16,458 research outputs found
Expressing Bayesian Fusion as a Product of Distributions: Application to Randomized Hough Transform
Data fusion is a common issue of mobile robotics, computer assisted
medical diagnosis or behavioral control of simulated character for instance. However
data sources are often noisy, opinion for experts are not known with absolute
precision, and motor commands do not act in the same exact manner on the environment.
In these cases, classic logic fails to manage efficiently the fusion process.
Confronting different knowledge in an uncertain environment can therefore be adequately
formalized in the bayesian framework.
Besides, bayesian fusion can be expensive in terms of memory usage and processing
time. This paper precisely aims at expressing any bayesian fusion process as a
product of probability distributions in order to reduce its complexity. We first study
both direct and inverse fusion schemes. We show that contrary to direct models,
inverse local models need a specific prior in order to allow the fusion to be computed
as a product. We therefore propose to add a consistency variable to each local
model and we show that these additional variables allow the use of a product of the
local distributions in order to compute the global probability distribution over the
fused variable. Finally, we take the example of the Randomized Hough Transform.
We rewrite it in the bayesian framework, considering that it is a fusion process
to extract lines from couples of dots in a picture. As expected, we can find back
the expression of the Randomized Hough Transform from the literature with the
appropriate assumptions
A Model for an Intelligent Support Decision System in Aquaculture
The paper purpose an intelligent software system agents–based to support decision in aquculture and the approach of fish diagnosis with informatics methods, techniques and solutions. A major purpose is to develop new methods and techniques for quick fish diagnosis, treatment and prophyilaxis at infectious and parasite-based known disorders, that may occur at fishes raised in high density in intensive raising systems. But, the goal of this paper is to presents a model of an intelligent agents-based diagnosis method will be developed for a support decision system.support decision system, diagnosis, multi-agent system, fish diseases
Learning models of plant behavior for anomaly detection and condition monitoring
Providing engineers and asset managers with a too] which can diagnose faults within transformers can greatly assist decision making on such issues as maintenance, performance and safety. However, the onus has always been on personnel to accurately decide how serious a problem is and how urgently maintenance is required. In dealing with the large volumes of data involved, it is possible that faults may not be noticed until serious damage has occurred. This paper proposes the integration of a newly developed anomaly detection technique with an existing diagnosis system. By learning a Hidden Markov Model of healthy transformer behavior, unexpected operation, such as when a fault develops, can be flagged for attention. Faults can then be diagnosed using the existing system and maintenance scheduled as required, all at a much earlier stage than would previously have been possible
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
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