• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.1098_1099
• /
• 2009

In this paper, we present the estimation of wind turbine power generation using Cascade Architectures of Fuzzy Neural Networks(CAFNN). The proposed model uses the wind speed average, the standard deviation and the past output power as input data. The CAFNN identification process uses a 10-min average wind speed with its standard deviation. The method for rule-based fuzzy modeling uses Gaussian membership function. It has three fuzzy variables with three modifiable parameters. The CAFNN's configuration has three Logic Processors(LP) that are constructed cascade architecture and an effective optimization method uses two-level genetic algorithm. First, The CAFNN is trained with one-day average input variables. Once the CAFNN has been trained, test data are used without any update. The main advantage of using CAFNN is having simple structure of system with many input variables. Therefore, The proposed CAFNN technique is useful to predict the wind turbine(WT) power effectively and hence that information will be helpful to decide the control strategy for the WT system operation and application.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.1
• /
• pp.167-174
• /
• 2005

The design for cold extrusion dies is very important, because the die insert is subjected to very high radial and hoop stresses. The design of cold extrusion dies has many constrained conditions. In this paper, the used assumptions are such that the yield strength of each ring is selected according to the allowable tensile or compressive hoop stress in each ring and the maximum allowable inner pressure, when yielding occurs in one ring of the dies, is obtained by the proposed equation. In order to obtain design variables, such as diameter ratios and interferences, using the maximum inner pressure, the flexible tolerance method was used for shrink-fitted thick-walled cylinders. ANSYS APDL was used to perform the repeated analysis of deformation of the dies due to the variation of the design variables. The response surface methodology is utilized to analyze the relationship between the design variables and the maximum radial displacement of the die insert during extrusion. From the results, it is found that outer diameter of the die insert has the largest effect on the minimization of maximum radial displacement at the inner surface of the dies.

• International Journal of Precision Engineering and Manufacturing
• /
• v.1 no.1
• /
• pp.62-70
• /
• 2000

The design of gear train is a kind of mixed problems which have to determine various types of design variables; i,e., continuous, discrete, and integer variables. Therefore, the most common practice of optimum design using the derivative of objective function has difficulty in solving those kinds of problems and the optimum solution also depends on initial guess because there are many sophisticated constrains. In this study, the Genetic Algorithm is introduced for the optimum design of gear trains to solve such problems and we propose a genetic algorithm based gear design system. This system is applied for the geometrical volume(size) minimization problem of the two-stage gear train and the simple planetary gear train to show that genetic algorithm is better than the conventional algorithm solving the problems that have continuous, discrete, and integer variables. In this system, each design factor such as strength, durability, interference, contact ratio, etc. is considered on the basis of AGMA standards to satisfy the required design specification and the performance with minimizing the geometrical volume(size) of gear trains

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.5
• /
• pp.422-428
• /
• 2013

In many cases, an $\bar{X}$ control chart which is based on the performance variable is used in industrial fields. However, if the performance variable is too costly or impossible to measure and a less expensive surrogate variable is available, the process may be more efficiently controlled using surrogate variables. In this paper, we propose a model for the economic design of a VSI (Variable Sampling Interval) $\bar{X}$ control chart using a surrogate variable that is linearly correlated with the performance variable. The total average profit model is constructed, which involves the profit per cycle time, the cost of sampling and testing, the cost of detecting and eliminating an assignable cause, and the cost associated with production during out-of-control state. The VSI $\bar{X}$ control charts using surrogate variables are expected to be superior to the Shewhart FSI (Fixed Sampling Interval) $\bar{X}$ control charts using surrogate variables with respect to the expected profit per unit cycle time from economic viewpoint.

• Communications for Statistical Applications and Methods
• /
• v.25 no.2
• /
• pp.235-243
• /
• 2018

Many studies exist on the influence of one or few observations on estimators in a variety of statistical models under the "large n, small p" setup; however, diagnostic issues in the regression models have been rarely studied in a high dimensional setup. In the high dimensional data, the influence of observations is more serious because the sample size n is significantly less than the number variables p. Here, we investigate the influence of observations on the least absolute shrinkage and selection operator (LASSO) estimates, suggested by Tibshirani (Journal of the Royal Statistical Society, Series B, 73, 273-282, 1996), and the influence of observations on selected variables by the LASSO in the high dimensional setup. We also derived an analytic expression for the influence of the k observation on LASSO estimates in simple linear regression. Numerical studies based on artificial data and real data are done for illustration. Numerical results showed that the influence of observations on the LASSO estimates and the selected variables by the LASSO in the high dimensional setup is more severe than that in the usual "large n, small p" setup.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.11a
• /
• pp.699-702
• /
• 2000

The automation and control of robotic welding process is a very complex assignment because the system is affected by a number of variables which are very difficult to determine or predict in practice. Not only the optimization of the robotic welding process is considered from the point of view of the time and the cost of manufacturing. as well as quality of the weldment. the human factors of the production and many other factors must taken into consideration. hi order to determine the optimal parameters of robotic welding process, it is necessary to build a computer model representing all parameters influencing the welding process as well as the mutual dependence between them. This paper presents an approach to modeling the robotic welding process in which all parameters affecting the welding process are included using a neural network. A detailed analysis of the simulation results has been carried out to evaluate the proposed neural network model.

