scholarly journals Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fan Gao ◽  
Fan Li ◽  
Zhifei Wang ◽  
Wenqi Ge ◽  
Xinqin Li
Keyword(s):  
Text Mining ◽  
High Speed ◽  
Single Layer ◽  
Feature Representation ◽  
Classification Model ◽  
High Speed Railway ◽  
Railway Signal ◽  
Representation Method ◽  
Multilevel Classification

In this paper, the multilevel classification model of high-speed railway signal equipment fault based on text mining technology is proposed for the data of high-speed railway signal fault. An improved feature representation method of TF-IDF is proposed to extract the feature of fault text data of signal equipment. In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary learners, and k-fold cross verification was used to train the stacking model. The multitask cooperative voting decision tree was designed to correct the membership relationship of classification results of each layer. Ten years of signal switch machine fault data of high-speed railway are used for experimental analysis; the experiment shows that the multilevel classification model can effectively improve the classification of signal equipment fault multilevel classification task evaluation index and can ensure the correctness of the subordinate relations’ classification results.

Reliability Models of GSM-R Redundant Network on High-Speed Railway

2012 ◽  
Vol 198-199 ◽  
pp. 1783-1788
Author(s):  
Jun Ting Lin ◽  
Jian Wu Dang
Keyword(s):  
Network Architecture ◽  
High Speed ◽  
Failure Time ◽  
Network Reliability ◽  
Single Layer ◽  
Double Layers ◽  
High Speed Railway ◽  
Reliability Models ◽  
Single Layer Network ◽  
Wireless Architectures

As a dedicated digital mobile communication system designed for railway application, GSM-R must provide reliable bidirectional channel for transmitting security data between trackside equipments and on-train computer on high-speed railways. To ensure the safety of running trains, redundant network architecture is commonly used to guarantee the reliability of GSM-R. Because of the rigid demands of railway security, it is important to build reliability mathematical models, predict the network reliability and select a suitable one. Two common GSM-R wireless architectures, co-sited double layers network and intercross single layer network, are modeled and contrasted in this paper. By calculating the reliabilities of each reliable model, it is clear that more redundant the architecture is, more reliable the system will be, the whole system will bear a less failure time per year as the benefit. Meanwhile, as the redundancy of GSM-R system raises, its equipment and maintenance will cost much, but the reliability raise gently. From the standpoint of transmission system interruption and network equipment failure, the reliability of co-sited double layer network architecture is higher than the intercross single layer one, while the viability and cost of the intercross redundant network is better than co-sited one in natural disasters such as flood and lightning. Taking fully into account reliability, viability and cost, we suggest that intercross redundant network should be chosen on high-speed railway.

Real-Time Database System Implementation of Railway Signal Power Source Remote Monitoring

2011 ◽  
Vol 128-129 ◽  
pp. 961-964
Author(s):  
Zhi Jian Qu ◽  
Li Liu
Keyword(s):  
Power Supply ◽  
Remote Monitoring ◽  
High Speed ◽  
Low Voltage ◽  
Signal Power ◽  
High Speed Railway ◽  
Railway Traffic ◽  
Power Failure ◽  
Railway Signal ◽  
Real Time Database

Railway signal is a key technology for high-speed railway，which is the foundation to keep the high-speed train line. Railway signal power is the automatic blocking of railway lines and 10kV lines transform into 380V power through after the power supply for railway signals. Signal power as the railway traffic signal of the power supply, it belongs to the first level of power system load. Its 10kV high voltage side stik up by the major of electric, 380V low voltage side maintenance by the signal major. When the signal power failure, often occur shirk responsibilities between the different majors, in order to define the responsibilities of the accident better, which need automation remote monitoring for main railway lines of the railway signal power and scheduling control as soon as possible[1-3].

High-Speed Railway Passenger Node Classification Method and Train Stops Scheme

2014 ◽  
Vol 505-506 ◽  
pp. 632-636 ◽  
Author(s):  
Peng Fei Zhou ◽  
Bao Ming Han ◽  
Qi Zhang
Keyword(s):  
High Speed ◽  
Passenger Transport ◽  
High Speed Rail ◽  
Plan Evaluation ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Railway Passenger Transport ◽  
Train Operation ◽  
Railway Passenger

The development of high-speed railway has been very fast, while there are still existing many problems to be further studied and discussed, especially the design of high-speed railway Train stops program. The research of classification of high-speed passenger railway nodes has a vital significance for forecast of high-speed railway passenger flow, passenger train operation plan, evaluation and optimization and so on, especially for highspeed railway stopping schedule .This paper analyzes the significance and methods of high-speed passenger railway nodes classification, and designs high-speed rail train line stops program based on the classification. Finally, analyzing the case on the basis of Beijing-Guangzhou high-speed railway, a train stops program will be made bases on the classification of Beijing-Guangzhou high-speed railway passenger transport nodes to verify the feasibility of this study.

scholarly journals An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongyu Zhang ◽  
Limin Jiang ◽  
Jijun Tang ◽  
Yijie Ding
Keyword(s):  
Lung Cancer ◽  
Feature Representation ◽  
The Cancer Genome Atlas ◽  
Classification Model ◽  
Kernel Learning ◽  
Support Vector ◽  
Cancer Subtypes ◽  
Operation Speed ◽  
Kernel Fusion

In recent years, cancer has become a severe threat to human health. If we can accurately identify the subtypes of cancer, it will be of great significance to the research of anti-cancer drugs, the development of personalized treatment methods, and finally conquer cancer. In this paper, we obtain three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA). Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification model. By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion. Finally, we put the combined kernel function into the support vector machine (SVM) and get excellent results. Among them, in the classification of renal cell carcinoma subtypes, the maximum accuracy can reach 0.978 by using the method of MKL (HSIC calculation weight), while in the classification of lung cancer subtypes, the accuracy can even reach 0.990 with the same method (FKL calculation weight).

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