• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.318-323
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• 2011

Illegal and unethical behaviors of the construction industry affect people's lives and health more than the same problems of the other industries. Among these behaviors, the construction industry is mostly criticized for bribery scandals. According to the survey of the Ministry of Justice in Taiwan over the past years, bribery cases involving public engineering projects and governmental procurements account for a rather large portion of the indicted corruption cases. Transparency International's "Bribe Payer Index" indicates people in construction-related industries are the most likely to pay bribes. Poor construction quality directly and indirectly caused by bribery poses a great threat to public safety, organizational reputation and economic development. However, there is a limited number of existing research on the bribery problem of the construction industry. This study is an empirical attempt to explore bribery intention and its affecting factors among the construction organizations in Taiwan by conducting a questionnaire survey. The theory of planned behavior was used in this study to build its research model (covering elements of attitude, subjective norm, perceived behavior control, and intention). Totally 431 valid samples were returned. To explore the factors affecting bribery intention, this study adopted Pearson's correlation analysis to discuss about the connections among the questionnaire respondents' attitudes to bribery, subjective norms, perceived behavior control, and bribery intention. A multi-regression analysis was then conducted to test if the planned behavior theory can effectively predict bribery intention. The research found (1) according to the results of Pearson's correlation analysis, the respondents' bribery intention, attitudes, subjective norms, and perceived behavior control are positively correlated with one another; (2) according to the results of the multi-regression analysis, bribery intention can be explained through attitudes, subjective norms, and perceived behavior control with an adjusted R² value of 0.591, meaning 59.1% of the bribery intention's variances can be explained through the three dimensions. In addition, each of the three dimensions has a significant influence on the respondents' behavior intentions.

• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1993년도 추계학술대회논문집
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.191-202
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• 2024

The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.

• 한국건축시공학회지
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• 제4권4호
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• pp.63-70
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• 2004

The objective of this study is to evaluate the compressive strength of recycled aggregate concrete by the non-destructive testing. Main experimental variables were the replacement level of recycled aggregate and blast-furnace slag, which were divided into two series according to recycled aggregate maximum size. Test results showed that a recycled aggregate had a significant influence on the non-destructive testing results, such as rebound number, Ultrasonic pulse velocity, and frequency. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results.

• 한국안전학회지
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• 제24권1호
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• pp.69-77
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• 2009

This paper reports the aging driver traffic accident severity modeling results. For the modeling, Poisson regression approach is applied using the data set obtained from the Korea Transportation Safety Authority's simulator-based driver aptitude test results. The test items include the estimations of moving objects' speed and stopping distance, drivers' multi-task capability, and kinetic depth perception and so on. The resulting model with the response variable of equivalent property damage only(EPDO) indicated that EPDO is significantly influenced by moving objects' speed estimation and drivers' multi-task capabilities. More interestingly, a comparison with the younger driver model revealed that the degradation of such capabilities may result in severer crashes for older drivers as suggested by the higher estimated parameters for the older driver model.

• 한국건설관리학회논문집
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• 제13권2호
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• pp.91-101
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• 2012

공공아파트 프로젝트의 기획단계에서 수행되는 적정 공사비 예측은 기획부터 유지관리까지 전(全) 단계에 걸쳐서 영향을 미치게 되므로 명확한 예측기준 및 방법이 제시되어야 한다. 그러나 현재까지 다양한 다중회귀모델을 활용한 공사비 예측 방법이 개발되어 왔으나, 정성변수를 포함하여 공사비를 예측하는 방법에 대한 연구는 부족한 상황이다. 따라서 본 연구에서는 기획단계 활용을 위한 정성변수를 포함하는 공사비 예측모델을 개발하고자 하였다. 이를 위하여 공사비 영향요인을 분석 및 추출하고, 회귀분석을 위한 독립변수를 선정하였다. 그리고 정성변수를 포함하는 공사비 예측모델을 개발하며 사례적용을 통한 검증을 실시하였다. 개발된 공사비 예측모델과 "RESAMPLING 기법"을 사용하여 구조형식별 공사비 가산비율을 제시하였다. 본 연구에서 제시한 더미회귀모델과 가산비율을 활용하면, 일반적인 공사비 예측과 함께 동일한 평형, 세대수, 연면적에서 평면형식과 구조형식을 변경시켰을 때의 공사비 예측이 가능할 것으로 기대된다.

