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system Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Jiahui Huang Xiangxiang Zheng Guodong Lv Xiaojing Li Guohua Wu Liang Xu Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China State Key Laboratory of Pathogenesis Prevention and Treatment of High Incidence Diseases in Central Asia The First Affiliated Hospital of Xinjiang Medical University Urumqi China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Alveolar echinococcosis (AE) remains a serious worldwide public health problem as a very harmful human tapeworm disease. Rapid and accurate diagnosis of AE is crucial for treatment and postoperative recovery. This study explored the applicability of tissue surface Raman spectroscopy (SERS) combined with machine learning for rapid AE detection. Specifically, liver tissue section samples were obtained by creating an AE mouse model and SERS spectra were acquired. Five kinds of machine learning methods were used to construct AE diagnosis model, among which the linear kernel support vector machine (SVM) model had the best classification result, and the accuracy rate reached $95.7 \%$. Then, the linear SVM model was subjected to feature significance analysis, which demonstrated that the biochemicals corresponding to the spectra of 641,716 and $1372 \mathrm{~cm}^{-1}$ might be potential biomarkers. Gaussian noise is further added to test the noise interference resistance of different machine learning algorithms, and it is found that SVM with Gaussian radial basis function (RBF) kernel function has better noise interference resistance. The results show that tissue SERS spectroscopy combined with SVM algorithm for rapid detection of AE has a promising application.

关键词： Support vector machines Analytical models Machine learning algorithms Accuracy Biological system modeling Surface resistance Raman scattering Interference Machine learning Kernel

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Consensus-based Distributed Augmented Lagrangian Method for Nonconvex Optimization with Coupled Constraints

Consensus-based Distributed Augmented Lagrangian Method for ...

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Chinese Automation Congress (CAC)

作者： Yanxu Su School of Artificial Intelligence Anhui University Hefei P. R. China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing P. R. China

Abstract: In this paper, we study the nonconvex optimization problems with consideration of globally coupled constraints. The constrained optimization problem is converted into an augmented Lagrangian function, and a distributed primal-dual algorithm is exploited in the context of finite-time consensus for solving such a problem. Particularly, we do not require the globally coupled constraints to be separable. Instead, we introduce some local variables for estimating the constraint violations. Under some mild assumptions in the research of nonconvex optimization, we prove the convergence of the presented algorithm to a KKT point of the problem. The numerical experiment verifies the correctness and the effectiveness of the theoretical results.

关键词： Automation Heuristic algorithms Aggregates Numerical simulation Linear programming Optimization Lagrangian functions

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Flexible Active Safety Motion control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach

arXiv

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arXiv 2024年

作者： Liu, Jinhao Yang, Jun Mao, Jianliang Zhu, Tianqi Xie, Qihang Li, Yimeng Wang, Xiangyu Li, Shihua The School of Automation The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Aeronautical and Automotive Engineering Loughborough University LoughboroughLE11 3TU United Kingdom The College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China

A flexible active safety motion (FASM) control approach is proposed for the avoidance of dynamic obstacles and the reference tracking in robot manipulators. The distinctive feature of the proposed method lies in its utilization of control barrier functions (CBF) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, thereby offering flexibility and active safety for robot manipulators in dynamic environments. First, discrete-time CBFs are employed to formulate the novel flexible CBFSC with dynamic decay rates for robot manipulators. Following that, the model predictive control (MPC) philosophy is applied, integrating flexible CBFSC as safety constraints into the receding-horizon optimization problem. Significantly, the decay rates of the designed CBFSC are incorporated as decision variables in the optimization problem, facilitating the dynamic enhancement of flexibility during the obstacle avoidance process. In particular, a novel cost function that integrates a penalty term is designed to dynamically adjust the safety margins of the CBFSC. Finally, experiments are conducted in various scenarios using a Universal Robots 5 (UR5) manipulator to validate the effectiveness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Application of Integrated Sensing and Communication in Structural Health Monitoring

Application of Integrated Sensing and Communication in Struc...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Jie Yang Chao-Kai Wen Shi Jin Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798331539658

