have proven to be highly effective tools for automated skin cancer classification, significantly advancing the field of dermatology. This paper introduces a novel approach to generate edge maps from dermoscopic images using a holistically nested edge detector model. These edge maps enhance the detection of shape and symmetry irregularities, which are key indicators of malignancy, and improve the focus on relevant regions of interest. We then propose an edge-guided dual-branch neural network, called EDB-Net, for the classification task. Branch 1 handles edge maps, while Branch 2 processes original dermoscopic images. To highlight significant regions and focus on specific lesion areas, we incorporate a novel channel-spatial synergistic attention block within Branch 2. Additionally, we introduce a unique strategy to modulate the generated attention maps using edge features extracted from the edge maps in Branch 1, creating edge-guided features that refine the overall feature representation. In the final stage, both edge-guided and attention-aided features are combined, producing more distinct and contextually relevant outputs, thereby significantly enhancing classification performance. Our model achieves accuracies of 0.927 and 0.848 on the challenging HAM10000 and ISIC 2016 datasets, respectively, without employing any data augmentation. The source code of the proposed model is available at: https://***/Cmatermedicalimageanalysis/EDB_Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Optical flow-based angular rate sensor fault detection on UAVs*

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IFAC-PapersOnLine 2022年第14期55卷 46-51页

作者： Peter Bauer Szabolcs Kun Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) ELKH Budapest Hungary

The paper first extends the previous work of the authors dealing with optical flow-based angular rate estimation. Extension means consideration of camera position and orientation relative to the UAV body system (non-aligned camera). After the extension the proposed method is evaluated with virtual reality (Unreal-Carla) image sequences considering simulated aircraft flight trajectories and different frames per second (fps) rates. Based-on the results showing high fps requirements for agile flight scenarios a reversed method is proposed for angular rate sensor fault detection considering the integration of system dynamics based-on angular rate and velocity measurements and comparing the predicted image feature positions with the measured ones. The feature position differences are the error measures completed with up-down counters. The results are promising however, only basic evaluation of the proposed methods is done paving the way for detailed evaluation and development outlined in the conclusion.

关键词： angular rate fault detection optical flow UAV

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Modeling the Variety of Trip Opportunities in Recreational Route Networks

Frontiers in Artificial Intelligence and Applications

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Frontiers in Artificial Intelligence and Applications 2021年 343卷 264-268页

作者： Molnár, András J. SZTAKI Institute for Computer Science and Control Kende u. 13-17 BudapestH-1111 Hungary

ISBN: (纸本)9781643682426

In this paper, we deal with the question of how the variety of trip opportunities can be modeled in - possibly complex - recreational trail networks (such as hiking paths or cycling ways). In order to quantify the variety of possible loop trips starting from specific trailheads (starting nodes accessible from outside the network) and the variety of connecting trips between specific origin-destination pairs, two novel measures of Loop Trip Variety Index (LTVI) and Connecting Trip Variety Index (CTVI) are proposed preliminarily and informally in [12], respectively, in the frame of assessing the impacts of some recent trail network developments. This paper establishes the formal definitions of improved variants of these measures, shows their well-definedness, presents the algorithms of their computation, investigates on their properties and benefits, and gives reasons of how and to what extent they can be treated as models of trip variety. Possible uses, application areas and future improvements are sketched especially for visitor management planning and profile-based trip recommendation systems. © 2021 The authors and IOS Press.

关键词： Information management

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Multiple Robots Autonomous Task Planning Based on Improved Genetic Algorithm

Multiple Robots Autonomous Task Planning Based on Improved G...

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IEEE International Conference on Robotics and Biomimetics

作者： Zhiyuan Yang Qingjie Zhao Xingchen Lv Lei Wang Wangwang Liu School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This paper presents an autonomous task planning method for multiple collecting robots on improved genetic algorithm. Considering the limited capacity of collecting robots and the suitability of hopping robots in the microgravity environment of small celestial bodies, we developed a task planning model based on the Capacitated Vehicle Routing Problem (CVRP) and proposed an improved genetic algorithm to simultaneously allocate tasks and plan paths for multiple collecting robots. During robot movement, the A * algorithm is employed for active obstacle avoidance when unknown obstacles are encountered. Experimental results demonstrated that our algorithm, can efficiently allocate tasks for the robot group, and each robot actively avoiding obstacles during movement.

