Metrorail Train Safety Monitoring Central Node Network Computing Environment

2013 ◽  
Vol 455 ◽  
pp. 431-433
Author(s):  
Hai Feng Hong ◽  
Zhen Chen Chang ◽  
Chen Hui Yang ◽  
Xue Dong Wang
Keyword(s):  
Working Condition ◽  
Preventive Measures ◽  
Safety Monitoring ◽  
Network Computing ◽  
Computing Environment ◽  
Central Node ◽  
Circulation Mechanism ◽  
Node Network ◽  
Diagnosis Accuracy ◽  
Train Safety

Metrorailtrain safetyguarantee and accident preventive measures become more stringent as severity of collisions and derailments happens frequently. Aninnovative safety monitoring and early warning network technology is discussed. This computing environment named Centralnode is used in several Metrorail trains under working condition. It integrates trains real-time dataaccording to aspects of vehiclesinterconnection, function service models and characteristic, uniform storage structure, circulation mechanism, diagnosis fusion interface. It improves the vehicles fault diagnosis accuracy and effectiveness.

Study on LTE Data Transmission Quality Test of TSMD (Train Safety Monitoring Device)

2018 ◽  
Vol 6 (3) ◽  
pp. 213-221
Author(s):  
Soo-Hwan Lee ◽  
You-Ho Kim ◽  
Sang-ahm Kim ◽  
Hyun-Ju Hwang ◽  
Yong-Woon Choi
Keyword(s):  
Data Transmission ◽  
Safety Monitoring ◽  
Monitoring Device ◽  
Quality Test ◽  
Transmission Quality ◽  
Train Safety

System architecture of a train sensor network for automatic train safety monitoring

2019 ◽  
Vol 127 ◽  
pp. 1183-1192 ◽  
Author(s):  
Guoqiang Cai ◽  
Jiaqing Zhao ◽  
Qiong Song ◽  
Mengchu Zhou
Keyword(s):  
Sensor Network ◽  
System Architecture ◽  
Safety Monitoring ◽  
Train Safety

scholarly journals HeNCE: A Heterogeneous Network Computing Environment

1994 ◽  
Vol 3 (1) ◽  
pp. 49-60 ◽  
Author(s):  
Adam Beguelin ◽  
Jack J. Dongarra ◽  
George Al Geist ◽  
Robert Manchek ◽  
Keith Moore
Keyword(s):  
Heterogeneous Network ◽  
Local Area Network ◽  
Directed Graphs ◽  
Local Area ◽  
Control Flow ◽  
Future Research ◽  
Area Network ◽  
Network Computing ◽  
Computing Environment ◽  
Data Dependencies

Network computing seeks to utilize the aggregate resources of many networked computers to solve a single problem. In so doing it is often possible to obtain supercomputer performance from an inexpensive local area network. The drawback is that network computing is complicated and error prone when done by hand, especially if the computers have different operating systems and data formats and are thus heterogeneous. The heterogeneous network computing environment (HeNCE) is an integrated graphical environment for creating and running parallel programs over a heterogeneous collection of computers. It is built on a lower level package called parallel virtual machine (PVM). The HeNCE philosophy of parallel programming is to have the programmer graphically specify the parallelism of a computation and to automate, as much as possible, the tasks of writing, compiling, executing, debugging, and tracing the network computation. Key to HeNCE is a graphical language based on directed graphs that describe the parallelism and data dependencies of an application. Nodes in the graphs represent conventional Fortran or C subroutines and the arcs represent data and control flow. This article describes the present state of HeNCE, its capabilities, limitations, and areas of future research.

scholarly journals End-to-end diagnosis of cloud systems against intermittent faults

2020 ◽  
pp. 40-40
Author(s):  
Chao Wang ◽  
Zhongchuan Fu ◽  
Yanyan Huo
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Computing Environment ◽  
Bp Network ◽  
Cloud Computing Environment ◽  
Intermittent Faults ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
End To End ◽  
Detection Mechanisms

The diagnosis of intermittent faults is challenging because of their random manifestation due to intricate mechanisms. Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing. This paper proposes a fault diagnosis method that can effectively identify and locate intermittent faults originating from (but not limited to) processors in the cloud computing environment. The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods. It can be implemented with no additional fault detection mechanisms, and is realized by software with almost zero hardware cost. The proposed method shows a higher fault diagnosis accuracy than BP network, reaching 97.98% with low latency.

The Open Network Computing Environment

1990 ◽  
pp. 3-12
Author(s):  
Larry Garlick ◽  
Robert Lyon ◽  
Louis Delzompo ◽  
Brent Callaghan
Keyword(s):  
Network Computing ◽  
Computing Environment ◽  
Open Network
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