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NASTCNN: a spatio-temporal convolutional neural network with non-local attention mechanism for image-induced brain activity pattern recognition

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Optoelectronics Letters 2025年

作者： TAN Xiangxin SHAO Mingxiong ZENG Ke GU Yue School of Computer Science and Engineering， Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， Tianjin University of Technology Krembil Research Institute， University Health network

Deciphering the brain’s visual processing mechanisms has always been a hot topic. Using neural signal patterns to predict the corresponding external visual stimuli is crucial in understanding the interaction of real visual scenes. There are some challenges in the study of image-induced brain activity pattern recognition based on electroencephalogram (EEG)， e.g. how to fully utilize the effective features of EEG signals to improve the classification， and how the frequency and the length of EEG effect the model performance. We design an experimental paradigm that minimizes the interaction between visual stimuli， and propose a spatio-temporal convolutional neural network with non-local attention mechanism (NASTCNN) for image-induced brain activity pattern recognition. The framework utilizes non-local feature extraction operations to realize global information interaction between EEG signals over long distances and enhance the feature expression of some important information. Our model achieves the best performance on both MNIST-EEG and ImageNet-EEG datasets. The classification results are 24.00% and 50.10% respectively， higher than the random probability. We also analyze the effects of different EEG frequency bands and time subsequences of different lengths on the classification performance and which brain regions are activated over time. The results show that when full frequency band and entire length sequence are selected， the classification result is optimal. The occipital region is essential for processing visual tasks， and the temporal region and some frontal regions also play important role in the later stage of visual decoding. This research can help us better understand the visual processing mechanism of the brain.

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Analyzing the causation of a railway accident based on a complex network

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Chinese Physics B 2014年第2期23卷 644-650页

作者：马欣李克平罗自炎周进 State Key Laboratory of rail Traffic Control and Safety Beijing Jiaotong University

In this paper, a new model is constructed for the causation analysis of railway accident based on the complex network theory. In the model, the nodes are defined as various manifest or latent accident causal factors. By employing the complex network theory, especially its statistical indicators, the railway accident as well as its key causations can be analyzed from the overall perspective. As a case, the ＂7.23＂ China-Yongwen railway accident is illustrated based on this model. The results show that the inspection of signals and the checking of line conditions before trains run played an important role in this railway accident. In conclusion, the constructed model gives a theoretical clue for railway accident prediction and, hence, greatly reduces the occurrence of railway accidents.

关键词： railway accident complex network accident network model

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Scheduling trains on railway network using random walk method

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Chinese Physics B 2010年第3期19卷 227-232页

作者：李克平 State Key Laboratory of rail Traffic Control and Safety Beijing Jiaotong University

According to random walk, in this paper, we propose a new traffic model for scheduling trains on a railway network. In the proposed method, using some iteration rules for walkers, the departure and the arrival times of trains at each station are determined. We test the proposed method on an assumed railway network. The numerical simulations and the analytical results demonstrate that the proposed method provides an effective tool for scheduling trains. Some characteristic behaviours of train movement can be reproduced, such as train delay.

关键词： train schedule random walk model passenger transportation plan

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Learning a Deep Metric:A Lightweight Relation network for Loop Closure in Complex Industrial Scenarios

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Chinese Journal of Electronics 2021年第1期30卷 45-54页

作者： JIN Sheng CHEN Liang GAO Yu SHEN Changqing SUN Rongchuan School of Mechanical and Electric Engineering Soochow University School of rail Transportation Soochow University

The similarity metric in Loop closure detection(LCD) is still considered in an old fashioned way, i.e. to pre-define a fixed distance function, leading to a limited performance. This paper proposes a general framework named LRN-LCD, i.e. a Lightweight relation network for LCD, which combines the feature extraction module and similarity metric module into a simple and lightweight network. The LRN-LCD, an end-to-end framework, can learn a non-linear deep similarity metric to detect loop closures from different scenes. Moreover, the LRN-LCD supports image sequences as input to speed up the similarity metric in real-time applications. Extensive experiments on several open datasets illustrate that LRN-LCD is more robust to strong condition variations and viewpoint variations than the mainstream methods.

