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Chinese Journal of Electronics 2024年第3期33卷 803-813页

作者： Yongkui SUN Yuan CAO Peng LI Guo XIE Tao WEN Shuai SU National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Electronic and information Engineering Beijing Jiaotong University Shaanxi key laboratory of Complex System Control and intelligent information processing Xi'an University of Technology Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

As one of the most important railway signaling equipment,railway point machines undertake the major task of ensuring train operation safety.Thus fault diagnosis for railway point machines becomes a hot topic.Considering the advantage of the anti-interference characteristics of vibration signals,this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals.A feature extraction method combining variational mode decomposition(VMD) and multiscale fluctuation-based dispersion entropy is developed,which is verified a more effective tool for feature selection.Then,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection methods.Finally,support vector machine is utilized for fault diagnosis.Experiment comparisons show that the proposed method performs best.The diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% respectively.Especially,it is a try to use new means for fault diagnosis on railway point machines,which can also provide references for similar fields.

关键词： Fault diagnosis Vibrations Support vector machines Diversity reception Switches Feature extraction Rail transportation

Wide Stopband Substrate Integrated Waveguide Filter Using Bisection and Trisection Coupling in Multilayer

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Chinese Journal of Electronics 2024年第2期33卷 436-442页

作者： Peng CHU Jianguo FENG Peng ZHU Lei GUO Long ZHANG Leilei LIU Guoqing LUO Ke WU College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications National and Local Joint Engineering laboratory of RF Integration and Micro-Assembly Technology School of information and Communication Engineering Dalian University of Technology guangdong Provincial key laboratory of Optoelectronic information processing Chips and Systems Sun Yat-sen University College of Electronics and information Engineering Shenzhen University School of Electronics and information Hangzhou Dianzi University Poly-Grames Research Center Polytechnique Montreal

This article presents a highly efficient method for substrate-integrated-waveguide（SIW） filters to achieve very wide stopbands. By employing the proposed trisection slots in addition to the bisection slots as the inter-coupling structures, all spurious modes below TE505 of a SIW filter working in the fundamental mode TE101（f0）can be eliminated without requiring additional structure or complex theoretical analysis, without affecting the design of the fundamental passbands, and without degrading the performance of the filters. For verification, two prototype filters are designed, fabricated, and measured with wide stopbands up to 4.15f0 and 4.83f0, respectively. The proposed technique could facilitate the development of high-performance wide-stopband SIW filters for microwave/wireless circuits and systems.

关键词： Bandpass filter Wide stopband Substrate integrated waveguide Spurious mode suppression Multilayer

Adaptive sampling for corrugated plate digitization using a laser displacement sensor

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Science China(Technological Sciences) 2024年第5期67卷 1510-1523页

作者： WU ChengXing QI Qi CHEN BaiJin YANG JiXiang DING Han State key laboratory of intelligent Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China State key laboratory of intelligent Manufacturing Equipment and Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China

The surface quality of a corrugated plate directly determines the heat transfer property of the thermal power mechanical apparatus.Traditional detection methods are impractical for real-world production,being slow and destructive.In contrast,the point laser displacement sensor,employing the optical triangle method,emerges as a promising device for assessing parts with variable curvature and highly reflective surfaces.Despite its benefits,high-density sampling by an innate frequency introduces challenges such as data redundancy and a poor signal-to-noise ratio,potentially affecting the efficiency and precision of subsequent data processing.To address these challenges,adjustable frequency data sampling has been developed for this sensor,allowing adaptive sampling for corrugated plate digitization.The process begins with surface digitization to extract discrete points,which are transformed into intersection curves using the B-spline fitting technique.Subsequently,dominant points are identified,considering multigeometric constraints for curvature and arch height.Finally,the sampling signal is adjusted based on the distribution information of dominant points.Comparative results indicate that the proposed method effectively minimizes redundant sampling without compromising the accurate capture of essential geometric features.

