The Experts below are selected from a list of 102033 Experts worldwide ranked by ideXlab platform
Yan Zi-qin - One of the best experts on this subject based on the ideXlab platform.
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Application of Neural Network in Information Fusion
Computer Simulation, 2005Co-Authors: Yan Zi-qinAbstract:Information Fusion is a special technology in Information process research.In social practice,people have always synthetically dealt with every Information consciously or unconsciously.Information Fusion gets every Information as a whole to be synthetically analysed and dealt with for the base of making policy and controlling.With the development of computer technology and the application of neural network,up to now,there have been many models of the neural network and corresponding calculation methods.This article takes the running equipment of controlling protection as an example,to analyse and study the Fuzzy Neuron Network technology and Information Fusion technology together thus getting the theory with more practical meaning and so that the application of neural network in Information Fusion can be more used.
Erik Blasch - One of the best experts on this subject based on the ideXlab platform.
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Context-Enhanced Information Fusion - Contextual Tracking Approaches in Information Fusion
Context-Enhanced Information Fusion, 2016Co-Authors: Erik Blasch, Lauro Snidaro, Jesús García, Chun Yang, James LlinasAbstract:Many Information Fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this chapter, we seek to define and categorize different types of contextual Information. We describe five contextual Information categories that support target tracking: (1) domain knowledge from a user to aid the Information Fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road Information for target tracking and identification. Appropriate characterization and representation of contextual Information is needed for future high-level Information Fusion designs to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.
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Contextual Tracking Approaches in Information Fusion
Context-Enhanced Information Fusion, 2016Co-Authors: Erik Blasch, Lauro Snidaro, Jesús García, Chun Yang, James LlinasAbstract:Many Information Fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this chapter, we seek to define and categorize different types of contextual Information. We describe five contextual Information categories that support target tracking: (1) domain knowledge from a user to aid the Information Fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road Information for target tracking and identification. Appropriate characterization and representation of contextual Information is needed for future high-level Information Fusion designs to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.
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Context-Enhanced Information Fusion - Context-Enhanced Information Fusion
Advances in Computer Vision and Pattern Recognition, 2016Co-Authors: Lauro Snidaro, James Llinas, Jesús García, Erik BlaschAbstract:Robotics systems need to be robust and adaptable to multiple operational conditions, in order to be deployable in different application domains. Contextual knowledge can be used for achieving greater flexibility and robustness in tackling the main tasks of a robot, namely mission execution, adaptability to environmental conditions and self-assessment of performance. In this chapter, we review the research work focusing on the acquisition, management, and deployment of contextual Information in robotic systems. Our aim is to show that several uses of contextual knowledge (at different representational levels) have been proposed in the literature, regarding many tasks that are typically required for mobile robots. As a result of this survey, we analyze which notions and approaches are applicable to the de- sign and implementation of architectures for Information Fusion. More specifically, we sketch an architectural framework which enables for an effective engineering of systems that use contextual knowledge, by including the acquisition, representation, and use of contextual Information into a framework for Information Fusion
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Trust metrics in Information Fusion
Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, 2014Co-Authors: Erik BlaschAbstract:ABSTRACT Trust is an important concept for machine intelligence and is not consistent across many applications. In this paper, we seek to understand trust from a variety of factors: humans, sensors, communications, intelligence processing algorithms and human-machine displays of Information. In modeling the various aspects of trust, we provide an example from machine intelligence that supports the various attributes of measuring trust such as sensor accuracy, communication timeliness, machine processing confidence, and display throughput to convey the various attributes that support user acceptance of machine intelligence results. The example used is fusing video and text whereby an analyst needs trust Information in the identified imagery track. We use the proportional conflict redistribution rule as an Information Fusion technique that handles conflicting data from trusted and mistrusted sources. The discussion of the many forms of trust explored in the paper seeks to provide a systems-level design perspective for Information Fusion trust quantification. Keywords: Trust, Metrics, High-Level Information Fusion, Situation Assessment, Situation Awareness.
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Information Fusion in aC loud-Enabled Environment
2014Co-Authors: Erik Blasch, Yu Chen, Genshe Chen, Dan Shen, Ralph KohlerAbstract:Recent advances in cloud computing pose interesting capabilities for Information Fusion which have similar requirements of big data computations. With a cloud enabled environment, Information Fusion systems could be conducted over vast amounts of entities across multiple databases. In order to properly implement Information Fusion in a cloud, Information management, system design, and real-time execution must be considered. In this chapter, three aspects of current developments integrating low/high-level Information Fusion (LLIF/HLIF) and cloud computing are discussed: (1) agent-based service architectures, (2) ontologies, and (3) metrics (timeliness, confidence, and security). We introduce the Cloud- Enabled Bayes Network (CEBN) for wide area motion imagery target tracking and identification. The Google Fusion Tables service is also selected as a case study to illustrate commercial cloud-based Information Fusion applications.
Liu Zhun-ga - One of the best experts on this subject based on the ideXlab platform.
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Basic methods and progress of Information Fusion (IIII)
Control theory & applications, 2012Co-Authors: Liu Zhun-gaAbstract:Because of the demands from military technology,automation and intelligence,Information Fusion has been broadly concerned in academia and industry.Since a number of methods and algorithms have been emerging in recent years,it is necessary to analyze and review their recent developments.We start by analyzing the inherent problems and challenges in Information Fusion,including Fusion architecture,uncertainty of Information,multimodal Information,conflict Information,correlative Information,networked Information and nonlinearity of Information;and then,we review various methods and algorithms developed in the past ten years for implementing the Fusion of the above types of Information.In addition,several future directions of research are highlighted and described,including the joint processing for Information,human-centered Information Fusion,joint optimization for Information gathering and Fusion,architecture design for complex multisensor Information Fusion systems,simulation and performance evaluation for Information Fusion and the application of more mathematical theories.
Chen Bai-fan - One of the best experts on this subject based on the ideXlab platform.
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Multi-Sensor Information Fusion in Intelligent Car
Computer Simulation, 2012Co-Authors: Chen Bai-fanAbstract:Research the Information Fusion of intelligent car to improve the effective data quantity.This paper put forward an adaptive multi-sensor Fusion algorithm.Through estimating the data Fusion degree,filtering technology was used to improve the integration ability and increase the effective Information after data Fusion.Experiments show that this algorithm can greatly improve the diversity Information Fusion rate of multi-sensor and increase effective Information quantity.
Baoling Han - One of the best experts on this subject based on the ideXlab platform.
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Survey on robot multi-sensor Information Fusion technology
2008 7th World Congress on Intelligent Control and Automation, 2008Co-Authors: Xiaochuan Zhao, Qingsheng Luo, Baoling HanAbstract:Multi-sensor Information Fusion technology is an emerging technology, which is the foundation of robot intelligent control. This paper introduces the definition of multi-sensor Information Fusion technology from bionic, mathematic and engineering aspects respectively. Multi-sensor Information Fusion technologypsilas working process is explained. The principles, advantages and shortcomings of some common multi-sensor Information algorithms such as weighted average method, Kalman filtering, Bayes estimation, Dempster-Shafer evidential reasoning, fuzzy logic and neural networks are proposed. This survey overviews and analyses the study status of multi-sensor Information Fusion technology applied in robotics field. Some typical applications are given to illustrate the advantages of robot multi-sensor Information Fusion technology. The authors forecast the developing trend of robot multi-sensor Information Fusion technology from the following four aspects: sensor manufacture, sampling rate, Information Fusion algorithm and bio-inspired Information.
