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Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

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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 *** fault diagnosis for railway point machines becomes a hot *** 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 ***,a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed,which is validated more powerful than single feature selection ***,support vector machine is utilized for fault *** comparisons show that the proposed method performs *** diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% ***,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 Railway point machine Vibration signal Variational mode decomposition Two-stage feature selection

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An approach for formation design and flight performance prediction based on aerodynamic formation unit:Energy-saving considerations

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Chinese Journal of Aeronautics 2024年第3期37卷 77-91页

作者： Nanxuan QIAO Tielin MA Xiangsheng WANG Jie WANG Jingcheng FU Pu XUE School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China Institute of Unmanned System Beihang UniversityBeijing 100191China Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System Ministry of Industry and Information TechnologyBeijing 100191China School of Transportation Science and Engineering Beihang UniversityBeijing 102206China Beijing Institute of Space Long March Vehicle Beijing 100076China

The performance improvement of swarm drones through aerodynamic shape optimization may be challenging due to folded size constraints imposed by the specific launch ***,fixed-wing aircraft swarms can benefit from formation flight in terms of energy *** study introduces the concept of the"aerodynamic formation unit",which consists of two or three aircraft that form an inseparable unit of the *** the Unmanned Aerial Vehicle(UAV)distribution and wingtip vortex interference in the formation,two typical aerodynamic formation units are optimized by the variable-fidelity aerodynamic optimization method based on space *** aerodynamic characteristics of the formation UAVs that affect flight performance,such as lift-to-drag ratio(L/D ratio)and static stability are analyzed by Computational Fluid Dynamics(CFD)*** L/D ratio(cruising condition)of the following aircraft can be increased by 22.8%and 57.5%in the optimal units that involve two and three aircraft ***,this study conducts several CFD simulations for multi-aircraft formations formed by the units,which show that the average L/D ratio of the formation can be improved by more than 19%.These results verify the feasibility and effectiveness of the"aerodynamic formation unit"concept and the optimization framework for formation parameters.

关键词： Formation flight CFD Aerodynamic formation unit Wingtip vortices Foldable tandem-wing aircraft

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ResPocket: A Multi-Scale Feature Fusion Method for Improving Protein Binding Site Detection

ResPocket: A Multi-Scale Feature Fusion Method for Improving...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Yang, Wenli Chen, Yaoyu Liao, Xiongwei Wu, Zimo Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology China

ISBN: (纸本)9798350386226

Accurate detection of protein binding sites is critical for facilitating drug design. Most existing models rely on features at a single scale, leading to the loss of crucial global or local structural information. Therefore, this paper proposes a novel 3D model (ResPocket), to fully capture the multi-scale features of proteins. First, Residual Block (ResB) based on U-Net is used to capture features from different scales, including global and local structural information. Then, an Enhanced Attention Module (EAM) is designed to encode semantic dependencies by effectively capturing global context information. In addition, ResPocket focuses on learning local information about protein pockets with Self-Guided Mask Block (SGMB), to increase the robustness of the overall model and reduce its dependence on external mask guidance. Experimental results show that ResPocket improves by an average of 4.89% on DCA Top-n, by an average of 1.48% and 2.25% on DCC and DVO metrics, respectively. © 2024 IEEE.

关键词： Binding sites

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Fuzzy identification of nonlinear dynamic system based on selection of important input variables

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Journal of systems Engineering and Electronics 2022年第3期33卷 737-747页

作者： LYU Jinfeng LIU Fucai REN Yaxue Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan UniversityQinhuangdao 066004China School of Mathematics and Information Science and Technology Hebei Normal University of Science and TechnologyQinhuangdao 066004China

Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the ***, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.

关键词： Takagi-Sugeno(T-S)fuzzy modeling input variable selection(IVS) fuzzy identification fuzzy c-means clustering algorithm

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intelligent Decision Support system for Higher Education Institutions 9

Intelligent Decision Support System for Higher Education Ins...

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9th International Conference on Signal Processing and intelligent systems, ICSPIS 2023

作者： Afriliana, Nunik Meyliana Gaol, Ford Lumban Soeparno, Haryono Bina Nusantara University Binus Graduate Program Computer Science Program Computer Science Department Jakarta Indonesia Bina Nusantara University School of Information System Information System Department Jakarta Indonesia

ISBN: (纸本)9798350369922

To ensure the sustainability of their operations, higher education institutions have to establish a robust strategic plan. Higher education institutions have an obligation to continuously assess the quality of their educational offerings in every program to comply with the standards established by the government and other educational entities. The implementation of intelligent decision support systems is a feasible approach among other techniques for addressing the aforementioned challenges. An intelligent decision support system has the potential to enable the business to uphold its adherence to regulations, enhance operational effectiveness, and optimize overall performance. This study aimed to determine the current state of intelligent decision support systems through the implementation of a comprehensive literature review. The second goal is to evaluate the best practices, methods, and implementation of intelligent decision support systems within higher education institutions. The findings indicate that intelligent decision support systems have the capacity to serve as a viable option for higher education, particularly in terms of enhancing the accuracy and efficiency of decision-making processes. intelligent decision support systems exhibit a strong correlation with the fields of data mining and computational intelligence techniques. Nevertheless, the amount of research conducted on this subject is limited. © 2023 IEEE.

