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Prognostics and System Health management Conference (PHM-Qingdao)

作者： Nahao Shen Honghui Li Yanhui Bai Xiaorui Yang Ruiyi He School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway of MOE Beijing China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

The rapid expansion of railway networks and technological advancements has introduced significant challenges in ensuring railway safety. To address the critical need for effective prediction and prevention mechanisms, this paper proposes the FCAM-TCN-DSInformer model, a novel approach to railway accident prediction. The model integrates the Fourier-based Channel Attention Mechanism (FCAM), Temporal Convolutional Network (TCN), and De-stationary Informer (DSInformer) to capture both temporal and spectral features from time series data effectively. Extensive experiments and ablation studies were conducted to validate the model's performance. The results demonstrate that the FCAM-TCN-DSInformer model significantly outperforms both traditional and advanced models, including ARIMA, LSTM, GRU, Transformer, Autoformer, and FEDformer by achieving the highest $\mathrm{R}^{{2}}$ score and substantial reductions in error metrics such as RMSE, MAE, and MAPE. These improvements confirm the model's accuracy and robustness, making it a powerful tool for railway safety management. Moreover, the model's adaptability to different univariate time series datasets, such as the Daily Minimum Temperatures Dataset and the US Births Dataset, highlights its strong generalization capabilities. Future work will focus on optimizing the model for real-time predictions and expanding its application to multi-step prediction tasks, thereby fully realizing its potential across diverse forecasting contexts.

关键词： Adaptation models Attention mechanisms Computational modeling Railway accidents Time series analysis Railway safety Predictive models Transformers Data models Forecasting

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Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Domain Power Allocation 15

Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Dom...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Li, Yeshen Xiong, Ke Li, Han Ai, Bo Fan, Pingyi Letaief, Khaled Ben Beijing Jiaotong University Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China Beijing Jiaotong University State Key Laboratory of Electronic and Information Engineering School Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China Department of Electrical and Computer Engineering Hong Kong

ISBN: (纸本)9798350324662

This paper presents a cache-aided time-domain power allocation (CaTDPA) for 5G high-speed railway communications (HSRC), where two kinds of users, online interactive application users (OIUs) and online non-interactive application users (ONIUs), are jointly taken into account. For OIUs, their minimum ground-train data rate demand is guaranteed. For ONIUs, their minimum cache-releasing data rate demand is guaranteed. To achieve the green HSRC design, an optimization problem is formulated to maximize the system energy efficiency (EE) by jointly optimizing the power allocation (PA) and cache releasing start time (CRST) subject to the constraints of total available energy, maximum transmit power, ONIUs' ground-train data rate threshold, ONIUs' cache-releasing data rate threshold, and causality of CRST. To address such a non-convex and fractional problem, a two-step solution is designed. First, with a given CRST, a Dinkelbach-based method is applied to obtain the correspondingly optimal PA. Second, the one-dimensional traversal method is used to find the optimal CRST. Experimental results show that our proposed CaTDPA achieves the maximum EE among multiple compared methods while meeting the requirements of OIUs and ONIUs. Moreover, EE will decrease as the OIUs' ground-train data rate threshold and the train's moving speed increase, while it is observed that the ONIUs' cache-releasing data rate threshold has a relatively small effect on the EE of HSRC. © 2023 IEEE.

关键词： cache Energy efficiency high-speed railway power allocation

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Using PEM to Establish the Performance Evaluation Model of Bus APP 27

Using PEM to Establish the Performance Evaluation Model of B...

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27th ISSAT International Conference on Reliability and Quality in Design

作者： Yu, Chun-Min Li, Shao-Yu Chen, Yen Po Chen, Yu-Ling Lin, Jyun Ze Department of Industrial Engineering and Management National Chin-Yi University of Technology Taichung Taiwan Language Center of Chaoyang University of Technology Taichung Taiwan National Chung Hsing University Taichung Taiwan Taichung Municipal Chang Yi High School Taichung Taiwan

