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

作者： Duan, Lianlang Xu, Fei Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Solving nonlinear equation systems (NESs) is a challenging problems in numerical computation. Two goals should be considered for solving NESs. One is to locate as many roots as possible and the other is to improve the solving precision. In this work, in order to achieve these two goals, a two-phase niching differential evolution (TPNDE) is proposed. The innovation of TPNDE is mainly reflected in the following three aspects. First, a probabilistic mutation mechanism is designed to increase the diversity of the population. Second, a novel reinitialization procedure is proposed to detect converged individuals and explore more new regions that may contain global optima. Third, distribution adjustment local search (DALS) is proposed to deal with the isolated solution and enhance the convergence of the algorithm. To evaluate the performance of TPNDE, we test our TPNDE and 5 state-of-the-art algorithms on 20 NESs with diverse features. The experimental results show that the proposed TPNDE algorithm is better than the other methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Nonlinear equations

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HSDet: A Representative Sampling Based Object Detector in Cervical Cancer Cell images 15th

HSDet: A Representative Sampling Based Object Detector in Ce...

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

作者： Yan, Xiaoli Zhang, Zhongsi Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Automated detection of cervical cancer cells has the potential to reduce error and increase productivity in cervical cancer screening. However, the existing object detection methods to detect the cervical cancer cells make inadequate use of negative samples and fail to balance the number of hard samples and easy samples. In this work, we propose a image-level sampling method, called pair sampling, which can extract the representative negative samples and generate the sample pair image. Then, based on the sample pair image, we propose the hybrid sampling, a proposal-level sampling method, to balance the number of hard samples and easy samples. Combining the proposed sampling methods, we design a representative sampling based cervical cell detector, which can detect the cervical cancer cells effectively. In order to comprehensively evaluate our method, we use a dataset which is consisted of 16000 annotated cervical cell images with size of 1024 × 1024. The experimental result shows that our method achieves 57.1% mean Average Precision (mAP), and it is higher than cascade R-CNN and Faster R-CNN by 4.7% and 5.8%, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Cortical Neural P Systems 15th

Cortical Neural P Systems

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

作者： Fu, Zhongzheng Zhang, Luping Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

P systems are distributed and parallel computing models inspired from living cells. In this work, a variant of spiking neural P systems, cortical neural P systems, are proposed in the frame work of P systems combining the topology structure of basic P system and information processing style in spiking neurons. The computational power of cortical neural P systems is investigated. It is proved that cortical neural P systems are equivalent to Turing machines as number generators. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Turing machines

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Trimmed Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles 15th

Trimmed Data-Driven Evolutionary Optimization Using Selectiv...

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

作者： Shan, Yawen Hou, Yongjin Wang, Mengzhen Xu, Fei Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Data-driven evolutionary algorithms (DDEAs) have attracted much attention in recent years, due to their effectiveness and advantages in solving expensive and complex optimization problems. In an offline data-driven evolutionary algorithm, no additional information is available to update surrogate models, so we need to ensure the quality of the surrogate models built before optimization. Based on the offline data-driven evolutionary algorithm using selective surrogate ensembles (DDEA-SE), we propose a trimmed bagging based DDEA-SE (TDDEA-SE) to construct a more accurate model pool. To this end, we use trimmed bagging to prune models with large errors calculated by out-of-bag samples, thus improving the accuracy of the selective surrogate ensembles and promoting the optimization. The experimental results on benchmark problems show that the proposed algorithm can strike a balance between diversity and accuracy of models, and its competitiveness on solving offline data-driven optimization problems. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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Multiobjective Brainstorm Optimization with Diversity Preservation Mechanism for Nonlinear Equation Systems 15th

Multiobjective Brainstorm Optimization with Diversity Preser...

