6,584 research outputs found

    Adaptation of WASH Services Delivery to Climate Change and Other Sources of Risk and Uncertainty

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    This report urges WASH sector practitioners to take more seriously the threat of climate change and the consequences it could have on their work. By considering climate change within a risk and uncertainty framework, the field can use the multitude of approaches laid out here to adequately protect itself against a range of direct and indirect impacts. Eleven methods and tools for this specific type of risk management are described, including practical advice on how to implement them successfully

    Comparative Analysis of Naive Bayes and Tree Augmented Naive Bayes Models

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    Naive Bayes and Tree Augmented Naive Bayes (TAN) are probabilistic graphical models usedfor modeling huge datasets involving lots of uncertainties among its various interdependentfeature sets. Some of the most common applications of these models are image segmentation,medical diagnosis and various other data clustering and data classification applications. Aclassification problem deals with identifying to which category a particular instance belongs to,based on previous knowledge acquired by analysis of various such instances. The instances aredescribed using a set of variables called attributes or features. A Naive Bayes model assumes thatall the attributes of an instance are independent of each other given the class of that instance.This is a very simple representation of the system, but the independence assumptions made inthis model are incorrect and unrealistic. The TAN model improves on the Naive Bayes model byadding one more level of interaction among attributes of the system. In the TAN model, everyattribute is dependent on its class and one other attribute from the feature set. Since this modelincorporates the dependencies among the attributes, it is more realistic than a Naive Bayesmodel. This project analyzes the performance of these two models on various datasets. The TANmodel gives better performance results if there are correlations between the attributes but theperformance is almost the same as that of Naive Bayes model, if there are not enoughcorrelations between the attributes of the system

    Uncertainty analysis in product service system: Bayesian network modelling for availability contract

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    There is an emerging trend of manufacturing companies offering combined products and services to customers as integrated solutions. Availability contracts are an apt instance of such offerings, where product use is guaranteed to customer and is enforced by incentive-penalty schemes. Uncertainties in such an industry setting, where all stakeholders are striving to achieve their respective performance goals and at the same time collaborating intensively, is increased. Understanding through-life uncertainties and their impact on cost is critical to ensure sustainability and profitability of the industries offering such solutions. In an effort to address this challenge, the aim of this research study is to provide an approach for the analysis of uncertainties in Product Service System (PSS) delivered in business-to-business application by specifying a procedure to identify, characterise and model uncertainties with an emphasis to provide decision support and prioritisation of key uncertainties affecting the performance outcomes. The thesis presents a literature review in research areas which are at the interface of topics such as uncertainty, PSS and availability contracts. From this seven requirements that are vital to enhance the understanding and quantification of uncertainties in Product Service System are drawn. These requirements are synthesised into a conceptual uncertainty framework. The framework prescribes four elements, which include identifying a set of uncertainties, discerning the relationships between uncertainties, tools and techniques to treat uncertainties and finally, results that could ease uncertainty management and analysis efforts. The conceptual uncertainty framework was applied to an industry case study in availability contracts, where each of the four elements was realised. This application phase of the research included the identification of uncertainties in PSS, development of a multi-layer uncertainty classification, deriving the structure of Bayesian Network and finally, evaluation and validation of the Bayesian Network. The findings suggest that understanding uncertainties from a system perspective is essential to capture the network aspect of PSS. This network comprises of several stakeholders, where there is increased flux of information and material flows and this could be effectively represented using Bayesian Networks

    A problem-structuring model for analyzing transportation–environment relationships

