• The Korean Journal of Applied Statistics
• /
• v.23 no.2
• /
• pp.295-304
• /
• 2010

Coating thickness is one of target variables in quality control process in steel industry. To predict coating thickness and to control quality of anti-fingerprint steel coils, ultraviolet-visible spectra are measured. We propose a variable-interval selection procedure based on the variable importance in projection in partial least square model. Using the proposed variable interval selection method, prediction performance gets better in the reduced model than the full model with full spectra absorbance. It is also shown that the first differencing as a data preprocessing technique does work well for the prediction of coating thickness.

• Explosives and Blasting
• /
• v.20 no.1
• /
• pp.5-14
• /
• 2002

파쇄 암석의 파쇄도는 발파효율을 나타내는 중요 척도로서 적재와 분쇄 작업에 큰 영향을 미친다. 그러나 현장에 쌓여 있는 발파암 더미로부터 파쇄도를 조사한다는 것은 용이한 작업이 아니다. 본 연구에서는 석산 발파에서 가장 중요한 요소인 천공방식과 암질의 변화가 파쇄도와 발파진동에 미치는 영향을 조사하였다. 파쇄 입도에 영향을 미치는 중요한 변수인 천공방식, 암질등급(RMR), 현지 암반의 블록 크기, 발파진동 등의 영향을 파쇄암의 평균입도(MFS)와 상위 5개의 대괴 평균치$(L_5)$로 나타내었다. 연구결과, 파쇄암의 평균입도는 상위 5개의 대괴 평균치와 선형적인 관계를 보였다. 발파방법과 파쇄도 평가 결과 재래식 발파에서는 파쇄도를 예측할 수 없었고 커다란 옥석이 생성되었으나, 벤치발파에서는 평균파쇄암의 크기가 비교적 양호한 파쇄 상태를 유지하였다. 현지 암반 블록 크기는 평균 파쇄암의 크기와 선형적 관계를 나타내었다. RMR값이 커짐에 따라 발파진동 추정식의 진동상수 K와 감쇠지수 n의 절대값과 평균 파쇄암의 크기, 그리고 상위 대괴의 크기는 대체적으로 증가하는 경향을 나타내었고, 진동상수 K와 감쇠지수 n도 증가하는 경향을 보였다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.347-347
• /
• 2023

홍수 예·경보 시에는 시간-단위 또는 그 이하의 시간 척도에서 작용하는 강우에 대한 고도의 영향이 중요하게 되며, 특히 상대적으로 더 드문 관측 밀도가 있는 산악지역에서 강우의 공간분포에 대한 산악 효과의 중요도가 더 높아지게 된다. 일반적으로 1시간 시간스케일에서 강우-고도의 관계를 살펴보기 위해서는 대략 5km 내외의 관측 밀도를 가져야 하는 것으로 알려져 있으나 이러한 지역은 매우 드물다. 최근 기상 예측 수치모델로부터 모의된 강우량의 품질이 눈에 띄게 향상됨에 따라 국내에도 다양한 연구가 수행된 바 있다. 본 연구에서는 WRF를 이용하여 남강댐 지역의 과거 호우 사상을 재현한 후, 이로부터 생산된 공간적인 강우장을 이용하여 시간-단위의 시간 척도에서 강우량과 고도 사이의 관계를 고려할 수 있는 WREPN(WRF Rainfall-Elevation Parameterized Nowcasting) 모형을 제안한다. 홍수량 분석을 위해 WREPN 모형을 이용하였으며, 비교군으로 실무적으로 많이 사용되는 IDW, Kriging 기반 격자강우가 사용되었다. 격자기반 분포형 강우자료로부터 홍수량을 분석하기 위해 Mod-Clark 모형이 적용되었으며, 입력된 강우자료별매개변수의 불확실성을 분석하기 위해 베이지안 기법이 적용되었다. 매개변수의 불확실성 분석으로부터 강우-고도 관계가 고려된 WREPN 모형의 강우자료가 상대적으로 불확실성이 낮다는 것을 확인할 수 있었다.

• Journal of the Korea Convergence Society
• /
• v.10 no.3
• /
• pp.23-30
• /
• 2019

The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.

• Journal of Korean Society of Transportation
• /
• v.20 no.1
• /
• pp.65-76
• /
• 2002

This study is realized using fuzzy evaluation and AHP(the analytic hierarchy process) for the optimum search of traffic route and estimated by the quantitative analysis in the vague subjective judgement. It is different from classical route search and noticed thinking method of human. Appraisal element, weight, appraisal value of route is extracted from basic of the opinion gathering fur the driving expert and example of route model was used for the finding of practice utility. Model assessment was performed attribute membership function making of estimate element, estimate value setting, weight define by the AHP, non additive presentation of weight according to $\lambda$-fuzzy measure, Choquet fuzzy integral.

