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IEEE Conference on Decision and control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors

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Day-ahead Optimal Scheduling Model of Cascade Hydro-wind-photovoltaic Complementary System Considering Multidimensional Uncertainties and Inertia Constraints

Day-ahead Optimal Scheduling Model of Cascade Hydro-wind-pho...

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Power Science and Technology (ICPST), IEEE International Conference on

作者： Pengyu Wang Siyuan Chen Jian Xu Deping Ke Yaning Li Yi Yu Junxian Li Jing Chen School of Electrical Engineering and Automation Laboratory of Hydro-Wind-Solar Multi-Energy Control Coordination Wuhan University Wuhan China Laboratory of Hydro-Wind-Solar Multi-Energy Control Coordination Wuhan China China Three Gorges Corporation Science and Tecnology Research Institute Beijing China China Three Gorges Renewables (Group) Co. Ltd Beijing China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Taking hydropower as a forerunner, forming a new pattern of development and operation of clean energy bases in river basins by complementing hydro-wind-photovoltaic power is a key way to realize the "dual-carbon goals". This paper fully exploits the flexibility supply of the hydro-wind-photovoltaic system and proposes a day-ahead optimal scheduling model of the cascade hydro-wind-photovoltaic complementary system. First, the scenario generation method considering the spatio-temporal correlation of prediction errors is proposed in combination with Latin Hypercubic Sampling and differential evolution algorithm. Secondly, the effective mechanical inertia of hydropower units and the effective virtual inertia of wind power are quantified when the frequency is adjusted upward/downward, and a day-ahead scheduling model considering inertia constraints and the flexibility supply and demand is proposed based on the scenario method to effectively dig the flexibility of the hydro-wind-photovoltaic complementary system and improve the frequency stabilization capacity of the grid.

关键词： Photovoltaic systems Correlation Uncertainty Supply and demand Hydroelectric power generation Optimal scheduling Wind power generation

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An Approach for Rapid Online Robustness Enhancement of Distributed Power Systems using Load Classifier Forecasting

An Approach for Rapid Online Robustness Enhancement of Distr...

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International (EPE-PEMC) Power Electronics and Motion control Conference

作者： Xin Wei Denis Sidorov Aliona Dreglea Alexey Iskakov Zongjie Wang Liguo Wang School of Electrical Engeering & Automation Harbin Institute of Technology Harbin China Energy Systems Institute of Russian Academy of Sciences Irkutsk Russia Baikal School of BRICS Irkutsk National Research Technical University Irkutsk Russia V.A.Trapeznikov Institute of Control Sciences Russian Academy of Sciences Moscow Russia Electrical and Computer Engineering Department University of Connecticut Storrs US

ISBN: (数字)9798350385236

ISBN: (纸本)9798350385243

In this paper, we propose an online approach to rapidly and efficiently improve the robustness of distributed power systems (DPSs). Based on real-time monitoring of sudden voltage sag the main work includes the following two steps: offline classifier identification and online instantaneous disturbance suppression. In order to accurately forecast load classifiers, we adopt an automatic analytical solution, Wu’s Elimination Method (WEM), to derive the expressions of differential equations that are used to describe the phenomena in DPS. Then the corresponding load classifiers have been obtained by BCU method (Boundary of stability region based controlling Unstable equilibrium point method). The optimum switching time of the proposed Active Damping Generator can be chosen based on combining the obtained classifiers with digital signal processing. Simulation and experimental results show that through our work the suddenly changed voltage and distorted current of the DPS can be quickly recovered within 15ms which is faster than the standard IEC TS62749-2015.

