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2023 International Conference on Automation, control and Electronics Engineering, CACEE 2023

作者： Li, Yangxuan Lu, Junguo Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Department of Automation Shanghai China

ISBN: (纸本)9798350309201

This paper presents a study on the robust stability analysis of linear time-invariant systems with parameter uncertainties and norm-bounded uncertainties. By utilizing the structured singular value, necessary and sufficient conditions for robust stability are derived. Based on the stability condition, the stability margin of the uncertain system is obtained from the skewed structured singular value. Additionally, numerical simulation results are provided to validate the effectiveness of the proposed methods. © 2023 IEEE.

关键词： Linear time-invariant system

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A Timeslot Clustering-Based Hybrid Traffic Scheduling in Time-Sensitive Networking 22

A Timeslot Clustering-Based Hybrid Traffic Scheduling in Tim...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Yang, Zhihao Wang, Shouliang Xu, Qimin Li, Xin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9798331527471

The development of Industrial Internet of Things necessitates deterministic transmission of hybrid traffic with varying real-time requirements. Time-Sensitive Networking offers schemes combining scheduling mechanisms like the Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). Current methods utilize the combination of TAS&CQF to schedule Time-Triggered (TT) and Audio-Video Bridging (AVB) traffic under corresponding mechanisms, respectively. However, the prior TT traffic orchestration restricts the scheduling solution space of AVB traffic, decreasing the effectiveness of combination and finally resulting in low schedulability. In this paper, we focus on enhancing the schedulability of TT&AVB scheduling by integrating TAS and Cyclic Specified Queuing and Forwarding (CSQF), which brings larger scheduling solution space to AVB traffic. The integration of TAS&CSQF is proposed by analyzing the impact of TAS on CSQF to expand the allocatable resource for AVB traffic. Then TAS&CSQF scheduling model is formulated to ensure deterministic requirements. Within this model, Timeslot Clustering Optimization (TCO) is proposed to optimize the scheduling solution space of AVB traffic and slice timeslot by refining the scheduling of TAS. Based on TCO, Flow Congestion Metric (FCM) is designed to improve schedulability by the congestion level of each link. An FCM-based algorithm is further presented to generate TT&AVB solutions incrementally. Simulation results demonstrate that our method reduces scheduling time cost by over 168 times and improves schedulability by 32% for 3000 flows compared to existing methods. © 2024 IEEE.

关键词： Traffic congestion

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3D Machine Vision Enabled Shrinkage Pin Detection of Aviation Connectors in the Aircraft EWIS 43

3D Machine Vision Enabled Shrinkage Pin Detection of Aviatio...

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43rd Chinese control Conference, CCC 2024

作者： Wang, Yao Ren, Cheng Xu, Jiaxin Chen, Cailian Guan, Xinping Shanghai Jiao Tong University And Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581581

Aviation connectors are responsible for information and energy transmission in the aircraft electrical wiring interconnection system (EWIS). Even a tiny shrinkage of any connector pin may lead to a critical impact on the reliability and stability of the EWIS. However, the current detection method of connector pin shrinkage mainly relies on manual visual inspection, which is prone to missed and false detection. Additionally, traditional 2D machine vision methods face challenges in dealing with fine pins and deep calibers of connectors. To address this issue, this paper designs a defect detection device and proposes a 3D-enabled shrinkage pin detection (3D-SPD) algorithm. For the device, a 4-degree-of-freedom (4-DOF) support mechanism is designed to collect the connector point cloud based on the imaging principle of a 3D structured light camera. Then, a preprocessed method is presented to extract defective regions from the raw point cloud. Finally, 3D-SPD algorithm can automatically select the reference plane and detect the pin position, enabling online detection of connector shrinkage pins. Experiment results show that 3D-SPD algorithm is effective in detecting shrinkage pin defects on various types of aviation connectors, with an accuracy rate of 98.62% and a shrinkage pin detection rate of 100%. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Machine vision

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Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

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Intelligent Automation & Soft Computing 2023年第8期37卷 1753-1771页

作者： Qi Liu Jing Li Xianmin Wang Wenpeng Zhao School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510002China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang UniversityFuzhou350121China

Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core *** methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic *** tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense ***,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding ***,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor ***,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target *** carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.