关键词： corrugated plate laser displacement sensor adaptive sampling dominant point curvature arch height

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Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

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China Communications 2024年第4期21卷 53-73页

作者： Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue School of Communications and information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of intelligent Communication and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China key laboratory of Big Data intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China School of information Engineering Guangdong University of TechnologyGuangzhou 510006China

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

关键词： benefit maximization deep reinforcement learning multi-access edge cloud task offloading

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of Systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China key laboratory of System Control and information processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of intelligent Control and Management Shanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR problem.In particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value function.This method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing methods.Three numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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Data-driven Anomaly Detection Method Based on Similarities of Multiple Wind Turbines

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 803-818页

作者： Xiangjun Zeng Ming Yang Chen Feng Mingqiang Wang Lingqin Xia the College of Electrical Engineering & New Energy China Three Gorges University the key laboratory of Power System intelligent Dispatch and Control Shandong University

The operating conditions of wind turbines(WTs) in the same wind farm(WF) may share similarities due to their shared manufacturing process, control strategy, and operating environment. However, the similarities of WTs are seldom considered in WT anomaly detection, resulting in the disregard of useful information. This paper proposes a method to improve the reliability and accuracy of WT anomaly detection using the supervisory control and data acquisition(SCADA) data of multiple WTs in the same WF. First, a similarity assessment method based on a comparison of different observation time series is proposed, which objectively quantifies the similarities of WT operating conditions. Then, the SCADA data of the target WT and selected WTs that are similar are used to establish several estimation models through a long short-term memory(LSTM) algorithm. LSTM models that exhibit good estimation performance are used to construct a combined estimation model that estimates the variations in the monitored variables of the target WT. Finally, an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify anomalies. The effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.

关键词： Estimation Anomaly detection Data models Fault detection Time series analysis Training data Signal processing algorithms

Linear-fitting-based recursive filtering for nonlinear systems under encoding-decoding mechanism

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Science China(information Sciences) 2024年第5期67卷 183-196页

作者： Bo JIANG Hongli DONG Zhiwei GAO Yuxuan SHEN Fan YANG Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial key laboratory of Networking and intelligent Control Sanya Offshore Oil & Gas Research Institute Northeast Petroleum University School of Electrical & information Engineering Northeast Petroleum University

This paper deals with a recursive filtering problem for a class of discrete time-varying nonlinear networked systems with the encoding-decoding mechanism. The linear fitting method is introduced to handle the nonlinearity. An encoding-decoding mechanism is constructed to describe the data transmission process in wireless communication networks(WCNs). To be specific, the measurement outputs are mapped by a quantizer to unique codewords for transmission in WCNs. Then, the codewords are decoded by the decoder to recover the measurement outputs which are sent to the filter. The processing/encoding delay and network delay have been considered. Firstly, on the premise that the upper bound of the filtering error covariance is minimum, the appropriate filtering gain is calculated. Then, the mean square exponential boundedness of the filtering error is analyzed. Finally, two simulation examples are presented to verify the effectiveness of the proposed algorithm.

关键词： encoding-decoding mechanism uniform quantizer linear fitting networked systems recursive filtering

RSPSSL:A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization

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Light(Science & Applications) 2024年第2期13卷 366-386页

作者： Jiaqi Hu Gina Jinna Chen Chenlong Xue Pei Liang Yanqun Xiang Chuanlun Zhang Xiaokeng Chi Guoying Liu Yanfang Ye Dongyu Cui De Zhang Xiaojun yu Hong Dang Wen Zhang Junfan Chen Quan Tang Penglai Guo Ho-Pui Ho Yuchao Li Longqing Cong Perry Ping Shum State key laboratory of Optical Fiber and Cable Manufacture Technology Guangdong Key Laboratory of Integrated Optoelectronics IntellisenseDepartment of EEESouthern University of Science and TechnologyShenzhen 518055China College of Optical and Electronic Technology China Jiliang UniversityHangzhou 310018China Department of Nasopharyngeal Carcinoma Sun Yat-sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapyGuangzhou 510060China Department of Ocean Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Nephrology Chaozhou People’s HospitalChaozhou 521011China Clinical Research Design Division Sun Yatsen Memorial HospitalGuangzhouGuangdong 510120China School of Automation Northwestern Polytechnical UniversityXi’anShaanxi 710072China Department of Biomedical Engineering The Chinese University of Hong KongHong KongChina guangdong Provincial key laboratory of Nanophotonic Manipulation Institute of NanophotonicsJinan UniversityGuangzhou 511443China

Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology.It is also an emerging omics technique for metabolic profiling to shape precision medicine.However,precisely attributing vibration peaks coupled with specific environmental,instrumental,and specimen noise is problematic.intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction.Here,we propose a novel Raman spectral preprocessing scheme based on self-supervised learning(RSPSSL)with high capacity and spectral fidelity.It can preprocess arbitrary Raman spectra without further training at a speed of~1900 spectra per second without human interference.The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88%reduction in root mean square error and a 60%reduction in infinite norm(L__(∞))compared to established techniques.With this advantage,it remarkably enhanced various biomedical applications with a 400%accuracy elevation(ΔAUC)in cancer diagnosis,an average 38%(few-shot)and 242%accuracy improvement in paraquat concentration prediction,and unsealed the chemical resolution of biomedical hyperspectral images,especially in the spectral fingerprint region.It precisely preprocessed various Raman spectra from different spectroscopy devices,laboratories,and diverse applications.This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput,cross-device,various analyte complexity,and diverse applications.

关键词： spectral scheme resolution

Low-Rank Optimal Transport for Robust Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1667-1680页

作者： Bingrong Xu Jianhua Yin Cheng Lian Yixin Su Zhigang Zeng IEEE the School of Automation Wuhan University of TechnologyWuhan 430070China intelligent Transportation Systems Research Center Wuhan University of TechnologyWuhan 430063 Chongqing Research Institute Wuhan University of TechnologyChongqingChina the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 the key laboratory of Image processing and intelligent Control of Education Ministry of China Wuhan 430074China

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

关键词： Domain adaptation low-rank constraint noise corruption optimal transport

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Facile intercalation of alkali ions in WOfor modulated electronic and optical properties: Implications for artificial synapses and chromogenic application

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Science China(Physics,Mechanics & Astronomy) 2024年

作者： Changmeng Huan Zihan Lu Silin Tang Yongqing Cai Qingqing Ke guangdong Provincial key laboratory of Optoelectronic information processing Chips and Systems Sun Yat-sen University Institute of Applied Physics and Materials Engineering University of Macau School of Microelectronics Science and Technology Sun Yat-sen University

Tungsten oxides(WO3) are widely recognized as multifunctional systems owing to the existence of rich polymorphs. These diverse phases exhibit distinct octahedra-tilting patterns, generating substantial tunnels that are ideally suited for iontronics.However, a quantitative comprehension regarding the impact of distinct phases on the kinetics of intercalated conducting ions remains lacking. Herein, we employ first-principles calculations to explore the spatial and orientational correlations of ion transport in γ-and h-WO3, shedding light on the relationship between diffusion barriers and the size of the conducting ions. Our findings reveal that different types and concentrations of alkali-metals induce distinct and continuous lattice distortions in WO3polymorphs. Specifically, γ-WO3is more appropriate to accommodate Li+ions, exhibiting a diffusion barrier and coefficient of0.25 e V and 9.31×10-8cm2s-1, respectively. Conversely, h-WO3features unidirectional and sizeable tunnels that facilitate the transport of K+ions with an even lower barrier and a high coefficient of 0.11 eV and 2.12×10-5cm2s-1, respectively. Furthermore, the introduction of alkali-metal into WO3tunnels tends to introduce n-type conductivity by contributing s-electrons to the unoccupied W 5d states, resulting in enhanced conductivity and tunable electronic structures. These alkali metals in WO3tunnels are prone to charge transfer, forming small polaronic states and modulating the light absorption in the visible and nearinfrared regions. These tunable electronic and optical properties, combined with the high diffusion coefficient, underscore the potential of WO3in applications such as artificial synapses and chromogenic devices.

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