• Journal of Environmental Science International
• /
• v.13 no.6
• /
• pp.505-512
• /
• 2004

This study was attempted to review the information data for minimizing the destruction of environmental naturalness and the visual damage of landscape from road construction by establishing a model of environmental naturalness for national roads in the suburb areas to suggest an answer to a research question, ' hat does decide the environmental naturalness of roadscape?'. We found that 1) The road-side slope showed no statistical significance in the description of environmental naturalness of roadscape, but the fact that the road-side slope from road construction is the destruction of natural topography cannot be overlooked. 2) In terms of the direction of value variations for independent variables, signboard and telegraph post, soundproofing and protection wall, structure, and building acted toward negative (-) direction, while mountains, sky, road trees, fields, and surrounding green including the road-side slope acted toward positive(+) direction. 3) The variable with highest relative contribution to dependent variables among independent variables is building, which has importance as many as 148 times of road-side slope, while the variable road-side slope has the least importance. Building has the importance of 7.22 times, mountains 5.51 times, road trees 2.59 times, surrounding green 2.54 times, structure 2.41 times, signboard and telegraph post 2.37 times, soundproofing and protection wall 2.20 times, and sky 1.32 times of the fields as a standard criterion values 1.

• Korean Journal of Oriental Medicine
• /
• v.12 no.2 s.17
• /
• pp.85-92
• /
• 2006

Pulse is one of the basic diagnostic information of TKM(Traditional Korean Medicine). To quantify and standardize pulse diagnosis, we had collected an amount of clinical data from May 2005 by using newly developed pulse analyzer. But there were many noises in pulse wave according to measuring method, environment, operator and condition of patient. So some data can’t be included for analyzing diagnosis. To reduce noises from measuring pulse and to collect reliable pulse wave data, we made the process map of measuring method and applied six sigma project. With this we can improved the method of measuring pulse wave in collecting clinical data. The project follows a disciplined process of five macro phases: define, measure, analyze, improve and control (DMAIC). A process map and C-E diagram are used to identify process input and output variables. The major input variables are selected by using C&E matrix, and process map is developed by analyzing input variables. And the optimum process conditions are going to be controled to avoid in increasing loss of collecting pulse wave data.

• Journal of Home Health Care Nursing
• /
• v.26 no.3
• /
• pp.265-277
• /
• 2019

Purpose: This study was performed to explore research trends in the intention to stay of hospital nurses and provide basic data to establish nursing management strategies to increase the intention to stay. Methods: Articles published between 2009 and 2018 were searched. The search terminologies were "intention to stay," "nurse", and "hospital". In the first search, 381 articles were extracted from academic databases. Thirty articles were used in the systematic review, and 29 articles were used in the correlation meta-analysis. Results: Thirty-two variables were explored in relation to the intention to stay. In the correlation analysis, job satisfaction and work environment showed statistically significant positive correlations in many studies. In the correlation meta-analysis, 7 variables including organizational commitment showed statistically significant effect sizes. Conclusion: We suggest that structural equation model analysis to identify causal relations among influencing variables of the intention to stay of hospital nurses may be conducted. This study can be used as a guideline to develop intention-to-stay enhancement programs for hospital nurses.

• Journal of Information Technology Applications and Management
• /
• v.21 no.1
• /
• pp.177-184
• /
• 2014

This research analyzes the effects of factors on the demands for outbound to the countries such as Japan, China, the United States of America, Thailand, Philippines, Hong Kong, Singapore and Australia, the countries preferred by many Koreans. The factors for this research are (1) economic variables such as Korea Composite Stock Price Index (KOSPI), which could have influences on outbound tourism and exchange rate and (2) unpredictable events such as diseases, financial crisis and terrors. Regression analysis was used to identify relationship based on the monthly data from January 2001 to December 2010. The results of the analysis show that both exchange rate and KOSPI have impacts on the demands for outbound travel. In the case of travels to the United States of America and Philippines, Korean tourists usually have particular purposes such as studying, visiting relatives, playing golf or honeymoon, thus they are less influenced by the exchange rate. Moreover, Korean tourists tend not to visit particular locations for some time when shock reaction happens. As the demands for outbound travels are different from country to country accompanied by economic variables and shock variables, differentiated measure to should be considered to come close to the target numbers of tourists by switching as well as creating the demands. For further study we plan to build outbound tourism forecasting models using Artificial Neural Networks.