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    This is the post-print version of the final paper published in European Journal of Operational Research. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.This study discusses a decision support framework that guides policy makers in their strategic transportation related decisions by using multi-methodology. For this purpose, a methodology for analyzing the effects of transportation policies on environment, society, economy, and energy is proposed. In the proposed methodology, a three-stage problem structuring model is developed. Initially, experts’ opinions are structured by using a cognitive map to determine the relationships between transportation and environmental concepts. Then a structural equation model (SEM) is constructed, based on the cognitive map, to quantify the relations among external transportation and environmental factors. Finally the results of the SEM model are used to evaluate the consequences of possible policies via scenario analysis. In this paper a pilot study that covers only one module of the whole framework, namely transportation–environment interaction module, is conducted to present the applicability and usefulness of the methodology. This pilot study also reveals the impacts of transportation policies on the environment. To achieve a sustainable transportation system, the extent of the relationships between transportation and the environment must be considered. The World Development Indicators developed by the World Bank are used for this purpose

    A Bayesian Approach to Sensor Placement and System Health Monitoring

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    System health monitoring and sensor placement are areas of great technical and scientific interest. Prognostics and health management of a complex system require multiple sensors to extract required information from the sensed environment, because no single sensor can obtain all the required information reliably at all times. The increasing costs of aging systems and infrastructures have become a major concern, and system health monitoring techniques can ensure increased safety and reliability of these systems. Similar concerns also exist for newly designed systems. The main objectives of this research were: (1) to find an effective way for optimal functional sensor placement under uncertainty, and (2) to develop a system health monitoring approach with both prognostic and diagnostic capabilities with limited and uncertain information sensing and monitoring points. This dissertation provides a functional/information --based sensor placement methodology for monitoring the health (state of reliability) of a system and utilizes it in a new system health monitoring approach. The developed sensor placement method is based on Bayesian techniques and is capable of functional sensor placement under uncertainty. It takes into account the uncertainty inherent in characteristics of sensors as well. It uses Bayesian networks for modeling and reasoning the uncertainties as well as for updating the state of knowledge for unknowns of interest and utilizes information metrics for sensor placement based on the amount of information each possible sensor placement scenario provides. A new system health monitoring methodology is also developed which is: (1) capable of assessing current state of a system's health and can predict the remaining life of the system (prognosis), and (2) through appropriate data processing and interpretation can point to elements of the system that have or are likely to cause system failure or degradation (diagnosis). It can also be set up as a dynamic monitoring system such that through consecutive time steps, the system sensors perform observations and send data to the Bayesian network for continuous health assessment. The proposed methodology is designed to answer important questions such as how to infer the health of a system based on limited number of monitoring points at certain subsystems (upward propagation); how to infer the health of a subsystem based on knowledge of the health of the main system (downward propagation); and how to infer the health of a subsystem based on knowledge of the health of other subsystems (distributed propagation)

    Ports’ congestion factors: Applying risk analysis as a problem identification tool to figure out the interrelated complex factors that contribute to the problem by assigning weights and probabilities to each factor

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    Ports’ congestion is a recurring problem that is caused by several factors. There are several past attempts to resolve ports’ congestion by applying governing and constructional reforms. Due to divergence and instability of congestion causal factors, the available studies and solutions are specific to individual ports. The main objective of this master thesis is to apply risk analysis as a problem identifier to figure out the interrelated complex factors that contribute to the congestion problem by assigning weights and probabilities to each factor. The research is based on qualitative data from secondary sources to gather all available information about the causal factors for ports’ congestion. A structured questionnaire was carried out and sent to various ports’ managers to figure out the most effective causal factors globally, as a means of validation for the secondary data and to ensure that the data reflect the current congestions causing factors from the port’s users themselves. Congestion’s factors can be human, technical, or organizational with different magnitudes based on the port’s features and capabilities. They are vulnerable to sudden and quick changes due to their interrelated and complex structure. Bayesian network (BN) is a risk analysis tool that fits the complex and changing scenarios of the congestion problem. It can incorporate the newly received information into the pre-established network of causal factors for port congestion. BN managed to reflect the cause-and-effect relationship between the causal factors and by means of appropriate software, the effect of any new event on congestion occurrence is visualized. Furthermore, the application of BN needs to be integrated into the port information management system as a permanent warning system that predicts the congestion and virtually shows the results of applying suggested solutions before applying it. Keywords: port congestion, congestion factors, Bayesian network, port productivit
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