ISBN: (纸本)9798986576107

With the high development of economy and the continuous growth of the population of major cities in the world, the problem of traffic jams has become a serious problem that local governments must solve. In addition, facing the impact of global warming, how to save energy and reduce carbon emission is another critical problem that local governments need to face. An effective smart bus APP can enhance people's satisfaction with the use of the APP while it can increase their willingness to take the bus, thereby reducing the number of people driving their own cars. It is of some help to alleviating the problem of urban traffic congestion. At the same time, it also reduces the total amount of carbon emitted from cars so as to achieve the effects of energy saving and carbon reduction. Therefore, this paper, through questionnaires, gathered opinions from passengers who used the bus APP and established a performance evaluation model of the bus APP. This model can assist the designer or management engineer of the bus APP to find out the service items of the bus APP that need to be improved, in order to make the bus APP more intelligent and efficient in use. © 2022 International Society of Science and Applied Technologies

关键词： Global warming

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ED-YOLO: A Deep Learning and Edge Detection Based Turnout Switch Gap Monitor Method

ED-YOLO: A Deep Learning and Edge Detection Based Turnout Sw...

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International Conference on Electronics Information and Emergency Communication

作者： Qinghao Tian Yuping Lu Yeshen Li Ke Xiong Engineering Research Center of Network Management Technology for High-Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

A real-time monitoring method for turnout switch gaps based on deep learning and edge detection, named EDYOLO, is proposed to accurately monitor the fastening status of turnouts by identifying the scale readings of the turnout extension indicator rods. The difference between these readings and the baseline scale is calculated to obtain the distance of the switch indicator gaps. To transmit data from embedded clients deployed at the turnout edge to a remote monitoring center server, the system integrates two wireless communication technologies: 4G and LoRa. Additionally, Bluetooth communication technology is integrated to allow railway administrators to configure the embedded clients conveniently using handheld terminals. A prototype is implemented based on Raspberry Pi, and experimental results show that the maximum error of the proposed system in gap monitoring is less than 0.1 millimeters. Furthermore, the server can poll and remotely collect gap data from at least 60 clients per minute.

关键词： Wireless communication Deep learning YOLO Image edge detection Switches Real-time systems Rail transportation

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FGLNoRe: Federated Graph Learning on Non-IID RF Fingerprints for Building-Floor Positioning

FGLNoRe: Federated Graph Learning on Non-IID RF Fingerprints...

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International Conference on Electronics Information and Emergency Communication

作者： Zhenhuang Huang Bo Gao Ke Xiong Engineering Research Center of Network Management Technology for High-Speed Railway of Ministry of Education School of Computer Science and Technology Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

In recent years, deep learning has revolutionized indoor positioning based on radio frequency fingerprints. Furthermore, federated learning has been introduced to address the challenges of data sharing without compromising location privacy in indoor positioning. In the context of federated learning, there is the inherent heterogeneity among fingerprint data from diverse mobile clients. Moreover, federated learning approaches overlook the underlying spatial correlations among access points (APs). Therefore, we need to improve model aggregation techniques and integrate graph learning techniques into federated learning to effectively learn from heterogeneous and spatially correlated APs. This paper proposes the FGLNoRe algorithm (Federated Graph Learning via Normalized Averaging with Regularization), a federated graph learning method for fingerprinting-based building-floor positioning in large-scale three-dimensional scenarios. The method introduces federated graph learning via normalized averaging with regularization to integrate federated learning with graph convolutional network (GCN). Extensive experiments on a real Wi-Fi fingerprint dataset validate that our method achieves a building-floor positioning classification accuracy of 0.85 in the presence of high-level data heterogeneity.

关键词： Radio frequency Learning systems Deep learning Data privacy Accuracy Federated learning Fingerprint recognition

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Efficient Multi-Objective Evolutionary Algorithms for Railway Train Platforming Rescheduling Under Arrival Delays

Efficient Multi-Objective Evolutionary Algorithms for Railwa...

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2024 China Automation Congress, CAC 2024

作者： Ding, Shuxin Zhang, Tao Yuan, Zhiming Sun, Yanhao Fu, Qingyun Hu, Ping Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Traffic Management Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China Postgraduate Department China Academy of Railway Sciences Beijing100081 China Yibin Track Signal and Communication Depot China Railway Chengdu Group Co. Ltd. Yibin644600 China

