• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.1
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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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.

• The Journal of the Acoustical Society of Korea
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• v.29 no.1E
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• pp.45-53
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• 2010

In this paper, we present the use of the Features Weighted Mahalanobis Distance (FWMD) in improving the performance of Likelihood Maximizing Beamforming (Limabeam) algorithm in speech recognition for microphone array. The proposed approach is based on the replacement of the traditional distance measure in a Gaussian classifier with adding weight for different features in the Mahalanobis distance according to their distances after the variance normalization. By using Features Weighted Mahalanobis Distance for Limabeam algorithm (FWMD-Limabeam), we obtained correct word recognition rate of 90.26% for calibrate Limabeam and 87.23% for unsupervised Limabeam, resulting in a higher rate of 3% and 6% respectively than those produced by the original Limabearn. By implementing a HM-Net speech recognition strategy alternatively, we could save memory and reduce computation complexity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.1
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• pp.41-47
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• 2007

Mahalanobis Taguchi-System (MTS) is a pattern information technology, which has been used in different diagnostic ap plications to make quantitative decisions by constructing a multivariate system using data analytic methods without any as sumption regarding statistical distribution. MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric In this work, MTS used for analyzing automotive brake feeling system, which measured as a brake feel index (BFI) from 9 attributes. The automobile which has a good BFI score treated as a normal group for constructing Mahalanobis space. The results of this research show that two attributes (Pre load & Max deceleration) have a minus gain value and can be removed from further analysis. The difference of MD value between using all 9 attributes and just using significant attribute compared.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.122-129
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• 2010

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.300-310
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• 2005

Mahalanobis Taguchi System (MTS) is developed by Genishi Taguchi as a part of his quality engineering methodology. The basic idea of Taguchi's quality engineering is looking for the way of effectiveness of analyzing multivariate system. In the MTS, with the standardized variables of healthy normal data, Mahalanobis Distance(MD) calculated and that can be discriminate between normal and abnormal objects. If this discrimination process is successful, next step is optimization which is try to reduce number of attributes by neglecting less effective attributes to MD. Orthogonal Array (OA) and Signal to Noise ratio (S/N) are used to evaluate the amount contribution of each attribute to the MD. Wisconsin Breast Cancer study, from machining learning repository at University of California at Irvine, used for examining the discriminant ability of MTS.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.1
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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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.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.99-107
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• 2009

Mahalanobis Taguchi-System (MTS) has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate system using data analytic methods without any assumption regarding statistical distribution. MTS performs Taguchi's fractional factorial design based on the Mahahlanobis distance as a performance metric. In this study, MTS used for analyzing automotive ride satisfaction, which measured as a CSR(Customer Satisfaction Rating). The automobile which has a good CSR score treated as a normal group for constructing Mahalanobis space. The results of this research show that two attribute (Impact Hardness and Memory Shake) have a minus gain value and can be removed from further analysis. With the linear regression model, the difference of CSR between using all 6 attributes and just using significant 4 attributes compared.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.216-221
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• 2010

In this paper, we propose speaker verification method using Support Vector Machine (SVM) kernel with Gaussian Mixture Model (GMM)-supervector based on the Mahalanobis distance. The proposed GMM-supervector SVM kernel method is combined GMM with SVM. The GMM-supervectors are generated by GMM parameters of speaker and other speaker utterances. A speaker verification threshold of GMM-supervectors is decided by SVM kernel based on Mahalanobis distance to improve speaker verification accuracy. The experimental results for text-independent speaker verification using 20 speakers demonstrates the performance of the proposed method compared to GMM, SVM, GMM-supervector SVM kernel based on Kullback-Leibler (KL) divergence, and GMM-supervector SVM kernel based on Bhattacharyya distance.