• 한국대기환경학회지
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• 제10권4호
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• pp.224-232
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• 1994

A new neural network model has been developed to predict short-term air pollution concentration. In addition, a multiple regression model widely used in statistical analysis was tested. These models were applied for prediction of daily maximum ozone concentration in Seoul during the summer season of 1991. The time periods between May and September 1989 and 1990 were utilized to train set of learning patterns in neural network model, and to estimate multiple regression model. To evaluate the results of the different models, several Performance indices were used. The results indicated that the multiple regression model tended to underpredict the daily maximum ozone concentration with small r$^{2}$(0.38). Also, large errors were found in this model; 21.1 ppb for RMSE, 0.324 for NMSE, and -0.164 for MRE. On the other hand, the results obtained from the neural network model were very promising. Thus, we can know that this model has a prominent efficiency in the adaptive control for the non-linear multi- variable systems such as photochemical oxidants. Also, when the recent new information was added in the neural network model, prediction accuracy was increased. From the new model, the values of RMSE, NMSE and r$^{2}$ were 13.2ppb, 0.089, 0.003 and 0.55 respectively.

• Journal of Welding and Joining
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• 제17권5호
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• pp.98-106
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• 1999

In GMAW, the spatters are generated according to the variation of the arc. Of the arc is stable, Few spatters are generated. But if unstable, too many spatters are generated. So, this means the spatters are dependent on the arc state. The aim of this study is to accurately estimate the arc state. To do this, the generated spatters were captured under the some welding conditions, and the waveforms of the arc voltage and welding current were collected. From the collected signals, the waveform factors and their standard deviations were extracted. Using these factors as input parameters of multi-layer artificial neural network, the learning for the weight of the generated spatters is performed and the estimation results to the real spatter are assessed. Obtained results are as follow: the linear correlation coefficient between the estimated result and the real spatters was 0.9986. And although the average convergence error was set 0.002, the estimated error to the real spatter was within 0.1 gr/min at each welding condition. In the estimation for the weight generated spatters, the result with multi-layer neural network was far better than with multiple regression analysis. Especially, even though under the welding condition which the arc state is unstable (the spatter is generated much more), very excellent estimation performance was shown.

• Journal of Biosystems Engineering
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• 제38권1호
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• pp.64-71
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• 2013

Purpose: The purpose of this study was to select standing posture parameters that have a significant difference according to the severity of spinal deformity, and to develop a novel Cobb angle prediction model for adolescent girls with idiopathic scoliosis. Methods: Five normal adolescents girls with no history of musculoskeletal disorders, 13 mild scoliosis patients (Cobb angle: $10^{\circ}-25^{\circ}$), and 14 severe scoliosis patients (Cobb angle: $25^{\circ}-50^{\circ}$) participated in this study. Six infrared cameras (VICON) were used to acquire data and 35 standing parameters of scoliosis patients were extracted from previous studies. Using the ANOVA and post-hoc test, parameters that had significant differences were extracted. In addition, these standing posture parameters were utilized to develop a Cobb-angle prediction model through multiple regression analysis. Results: Twenty two of the parameters showed differences between at least two of the three groups and these parameters were used to develop the multi-linear regression model. This model showed a good agreement ($R^2$ = 0.92) between the predicted and the measured Cobb angle. Also, a blind study was performed using 5 random datasets that had not been used in the model and the errors were approximately $3.2{\pm}1.8$. Conclusions: In this study, we demonstrated the possibility of clinically predicting the Cobb angle using a non-invasive technique. Also, monitoring changes in patients with a progressive disease, such as scoliosis, will make possible to have determine the appropriate treatment and rehabilitation strategies without the need for radiation exposure.

• 반도체디스플레이기술학회지
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• 제20권4호
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• pp.146-150
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• 2021

Plasma information based virtual metrology (PI-VM) that predicts wafer-to-wafer etch rate variation after wet cleaning of plasma facing parts was developed. As input parameters, plasma information (PI) variables such as electron temperature, fluorine density and hydrogen density were extracted from optical emission spectroscopy (OES) data for etch plasma. The PI-VM model was trained by stepwise variable selection method and multi-linear regression method. The expected etch rate by PI-VM showed high correlation coefficient with measured etch rate from SEM image analysis. The PI-VM model revealed that the root cause of etch rate variation after the wet cleaning was desorption of hydrogen from the cleaned parts as hydrogen combined with fluorine and decreased etchant density and etch rate.

