• 한국대기환경학회지
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• 제23권1호
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• pp.14-28
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• 2007

This study presented the selection of ozone ($O_3$) potential factors and designed and assessed its potential prediction model using multiple-linear regression equations in Ulsan area during the springtime from April to June, $2000{\sim}2004$. $O_3$ potential factors were selected by analyzing the relationship between meterological parameters and surface $O_3$ concentrations. In addition, cluster analysis (e.g., average linkage and K-means clustering techniques) was performed to identify three major synoptic patterns (e.g., $P1{\sim}P3$) for an $O_3$ potential prediction model. P1 is characterized by a presence of a low-pressure system over northeastern Korea, the Ulsan was influenced by the northwesterly synoptic flow leading to a retarded sea breeze development. P2 is characterized by a weakening high-pressure system over Korea, and P3 is clearly associated with a migratory anticyclone. The stepwise linear regression was performed to develop models for prediction of the highest 1-h $O_3$ occurring in the Ulsan. The results of the models were rather satisfactory, and the high $O_3$ simulation accuracy for $P1{\sim}P3$ synoptic patterns was found to be 79, 85, and 95%, respectively ($2000{\sim}2004$). The $O_3$ potential prediction model for $P1{\sim}P3$ using the predicted meteorological data in 2005 showed good high $O_3$ prediction performance with 78, 75, and 70%, respectively. Therefore the regression models can be a useful tool for forecasting of local $O_3$ concentration.

• 아시안잔디학회지
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• 제23권1호
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• pp.77-90
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• 2009

Near Infrared Reflectance Spectroscopy(NIRS)는 짧은 시간 안에 식물의 다양한 영양소를 동시에 정확하고 빠르게 측정할 수 있다. 본 연구는 creeping bentgrass 'CY2' 엽의 여러 가지 기본 요소의 값을 예측하기 위해서 NIRS(근적의선 분광분석기)를 사용하여 측정하였다. 그 결과, 질소와 수분 그리고 탄수화물의 $r^2$은 각각 0.892, 0.925, 0.971이었다. 검량식에 대한 검증에서 $r^2$이 높은 상관관계를 나타냈으므로, 잔디에서 더 많은 연구를 위한 실용화 가능성을 확인 할 수 있었다.

• Journal of Welding and Joining
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• 제14권4호
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• pp.33-42
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• 1996

In recent years there has been a significant growth in the use of the automated and/or robotic welding system, carried out as a means of improving productivity and quality, reducing product costs and removing the operator from tedious and potentially hazardous environments. One of the major difficulties with the automated and/or robotic welding process is the inherent lack of mathematical models for determination of suitable welding process parameters. Partial-penetration, single-pass bead-on-plate welds were fabricated in 12mm AS 1204 mild steel flats employing five different welding process parameters. The experimental results were used to develop three empirical equations: curvilinear; polynomial; and linear equations. The results were also employed to find the best mathematical equation under weld bend width to assist in the process control algorithms for the Gas Metal Arc Welding(GMAW) process and to correlate welding process parameters with weld bead width of bead-on-plates deposited. With the help of a standard statistical package program. SAS, multipe regression analysis was undertaken for investigating and modeling the GMAW process, and significance test techniques were applied for the interpretation of the experimental data.

• 한국농공학회논문집
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• 제55권6호
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• pp.127-133
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• 2013

The purpose of this study was to investigate maximum and minimum grain size which eroded by wind according to soil and wind conditions, such as top soil water content, roughness, land slope, wind velocity and proportion of grain size under 0.84mm. For performing this study, portable wind erosion tunnel was designed and utilized during field test, which facilitated measuring actual wind erosions under artificially controlled wind conditions. In the result, maximum, minimum grain size had strong negative correlation with roughness while weak positive correlation with wind velocity. Also, Slope which means the effect of gravity also influence grain size erodible by winds. Based on these results, regression equations were suggested for predicting maximum and minimum grain sizes by using multiple linear regression analysis from SPSS 20.0. The equation for maximum grain size erodible by winds showed a good agreement with the observed data with $R^2$=0.896. Other equation for minimum grain size had $R^2$=0.777.

