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The Performance Evaluation and Resilience Analysis of Supply Chain Based on Logistics Network

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The Performance Evaluation and Resilience Analysis of Supply...

第三十九届中国控制会议

作者： Weiqi Sun Yuanlong Li Liangren Shi Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of the Ministry of Education Shanghai Key Laboratory of Navigation and Location-based Services and Academy of Information Technology and Electrical Engineering Shanghai Jiao Tong University

With the development of globalization, more and more enterprises are involved in the supply chain network with increasingly complex structure. In this paper, enterprises and relations in the logistics network are abstracted as nodes and edges of the complex network. A graph model for a supply chain network to specified industry is constructed, and the Neo4 j graph database is employed to store the graph data. This paper uses the theoretical research tool of complex network to model and analyze the supply chain, and designs a supply chain network evaluation system which include static and dynamic measurement indexes according to the statistical characteristics of complex network. In this paper both the static and dynamic resilience characteristics of the the constructed supply chain network are evaluated from the perspective of complex network. The numeric experimental simulations are conducted for validation. This research has practical and theoretical significance for enterprises to make strategies to improve the anti-risk capability of supply chain network based on logistics network information.

关键词： Complex network Supply chain network Graph database Neo4j Resilience evaluation

GTNet: Generative transfer network for zero-shot object detection

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arXiv 2020年

作者： Zhao, Shizhen Gao, Changxin Shao, Yuanjie Li, Lerenhan Yu, Changqian Ji, Zhong Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electrical and Information Engineering Tianjin University

We propose a Generative Transfer Network (GTNet) for zero-shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Detection Module can learn large-scale seen domain knowledge. The Knowledge Transfer Module leverages a feature synthesizer to generate unseen class features, which are applied to train a new classification layer for the Object Detection Module. In order to synthesize features for each unseen class with both the intra-class variance and the IoU variance, we design an IoU-Aware Generative Adversarial Network (IoUGAN) as the feature synthesizer, which can be easily integrated into GTNet. Specifically, IoUGAN consists of three unit models: Class Feature Generating Unit (CFU), Foreground Feature Generating Unit (FFU), and Background Feature Generating Unit (BFU). CFU generates unseen features with the intra-class variance conditioned on the class semantic embeddings. FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively. We evaluate our method on three public datasets and the results demonstrate that our method performs favorably against the state-of-the-art ZSD approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution

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arXiv 2021年

作者： Gao, Guangwei Tang, Lei Wu, Fei Lu, Huimin Yang, Jian The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The College of Automation Nanjing University of Posts and Telecommunications Nanjing China The Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu804-8550 Japan The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

With the growing importance of preventing the COVID-19 virus in cyber-manufacturing security, face images obtained in most video surveillance scenarios are usually low resolution together with mask occlusion. However, most of the previous face super-resolution solutions can not efficiently handle both tasks in one model. In this work, we consider both tasks simultaneously and construct an efficient joint learning network, called JDSR-GAN, for masked face super-resolution tasks. Given a low-quality face image with mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some competitive methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surface Vehicle in Marine Environment

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Point Cloud Preprocessing on 3D LiDAR data for Unmanned Surf...

information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xianzhi Qi Wenxing Fu Pei An Bingli Wu Jie Ma National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

Point cloud preprocessing is still a challenging task in the marine environment, for it is difficult to filter out non-obstacle points while avoiding damage to the obstacle completeness. In this paper, we propose a novel data preprocessing method on 3D LiDAR data for the unmanned surface vehicle in the marine environment. It consists of two tasks: outlier removal and wake filtering. As the spatial resolution of LiDAR changes with distance, we exploit distance normalization on statistical outlier filter for robust outlier removal. Considering the gradient difference between wave wake and obstacle surface near the water, we define and calculate the vertical state of point cloud on the range image to obtain the pre-filtering point set, and then use the RANSAC method to filter out the wake points. Experiments on real data has demonstrated the effectiveness of the proposed algorithm both in terms of feasibility and accuracy.

关键词： Three-dimensional displays Laser radar Filtering Filtering algorithms Spatial resolution Task analysis Measurement by laser beam

Attitude Coupling control and Lateral Stability Analysis of Hypersonic Vehicle

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Attitude Coupling Control and Lateral Stability Analysis of ...

Chinese control and Decision Conference, CCDC

作者： Xu Li Ye Yang Qianwei He Zhongtao Cheng Yongji Wang Lei Liu National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

For the strong coupling characteristics of pitch, yaw and roll channels in the attitude control system of a hypersonic vehicle, a coupling controller is designed based on the state feedback and the dominant pole assignment of the Dutch roll mode to realize the system decoupling but improve the maneuverability of the vehicle. The new diagonal dominance matrix and row radius coefficient are introduced to analyze the coupling characteristics and degree of the system. Then based on this, the coupling PID controller is briefly designed as a comparison with the coupling controller. In addition, the lateral stability of the vehicle is analyzed, and the sensitivity of the coupling control system to runaway is measured based on the Weissman criterion. Finally, the simulation results show that the designed coupling controller has more precise control effect and less rudder deflection than the decoupling controller, which has a certain practical engineering significance.