ISBN: (纸本)9798331539665

Integrated sensing and communication (ISAC) is a primary goal in future cellular systems. Numerous researchers have explored how cellular systems can incorporate sensing capabilities in various use cases, such as intruder detection, monitoring, tracking, and collision avoidance. However, the potential of ISAC in structural health monitoring (SHM), which involves detecting slow and subtle changes in structures, remains unexplored. This study introduces a pioneering problem model and a solution framework for SHM using ISAC technology, employing reconfigurable intelligent surfaces (RIS) as reference points for monitoring building structures. Our method is inspired by the dynamic phase configuration of RIS to generate varying radio signals, which differ from background interference and thus facilitate the acquisition of precise radio measurements from the reference points. We conduct a theoretical analysis of these radio measurements through the lens of Fisher information. Our findings confirm that increasing observation time, adding more receivers, and optimizing RIS phases significantly enhance the position error bound (PEB) and orientation error bound (OEB) of the reference points, meeting the stringent accuracy requirements of SHM. Furthermore, we develop a Bayesian inference model to recognize structural states based on the calculated PEB and OEB, enabling the validation of detection probabilities of cellular systems in identifying damaged structural conditions. Our comprehensive theoretical and numerical analysis reveals the capability of ISAC to effectively detect millimeter-level deformations.

关键词： Wireless communication Wireless sensor networks Deformation Numerical analysis Buildings Receivers Reconfigurable intelligent surfaces Integrated sensing and communication Numerical models Monitoring

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Tracking Performance of Networked systems with Limited Bandwidth and Colored Noise

Tracking Performance of Networked Systems with Limited Bandw...

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2022 Chinese Automation Congress, CAC 2022

作者： Zhang, Bin Jiang, Xiaowei Sun, Li Zhang, Xianhe School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Statistics and Mathematics Zhongnan University of Economics and Law Wuhan430074 China School of Electrical Engineering and Automation Hubei Normal University Huangshi435002 China

ISBN: (纸本)9781665465335

This study investigated the optimal tracking performance (OTP) of multi-input multi-output (MIMO), discrete- time networked control systems (NCSs). The limits of tracking performance (TP) under the influences of bandwidth, encoding- decoding, and additive coloured Gaussian noise (ACGN) are derived using the techniques of coprime decomposition and all-pass decomposition. The results reveal the negative impact of non-minimum phase (NMP) zeros and unstable poles of the plant as well as network communication constraints on the TP of NCSs. Finally, a numerical simulation is discussed and verifies our conclusions. © 2022 IEEE.

关键词： Networked control systems

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Deep Learning-Based LOS and NLOS Parameters Estimation, Tracking, and Identification

Deep Learning-Based LOS and NLOS Parameters Estimation, Trac...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Kai Chen Jun Wang Jie Yang Le Liang Chao-Kai Wen Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Millimeter-wave (mmWave) communication systems have large bandwidths and, combined with large-scale antenna arrays and beamforming technology, facilitate higher resolution of angle and delay measurements. This, in turn, provides opportunities for integrated sensing and communication (ISAC). Accurate and reliable estimation of mmWave channel state information is crucial for ISAC. In this paper, we propose a deep learning-based method for estimating channel multipath parameters, tracking, and identifying line-of-sight (LOS) and non-line-of-sight (NLOS) paths. Specifically, we leverage a network named “you only look once (YOLO)”, a highly efficient object detection network in computer vision, to detect the angles of arrival (AOAs) and angles of departure (AODs) of multipaths from received radio frequency signals. Moreover, we use a tracking-by-detection algorithm, ByteTrack, to track the variation of each path's AOA and AOD under a mobile scenario. Thanks to these vision-based channel estimation approaches, we reveal that LOS and NLOS paths exhibit different trajectory properties, allowing us to precisely identify LOS and NLOS paths, a challenge originally in the context of ISAC. We validate the proposed estimation, tracking, and identification methods on real measurement data and demonstrate that the proposed estimation method shows better performance compared to existing methods.

关键词： Antenna measurements Computer vision Simultaneous localization and mapping Millimeter wave measurements Estimation Channel estimation Transforms Integrated sensing and communication Millimeter wave communication Antenna arrays

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Adaptive terminal sliding mode control for flexible air-breathing hypersonic vehicle

Adaptive terminal sliding mode control for flexible air-brea...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021

作者： Qin, Decong Chen, Haibing Lin, Wei Tang, Peng Ma, Tielin Xu, Cheng School of Transportation Science and Engineering Beihang University Beijing China Aircraft/Engine Integrated System Safety Beijing Key Laboratory Beijing China Beijing System Design Institute of the Electro-mechanic Engineering Beijing China Institute of Unmanned System Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781624106095