• 한국식품영양학회지
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• 제8권1호
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• pp.23-31
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• 1995

To provide the basic information of buckwheat insoluble dietary fiber (IDF) and efficient material for the buckwheat processed foodstuffs, buckwheat (Fagopyrum egculentum Moench) was germinated at 1$0^{\circ}C$ for 7 days and the contents and composition of the insoluble dietary fiber were examined at 24 hour intervals. The NDF and ADF contents in ungerminated seeds were 20.27%, 18.95% on dry weight basis, respectively. During the germination period, the NDF and ADF contents increased gradually. The cellulose, hemicellulose and lignin contents in ungerminated seeds were 11.10%, 1.32% and 7.85%, respectively. During the germination period, the cellulose and lignin contents increased. However, there was no significant change in the hemicellulose contents. The composition of cellulose and hemicellulose in the NDF decreased with germination time, whereas that of lignin increased. The IDF contents obtained by Prosky method were higher than the NDF contents obtained by Van Soest method. However, the IDF and NDF contents were closely correlated(r=0.9785, p<0.01). The cellulose(Xl), hemicellulose(X2), lignin(X3) and soluble dietary fiber (SDF) (BL) showed the significant regression equation(p<0.001) with the root length(Y). The multiple regression equation was Y = -12.1306 + 0.9213xl - 0.1615$\times$2 + 0.1071$\times$3 + 0.7397$\times$4 and R2(coefficient of determination) was 0.942.

• 한국지반환경공학회 논문집
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• 제18권12호
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• pp.65-75
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• 2017

현장의 목적에 따라 유동성 채움재(Controlled Low-Strength Material, CLSM)의 유동성과 재령일에 따른 강도 특성은 다르게 요구될 수 있으며, 본 연구에서는 현장에 맞는 유동성 채움재의 배합설계가 가능하도록 다중회귀분석을 이용하여 유동성 채움재의 배합 비율로써 강도 및 플로우 특성을 예측하고자 하였다. CSA 팽창재, 보통 포틀랜드 시멘트, 플라이애시, 모래, 실트, 물 그리고 급결제로 구성된 유동성 채움재의 배합비에 따라 플로우값 그리고 12시간 및 7일 강도를 측정하였다. 유동성 채움재의 재료비율을 독립변수로, 측정된 각 특성을 종속변수로 선정 후, 통계 분석 프로그램인 SPSS Statistics 23을 통하여 다중회귀분석을 수행하였으며, 독립변수항이 1~3차식으로 이루어진 회귀모형의 회귀계수를 추정하였다. 회귀분석 결과, 플로우값은 3차식의 모형, 12시간 및 7일 강도는 1차식의 모형으로 설명하는 것이 가장 적절한 것으로 판단하였다. 본 논문에서 제안된 CLSM의 플로우값 및 강도 특성에 대한 회귀모형을 통하여, 실험을 수행하기 전, 요구되는 특성에 맞는 배합비를 추정할 수 있을 것으로 기대된다.

• 전기학회논문지
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• 제66권11호
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• pp.1628-1633
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• 2017

The purpose of this study was to identify the difference and correlation in elbow joint maximal flexion strength according to measurement methods and characteristics of muscular contraction, and to develop the predictive equation of elbow joint maximal flexion strength for the optimal exercise intensity setting and accurate measurement. Subjects were 30 male university students. Elbow joint maximal flexion strength of isokinetic contraction, isometric contraction at $75^{\circ}$ elbow joint flexion position, isotonic concentric 1RM, manual muscle strength (MMT) were measured with isokinetic dynamometer, dumbbell, and manual muscle tester. Pearson's r, linear regression equation, and multiple regression equation between variables were calculated. As a result, the highest value was isometric contraction. The second highest value was MMT. The third highest value was isokinetic contraction. 1RM was the lowest. Predictive equations of elbow joint maximal flexion strength between isometric and isokinetic contraction, between isometric contraction and 1RM, among isometric contraction, 1RM, and body weight were developed. In conclusion, 1RM and isokinetic elbow joint maximal flexion strength could be seemed to underestimate the practical elbow joint maximal flexion strength. And it is suggested that the developed predictive equations in this study should be useful in criteria- and goal-setting for resistant exercise and sports rehabilitation after elbow joint injury.

