Adaptive fault diagnosis in interactive electronic technical manuals (IETMs)

An Interactive Electronic Technical Manual (IETM) is a technical manual that is prepared in digital format to provide information about the diagnostics and maintenance of complex systems.;In this thesis we present a methodology for adaptive fault diagnosis in IETMs, a methodology that constantly adapts the fault diagnosis procedure, according to the experience of the user performing the diagnosis. We develop a framework of adaptation that constantly monitors user behavior, and learns about the fault and its possible causes as the system is used, hence making it easier to perform such procedures, which increases efficiency of usage of such a manual, an essential factor in performing fault diagnosis. We will also extend S1000D to incorporate all information necessary for our adaptation methodology.;The outcome of our methodology will be an IETM which contains adaptable fault diagnosis procedures that adapt to users according to their expertise levels making these procedures less cumbersome for users to accomplish, hence increasing their productivity and efficiency. (Abstract shortened by UMI.)

Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled separately. The objective of this paper was to implement a simple version of their framework, and to propose an alternative procedure based on a Bayesian Network (BN). These approaches were illustrated in modeling the risk of diarrhea infection for 2740 children aged 0 to 59 months in Cameroon. The authors implemented a (naïve) logistic regression, a step-level logistic regression and also a BN. While the first approach is inadequate, the two others approaches both account for the hierarchical structure but to different estimates and interpretations. BN implementation showed that a child in a family in the poorest group has respectively 89%, 40% and 18% probabilities of having poor sanitation, being malnourished and having diarrhea. An advantage of the latter approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in a given state, given the states of the others. Although the BN considered here is very simple, the method can deal with more complicated models.Bayesian networks; hierarchical model; diarrhea infection; disease determinants; logistic regression

デンリョク キキ セツビ ゼツエン レッカ シンダン ノ タメ ノ デンジハ センシング システム ノ カイハツ

Partial discharge (PD) is a symptom of the insulation degradation of the electric power apparatus and facilities. In this study a new method was developed for finding the direction of arrival (DOA) of the electromagnetic waves emitted from PD using Bayesian Network. The time delay between two antennas computed from digital data generally has error because of the effect of sampling time. Therefore the angle of arrival estimated from the time delay also has the estimation error. The probabilistic method of reasoning with uncertain information was investigated. This method provides the probabilities of each angle based on Bayes’ Theorem. The proposed method is able to estimate the angle of arrival of the electromagnetic waves

Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled separately. The objective of this paper was to implement a simple version of their framework, and to propose an alternative procedure based on a Bayesian Network (BN). These approaches were illustrated in modeling the risk of diarrhea infection for 2740 children aged 0 to 59 months in Cameroon. The authors implemented a (naïve) logistic regression, a step-level logistic regression and also a BN. While the first approach is inadequate, the two others approaches both account for the hierarchical structure but to different estimates and interpretations. BN implementation showed that a child in a family in the poorest group has respectively 89%, 40% and 18% probabilities of having poor sanitation, being malnourished and having diarrhea. An advantage of the latter approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in a given state, given the states of the others. Although the BN considered here is very simple, the method can deal with more complicated models

Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled separately. The objective of this paper was to implement a simple version of their framework, and to propose an alternative procedure based on a Bayesian Network (BN). These approaches were illustrated in modeling the risk of diarrhea infection for 2740 children aged 0 to 59 months in Cameroon. The authors implemented a (naïve) logistic regression, a step-level logistic regression and also a BN. While the first approach is inadequate, the two others approaches both account for the hierarchical structure but to different estimates and interpretations. BN implementation showed that a child in a family in the poorest group has respectively 89%, 40% and 18% probabilities of having poor sanitation, being malnourished and having diarrhea. An advantage of the latter approach is that it enables one to determine the probability that a risk factor (and/or the outcome) is in a given state, given the states of the others. Although the BN considered here is very simple, the method can deal with more complicated models

Finding the bandit in a graph: Sequential search-and-stop

We consider the problem where an agent wants to find a hidden object that is randomly located in some vertex of a directed acyclic graph (DAG) according to a fixed but possibly unknown distribution. The agent can only examine vertices whose in-neighbors have already been examined. In this paper, we address a learning setting where we allow the agent to stop before having found the object and restart searching on a new independent instance of the same problem. Our goal is to maximize the total number of hidden objects found given a time budget. The agent can thus skip an instance after realizing that it would spend too much time on it. Our contributions are both to the search theory and multi-armed bandits. If the distribution is known, we provide a quasi-optimal and efficient stationary strategy. If the distribution is unknown, we additionally show how to sequentially approximate it and, at the same time, act near-optimally in order to collect as many hidden objects as possible.Comment: in International Conference on Artificial Intelligence and Statistics (AISTATS 2019), April 2019, Naha, Okinawa, Japa