• Steel and Composite Structures
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• v.20 no.3
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• pp.469-486
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• 2016

Codes EN 1993 and EN 1994 require to take into account actual joint characteristics in the global analysis. In order to implement the semi-rigid connection effects in frame design, knowledge of joint rotation characteristics ($M-{\phi}$ relationship), or at least three basic joint properties, namely the moment resistance $M_R$, the rotational stiffness $S_j$ and rotation capacity, is required. To avoid expensive experimental tests many methods for predicting joint parameters were developed. The paper presents a comprehensive analytical model that has been developed for predicting the moment resistance $M_R$, initial stiffness $S_{j.ini}$ and rotation capacity of the minor axis, composite, semi-rigid joint. This model is based on so-called component method included in EN 1993 and EN 1994. Comparison with experimental test results shows that a quite good agreement was achieved. A computer program POWZ containing proposed procedure were created. Based on the numerical simulation made with the use of this program and applying regression analysis, simplified equations for main joint properties were also developed.

• Journal of Fashion Business
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• v.19 no.5
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• pp.77-92
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• 2015

The purpose of this study is to examine and compare effects of psychological factors on market mavenism and fashion leadership in order to determine the differences of two influential groups in the marketplace. The data were collected from 20's-50's consumers through an online survey institute and a total of 857 questionnaires were analyzed. Demographic variables (gender, age, and income level) were entered into the regression model 1 as independent variables, and 6 factors of consumer self-confidence, clothing involvement, status consumption, and price consciousness were entered into the regression model 2. In the regression model 1, gender (female) alone was significant in explaining market mavenism, while the income level had a positive relationship with fashion leadership. In the regression model 2, information acquisition, social outcome, persuasion knowledge among consumer self-confidence, and status consumption were significant predictors of market mavenism. On the other hand, personal outcome, social outcome, persuasion knowledge, clothing involvement, and status consumption had an effect on the fashion leadership. When comparing magnitudes of effects in predicting market mavenism and fashion leadership, social outcome and status consumption showed to have stronger impacts on fashion leadership than on market mavenism. Psychological factors showed to be more powerful in predicting market mavenism or fashion leadership, as compared to demographic variables.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

• Asia pacific journal of information systems
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• v.9 no.3
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• pp.93-110
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• 1999

The previous studies regarding the stock returns have advocated that industry effects exist over entire industry. As the industry categories are more rigid, the demand for predicting the industry sectors is rapidly increasing. The advances in Artificial Intelligence and Neural Networks suggest the feasibility of a valuable computational model for stock returns prediction. We propose a sector-factor model for predicting the return on industry stock index using neural networks. As a substitute for the traditional models, neural network model may be more accurate and effective alternative when the dynamics between the underlying industry features are not well known or when the industry specific asset pricing equation cannot be solved analytically. To assess the potential value of neural network model, we simulate the resulting network and show that the proposed model can be used successfully for banks and general construction industry. For comparison, we estimate models using traditional statistical method of multiple regression. To illustrate the practical relevance of neural network model, we apply it to the predictions of two industry stock indexes from 1980 to 1995.

• Transactions of Materials Processing
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• v.20 no.8
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• pp.548-554
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• 2011

A Finite Element (FE)-based model is proposed for predicting the roll force in an edger. The model is developed on the basis of the hypothetical mode of rolling and the least-squares regression analysis from the result of the FE approach. This model reflects the effect of process variables affected by the roll force, and has three dimensionless parameters, I.e., shape factor, reduction ratio and width-to-thickness ratio. The model prediction compared satisfactorily with experiment observations.

• Computers and Concrete
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• v.11 no.4
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• pp.291-304
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• 2013

By considering the creep characteristics of concrete core under eccentric compression, a creep model of concrete filled steel tubes (CFT) columns under eccentric compressive loads is proposed based on the concrete creep model B3. In this proposed model, a discrete element method is introduced to transform the eccentric loading into axial loading. The validity of the model is verified by comparing the predicting results with the published creep experiments results on CFT specimens under compressive loading, together with the predicting values based on other concrete creep models, such as ACI209, CEB90, GL2000 and elastic continuation and plastic flow theory. By using the proposed model, a parameters study is carried out to analysis the effects of practical design parameters, such as concrete mix (e.g. water to cement ratio, aggregate to cement ratio), steel ratio and eccentricity ratio, on the creep of CFT columns under eccentric compressive loading.

• Transactions of Materials Processing
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• v.23 no.3
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• pp.145-150
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• 2014

Precision control of the slab is crucial for product quality and production economy in hot strip mills. The current study presents a new model for predicting width spread of a slab with a rectangular cross section during roughing. The model is developed on the basis of the extremum principle for a rigid plastic material and a three dimensional admissible velocity field. This model incorporates the effect of process variables such as the shape factor and the ratio of width to thickness. We compare the results of this model to 3-D finite element (FE) process simulations and also to results from a previous study.

• Journal of Korean Library and Information Science Society
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• v.13
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• pp.131-166
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• 1986

This study is designed to review the methods of duplicate copies decision making in the academic library. In this thesis, I surveyed queueing & markov model, statistical model, and simulation model. The contents of the study can be summarized as follows: 1) Queueing and markov model is used for one of duplicate copies decision-making methods. This model was suggested by Leimkuler, Morse, and Chen, etc. Leimkuler proposed growth model, storage model, and availability model through using system analysis method. Queueing theory is a n.0, pplied to Leimkuler's availability model. Morse ad Chen a n.0, pplied queueing and markov model to their theory. They used queueing theory for measuring satisfaction level and Markov model for predicting user demand. 2) Another model of duplicate copies decision-making methods is statistical model. This model is suggested by Grant and Sohn, Jung Pyo. Grant suggested a model with a formula to satisfy the user demand more than 95%, Sohn, Jung Pyo suggested a model with two formulars: one for duplicate copies decision-making by using standard deviation and the other for duplicate copies predicting by using coefficient of variation. 3) Simulation model is used for one of duplicate copies decision-making methods. This model is suggested by Buckland and Arms. Buckland considered both loan period and duplicate copies simultaneously in his simulation model. Arms suggested computer-simulation model as one of duplicate copies decision-making methods. These methods can help improve the efficiency of collection development and solve some problems (space, staff, budget, etc, ) of Korean academic libraries today.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• Journal of Integrative Natural Science
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• v.13 no.1
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• pp.34-40
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• 2020

This study aims to create a model for predicting the number of extinguishment manpower to put out forest fires by taking into account the climate, the situation, and the extent of the damage at the time of the forest fires. Past research has been approached to determine the cause of the forest fire or to predict the occurrence of a forest fire. How to deal with forest fires is also a very important part of how to deal with them, so predicting the number of extinguishment manpower is important. Therefore predicting the number of extinguishment manpower that have been put into the forest fire is something that can be presented as a new perspective. This study presents a model for predicting the number of extinguishment manpower inputs considering the scale of the damage with forest fire on a scale bigger than 0.1 ha as data based on the forest fire annual report(Korea Forest Service; KFS) from 2015 to 2018 using the moderated multiple regression analysis. As a result, weather factors and extinguished time considering the damage show that affect forest fire extinguishment manpower.