Earticle

생활패턴의 변화는 통행의 목적을 변화시키기 때문에 궁극적으로 통행패턴의 변화를 야기한다. 이에 따라 본 연구에서는 1999년부터 2014년까지 통계청에서 조사한 생활시간조사자료를 활용하여 연도별 통행시간에 영향을 미치는 요인을 분석하고자 한다. 가구관련, 개인관련, 시간관련 변수들을 고려한 통행시간에 관한 다중선형회귀모형을 구축하였으며, 요일별차이를 보기위해 주중과 주말을 분리하여 추정하였다. 모형 추정 결과, 가구관련, 개인관련, 시간관련 변수가 통행시간에 영향을 미치는 것으로 분석되었다. 가구관련변수의 경우 개인관련변수에 비해 비교적 적은 변수들이 유의한 것으로 나타났는데, 이는 통행이 개인특성에더 큰 영향을 받기 때문인 것으로 판단된다. 각 활동시간들은 통행시간에 양의 영향을 미치는 것으로 분석되어 통행이 파생수요임을 나타내고 있다. 또한 여가활동시간이 가장 큰 영향력이 있는 것으로 도출되어, 여가를 위한 통행시간이 타활동에 비해 증가하는 것으로 분석되었다.

Changes in the life style might vary trip purposes, ultimately leading to the change in the travel behavior. Therefore, this study analyzed the factors affecting travel time change by using the time use survey data in Seoul, surveyed by the Statistics Korea in 1999~2014. We developed multiple linear regression models for travel time, considering individual, household and time-related variables as independent variables. The models were separately estimated weekday and weekend. the model results show that the household, individual, and time related variables have an significant effect on the travel time. In addition, travel time is more influenced by individual characteristics thn household ones. Each activity time positively affects the travel time, indicating that travel is derived demand. The variable that have the greatest influence on the travel time is the activity time for leisure.

본 연구는 선형-선형 다중회귀모형을 통해 여과속도, 탈진압력, 탈진간격, 입구농도 등의 운전조건에서 벤츄리 설치가 압력손실에 미치는 영향을 예측하여 충격기류식 여과집진장치의 효율적인 운전조건과 경제적인 설계 기초자료로 활용하 고자 하였다. 여과면적 6 m2인 파이로트형 충격기류식 여과집진장치를 이용하여 제철소 coke분진으로 여과속도, 입구농 도, 탈진압력, 탈진간격 및 벤츄리 설치 유·무의 조건에 따라 여과포 압력손실을 측정하였으며, 여과포 압력손실(P)의 변화에 기여하는 여과속도(Vf), 입구농도(Ci), 탈진압력(PP), 탈진간격(Pi), 벤츄리 설치 유·무 등에 따른 영향력을 파악하 기 위해 SAS 9.4(SAS Institute, USA) 프로그램으로 통상적인 최소자승 추정법(Ordinary Least Square; OLS)인 선형-선 형의 다중회귀모형 함수를 이용하여 벤츄리가 압력손실에 미치는 영향을 예측하였다. 예측 결과를 종합해 보면 압력손실 최소화를 위해서 벤츄리를 설치할 경우 20.3% 효율 향상이 기대되며, 압력손실에 기여하는 순서는 여과속도(Vf) > 탈진 압력(Pp) > 입구농도(Ci) > 탈진간격(Pi)임을 알 수 있었다.

The aim of this study is to predict the pressure drop due to the presence or absence of Venturi under various operating conditions such as filtration velocity, pulse pressure and interval, and dust concentration using linear-linear regression model and use it as an efficient operating condition and economic data for a shock-type filter collection system. A pilot filtration collector with a filter area of 6 m2 was used, and the filtering pressure drop was measured according to filtration velocity, inlet concentration, pulse pressure, pulse interval, and the flow and absence conditions of the Venturi installation by a steel plant. The SAS 9.4 program (SAS Institute, USA) was used to predict and to determine the effects of filtration velocity (Vf), inlet concentration (Ci), pulse pressure (Pp) and interval (Pi), presence or absence of venturi, etc. and also analyze the changes in pressure drop according to the presence of Venturi using the linear-linear multiple regression model, and the least-squares estimation method (OLS). The results re shown below. In order to minimize the pressure drop, the improvement in efficiency is expected to be 20.3% when the Venturi is installed, and the order of contributing to the pressure drop is filtration rate> pulse pressure> inlet concentration> pulse interval.

