• 대한토목학회논문집
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• 제42권4호
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• pp.571-579
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• 2022

본 연구는 부산·울산·경남 지역의 관광호텔을 대상으로 에너지사용량과 이들의 개별적인 호텔특성 및 입지특성 간의 관계를 실증분석하였다. 복합적인 관계식 도출을 위해 다중회귀모형에서부터 다수준회귀분석(multi-level regression analysis)으로 모형을 확장하였고, 이를 통해 건축물의 개별적인 특성만을 고려한 대부분의 선행연구에서 나아가 호텔이 위치한 지역의 입지적 특성과 호텔-지역 간 위계적 구조를 고려하여 좀 더 개선된 모형을 도출하였다. 분석결과에 따르면, 호텔의 규모, 연한, 서비스 등급과 같은 개별적인 특성은 에너지사용량을 설명하는 주요 변수이고, 그들의 영향은 지역적으로 유의한 차이를 보이는 것으로 나타났다. 또한, 중심상업지에 인접하거나 다수의 관광호텔이 밀집한 지역에 위치할수록 에너지사용량은 달라지는 것으로 나타났으며, 이러한 입지특성 또한 개별호텔의 에너지사용량을 설명함에 있어 주요한 요인임을 확인하였다. 이와 같은 결과는 건축물단위의 에너지정책과 소비수준이 높고 에너지 집약시설이 밀집한 지역에 대한 지역단위의 에너지정책이 함께 고려될 필요성을 시사하며, 관광산업의 지속가능성을 높이기 위한 지역적 책임을 제언한다.

• 스마트미디어저널
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• 제12권9호
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• pp.81-88
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• 2023

최근 코로나19로 인한 비대면 서비스의 확산으로 온라인을 통한 소통이 증가하고 있다. 비대면 상황에서는 텍스트나 음성, 이미지 등의 모달리티를 통해 상대방의 의견이나 감정을 인식하고 있다. 현재 다양한 모달리티를 결합한 멀티모달 감정인식에 관한 연구가 활발하게 진행되고 있다. 그중 음성 데이터를 활용한 감정인식은 음향 및 언어정보를 통해 감정을 이해하는 수단으로 주목하고 있으나 대부분 단일한 음성 특징값으로 감정을 인식하고 있다. 하지만 대화문에는 다양한 감정이 복합적으로 존재하기 때문에 다중 감정을 인식하는 방법이 필요하다. 따라서 본 논문에서는 복합적으로 존재하는 내재된 감정인식을 위해 음성 데이터를 전처리한 후 특징 벡터를 추출하고 시간의 흐름을 고려한 다중 감정 회귀 모델을 제안한다.

• 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.

• 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.

• 한국환경보건학회지
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• 제35권3호
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• pp.191-200
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• 2009

We investigated a trait of odor and the relationship between odor unit and odorous compounds using multiple regression analysis based on data compiled from Sihwa (SIC), Banwol (BIC), Banwol plating (BPIC) and Poseung industrial complex (PIC). These areas are odor control areas in Gyeonggi province. It was revealed that $NH_3$ and styrene concentrations in SIC and BPIC were relatively higher and $H_2S$ concentration especially in mc was more than five times higher than other areas. As a result of regression analysis using SAS, intensity of odor unit was highly related to concentrations of $H_2S$, TMA, styrene and n-valeraldehyde in SIC, $H_2S$, acetaldehyde, and butyraldehyde in BPIC and $NH_3$ in BIC.

• 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.

• 대기
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• 제25권4호
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• 한국치위생학회지
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• 제16권5호
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• pp.669-676
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• 2016

Objectives: The purpose of the study was to investigate the related factors of scaling experience of multi-cultural adolescents in Korea. Methods: The subjects were 698 multi-cultural adolescents from web-based survey of the 11th(2015) Korean Youth Risk Behavior. Multi-cultural adolescents are defined as the children of marriage migrant women. The study instruments included demographical characteristics of the subjects, oral health behaviors, daily tooth brushing times, health behaviors, and experience of smoking and alcohol consumption. Data were analyzed using PASW statistics 18.0. Results: The experience rate of scaling was 18.8%. Multiple logistic regression analysis revealed that experience of scaling were related with experiences of sealant and fruit consumption. Conclusions: It is very important to provide the continuing oral health prevention program for the adolescents and investigate the cost-benefit effectiveness of oral health care program.

• 한국정밀공학회지
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• 제21권9호
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• pp.127-134
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• 2004

It is difficult to have the existing structural optimization using coupled field analysis from CFD to structure analysis when the structure is influenced of fluid. Therefore in an initial model of this study after doing parameter design from the background of shape using topology optimization. and it is making a approximation formula using by the CFD-structure coupled-field analysis and design of experiment. By using this result, we conducted multi-objective optimization. We could confirm efficiency of stochastic method applicable in the scene of structure reliability design to be needed multi-objective optimization. And we presented a way of design that could overcome the time and space restriction in structural design such as the butterfly valve with the less experiment.