An Improved Algorithm for Multi-source Location Information Fusion Based on Comprehensive Probability Distance Matrix

2018 ◽  
Author(s):  
Cai Xi ◽  
Liu Yue ◽  
Xie Song
Keyword(s):  
Information Fusion ◽  
Distance Matrix ◽  
Source Location ◽  
Location Information ◽  
Probability Distance ◽  
Improved Algorithm

Identification of the D-S Evidence Conflict Based on Multi-Source Information

2013 ◽  
Vol 347-350 ◽  
pp. 2191-2196
Author(s):  
Feng Nan ◽  
Yang Li
Keyword(s):  
Information Fusion ◽  
Distance Matrix ◽  
Source Information ◽  
Independent Evidence ◽  
Information Distance ◽  
Convergence Results ◽  
Conflicting Evidence ◽  
Conflict Identification ◽  
Paper Construct ◽  
Systematic Identification

TO solve the applying constraints problem of D-S evidence conflict in multi-information fusion and achieve the systematic identification of evidence conflict,we introduce the K-L information on the distance function to describe the characteristics of the conflict between the evidence in the paper, construct the distance matrix defined the level of conflict of independent evidence in the whole system. Simulating results show that: the effective conflict identification of K-L information distance can complete the application constraint of D-S theorys synthetic rule, obtaining the optimized convergence results for synthesis of normal conflicting evidences, isolating the highly conflicting evidence.

scholarly journals A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

2020 ◽  
Author(s):  
Lifan Sun ◽  
Yuting Chang ◽  
Jiexin Pu ◽  
Haofang Yu ◽  
Zhe Yang
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Combination Method ◽  
Uncertain Information ◽  
Combination Rule ◽  
Simulation Experiments ◽  
Sensor Information ◽  
Bodies Of Evidence ◽  
Probability Distance ◽  
Deng Entropy

The Dempster-Shafer (D-S) theory is widely applied in various fields involved with multi-sensor information fusion for radar target tracking, which offers a useful tool for decision-making. However, the application of D-S evidence theory has some limitations when evidences are conflicting. This paper proposed a new method combining the Pignistic probability distance and the Deng entropy to address the problem. First, the Pignistic probability distance is applied to measure the conflict degree of evidences. Then, the uncertain information is measured by introducing the Deng entropy. Finally, the evidence correction factor is calculated for modifying the bodies of evidence, and the Dempster’s combination rule is adopted for evidence fusion. Simulation experiments illustrate the effectiveness of the proposed method dealing with conflicting evidences.

Simulation of Accurate Navigation Technology for Aircraft Landing Approach

2013 ◽  
Vol 329 ◽  
pp. 406-410
Author(s):  
Ang Tai Li ◽  
Xiao Jiao Ma
Keyword(s):  
Information Fusion ◽  
Navigation System ◽  
Location Information ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
System Models ◽  
Navigation Algorithm ◽  
Aircraft Landing ◽  
Simulation Results

The purpose of this paper is to find a solid application and improve the integrated navigation system of aircraft independent landing. The main researches are focused on the issue how to improve the accuracy and reliability of the integrated navigation system, by means of the detailed analyzing about information fusion, and investigating accurate navigation technology for independent landing approach of aircraft. Next, the integrated navigation system models SINS/DGPS/TAN/ILS based on Federated filter are established, and the accurate navigation algorithm of landing is also researched. At the end, the paper gives its simulation results for independent lading approach, which show this algorithm is able to greatly improve the accuracy of speed and location information for aircraft accurate landing.

scholarly journals An Improved Algorithm for Detecting Pneumonia Based on YOLOv3

2020 ◽  
Vol 10 (5) ◽  
pp. 1818 ◽  
Author(s):  
Shangjie Yao ◽  
Yaowu Chen ◽  
Xiang Tian ◽  
Rongxin Jiang ◽  
Shuhao Ma
Keyword(s):  
Binary Classification ◽  
Image Data ◽  
Location Information ◽  
Human Beings ◽  
Localization Accuracy ◽  
X Ray ◽  
Detection Algorithms ◽  
Radiological Society ◽  
Aided Diagnosis ◽  
Improved Algorithm

Pneumonia is a disease that develops rapidly and seriously threatens the survival and health of human beings. At present, the computer-aided diagnosis (CAD) of pneumonia is mostly based on binary classification algorithms that cannot provide doctors with location information. To solve this problem, this study proposes an end-to-end highly efficient algorithm for the detection of pneumonia based on a convolutional neural network—Pneumonia Yolo (PYolo). This algorithm is an improved version of the Yolov3 algorithm for X-ray image data of the lungs. Dilated convolution and an attention mechanism are used to improve the detection results of pneumonia lesions. In addition, double K-means is used to generate an anchor box to improve the localization accuracy. The algorithm obtained 46.84 mean average precision (mAP) on the X-ray image dataset provided by the Radiological Society of North America (RSNA), surpassing other detection algorithms. Thus, this study proposes an improved algorithm that can provide doctors with location information on lesions for the detection of pneumonia.

A WSN(Wiress Sensor Network) Building Scheme using Clustering and Location information

2020 ◽  
Vol 20 (3) ◽  
pp. 13-20
Author(s):  
Jinsoo Kim ◽  
◽  
Hyukjin Kwon ◽  
Dongkyoo Shin ◽  
Sunghoon Hong
Keyword(s):  
Sensor Network ◽  
Location Information ◽  
Network Building
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