Multi-Source Information Fusion Model of Traffic Lifeline Based on Improved D-S Evidence Theory

2018 ◽  
Author(s):  
Xianyu Li ◽  
Zhongyin Guo
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Source Information ◽  
Fusion Model

scholarly journals Application of Two-Level Joint Information Fusion Model in Intelligent Vehicle

2016 ◽  
Vol 12 (03) ◽  
pp. 77
Author(s):  
Yan Ting Lan ◽  
Jiinying Huang ◽  
Xiaodong Chen
Keyword(s):  
Information Fusion ◽  
Feature Fusion ◽  
Target Identification ◽  
Information Source ◽  
Evidence Theory ◽  
Intelligent Vehicles ◽  
Fusion Model ◽  
Multiple Features ◽  
Model Combining ◽  
Real Vehicle

This paper proposes a two-level joint information fusion model combining BP neural network and D-S evidence theory. The model of great practical value reduces target identification error probability by multiple features of the target information, shows good scalability with its two steps of information fusion model, and conveniently increases/reduces feature fusion information source according to different situations and different objects. The method used for intelligent vehicles has good flexibility and robustness in tracking and avoiding obstacle. The simulation and real vehicle tests have verified effectiveness of the method.

Research on Evaluation of Vehicle Dangerous Traveling State Based on Information Fusion Method

2013 ◽  
Vol 791-793 ◽  
pp. 1018-1022
Author(s):  
Peng Wang ◽  
Zhi Qiang Liu
Keyword(s):  
Information Fusion ◽  
Evaluation System ◽  
Evidence Theory ◽  
Recognition System ◽  
Fusion Method ◽  
Source Information ◽  
State Recognition ◽  
Research On Evaluation ◽  
Lane Departure ◽  
Level Information

An evaluation system of vehicle traveling state was proposed,and an unsafe vehicle traveling state recognition system was established using multi-level information fusion method. In view of the effects of the complexity of the driving environment, a variety of working conditions and the diversity of vehicle traveling characteristics, combing BP neural network with Dempster-Shafer evidence theory technique, the multi-information decision-level fusion was proposed to estimate the different kind model of the vehicle status. To verify the proposed strategies,the vehicle traveling posture evaluation system was established. The lane departure parameters and the relative distance parameters were studied in order to get the characterization of the vehicle traveling status information. The simulation results indicate that the adaptability and accuracy and the intelligence level of driving characterization estimation are significantly improved by using the pattern classification and decision technology of multi-source information fusion.

scholarly journals Innovative assessment scheme of navigation risk based on improved multi-source information fusion techniques

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877254
Author(s):  
Bo Li ◽  
Fuwen Pang
Keyword(s):  
Information Fusion ◽  
Time Complexity ◽  
Euclidean Distance ◽  
Evidence Theory ◽  
Classification Model ◽  
Support Vector ◽  
Source Information ◽  
Distance Ratio ◽  
Slack Variable ◽  
Nonlinear Support

To deal with highly time complexity and unstable assessments for conflicting evidences from various navigation factors, we put forward an innovative assessment scheme of navigation risk based on the improved multi-source information fusion techniques. Different from the existing studies, we first deduce the nonlinear support vector machine classification model for the general scenario. The slack variable is adaptively computed based on the Euclidean distance ratio. Considering the unsatisfactory characteristics of the standard Dempster–Shafer evidence theory, the optimal combination rule is derived step by step. What"s more, the lowly dimensional Kalman filter is applied to forecast the navigation risk. Simultaneously, the time complexity of each technique is analyzed. With respect to the vessel navigation risk, the assessment results are provided to indicate the reliability and efficiency of the proposed scheme.

