Data mining has emerged to address the problem of transforming data into useful knowledge. Although most data mining techniques, such as the use of association rules, may substantially reduce the search effort over large data sets, often, the consequential outcomes surpass the amount of information humanly manageable. On the other hand, important association rules may be overlooked owing to the setting of the support threshold, which is a very subjective metric, but rooted in most data mining techniques. This paper presents a study on the effects, in terms of precision and recall, of using a data preparation technique, called SemPrune, which is built on domain ontology. SemPrune is intended for pre- and post-processing phases of data mining. Identifying generalization/specialization relations, as well as composition/decomposition relations, is the key to successfully applying SemPrune.
Early failure detection in motor pumps is an important issue in prediction maintenance. An efficient condition-monitoring scheme is capable of providing warning and predicting the faults at early stages. Usually, this task is executed by humans. The logical progression of the condition-monitoring technologies is the automation of the diagnostic process. To automate the diagnostic process, intelligent diagnostic systems are used. Many researchers have explored artificial intelligence techniques to diagnose failures in general. However, all papers found in literature are related to a specific problem that can appear in many different machines. In real applications, when the expert analyzes a machine, not only one problem appears, but more than one problem may appear together. So, it is necessary to propose new methods to assist diagnosis looking for a set of occurring fails. For some failures, there are not sufficient instances that can ensure good classifiers induced by available machine learning algorithms. In this work, we propose a method to assist fault diagnoses in motor pumps, based on vibration signal analysis, using expert systems. To attend the problems related to motor pump analyses, we propose a parametric net model for multi-label problems. We also show a case study in this work, showing the applicability of our proposed method.Please use the following format when citing this chapter:
Design consists of analyzing scenarios and proposing artifacts, obeying the initial set of requirements that lead from initial to goal state. Finding or creating alternative solutions, analyzing them, and selecting the best one are expected steps in the designer's decision making process. Very often, not a sole designer, but a team of them is engaged in the design process, sharing their expertise and responsibility to achieve optimum projects. In a design team, most conflicts occur due to misunderstanding of one's assessment of specifications and contexts. Decision explanations play a key role in teamwork success. Designers are rational agents trained to follow rational methods. Acceptable justifications include value function, requirements, constraints, and criteria. Generally, explanations are delivered in a multimedia fashion, composed of text, graphics and gestures, to provide the audience the ability to perceive what was contextually imagined. The more spatial the reasoning is, the richer the explanation channel should be. This paper presents CineADD, a design explanation generation model based on cinema techniques such as animation, scripting, editing, and camera movements. The idea is to provide designers with a tool for describing the way their projects should be visually explained, as in a movie. Designers develop their projects in an active design document environment. Rationale is captured as a design model, so explanations can be generated instead of retrieved. The captured design model serves as a base to visually reconstruct design, giving emphasis and guidance by using movie storytelling techniques. CineADD was implemented for the domain of oil pipeline layout showing the feasibility of this approach. We expect CineADD to become a commodity attachable to any intelligent CAD system.
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