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IEEE International Conference on Industrial Informatics (INDIN)

作者： Ting Cheng Yonghui Liang Qimin Xu Shanying Zhu 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 Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In industrial sites, multiple real-time control systems often share computing resources. However, burst computing tasks may lead to the random dropping of control computing subtasks, resulting in control failures and potential hazards. To address this issue, we propose an energy-efficient safety-aware task scheduling scheme based on mixed-integer programming. When burst computing tasks are triggered, this scheduling scheme adaptively releases resources from low-criticality control computing sub tasks and adjusts the processor speed based on the dynamic voltage and frequency scaling technology (DVFS), ensuring the timely completion of burst computing tasks and keeping the control system state deviation within a safe threshold. To achieve the scheduling scheme, we propose an efficient algorithm called EESASA. Simulation results show that EESASA can minimize the overall cost of control and processor energy while ensuring the safety of the control system and the timely completion of burst computing tasks.

关键词： Time-frequency analysis Job shop scheduling Costs Processor scheduling Process control control systems Dynamic scheduling Real-time systems Energy efficiency Voltage control

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Vision Based Deflection Angle Measurement of Flight control Surfaces in Aircraft Testing

Vision Based Deflection Angle Measurement of Flight Control ...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Jiaxin Xu Cheng Ren Cailian Chen Yehan Ma Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

During the flight control system testing (FCST), it is crucial to accurately measure the deflection angles of flight control surfaces to determine whether they respond precisely to commands. However, the traditional measurement method, which relies on the manual use of angle measuring rulers for inspections, is inefficient, prone to wear, and lacks precision. To address this issue, we introduce a vision-based angle measurement method for FCST that replaces manual measurements, thereby significantly enhancing testing efficiency and accuracy. Our proposed triangular mesh alignment based angle measurement algorithm (TMA-AM) is a non-contact measurement method that involves two key procedures, capturing 3D coordinates from images and calculating deflection angles. The TMA-AM algorithm converts deflected angles into angular differences between two coordinate systems, while accounting for the curved characteristics of control surfaces. We evaluate TMA-AM algorithm on a 3D-printed wing test platform and an aircraft wing test platform. Experimental results demonstrate that our method achieves high accuracy, with an average angular measurement error below 0.05 ○ .

关键词： Measurement errors system testing Three-dimensional displays Coordinate measuring machines Accuracy Heuristic algorithms Process control Aircraft Surface treatment Aerospace control

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Distributed Optimization of Heterogeneous Linear Multi-Agent systems with Unknown Disturbances

Distributed Optimization of Heterogeneous Linear Multi-Agent...

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IEEE Conference on Decision and control

作者： Mengmeng Duan Shanying Zhu Ziwen Yang Cailian Chen Xinping Guan 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 Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. By applying the primal-dual method and the time-scale separation technique, a distributed dynamic controller without using any disturbance information is proposed. Based on the optimal condition, the relationship between the optimal solution and the equilibrium point of the system is established, and it is shown that the distributed optimization problem is solved provided that the closed-loop system is stable. Then, inspired by the nonsingular perturbation analysis, it is proved that the closed-loop system is exponentially input-to-state stable with respect to the derivative of the disturbance. Numerical examples are provided to illustrate the theoretical results.

关键词： Perturbation methods Trajectory Closed loop systems Optimization Multi-agent systems

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Multi-Attention Gate Based U-net for Retinal Vessel Segmentation 12

Multi-Attention Gate Based U-net for Retinal Vessel Segmenta...

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12th International Conference on information Technology in Medicine and Education, ITME 2022

作者： Zhu, Yu Li, Rui Teng, Shenghua Cao, Xinrong Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China College of Computer and Control Engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350310153

Retinal blood vessel segmentation images can be used to detect and evaluate various cardiovascular and ophthalmic diseases. However, due to the intricate vessel structures and blurred boundaries of vessels, it is a huge challenge to efficiently and accurately segment blood vessels. To deal with the above problems, this paper improves on the U-net by firstly using multi-scale feature convolution with kernels of varying size for feature extraction. Second, a non-local attention mechanism is applied to obtain richer global semantic information. Then multi-attention gate is used in the skip connection part by inputting feature maps of various scales and dimensions and selectively learning the interrelated regions, which improves the segmentation ability of the network model for the tiny structure of blood vessels. Quantitative and qualitative experimental results on two public datasets, DRIVE and CHASE-DB1, demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Blood vessels

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ST-Petri: A Visual Executable Semantic Model for PLC Structured Text Language

ST-Petri: A Visual Executable Semantic Model for PLC Structu...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Xiaoyu Hu Yonghui Liang Shibo Zhu Hui Li Shanying Zhu 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 Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

As very important controllers in automated facto-ries, the correctness and safety of Programmable Logic controller (PLC) and their programs determine the stability of the production process. Structured Text (ST) is one of the most widely used PLC programming languages. However, there are not enough formal semantics and different venders may have their own implementations. In this work, a visual executable semantic model ST-Petri for ST has been proposed. The model uses Petri nets to formally represent ST semantics while using graphs to visualise program control flow. To validate the correctness of the semantic model, we use programs from Github and mutations of existing source programs as a test set to execute and compare the results with the open source OpenPLC platform. Experimental results show that ST-Petri has significant advantages in terms of compilation pass rate and error indication as a semantic model and a ST program compiler.

