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. 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.

Identifying the water circulation status is one of the indispensable processes for watershed management in an urban area. Recently, various water circulation models have been developed to simulate the water circulation, but it takes a lot of time and cost to make a water circulation model that could adapt the characteristics of the watershed. This paper aims to develop a water circulation state estimation model that could easily calculate the status of water circulation in an urban watershed by using multiple linear regression analysis. The study watershed is a watershed in Seoul that applied the impermeable area ratio in 1962 and 2000. And, It was divided into 73 watersheds in order to consider changes in water circulation status according to the urban characteristic factors. The input data of the SHER(Similar Hydrologic Element Response) model, a water circulation model, were used as data for the urban characteristic factors of each watershed. A total of seven factors were considered as urban characteristic factors. Those factors included annual precipitation, watershed area, average land-surface slope, impervious surface ratio, coefficient of saturated permeability, hydraulic gradient of groundwater surface, and length of contact line with downstream block. With significance probabilities (or p-values) of 0.05 and below, all five models showed significant results in estimating the water circulation status such as the surface runoff rate and the evapotranspiration rate. The model that was applied all seven urban characteristics factors, can calculate the most similar results such as the existing water circulation model. The water circulation estimation model developed in this study is not only useful to simply estimate the water circulation status of ungauged watersheds but can also provide data for parameter calibration and validation.

The prediction of Jominy hardness curves and the effect of alloying elements on the hardenability of boron steels (19 different steels) are investigated using multiple regression analysis. To evaluate the hardenability of boron steels, Jominy end quenching tests are performed. Regardless of the alloy type, lath martensite structure is observed at the quenching end, and ferrite and pearlite structures are detected in the core. Some bainite microstructure also appears in areas where hardness is sharply reduced. Through multiple regression analysis method, the average multiplying factor (regression coefficient) for each alloying element is derived. As a result, B is found to be 6308.6, C is 71.5, Si is 59.4, Mn is 25.5, Ti is 13.8, and Cr is 24.5. The valid concentration ranges of the main alloying elements are 19 ppm < B < 28 ppm, 0.17 < C < 0.27 wt%, 0.19 < Si < 0.30 wt%, 0.75 < Mn < 1.15 wt%, 0.15 < Cr < 0.82 wt%, and 3 < N < 7 ppm. It is possible to predict changes of hardenability and hardness curves based on the above method. In the validation results of the multiple regression analysis, it is confirmed that the measured hardness values are within the error range of the predicted curves, regardless of alloy type.

국내 수출입 물동량의 증가와 해운산업의 발달에 따라 항만시설물의 사용빈도 또한 증가 추세에 있으나, SOC의 해운항만 부문의 투입 정부예산은 감축되어 왔다. 증가하는 사용빈도에 반하여 줄고 있는 예산으로 인해 항만시설물의 체계적이고 효율적인 유지관리 및 운영이 필요하다. 효율적인 유지관리 시스템 구축을 위해서 항만시설물이 위치한 지역, 구조물의 형태 및 취급화종, 시공 및 유지관리 수준과 같은 특성을 고려한 열화모델 개발이 필요하다. 항만시설물의 열화모델 개발은 시설물의 열화요인 분석과 열화데이터 수집 및 열화 모델 개발의 과정으로 수행하였다. 열화 모델 개발기법은 변수 특성에 따른 시간 의존적 상태변화를 반영할 수 있는 결정론적 방법인 다중 회귀분석과 변동성이 큰 자료들의 상태이력을 반영할 수 있는 확률론적 방법인 마코브 체인 이론을 이용하였다. 각 방법을 통해 잔교식 구조물과 블록식 구조물의 Project level의 상태 열화모델을 제시하였다.

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

Multivariate control charts are widely needed to monitor the production processes in various industry. Among the several multivariate control charts, control chart have been used of the typical technique. The control chart shows a statistic that represents observed variables and monitors the process through the statistic. In this case, the statistic generally have the limit that any variables affect to that statistic. To solve this problem, some studies have been progressed in the meantime. The representative method is to disassemble total statistic into each of the variable value and make a decision the parameters with large values than threshold value as a main cause. However, the means is requested to follow the normal distribution. To settle this problem, the bootstrap technique that don't be needed the probability distribution was introduced in 2011. In this paper, I introduced the detection technique of the fault variables using multiple regression analysis. There are two advantages; First, it is possible to use less samples than the ascertainment technique applying to bootstrap. Second, the technique using the regression analysis is easy to apply to the actual environment because the global threshold value is used.

In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

  Ultra-high voltage transformer industry has characteristic of small quantity batch production system by other order processing unlike general mass production systems. In this industry, observance of time deadline is very important in market competitive

In this study, we attempted to evaluate the relationship between air dilution sensory test and instrumental detection method for samples containing various odorous compounds. For the purpose of our comparative study, the analysis of malodor compounds was made using a total of 70 samples collected from three industrial sectors which include: Food & beverage, Waste treatment and cleaning, and Miscellaneous facilities. The results of instrumental analyses converted into three different odor indices (the odor concentration (OC), odor quotient (OQ), and odor intensity (OI)) were used to statistically sort out individual odorous components with the major impact. The results of multiple regression analysis between air dilution ratio value and instrumental odor concentration (of individual 12 compounds) indicate that butyraldehyde, CH₃SH, NH₃, and H₂S are the major odorous compounds that contribute most significantly to odor strength for the sample types investigated in this study.

