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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Zhang, Jinghao Chen, Zhe Li, Ning Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9798350327182

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling. © 2023 IEEE.

关键词： Genetic algorithms

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Wood Surface Discoloration Defect Detection Based on Improved HRNet

Wood Surface Discoloration Defect Detection Based on Improve...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Haoran Xu, Zhezhuang Zhu, Yuhang Lin, Ye College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Discoloration defect detection is a significant task in the wood industry for ensuring the appearance and quality of wooden products. However, discoloration defect detection still faces some challenges due to the low contrast, blurred boundary, and slender shape of discoloration defects. To overcome these challenges, we propose a multi-branch feature encoding HRNet(MFE-HRNet) for wood surface discoloration defect detection. The network maintains high-resolution features and builds a parallel multi-resolution architecture to prevent the dilution of low-contrast discoloration defect features. Through repeated cross-fusion of multi-scale features, the network enhances the global representation of blurry features. In order to effectively preserve the detail features of the defect edge, a multi-branch feature encoding module (MFEM) is proposed as the encoding layer of the network. In the MFEM, multiple branches concurrently extract features, generating feature maps with complementary characteristics, thereby enhancing the ability of network to recognize fine details of defects. Experimental results verify the effectiveness of our method in detecting discoloration defects on wood surface. © 2024 IEEE.

关键词： Discoloration

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Optimization Deployment of Temperature Measurement Points for CZ Silicon Single Crystal Growth Equipment Based on Improved Fuzzy C-Means Clustering

Optimization Deployment of Temperature Measurement Points fo...

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第43届中国控制会议

作者： Weichao Huang Siyuan Cao Ding Lui School of Automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the problems of low accuracy and difficult selection of temperature measurement point locations. To solve this problem, this paper proposes an optimization algorithm for measurement point locations based on improved fuzzy c-means clustering(IFCM). The method first generates the initial clustering center through a specific density function and introduces the Mahalanobis distance to solve the problem that the traditional fuzzy c-means clustering results depend on the initial clustering center and the results are unstable. Then, an optimized deployment algorithm based on IFCM temperature measurement points is proposed and compared with other mean value clustering algorithms. Finally, the optimal temperature measurement position on the crystal surface is selected by the IFCM temperature measurement point optimization deployment algorithm on the CZ(Czochraski)silicon single crystal growth equipment, and the inversion results of the randomly selected temperature measurement point scheme and the optimized temperature measurement point scheme are compared by the real-time inversion algorithm based on the dynamic matrix control multi-boundary heat flux, and the results of the optimized temperature measurement point scheme are obtained to be better than that of the randomly selected temperature measurement point scheme.

关键词： Fuzzy c-means clustering IFCM Temperature measurement point optimization Inverse heat transfer

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Learning-Based Motion Planning with Mixture Density Networks

Learning-Based Motion Planning with Mixture Density Networks

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yinghan Wang Xiaoming Duan Jianping He Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The trade-off between computation time and path optimality is a key consideration in motion planning algorithms. While classical sampling based algorithms fall short of computational efficiency in high dimensional planning, learning based methods have shown great potential in achieving time efficient and optimal motion planning. The SOTA learning based motion planning algorithms utilize paths generated by sampling based methods as expert supervision data and train networks via regression techniques. However, these methods often overlook the important multimodal property of the optimal paths in the training set, making them incapable of finding good paths in some scenarios. In this paper, we propose a Multimodal Neuron Planner (MNP) based on the mixture density networks that explicitly takes into account the multimodality of the training data and simultaneously achieves time efficiency and path optimality. For environments represented by point clouds, MNP first efficiently compresses point clouds into a latent vector by encoding networks that are suitable for processing point clouds. We then design multimodal planning networks which enables MNP to learn and predict multiple optimal solutions. Simulation results show that our method outperforms SOTA learning based method MPNet and advanced sampling based methods IRRT* and BIT*.

关键词： Point cloud compression Learning systems Training Simulation Neurons Training data Encoding

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A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior information of information Matrix

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Journal of Shanghai Jiaotong university(Science) 2022年第5期27卷 671-679页

作者： HUANG Yinghao WU Yi YAO Lixiu CAI Yunze Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai200240China Alibaba(China)Co. Ltd.Hangzhou311121China

The tracking of maneuvering targets in radar networking scenarios is studied in this *** the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking *** adaptive grid-expected-mode augmentation variable structure multiple model algorithm is *** adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model ***,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is *** the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.

关键词： radar network system variable structure multiple model consistency filtering information matrix particle swarm optimization

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Distributed Optimization of Heterogeneous Linear Multi-Agent systems With Unknown Disturbances and Optimal Gain Tuning

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IEEE Transactions on Signal and information processing over Networks 2025年 11卷 563-576页

作者： Mengmeng Duan Shanying Zhu Ziwen Yang Cailian Chen Xinping Guan State Key Laboratory of Submarine Geoscience Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Key Laboratory for Perception and Control in Industrial Network Systems Shanghai China

In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. To solve this problem, we propose a distributed controller design framework, which reduces the controller design for heterogeneous linear multi-agent systems to the stabilizer design for first-order multi-agent systems. By this framework, we propose two kinds of dynamic controllers under the strong convexity of the objective function and the restricted secant inequality condition, respectively. Based on the optimal condition and singular perturbation analysis technique, we prove that the system converges to the optimal state if the disturbances tend to be constant or vary slowly. To further optimize the performance criterion under system stability and input constraint, we provide an optimal gain tuning algorithm such that the system stability, optimality and feasibility are simultaneously achieved. Numerical examples are provided to illustrate the effectiveness of the theoretical results.

