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Asian control Conference

作者： Yuzhu Duan Ziwen Yang Shanying Zhu Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education of China Shanghai P. R. China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The Industrial Internet of Things (IIoT) is increasingly emerging as a novel paradigm for information exchange within industrial production environments. To facilitate the efficient operation of IIoT, there is an urgent requirement for communication-efficient methods to transmit extensive production data. This paper proposes a communication-efficient distributed optimization algorithm tailored for the strongly convex and smooth convex optimization problems over a directed graph. To encompass a broader class of compressors, the assumption of compressor is relaxed to contain both relative and absolute errors. To mitigate compression errors, we adopt differential compression technique and dynamic scaling factor in the algorithm. In particular, under this compression assumption, a non-uniform quantizer is introduced to enable finite-bit transmission over communication channels and an upper bound of the required bits with this quantizer is provided. Moreover, we prove the linear convergence of the proposed algorithm with compression, and numerical examples are provided to verify the performance of the proposed algorithm.

关键词： Upper bound Heuristic algorithms Directed graphs Production Compressors Convex functions Optimization

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The Application of the Alternate Descent Conditional Gradient Method in Tomographic SAR Off-grid Imaging

The Application of the Alternate Descent Conditional Gradien...

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IET International Radar Conference 2023, IRC 2023

作者： Shao, Mingxiao Su, Cikang Zhang, Zhe Zhang, Bingchen The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Compressed sensing (CS) technology has been applied to topographic synthetic aperture radar (TomoSAR) imaging due to the sparsity of elevation signals. The traditional CS algorithms discretize the elevation into many grids and assume that the scatterers are located on grids, therefore, the accuracy of the traditional CS algorithms is limited by the off-grid effect. Although there are currently methods to address the off-grid effect, such as atomic norm minimization (ANM), its application is limited by the baseline distribution form of TomoSAR, which only applies to uniformly observed scenarios. Alternating Descent Conditional Gradient (ADCG) has been applied in many fields as a gridless imaging algorithm. In this paper, we apply ADCG to TomoSAR imaging for the first time, achieving the reconstruction of non-uniform baseline observation scenes. The effectiveness of ADCG in TomoSAR imaging was verified by comparing it with iterative soft thresholding (IST) and orthogonal matching pursuit (OMP) through simulation experiments. © The Institution of Engineering & Technology 2023.

关键词： Compressed sensing

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Image Quality Assessment Method Based on Cross-Modal 20th

Image Quality Assessment Method Based on Cross-Modal

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Long, Teng Chen, Li Wang, Yubo School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9789819970247

Deep learning methods have achieved remarkable results in the direction of image quality assessment tasks. However, most of the related studies focus only on image unimodal, ignoring the potential advantages that come with the development of cross-modal techniques. Cross-modal models have implied a wealth of information, which provides new research directions and possibilities in the field of image quality assessment. In this paper, the feasibility of cross-modal models in image quality assessment is first explored for the image quality binary classification task. Subsequently, the optimization prompting method is combined with the tuning of the image encoder in the cross-modal model so that the cross-modal model can be used for image quality assessment scoring. To verify the feasibility of the cross-modal model on the image quality assessment task, an empirical analysis was conducted on the binary image quality dataset PQD, and it was found that the F1 score improved by 18% over the baseline model. Further, we propose an adaptive cross-modal image quality assessment method AC-IQA. On the image quality scoring dataset, compared with the previous optimal methods, AC-IQA improves the PLCC and SROCC metrics on the TID2013 dataset by 5.5% and 9.5%, respectively, and on the KADID dataset by 6.2% and 5.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image quality

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Minimizing Data Collection Latency for Coexisting Time-Critical Wireless Networks with Tree Topologies

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2354-2369页

作者： Zhang, Jialin Liang, Wei Yang, Bo Shi, Huaguang Liang, Ying-Chang Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Northwest A and F University College of Information Engineering Yangling712100 China Henan University School of Artificial Intelligence Zhengzhou450046 China University of Electronic Science and Technology of China Center for Intelligent Networking and Communications Chengdu611731 China

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot. © 2004-2012 IEEE.

关键词： Scheduling algorithms

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Model Predictive control for T-S Fuzzy Markovian Jump systems Using Dynamic Prediction Optimization

arXiv

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arXiv 2024年

作者： Zhang, Bin Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

In this paper, the model predictive control (MPC) problem is investigated for the constrained discrete-time Takagi-Sugeno fuzzy Markovian jump systems (FMJSs) under imperfect premise matching rules. To strike a balance between initial feasible region, control performance, and online computation burden, a set of mode-dependent state feedback fuzzy controllers within the frame of dynamic prediction optimizing (DPO)-MPC is delicately designed with the perturbation variables produced by the predictive dynamics. The DPO-MPC controllers are implemented via two stages: at the first stage, terminal constraints sets companied with feedback gain are obtained by solving a "min-max" problem;at the second stage, and a set of perturbations is designed felicitously to enlarge the feasible region. Here, dynamic feedback gains are designed for off-line using matrix factorization technique, while the dynamic controller state is determined for online over a moving horizon to gradually guide the system state from the initial feasible region to the terminal constraint set. Sufficient conditions are provided to rigorously ensure the recursive feasibility of the proposed DPO-MPC scheme and the mean-square stability of the underlying FMJS. Finally, the efficacy of the proposed methods is demonstrated through a robot arm system example. © 2024, CC BY.

