Kuo-Chen Hung

On the Mitchell similarity measure and its application to pattern recognition

This paper is a response to the similarity measure and pattern recognition problem of Mitchell that was published in Pattern Recognition Letters, 2003. The purpose of this paper is threefold. First, we reviewed and revised her computation for similarity measures. Second, we proved that the similarity values for the one-norm should be larger than that for the two-norm for her pattern recognition problem. Third, we proposed a more scattered similarity measure to help researchers determine patterns. Our findings may shed light on the ongoing debate between Li and Cheng (2002) and Mitchell (2003).

An enhanced method and its application for fuzzy multi-criteria decision making based on vague sets

Ye [Ye Jun. Improved method of multicriteria fuzzy decision making based on vague sets. Computer-Aid Design 2007;39:164-9] presented an improved method to handle multi-criteria fuzzy decision-making problems based on vague set theory. He/She provided some functions to measure the degree of suitability of each alternative with respect to a set of criteria presented by vague values. However, in some cases, these functions do not give sufficient information about alternatives. Therefore, in this paper, an enhanced method is provided to measure the accuracy membership of each alternative so as to give additional information for the decision maker. In addition, to making computing and ranking results easier and to increase the recruiting productivity, a computer-based decision-support system is also developed, which may help to make a decision more efficiently.

A decision support system for engineering design based on an enhanced fuzzy MCDM approach

Design concept is an important wealth-creating activity in companies and infrastructure. However, the process of designing is very complex. Besides, the information required during the conceptual stage is incomplete, imprecise, and fuzzy. Hence, fuzzy set theory should be used to handle linguistic problem at this stage. This paper presents a fuzzy integrated approach to assess the performance of design concepts. And those criteria rating, relative weights and performance levels are captured by fuzzy numbers, and the overall performance of each alternative is calculated through an enhanced fuzzy weighted average (FWA) approach. A practical numerical example is provided to demonstrate the usefulness of this study. In addition, this paper, in order to make computing and ranking results easier to increase the recruiting productivity, develops a computer-based decision support system to help make decisions more efficiently.

Evaluating the manufacturing capability of a lithographic area by using a novel vague GERT

This study proposes a novel vague graphical evaluation and review technique (GERT) for evaluating wafer manufacturing yield and finishing time in lithographic area. Wafer manufacturing reparability in lithographic area often requires reentry operations. Besides, many manufacturing steps, variable products, and flows can cause many difficulties and uncertainties. Hence, lithographic area is always the bottleneck in wafer fab manufacturing procedures. The main purpose of this study is to resolve the reentry problem in wafer manufacturing by GERT, and to solve the uncertainty problem by using vague set. Based on the manufacturing procedure of lithographic area in the 300mm wafer fab, the algorithm steps for vague GERT are proposed, and a simple decision support system is developed to process the complex calculation procedure for providing more information to managers. We also hope to enhance the capability of lithographic area in order to improve overall system performance.

Note on inexact optimal solution of fuzzy mathematical programming

Abstract In Tang and Wang (Computers and Operations Research, pp. 413–422, 1997), they tried to develop an interactive approach for quadratic programming problems with fuzzy objective and resources. Based on their approach, they claimed that the exact optimal solution for inventory model under fuzzy objective and resources is useless. We will point out that their approach is questionable, from complete and contains severe drawbacks so that their comments about the exact optimal solution should be treated as seriously questionable.

Applying Least Squares Support Vector Regression with Genetic Algorithms for Radio-Wave Path-Loss Prediction in Suburban Environment

This paper presents least squares support vector regression with genetic algorithms (LS-SVRGA) models for the prediction of radio-wave path-loss in suburban environment. The least squares support vector regression (LS-SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the application of LS-SVR models in a radio-wave path-loss forecasting has not been widely investigated. This study aims at developing a LS-SVRGA model to forecast radio-wave path-loss data. Furthermore, in the LS-SVRGA model genetic algorithms is applied in order to select two parameters of LS-SVR models. In this study, four forecasting models, Egli, Walfisch and Bertoni (W&B), generalized regression neural networks (GRNN), and support vector regression with genetic algorithms (SVRGA) models are employed for forecasting the same data sets. Empirical results indicate that the LS-SVRGA outperforms others models in terms of forecasting accuracy. Thus, the LS-SVRGA model is an effective method for radio-wave path-loss forecasting in suburban environment.

AN EFFICIENT NEWTON-RAPHSON PROCEDURE FOR DETERMINING THE OPTIMAL INVENTORY REPLENISHMENT POLICY

ABSTRACT In general, using the Newton-Raphson method to find the root of an equation is a simple and popular algorithm. And it is a suitable process to locate the optimal ordering time for the inventory model taking into account the time value as mentioned in Dohi et al. [RAIRO: Oper. Res. 26 (1992) 1–14]. However, it sometimes cannot obtain the optimal solution because of the selection of a starting point. When the objective function has two roots, arbitrarily selecting a starting point may cause the iterated sequence not to converge to the optimal solution. Hence, in order to overcome this problem, we apply the Silver-Meal heuristic approach which produces a point as its starting point for the Newton-Raphson method to establish the steps of the algorithm. From the numerical examples, we show that the proposed method is more efficient than the bisection method that is cited by two recent papers.

Developing kernel intuitionistic fuzzy c-means clustering for e-learning customer analysis

This study develops the kernel intuitionistic fuzzy c-means clustering (KIFCM), and applies KIFCM in E-learning customer analysis. KIFCM combines intuitionistic fuzzy sets (IFSs) with kernel fuzzy c-means clustering (KFCM). The KIFCM has advantages of IFSs and KFCM which can effectively handle uncertain data and simultaneously map data to kernel space. The proposed KFCM has better performance than k-mean (KM) and fuzzy c-means (FCM) in numerical example. Furthermore, the study adopts the advanced clustering technology in E-learning customer clustering analysis, and analyses customer data based on clustering results by correlation analysis. The customer analysis result can provide for sales department, and assist to obtain customers learning tendency in E-learning platform.

Grey Model with Rolling Mechanism for Radio-Wave Path-Loss Forecasting in Suburban Environment

The grey prediction model, GM (1,1), with the property of processing with a minimum of data, has been successfully applied in various fields. However, applying grey prediction with rolling mechanism (GPRM) to predict radio-wave path-loss has not been widely investigated. Thus, this paper aims applying GPRM approach for the prediction of radio-wave path loss in suburban environment. Furthermore, a comparison has been discussed with traditional other radio-wave path-loss prediction approaches and the proposed approach. An illustrative example, we find that the GPRM method can effectively fitting the actual value than other current models. Consequently, this method can help designer to evaluate radio-wave path-loss in uncertain environment.

Hybrid support vector regression and GA/TS for radio-wave path-loss prediction

This paper presents support vector regression with hybrid genetic algorithms and tabu search (GA/TS) algorithms (SVRGA/TS) models for the prediction of radio-wave path-loss in suburban environment. The support vector regression (SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the application of SVR model in a radio-wave path-loss forecasting has not been widely investigated. This study aims at developing a SVRGA/TS model to forecast radio-wave pathloss data. Furthermore, the genetic algorithm and tabu search techniques have be applied to select important parameters for SVR model. In this study, four forecasting models, Egli, Walfisch and Bertoni (W&B), generalized regression neural networks (GRNN) and SVRGA/TS models are employed for forecasting the same data sets. Empirical results indicate that the SVRGA/TS outperforms other models in terms of forecasting accuracy. Thus, the SVRGA/TS model is an effective method for radio-wave path-loss forecasting in suburban environment.