ISBN: (纸本)9798350368604

This paper studies the railway train platforming rescheduling problem (TPRP). Rescheduling train arrival/departure time and routes are conducted when a train arrives with delays. A bi-objective optimization problem is formulated to minimize the total train delays and rescheduling costs. Multi-objective evolutionary algorithms (MOEAs) are applied to solve the problem. An integer-permutation-based encoding method is developed to represent the platform track assignment and train departure priority at the station. The arrival and departure times of the trains are obtained through a decoding method, which minimizes train delays and handles the constraints. Based on the above encoding and decoding methods, three state-of-the-art MOEAs are adopted, that is, nondominated sorting genetic algorithm-II (NSGA-II), MOEA based on decomposition (MOEA/D), and multi-objective particle swarm optimization with crowding distance and elitist learning strategy (MOPSO-CD-ELS). The experimental results show the effectiveness of the encoding and decoding methods in obtaining the true Pareto front (PF). Among the compared algorithms, NSGA-II and MOPSO-CD-ELS outperform MOEA/D, nearly covering the entire true PF. © 2024 IEEE.

关键词： Genetic algorithms

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Climate warming advances plant reproductive phenology in China’s northern grasslands

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Journal of Plant Ecology 2024年第6期17卷 225-240页

作者： Lu Bai Lei Tian Zhiguo Ren Xiaohui Song Kailiang Yu Lin Meng Zhanfeng Hou Haiyan Ren Key Laboratory of Grassland Resources of the Ministry of Education Key Laboratory of Forage CultivationProcessing and High Effcient Utilization of the Ministry of Agriculture and Rural AffairsInner Mongolia Key Laboratory of Grassland Management and UtilizationCollege of Grassland ScienceInner Mongolia Agricultural UniversityHohhot 010011China National Center of Pratacultural Technology Innovation(under preparation Mengcao Ecological Environment(Group)Co. Ltd.Hohhot 010070China New Path Education Co.Ltd Hohhot 010010China High Meadows Environmental Institute Princeton UniversityPrincetonNJ 08544USA Department of Earth and Environmental Sciences Vanderbilt UniversityNashvilleTN 37240USA College of Mechanical and Electrical Engineering Inner Mongolia Agricultural UniversityHohhot 010018China

Despite much recent progress,our understanding of plant phenology response to climate change remains *** particular,how and to what extent climate warming affects the vegetative and reproductive phenology of different plant functional groups in northern grassland ecosystems remains largely ***,we compiled data of 1758 observations from 25 individual studies and carried out a meta-analysis of plant phenology in relation to temperature changes across a range of plant species and functional groups in northern *** show that climate warming tended to extend the duration of reproductive phenology while having no effect on the duration of vegetative *** also identified specific temperature sensitivities for different phenological stages:1.73 days°C^(−1) for budding,−3.38 days°C^(−1) for leaf spreading and 0.56 days°C^(−1) for yellow withered stage,***,warming resulted in earlier leaf spreading in shrubs and semi-shrubs,but caused a delay in the budding time of *** terms of reproductive phenology,temperature sensitivity was−1.73 days°C^(−1) for flowering time,−2.53 days°C^(−1) for fruit ripening and−0.11 days°C^(−1) for fruit shedding,*** advanced the flowering and fruit repining time of all functional groups except for *** results indicate that elevated temperatures advanced reproductive phenology and extended its duration in northern grasslands,while showing no impact on vegetative *** findings demonstrate the differential responses of different functional groups to warming,highlighting the diverse growth strategies and adaptation of grassland plants in a warming world.

关键词： Climate change grassland reproductive phenology vegetative phenology plant functional groups

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A GAN-Based Data Poisoning Attack Against Federated Learning Systems and Its Countermeasure

arXiv

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arXiv 2024年

作者： Sun, Wei Gao, Bo Xiong, Ke Wang, Yuwei The Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer Science and Technology The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

As a distributed machine learning paradigm, federated learning (FL) is collaboratively carried out on privately owned datasets but without direct data access. Although the original intention is to allay data privacy concerns, "available but not visible" data in FL potentially brings new security threats, particularly poisoning attacks that target such "not visible" local data. Initial attempts have been made to conduct data poisoning attacks against FL systems, but cannot be fully successful due to their high chance of causing statistical anomalies. To unleash the potential for truly "invisible" attacks and build a more deterrent threat model, in this paper, a new data poisoning attack model named VagueGAN is proposed, which can generate seemingly legitimate but noisy poisoned data by untraditionally taking advantage of generative adversarial network (GAN) variants. Capable of manipulating the quality of poisoned data on demand, VagueGAN enables to trade-off attack effectiveness and stealthiness. Furthermore, a cost-effective countermeasure named Model Consistency-Based Defense (MCD) is proposed to identify GAN-poisoned data or models after finding out the consistency of GAN outputs. Extensive experiments on multiple datasets indicate that our attack method is generally much more stealthy as well as more effective in degrading FL performance with low complexity. Our defense method is also shown to be more competent in identifying GAN-poisoned data or models. The source codes are publicly available at https://***/SSssWEIssSS/VagueGAN-Data-Poisoning-Attack-and-Its-Countermeasure. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost effectiveness