关键词： Damping Power quality Signal processing algorithms Power system stability Robustness Generators Real-time systems Classification algorithms Surges Standards

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Atten-DilatedNet: fault diagnosis method of bearing for mine drilling rig

Atten-DilatedNet: fault diagnosis method of bearing for mine...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Guoqing Sun Xiaoyu Zou Jie Pan Zhongbin Wang Hongquan Ji Dezhong Xin School of Mechanical and Electrical Engineering China University of Mining and Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Chongqing Research Institute of China Coal Science and Industry Group Chongqing China

Currently, bearing fault signal diagnosis of mine drilling rig suffers from strong time dependency and serious signal noises, which leads to low diagnostic accuracy. Aiming at solving the problem of inadequate extraction of key features hidden in strong noise, a temporal attention embedding dilated convolutional neural network (Atten-DilatedNet) is designed for fault diagnosis of mine drilling rig bearing. The method takes the original vibration signals of different faulty bearings of drilling rigs as the input of the model. It adopts a one-dimensional dilated convolutional neural network to automatically extract features from the vibration signals, with larger receptive field and longer time range than traditional convolutional operator. A temporal attention mechanism is further embedded to the network, which adaptively assigns different weights to above feature sequences and model their time dependency. Thus, Atten-DilatedNet can enhance attention to critical-to-fault feature, and restrain interference of noise. Case studies on public data sets and experimental drilling rig demonstrate the validaty and superiority of the proposed Atten-DilatedNet based fault diagnosis method. It shows higher accuracy, better generalization performance, and stronger robustness than some traditional machine learning methods.

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Adaptive Multi-Core Real- Time Scheduling Based on Reinforcement Learning

Adaptive Multi-Core Real- Time Scheduling Based on Reinforce...

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International Conference on control and automation (ICCA)

作者： Yonghui Liang Hui Li Fei Shen Qimin Xu Shuna Hua Shanying Zhu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Re-search Center of Intelligent Control and Management Shanghai China Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China North Information Control Research Academy Group Company Ltd. Nanjing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

With the transformation towards industrial intel-ligence, multi-core processors are increasingly being applied in real-time networked control systems to ensure secure exe-cution of sensing, computing and actuating tasks under time constraints. However, existing scheduling methods result in either low CPU utilization or many missed task deadlines in dynamic systems. In this paper, we propose a two-layer scheduling architecture to address this issue by fully exploring the complex dependency between real-time tasks. To be specific, the local layer determines task execution priorities considering both dependency between tasks and deadline constraints by utilizing a reinforcement learning approach. Moreover, to better utilize the parallel capabilities of multi-core processors and reduce temporal collisions, this paper minimizes the requested core count for the task set based on a greedy strategy. The global layer designs a scheduling algorithm based on the pre-empt method and provides schedulability analysis of multiple task sets. Experimental results validate the correctness of the proposed scheduling approach, and efficiency is demonstrated through comparisons with baseline method.

关键词： Job shop scheduling Multicore processing Scheduling algorithms Networked control systems Reinforcement learning Real-time systems Sensors

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Quantitative benefits of the digital product passport and data sharing in remanufacturing

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Procedia CIRP 2023年 120卷 928-933页

作者： Ádám Szaller Viola Gallina Barna Gal Alexander Gaal Christian Fries Institute for Computer Science and Control Kende str. 13-17 1111 Budapest Hungary EPIC InnoLabs Nonprofit Ltd. Kende str. 13-17 1111 Budapest Hungary Fraunhofer Austria Research GmbH Theresianumgasse 7 A-1040 Wien Austria Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstr. 12 70569 Stuttgart Germany

As a part of the circular economy – which aims at closing the loop of materials – remanufacturing is getting more and more attention in recent years. With remanufacturing, companies are carrying out a recovery operation of used products with the aim of rebuilding a like-new condition. As various members of the supply chain are involved in this process, and product data needed for optimal operation is often missing, remanufacturing processes can become uncertain and hard to optimize. The goal of a new European Commission initiative, the digital product passport (DPP), is to facilitate information exchange between stakeholders, in order to reduce uncertainties. DPP, assigned to a product, could contain information about e.g., its origin, composition, repair and dismantling possibilities. In the paper, the essence of DPP is introduced, and a case study is presented to show the qualitative benefits of the increased amount of information about the product. A discrete event-based simulation model of three real production lines that are (re)manufacturing cylinder heads is presented. Experiments are also conducted, where quantitative benefits of using DPP are highlighted, such as decreasing unnecessary production time, increasing ratio of remanufactured parts and increasing output.

