• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.158-158
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• 2019

Recent studies show the possibility of more frequent extreme events as a result of the changing climate. These weather extremes, such as excessive rainfall, result to debris flow, river overflow and urban flooding, which post a substantial threat to the community. Therefore, an effective flood model is a crucial tool in flood disaster mitigation. In recent years, a number of flood models has been established; however, the major challenge in developing effective and accurate inundation models is the inconvenience of running multiple models for separate conditions. Among the solutions in recent researches is the development of the combined 1D-2D flood modeling. The coupled 1D-2D river flood modeling allows channel flows to be represented in 1D and the overbank flow to be modeled over two-dimension. To test the efficiency of this approach, this research aims to assess the capability of HEC-RAS model's implementation of the combined 1D-2D hydraulic simulation of river overflow inundation, and compare with the results of GERIS and FLUMENS 2D flood model. Results show similar output to the flood models that had used different methods. This proves the applicability of the HEC-RAS 1D-2D coupling method as a powerful tool in simulating accurate inundation for flood events.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.4
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• pp.72-77
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• 2002

The load interaction effect can be best illustrated by the phenomenon of overload retardation. Some prediction methods for retardation are reviewed and the problems discussed in the present paper. The so-called under-load effect much of the retardation disappears if a very low level minimum stress follows the overload, is also of importance for a prediction model to work properly under random load spectrum. The concept of Interactive Zone (IZ) fully considering reversed plasticity during unloading was discussed. This IZ concept can be combined with existing models to derive some improved models that can naturally take account of the under-load effect. Some simulations by IZ improved models for test under complex load sequences including multiple overloads and both over/under loads are compared with test results. It is seen that the improvement by IZ concept greatly enhanced the ability of existing models to accommodate complex load interaction effects.

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.7
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• pp.1015-1018
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• 1999

Single trait mixed models have been dominantly utilized for genetic evaluation of the reproductive traits in swine. However employing multiple trait approach may lead to more accurate genetic evaluations. For 5 litter size and litter weight traits of Danish Landrace, genetic parameters were estimated with a multiple trait mixed model. The heritability estimates were 0.02, 0.03, 0.03, 0.05, and 0.07, respectively for litter size at birth, litter size born alive, litter weight at birth, litter size at weaning, and litter weight at weaning. Negative genetic correlations were all positive. The litter weight at birth showed genetic antagonism with litter size born alive (-0.65) and litter size at weaning (-0.31), but positive with litter size at birth (0.47) and litter weight at weaning (0.31). The estimates of environmental correlations were larger than their corresponding genetic correlation estimates except for those between litter weight at birth and the other four traits. This study recommends simultaneous selection for two or more traits with multivariate mixed models in order to improve overall economic response.

• Korean journal of applied entomology
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• v.56 no.1
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• pp.1-18
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• 2017

Temperature-dependent development model is an essential component for forecasting models of insect pests as well as for insect population models. This study reviewed the nonlinear models which explain the relationship between temperature and development rate of insects. In the present study, the types of models were classified largely into empirical and biophysical model, and the groups were subdivided into subgroups according to the similarity of mathematical equations or the connection with original idea. Empirical models that apply analytical functions describing the suitable shape of development curve were subdivided into multiple subgroups as Stinner-based types, Logan-based types, performance models and Beta distribution types. Biophysical models based on enzyme kinetic reaction were grouped as monophyletic group leading to Eyring-model, SM-model, SS-mode, and SSI-model. Finally, we described the historical development and characteristics of non-linear development models and discussed the availability of models.

• Journal of Korea Trade
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• v.26 no.3
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• pp.63-78
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• 2022

Purpose - This study's purpose is to analyze which factors are more important to strengthening the competitiveness of global innovative companies by firstly sampling global 40 enterprises, secondly investigating of study models empirically, thirdly finding out significant implications through research, and finally using this result to help improve global companies' competitive edges. Design/methodology - Developing three research models of hypothesis and using 5 variables such as technology innovation, knowledge management, human resource development, sustainable management, and corporate life, this study was empirically carried out by reliability and validity testing, correlation analysis of variables, and multiple regression analysis of three research models. Findings - Through proceeding empirical analysis study, we found out that technology innovation and sustainable management had a significant impact on strengthening competitiveness through the hypothesis test. Those two factors had positive results and a synergy effect through correlation analysis along with process change and human resource development, which are also important areas in global innovative companies. Originality/value - In line with the fourth industrial revolution era's acceleration and COVID-19's large impact on all industries, global companies are newly developing their business models to cope with external environment change. This study's results would be meaningful for global enterprises and domestic companies to improve their overall competitive edge by reinforcing their innovation strategy, preparing next growth engines, diversifying business portfolios, and setting business milestones.

• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.63-68
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• 2008

This study was carried out to find a useful method to predict hardness of tomato using optical spectrum data. Optical spectrum of reflectance and transmittance data were collected processed by 9 kind of preprocessing methods-normalizations of mean, maximum and range, SNV (standard normal variate), MSC (multiplicative scatter correction), the first derivative and second derivative of Savitzky-Golay and Norris-Gap. With the preprocessed and non-processed original spectrum data, prediction models of hardness of tomato were developed using analytical tools of PLS (partial least squares) and MLR (multiple linear regression) and tested for their validation. The test of validation resulted that the analytical tools of PLS and MLR output similar performances while the transmittance spectra showed much better result than the reflectance spectra.

• Korean Journal of Health Education and Promotion
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• v.17 no.2
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• pp.35-56
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• 2000

This study is research on physical health and mental health(physical and mental symptom at school) in perceived school environment among adolescents. The study subjects ere selected by senior high school students in seoul and random sampling. The 3,060 subjects were selected 18 schools. The duration for survey was for Nov. 25-Dec. 13, 1998. The reliability of Questionnaire was Cronbach's $\alpha=0.95$. This study used multiple regression through Factor Analysis in SPSS programs. The major findings of this study are as follows: (1) All of Multiple Regression Models were significant. (p<0.001). (2) Physical Mental health is related to gender, personal environment, and economic status. (3) Physical Health is related to perceived school environmental variable (therapeutic teacher-student relationships, classrom climate, and school life satisfaction) among adolescents. (4) Mental Health is related to perceived school environment variables(therapeutic teacher-student relationships, classroom climate, school life satisfaction, teachers' climate, and classmates' attitudes) among adolescents. This study could be used as the basis for the development of educational program, counseling, teacher in-service training, student teacher training and the establishment of educational and health policy.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1633-1641
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• 2020

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network. Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results: The R² values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R² values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.4
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• pp.609-628
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• 1995

This paper presents a reliability model and a data-analytic procedure for a repairable unit subject to failures due to multiple non-identifiable causes. We regard a failure cause as a state and assume the life distribution for each cause to be exponential. Then we represent the dependency among the causes by a Markov switching model(MSM) and estimate the transition probabilities and failure rates by maximum likelihood(ML) method. The failure data are incomplete due to masked causes of failures. We propose a specific version of EM(expectation and maximization) algorithm for finding maximum likelihood estimator(MLE) under this situation. We also develop statistical procedures for determining the number of significant states and for testing independency between state transitions. Our model requires only the successive failure times of a unit to perform the statistical analysis. It works well even when the causes of failures are fully masked, which overcomes the major deficiency of competing risk models. It does not require the assumption of stationarity or independency which is essential in mixture models. The stationary probabilities of states can be easily calculated from the transition probabilities estimated in our model, so it covers mixture models in general. The results of simulations show the consistency of estimation and accuracy gradually increasing according to the difference of failure rates and the frequency of transitions among the states.