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第41届中国控制会议

作者： Jianhao Li Xiaowei Jiang Hao Chen Shaoying Wang Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System College of Science Binzhou University School of Electrical Engineering and Automation Hubei Normal University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we focus on the tracking performance(TP) limitations of networked control systems(NCS s).The control system considered is a multi-input and multi-output(MIMO) discrete-time network control system with time delay,channel noise,packet dropouts,and logarithmic quantization communication *** modeling the channel,we assumed the channel noise to be additive white Gaussian noise(AWGN) and used a binary random process to model packet dropouts and a logarithmic quantizer to model the quantization *** explicit expression of the TP limitations of NCSs utilizing the two-degree-of-freedom(TDOF) controller is then derived using the frequency-domain approach,coprime decomposition,and Youla parameterization *** results show that the essential characteristics of the plant(locations and directions of nonminimum phase zeros and unstable poles) and network constraints(time delay,packet dropouts,quantization,and noise power spectral density) are closely related to the TP limitations.

关键词： Networked control systems tracking performance limitations packet dropouts logarithmic quantization

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Optimization Deployment of Temperature measurement Points for CZ Silicon Single Crystal Growth Equipment Based on Improved Fuzzy C-Means Clustering

Optimization Deployment of Temperature Measurement Points fo...

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第43届中国控制会议

作者： Weichao Huang Siyuan Cao Ding Lui School of Automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the problems of low accuracy and difficult selection of temperature measurement point locations. To solve this problem, this paper proposes an optimization algorithm for measurement point locations based on improved fuzzy c-means clustering(IFCM). The method first generates the initial clustering center through a specific density function and introduces the Mahalanobis distance to solve the problem that the traditional fuzzy c-means clustering results depend on the initial clustering center and the results are unstable. Then, an optimized deployment algorithm based on IFCM temperature measurement points is proposed and compared with other mean value clustering algorithms. Finally, the optimal temperature measurement position on the crystal surface is selected by the IFCM temperature measurement point optimization deployment algorithm on the CZ(Czochraski)silicon single crystal growth equipment, and the inversion results of the randomly selected temperature measurement point scheme and the optimized temperature measurement point scheme are compared by the real-time inversion algorithm based on the dynamic matrix control multi-boundary heat flux, and the results of the optimized temperature measurement point scheme are obtained to be better than that of the randomly selected temperature measurement point scheme.

关键词： Fuzzy c-means clustering IFCM Temperature measurement point optimization Inverse heat transfer

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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Object Detection and Multiple Objective Optimization Manipulation Planning for Underwater Autonomous Capture in Oceanic Natural Aquatic Farm

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Journal of Field Robotics 2025年第0期

作者： Hai, Huang Tao, Jiang Xinyu, Bian Hao, Zhou Xu, Yang Gang, Wang Hongde, Qin Xinyue, Han National Key Laboratory of Science and Technology on Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Bei Jing China

Underwater autonomous capture operations offer significant potential for reducing labor and health risks in sea organism industries. This study presents a comprehensive solution for cross-domain underwater object detection and autonomous capture. A novel unsupervised domain adaptive learning method is proposed, integrating multiscale domain adaptive modules and attention mechanisms into a Faster Region-Convolutional Neural Network framework. This approach enhances feature alignment across diverse aquatic domains without parameter tuning. Additionally, an efficient, parameterless constrained multiobjective optimization algorithm is introduced for underwater autonomous mobile capture, integrating parameterized trajectory planning with innovative features, such as adaptive mutation strategies and constraint violation tolerance. The proposed approaches are extensively validated through simulations, tank experiments, and real-world oceanic trials in the Natural Aquatic Farm of Zhangzidao Island. Results demonstrate the system's robustness in complex underwater environments with varying currents, with experimental outcomes validating the accuracy and reliability of detection and capture capabilities. This research significantly advances autonomous underwater systems' capabilities in object detection and capture tasks, addressing complex challenges in realistic organism capture applications across diverse aquatic environments. © 2025 Wiley Periodicals LLC.

关键词： Health risks

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A Secure Clock Synchronization Scheme for Wireless Sensor Networks Against Malicious Attacks

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Journal of systems Science & complexity 2021年第6期34卷 2125-2138页

作者： ZHANG Xuxin LIU Yanzhang ZHANG Ya School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096China China Electric Power Research Institute Nanjing 210003China

This paper studies the secure and accurate clock synchronization problem for sensor networks with time-varying delays and malicious attacks.A novel clock synchronization scheme based on the attack detection mechanism,attack compensation,and maximum consensus protocol is *** proposed scheme starts with the detection of the malicious attacks and the clock data under attacks is *** the basis,software clock parameters are updated so that all the nodes in the network can have the same software skew and offset,so the clock synchronization can be ***,it is theoretically proved that the proposed scheme can achieve the attack detection correctly,and further can guarantee a secure and accurate clock *** addition,extensive simulations are also conducted to validate the effectiveness of the proposed scheme.

关键词： Clock synchronization communication delays consensus malicious attacks wireless sensor networks

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A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise

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Defence Technology（防务技术） 2022年第2期18卷 183-193页

作者： Yu-xing Li Shang-bin Jiao Bo Geng Qing Zhang You-min Zhang School of Automation and Information Engineering Xi’an University of TechnologyXi’an710048China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an710048China Department of Mechanical Industrial and Aerospace EngineeringConcordia UniversityMontrealQuebec H3G 1M8Canada

Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault *** this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed *** results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and *** is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.