Arc Detection and Recognition in the Pantograph-Catenary System Based on Multi-Information Fusion

2020 ◽  
Vol 2674 (10) ◽  
pp. 229-240
Author(s):  
Shize Huang ◽  
Wei Chen ◽  
Bo Sun ◽  
Ting Tao ◽  
Lingyu Yang
Keyword(s):  
Visible Light ◽  
Information Fusion ◽  
High Speed ◽  
Evidence Theory ◽  
Detection Methods ◽  
Fusion Method ◽  
Fusion Model ◽  
Threshold Method ◽  
High Speed Trains ◽  
Catenary System

The pantograph-catenary system is critical to high-speed railways. Electric arcs in the pantograph-catenary system indicate possible damages to the whole railway system, and detecting them in time has been a critical task. In this paper, a fusion method for the pantograph-catenary arc detection based on multi-type videos is proposed. First, convolutional neural network (CNN) is employed to detect arcs in visible light images, and a threshold method is applied to identify arcs in infrared images. Second, the CNN-based environment perception model is established on visible light images. It obtains the dynamical adjustment of the reliability weights for different scenarios where trains usually work. Finally, the information fusion model based on evidence theory uses those weights and integrates the detection results on visible light images and infrared results. The experimental results demonstrate the fusion method can avoid misjudgments of the two individual detection methods in certain scenarios, and perform better than each of them. This approach can adapt to the complex environments of high-speed trains.

An Information Fusion Model Based on Dempster–Shafer Evidence Theory for Equipment Diagnosis

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Dengji Zhou ◽  
Tingting Wei ◽  
Huisheng Zhang ◽  
Shixi Ma ◽  
Fang Wei
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Information Fusion ◽  
Evidence Theory ◽  
Diagnostic System ◽  
Fusion Model ◽  
First Case ◽  
Evidence Algorithm ◽  
Data Layer ◽  
Analytical System

An abnormal operating effect can be caused by different faults, and a fault can cause different abnormal effects. An information fusion model, with hybrid-type fusion frame, is built in this paper, so as to solve this problem. This model consists of data layer, feature layer and decision layer, based on an improved Dempster–Shafer (D-S) evidence algorithm. After the data preprocessing based on event reasoning in data layer and feature layer, the information will be fused based on the new algorithm in decision layer. Application of this information fusion model in fault diagnosis is beneficial in two aspects, diagnostic applicability and diagnostic accuracy. Additionally, this model can overcome the uncertainty of information and equipment to increase diagnostic accuracy. Two case studies are implemented by this information fusion model to evaluate it. In the first case, fault probabilities calculated by different methods are adopted as inputs to diagnose a fault, which is quite different to be detected based on the information from a single analytical system. The second case is about sensor fault diagnosis. Fault signals are planted into the measured parameters for the diagnostic system, to test the ability to consider the uncertainty of measured parameters. The case study result shows that the model can identify the fault more effectively and accurately. Meanwhile, it has good expansibility, which may be used in more fields.

A Network Security Situational Awareness Model Based on Information Fusion

2013 ◽  
Vol 846-847 ◽  
pp. 1632-1635
Author(s):  
Abasi
Keyword(s):  
Network Security ◽  
Information Fusion ◽  
Situational Awareness ◽  
Evidence Theory ◽  
Arma Model ◽  
Situational Factors ◽  
Source Information ◽  
Network Nodes ◽  
Model Based ◽  
The Future

Security situational awareness has become a hot topic in the area of network securityresearch in recent years. The existing security situational awareness methods are analyzed and compared in details, and thus a newnetwork security situational awareness model based on information fusion is proposed. This modelfuses multi-source information from a mass of logs by introducing the modified D-S evidence theory,gets the values of nodes security situational awareness by situational factors fusion using attacks threat,and vulnerability information which network nodes have and successful attacks depend on, computesthe value of network security situational awareness by nodes situation fusion using service informationof the network nodes, and draws the security-situation-graph of network. Then, it analyzes the timeseries of the computing results by ARMA model to forecast the future threat in network security.Finally an example of actual network datasets is given to validate the network security situationalawareness model and algorithm. The results show that this model and algorithm is more effective andaccurate than the existing security situational awareness methods.

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