关键词： Vehicle routing Wheels Path planning Planning Safety Resource management Mobile robots Robots Genetic algorithms Capacity planning

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A Registration and Fusion Method of 3D Cross-source Point Cloud Data for Modeling Accurate Models of Small Celestial Bodies

A Registration and Fusion Method of 3D Cross-source Point Cl...

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IEEE International Conference on Robotics and Biomimetics

作者： Xuan He Qingjie Zhao Xingchen Lv Lei Wang Wangwang Liu School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

High-precision three-dimensional models of small celestial bodies are important for deep space exploration tasks. The homologous point cloud obtained from a single sensor has limitations in terms of accuracy and density. For example, the point cloud obtained by RGB camera has rich information and high density, but low precision. The point cloud obtained by laser scanning has high precision but low density. Therefore, we hope to register and fuse the point clouds obtained from different sensors, synthesize the advantages of cross-source point clouds obtained from different source domains, and improve the density, accuracy and other aspects of small celestial point clouds. To solve the common problems of cross-source point clouds, we adopt an end-to-end feature measurement point cloud registration framework, which does not need to search the corresponding points between two point clouds, and reduces the difficulty of processing cross-source point cloud data. Aiming at the problem that some stray points on the registered point cloud surface affect the accuracy of the point cloud, a point cloud fusion optimization method based on Euclidean distance is proposed. The stray points around the point cloud are removed to reduce the loss of normal points. The accuracy of point cloud is improved by the fusion after registration. Experiments show that the proposed method can effectively improve the density and accuracy of small celestial body models.

关键词： Point cloud compression Solid modeling Accuracy Three-dimensional displays Euclidean distance Feature extraction Data models Space exploration Laser fusion Surface treatment

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Landing location planning based on genetic cooperative particle swarm optimization

Landing location planning based on genetic cooperative parti...

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International Symposium on Autonomous Systems (ISAS)

作者： Xinyu Yao Qingjie Zhao Xingchen Lv Lei Wang Wangwang Liu School of Computer Science & Technology Beijing Institute of Technology Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

Being able to safely land on the surface is one of the primary challenges when a probe exploring an asteroid. In order to ensure landing safety, the landing location planning needs to comprehensively consider the terrain characteristics and avoid obstacles such as protruding rocks, craters and slopes. Relatively flat areas are suitable for landing. If multiple landers are landing together, in order to avoid collisions and maintain good communication, appropriate distance should be maintained between the landers. In this paper, we first establish a landing environment model, and then propose a landing location planning method for a lander-group using genetic cooperative particle swarm optimization (GCPSO), which ensures the autonomous movement of individual landers while maintaining a certain safe landing distance between them. Taking a group of three landers as an example, simulation experiments show that the algorithm proposed in this paper can ensure the group to achieve stable landing and keep safe distances between them.

关键词： Collaboration Land surface Genetics Rocks Planning Solar system Safety

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Face anti-spoofing based on multi-modal and multi-scale features fusion

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The Journal of China Universities of Posts and Telecommunications 2022年第6期29卷 73-82页

作者： Kong Chao Ou Weihua Gong Xiaofeng Li Weian Han Jie Yao Yi Xiong Jiahao School of Big Data and Computer Science Guizhou Normal UniversityGuiyang 550025China Special Key Laboratory of Artificial Intelligence and Intelligent Control of Guizhou Province Guizhou Institute of TechnologyGuiyang 550003China Guizhou Science and Technology Information Center Department of Science and Technology of Guizhou ProvinceGuiyang 550002China

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network(CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion(MMFF) is proposed. Specifically, first residual network(Resnet)-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network(FPN), finally squeeze-and-excitation fusion(SEF) module and self-attention network(SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.

关键词： face anti-spoofing multi-modal fusion multi-scale fusion self-attention network(SAN) feature pyramid network(FPN)

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