• 품질경영학회지
• /
• 제24권2호
• /
• pp.128-141
• /
• 1996

The advent of the quality-improvement movement caused a great expansion in the use of statistically designed experiments in industry. The regression method is often used for the analysis of data from such experiments. However, the data for a quality characterstic often takes the form of counts or the ratio of counts, e.g. fraction of defectives. For such data the analysis using generalized linear models is preferred to that using the simple regression model. In this paper we introduce the generalized linear model and show how it can be used for the analysis of non-normal data from quality-improvement experiments.

• 한국소음진동공학회논문집
• /
• 제26권6_spc호
• /
• pp.736-743
• /
• 2016

This study describes optimization methods for adjustment of helicopter main rotor tracking and balancing (RTB). RTB is a essential process for helicopter operation and maintenance. Linear and non-linear models were developed with past RTB test results for estimation of RTB adjustment. Then global and sequential optimization methods were applied to the each of models. Utilization of the individual optimization method with each model is hard to fulfill the RTB requirements because of different characteristics of each blade. Therefore an ensemble model was used to integrate every estimated adjustment result, and an adaptive method was also applied to adjustment values of the linear model to update for next estimations. The goal of this developed RTB adjustment optimization program is to achieve the requirements within 2 run. Additional tests for comparison of weight factor of the ensemble model are however necessary.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제15권4호
• /
• pp.277-290
• /
• 2011

We define some multi-type asset models derved from L$\acute{e}$vy proceses which emphasize coefficients of stochastic differential equations. Also these asset models can be represented by Doleance-Dade linear equations derived from jump-type semimartingales which are decomposed by various terms of time basically. For these asset models, we can construct optimal portfolio strategy by using filtered various information at each check time.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2000년도 추계학술발표회 논문집
• /
• pp.187-191
• /
• 2000

In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.

• 한국경영과학회지
• /
• 제10권2호
• /
• pp.75-82
• /
• 1985

Reorganization is essential part of data base maintenanc work and the reasonable reorganization points can be determined from the trade-off between reorganization cost and performance degradation. There has been many reorganization models so far, but none of these models have assumed nonlinear search cost function. This paper presents the existensions of two existing linear reorganization models for the case where the search cost function is quadratic. The higher performance of these extended models was shown in quadratic search cost function case.

• Journal of Forest and Environmental Science
• /
• 제30권4호
• /
• pp.351-361
• /
• 2014

Leaf area ($A_0$) and leaf biomass ($M_0$) estimation are significant prerequisites to studying tree physiological processes and modeling in the forest ecosystem. The objective of this study was to develop allometric models for estimating $A_0$ and $M_0$ of Swietenia mahagoni L. from different tree parameters such as DBH and tree height of mahogany plantations in the northeastern region of Bangladesh. A total of 850 healthy and well formed trees were selected randomly for sampling in the five study sites. Then, twenty two models were developed based on different statistical criteria that propose reliable and accurate models for estimating the $A_0$ and $M_0$ using non-destructive measurements. The results exposed that model iv and xv were selected on a single predictor of DBH and showed more statistically accuracy than other models. The selected models were also validated with an additional test data set on the basis of linear regression and t-test for mean difference between observed and predicted values. After that, a comparison between the best logarithmic and non-linear allometric model shows that the non-linear model produces systematic biases and underestimates $A_0$ and $M_0$ for larger trees. As a result, it showed that the bias-corrected logarithmic model iv and xv can be used to help quantify forest structure and functions, particularly valuable in future research for estimating $A_0$ and $M_0$ of S. mahagoni in this region.

