• Computers and Concrete
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• v.32 no.3
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.9-21
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• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.103-114
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• 2015

The role of small and medium enterprises as an economic growth factor has been accentuated; consequently, the need to develop a business forecast model and indexes that accurately examine business situation of small and medium enterprises has increased. Most current business model and indexes concerning small and medium enterprises, released by public and private institutions, are based on Business Survey Index (BSI) and depend on subjective (business model and) indexes; therefore, the business model and indexes lack a capacity to grasp an accurate business situation of these enterprises. The business forecast model and indexes suggested in the study have been newly developed with Principal Component Analysis(PCA) and weight method to accurately measure a business situation based on reference dates addressed by the National Statistical Office(NSO). Empirical studies will be presented to prove that the newly proposed business model and indexes have their basis in statistical theory and their trend that resembles the existing Composite Index.

• Journal of Families and Better Life
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• v.5 no.2
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• pp.11-27
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• 1987

This study examined job satisfaction levels between employed single women and employed married women. The specific objectives of this study were; (1) to investigate differences in the level of job satisfaction between employed single women and employed married women; (2)to investigate the factors which influence the level of job satisfaction; (3)to investigate the relationship between job satisfaction and life satisfaction. the data used in this study included 441 working women from 290 (65.8 %) single women and 151 (34.2%) married women. Statistical analyses were conducted using frequencies, percentiles, mean , t-test , ANOVA, pearson's correlation and a stepwise multiple regression. The major findings were ; (1) at the P<.005 level, there was a significant difference in the job Satisfaction Index (JSI) between employed single and married women by using the t-test ; (2) Occupation, type of organization , job experiences, travel time from home to work. unionization, age, and health status were significantly related to the JSI by using the one-way ANOVA; (3) Thee was an interaction effect between income and marital status on the JSI, and between education and marital status on the JSI; (4) Using a stepwise multiple regression method, the order of variables as they were entered in the model were different between employed single and married women; (5) There was a positive relationship between the JSI and Life Satisfaction Index(r=.41)

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1125-1132
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• 2017

In a situation where the severity and frequency of drought events getting stronger and higher, many studies related to drought forecast have been conducted to improve the drought forecast accuracy. However it is difficult to predict drought events using a single model because of nonlinear and complicated characteristics of temporal behavior of drought events. In this study, in order to overcome the shortcomings of the single model approach, we first build various single models capable to explain the relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and other independent variables such as world climate indices. Then, we developed a combined models using Stochastic Gradient Descent method among Ensemble Learnings.

• Korea-Australia Rheology Journal
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• v.21 no.4
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• pp.289-297
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• 2009

We numerically analyzed flow characteristics of the polymer melt in the screw equipment using a proper modeling and investigated design parameters which have influence on the mixing performance as the capability of the screw equipment. We considered the non-Newtonian and non-isothermal flow in a single rotor equipment to investigate the mixing performance with respect to screw dimensions as shape parameter of the single rotor equipment and screw speed as process parameter. We used Bird-Carreau-Yasuda model as a viscous model of the polymer melt and the particle tracking method to investigate the mixing performance in the screw equipment and considered four mixing performance indexes: residence time distribution, deformation rate, total strain and particle standard deviation as a new mixing performance index. We compared these indexes to determine design parameters and object function. On basis of the analysis results, we carried out the optimal design by using the response surface method and design of experiments. In conclusion, the differences of results between the optimal value and numerical analysis are about 5.0%.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.3
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• pp.17-30
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• 2004

This study aimed to develop an agricultural drought assessment methodology for irrigated paddy field districts from a single reservoir. Agricultural drought was defined as the reservoir storage shortage state that cannot satisfy water requirement from the paddy fields. The suggested model, SRADEMP (a Single Reservoir Agricultural Drought Evaluation Model for Paddy), was composed of 4 submodels: PWBM (Paddy Water Balance Model), RWBM (Reservoir Water Balance Model), FA (Frequency and probability Analysis model), and DCI (Drought Classification and Indexing model). Two indices, PDF (Paddy Drought Frequency) and PDI (Paddy Drought Index) were also introduced to classify agricultural drought severity Both values were divided into 4 steps, i.e. normal, moderate drought, severe drought, and extreme drought. Each step of PDI was ranged from +4.2 to -1.39, from -1.39 to -3.33, from -3.33 to -4.0 and less than -4.0, respectively. SRADEMP was applied to Jangheung reservoir irrigation district, and the results showed good relationships between simulated results and the observed data including historical drought records showing that SRADEMP explains better the drought conditions in irrigated paddy districts than PDSI.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1174-1179
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• 2008

Many modeling and calibration methods have been developed to analyze and design the biological wastewater treatment process. For the systematic use of activated sludge model (ASM) in a real treatment process, a most important step in this usage is a calibration which can find a key parameter set of ASM, which depends on the microorganism communities and the process conditions of the plants. In this paper, a standardized calibration protocol of the ASM model is developed. First, a weighted effluent quality index(WEQI) is suggested far a calibration protocol. Second, the most sensitive parameter set is determined by a sensitive analysis based on WEQI and then a parameter optimization method are used for a systematic calibration of key parameters. The proposed method is applied to a calibration problems of the single carbon removal process. The results of the sensitivity analysis and parameter estimation based on a WEQI shows a quite reasonable parameter set and precisely estimated parameters, which can improve the quality and the efficiency of the modeling and the prediction of ASM model. Moreover, it can be used for a calibration scheme of other biological processes, such as sequence batch reactor, anaerobic digestion process with a dedicated methodology.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.3
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• pp.296-303
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• 2004

The heat and flow characteristics in a single-phase parallel-flow heat exchanger was examined numerically to obtain its optimal shape. A response surface method was introduced to approximately predict its performance with respect to the design parameters over the design domain. The inflow/outflow angle of the working fluid, the location of inlet/outlet, the protruding height of flat tube and the height of header were chosen as a design parameter The evaluation of the relative importance of the design parameters was performed based on a sensitivity analysis. An efficiency index was used as an evaluation characteristics value to simultaneously consider both the heat transfer and the pressure drop. The efficiency index of the optimum model, compared to that of the base model, was increased by 9.3%.