Inference Degradation in Information Fusion

2010 ◽  
pp. 92-109
Author(s):  
Xiangyang Li
Keyword(s):  
Information Fusion ◽  
Information Integration ◽  
Dynamic Bayesian Network ◽  
Recognition Task ◽  
Selection Strategy ◽  
Optimization Approach ◽  
Sensor Planning ◽  
Multiple Sensors ◽  
Information Awareness ◽  
Security Surveillance

Dynamic and active information fusion processes select the best sensor based on expected utility calculation in order to integrate the evidences acquires both accurately and timely. However, inference degradation happens when the same/similar sensors are selected repeatedly over time if the selection strategy is not well designed that considers the history of sensor engagement. This phenomenon decreases fusion accuracy and efficiency, in direct conflict to the objective of information integration with multiple sensors. This chapter tries to provide a mathematical scrutiny of this problem in the myopia planning popularly utilized in active information fusion. In evaluation it first introduces the common active information fusion context using security surveillance applications. It then examines the generic dynamic Bayesian network model for a mental state recognition task and analyzes experimentation results for the inference degradation. It also discusses the candidate solutions with some preliminary results. The inference degradation problem is not limited to the discussed task and may emerge in variants of sensor planning strategies even with more global optimization approach. This study provides common guidelines in information integration applications for information awareness and intelligent decision.

Multiple-Sensor Planning and Information Integration for Automatic Coordinate Metrology

2001 ◽  
Vol 1 (2) ◽  
pp. 167-179 ◽  
Author(s):  
Tzung-Sz Shen ◽  
Jianbing Huang ◽  
Chia-Hsiang Menq
Keyword(s):  
High Precision ◽  
Information Integration ◽  
Vision System ◽  
Sensor Integration ◽  
Sensor Planning ◽  
Multiple Sensors ◽  
Surface Areas ◽  
Coordinate Metrology ◽  
Key Issues ◽  
Multiple Sensor

Multiple-sensor integration of vision and touch probe sensors has been shown to be a feasible approach for rapid and high-precision coordinate acquisition [Shen, T. S., Huang, J., and Meng, C. H., 2000, “Multiple-sensor integration for rapid and high-precision coordinate metrology,” IEEE/ASME Trans. Mechatron. 5, pp. 110–121]. However, the automation of coordinate measurements is still hindered by unknown surface areas that cannot be digitized using the vision system due to occlusions. It is identified that the estimation and reasoning of unknown surface areas, and automatic sensor planning using multiple sensors are two key issues. In order to advance multiple-sensor integration technologies toward a fully automatic and agile coordinate metrology, information integration algorithms for estimating and reasoning unknown surface areas, and an automatic multiple-sensor planning environment are developed in this paper. Experimental and simulation results are also demonstrated.

Grounding Failure Detection Based on ELM Information Fusion in Distribution Power Systems

2013 ◽  
Vol 325-326 ◽  
pp. 565-568
Author(s):  
Yu Sheng Zhou ◽  
Yong Feng Liu ◽  
Xiang Jun ◽  
Zheng Pan
Keyword(s):  
Power Systems ◽  
Information Fusion ◽  
Information Integration ◽  
Single Phase ◽  
Wavelet Packet ◽  
Distribution Network ◽  
Failure Detection ◽  
Fault Line ◽  
Fault Line Selection ◽  
Line Selection

For the complexity of the distribution network and the particularity of single-phase grounding fault, intelligent distribution network grounding fault line selection model based on the Extreme Learning Machine (ELM) information integration is proposed in the paper. When a single-phase grounding fault happened, the relation functions for the wavelet packet decomposition, the fifth harmonic method and the traveling wave method are respectively determined, the fault estimate data of transient zero-sequence current are calculated. The fault line is accurately judged by the ELM networks information fusion. Through analyzing MATLAB simulation result about different ground fault line selection, the validity and accuracy of the method are verified.

scholarly journals Monitoring and Analysis of Physical Exercise Effects Based on Multisensor Information Fusion

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Xinliang Zhou ◽  
Shantian Wen
Keyword(s):  
Physical Exercise ◽  
Data Fusion ◽  
Information Fusion ◽  
Development Process ◽  
System Optimization ◽  
Physiological Parameters ◽  
Fusion Technology ◽  
Multisensor Data Fusion ◽  
Multiple Sensors ◽  
Multisensor Information Fusion

In this paper, multiple sensors are used to track human physiological parameters during physical exercise, and data information fusion technology is used to extract useful information for monitoring and analyzing the effects of physical exercise. This paper explores the interaction and developmental dynamics of multisensor information fusion technology and physical exercise data monitoring based on the interrelationship and interpenetration between the two. The design ideas and principles that should be followed for the software designed in this study are discussed from the perspective of the portable design of measurement instruments and the perspective of multisensor information fusion, and then, the overall architecture and each functional module are studied to propose a scientific and reasonable design model. The general methodological model to be followed for the development of this resource is designed, and the basic development process of the model is explained and discussed, especially the requirement analysis and structural design, and how to build the development environment are explained in detail; secondly, based on the course unit development process in this model, we clarify the limitations of the system through meticulous analysis of the measurement results, which provides a solid foundation for the next step of system optimization. Finally, with a focus on future development, we elaborate on the potential possible role and development trend of multisensor information fusion in the future period. In this paper, we propose to apply the multisensor data fusion algorithm to the monitoring, analysis, and evaluation of the effect of physical exercise, by collecting multiple human physiological parameters during physical exercise through multiple sensors and performing data fusion processing on the collected physiological parameters to finally evaluate the effect of physical exercise.