• 한국환경농학회지
• /
• 제39권3호
• /
• pp.204-213
• /
• 2020

BACKGROUND: Since the number of crops cultivated in reclaimed land is huge, it is very difficult to quantify the total crop production. Therefore, a non-destructive method for predicting crop production is needed. Salt tolerant root vegetables such as red beets and sugar beet are suitable for cultivation in reclaimed land. If their underground biomass can be predicted, it helps to estimate crop productivity. Objectives of this study are to investigate maximum leaf length and weight of red beet, sugar beet, and turnips grown in reclaimed land, and to determine optimal model with regression analysis for linear and allometric growth models. METHODS AND RESULTS: Maximum leaf length, width, and root fresh weight of red beets, sugar beets, and turnips were measured. Ten linear models and six allometric growth models were selected for estimation of root fresh weight and non-linear regression analysis was conducted. The allometric growth model, which have a variable multiplied by square of maximum leaf length and maximum leaf width, showed highest R² values of 0.67, 0.70, and 0.49 for red beets, sugar beets, and turnips, respectively. Validation results of the models for red beets and sugar beets showed the R² values of 0.63 and 0.65, respectively. However, the model for turnips showed the R² value of 0.48. The allometric growth model was suitable for estimating the root fresh weight of red beets and sugar beets, but the accuracy for turnips was relatively low. CONCLUSION: The regression models established in this study may be useful to estimate the total production of root vegetables cultivated in reclaimed land, and it will be used as a non-destructive method for prediction of crop information.

• 한국의류학회지
• /
• 제44권1호
• /
• pp.84-95
• /
• 2020

Wearing ease is a critical factor when designing special uniforms such as flight pilot's garment and should reflect occupational properties for better performance. This study measured skin surface on 31 areas in seven postures that refer to the pilot's occupational postures as well as made six prediction models including linear mixed model (LMM) for each body part to find the best fit model. Skin surface measured from 3D body scanned images of 11 male pilot participants. There were significantly positive and negative changes in various areas from standing posture (P1) to dynamic postures (P2-P7). Six models were designed in various compositions using stature and chest circumference as fixed effects and subject and posture as random effects. The best models were linear mixed models with one fixed effect (chest circumference or stature, varies with body parts) and two random effects (subject and posture). The results of this study provide reference data to set wearing ease for pilot's garment and suggests a new methodology in this research area, but verifying the effect of diverse independent variables is left for future studies.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2019년도 학술발표회
• /
• pp.147-147
• /
• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• 대한원격탐사학회지
• /
• 제36권6_3호
• /
• pp.1711-1720
• /
• 2020

최근 급속한 산업화와 도시화로 인해 인위적으로 발생하는 미세먼지(Particulate matter, PM)는 기상 조건에 따라 이동 및 분산되면서 피부와 호흡기 등 인체에 악영향을 미친다. 본 연구는 기상인자를 multiple linear regression(MLR), support vector machine(SVM), 그리고 random forest(RF) 모델의 입력자료로 하여 서울시 PM10 농도를 예측하고, 모델 간 성능을 비교 평가하는데 그 목적을 둔다. 먼저 서울시에 소재한 39개소 대기오염측정망(air quality monitoring sites, AQMS)에서 관측된 PM10 농도 자료를 8:2 비율로 구분하여 모델 훈련과 검증 데이터셋으로 사용되었다. 또한 기상관측소(automatic weather system, AWS)에서 관측되고 있는 자료 중 9개 기상인자(평균기온, 최고기온, 최저기온, 일 강수량, 평균풍속, 최대순간풍속, 최대순간풍속풍향, 황사발생유무, 상대습도)가 모델의 입력자료로 선정되었다. 각 AQMS에서 관측된 PM10 농도와 MLR, SVM, 그리고 RF 모델에 의해 예측된 PM10 농도 간 결정계수(R2)는 각각 0.260, 0.772, 그리고 0.793이었고, RF 모델이 PM10 농도 예측에 가장 높은 성능을 나타냈다. 특히 모델 검증에 사용되는 AQMS 중 관악구와 강남대로 AQMS는 상대적으로 AWS에 가까워 SVM과 RF 모델에서 높은 정확도를 나타냈다. 종로구 AQMS는 AWS에서 비교적 멀리 떨어져 있지만, 인접한 두 AQMS 데이터가 모델 학습에 사용되었기 때문에 두 모델에서 높은 정확도를 나타냈다. 반면 용산구 AQMS는 AQMS 및 AWS에서 비교적 멀리 떨어져 있기에 두 모델의 성능이 낮게 나타냈다.