关键词： Visualization Program processors Semantics Programmable logic devices Petri nets Process control Production Stability analysis Safety Software development management

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Time Series Data Association Mining With High-Frequency Data Interference 24

Time Series Data Association Mining With High-Frequency Data...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhu, Rongjing Li, Peng Liu, Yu Deng, Miao School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

Time series data mining is an important problem in data mining. It discovers the intrinsic correlation rules by analyzing a large amount of time series data. However, high-frequency data may exist in the dataset. High-frequency data is the data that occurs frequently in time series data, which will affect the mining of association rules. Traditional time series association mining tends to classify high-frequency data as correlated data because they co-occur too many times. In this paper, we propose the list mining framework to mine the association data by list co-occurrence to achieve the mining of massive data. We divide it into three phases, list generation phase, matrix generation phase, and rule generation phase. First, we create data lists based on time windows. Second, we create the initial count matrix, update the count matrix based on the list, and propose a GCPM algorithm to generate a conditional probability matrix based on the count matrix. Third, we propose a List Mining (LM) algorithm to generate the initial rules based on the conditional probability matrix and optimize the initial rules to get the final association rules. In addition, we weigh the rules according to the list mining method and propose a Weighted List Mining (WLM) algorithm to filter rules by reducing the weight of frequent item interference. The experimental results show that LM and WLM can effectively improve the accuracy of mining results. © 2022 IEEE.

关键词： Data mining

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Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature

arXiv

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arXiv 2024年

作者： Yang, Ruikai He, Fan He, Mingzhen Yang, Jie Huang, Xiaolin The Institute of Image Processing and Pattern Recognition The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven LeuvenB-3001 Belgium

Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on coefficients of features, necessitating that the random features on different nodes are identical. However, in many applications, data on different nodes varies significantly on the number or distribution, which calls for adaptive and data-dependent methods that generate different RFs. To tackle the essential difficulty, we propose a new decentralized KRR algorithm that pursues consensus on decision functions, which allows great flexibility and well adapts data on nodes. The convergence is rigorously given and the effectiveness is numerically verified: by capturing the characteristics of the data on each node, while maintaining the same communication costs as other methods, we achieved an average regression accuracy improvement of 25.5% across six real-world data sets. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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Modeling of All-Vanadium Redox Battery Based on Dual Stream Network with Attention Mechanism

Modeling of All-Vanadium Redox Battery Based on Dual Stream ...

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International Conference on Computer and Communications (ICCC)

作者： Jiajie Zhu Bo Yang Mingxuan Cai 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 Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Accurate voltage prediction for all-vanadium redox batteries (VRBs) affects the efficiency and safety of battery. In this paper, we propose a self-attention-based dual-stream neural network (SA-DSNN) model for voltage prediction under various working conditions. This approach independently processes temporal and condition data streams to reduce the noise interference. Using neural networks tailored to each of stream can more effectively extract intrinsic features. In particular, self-attention mechanisms are employed to enhance critical information. The features from both streams are fully integrated in output layer to generate final predictions. Experimental results indicate that the proposed method enhances the generalization and accuracy of VRBs model across different time scale.

关键词： Accuracy Neural networks Noise Voltage Interference Predictive models Redox Feature extraction Batteries Streams

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Robust ∞ model reduction for the continuous fractional-order two-dimensional Roesser system: The 0 < Ε ≤ 1 case

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Mathematical Methods in the Applied Sciences 2024年第2期47卷 782-798页

作者： Zhang, Jia-Rui Lu, Jun-Guo 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 Engineering Research Center of Intelligent Control and Management Shanghai China

This study analyzes the (Formula presented.) model reduction methods and the robust (Formula presented.) model reduction methods for the continuous fractional-order (FO) two-dimensional (2D) Roesser system with the FO ranging from 0 to 1. A sufficient LMI-based condition is given by Projection Lemma, the FO-stable-region-analyze method and matrix analysis, guaranteeing that the error system is stable and that its transfer function's (Formula presented.) -norm is bounded. Then, the approach to design the reduced-model system of the original continuous FO 2D Roesser model is given. The robust (Formula presented.) model reduction methods for continuous FO 2D Roesser systems with polytopic uncertainties are proposed based on the results above. In the end, two numerical examples are used to demonstrate the effectiveness of the results. © 2023 John Wiley & Sons, Ltd.

关键词： Linear matrix inequalities

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Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression

arXiv

引用

arXiv 2024年

作者： Sheng, Xihua Li, Li Liu, Dong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Video compression performance is closely related to the accuracy of inter prediction. It tends to be difficult to obtain accurate inter prediction for the local video regions with inconsistent motion and occlusion. Traditional video coding standards propose various technologies to handle motion inconsistency and occlusion, such as recursive partitions, geometric partitions, and long-term references. However, existing learned video compression schemes focus on obtaining an overall minimized prediction error averaged over all regions while ignoring the motion inconsistency and occlusion in local regions. In this paper, we propose a spatial decomposition and temporal fusion based inter prediction for learned video compression. To handle motion inconsistency, we propose to decompose the video into structure and detail (SDD) components first. Then we perform SDD-based motion estimation and SDD-based temporal context mining for the structure and detail components to generate short-term temporal contexts. To handle occlusion, we propose to propagate long-term temporal contexts by recurrently accumulating the temporal information of each historical reference feature and fuse them with short-term temporal contexts. With the SDD-based motion model and long short-term temporal contexts fusion, our proposed learned video codec can obtain more accurate inter prediction. Comprehensive experimental results demonstrate that our codec outperforms the reference software of H.266/VVC on all common test datasets for both PSNR and MS-SSIM. Copyright © 2024, The Authors. All rights reserved.

关键词： Image compression

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