关键词： lightweight relation network image sequences similarity metric feature extraction module SLAM (robots) loop closure detection robot vision nonlinear deep similarity loop closures fixed distance function learning (artificial intelligence) LRN-LCD framework feature extraction deep metric

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Cascading Delays for the High-Speed rail network Under Different Emergencies:A Double Layer network Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2014-2025页

作者： Xingtang Wu Mingkun Yang Wenbo Lian Min Zhou Hongwei Wang Hairong Dong School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 100096China State Key Laboratory of Advanced rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China National Research Center of railway Safety Assessment Beijing Jiaotong UniversityBeijing 100044China

High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

关键词： Delay propagation double layer network high speed rail network max-plus algebra

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Preliminary Study on Selling Tickets in Reason for Last Trains on Beijing rail Transit network

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Journal of Intelligent Learning Systems and Applications 2013年第4期5卷 254-260页

作者： Yang Wang Jie Xu Limin Jia Jianyuan Guo Ping Liang Bo Wang Jinxin Xie Beijing Metro Beijing China. School of Traffic and Transportation Beijing Jiaotong University Beijing China State Key Laboratory of rail Traffic Control & Safety Beijing Jiaotong University Beijing State Key Laboratory of rail Traffic Control & Safety Beijing Jiaotong University Beijing China

With the increase of Beijing urban rail transport network, the structure of the road network is becoming more complex, and passengers have more travel options. Together with the complex paths and different timetables, taking the last train is becoming much more difficult and unsuccessful. To avoid losses, we propose feasible suggestions to the last train with reasonable selling tickets system.

关键词： Intelligent Transportation Urban rail Transport network Automatic Ticketing System The Last Train Transfer

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An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

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Chinese Journal of Electronics 2025年第1期34卷 365-372页

作者： Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li School of Electronics and Information Engineering Liaoning University of Technology Key Laboratory of Security for network and Data in Industrial Internet of Liaoning Province Faculty of Information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluate the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关键词： Data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet Protection

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Glia-to-neuron reprogramming to the rescue?

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Neural Regeneration Research 2025年第5期20卷 1395-1396页

作者： Jack W.Hickmott Cindi M.Morshead Department of Surgery University of TorontoTorontoONCanada Donnelly Centre for Cellular and Biomolecular Research TorontoONCanada Institute of Biomedical Engineering University of TorontoTorontoONCanada The KITE Research Institute Toronto Rehabilitation InstituteUniversity Health NetworkTorontoONCanada

Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative *** brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be *** exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.

关键词： programming passing proof

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A Complex network Model for Analyzing railway Accidents Based on the Maximal Information Coefficient

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Communications in Theoretical Physics 2016年第10期65卷 459-466页

作者： Fu-Bo Shao Ke-Ping Li State Key Laboratory of rail Traffic Control and Safety Beijing Jiaotong University

It is an important issue to identify important influencing factors in railway accident *** this paper,employing the good measure of dependence for two-variable relationships,the maximal information coefficient(MIC),which can capture a wide range of associations,a complex network model for railway accident analysis is designed in which nodes denote factors of railway accidents and edges are generated between two factors of which MIC values are larger than or equal to the dependent *** variety of network structure is *** the increasing of the dependent criterion,the network becomes to an approximate scale-free ***,employing the proposed network,important influencing factors are *** we find that the annual track density-gross tonnage factor is an important factor which is a cut vertex when the dependent criterion is equal to *** the network,it is found that the railway development is unbalanced for different states which is consistent with the fact.

关键词： railway accidents complex network the maximal information coefficient

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A Control Simulation Method of High-Speed Trains on railway network with Irregular Influence

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Communications in Theoretical Physics 2011年第9期56卷 411-418页

作者：杨立兴李想李克平 State Key Laboratory of rail Traffic Control and Safety Beijing Jiaotong University

Based on the discrete time method, an effective movement control model is designed for a group of high- speed trains on a rail network. The purpose of the model is to investigate the specific traffic characteristics of high-speed trains under the interruption of stochastic irregular events. In the model, the high-speed rail traffic system is supposed to be equipped with the moving-block signalling system to guarantee maximum traversing capacity of the railway. To keep the safety of trains＇ movements, some operational strategies are proposed to control the movements of trains in the model, including traction operation, braking operation, and entering-station operation. The numerical simulations show that the designed model can well describe the movements of high-speed trains on the rail network. The research results can provide the useful information not only for investigating the propagation features of relevant delays under the irregular disturbance but also for rerouting and reseheduling trains on the rail network.

关键词： high-speed train rail network discrete-time simulation irregular event

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