Research of Selective and Incremental Information Fusion Method Based on Bayesian Network

2013 ◽  
Vol 397-400 ◽  
pp. 2060-2063
Author(s):  
Li Wei Zhang ◽  
Jing Zhang ◽  
Yan Sun
Keyword(s):  
Decision Making ◽  
Bayesian Network ◽  
Information Fusion ◽  
Relevant Information ◽  
Fusion Method ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Information Selection ◽  
Incremental Information ◽  
Better Than

This paper presents a selective incremental information fusion method based on Bayesian network, so that the fusion algorithm can actively select the most relevant information and decision-making, and can make the fusion model to adapt to the dynamic changes in the external environment, and sensor information selection, fusion, decision-making integrated in the framework of Bayesian network . The experimental results show that this method is better than the traditional method.

scholarly journals Complex Entropy and Its Application in Decision-Making for Medical Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fuyuan Xiao ◽  
Xiao-Guang Yue
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Medical Diagnosis ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Numerical Examples ◽  
Open Issue ◽  
Complex Planes ◽  
Complex Valued ◽  
Complex Entropy

In decision-making systems, how to measure uncertain information remains an open issue, especially for information processing modeled on complex planes. In this paper, a new complex entropy is proposed to measure the uncertainty of a complex-valued distribution (CvD). The proposed complex entropy is a generalization of Gini entropy that has a powerful capability to measure uncertainty. In particular, when a CvD reduces to a probability distribution, the complex entropy will degrade into Gini entropy. In addition, the properties of complex entropy, including the nonnegativity, maximum and minimum entropies, and boundedness, are analyzed and discussed. Several numerical examples illuminate the superiority of the newly defined complex entropy. Based on the newly defined complex entropy, a multisource information fusion algorithm for decision-making is developed. Finally, we apply the decision-making algorithm in a medical diagnosis problem to validate its practicability.

scholarly journals An Algorithm of Wireless Sensor Monitoring System

2018 ◽  
Vol 14 (01) ◽  
pp. 52
Author(s):  
Li Hongri
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Monitoring System ◽  
Information Fusion ◽  
Design Method ◽  
Storage System ◽  
Wireless Sensor ◽  
Fusion Method ◽  
Fusion Model ◽  
Accurate Identification

In order to realize more intelligent storage monitoring system, the information fusion model of wireless sensor network for storage environment monitoring is studied on the basis of analyzing information fusion technology. By analyzing the structure of storage monitoring system based on wireless sensor network, a two-layer information fusion method is established. The information fusion of homogeneous sensor based on adaptive weighting and the fusion method of heterogeneous sensor based on radial basis function neural network are designed and verified. The experimental results show that the design method can fuse the storage environment information and realize the accurate identification of the environmental state. Therefore, the algorithm can effectively improve the speed of network training, and the classification effect is good. To a certain extent, it can help enterprises to establish a safe and efficient storage system, to enhance the efficiency of enterprise warehousing operations.

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.

A Research on the Improved Information Fusion Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1909-1912
Author(s):  
Guo Dong Zhang ◽  
Rui Min Qi
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Evidence Theory ◽  
Source Information ◽  
Fusion Algorithm ◽  
Mutually Independent

In the field of information confusion, evidence theory takes advantage of its uncertainties. But in practical evidences are always mutually independent, However, multi-source information fusion is a comprehensive integration and then obtaining decision-making, sometimes the result of information fusion give us wrong conclusion. So an improved information fusion algorithm is proposed in this paper. It can heighten the information confusion reliability and accuracy in a practical example.

Multi-Source Information Fusion Based on Kalman Filtering

2015 ◽  
Vol 727-728 ◽  
pp. 863-866
Author(s):  
Meng Meng Zhou ◽  
G.M. Gao ◽  
Hong Bo Yang
Keyword(s):  
Kalman Filtering ◽  
Information Fusion ◽  
Attitude Determination ◽  
Low Frequency ◽  
Fusion Method ◽  
Source Information ◽  
Satellite Platform ◽  
Pointing Accuracy ◽  
Micro Vibration ◽  
Better Than

Thehigh-frequency angular micro-vibration on satellite platform results in theoptical axis pointing decreasing accuracy. The Kalman filtering based on attitudeinformation fusion method is presented to solve this case and improve the pointing accuracy of attitude determination. Thesimulation results indicate that the estimated accuracy of Kalman filteringattitude information fusion method is better than the result only fromconventional low frequency sensor. Accordingly, the attitude information fusionmethod is verified and accuracy.

FIF: A fuzzy information fusion algorithm based on multi-criteria decision making

2014 ◽  
Vol 58 ◽  
pp. 23-32 ◽  
Author(s):  
Rita A. Ribeiro ◽  
António Falcão ◽  
André Mora ◽  
José M. Fonseca
Keyword(s):  
Decision Making ◽  
Information Fusion ◽  
Multi Criteria Decision Making ◽  
Fuzzy Information ◽  
Fusion Algorithm

scholarly journals A Multi-Objective Approach for Materialized View Selection

2019 ◽  
Vol 10 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jay Prakash ◽  
T.V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Relevant Information ◽  
Maintenance Cost ◽  
View Selection ◽  
Materialized View Selection ◽  
Speed Up ◽  
Transactional Data ◽  
Selection Of ◽  
Better Than

In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.

scholarly journals Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Chen ◽  
Jie Han ◽  
Wenping Lei ◽  
Yongxiang Cui ◽  
Zhenhong Guan
Keyword(s):  
Data Acquisition ◽  
Information Fusion ◽  
Prediction Method ◽  
Fault Prediction ◽  
Vibration Measurement ◽  
Signal Acquisition ◽  
Vector Data ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Full Vector

Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.

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