关键词： Predictive control systems

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Research on Risk Management of Petrochemical Supply Chain Based on Network Dynamic Evolution Model

Research on Risk Management of Petrochemical Supply Chain Ba...

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Chinese Intelligent Automation Conference, CIAC 2021

作者： Pan, Chang-chun Huang, Xiaohao Sun, Weiqi Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9789811663710

This paper studies the quantitative measurement method of risk occurrence and possible spread among petrochemical supply chain enterprise nodes. Combined with the characteristics of petrochemical supply chain network, a risk propagation model of the supply chain based on cascading failure process is proposed, and a simulation experiment of risk propagation is carried out to investigate the different model parameters and different risk source enterprises on the communication process. According to experimental results, this paper puts forward targeted preventive measures to achieve the prevention and control of supply chain risk, which has important guiding significance for the regular operation of the petrochemical supply chain. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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An Unsupervised Person Search Method for Video Surveillance 22

An Unsupervised Person Search Method for Video Surveillance

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Feng, Deying Yang, Jie Wei, Yanxia Xiao, Hairong Zhang, Laigang School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Liaocheng University China

ISBN: (纸本)9781450396110

We propose an unsupervised person search method for video surveillance. This method considers both the spatial features of persons within each frame and the temporal relationship of the same person among different frames. Thus, the spatial features are extracted by region convolutional neural network, and the temporal relationship is organized by gate recurrent unit. The spatio-temporal features are generated by the following average pooling layer and indexed by locality sensitive hashing. A surveillance video database is constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves the search accuracy by utilizing the spatio-temporal features. © 2022 ACM.

关键词： Monitoring

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SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds

arXiv

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arXiv 2024年

作者： Wang, Yanbo Zhao, Wentao Cao, Chuan Deng, Tianchen Wang, Jingchuan Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Although LiDAR semantic segmentation advances rapidly, state-of-the-art methods often incorporate specifically designed inductive bias derived from benchmarks originating from mechanical spinning LiDAR. This can limit model generalizability to other kinds of LiDAR technologies and make hyperparameter tuning more complex. To tackle these issues, we propose a generalized framework to accommodate various types of LiDAR prevalent in the market by replacing window-attention with our sparse focal point modulation. Our SFPNet is capable of extracting multi-level contexts and dynamically aggregating them using a gate mechanism. By implementing a channel-wise information query, features that incorporate both local and global contexts are encoded. We also introduce a novel large-scale hybrid-solid LiDAR semantic segmentation dataset for robotic applications. SFPNet demonstrates competitive performance on conventional benchmarks derived from mechanical spinning LiDAR, while achieving state-of-the-art results on benchmark derived from solid-state LiDAR. Additionally, it outperforms existing methods on our novel dataset sourced from hybrid-solid LiDAR. Code and dataset are available at https://***/Cavendish518/SFPNet and https://***. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Application of improved sparrow search algorithm in wave compensation

Application of improved sparrow search algorithm in wave com...

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2024 International Conference on Image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Hu, Ao Lin, Yunhan Chen, Yaojie School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real Time Industrial System Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9781510680425

To solve the problem of ship heave motion in harsh marine environments, which affects the positioning accuracy and safety of its robotic arm, this paper adopts a PID parameter optimization method based on Tent cold light source improved sparrow search ***, utilizing Tent chaotic mapping to enhance the diversity of sparrow populations, and inspired by the fluorescence attraction effect in the firefly algorithm, this paper perturbs sparrows with a cold light source strategy to obtain better positions and improve their search ***, simulation experiments were conducted to optimize the PID control system using improved *** results showed that compared with the three population intelligent optimization algorithm, the improved algorithm had better convergence accuracy, system response speed, and ***, the improved algorithm was applied to the actual experiment of compensating for the heave direction at the end of a shipborne robotic *** comparison of the four algorithms in compensating for the height difference in the heave direction showed that the improved algorithm had the best heave compensation effect, reflecting the effectiveness of the algorithm in engineering *** provides a certain reference for the active compensation of swarm intelligence optimization algorithms in the heave direction of ships. © 2024 SPIE.

关键词： Robotic arms

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Topology-Preserving Second-Order Consensus: A Strategic Compensation Approach

Topology-Preserving Second-Order Consensus: A Strategic Comp...

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IEEE Conference on Decision and control

作者： Zitong Wang Yushan Li Xiaoming Duan Jianping He Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

The interaction topology plays a significant role in the collaboration of multiagent systems. How to preserve the topology against inference attacks has become an imperative task for security concerns. In this paper, we propose a distributed topology-preserving algorithm for second-order multi-agent systems by adding noisy inputs. The major novelty is that we develop a strategic compensation approach to overcome the noise accumulation issue in the second-order dynamic process while ensuring the exact second-order consensus. Specifically, we design two distributed compensation strategies that make the topology more invulnerable against inference attacks. Furthermore, we derive the relationship between the inference error and the number of observations by taking the ordinary least squares estimator as a benchmark. Extensive simulations are conducted to verify the topology-preserving performance of the proposed algorithm.

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