关键词： Couplings State feedback Stability criteria Attitude control Aerodynamics Aircraft

Relevant Region Prediction for Crowd Counting

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arXiv 2020年

作者： Chen, Xinya Bin, Yanrui Gao, Changxin Sang, Nong Tang, Hao Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Department of Information Engineering and Computer Science University of Trento

Crowd counting is a concerned and challenging task in computer vision. Existing density map based methods excessively focus on the individuals’ localization which harms the crowd counting performance in highly congested scenes. In addition, the dependency between the regions of different density is also ignored. In this paper, we propose Relevant Region Prediction (RRP) for crowd counting, which consists of the Count Map and the Region Relation-Aware Module (RRAM). Each pixel in the count map represents the number of heads falling into the corresponding local area in the input image, which discards the detailed spatial information and forces the network pay more attention to counting rather than localizing individuals. Based on the Graph Convolutional Network (GCN), Region Relation-Aware Module is proposed to capture and exploit the important region dependency. The module builds a fully connected directed graph between the regions of different density where each node (region) is represented by weighted global pooled feature, and GCN is learned to map this region graph to a set of relation-aware regions representations. Experimental results on three datasets show that our method obviously outperforms other existing state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

Correction to: Visual Servoing of Unknown Objects for Family Service Robots

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Journal of Intelligent & Robotic Systems 2022年第4期106卷 1-1页

作者： Xin, Jing Dong, Caixia Zhang, Youmin Yao, Yumeng Gong, Ailing Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada

Prescribed performance-based privacy-preserving quantized consensus for distributed energy dispatch optimization

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Energy 2025年 331卷

作者： Minxue Kong Feifei Shen Xin Peng Linlin Li Weimin Zhong Zhi Li Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai 200237 China School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China State Key Laboratory of Industrial Control Technology Ministry of Education East China University of Science and Technology Shanghai 200237 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China State Key Laboratory of Chemical Engineering and Low-Carbon Technology East China University of Science and Technology Shanghai 200237 China

To improve energy efficiency and optimize energy dispatching of combined heat and power (CHP) systems, a distributed economic optimization framework based on the equal incremental principle is established, and a distributed fixed-time quantized privacy-preserving consensus algorithm is adopted to achieve consistent incremental costs. First, a privacy mask with predetermined performance is designed to avoid the potential leakage of sensitive information in distributed communication. Based on this, fixed-time privacy-preserving leader–follower and weighted average consensus algorithms are proposed, where the setting time is independent of the initial state conditions. In contrast to existing works, the proposed algorithms can achieve complete consensus instead of converging within a small region, ensuring the global optimality of the solutions. Additionally, a quantization mechanism and time-varying gains are introduced into the algorithms, reducing transmission constraints and resource waste. Finally, the proposed algorithms are applied to the distributed economic dispatch problem, demonstrating precise convergence and excellent privacy protection effect.

关键词： Consensus Distributed economic dispatch Prescribed performance Privacy-preserving

BAANet: Learning Bi-directional adaptive attention gates for multispectral pedestrian detection

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arXiv 2021年

作者： Yang, Xiaoxiao Qian, Yeqiang Zhu, Huijie Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Science and Technology on Near-Surface Detection Laboratory Wuxi214035 China

Thermal infrared (TIR) image has proven effectiveness in providing temperature cues to the RGB features for multispectral pedestrian detection. Most existing methods directly inject the TIR modality into the RGB-based framework or simply ensemble the results of two modalities. This, however, could lead to inferior detection performance, as the RGB and TIR features generally have modality-specific noise, which might worsen the features along with the propagation of the network. Therefore, this work proposes an effective and efficient cross-modality fusion module called Bi-directional Adaptive Attention Gate (BAA-Gate). Based on the attention mechanism, the BAA-Gate is devised to distill the informative features and recalibrate the representations asymptotically. Concretely, a bi-direction multi-stage fusion strategy is adopted to progressively optimize features of two modalities and retain their specificity during the propagation. Moreover, an adaptive interaction of BAA-Gate is introduced by the illumination-based weighting strategy to adaptively adjust the recalibrating and aggregating strength in the BAA-Gate and enhance the robustness towards illumination changes. Considerable experiments on the challenging KAIST dataset demonstrate the superior performance of our method with satisfactory speed. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction