• International journal of advanced smart convergence
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• 제10권1호
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• Journal of Korea Artificial Intelligence Association
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• 제1권2호
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• 대한산업공학회지
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• 제40권3호
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• pp.283-290
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• 2014

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

• 한국과학교육학회지
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• 제16권1호
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• pp.13-34
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• 1996

The purpose of this study is to develop appropriate science teaching models which can be applied effectively to relevant situations. Five science teaching models; cognitive conflict teaching models, generative teaching model, learning cycle teaching model, hypothesis verification teaching model and discovery teaching model, were identified from the existing models. The teaching models were modified and in primary and secondary students using a nonequivalent pretest-posttest control group design. Major findings of this study were as follows: 1. For teaching science concepts, three teaching models were found more effective; cognitive conflict teaching model, generative teaching model and discovery teaching model. 2. For teaching inquiry skills, two teaching models were found more effective; learning cycle teaching model and hypothesis verification teaching model. 3. For teaching scientific attitudes, two teaching models were found more effective; learning cycle teaching models and discovery teaching model. Each teaching model requires specific learning environment. It is strongly suggested that teachers should select a suitable teaching model carefully after evaluating the learning environment including teacher and student variables, learning objectives and curricular materials.

• 한국항해항만학회지
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• 제27권3호
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• pp.307-313
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• 2003

Off-line ship chartering is done nearly through the brokers. Because of the international scale of chartering market, brokers spend too much times and costs on searching the most appropriate product which the consumers want. In this research, we propose the on-line Charter Product Categorization Model to search the products efficiently in the Cyber Chartering System. This Model will make concerned parties of the ship chartering to get unified product information efficiently, and the select the most appropriate product. In this research, we classified the ship chartering products into categories of cargo, ship type, and sea routes, and defined mutual relation of each products, and we verified that this classification is necessary to search the products through the product searching experiment.

• 환경정책연구
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• 제2권1호
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• pp.77-86
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• 2003

입도분포는 토양의 수리특성을 추정하는 데 많이 사용되고 있다. 본 연구는 다양한 가정조건을 가진 9개의 입도분포모형을 가지고 한국토양을 대상으로 어떤 모형이 가장 잘 입도분포를 모사하는지를 조사하였다. 4개의 추정변수를 가진 Fredlund모형, 로지스틱성장곡선, 그리고 Weibull분포가 다른 모형에 비해 PSD를 잘 모사하였다. 특히 추정변수가 없는 로지스틱 성장곡선 함수가 좋은 모사를 나타낸 것이 흥미로웠다.

• 한국인터넷방송통신학회논문지
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• 제9권5호
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• pp.169-174
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• 2009

본 논문에서는 정확한 분석 모델을 제시할 수 있는 객체 모델링과 정형화 방법을 통해 개발자와 사용자간 효과적인 의사소통을 제공하고 객체모델의 정형화와 표준화에 필요한 형식명세로의 변환 규칙을 제안한다. 사용자의 요구에 따라 최적의 객체모델 선택을 위한 객체 모델 검색 프로토타입을 제시한다. 이를 통해서 적합한 모델을 선택할 수 있으므로 소프트웨어 개발시 비용과 노력을 최소화할 수 있다.

• 대한산업공학회지
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• 제42권5호
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• pp.314-326
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• 2016

The purpose of variable selection techniques is to select a subset of relevant variables for a particular learning algorithm in order to improve the accuracy of prediction model and improve the efficiency of the model. We conduct an empirical analysis to evaluate and compare seven well-known variable selection techniques for multiple linear regression model, which is one of the most commonly used regression model in practice. The variable selection techniques we apply are forward selection, backward elimination, stepwise selection, genetic algorithm (GA), ridge regression, lasso (Least Absolute Shrinkage and Selection Operator) and elastic net. Based on the experiment with 49 regression data sets, it is found that GA resulted in the lowest error rates while lasso most significantly reduces the number of variables. In terms of computational efficiency, forward/backward elimination and lasso requires less time than the other techniques.

• 산업경영시스템학회지
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• 제33권4호
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• pp.10-22
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• 2010

The first full-scale u-City operation has started in Dongtan u-City. As local cities take over and operate the u-City, unexpected issues arise such as securing the budget of operating costs and self-providing the costs with business models utilizing the u-City assets. The paper presents a strategy for solving these issues. The strategy provides a foundation(infrastructure) for long-term operation models which may reduce the long-term operating costs. In order to establish the economic operating framework of u-City, suggested are some cost-reduction models based on the operating costs structure. For each model, a base framework with comparative analysis of operating costs is provided. With these models, each u-City may select a relevant model according to the characteristics of it. We hope that the framework provides the foundation for efficient and sustainable u-City operations.