The fit of military apparel affects soldiers' mobility, protection, and musculoskeletal health, while poor fit inflates logistics costs through excess inventory and exchanges. Conventional sizing relies on a bivariate percentile grid over one or two control dimensions, which cannot reflect multivariate body-shape diversity. This study formulates sizing-system design as a multivariate p-median (facility-location) problem and develops a data-driven, explainable framework using the publicly available 2012 U.S. Army Anthropometric Survey (ANSUR II; 6,068 personnel, 93 measurements). Over a ten-dimension garment-fit space with grading- based tolerances, three size generators—the traditional percentile grid, k-means (centroid prototypes), and p-median/k-medoids (real-body prototypes)—are compared on a size-count versus fit trade-off curve. The data-driven generators dominate the percentile grid: k-means attains with only 15 sizes a higher mean dimensional accommodation than the grid achieves with 30 sizes, and reduces the mean misfit ratio by about 14% at an equal number of sizes, while p-median offers interpretable prototypes that correspond to actual soldiers usable as fit models. A measurement-minimization pipeline then reconstructs the full fit space from a few low-cost measurements using machine learning and builds the sizing system on the reconstructed bodies, evaluating accommodation on the true dimensions to avoid circularity; three measurements (weight, neck circumference, stature) recover 97% of the full-measurement accommodation, and two recover 94.5%. Permutation importance and SHAP (SHapley Additive ex- Planations) identify weight and neck circumference as the most informative measurements. A bootstrap analysis shows that gender-integrated sizing is marginally but significantly better than gender-separated sizing, justifying a unified system for stock-keeping simplicity.

본 연구에서는 커피(C. Arabica)의 FT-IR 스펙트럼 데이터 를 기반한 다변량통계분석을 이용한 대사체 분석을 통해 품종 식별을 하여 육종 연구에 기초자료로 활용하고자 한다. 1. FT-IR 스펙트럼 데이터를 이용한 PCA(principal component analysis), PLS-DA(partial least square discriminant analysis) 그리고 HCA(hierarchical clustering analysis) 분석을 통해 품종 분류가 가능하였다. 2. 커피 품종들은 FT-IR 스펙트럼 부위인 1700-1500-1 (Amide I 과 II을 포함하는 아미노산 및 단백질계열의 화합물 들), 1500-1300-1 (phosphodiester group을 포함한 핵산 및 인지질의 정보), 1100-950cm-1 (단당류나 복합 다당류를 포함하는 carbohydrates 계열의 화합물)에서 질적, 양적 정보의 차이가 나타났다. 3. PCA 상에 나타난 8품종의 커피 품종이 각각 그룹을 형성하였다. 그 중 ‘Caturra’와 ‘Mahsellesa’ 품종은 각각의 그룹을 나타내면서 C. arabica 종에서도 다른 대사체 정보를 나타내는 것으로 확인하였고, ‘Catuai’, ‘CR-95’, ‘Geisra’, ‘Obata’, ‘Vemecia’ 그리고 ‘non’ 품종은 유사한 대사체 정보를 나타내는 것으로 확인하였다. 4. PLS-DA 분석의 경우 PCA 분석 보다 커피 품종간 식별이 뚜렷하게 나타났다. 5. 본 연구에서 확립된 대사체 수준에서 커피의 품종 식별 기술은 품종, 계통의 신속한 선발 수단으로 활용이 가능할 것으로 기대되며 육종을 통한 품종개발 가속화에 기여 할 수 있을 것으로 예상된다.

Mahalanobis Taguchi System (MTS) is a pattern information technology, which has been used in different diagnostic applications make quantitative decisions by constructing a multivariate measurement scale using data analytic methods without any assumption

Mahalanobis Taguchi System (MTS) is a pattern information technology, which 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. The MTS performs Taguchi's fractional factorial design based on the Mahahlanobis distance as a performance metric. In this work, MTS used for analyzing Wisconsin Breast Cancer data which have 10 attributes. With the data, which have 10 different tests, collected from patients determine who has cancer or not. At this research MTS used for reducing the number of test with define the relationship between each attrivute and diagnosis result. The accuracy of diagnosis compare to previous research.

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.

As we understand it, Process Capability indices are intended to provide single-number assessments of ability to meet specification limits on quality characteristics of interest. As a consequence of the varied ways in which PCIs are used, there have been two natural lines of research work: ① studies on the properties of PCIs and their estimators in many different environments; ② construction of new PCIs purporting to have better properties in certain circumstances. The most of existing process capability indices are concerned with the single variable. But, in many cases, a quality characteristic is composed with several factors. In that case, we want to know the integrated process capability of a quality characteristic not those of each factor. In this paper, we proposed a new multivariate system process capability index called MSPCI:SCpsk which is the geometric mean of performance measure Cpsk'S, and will be used as the criterion to assess multiple response process designs. Numerical illustration is done for SCpsk, Cp(f), Cp, Cpk, Cpm, and Cpsk.