• Journal of Ecology and Environment
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• 제40권1호
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• pp.34-44
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• 2016

Background: Little is known about how chemical water quality is associated with ecological stream health in relation to landuse patterns in a watershed. We evaluated spatial characteristics of water quality characteristics and the ecological health of Dongjin-River basin, Korea in relation to regional landuse pattern. The ecological health was assessed by the multi-metric model of Index of Biological Integrity (IBI), and the water chemistry data were compared with values obtained from the health model. Results: Nutrient and organic matter pollution in Dongjin-River basin, Korea was influenced by land use pattern and the major point sources, so nutrients of TN and TP increased abruptly in Site 4 (Jeongeup Stream), which is directly influenced by wastewater treatment plants along with values of electric conductivity (EC), bacterial number, and sestonic chlorophyll-a. Similar results are shown in the downstream (S7) of Dongjin River. The degradation of chemical water quality in the downstream resulted in greater impairment of the ecological health, and these were also closely associated with the landuse pattern. Forest region had low nutrients (N, P), organic matter, and ionic content (as the EC), whereas urban and agricultural regions had opposite in the parameters. Linear regression analysis of the landuse (arable land; $A_L$) on chemicals indicated that values of $A_L$ had positive linear relations with TP ($R^2=0.643$, p < 0.01), TN ($R^2=0.502$, p < 0.05), BOD ($R^2=0.739$, p < 0.01), and suspended solids (SS; ($R^2=0.866$, p < 0.01), and a negative relation with TDN:TDP ratios ($R^2=0.719$, p < 0.01). Conclusions: Chemical factors were closely associated with land use pattern in the watershed, and these factors influenced the ecological health, based on the multimetric fish IBI model. Overall, the impairments of water chemistry and the ecological health in Dongjin-River basin were mainly attributes to point-sources and land-use patterns.

• Journal of the Korean Data and Information Science Society
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• 제26권1호
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• pp.65-75
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• 2015

본 논문에서는 전문대 및 4년제 대졸 신규취업자의 임금을 결정하는 요인을 분석하기 위해 개인수준의 인적특성 변수들과 업종수준의 특성변수들을 사용하였다. 본 논문은 개인수준의 임금이 개인수준의 인적특성 (1-수준)과 업종 수준의 산업특성 (2-수준)에 의해 영향을 받는 다층구조 (multi-level)를 가지게 된다는 점을 주목하였다. 이와 같이 위계적 자료 특성을 가지는 복수의 분석단위 구조가 되면, 전통적인 회귀분석에서와 같이 개인수준의 임금이 독립이라는 가정을 할 수 없게 된다. 따라서 본 논문에서는 개인수준의 임금에 영향을 미치는 다층구조의 특성을 가진 변수들의 영향력을 보다 타당하게 분석하기 위한 방법으로 위계선형모형 (HLM; hierarchical linear model)을 이용하였다. 주요 결과는 다음과 같다. 첫째, 개인수준과 업종수준 변수들 모두를 포함한 다중대응분석의 결과에 의하면, 개인수준의 임금이 서로 다른 그룹에 대응되는 개인 특성 변수값과 업종 특성 변수값이 그룹별로 서로 상이하여 개인특성 변수만이 아니라 업종특성 변수도 개인수준의 임금에 영향을 미치고 있다는 점이 발견되었다. 둘째, 개인수준과 업종수준 변수들 모두를 포함한 의사결정나무분석의 결과에 의하면, 개인수준의 임금에 가장 많은 영향을 미치는 변수가 업종별 임금이고, 그 다음으로는 업종별 근로시간, 연령, 성별 등의 순으로 나타났다. 이와 같이 개인수준의 임금을 결정하는 데 있어서 업종의 특성이 매우 중요한 것으로 나타났다는 점은 위계적 선형모형의 활용이 타당하다는 것을 시사하는 것이다. 셋째, 개인수준의 인적특성과 업종 수준의 산업특성 변수들을 모두 포함한 모형이 다른 모형들에 비해서 모형 적합도가 가장 개선되어 위계적 선형모형이 적합한 것으로 나타났다.