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

作者： Zhao, Yi Zhu, Zelin Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

In many fields of engineering and science, it is necessary to solve nonlinear equation systems (NESs). When using multiobjective optimization to solve NESs, there are two problems: 1) how to transform an NES into a multiobjective optimization problem and 2) how to design an algorithm to solve the transformed problems. In this work, we propose a multilayer bi-objective transformation technique, which can transform an NES into a bi-objective optimization problem, and it overcomes the curse of dimensionality and the problem of missing roots caused by the decrease of solutions discernibility in previous transformation techniques. Then, combining the multilayer bi-objective transformation technique, we design a multiobjective brainstorm optimization with a diversity preservation mechanism, which can effectively locate multiple roots of NESs in a single run. Compared with several state-of-art methods on 30 NESs with different features, our approach provides very competitive performance with the highest root ratio and success ratio. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Bearing-Based Target Entrapping control of Multiple Uncertain Agents With Arbitrary Maneuvers

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IFAC-PapersOnLine 2023年第2期56卷 8821-8826页

作者： Haifan Su Ziwen Yang Shanying Zhu Cailian Chen Wenbin Yu Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

This paper is concerned with bearing-based cooperative target entrapping control of multiple uncertain agents with arbitrary maneuvers including shape deformation, rotations, scalings, etc. A leader-follower structure is used, where the leaders move with the predesigned trajectories, and the followers are steered by an estimation-based control method, integrating a distance estimator using bearing measurements and a stress matrix-based formation controller. The signum functions are used to compensate for the uncertainties so that the agents’ accelerations can be piecewise continuous and bounded to track the desired dynamics. With proper design of the leaders’ trajectories and a geometric configuration, an affine matrix is determined so that the inter-agent relative bearings can be persistently exciting since the bearing rates are related to different weighted combinations of the affine matrix vectors. The asymptotic convergence of the estimation and control error is proved using Filipov properties and cascaded system theories. A sufficient condition for inter-agent collision avoidance is also proposed. Finally, simulation results are given to validate the effectiveness of the method.

关键词： Target entrapping control bearing measurement persistent excitation uncertainty

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期PP卷 PP页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： image-Text Matching Medical Report Generation Sample-graph Consistency Self-boosting framework

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Efficient Fabric Defect Detection Algorithm Based on Improved YOLOv5

Efficient Fabric Defect Detection Algorithm Based on Improve...

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Chinese Automation Congress (CAC)

作者： LiHua Tian AoZe Wang ZhiGang Sun Li Xiao dept. School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Huazhong University of Science and Technology Wuhan China

In order to address the issues of real-time performance and the low dependency between feature channels in fabric defect detection networks, this paper proposes the ESE_YOLOv5 network based on YOLOv5. Firstly, to address the relative redundancy of the neck detection network feature channels, a relatively lightweight and efficient convolution module is adopted to ensure accuracy while reducing computation and parameter volume. Furthermore, the Efficient Squeeze-Excitation (ESE) module is introduced into the backbone to optimize the dependency of feature channels, which enhances the model's feature extraction capacity and improves detection accuracy. Experimental results show that compared to YOLOv5, the proposed ESE_YOLOv5 model reduces computation and parameter volume while improving accuracy, meeting the needs of fabric defect detection for recognizing fabric defects that have similar characteristics to the background while maintaining real-time performance.

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Efficient Reinforcement Learning for 3D LiDAR Navigation of Mobile Robot 41

Efficient Reinforcement Learning for 3D LiDAR Navigation of ...

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第41届中国控制会议

作者： Yu Zhai Zhe Liu Yanzi Miao Hesheng Wang China University of Mining and Technology MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Developing an efficient automatic navigation system for mobile robots is challenging in the strange scenarios where robots can only observe the environment of the surrounding limited *** other distributed automatic navigation systems exist,they often require extracting semantic information to calculate navigation action,which requires extra modules to provide perceptual information and is not *** propose an end-to-end automatic navigation system based on the reinforcement learning *** particular,the raw 3 D LiDAR data is used to directly map an efficient navigation *** design a novel dense reward function to handle the reward sparsity issue and provide a graphical representation method to enable the efficient feature learning from the raw 3 D LiDAR data in our navigation *** addition,an imitation learning based policy initialization is introduced before the subsequent reinforcement learning,which increases the learning efficiency and,in the meantime,still encouraging the robot to explore all the potential states to achieve advanced performance than the imitated *** navigation model is trained in the Webots environment and the experimental results show that our model has efficient and flexible navigation performance in complex *** importantly,trained model can be easily extended to unfamiliar environments.

关键词： End-to-end Navigation Mobile robot Reinforcement Learning 3D LiDAR

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Digital-Twin-Based Clock Synchronization in Industrial IoT Systems 13

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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13th IEEE International Conference on CYBER Technology in Automation, control, and intelligent Systems, CYBER 2023

作者： Cheng, Ting Liu, Shuai Xu, Qimin Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798350315196

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources. © 2023 IEEE.

关键词： Internet of things

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