• 소성∙가공
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• 제26권5호
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• pp.271-276
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• 2017

A tee is the most common pipefitting used to combine or divide fluid flow. Tees can connect pipes of different diameters or change the direction of a pipe run. To manufacture tee type of stainless steel pipe, combinations of punch piercing and burr forming have been widely used in the industry. However, such method is considerably time consuming with regard to performing empirical work necessary to attain process conditions to prevent upper end tearing of the tee product and meet target tee height. Numerous experiments have shown that the piercing profile is the main cause of defects mentioned above. Furthermore, the mold design is formed through trial and error according to pipe diameters and changes in requirements. Thus, the objective of this study was to perform piercing and burring process analysis via finite element analysis using DYNAFORM to resolve problems mentioned above. An optimization design method was used to determine the piercing punch profile. Three radii of the piercing punch (i.e., large, small, and joined radii) were selected as design variables to minimize thinning of a tee pipe. Based on results of correlation and multiple regression analyses, we developed a predictive approximation model to satisfy requirements for both thickness reduction and target height. The new piercing punch profile was then applied to actual tee forming using the developed prediction equation. Model results were found to be in good agreement with experimental results.

• 한국농공학회논문집
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• 제49권4호
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• pp.3-12
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• 2007

The purpose of this paper is to analyze the relationship between meteorological factors and agricultural reservoir storage, and predict the reservoir storage by multiple regression equation selected by high correlated meteorological factors. Two agricultural reservoirs (Geumgwang and Gosam) located in the upsteam of Gongdo water level gauging station of Anseong-cheon watershed were selected. Monthly reservoir storage data and meteorological data in Suwon weather station of 21 years (1985-2005) were collected. Three cases of correlation (case 1: yearly mean, case 2: seasonal mean dividing a year into 3 periods, and case 3: lagging the reservoir storage from 1 month to 3 months under the condition of case 2) were examined using 8 meteorological factors (precipitation, mean/maximum/minimum temperature, relative humidity, sunshine hour, wind velocity and evaporation). From the correlation analysis, 4 high correlated meteorological factors were selected, and multiple regression was executed for each case. The determination coefficient ($R^{2}$) of predicted reservoir storage for case 1 showed 0.45 and 0.49 for Geumgwang and Gosam reservoir respectively. The predicted reservoir storage for case 2 showed the highest $R^{2}$ of 0.46 and 0.56 respectively in the period of April to June. The predicted reservoir storage for 1 month lag of case 3 showed the $R^{2}$ of 0.68 and 0.85 respectively for the period of April to June. The results showed that the status of agricultural reservoir storage could be expressed with couple of meteorological factors. The prediction enhanced when the storage data are divided into periods rather than yearly mean and especially from the beginning time of paddy irrigation (April) to high decrease of reservoir storage (June) before Jangma.

• 수산해양기술연구
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• 제58권4호
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• pp.299-309
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• 2022

Among the fishing vessels operating in the coastal waters, jigging fishing vessels were considered representative vessels engaged only by wind, sea, tide, and external force. Then, a fishing vessel with a length of shorter than 10 m from July 1, 2018 to August 5, 2019 was studied to obtain a drift prediction model by multiple regression analysis. In the correlation analysis between variables for leeway of speed and direction, the speed and direction of tidal seem to be the most affected in coastal waters. Therefore, it should be considered an explanatory variable when conducting drift tests. As a result of multiple regression analysis on the predicted equations of leeway speed and direction due to the external force on the drift of the fishing vessel, p < 0.000 was considered significant in the F-test, but the coefficient of determination was 55.2% and 37.8%. The effect on the predicted leeway speed was in the order of the tidal speed and current speed. In addition, the impact on the predicted leeway direction was in the order of the tidal speed and current speed. ŷ(m/s) = - 0.0011(x1) + 0.9206(x2) + 0.0001(x3) + 0.0002(x4) + 0.0050(x5) + 0.0529(x6) + 0.2457 ŷ(degree) = 0.6672(x1) + 93.1699(x2) + 0.0585(x3) - 0.0244(x4) - 1.2217(x5) + 4.6378(x6) - 0.0837