모바일 컨버전스가 확산되면서 많은 기업들은 다양한 기능을 통합한 컨버전스 제품을 출시하고 있다. 이러한 모바일 컨버전스는 휴대의 편의성과 디지털 기기의 특성상 기기들 간의 공통된 요소를 함께 사용할 수 있는 경제적인 측면과 사용상의 편리성 증가 등 여러 가지 장점을 가지고 있다. 그러나 컨버전스 경향에 힘입어 많은 제품이 시장에 출시되었으나 기대와는 달리 실패하는 사례가 발생하고 있다. 이것은 제품개발에 있어서 컨버전스 제품에 대한 소비자의 평가를 반영하는데 미흡한 결과로 볼 수 있다. 컨버전스 제품이 시장에서 살아남기 위해서는 제품의 우수성과 인프라 구축도 중요하지만 무엇보다도 소비자가 컨버전스 제품을 어떻게 평가하고 어떠한 요인의 영향을 받는지에 대한 이해가 필수적이라 할 수 있다. 그럼에도 불구하고 현재까지 모바일기기를 중심으로 제품 평가에 대한 연구가 미비했다. 따라서 본 연구는 모바일 컨버전스 제품에 대한 소비자 평가와 영향 요인을 알아보고자 한다. 본 연구는 설문조사 방법에 의해 이루어졌으며, 158명으로부터 수집한 자료는 분산분석과 다중회귀분석을 통해 분석하였다. 연구결과 상대적 이점, 복잡성, 관찰가능성(MP3, 게임), 지각된 편리성 등의 요인들이 유의적인 영향을 미치는 것으로 나타났다.

Landfill and incineration tax was introduced in 2018 to reduce waste and promote recycling. However, there is a debate about tax rate. An analysis of the external effects of waste-treatment facilities is necessary, but first, an analysis of direct costs (construction, operation) is compulsory and must be conducted precisely. This study analyzed factors that affect operating cost. Ultimately, an estimation of annual operating cost was achieved by applying a multiple regression analysis to the previously-recorded data from 33 incineration facilities and 199 reclamation facilities. The results showed that incineration operating cost is affected by capacity, capacity utilization rate, and use of electricity. Annual landfill amount, area, and leachate treatment affect landfill operating cost, as well. The coefficient of determination is 0.6 or higher. Significance and collinearity between independent variables is at an acceptable level.

Wastewater treatment plant(WWTP) has been recognized as a high energy consuming plant. Usually many WWTPs has been operated in the excessive operation conditions in order to maintain stable wastewater treatment. The energy required at WWTPs consists of various subparts such as pumping, aeration, and office maintenance. For management of energy comes from process operation, it can be useful to operators to provide some information about energy variations according to the adjustment of operational variables. In this study, multiple regression analysis was used to establish an energy estimation model. The independent variables for estimation energy were selected among operational variables. The R2 value in the regression analysis appeared 0.68, and performance of the electric power prediction model had less than ±5% error.

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

The purpose of this study is to compare the relative growth of annual ring width of red pine(Pinus densiflora), black pine(Pinus thunbergii) and pitch pine(Pinus rigida) by means of multiple regression method according to Graybill hypothesis. The obtained results are as follows. 1. The changes of rainfall have affected to tree growth during the periods of 1975 through 1978. 2. Among these pine trees, red pine was mostly influenced by environmental factors. 3. The growth of annual ring width was sensitively responded to the changes of rainfall and air temperature. 4. Among the heavy metals analyzed, the concentrations(ppm) of Lead(Pb) and Copper(Cu) were negatively effected on the growth of annual ring width of pine trees. 5. The analytical technique of annual ring width may be useful for estimation of the pollution in forest areas near industrial complexes.

본 연구에서는 다중회귀분석을 이용하여 산악효과를 야기하는 지형인자와 강수와의 관계를 파악하였다. 섬 전체가 산악지형인 제주도의 연평균강수량과 지수홍수법으로 산출한 확률강우량을 강수자료로 사용하여 산악효과를 야기하는 지형인자로 선정한 고도, 위 경도와 회귀모형을 구성하였다. 회귀분석 결과 연평균강수량과 고도와의 선형관계가 확률강우량에서도 동일하게 나타났으며, 고도이외에 위도, 경도를 각각 추가인자로 고려할 경우 강우량과 더욱 강한 상관성을 보였다. 또한,

본 연구는 저수량 지역 빈도분석(regional low flow frequency analysis)을 수행하기 위하여 일반최소자승법(ordinary least squares method)을 이용한 Bayesian 다중회귀분석을 적용하였으며, 불확실성측면에서의 효과를 탐색하기 위하여 Bayesian 다중회귀분석에 의한 추정치와 t 분포를 이용하여 산정한 일반 다중회귀분석의 추정치의 신뢰구간을 비교분석하였다. 각 재현기간별 비교결과를 보면 t 분포를 이용하