광산란법을 이용한 초미세먼지 측정기는 초 단위의 측정이 가능하고 휴대할 수 있는 크기로 설계될 수 있다. 또한 하나의 센서로 여러 크기별 (PM1.0, PM2.5, PM4.0, 및 PM10) 농도를 측정할 수 있다. 이 방식은 입자의 개수와 크기를 측정하고 이를 단위 부피당 무게인 농도로 변환하는 과정 때문에 큰 밀도를 가지는 황사에 대해서는 큰 오차를 나타낸다. 본 논문은 광산란 초미세먼지 측정기가 여러 크기별 PM 농도를 이용하여 황사 발생 시 초미 세먼지(PM2.5)의 농도의 오차를 정확히 보정할 수 있고, 황사가 발생하지 않을 때도 영향을 받지 않는, 다중 선형 외기 기법의 기계학습에 의한 보정 기법을 제시한다. 두 가지 또는 세 가지의 PM 크기 입력만으로도 광산란 미세 먼지 측정 장치의 황사 오류를 크게 보정할 수 있음을 보인다. 한 달 동안 중부권대기환경연구소의 베타레이 측정 기와 광산란 측정기의 측정값을 비교·분석하였다. 황사가 없는 구간에서 이 두 장비의 상관계수(R2)는 0.927이었 고, 황사를 포함한 전 구간에서 상관계수는 0.763이었지만, 기계학습을 통하여 상관계수가 0.944로 향상되었다.

Light-scattering fine particulate matter monitors can measure particulate matter (PM) concentrations in every second and can be designed in a portable size. They can measure the concentrations of various PM sizes (PM1.0, PM2.5, PM4.0 and PM10) with a single sensor. They measure the number and size of particulate matters and convert them to weight per volume (concentration). These devices show a large error for asian dust. This paper proposes a scheme that compensates the PM2.5 concenstration error for asian dust by multiple linear regression machine learning in light-scattering PM monitors. This scheme can be effective with only two or three types of PM sizes. The experimental results compare a beta-ray PM monitor of national institute of environmental research and a light-scattering PM monitor during a month. The correlation coefficient (R2) of theses two devices was 0.927 without asian dust, but it was 0.763 due to asian dust during the entire experimental period and improved to 0.944 by the proposed machine learning.

Recent years, as China is experiencing a rapid development, the educational investments are undergoing a sharp increase compared to past decades. Educational investment has become a crucial aspect that deeply influencing the future development of a country. What's more importantly, knowing the future investments of education is a crucial affair that decides the future tendency of related projects such as government management, cooperation investment and personnel training. Decision makers cannot make any effective decisions without knowing the future investments of education under all these circumstances. However, it is fairly difficult for people to obtain the predicted data of educational investments without the aid of mathematical and computational modeling. Therefore, in this article, we aim at presenting a series of solutions for the prediction of educational investment for China based on multiple linear regression (MLR), artificial neural networks (ANNs) and grey model GM (1,1). Multiple comparisons are made for deciding whether model should be used under different external conditions. Our research successfully shows that all these models are available for practical applications and scientists and other related people can choose their suitable models alternatively for the sake of making a better prediction under different circumstances.

With the development of Chinese economics, there is an increasing tendency that people have the abilities to purchase private vehicles. However, the increase of private vehicles rapidly increases the air pollution. Therefore, it is necessary that we should study the change regulation of the private vehicle ownership in Chinese area. Due to the impact factors of the change regulation of the private vehicle ownership is various and uncertain, we should take various factors into consideration as much as possible. In this article, we take 11 indicators as the independent variables, while the private vehicle ownership as the dependent variable. Multiple linear regression (MLR) model and artificial neural networks (ANNs) models were developed respectively in order to predict the private vehicle ownership. As an alternative model, we developed grey model GM (1, 1) according to the regulation of the private vehicle ownership. Detailed comparisons among the MLR, ANNs and GM (1, 1) models were made and results show that they have different advantages respectively. Our results shows that all the three types of computation models can be used for the prediction of the private vehicle ownership in Chinese area.

Texture harmony pursues a suitable texture matching to meet the customers Kansei image requirements. The texture harmony method which is based on Kansei Engineer is developed. Several questionnaires are made to obtain the Kansei words, design elements and texture factors. The representative Kansei words, representative design elements and the representative texture factors are selected using Pareto Diagram, Likert scale, multidimensional scaling analysis and cluster analysis. After developing the virtual samples, the respondents are asked to evaluate the Kansei image score of each sample according to the different Kansei image word. The Kansei image evaluating matrix is obtained by combining the Kansei image score with the texture combination code. The multiple linear regression model is supposed to explain the relationship of the Kansei image score and the texture factors. Based on the Kansei image evaluate matrix, the hypothesis is verified using the SPSS software. The case of electric kettle texture harmony design is studied to verify the method. The method can facilitate designers work, and lay a foundation of computer aided texture design system.

본 연구의 목적은 관람 서비스 국립수목원 방문 서비스 질이 국립수목원의 방문 만족에 미치는 영향을 규명하고자 함이다. 본 연구에서는 서비스 질 척도를 국립수목원 관람 환경에 맞게 개발하여 가설을 설정하였다. 또한 조사는 설문조사를 통하여 데이터를 구축하였으며 2005년에서 2007년까지 1,772명의 방문객을 대상으로 조사한 것이다. 기술통계분석과 요인분석, 다중회귀분석을 실시하여, 1) 요인분석의 결과로 5개의 방문 서비스 유형으로 확신성, 대응성, 유형성, 공감성, 신뢰성 유형을 추출하였고 2) 회귀분석을 통하여 서비스질은 1% 유의수준 내에서 유의한 것으로 분석되었으며 3) 확신성의 상대적 중요도는 공감성에 비해 2.58배 중요한 것으로 나타났다. 연구 결과들은 국립수목원의 교육적 가치와 경관매력요소의 만족, 자원관리에 대한 만족이 전반적 방문만족을 높이는 매우 중요한 요소라는 것을 시사하고 있다

The purpose of this paper was to identify the effects of visitor service quality affecting visitor satisfaction in Korea National Arboretum (KNA). After reviewing the literature, this research selected and developed the indicators visitor's service (VS) and has formulated the hypothesis of this research. This research has obtained data through a questionnaire, which surveyed 1,772 visitors in 2005~2007. I have analyzed the data using descriptive statistical methods, factor analysis, Pearson's correlation analysis, and multiple linear regression method. We found that 1) the results of factor analysis, the five factors of visitor's service (FVSs) i.e., assurance, responsiveness, tangibles, empathy, reliability have been extracted. 2) in multiple regression, FVSs have been statistically significant at one percent level, and 3) the relative contribution of the assurance of VS on user's satisfaction has been determined to have respectively 2.58 times more importance than that of the empathy of VS. The research results suggest that educational value, satisfaction of scenery attraction, and satisfactory management of resources were the most important factors in KNA to increase visitor's satisfaction.

Forecasting quality of complex products is a major concern for quality engineers and enterprises' decision-makers. But only a few researchers have investigated how multiple linear regression analysis can improve forecasts. This article presents a predictive control model for personalized forecasting system of complex product quality using multiple linear regression analysis. First, this paper compares the performance of personalized forecasting system for product quality which leads to a better understanding of the applicability of the forecasting model. Second, this paper identifies the effect of predictive control model of multiple linear regression analysis on forecasting accuracy. Then, an example shows the feasibility of using predictive control model in complex product quality personalized forecasting. Finally, it makes a conclusion that the approved prediction system embraces superiority over the prediction of the quality factors and manufacturing resources.

한자 급수와 같이 기존 한자 난이도 선정 방식에 문제점이 있다. 실생활에서 쓰이는 한글 단어와 차이가 나며 해당 급수가 실제로 얼마나 많이 쓰이는지 알 수가 없다. 이러한 문제를 해결하기 위해 빈도수를 이용하여 다중 회귀 분석을 이용하여 한자 난이도를 측정한다. 초등 교과서를 기반으로 한자활용빈도수와 한글의미빈도수를 집계한다. 두 빈도수와 획수를 함께 사용하여 설문지를 작성하여 해당 한자의 학습 적정 시기를 답변 받아 이를 회귀에서 사용할 타겟 변수로 이용한다. 단계별 회귀분석을 이용하여 적절한 피처를 선택하고 다중 선형 회귀 분석을 한다. 모델의 R2는 0.1105가 나왔으며 RMSE는 0.1105의 결과가 나왔다.

There is a problem with the existing method of selecting the difficulty levels of Hanja characters. Some Hanja characters selected by the existing methods are different from Sino-Korean words used in real life and it is impossible to know how many times the Hanja characters are used. To solve this problem, we measure the difficulty of Hanja characters using the multiple regression analysis with the frequency as the features. Based on the elementary textbooks, FWS and FHU are counted. A questionnaire is written using the two frequencies and stroke together to answer the appropriate timing of learning the Hanja characters and use them as target variables for regression. Use stepwise regression to select the appropriate features and perform multiple linear regression. The R2 score of the model was 0.1105 and the RMSE was 0.1105.

국내 석유 시장은 국제 석유 가격의 변동에 매우 민감하기 때문에 그 변동성에 대한 파악과 대처가 중요하다. 특히, 높은 소비량을 보이는 휘발유의 가격이 어떠한 요인에 인해 변화하는지 명확하게 파악하는 것이 필요하다. 국 제 휘발유 가격은 휘발유 수급, 지정학적 사건, 미국 달러화 가치 변동 등 글로벌 요인에 영향을 받는다. 그러나 기존 의 연구들은 휘발유의 수급에만 초점에 맞추어 진행하였다는 한계가 존재한다. 본 연구에서는 다양한 머신러닝 기반 의 회귀 모델을 활용하여 거시적 경제지표와 국제 휘발유 가격 간의 인과관계를 탐색한다. 첫째, 다양한 세계 경제지 표 데이터를 수집한다. 둘째, 데이터 전처리를 진행한다. 셋째, 다중선형회귀, Ridge 회귀, Lasso(Least Absolute Shrinkage and Selection Operator) 회귀 모델을 활용하여 모델링한다. 실험 결과, 테스트 데이터 셋에서 다중선형회 귀 모델이 가장 높은 정확도(97.3%)를 보였다. 우리는 국제 휘발유 가격의 예측은 국내 경제 안정성과 에너지 정책 결정에 도움이 될 수 있을 것으로 기대한다.

The domestic petroleum market is highly sensitive to changes in international oil prices. So, it is important to identify and respond to those changes. In particular, it is necessary to clearly understand the factors causing the price fluctuations of gasoline, which exhibits high consumption. International gasoline prices are influenced by global factors such as gasoline supplies, geopolitical events, and fluctuations in the U.S. dollar. However, previous studies have only focused on gasoline supplies. In this study, we explore the causal relationship between economic indicators and international gasoline prices using various machine learning-based regression models. First, we collect data on various global economic indicators. Second, we perform data preprocessing. Third, we model using Multiple linear regression, Ridge regression, and Lasso(Least Absolute Shrinkage and Selection Operator) regression. The multiple linear regression model showed the highest accuracy at 96.73% in test sets. As a result, Our Multiple linear regression model showed the highest accuracy at 96.73% in test sets. We will expect that our proposed model will be helpful for domestic economic stability and energy policy decisions.

이 연구는 북서태평양에서 여름철(7-9월) 동안 발생하는 태풍 빈도를 예측하기 위한 다중회귀모델을 4가지 원격패턴을 이용하여 개발하였다. 이 패턴은 4-5월 동안 동아시아 대륙에서의 시베리아 고기압 진동, 북태평양에서의 북태평양 진동, 호주근처의 남극진동, 적도 중앙태평양에서의 대기순환으로 대표된다. 이 통계모델은 이 모델로부터 예측된 높은 태풍발생빈도의 해와 낮은 태풍발생빈도의 해 사이에 차를 분석함으로써 검증되었다. 높은 태풍발생빈도의 해에는 다음과 같은 4가지의 아노말리 특성을 나타내었다: i) 동아시아 대륙에 고기압성 순환 아노말리(양의 시베리아 고기압진동), ii) 북태평양에 남저북고의 기압계 아노말리, iii) 호주 근처에 저기압성 순환 아노말리(양의 남극진동), iv) 봄부터 여름 동안 니뇨3.4 지역에 저기압성 순환 아노말리. 따라서 적도 서태평양에서 무역풍 아노말리는 양반구의 아열대 서태평양에 위치한 저기압성 순환 아노말리에 의해 약화되었다. 결국, 이러한 기압계 아노말리의 공간분포는 열대 서태평양에 대류를 억제하는 대신 아열대 서태평양에 대류를 강화시켰다.

This study has developed a multiple linear regression model (MLRM) for the seasonal prediction of the summer tropical cyclone genesis frequency (TCGF) over the western North Pacific (WNP) using the four teleconnection patterns. These patterns are representative of the Siberian high Oscillation (SHO) in the East Asian continent, the North Pacific Oscillation (NPO) in the North Pacific, Antarctic oscillation (AAO) near Australia, and the circulation in the equatorial central Pacific during the boreal spring (April-May). This statistical model is verified by analyzing the differences hindcasted for the high and low TCGF years. The high TCGF years are characterized by the following anomalous features: four anomalous teleconnection patterns such as anticyclonic circulation (positive SHO phase) in the East Asian continent, pressure pattern like north-high and south-low in the North Pacific, and cyclonic circulation (positive AAO phase) near Australia, and cyclonic circulation in the Nino3.4 region were strengthened during the period from boreal spring to boreal summer. Thus, anomalous trade winds in the tropical western Pacific (TWP) were weakened by anomalous cyclonic circulations that located in the subtropical western Pacific (SWP) in both hemispheres. Consequently, this spatial distribution of anomalous pressure pattern suppressed convection in the TWP, strengthened convection in the SWP instead.

This paper focuses on the developed empirical models for the prediction on top-bead width in GMA(Gas Metal Arc) welding process. Three empirical models have been developed: linear, curvilinear and an intelligent model. Regression analysis was employed fur optimization of the coefficients of linear and curvilinear model, while Genetic Algorithm(GA) was utilized to estimate the coefficients of intelligent model. Not only the fitting of these models were checked, but also the prediction on top-bead width was carried out. ANOVA analysis and contour plots were respectively employed to represent main and interaction effects between process parameters on top-bead width.

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

The development of information technology is bringing many changes to everyday life, and machine learning can be used as a technique to solve a wide range of real-world problems. Analysis and utilization of data are essential processes in applying machine learning to real-world problems. As a method of processing data in machine learning, we propose an approach based on applying multiple linear regression models by interlacing data to the task of classifying similar software. Linear regression is widely used in estimation problems to model the relationship between input and output data. In our approach, multiple linear regression models are generated by training on interlaced feature data. A combination of these multiple models is then used as the prediction model for classifying similar software. Experiments are performed to evaluate the proposed approach as compared to conventional linear regression, and the experimental results show that the proposed method classifies similar software more accurately than the conventional model. We anticipate the proposed approach to be applied to various kinds of classification problems to improve the accuracy of conventional linear regression.