• 한국도로학회논문집
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• 제10권4호
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• pp.139-150
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• 2008

본 연구는 도로연장측면에서 본선에 비해 상대적으로 연결로에서 발생하는 사고빈도가 높고, 교통사고가 증가하는 추세인 고속도로 연결로의 교통사고 예측모형의 개발에 초점을 두었다. 연결로 유형별(직결, 준직결, 루프)로 통계적으로 유의한 사고인자를 선정하고, 사고율과의 관계가 비선형 임을 분석하여 변수를 변형(Variables Transformation)하여 All possible 방식으로 예측모형을 개발하고, 통계적 진단 및 검증을 거쳐 유의성을 확인하였으며 이에 기존 개발 모형에 비해 예측력이 더욱 우수한 결과를 보였다. 개발된 사고예측모형은 보다 비용면에서 효율적이고, 안전한 트럼펫형 IC 연결로의 설계와 연결로 교통사고 원인분석에 활용될 수 있을 것으로 기대된다.

• 수산해양기술연구
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• 제57권3호
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• pp.236-245
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• 2021

In this study, a drifting test using a experimental vessel (2,966 tons) in the northern waters of Jeju was carried out for the first time in order to obtain the fundamental data for drift. During the test, it was shown that the average leeway speed and direction by GPS position were 0.362 m/s and 155.54° respectively and the leeway rate for wind speed was 8.80%. The analysis of linear regression modes about leeway speed and direction of the experimental vessel indicated that wind or current (i.e. explanatory variable) had a greater influence upon response variable (e.g. leeway speed or direction) with the speed of the wind and current rather than their directions. On the other hand, the result of multiple regression model analysis was able to predict that the direction was negative, and it was demonstrated that predicted values of leeway speed and direction using an experimental vessel is to be more influential by current than wind while the leeway speed through variance and covariance was positive. In terms of the leeway direction of the experimental vessel, the same result of the leeway speed appeared except for a possibility of the existence of multi-collinearity. Then, it can be interpreted that the explanatory variables were less descriptive in the predicted values of the leeway direction. As a result, the prediction of leeway speed and direction can be demonstrated as following equations. Ŷ₁= 0.4031-0.0032X₁+0.0631X₂-0.0010X₃+0.4110X₄ Ŷ₂= 0.4031-0.6662X₁+27.1955X₂-0.6787X₃-420.4833X₄ However, many drift tests using actual vessels and various drifting objects will provide reasonable estimations, so that they can help search and rescue fishing gears as well.

• Geomechanics and Engineering
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• 제22권1호
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• pp.97-108
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• 2020

The time-consuming and less objectivity are the main problems of conventional micromechanical parameters calibration method of Particle Flow Code simulations. Thus this study aims to address these two limitation of the conventional "trial-and-error" method. A new calibration method for the linear parallel bond model (CM-LPBM) is proposed. First, numerical simulations are conducted based on the results of the uniaxial compression tests on limestone. The macroscopic response of the numerical model agrees well with the results of the uniaxial compression tests. To reduce the number of the independent micromechanical parameters, numerical simulations are then carried out. Based on the results of the orthogonal experiments and the multi-factor variance analysis, main micromechanical parameters affecting the macro parameters of rocks are proposed. The macro-micro parameter functions are ultimately established using multiple linear regression, and the iteration correction formulas of the micromechanical parameters are obtained. To further verify the validity of the proposed method, a case study is carried out. The error between the macro mechanical response and the numerical results is less than 5%. Hence the calibration method, i.e., the CM-LPBM, is reliable for obtaining the micromechanical parameters quickly and accurately, providing reference for the calibration of micromechanical parameters.

• 품질경영학회지
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• 제32권3호
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• pp.109-125
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• 2004

This paper deals with an optimal process control problem in production of hull structural steel plate with high defective rate. The main quality characteristic(dependent variable) is the internal quality(defect) of plates and is dependent on process parameters(independent variables). The dependent variable(quality characteristics) has three categorical ordinal data and there are 35 independent variables(29 continuous variables and 6 categorical variables). In this paper, we determine the main factors and to develop the mathematical model between internal quality predicted probabilities and the main factors. Secondly, we find out the optimal process condition of main factors through analysis of variance(ANOVA) using simulation. We consider three models to obtain the main factors and the optimal process condition: linear, quadratic, error models.