关键词： Nonlinear dynamic Refined composite multi-scale dispersion entropy(RCMDE) Multi-scale dispersion entropy(MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy(MPE) Classification of ship-radiated noise

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Nonrigid Object Contact Estimation With Regional Unwrapping Transformer

arXiv

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arXiv 2023年

作者： Xie, Wei Zhao, Zimeng Li, Shiying Zuo, Binghui Wang, Yangang Southeast University China The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

Acquiring contact patterns between hands and nonrigid objects is a common concern in the vision and robotics community. However, existing learning-based methods focus more on contact with rigid ones from monocular images. When adopting them for nonrigid contact, a major problem is that the existing contact representation is restricted by the geometry of the object. Consequently, contact neighborhoods are stored in an unordered manner and contact features are difficult to align with image cues. At the core of our approach lies a novel hand-object contact representation called RUPs (Region Unwrapping Profiles), which unwrap the roughly estimated hand-object surfaces as multiple high-resolution 2D regional profiles. The region grouping strategy is consistent with the hand kinematic bone division because they are the primitive initiators for a composite contact pattern. Based on this representation, our Regional Unwrapping Transformer (RUFormer) learns the correlation priors across regions from monocular inputs and predicts corresponding contact and deformed transformations. Our experiments demonstrate that the proposed framework can robustly estimate the deformed degrees and deformed transformations, which makes it suitable for both nonrigid and rigid contact. © 2023, CC BY-NC-SA.

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Secure output synchronization of heterogeneous multi-agent systems against false data injection attacks

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Science China(Information Sciences) 2022年第6期65卷 142-154页

作者： Shicheng HUO Dalin HUANG Ya ZHANG School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Cyber Science and Engineering Southeast University

This paper studies secure output synchronization for heterogeneous multi-agent systems against false data injection(FDI) attacks. Both the sensors and actuators of agents may be injected into FDI *** mitigate the malicious impact of these attacks on synchronization performance, a desired reference model,which is combined with an auxiliary system, is first designed for each agent to simulate its normal system dynamics. Then, based on this reference model, a state feedback cooperative controller and a static output feedback cooperative controller, which consist of adaptive compensators, are proposed, respectively. The integrated control protocols can ensure that the output synchronization error is small by adjusting some designed parameters even in the presence of FDI attacks. An illustrative example is employed to demonstrate the effectiveness of the proposed method.

关键词： output synchronization false data injection attacks adaptive compensators heterogeneous multi-agent systems secure control

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Effectiveness of Koopman Autoencoder Predicting Nonlinear system States: Effect of Latent Layer Neuron Number

Effectiveness of Koopman Autoencoder Predicting Nonlinear Sy...

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2022 Chinese Automation Congress, CAC 2022

作者： Zhou, Jun Jia, Yubin Liu, Zhimin Yong, Panxiao Guo, Jun The School of Automation Southeast University Jiangsu Nanjing210096 China The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

ISBN: (纸本)9781665465335

Researchers have always been popular with analyzing and controlling nonlinear systems, yet most methods are based on local linearized models. As an alternative, Koopman theory was introduced to solve global linearization problems. Koopman operator is infinite-dimensional and has the ability to globally linearize nonlinear systems without information loss. In this work, a Koopman autoencoder, which combines the Koopman operator and autoencoder network, is used for predicting nonlinear systems. The autoencoder network is designed to find an approximate finite-dimensional Koopman operator. An inverted pendulum dynamic system is used to evaluate the effectiveness of the Koopman autoencoder, and accurate prediction results have been achieved. Furthermore, we discussed the effect of latent layer neuron number in the Koopman autoencoder, prediction results are given, and the best number of latent layer neurons for this system is found. © 2022 IEEE.

关键词： Nonlinear systems

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4D Myocardium Reconstruction with Decoupled Motion and Shape Model

arXiv

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arXiv 2023年

作者： Yuan, Xiaohan Liu, Cong Wang, Yangang Southeast University China The Key Laboratory of Measurement Control of Complex Systems of Engineering Ministry of Education Nanjing China

Estimating the shape and motion state of the myocardium is essential in diagnosing cardiovascular diseases. However, cine magnetic resonance (CMR) imaging is dominated by 2D slices, whose large slice spacing challenges inter-slice shape reconstruction and motion acquisition. To address this problem, we propose a 4D reconstruction method that decouples motion and shape, which can predict the inter-/intra- shape and motion estimation from a given sparse point cloud sequence obtained from limited slices. Our framework comprises a neural motion model and an end-diastolic (ED) shape model. The implicit ED shape model can learn a continuous boundary and encourage the motion model to predict without the supervision of ground truth deformation, and the motion model enables canonical input of the shape model by deforming any point from any phase to the ED phase. Additionally, the constructed ED-space enables pre-training of the shape model, thereby guiding the motion model and addressing the issue of data scarcity. We propose the first 4D myocardial dataset as we know and verify our method on the proposed, public, and cross-modal datasets, showing superior reconstruction performance and enabling various clinical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion estimation

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