• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.702-703
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• 2015

Currently, according to the climate change, serious damage by Typhoon has been occurred in the world. In this respect, the research on the damage prediction model to minimize the damage from various natural disaster has been conducted in several developed countries. In the case of U.S, various damage prediction models of buildings from natural disasters have been used widely in many organizations such as insurance companies and governments. In South Korea, although studies regarding damage prediction model of hurricane have been conducted, the scope has been only limited to consider the property of hurricane. However, it is necessary to consider various factors such as socio-economic, physical, geographical, and built environmental factors to predict the damages. Therefore, to address this issue, correlation analysis is conducted between various variables based on the data of hurricane from 2003 to 2012. The findings of this study can be utilized to develop for predicting the damage of hurricane on buildings.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.19-22
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• 1996

A traditional approach to the prediction of economic and financial variables takes the form of statistical models to summarize past observations and to project them into the envisioned future. Over the past decade, an increasing number of organizations has turned to the use of neural networks. To date, however, many spheres of interest still lack a systematic evaluation of the statistical and neural approaches. One of these lies in the prediction of corporate bond yields for Korea. This paper reports on a comparative evaluation of ARIMA models and neural networks in the context of interest rate prediction. An additional experiment relates to an integration of the two methods. More specifically, the statistical model serves as a filter by providing estimtes which are then used as input into the neural network models.

• Asian-Australasian Journal of Animal Sciences
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• 제11권5호
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• pp.580-585
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• 1998

The study was conducted to determine if variation in protein yield can be explained by expressions of early lactation somatic cell score (SCS) and if prediction can be improved by including SCS among the predictors. A data set was prepared (n = 663,438) from Wisconsin Dairy Improvement Association (USA) records for protein yield with sample days near 20. Stepwise regression was used requiring F statistic (p < .01) for any variable to stay in the model. Separate analyses were run for 12 combinations of four seasons and first three parities. Selection of SCS variables was not consistent across seasons or lactations. Coefficients of detennination ($R^2$) ranged from 51 to 61% with higher values for earlier lactations. Including any expression of SCS in the prediction equations improved $R^2$ by < 1 %. SCS was associated with milk yield on the sample day, but the association was not strong enough to improve the prediction of future yield when other expressions of milk yield were in the model.

• Ocean Systems Engineering
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• 제7권2호
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• pp.75-87
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• 2017

A breakwater has always been an ideal option to prevent shoreline erosion due to wave action as well as to maintain the tranquility in the lagoon area. The effects of the impinging wave on the structure could be analyzed and evaluated by several physical and numerical methods. An alternate approach to the numerical methods in the prediction of performance of a breakwater is Artificial Intelligence (AI) tools. In the recent decade many researchers have implemented several Artificial Intelligence (AI) tools in the prediction of performance, stability number and scour of breakwaters. This paper is a comprehensive review which serves as a guide to the current state of the art knowledge in application of soft computing techniques in breakwaters. This study aims to provide a detailed review of different soft computing techniques used in the prediction of performance of different breakwaters considering various combinations of input and response variables.

• Communications for Statistical Applications and Methods
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• 제28권3호
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• pp.217-232
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• 2021

The Korea film industry has matured and the number of movie-watching per capita has reached the highest level in the world. Since then, movie industry growth rate is decreasing and even the total sales of movies per year slightly decreased in 2018. The number of moviegoers is the first factor of sales in movie industry and also an important factor influencing additional sales. Thus it is important to predict the number of movie audiences. In this study, we predict the cumulative number of audiences of films using stacking, an ensemble method. Stacking is a kind of ensemble method that combines all the algorithms used in the prediction. We use box office data from Korea Film Council and web comment data from Daum Movie (www.movie.daum.net). This paper describes the process of collecting and preprocessing of explanatory variables and explains regression models used in stacking. Final stacking model outperforms in the prediction of test set in terms of RMSE.

• 대한기계학회논문집A
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• 제20권6호
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• pp.1810-1818
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• 1996

A new finite elemetn approach is introduced for direct prediction of bland shapes and strain distributions from desired final shapes in sheet metal forming. The approach deals with the geometric compatibility of finite elements, plastic deformation theory, minimization of plastic work with constraints, and a proper initial guess. The algorithm developed is applied to cylindrical cup drawing, square cup drawing, and fron fender forming to confirm its validity by demonstratin reasonable accurate numerical results of each problems. Rapid calculation with this algorithm enables easy determination of various process variables for design of sheet metal forming process.

• 대한기계학회논문집B
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• 제24권8호
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• pp.1077-1084
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• 2000

In this work, a modified stage-stacking method for the performance prediction of multi-stage axial flow compressors is proposed. The method is based on a simultaneous calculation of all interstage variables (temperature, pressure, flow velocity) instead of the conventional sequential stage-by-stage scheme. The method is also very useful in simulating the effect of changing angles of the inlet guide vane and stator vanes on the compressor operating characteristics. Generalized stage performance curves are used in presenting the performance characteristics of each stage. General assumptions enable determination of flow path data and stage design performance. Performance of various real compressors is predicted and comparison between prediction and field data validates the usefulness of the present method.

• International journal of advanced smart convergence
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• 제11권2호
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• pp.30-37
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• 2022

Because fine dust (PM10) has a close effect on the environment, fine dust generated in the climate and living environment has a bad effect on the human body. In this study, the LSTM model was applied to predict and analyze the effect of fine dust on Gwangju Metropolitan City in Korea. This paper uses prediction values of input variables selected through correlation analysis to confirm fine dust prediction performance. In this paper, data from the Gwangju Metropolitan City area were collected to measure fine dust. The collection period is one year's worth of data was used from january to December of 2021, and the test data was conducted using three-month data from January to March of 2022. As a result of this study, in the as a result of predicting fine dust (PH10) and ultrafine dust (PH2.5) using the LSTM model, the RMSE was 4.61 and the test result value was as low as 4.37. This reason is judged to be the result of the contents of the one-year sample.

• ETRI Journal
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• 제26권4호
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• pp.297-306
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• 2004

A plasma is a collection of charged particles and on average is electrically neutral. In fabricating integrated circuits, plasma etching is a key means to transfer a photoresist pattern into an underlayer material. To construct a predictive model of plasma-etching processes, a polynomial neural network (PNN) is applied. This process was characterized by a full factorial experiment, and two attributes modeled are its etch rate and DC bias. According to the number of input variables and type of polynomials to each node, the prediction performance of the PNN was optimized. The various performances of the PNN in diverse environments were compared to three types of statistical regression models and the adaptive network fuzzy inference system (ANFIS). As the demonstrated high-prediction ability in the simulation results shows, the PNN is efficient and much more accurate from the point of view of approximation and prediction abilities.