scholarly journals Research on Multi-point Hopping Communication System in Power Network Based on Information Fusion Model

2021 ◽  
Vol 2108 (1) ◽  
pp. 012048
Author(s):  
Lifeng Yu ◽  
Kaibo Hu ◽  
Jiachen Lv ◽  
Zhiling Xia
Keyword(s):  
Information Fusion ◽  
Communication System ◽  
Evidence Theory ◽  
Poor Performance ◽  
Power Network ◽  
Fusion Model ◽  
Communication Performance ◽  
Distributed Information ◽  
Acquisition Scheme ◽  
The Time Domain

Abstract Multi-source heterogeneous information will cause access burden to the power network, resulting in poor performance of multi-point hop communication. Therefore, a multi-point hop communication system based on information fusion model is designed. The IEC61970/61968CIM is selected as the integrated bus general data acquisition scheme of the information fusion model. The local-global distributed information fusion mechanism is used to realize the communication of the power multi-point hop communication system. In this paper, the artificial neural network algorithm is used to fuse local information features, and according to D-S evidence theory, the global decision-level fusion is carried out from both the spatial domain and the time domain. Through the information fusion model, the multi-point hop communication information in the power network realizes efficient transmission. The experimental results show that the application of the system in the power network to implement multi-point hop communication, the packet loss rate is less than 0.25%, the transmission delay is less than 30ms, and the communication performance of the power network is improved

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.

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.

scholarly journals Research on Improved DS Evidence Theory

2021 ◽  
Author(s):  
Zhen Lin ◽  
Jinye Xie
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Weight Matrix ◽  
Fusion Model ◽  
Conflicting Evidence ◽  
Evidence Source ◽  
Sensor Information ◽  
Focal Element ◽  
New Evidence

Abstract In view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved DS evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the BPA of each focal element to obtain a new evidence source. Then, the concept of credibility is introduced, and the synthetic rules are improved by using the credibility of evidence and the average support of evidence focal elements, so as to obtain the fusion results. The results show that compared with other algorithms, this algorithm can solve the problems of DS evidence theory in dealing with highly conflicting evidence to a certain extent, and the fused results are more reasonable and converge faster.

scholarly journals Research on improved evidence theory based on multi-sensor information fusion

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhen Lin ◽  
Jinye Xie
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Fusion Model ◽  
Conflicting Evidence ◽  
Evidence Source ◽  
Basic Probability ◽  
Sensor Information ◽  
Focal Element ◽  
New Evidence ◽  
Evidence Credibility

AbstractIn view of the lack of effective information fusion model for heterogeneous multi-sensor, an improved Dempster/Shafer (DS) evidence theory algorithm is designed to fuse heterogeneous multi-sensor information. The algorithm first introduces the compatibility coefficient to characterize the compatibility between the evidence, obtains the weight matrix of each proposition, and then redistributes the basic probability distribution of each focal element to obtain a new evidence source. Then the concept of credibility is introduced, and the average support of evidence credibility and evidence focal element is used to improve the synthesis rule, so as to obtain the fusion result. Compared with other algorithms, the proposed algorithm can solve the problems existing in DS evidence theory when dealing with highly conflicting evidence to a certain extent, and the fusion results are more reasonable and the convergence speed is faster.

Distributed Information and Electric Power Network...

2005 ◽  
Author(s):  
Shinichi Nakasuka ◽  
Takashi Eishima ◽  
Hironori Sahara ◽  
Yuya Nakamura ◽  
Yoshiki Sugawara ◽  
...  
Keyword(s):  
Electric Power ◽  
Power Network ◽  
Distributed Information

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

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