Flexible air-breathing hypersonic vehicles show significant and complex aerodynamic, scramjet propulsion system, flight dynamic and elastic coupling characteristics, which usually are highly nonlinear. These result in huge uncertainties, parameter perturbation and internal disturbance, which can no longer be ignored, and therefore, extreme difficulties for flight control design. A unique parameter adaptive non-singular terminal sliding mode method is proposed for longitudinal control law design of a flexible coupling air-breathing hypersonic vehicle. This method uses adaptive reaching law gain instead of additional adaptive compensation term to handle the uncertainty to improve robustness. The stability of the close loop system is proved via a Lyapunov way. The longitudinal tracking control law for velocity and angle of attack is designed based on a rigid dynamic model of a flexible air-breathing hypersonic vehicle. A strong coupling model of the same vehicle, considering aerodynamic-scramjet engine-flight dynamic-elastic couplings, is established as the verification platform of control law designed. Simulation results obtained based on the coupling model demonstrate that the control law designed has good performance and acceptable robustness. © 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hypersonic vehicles

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Integrated Communication and Learned Recognizer with Customized RIS Phases and Sensing Durations

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IEEE Transactions on Communications 2025年

作者： Huang, Yixuan Yang, Jie Wen, Chao-Kai Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Southeast University Frontiers Science Center for Mobile Information Communication and Security National Mobile Communications Research Laboratory Nanjing210096 China

Future wireless communication networks are expected to be smarter and more aware of their surroundings, enabling a wide range of context-aware applications. Reconfigurable intelligent surfaces (RISs) are set to play a critical role in supporting various sensing tasks, such as target recognition. However, current methods typically use RIS configurations optimized once and applied over fixed sensing durations, limiting their ability to adapt to different targets and reducing sensing accuracy. To overcome these limitations, this study proposes an advanced wireless communication system that multiplexes downlink signals for environmental sensing and introduces an intelligent recognizer powered by deep learning techniques. Specifically, we design a novel neural network based on the long short-term memory architecture and the physical channel model. This network iteratively captures and fuses information from previous measurements, adaptively customizing RIS phases to gather the most relevant information for the recognition task at subsequent moments. These configurations are dynamically adjusted according to scene, task, target, and quantization priors. Furthermore, the recognizer includes a decision-making module that dynamically allocates different sensing durations, determining whether to continue or terminate the sensing process based on the collected measurements. This approach maximizes resource utilization efficiency. Simulation results demonstrate that the proposed method significantly outperforms state-of-the-art techniques while minimizing the impact on communication performance, even when sensing and communication occur simultaneously. Part of the source code for this paper can be accessed at https://***/kiwi1944/CRISense. © 1972-2012 IEEE.

关键词： Long short-term memory

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Finite-Time Continuous Terminal Sliding-Mode Speed control of Micro Gas Turbine system with Mismatched Disturbances

Finite-Time Continuous Terminal Sliding-Mode Speed Control o...

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2021 China Automation Congress, CAC 2021

作者： Sun, Yanjun Xu, Long Chen, Ning Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China China Electric Power Research Institute Nanjing210036 China

ISBN: (纸本)9781665426473

The micro gas turbine system acts as a key role in distributed renewable generation systems due to its flexibility, versatility, and low emissions. This paper focuses on the micro gas turbine speed control problem, which primarily determines the fuel flow for the turbine under variable load conditions. Due to nonlinear factors in MGTS, such as the coupling between systems parameters, and external disturbances such as electric load variation, it is a challenge to realize accurate speed control. In this paper, a continuous terminal sliding-mode control law based on a finite-time disturbance observer is designed for micro gas turbine control system. In this method, the integral of high-frequency switching function is introduced to control law. It guarantees the continuity of control signal and alleviates the chattering problem. Besides, the linear sliding mode surface is replaced by a full-order terminal sliding mode surface, and it obtains finite-time convergence and smaller speed tracking error. Furthermore, disturbance estimates are used in control law to eliminate the adverse effect of disturbances on control performance, which improves disturbance rejection ability. Finally, simulation comparison with conventional sliding-mode control validates the advantages of the composite controller. © 2021 IEEE

关键词： Disturbance rejection

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A new image denoising method based on steer ketnel regression

A new image denoising method based on steer ketnel regressio...

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2020 Jiangsu Annual Conference on Automation, JACA 2020

作者： Shi, Gongbiao Chai, Lin Jin, Lizuo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781839535635

In this paper, we propose a new image pyramid denoising model based on the iterative steer kernel regression (ISKR) method. By introducing an image pyramid to generate images at multiple scales, it can filter out large-scale noise on low-resolution images and preserve details on the high-resolution image, and then we fuse the denoised images of multiple resolutions into a high-quality image by up-sampling noise residual. This method can improve denoising performance efficiently. and reduce the cost of calculation. Experiments show that this model reduces calculation by about 58% compared with the ISKR method while ensuring a good denoising effect. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Image denoising

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