Investigation of Temporal Integration by Video Sampling

Perception ◽  
1973 ◽  
Vol 2 (4) ◽  
pp. 441-490 ◽  
Author(s):  
R Williams
Keyword(s):  
Information Integration ◽  
Temporal Integration ◽  
Recognition Task ◽  
Theoretical Models ◽  
Stereoscopic Depth ◽  
Critical Duration ◽  
Integration Time ◽  
Binocular Fusion ◽  
Central Process ◽  
Time Critical

The results from the preliminary set of experiments in which a new video sampling apparatus was used are reported. With the aid of this apparatus experiments were carried out to measure the maximum visual temporal integration time (critical duration) at various background intensities (0·034–34 cd m−2). The aim was to determine to what extent this phenomenon is attributable to either ‘central’ or ‘peripheral’ events. The extended integration period found for the number recognition task is interpreted as evidence of a ‘central’ process; to follow the argument further, an attempt was made to demonstrate information integration using a rotating form in a similar identification–discrimination situation. Monocular, binocular, and dichoptic arrangements were employed, and the amount of dichoptic summation of form information, achieved by both normal and strabismic subjects without stereoscopic depth perception, was used to test two theoretical models of binocular fusion. In addition, stereoscopic depth was generated with uncorrected sampling of the left and right images, which may be due to the action of a ‘fusion hierarchy’. Signal detection theory is suggested as a possible solution to the problem of expectation effects in identification-threshold experiments.

Integrated Automation System Using Information Fusion Techniques

2014 ◽  
Vol 533 ◽  
pp. 281-284
Author(s):  
Hong Wei Quan ◽  
Wan Bing Li
Keyword(s):  
Information Processing ◽  
Information Fusion ◽  
Information Integration ◽  
Research Topic ◽  
Automation System ◽  
Fusion Technology ◽  
Integrated Automation ◽  
Decision Supporting ◽  
Integrated Automation System ◽  
System Information

More and more problems emerged in integrated automation system such as data processing, database information integration, and decision supporting and fault diagnosis. Information fusion technology is currently a front research topic in the field of information processing. The application of information fusion in above four aspects suggests its an effective method to solve the problems in integrated automation system. Information fusion technology will get more widely applications in integrated automation system.

Based on D - S Evidence Theory of Solution Concentration Detection Method Research

2014 ◽  
Vol 494-495 ◽  
pp. 869-872
Author(s):  
Xian Bao Wang ◽  
Shi Hai Zhao ◽  
Guo Wei
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Solution Concentration ◽  
Sensor Data ◽  
Fusion Technology ◽  
Feedback Information ◽  
Multiple Sensors ◽  
Multi Sensor Data Fusion ◽  
Single Sensor ◽  
Sensor Information

According to the theory of multi-sensor information fusion technology, based on D - S evidence theory to fuse of multiple sensors feedback information from different angles for detecting solution concentration, and achieving the same judgment; This system uses of D - S evidence theory of multi-sensor data fusion method, not only make up the disadvantages of using a single sensor, but also largely reduce the uncertainty of the judgment. Additionally this system improves the rapidity and accuracy of the solution concentration detection, and broadens the application field of multi-sensor information fusion technology.

scholarly journals Fusion of the Brooks–Iyengar Algorithm and Blockchain in Decentralization of the Data-Source

2019 ◽  
Vol 8 (1) ◽  
pp. 17 ◽  
Author(s):  
Sitharama Iyengar ◽  
Sanjeev Ramani ◽  
Buke Ao
Keyword(s):  
Information Fusion ◽  
Local System ◽  
Physical World ◽  
Consensus Algorithm ◽  
Multiple Sources ◽  
Fusion Algorithm ◽  
Multiple Sensors ◽  
The Past ◽  
Distributed Ledger ◽  
Data Source

Information fusion has been a topic of immense interest owing to its applicability in various applications. This brings to the fore the need for a flexible and accurate fusion algorithm that can be versatile. The Brooks–Iyengar algorithm is one such fusion algorithm. It has since its inception found numerous applications that deal with the fusion of data from multiple sources. The uniqueness of the Brooks–Iyengar algorithm is the ease with which the data from multiple sensors in a local system can be fused and also reach consensus in a distributed system with the added capability of fault tolerance. Blockchain has found its use as a distributed ledger and has successfully supported and fueled many crypto-currencies over the years. Information fusion with regards to Blockchains is a topic of great research interest in the past couple of years. Since blockchain has no official node, the introduction of a decentralized network and a consensus algorithm is required in making the interactions and exchanges between multiple suppliers easier and thus leads to business being carried out without any hassles. In this paper, we attempt to understand and describe the deployment of multiple sensors to measure various aspects of the physical world. We discuss a novel technique of employing the Brooks–Iyengar algorithm in the design of the system that would decentralize the data source from the corresponding measurements and thus ensure the integrity of the transactions in the Blockchain. Finally, a theoretical analysis of the performance of the algorithm when used in a blockchain based decentralized environment is also discussed.

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