• 산업경영시스템학회지
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• 제46권1호
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• Journal of Korean Biological Nursing Science
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• 제25권3호
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• pp.160-171
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• 2023

Purpose: To identify factors influencing health-related quality of life for young single-person households, this study investigated physical and mental health status, health behavior, depression, resilience, and health-related quality of life. Methods: An online survey was administered to members of young single-person households from March 22 to 30, 2022. The data were analyzed using the chi-square test, independent t-test, one-way analysis of variance, Pearson correlation coefficients, multiple regression, and a simple mediation model applying the PROCESS macro model 4 with 95% bias-corrected bootstrapped confidence intervals. Results: The participants were 229 members of young single-person households. Health-related quality of life showed significant relationships with residence (t = 2.80, p = .006), month (F = 3.70, p = .026), mental health status (F = 20.33, p < . 001), and high-intensity exercise (F = 7.35, p = .001) among general and health-related characteristics. Health-related quality of life had significant correlations with depression (r = -.72, p < .001) and resilience (r = .58, p < .001). Multiple regression analysis showed that depression (β = -.57, p < .001) and resilience (β = .21, p < .001) influenced health-related quality of life. Moreover, resilience had a mediating effect between depression and health-related quality of life (indirect effect = -0.002, 95% bias-corrected bootstrapped confidence interval = -0.003 to -0.001). Conclusion: Members of young single-person households tended to be more vulnerable to emergency situations, such as during the coronavirus disease 2019 pandemic, when lockdowns and quarantines were frequent. To improve health-related quality of life in young single-person households, people with high levels of depression or low levels of resilience need special attention and support to promote mental health.

• 한국조리학회지
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• 제23권3호
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• pp.196-206
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• 2017

This study intended to examine the Korean restaurant customer's recognition about GIAP and to measure the reliability for them, and to figure out their effects on the customer's satisfaction and revisit intentions. Accordingly, research model had been set up and also hypotheses was made up according to pre-studies and pre-investigation results. Factor analysis and reliability analysis were concluded as valid results by the Cronbach's Alph for GIAP, customer satisfaction and revisit intention each other. Firstly, the effect of GIAP on the customer satisfaction was showed positive significance about four factors - perceived quality, reliability, authenticity and security on the customer satisfaction by the multiple regression analysis. Secondly, The effect of GIAP on the customer's revisit intention was showed positive significance about four factors - perceived quality, reliability, authenticity and security on the customer's revisit intention by the multiple regression analysis. Thirdly, the effect of the customer satisfaction on the customer's revisit intention was showed positive significance by the simple regression analysis. This study focused on medium level-cost Korean-pop restaurant so as to investigate general popular's recognition about the GIAP and intended to figure out their revisit intention. Therefore, the results are useful for increasing the food-service strategy system. Informations about the GIAP in restaurants can be credible to the restaurant customers.

• 보건교육건강증진학회지
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• 제23권4호
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• pp.89-104
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• 2006

Objectives: This study was done to find factors related with performance of hepatitis B Vaccination among some aged persons through health belief model. Methods: A questionnaire survey was made during September 2004 toward 230 elderly persons using institutions for the elderly of Gwangju City. The relations between subjects characteristics including health belief, mass media contact, hepatitis B experience and performance of hepatitis B vaccination were tested by t test or X2 test. Multiple logistic regression analysis was done to find final significantly related variables. Results: 24.8% of the subjects were vaccinated against hepatitis B. By simple analysis of relation between performance of hepatitis B vaccination and subjects characteristics including health belief, significant variables were chosen as 6 variables including perceived susceptibility, perceived seriousness, perception of benefits, knowledge on hepatitis B, age, experience of hepatitis through family or friend. After adjusting for confounding variables by multiple logistic regression analysis, hepatitis B vaccine performance showed significantly higher rate as the perception of disease seriousness increased(OR: 1.08, 95% CI: $1.03{\sim}1.14$) and in the group contacted with TV or radio information about hepatitis compared with non-contact. The group who experienced hepatitis among family or friends showed significantly higher hepatitis B vaccination performance rate compared with non-experienced. Conclusion: These results suggested that hepatitis B vaccine performance was related with health belief including hepatitis susceptibility, disease seriousness perception, acquisition of information through TV or radio and indirect hepatitis experience from family or friends.