关键词： Optimization Multi-agent systems Linear programming Tuning Heuristic algorithms Real-time systems Stability criteria Signal processing algorithms Symmetric matrices Convergence

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Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 313-328页

作者： Hanjiang Hu Hesheng Wang Zhe Liu Weidong Chen Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China IEEE Department of Automation Institute of Medical RoboticsKey Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Technology University of CambridgeCambridge CB30FDUnited Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous *** retrieval is an efficient and effective technique in image-based localization *** to the drastic variability of environmental conditions,e.g.,illumination changes,retrievalbased visual localization is severely affected and becomes a challenging *** this work,a general architecture is first formulated probabilistically to extract domain-invariant features through multi-domain image ***,a novel gradientweighted similarity activation mapping loss(Grad-SAM)is incorporated for finer localization with high *** also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised *** final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models with and without Grad-SAM *** experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons *** strong generalization ability of our approach is verified with the RobotCar dataset using models pre-trained on urban parts of the CMU-Seasons *** performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision,especially under challenging environments with illumination variance,vegetation,and night-time ***,real-site experiments have been conducted to validate the efficiency and effectiveness of the coarse-to-fine strategy for localization.

关键词： Deep representation learning place recognition visual localization

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Distributed Task Allocation with Time Constraints Under Limited Communication Resources in Multi-Agent system

Distributed Task Allocation with Time Constraints Under Limi...

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Asian control Conference

作者： Weilin Li Jing Wu Chengnian Long Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, a new distributed allocation algorithm based on performance impact with collective influence (PI-CI) is proposed. Considering inefficiency of creating time slots in PI-MaxAss, two completely independent phases with different communication frequencies are designed to ensure allocation both real-time and conflict-free under limited communication. To solve the misconvergence of the baseline algorithm PI-MaxAss in allocation results, an evaluation function is put forward in consensus phase to directly determines the final attribution of repeatedly assigned tasks, guaranteeing convergence of allocation. Comparison experiments validate the performance improvement of the PI-CI algorithm over the baseline algorithm.

关键词： Time-frequency analysis Real-time systems Resource management Time factors Multi-agent systems Radio spectrum management Convergence

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Analyzing the Correlation between Student Learning Behaviors and Academic Performance in Blended Learning Environments

Analyzing the Correlation between Student Learning Behaviors...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Ying Chen, Kang Xu, Zhezhuang Lin, Ye Zeng, Qingbo College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Blended learning integrates online and face-to-face (FTF) teaching methods to offer students in higher education a flexible and customized educational experience. Despite its significant advantages in promoting academic achievement and increasing student participation, blended learning presents challenges for educators due to difficulties in collecting and analyzing learning behavior data. Existing research mainly focuses on single learning modes, and data collection methods primarily rely on interviews and questionnaires, which can be subjective. To address these challenges, this study developed a mobile software called EasyClass, integrated with an online Small Private Online Course(SPOC) platform, to automatically record student behaviors in a blended learning environment. Obtaining data from an undergraduate blended learning course, statistical methods are used to analyze the correlation between student behaviors and final grades, as well as to identify factors affecting grades. The results of multivariate linear regression models confirmed the feasibility and effectiveness of blended instruction. This study provides a new approach to identifying and disseminating effective data collection strategies in blended learning, enabling educators and educational institutions to better understand and assess student behaviors, and to further explore their impact on final academic achievement. © 2024 IEEE.

关键词： Contrastive Learning

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Cooperative Optimization Method of UAV Swarm for Multi-objective High-Precision Location 5th

Cooperative Optimization Method of UAV Swarm for Multi-objec...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Li, Bowen Ji, Xiaoting Huang, Yuanling School of Information System Engineering Information Engineering University Zhengzhou450001 China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041 China

ISBN: (纸本)9789811939976

In the field of electromagnetic radiation source location, using UAVs to locate the radiation source target has become a new location method. At present, the UAV swarm cooperative optimization method for high-precision location has some challenges, such as insufficient ability of multi-objective high-precision location, poor timeliness and so on. To solve these problems, this paper proposes a cooperative optimization method of UAV swarm for multi-objective location. Firstly, the multi-objective joint location accuracy is derived, which is taken as the objective function of swarm cooperative optimization. Secondly, in order to allocate multiple UAVs to multiple targets efficiently, the initialization strategy based on target isolation and the random allocation method of UAV based on probability are proposed. Finally, by combining the above improved strategies, an optimization method of UAV swarm cooperative location is proposed. Through the analysis of experimental results, the cooperative optimization method in this paper has obvious advantages in global optimization and convergence ability, which can improve the joint location accuracy and optimization speed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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