• Genomics & Informatics
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• 제20권4호
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• pp.48.1-48.7
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• 2022

Penalized regression has been widely used in genome-wide association studies for joint analyses to find genetic associations. Among penalized regression models, the least absolute shrinkage and selection operator (Lasso) method effectively removes some coefficients from the model by shrinking them to zero. To handle group structures, such as genes and pathways, several modified Lasso penalties have been proposed, including group Lasso and sparse group Lasso. Group Lasso ensures sparsity at the level of pre-defined groups, eliminating unimportant groups. Sparse group Lasso performs group selection as in group Lasso, but also performs individual selection as in Lasso. While these sparse methods are useful in high-dimensional genetic studies, interpreting the results with many groups and coefficients is not straightforward. Lasso's results are often expressed as trace plots of regression coefficients. However, few studies have explored the systematic visualization of group information. In this study, we propose a multi-level polar Lasso (MP-Lasso) chart, which can effectively represent the results from group Lasso and sparse group Lasso analyses. An R package to draw MP-Lasso charts was developed. Through a real-world genetic data application, we demonstrated that our MP-Lasso chart package effectively visualizes the results of Lasso, group Lasso, and sparse group Lasso.

• 대한산업공학회지
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• 제39권5호
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• pp.393-402
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• 2013

The Mahalanobis-Taguchi System (MTS) is a diagnostic and predictive method for multivariate data. In the MTS, the Mahalanobis space (MS) of reference group is obtained using the standardized variables of normal data. The Mahalanobis space can be used for multi-class classification. Once this MS is established, the useful set of variables is identified to assist in the model analysis or diagnosis using orthogonal arrays and signal-to-noise ratios. And other several techniques have already been used for classification, such as linear discriminant analysis and logistic regression, decision trees, neural networks, etc. The goal of this case study is to compare the ability of the Mahalanobis-Taguchi System and logistic regression using a data set.

• 한국환경과학회지
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• 제16권11호
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• pp.1301-1312
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• 2007

The objective of this study is to propose a critical storm duration forecasting model on storm runoff in small river basin. The critical storm duration data of 582 sub-basin which introduced disaster impact assessment report on the National Emergency Management Agency during the period from 2004 to 2007 were collected, analyzed and studied. The stepwise multiple regression method are used to establish critical storm duration forecasting models(Linear and exponential type). The results of multiple regression analysis discriminated the linear type more than exponential type. The results of multiple linear regression analysis between the critical storm duration and 5 basin characteristics parameters such as basin area, main stream length, average slope of main stream, shape factor and CN showed more than 0.75 of correlation in terms of the multi correlation coefficient.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1449-1461
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• 2024

As technology advances, processor speeds are increasing at a rapid pace and digital systems require a significant amount of data bandwidth. As a result, careful consideration of signal integrity is required to ensure reliable and high-speed data processing. Crosstalk has become a vital area of research in signal integrity for electronic packages, mainly because of the high level of integration. Analytic formulas were analyzed in this study to identify the features that can predict crosstalk in multi-conductor transmission lines. Through the analysis, five variables were found and obtained a dataset consisting of 302,500, data points. The study evaluated the performance of various regression models for optimization via automatic machine learning by comparing the machine learning predictions with the analytic solution. Extra tree regression consistently outperformed other algorithms, with coefficients of determination exceeding 0.9 and root mean square logarithmic errors below 0.35. The study also notes that different algorithms produced varied predictions for the two metrics.