• 대한산업공학회지
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• 제39권5호
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• pp.393-402
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• 2013

The Mahalanobis-Taguchi System (MTS) is a diagnostic and predictive method for multivariate data. In the MTS, the Mahalanobis space (MS) of reference group is obtained using the standardized variables of normal data. The Mahalanobis space can be used for multi-class classification. Once this MS is established, the useful set of variables is identified to assist in the model analysis or diagnosis using orthogonal arrays and signal-to-noise ratios. And other several techniques have already been used for classification, such as linear discriminant analysis and logistic regression, decision trees, neural networks, etc. The goal of this case study is to compare the ability of the Mahalanobis-Taguchi System and logistic regression using a data set.

• 품질경영학회지
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• 제33권2호
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• pp.64-73
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• 2005

In this paper, we introduce MTS(Mahalanobis-Taguchi System) Method which is suggested Dr. Taguchi in later 1990. We apply MTS Method for Quality Competitive Appraisal System(QCAS) Model which is executived from 1997 by Agency for Technology and Standards, Ministry of Commerce, Industry and Energy(MOCIE). We can measure company Quality competitive by using MTS. MTS Measure can not be compared statistical sum by calculated QCAS. MTS can be possible distinct subtle which can not distinct using statistical sum. Also MTS Method can seek more strong effect factor among of many factor. If A Company use MTS Method, can find vital factor and level of destination.

• 산업경영시스템학회지
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• 제35권4호
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• pp.10-15
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• 2012

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is diagnosis of the spondylolisthesis from biomedical data that is derived from the shape and orientation of the pelvis and lumbar spine. The data set has six attributes including pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis and two class including normal and abnormal. From University of California at Irvine machine learning repository, 100 normal and 150 spondylolisthesis patient's data were used for this study. Mahalanobis Taguchi System (MTS) application process and the diagnosis results were described in this paper.

• 한국기계가공학회지
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• 제15권1호
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• pp.95-102
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• 2016

Wrinkle, spring-back, and fracture are major defects frequently found in the sheet metal forming process, and the reduction of such defects is difficult as they are affected by uncontrollable factors, such as variations in properties of the incoming material and process parameters. Without any countermeasures against these issues, attempts to reduce defects through optimal design methods often lead to failure. In this research, a new multi-attribute robust design methodology, based on the Mahalanobis Taguchi System (MTS), is presented for reducing the possibilities of wrinkle, spring-back, and fracture. MTS performs experimentation, based on the orthogonal array under various noise conditions, uses the SN ratio of the Mahalanobis distance as a performance metric. The proposed method is illustrated through a robust design of the sheet metal forming process of a cross member of automotive body.

• 한국기계가공학회지
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• 제16권1호
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• pp.1-8
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• 2017

Warpage is a major defect frequently found in the injection molding process, and the reduction of warpage is a very challenging problem because of the uncontrollable factors, such as variations in the process parameters. Without any countermeasure against these noises, attempts to reduce the defects often lead to failure. In this research, a new robust design methodology, based on the Mahalanobis Taguchi System (MTS) to reduce warpage, is presented. The MTS performs the orthogonal array experiments and uses the signal-to-noise (SN) ratio of the Mahalanobis distance as a performance metric. The validity of the proposed method is illustrated through an optimal design of the injection molding process of a CPU base plate.

• International Journal of Quality Innovation
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• 제5권2호
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• pp.1-16
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• 2004

Credit scoring is widely used to make credit decisions, to reduce the cost of credit analysis and enable faster decisions. However, traditional credit scoring models do not account for the influence of noises. This study proposes a robust credit scoring system based on Mahalanobis-Taguchi System (MTS). The MTS, primary proposed by Taguchi, is a diagnostic and forecasting method using multivariate data. The proposed approach's effectiveness is demonstrated by using real case data from a large Taiwanese bank. The results reveal that the robust credit scoring system can be successfully implemented using MTS technique.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.311-317
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• 2007

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. And, optimal characteristic values influenced on the result of the SQ evaluation were derived by signal to noise ratio(SN ratio) of the Taguchi method. Finally, the new method to evaluate SQ is constructed using Mahalanobis-Taguchi system(MTS). Furthermore, the MTS method for SQ evaluation was compared by the result of SQ grade table at the previous study and their virtues and faults introduced.

• 한국소음진동공학회논문집
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• 제18권4호
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• pp.393-399
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• 2008

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. And, optimal characteristic values influenced on the result of the SQ evaluation were derived by signal to noise ratio(SN ratio) of the Taguchi method. Finally, the new method to evaluate SQ is constructed using Mahalanobis-Taguchi system(MTS). Furthermore, the MTS method for SQ evaluation was compared by the result of SQ grade table at the previous study and their virtues and faults introduced.

• 응용통계연구
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• 제25권4호
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• pp.589-603
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• 2012

Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.

• Journal of Information Processing Systems
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• 제15권3호
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• pp.682-693
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• 2019

The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.