13,068 research outputs found

    Multi-segment multi-criteria approach for selection of trenchless construction methods

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    The research work presented in this thesis has two broad objectives as well as five individual goals. The first objective is to search and determine the minimum cost and corresponding goodness-of-fit by using a different combination of methods that are capable of resolving the problem that exists in multiple segments. This approach can account for variations in unit price and the cost of the design and the inspection associated with multiple methods. The second objective is to calculate the minimum risk for the preferred solution set. The five individual goals are 1) reduction in total cost, 2) application of Genetic Algorithm (GA) for construction method selection with focus on trenchless technology, 3) application of Fuzzy Inference System for likelihood of risk, 4) risk assessment in HDD projects, and 5) Carbon footprint calculation. In most construction projects, multiple segments are involved in a single project. However, there is no single model developed yet to aid the selection of appropriate method(s) based on the consideration of multiple-criteria. In this study, a multi-segment conceptualizes a combination of individuals or groups of mainlines, manholes, and laterals. Multi-criteria takes into account the technical viability, direct cost, social cost, carbon footprint, and risks in the pipelines. Three different segments analyzed are 1) an 8 inch diameter, 280 foot long gravity sewer pipe, 2) a 21 inch diameter, 248 foot long gravity sewer pipe, and 3) a 12 inch diameter, 264 foot long gravity sewer pipe. It is found that GA would not only eliminate the shortcomings of competing mathematical approaches, but also enables complex optimization scenarios to be examined quickly to the optimization of multi-criteria for multi-segments. Furthermore, GA follows a uniform iterative procedure that is easy to code and decode for running the algorithm. Any trenchless installation project is associated with some level of risk. Due to the underground installation of trenchless technologies, the buried risk could be catastrophic if not assessed promptly. Therefore, risk management plays a key role in the construction of utilities. Conventional risk assessment approach quantifies risk as a product of likelihood and severity of risk, and does not consider the interrelation among different risk input variables. However, in real life installation projects, the input factors are interconnected, somewhat overlapped, and exist with fuzziness or vagueness. Fuzzy logic system surpasses this shortcoming and delivers the output through a process of fuzzification, fuzzy inference, fuzzy rules, and defuzzification. It is found in the study that Mamdani FIS has the potential to address the fuzziness, interconnection, and overlapping of different input variables and compute an overall risk output for a given scenario which is beyond the scope of conventional risk assessment

    A hybrid traceability technology selection approach for sustainable food supply chains

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    Traceability technologies have great potential to improve sustainable performance in cold food supply chains by reducing food loss. In existing approaches, traceability technologies are selected either intuitively or through a random approach, that neither considers the trade-off between multiple cost–benefit technology criteria nor systematically translates user requirements for traceability systems into the selection process. This paper presents a hybrid approach combining the fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with integer linear programming to select the optimum traceability technologies for improving sustainable performance in cold food supply chains. The proposed methodology is applied in four case studies utilising data collected from literature and expert interviews. The proposed approach can assist decision-makers, e.g., food business operators and technology companies, to identify what combination of technologies best suits a given food supply chain scenario and reduces food loss at minimum cost.Cambridge Trust and Commonwealth Scholarship Commission

    Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

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    Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy

    Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

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    Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

    A Multiobjective Evolutionary Conceptual Clustering Methodology for Gene Annotation Within Structural Databases: A Case of Study on the Gene Ontology Database

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    Current tools and techniques devoted to examine the content of large databases are often hampered by their inability to support searches based on criteria that are meaningful to their users. These shortcomings are particularly evident in data banks storing representations of structural data such as biological networks. Conceptual clustering techniques have demonstrated to be appropriate for uncovering relationships between features that characterize objects in structural data. However, typical con ceptual clustering approaches normally recover the most obvious relations, but fail to discover the lessfrequent but more informative underlying data associations. The combination of evolutionary algorithms with multiobjective and multimodal optimization techniques constitutes a suitable tool for solving this problem. We propose a novel conceptual clustering methodology termed evolutionary multiobjective conceptual clustering (EMO-CC), re lying on the NSGA-II multiobjective (MO) genetic algorithm. We apply this methodology to identify conceptual models in struc tural databases generated from gene ontologies. These models can explain and predict phenotypes in the immunoinflammatory response problem, similar to those provided by gene expression or other genetic markers. The analysis of these results reveals that our approach uncovers cohesive clusters, even those comprising a small number of observations explained by several features, which allows describing objects and their interactions from different perspectives and at different levels of detail.Ministerio de Ciencia y Tecnología TIC-2003-00877Ministerio de Ciencia y Tecnología BIO2004-0270EMinisterio de Ciencia y Tecnología TIN2006-1287

    Layer of protection analysis applied to ammonia refrigeration systems

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    Ammonia refrigeration systems are widely used in industry. Demand of these systems is expected to increase due to the advantages of ammonia as refrigerant and because ammonia is considered a green refrigerant. Therefore, it is important to evaluate the risks in existing and future ammonia refrigeration systems to ensure their safety. LOPA (Layer of Protection Analysis) is one of the best ways to estimate the risk. It provides quantified risk results with less effort and time than other methods. LOPA analyses one cause-consequence scenario per time. It requires failure data and PFD (Probability of Failure on Demand) of the independent protection layers available to prevent the scenario. Complete application of LOPA requires the estimation of the severity of the consequences and the mitigated frequency of the initiating event for risk calculations. Especially in existing ammonia refrigeration systems, information to develop LOPA is sometimes scarce and uncertain. In these cases, the analysis relies on expert opinion to determine the values of the variables required for risk estimation. Fuzzy Logic has demonstrated to be useful in this situation allowing the construction of expert systems. Based on fuzzy logic, the LOPA method was adapted to represent the knowledge available in standards and good industry practices for ammonia refrigeration. Fuzzy inference systems were developed for severity and risk calculation. Severity fuzzy inference system uses the number of life threatening injuries or deaths, number of injuries and type of medical attention required to calculate the severity risk index. Frequency of the mitigated scenario is calculated using generic data for the initiating event frequency and PFD of the independent protection layers. Finally, the risk fuzzy inference system uses the frequency and severity values obtained to determine the risk of the scenario. The methodology was applied to four scenarios. Risk indexes were calculated and compared with the traditional approach and risk decisions were made. In conclusion, the fuzzy logic LOPA method provides good approximations of the risk for ammonia refrigeration systems. The technique can be useful for risk assessment of existing ammonia refrigeration systems

    An approach to automatic learning assessment based on the computational theory of perceptions

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    E-learning systems output a huge quantity of data on a learning process. However, it takes a lot of specialist human resources to manually process these data and generate an assessment report. Additionally, for formative assessment, the report should state the attainment level of the learning goals defined by the instructor. This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of the learning process and implement the automated generation of an assessment report. GLMP is based on fuzzy logic and the computational theory of perceptions. This technique is useful for implementing complex assessment criteria using inference systems based on linguistic rules. Apart from the grade, the model also generates a detailed natural language progress report on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. This is illustrated by applying the model to the assessment of Dijkstra’s algorithm learning using a visual simulation-based graph algorithm learning environment, called GRAPH
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