• The Korean Journal of Applied Statistics
• /
• v.34 no.4
• /
• pp.589-597
• /
• 2021

In simple and multiple regression, there is a difference in the meaning of regression coefficients, and not only are the estimates of regression coefficients different, but they also have different signs. Understanding the relative contribution of explanatory variables in a regression model is an important part of regression analysis. In a standardized regression model, the regression coefficient can be interpreted as the change in the response variable with respect to the standard deviation when the explanatory variable increases by the standard deviation in a situation where the values of the explanatory variables other than the corresponding explanatory variable are fixed. However, the size of the standardized regression coefficient is not a proper measure of the relative importance of each explanatory variable. In this paper, the estimator of the regression coefficient in multiple regression is expressed as a function of the correlation coefficient and the coefficient of determination. Furthermore, it is considered in terms of the effect of an additional explanatory variable and additional increase in the coefficient of determination. We also explore the relationship between estimates of regression coefficients and correlation coefficients in various plots. These results are specifically applied when there are two explanatory variables.

• Korean Journal of Psychosomatic Medicine
• /
• v.27 no.2
• /
• pp.101-110
• /
• 2019

Objectives : It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium. Methods : Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium. Results : There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity. Conclusions : The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.34 no.4
• /
• pp.283-289
• /
• 2014

This study was conducted to develop a non-destructive detection method for adulterated powder products using Raman spectroscopy and partial least squares regression(PLSR). Garlic and ginger powder, which are used as natural seasoning and in health supplement foods, were selected for this experiment. Samples were adulterated with corn starch in concentrations of 5-35%. PLSR models for adulterated garlic and ginger powders were developed and their performances evaluated using cross validation. The $R^2_c$ and SEC of an optimal PLSR model were 0.99 and 2.16 for the garlic powder samples, and 0.99 and 0.84 for the ginger samples, respectively. The variable importance in projection (VIP) score is a useful and simple tool for the evaluation of the importance of each variable in a PLSR model. After the VIP scores were taken pre-selection, the Raman spectrum data was reduced by one third. New PLSR models, based on a reduced number of wavelengths selected by the VIP scores technique, gave good predictions for the adulterated garlic and ginger powder samples.

• The Journal of Information Technology
• /
• v.2 no.2
• /
• pp.1-12
• /
• 1999

The purpose of this study is to classify the determinants of channel performance and to reveal the contribution of classified performance factor on the total distribution performance of individual channel members. The empirical results of this study confirm that channel member performance is too rich and complex to be accurately reflected in a single item or even limited domain performance measures. The respondents in our sample monitored a lot of different facets of performance, indicating their belief in the multidimensional nature of performance. Our finding that outcome-based monitoring was particularly pervasive was not surprising given that managerial attention to end results is expected since those results are necessary for continued survival of the dealership. However, the dealership owner were not content to rely exclusively on these outcome performance indicators. They also gathered information on the efforts and activities that lead to those outcomes as well as other facets of information such as customer satisfaction and selling skills of salesperson. Limiting the definition and measurement of performance to outcomes only, or to a single item measure would have failed to capture important facets of performance, and could distort he relationship between the managerial variables and performance.

• Korean Journal of Remote Sensing
• /
• v.37 no.2
• /
• pp.257-274
• /
• 2021

Although image segmentation is a critical part of object-based analysis of high resolution imagery, there has been lack of studies to evaluate the quality of image segmentation. In this study, we aimed to find practical and effective methods to obtain optimal parameters for image segmentation. Evaluations of image segmentation are divided into unsupervised, supervised, and qualitative visual interpretation methods. Using the multispectral UAV images, sampled from urban and forest over the Incheon Metropolitan City Park, three evaluation methods were compared. In overall, three methods showed very similar results regardless of the computational costs and applicability, although the optimal parameters determined by the evaluations were different between the urban and forest images. There is no single measure that outperforms in the unsupervised evaluation. Any combinations of intra-segment measures (V, COV, WV) and inter-segment measures (MI, BSH, DTNP) provided almost the same results. Although supervised method may be biased by subjective selection of reference data, it can be easily applied to detect object of interest. The qualitative visual interpretation on the segmentation results corresponded with the unsupervised and supervised evaluations.