关键词： Semi-supervised learning attention mechanism feature augmentation consistency regularization

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Fast Super-Resolution DOA Estimation for Unknown Number of Radiation Sources Based on Complex Convolutional Neural Networks 2

Fast Super-Resolution DOA Estimation for Unknown Number of R...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Ma, Hui Liu, Xianpeng Liu, Hongwei Yang, Junxiang Xidian University National Key Laboratory of Radar Signal Processing Xi'an China Xian Institute of Electromechanical Information Technology Electromechanical Dynamic Control Laboratory Xi'an China

ISBN: (纸本)9798331515669

To address the high computational complexity of traditional Direction of Arrival (DOA) estimation techniques and the limitations of previous neural networks in handling fixed signal sources, a new signal processing model was developed. This model employs sparse representation methods and deep learning through complex convolutional neural networks(CCNN). Focused research on the array's performance under unknown radiation sources using the complex convolutional neural network algorithm for super-resolution DOA estimation was conducted in a PyTorch environment. The results indicate that the complex convolutional model has a lower DOA computation complexity and higher precision in handling unknown radiation sources. After incorporating sparse representation with sampled data through the CCNN model for DOA estimation, an improvement in estimation accuracy and a reduction in computational complexity were observed compared to methods relying solely on sparse representation. Additionally, this model can effectively detect incoming waves from unknown radiation sources, unlike previous neural networks. © 2024 IEEE.

关键词： Convolutional neural networks

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controllability of neighborhood Corona product networks

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Science China(information Sciences) 2024年第12期67卷 327-328页

作者： Bo LIU Xuan LI Qiang ZHANG Junjie HUANG Housheng SU Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages School of Information Engineering Minzu University of China School of Mathematical Sciences Inner Mongolia University 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

controlling networks aims to study the models, structures,and related dynamics of complex networks. The primary problem of controlling networks is to determine whether they are controllable. Nowadays, controllability has been widely studied and applied to system engineering and control theory, power systems, aerospace, and quantum systems. Various classical criteria include the Gram matrix criterion, Kalman rank criterion, and PBH test.

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Path Planning Based on A-Star and Dynamic Window Approach Algorithm for Wild Environment

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Journal of Shanghai Jiaotong university(Science) 2024年第4期29卷 725-736页

作者： DONG Dejin DONG Shiyin ZHANG Lulu CAI Yunze Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai200240China Shanghai Electro-Mechanical Engineering Institute Shanghai201109China

The path planning problem of complex wild environment with multiple elements still poses *** paper designs an algorithm that integrates global and local planning to apply to the wild environmental path *** modeling process of wild environment map is *** optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning ***,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional *** the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple *** improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.

关键词： path planning path re-planning wild environment A-Star algorithm dynamic window approach

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

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Energy and AI 2024年第4期18卷 368-379页

作者： Weipeng Li Yuting Chong Xin Guo Jun Liu School of Automation and Information Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an710048ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No.5 Jinhua South RoadXi’an710048ShaanxiChina

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power *** data-driven forecasting methods are regarded as an effective ***,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting *** integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting *** on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is *** effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

关键词： Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism

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Synthesis of Failure-Robust Plans for Multi-Robot Systems Under Temporal Logic Specifications 19

Synthesis of Failure-Robust Plans for Multi-Robot Systems Un...

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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Huang, Feifei Li, Shaoyuan Yin, Xiang Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Department of Automation Shanghai200240 China

ISBN: (纸本)9798350320695

In this study, we address the multi-robot path planning problem for tasks specified by linear temporal logic (LTL) formulae. Unlike existing studies, we take into account the possibility of robot failures, where a failed robot can no longer contribute to the completion of the LTL task. Our objective is to find a failure-robust path, which ensures that the LTL task can always be fulfilled, even if a maximum number of robots fail at any point during execution. To achieve this, we extend the mixed-integer linear programming (MILP) approach to the failure-robust setting. To overcome the computational complexity, we identify a fragment of LTL formulae called the free-union-closed LTL, which allows for more scalable synthesis without considering the global combinatorial issue. We present case studies to demonstrate our findings. Our approach provides a novel solution to the problem of multi-robot path planning under robot failures, offering a practical and efficient way to achieve robustness in the face of unforeseen events. © 2023 IEEE.

关键词： Multipurpose robots

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