Four kinds of $Si_3N_4$-based ceramic cutting tools with different sintering time were fabricated to investigante the effect of sintering time on the microstructure, mechanical properties, grain sizes and the cutting performance. An endeavor was also made to determine the relation among mechanical property, Brain size and tool life. $Si_3N_4$ home made cutting tool sintered for 1 hour under $1760^{\circ}$ temperature and 25MPa pressure showed the best cutting performance among selected ceramic tools during machining both Bray cast iron and heat treated SCM440. Multiple linear regression model was used to estimate the tool lift from mechanical property, grain size and showed good result. It was also shown that hardness imposed the biggest offect on tool life.

Rice blast is the most serious disease of rice due to its harmfulness and its world wide distribution. $Magnaporthe$ $grisea$ is the cause of rice blast disease and destroys rice enough to feed several tens of millions of people each year. Fungicides are commonly used to control rice blast. But $M.$ $grisea$ acquires resistance to chemical treatments by genetic mutations. 2-Phenylimino-1,3-thiazolines were proposed as a novel class of fungicides against $M.$ $grisea$ in the previous study. To develop compounds with a higher biological activity, a new series of 2-phenylimino-1,3-thiazolines was synthesized and its fungicidal activity was determined against $M.$ $grisea$. The QSAR analysis was carried out on a series of 2-phenylimino-1,3-thiazolines. The QSAR results showed the dependence of fungicidal activity on the structural and physicochemical features of 2-phenylimino-1,3-thiazolines. Our results could be used as guidelines for the study of the mode of action and further design of optimal fungicides.

The relationships among the concentrations of chemical compositions, scores of sensory quality and price were studied based on 112 jasmine tea samples. Linear correlation analysis showed that 5 quality attributes (appearance, liquor color, aroma, taste and infused leaves) were positively correlated with each other and to the sum. 5 quality attributes and total quality score were logarithm relevant to price with the coefficients of determination R2 were 0.855, 0.667, 0.836, 0.900, 0.906 and 0.914. The six taste attributes could be grossly divided into two groups. Tenderness, sweetness, freshness and mellowness were grouped showing positive correlation with taste score, total quality score and price, while heaviness and thickness were grouped for opposite correlation. There were positive correlation within the same group and negative correlation between two different groups. The concentrations of polyphenols, amino acid, water extract, gallocatechin (GC), epigallocatechin (EGC), catechin (C), epicatechin (EC), epigallocatechin gallate (EGCG), gallocatechin gallate (GCG) and epicatechin gallate (ECG) in tea infusions were negatively correlated with tenderness, sweetness, freshness, mellowness, taste score, total quality score and price but positively correlated with heaviness and thickness (except some individual components), while gallic acid (GA) was the opposite. Multiple linear regression equations of taste score and total quality score were established. The back substitution was of high correlation between predicted and actual value and acceptance rate was over 90%.

The feature of resolution enhancement by derivative operation is linked to one of the multivariate analysis, which is multiple linear regression with two options, all possible and stepwise regression. Examined samples were synthetic mixtures of 5 vitamins, thiamine mononitrate, riboflavin phosphate, nicotinamide, pyridoxine hydrochloride and ascorbic acid. All components in mixture were quantified with reasonably good accuracy and precision. Whole data processing procedure was accomplished on-line by the development of three computer programs written in APPLESOFT BASIC language.

This paper is concerned with the detection of multiple change-points in linear regression models. The proposed procedure relies on the local estimation for global change-point estimation. We propose a multiple change-point estimator based on the local least squares estimators for the regression coefficients and the split measure when the number of change-points is unknown. Its statistical properties are shown and its performance is assessed by simulations and real data applications.

In recent years, many people use R as a statistics system. R is frequently updated by many R project teams. We are interested in the method of multiple outlier detection and know that R is not supplied the method of multiple outlier detection. In this talk, we review these procedures for detecting multiple outliers and provide more efficient procedures combined with direct methods and indirect methods using R.