• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.50
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• pp.171-181
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• 1999

Taguchi's robust design methodology has focus only a single characteristic or response, but the quality of most products is seldom defined by a characteristics, and is rather the composite of a family of characteristics which are often interrelated and nearly always measured in a variety of units. The multiple characteristics problem is how to compromise the conflicts among the selected levels of the design parameters for each individual characteristic. In this paper, Methodology using SN ratio optimized by univariate technique is proposed and a parameter design procedure to achive the optimal compromise among several different response variables is developed. One new case studies are solved by the proposed method and the results are compared with ones by the sum of SN ratios, the expected weighted loss, the desirability function, and EXTOPSIS model.

• Journal of Integrative Natural Science
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• v.13 no.2
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• pp.76-82
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• 2020

If normality of an observed data is not a viable assumption, we can carry out normal-theory analyses by suitable transforming data. Power transformation by Box and Cox, one of the transformation methods, is derived the power which maximized the likelihood function. But it doesn't induces the closed form in mathematical analysis. In this paper, we compose some R the syntax of which is easier than other statistical packages for deriving the power with using numerical methods. Also, by using R, we show the transformed data approximately distributed the normal through Q-Q plot in univariate and bivariate cases with some examples. Finally, we present the value of a goodness-of-fit statistic(AD) and its p-value for normal distribution. In the similar procedure, this method can be extended to more than bivariate case.

• Korean Journal of Health Education and Promotion
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• v.15 no.1
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• pp.165-178
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• 1998

The purpose of this explanatory study was to provide baseline information on the secondary prevention health behavior of cervical cancer in Korea which was related to the utilization of the Papanicolaou (Pap) smear screening test. The secondary data from the 1992 Korean Health Behavior Survey was used for analysis in order to determine sociodemographic profiles and the predictor variables. The sample analyzed for this study contained 1,489 Korean women residing in Korea aged 20-59 selected by multi-stage sampling method from the 1990 Korean census. Univariate, bivariate, and logistic regression analysis were performed to produce the findings of this study. Only 27.9% of the study sample had had a Pap test in 1992. It was found that the relative sociodemographic profiles of the Pap test between users and non-users were distinctive. The predictors variables were age, marital status, educational status, usual source of care, perceived household economic status, health check-up, and presence of chronic diseases

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.35-47
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• 2004

In this paper, we generalizes Kim and Bickel(2003)'s statistic for bivariate normality to that of multinormality, applying Fattorini(1986)'s method. Fattorini(1986) generalized Shapiro-Wilk's statistic for univariate normality to multivariate cases. The proposed statistic could be considered as an approximate statistic to Fattorini(1986)'s. It can be used even for a big sample size. Power performance of the proposed test is assessed in a Monte Carlo study.

• Journal of Korean Society for Quality Management
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• v.22 no.3
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• pp.34-53
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• 1994

Taguchi's robust design methodology has focused only on a single performance characteristic or response, but the quality of most products is seldom defined by a characteristic, and is rather the composite of a family of characteristics which are often interrelated and nearly always measured in a variety of units. The multiple performance characteristics problem is how to compromise the conflicts among the selected levels of the design parameters for each individual performance characteristic. In this paper, the modified desirability function using SN ratio which can be optimized by univariate technique is proposed and a parameter design procedure to achieve the best balance among several different response variables is developed We reanalyze two existing case studies by the proposed method and compare these results with ones by the sum of SN ratios and the expected weighted loss.

• Journal of the KSME
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• v.22 no.6
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• pp.456-462
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• 1982

재래의 공학적 해석과정에서는, 주어진 시스템에 대한 정량적 해석을 위해 우선 시스템에 대한 수학적 모형을 가정.도입하고, 미지계수에 대한 결정은 시스템으로부터 관측된 자료에 의존하게 된다. 반면 DDS에 의한 모형화는 시스템에 대한 사전 지식이 없어도 시스템으로부터의 자료에 대한 체계적인 정량적 해석을 통해서 시스템을 특성화시키는 미분방정식을 유도할 수 있게 하며 오히려 시스템에 대한 이해를 돕게 한다. 이 때 결정되는 모형은 최소오차자승법(least square error method)에 의한 통계학적으로 가장 적합한 근사모형으로 결정된 모형의 계수로부터 시 스템의 물리적 특성을 규명(identify)할 수 없는 경우에도 그 모형으로 표현되는 추상시스템을 바탕으로 특성화, 예측 내지 제어 목적으로 활용할 수 있다. DDS는 단일변수(univariate)및 다 변수(multivariate) 자료에 모두 적용 가능하며 전달 함수 규명(Transfer Function Identification) 및 닫힘 루우프 시스템(closedloop system)의 모형화 및 해석에도 이용되고 있다.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.236-241
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• 2022

We consider methods of estimating a binary regression function using a nonparametric kernel estimation when there is only one covariate. For this, the Nadaraya-Watson estimation method using single and double bandwidths are used. For choosing a proper smoothing amount, the cross-validation and plug-in methods are compared. In the real data analysis for case study, German credit data and heart disease data are used. We examine whether the nonparametric estimation for binary regression function is successful with the smoothing parameter using the above two approaches, and the performance is compared.

• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.8
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• pp.745-752
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• 2009

In robust design, the mean and variance of design performance are frequently used to measure the design performance and its robustness under uncertainties. In this paper, we present the Gauss-type quadrature formula as a rigorous method for mean and variance estimation involving arbitrary input distributions and further extend its use to robust design optimization. One dimensional Gauss-type quadrature formula are constructed from the input probability distributions and utilized in the construction of multidimensional quadrature formula such as the tensor product quadrature (TPQ) formula and the univariate dimension reduction (UDR) method. To improve the efficiency of using it for robust design optimization, a semi-analytic design sensitivity analysis with respect to the statistical moments is proposed. The proposed approach is applied to a simple bench mark problems and robust topology optimization of structures considering various types of uncertainty.