Tzu Liang Tseng

Rough set approach to case-based reasoning application

Abstract The case-based reasoning becomes a novel paradigm that solves a new problem by remembering a previous similar situation and by reusing information and knowledge of that situation. In general, the traditional representation of cases is too simple and is not well structured to support the decision-making in organization. Furthermore, the similarity testing of case-based reasoning is very time-consuming. Therefore, a novel approach to represent the knowledge of cases in an explicit manner and to search similar cases in an efficient way is desired. An Extensible Markup Language-based representation formulated with the Zachman framework is proposed in this paper. Through a rough set based approach, case-based reasoning becomes more efficient and complexity of computation of the similarity testing is significantly reduced.

Rule induction based on an incremental rough set

The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. There are numerous studies of incremental rough set based approaches. However, these approaches are applied to traditional rough set based rule induction, which may generate redundant rules without focus, and they do not verify the classification of a decision table. In addition, these previous incremental approaches are not efficient in a large database. In this paper, an incremental rule-extraction algorithm based on the previous rule-extraction algorithm is proposed to resolve there aforementioned issues. Applying this algorithm, while a new object is added to an information system, it is unnecessary to re-compute rule sets from the very beginning. The proposed approach updates rule sets by partially modifying the original rule sets, which increases the efficiency. This is especially useful while extracting rules in a large database.

Delayed product differentiation : a design and manufacturing perspective

Delayed product differentiation (DPD) is a design concept for improving customer satisfaction and manufacturing performance. In this paper, a methodology for implementing the delayed product differentiation strategy in manufacturing is presented. Three design rules are suggested. The impact of delayed product differentiation strategy on the performance of a manufacturing system is quantified and incorporated in the product design. The problem of selecting designs to minimize the total differentiation and manufacturing cost is formulated and solved. The methodology presented in the paper is illustrated with examples.

Alternative rule induction methods based on incremental object using rough set theory

The rough set (RS) theory can be seen as a new mathematical approach to vagueness and is capable of discovering important facts hidden in that data. However, traditional rough set approach ignores that the desired reducts are not necessarily unique since several reducts could include the same value of the strength index. In addition, the current RS algorithms have the ability to generate a set of classification rules efficiently, but they cannot generate rules incrementally when new objects are given. Numerous studies of incremental approaches are not capable to deal with the problems of large database. Therefore, an incremental rule-extraction algorithm is proposed to solve these issues in this study. Using this algorithm, when a new object is added up to an information system, it is unnecessary to re-compute rule sets from the very beginning, which can quickly generate the complete but not repetitive rules. In the case study, the results show that the incremental issues of new data add-in are resolved and a huge computation time is saved.

Capitalizing on knowledge: a novel approach to crucial-knowledge determination

Corporation managers make informed decisions based upon a combination of judgment and knowledge from various departments such as marketing, sales, research, development, manufacturing, and finance. Ideally, all relevant knowledge should be brought together before judgment is exercised. However, determining the knowledge requirements and obtaining pertinent, consistent, and up-to-date knowledge across a large company is a complex process. Crucial-knowledge determination is a complex process: specifically the identification of crucial knowledge and knowledge requirements. In this paper, a methodology is developed for modeling the knowledge requirements and the associated tasks for collecting the knowledge simultaneously. This methodology provides a valuable contribution in knowledge-management systems by defining a major plan for discovering issues-oriented knowledge. One procedure and one heuristic algorithm are illustrated with numerical examples.

A rough set based approach to distributor selection in supply chain management

Distributors selection is an important issue in Supply chain management, particularly in the current competitive environment. The current research works provide only conceptual, descriptive, and simulation results, focusing mainly on firm resources and general marketing factors. The selection and evaluation of distributors generally incorporate qualitative information; however, analyzing qualitative information is difficult by standard statistical techniques. Consequently, a more suitable approach is desired. In this paper, a method based on Rough set theory, which has been recognized as a powerful tool in dealing with qualitative data in the literature, is introduced and modified for preferred distributor selection. We derived certain decision rules which are able to facilitate distributor selection and identified several significant features based on an empirical study conducted in China.

A Novel Hybrid Approach Based on Wavelet Transform and Fuzzy ARTMAP Networks for Predicting Wind Farm Power Production

This paper presents a novel hybrid intelligent algorithm based on the wavelet transform (WT) and fuzzy ARTMAP (FA) network for forecasting the power output of a wind farm utilizing meteorological information such as wind speed, wind direction, and temperature. The prediction capability of the proposed hybrid WT +FA model is demonstrated by an extensive comparison with a benchmark persistence method, other soft computing models, and hybrid models as well. The test results show a significant improvement in forecasting error through the application of a proposed hybrid WT +FA model. The proposed hybrid wind power forecasting strategy is applied to real-life data from Kent Hill wind farm located in New Brunswick, Canada.

XML-based modeling of corporate memory

Analytical knowledge is distributed among domain experts, analysts, and data-storage systems. Extracting such knowledge from databases is of interest to corporations. The traditional top-down development of corporate memory is not appropriate for modern organizations because of the distributed nature of information. This paper proposes models of analytical knowledge and new ways of developing corporate memory by using an extensible markup language (XML). It aims at efficient exploration of useful knowledge by mining the Web. The proposed approach of modeling analytical knowledge is explicit and sharable. The concepts introduced in the paper have been demonstrated with a manufacturing case study.

A novel hybrid approach based on wavelet transform and fuzzy ARTMAP network for predicting wind farm power production

This paper presents a novel hybrid intelligent algorithm based on the wavelet transform (WT) and fuzzy ARTMAP (FA) network for forecasting the power output of a wind farm utilizing meteorological information such as wind speed, wind direction, and temperature. The prediction capability of the proposed hybrid WT +FA model is demonstrated by an extensive comparison with a benchmark persistence method, other soft computing models, and hybrid models as well. The test results show a significant improvement in forecasting error through the application of a proposed hybrid WT +FA model. The proposed hybrid wind power forecasting strategy is applied to real-life data from Kent Hill wind farm located in New Brunswick, Canada.

A novel approach to predict surface roughness in machining operations using fuzzy set theory

Abstract The increase of consumer needs for quality metal cutting related products with more precise tolerances and better product surface roughness has driven the metal cutting industry to continuously improve quality control of metal cutting processes. In this paper, two different approaches are discussed. First, design of experiments (DOE) is used to determine the significant factors and then fuzzy logic approach is presented for the prediction of surface roughness. The data used for the training and checking the fuzzy logic performance is derived from the experiments conducted on a CNC milling machine. In order to obtain better surface roughness, the proper sets of cutting parameters are determined before the process takes place. The factors considered for DOE in the experiment were the depth of cut, feed rate per tooth, cutting speed, tool nose radius, the use of cutting fluid and the three components of the cutting force. Finally the significant factors were used as input factors for fuzzy logic mechanism and surface roughness is predicted with empirical formula developed. Test results show good agreement between the actual process output and the predicted surface roughness.