关键词： Data mining

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D2SFormer: Dual attention-dynamic bidirectional Transformer for semantic segmentation of urban remote sensing images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12248-12262页

作者： Yan, Yi Li, Jiafeng Zhang, Jing Wang, Liuqian Zhuo, Li Beijing University of Technology School of Information Science and Technology Chaoyang Beijing100124 China Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System China

Semantic segmentation of urban remote sensing images (URSIs) is crucial for the development of smart cities and the acceleration of urban information. In view of the characteristics of URSIs with intricate backgrounds, dense distributions of feature objects, and varying object scales, we propose a dual attention-dynamic bidirectional Transformer (D2SFormer) for semantic segmentation of URSIs. First, a dual attention Transformer encoder has a global branch of cross-shaped window self-attention (CWSA) and a local branch of convolutional channel attention block (CCAB), which helps to enhance pixel-level semantic discrimination. Then, CCAB combines the local sensitivity of Conv with the non-local modeling capability of ECA for overlapping region resolution. Finally, the decoder dynamically fuses multiscale features to reconstruct boundaries and suppress artifacts by using a bidirectional feature pyramid network (BiFPN) and dysampling. Experimental results demonstrate that our D2SFormer achieves mIoUs of 87.84%, 81.24%, and 53.19% on the Potsdam, Vaihingen, and LoveDA datasets, respectively, which is highly competitive with existing methods. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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A Multi Target Drug Design Method Based on Target Protein Sequence and Feature Similarity

A Multi Target Drug Design Method Based on Target Protein Se...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Haoran Lin, Xiaoli Hu, Jing Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350386226

This paper describes a multi target drug design method based on features of the target proteins. The multi target drug which inhibit multiple proteins have prospective applications, but design is difficult. In this study, the target protein sequences are embedded to obtain target features. The features of each target are independently encoded as target latent vectors, and the features of multiple targets are jointly encoded as target similarity latent vectors. Based on the features of targets and the similarity features among the targets, multi target drugs are efficiently designed. In the experiments, the designed multi target drugs can be docked with target proteins with a proprietary molecular structure according to the requirements of different target pocket structures. The excellent fitness among the molecular structure of multi target drugs and the protein structure of multiple targets confirms the performance of the proposed method. In the molecular docking part of the experi¬ments, the binding affinity of designed multi target drugs is far better than that in the previous studies, which administrates the better performance of this work. © 2024 IEEE.

关键词： Molecular docking

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From Torch to Projector: Fundamental Tradeoff of Integrated Sensing and Communications

IEEE BITS the Information Theory Magazine

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IEEE BITS the information Theory Magazine 2024年第1期4卷 73-90页

作者： Xiong, Yifeng Liu, Fan Wan, Kai Yuan, Weijie Cui, Yuanhao Caire, Giuseppe Beijing University of Posts and Telecommunications School of Information and Electronic Engineering Beijing100876 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen518055 China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Technical University of Berlin Berlin10623 Germany

Sensing and communications (S&C) were historically developed in parallel. In the last decade, they have been evolving from separation to integration, giving rise to the integrated sensing and communication (ISAC) paradigm, that has been recognized as one of 6G key usage scenarios. Despite the plethora of research works dedicated to ISAC signal processing, the fundamental performance limits of S&C remain widely unexplored in ISAC systems. In this tutorial paper, we attempt to summarize the recent research findings in characterizing the performance boundary of ISAC systems and the resulting S&C tradeoff from an information-theoretical viewpoint. We begin with a folkloric "torch metaphor" that depicts the subspace tradeoff between S&C. Then, we elaborate on the fundamental capacity-distortion (C-D) theory, indicating the incompleteness of this metaphor. Toward that end, we further elaborate on the S&C tradeoff by discussing a special case within the C-D framework, namely the Cramér–Rao bound-rate region. In particular, S&C have preference discrepancies over both the signal subspace and the adopted codebook, leading to a "projector metaphor" complementary to the torch analogy. We also present two practical design examples by leveraging the lessons learned from fundamental theories. Finally, we conclude the article by identifying a number of open challenges. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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CFinDEE: A Chinese Fine-Grained Financial Dataset for Document-Level Event Extraction 24

CFinDEE: A Chinese Fine-Grained Financial Dataset for Docume...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Zhang, Tian Liu, Maofu Zhou, Bingying School of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400701726

Document-level event extraction faces numerous challenges in accurately modeling real-world financial scenarios, particularly due to the inadequacies in existing datasets regarding data scale and fine-grained annotations. The development of datasets is a crucial factor in driving research progress;therefore, we present a high-quality Chinese document-level event extraction dataset, CFinDEE. This dataset, grounded in real-world financial news, defines 22 event types and 116 argument roles, annotating 26,483 events and 107,096 event arguments. CFinDEE aims to address these shortcomings by providing more comprehensive annotations and data augmentation, offering richer resources for document-level event extraction in the financial domain. CFinDEE extends data both horizontally and vertically, where horizontal expansion enriches the types of financial events, enhancing the diversity of the dataset;vertical expansion, by increasing the scale of the data, effectively boosts the practical value of the dataset. Experiments conducted on multiple advanced models have validated the high applicability and effectiveness of the CFinDEE dataset for document-level event extraction tasks in the financial field. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Expansion

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Recent Progress in Reinforcement Learning and Adaptive Dynamic programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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