• Journal of Korean Neurosurgical Society
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• 제62권4호
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• 자원환경지질
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• 제39권3호
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• pp.255-268
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• 2006

58개 기상관측소에서 최근 22년간(1981-2002) 측정된 기상 자료를 이용하여 국내의 기온(SAT) 및 지면온도(GST)의 시공간적 변동 경향을 분석하였다. 먼저 관측 자료로부터 각 관측소의 평균기온(MSAT)과 평균지면온도(MGST)를 계산하였으며, 다중선형회귀분석을 통해 MSAT와 MGST를 예측할 수 있는 회귀식을 산정하였다. 회귀모형의 회귀변수는 관측소의 위도 및 고도이다. 회귀모형의 추정치와 실제 관측값의 결정계수($R^2$)는 각각 0,92와 0.94로 나타나 모형의 예측 정확성이 매우 높은 것으로 분석되었다. MGST는 지열펌프 시스템 설계의 주요 입력 변수이므로 최근 지열에너지자원 활용 분야에서 매우 중요하게 다루어지는 변수이다. 따라서 제시된 회귀모형은 신뢰할만한 관측 자료가 없는 지역에서 MGST를 추정하는데 매우 유용하게 이용될 수 있을 것으로 예상된다. SAT 자료에 대한 선헝회귀분석을 통해 지구온난화 및 도시화에 기인한 기온 상승의 장기 추세 변동성을 탐색하였다. 1개 관측소를 제외한 57개 관측소에서 $0.005{\sim}0.088^{\circ}C/yr$ 범위의 기온증가율을 가지는 추세 변동이 확인되었다. 또한 GST에 영향을 미치는 기상요소로서 일사량, 지구복사, 강수량 및 적설량 자료를 분석하였다. GST는 주로 SAT 및 일사량에 의하여 결정되지만 강수 및 증발에 의한 토양의 열용량 변화, 적설에 의한 대기와 지표면 차단, 지구복사에 영향을 줄 수 있는 대기의 조건 변화 등이 복합적인 변동 요인으로 작용하는 것으로 나타났다.

• 한국건설관리학회논문집
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• 제5권5호
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• pp.93-100
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• 2004

For accurate construction contract time estimation, few parameters are more significant than crew production rates and factors affecting the rates. However, statistical analysis techniques for finding such factors are not always simple mainly because there are many factors and the interaction between factors is not well quantitatively understood. This paper presents methodology of identifying factors driving crew production rates. The methodology is further demonstrated with representative data collected by the author from 13 on-going highway constructions. Three factors were identified as statistically significant drivers of Cap crew production rate: 'Cap Size (m3/ea)'; 'Cap Length (m)'; and 'Cap Shape (Rectangle vs. Inverted 'T')'. It was also found that the production rates are best explained by a multiple regression model with two of the drivers; 'Cap Size' and 'Cap Shape'.

• 환경위생공학
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• 제17권4호
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• pp.61-67
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• 2002

NIRS(Near infrared spectroscopy) scanning from 1300nm to 2400nm was appl ied for the DEHP(di-(2 ethylhexyl)phthalate) in PVC(polyvinyl chloride_packing material. All samples were devided into calibration group and validation group. As a result of conduction the multiple regression analysis on the correlation between the NIR spectrum data and chemical assay value obtained by the Korea Food Sanitation Act. The validation model for measuring the DEHP content had R of 0.997, SEC of 0.132, SEP of 0.176 by MLR and R of 0.996, SEC of 0.142, SEP of 0.198 by PLS and the detection limit was 0.1%. The obtained results indicate that the NIR procedure can potentially be used as a nondestructive analysis method for the purpose of rapid and simple measurement of DEHP in PVC packing material.

• Journal of the Korean Data and Information Science Society
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• 제28권2호
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• pp.435-442
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• 2017

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}$ (p ${\geq}$ 3) under the quadratic loss from multi-variate normal population. We find a James-Stein type estimator which shrinks towards the projection vectors when the underlying distribution is that of a variance mixture of normals. In this case, the norm ${\parallel}{\theta}-K{\theta}{\parallel}$ is known where K is a projection vector with rank(K) = q. The class of this type estimator is quite general to include the class of the estimators proposed by Merchand and Giri (1993). We can derive the class and obtain the optimal type estimator. Also, this research can be applied to the simple and multiple regression model in the case of rank(K) ${\geq}2$.

• Asian-Australasian Journal of Animal Sciences
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• 제27권10호
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• pp.1406-1410
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• 2014

Age at first calving is an important trait for achieving earlier reproductive performance. To detect quantitative trait loci (QTL) for reproductive traits, a genome wide association study was conducted on the 96 Hanwoo cows that were born between 2008 and 2010 from 13 sires in a local farm (Juk-Am Hanwoo farm, Suncheon, Korea) and genotyped with the Illumina 50K bovine single nucleotide polymorphism (SNP) chips. Phenotypes were regressed on additive and dominance effects for each SNP using a simple linear regression model after the effects of birth-year-month and polygenes were considered. A forward regression procedure was applied to determine the best set of SNPs for age at first calving. A total of 15 QTL were detected at the comparison-wise 0.001 level. Two QTL with strong statistical evidence were found at 128.9 Mb and 111.1 Mb on bovine chromosomes (BTA) 2 and 7, respectively, each of which accounted for 22% of the phenotypic variance. Also, five significant SNPs were detected on BTAs 10, 16, 20, 26, and 29. Multiple QTL were found on BTAs 1, 2, 7, and 14. The significant QTLs may be applied via marker assisted selection to increase rate of genetic gain for the trait, after validation tests in other Hanwoo cow populations.