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Structured Tensor Decomposition Based Channel Estimation and Double Refinements for Active RIS Empowered Broadband Systems

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Yirun Wang, Yongqing Shen, Yuyao Wang, Gongpu Tellambura, Chintha Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer Science and Technology Beijing100044 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Channel parameter recovery is critical for the next-generation reconfigurable intelligent surface (RIS)-empowered communications and sensing. Tensor-based mechanisms are particularly effective, inherently capturing the multi-dimensional nature of wireless channels. However, existing studies assume either a line-of-sight (LOS) scenario or a blocked TX-RX channel. This paper solves a novel problem: tensor-based channel parameter estimation for active RIS-aided multiple-antenna broadband connections in fully multipath environments with the TX-RX link. System settings are customized to construct a fifth-order canonical polyadic (CP) signal tensor that matches the five-dimensional channel. Four tensor factors contain redundant columns, rendering the classical Kruskal's condition for decomposition uniqueness unsatisfied. The fifth-order Vandermonde structured CP decomposition (VSCPD) is developed to address this challenge, making the tensor factorization problem solvable using only linear algebra and offering a relaxed general uniqueness condition. With VSCPD as a perfect decoupling scheme, a sequential triple-stage channel estimation algorithm is proposed based on one-dimensional parameter estimation. The first stage enables multipath identification and algebraic coarse estimation. The following two stages offer optional successive refinements at the cost of increased complexity. The closed-form Cramér-Rao lower bound (CRLB) is derived to assess the estimation performance. Herein, the noise covariance matrix depends on multipath parameters in our active-RIS scenario. Numerical results are provided to verify the effectiveness of proposed algorithms under various evaluation metrics. Our results also show that active RIS can significantly improve channel estimation performance compared to passive RIS. © 2025 IEEE.

关键词： Tensors

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An Anomaly Detection Method for Protection Relay System in Distribution Networks based on KPCA-IF Model 10

An Anomaly Detection Method for Protection Relay System in D...

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10th China International Conference on Electricity Distribution, CICED 2022

作者： Guo, Zhijie Su, Jianhong Liu, Yanzheng Sun, Chenhao Liao, Jinrong Liu, Shengxiang International College of Engineering Changsha University of Science &technology Changsha China School of Electrical & Information Engineering Changsha University of Science & Technology Changsha China Project Management Department Yueding Power Engineering Supervision Co. Ltd. ZhaoQing China Technical Service Center State Grid Hunan Extra High Voltage Substation Company Changsha China

ISBN: (数字)9781665452687

ISBN: (纸本)9781665452687

Due to the complex topology, multiple line branches, and dense spatial distributions of the distribution networks, the disturbances and failures cannot be eliminated it is difficult to completely avoid potential operation disturbances and faults. Thus, a reliable and stable protection system is necessary the protection system is bound to ensure a high level of reliability and stability. In that case, the monitoring and identification of the potential abnormal operation status of the protection system must be ensured the monitoring and identification of potential abnormal operation status of the protection system are facing new challenges. To this end, a data-driven-based real-time anomaly detection model is proposed in this paper. The kernel principal components analysis (KPCA) process is deployed to compress the dimensionality of raw data, which can then reduce the computational complexity within a high-dimensional data environment. Next, the isolated forest (IF) model is applied to excavate potential outliers according to the numeric range of the normal operating state of each feature. It can maintain high detection performance in the biased or sparse distributions and present a high reaction speed. Finally, the operation data of a relay system in a regional distribution network are utilized in the case study. The results verify the better performance of the proposed model in practical application, which can assist in the automatic identification and response of the risks of the distribution network. © 2022 IEEE.

关键词： Anomaly detection

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