关键词： remanufacturing simulation digital product passport data sharing, simulation

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Track Prediction Based on Spatio-Temporal Attention 10th

Track Prediction Based on Spatio-Temporal Attention

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10th China Conference on Command and control, C2 2022

作者： Wang, Peng Zhang, Junyi Zhang, Lu Jin, Jianglin Fan, Yuqi The 54th Research Institute of China Electronics Technology Group Corporation Hubei Shijiazhuang050080 China Hebei Key Laboratory of Electromagnetic Spectrum Cognition and Control Hubei Shijiazhuang050080 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789811960512

With the rapid development of aviation, the air traffic flow is increasing exponentially, leading to increasingly serious route congestion and route conflicts. Accurate prediction of aircraft trajectories is essential to ensure the safety of airspace. Trajectory data is a time-series sequence, and most of the existing methods for trajectory prediction are based on recurrent neural networks and attention mechanisms. However, these methods fail to effectively accumulate the correlation between the attributes of trajectory points and the time varying attribute features, and suffer from slow model training. In this paper, we propose STA-STCN, a novel track prediction model based on spatio-temporal dual attention (STA) and stacked temporal convolutional network (STCN). STA extracts the spatial and temporal features in parallel based on self-attention. The spatial feature characterizes the relationships between attributes of a track point, and the temporal feature represents the time varying characteristics of an attribute in track points. STA then fuses the extracted features to describe the accumulated time varying attributes correlation. STCN takes the fused features as the input to predict target track point attributes. We conduct experiments on the flight data provided by global flight data services website VariFight. Experimental results show that the model can reduce the root mean square error, mean absolute error, and track prediction model training time, indicating that model STA-STCN can achieve effective track prediction. © 2022, Chinese institute of Command and control.

关键词： Forecasting

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Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning

arXiv

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arXiv 2023年

作者： Tian, Lei Tang, Yongqiang Hu, Liangchen Zhang, Wensheng The Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Domain adaptation manages to transfer the knowledge of well-labeled source data to unlabeled target data. Many recent efforts focus on improving the prediction accuracy of target pseudo-labels to reduce conditional distribution shift. In this paper, we propose a novel domain adaptation method, which infers target pseudo-labels through cross-domain label propagation, such that the underlying manifold structure of two domain data can be explored. Unlike existing cross-domain label propagation methods that separate domain-invariant feature learning, affinity matrix constructing and target labels inferring into three independent stages, we propose to integrate them into a unified optimization framework. In such way, these three parts can boost each other from an iterative optimization perspective and thus more effective knowledge transfer can be achieved. Furthermore, to construct a high-quality affinity matrix, we propose a discriminative graph self-learning strategy, which can not only adaptively capture the inherent similarity of the data from two domains but also effectively exploit the discriminative information contained in well-labeled source data and pseudo-labeled target data. An efficient iterative optimization algorithm is designed to solve the objective function of our proposal. Notably, the proposed method can be extended to semi-supervised domain adaptation in a simple but effective way and the corresponding optimization problem can be solved with the identical algorithm. Extensive experiments on six standard datasets verify the significant superiority of our proposal in both unsupervised and semi-supervised domain adaptation settings. © 2023, CC BY-SA.

关键词： Iterative methods

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Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction

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Fuzzy PID control for suppressing stick-slip vibration of drill-string system

Fuzzy PID control for suppressing stick-slip vibration of dr...

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Chinese automation Congress (CAC)

作者： Shuaikang Hu Chengda Lu Min Wu Zhaoqing He Haitao Song Wangnian Li School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China China Coal Technology and Engineering Group Xi'an Research Institute Co. Ltd Xi'an China

In this paper, a fuzzy PID (Proportional Integral Derivative) controller is introduced to suppress the stick-slip vibration present in the drilling process. First, the dynamical model of stick-slip vibration is composed of the multi-DOF (Degree-Of-Freedom) drill-string model and the Karnopp model describing the interaction of the drill bit with the rock. Then, stability analysis is made for the initial PID parameters of our controller. To suppress the stick-slip vibration and at the same time mitigate the effects of the stratigraphic environment variation on the control performance, the fuzzy adaptive algorithm is chosen as the method of optimization controller parameters. From the experimental results, it is clear that our controller has good control performance in the complex and changing stratigraphic environment.

关键词： Vibrations Drilling PI control Delay effects Process control Rocks Stability analysis

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