• Korean Parent-Child Health Journal
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• v.5 no.2
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• pp.191-205
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• 2002

This study was conducted to provide the baseline data for preparing an educational program for mothers of pediatric asthma patients by identifying the knowledge about asthma, mother's educational demand and the perceived educational performance. This study used survey design. The subjects were chosen from the mothers whose children have received pediatric asthma treatment or who have admitted in the pediatric unit of major hospitals using selection criteria. The total number of subjects were 63 mothers. The data collection period was from May 1st, 2001 to April 17th, 2001. Instruments used for this study were knowledge examination, educational demand evaluation, and educational performance. The data were analysed using t-test, ANOVA with SPSS PC(Version 10.0). The results of this study were as follows. 1. Mean score of knowledge about pediatric asthma was 17.95, which suggests the mothers of children with asthma have a medium knowledge level. The highest grade was knowledge about treatment and follow management and the lowest grade was knowledge about diet. 2. Demand for education showed 4.23, which suggests the mothers of children with asthma have high educational demand. The highest score was about exercise and activity in daily life and the lowest score was medication. 3. Perceived educational performance score of themselves showed 2.40, which suggests the mothers of children with asthma thought that health team do not give enough education to them. The highest score was knowledge about pediatric asthma itself and the lowest score was exercise and activity in daily life. 4. Demand for education and perceived educational performance about pediatric asthma showed significant difference in all areas. 5. There were no statistically significant difference noted between general characteristics and degree of knowledge, educational demand and perceived educational performance about pediatric asthma. In conclusion, there needed a systematic educational program development for the mothers of children with asthma. Especially, an education program for mothers in the beginning period of pediatric asthma should be emphasized.

• Structural Engineering and Mechanics
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• v.60 no.6
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• pp.1021-1038
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• 2016

Considerable part of reinforced concrete building has suffered from destructive earthquakes in Turkey. This situation makes necessary to determine nonlinear behavior and seismic performance of existing RC buildings. Inelastic response of buildings to static and dynamic actions should be determined by considering both flexural plastic hinges and brittle shear hinges. However, shear capacities of members are generally neglected due to time saving issues and convergence problems and only flexural response of buildings are considered in performance assessment studies. On the other hand, recent earthquakes showed that the performance of older buildings is mostly controlled by shear capacities of members rather than flexure. Demand estimation is as important as capacity estimation for the reliable performance prediction in existing RC buildings. Demand estimation methods based on strength reduction factor (R), ductility (${\mu}$), and period (T) parameters ($R-{\mu}-T$) and damping dependent demand formulations are widely discussed and studied by various researchers. Adopted form of $R-{\mu}-T$ based demand estimation method presented in Eurocode 8 and Turkish Earthquake Code-2007 and damping based Capacity Spectrum Method presented in ATC-40 document are the typical examples of these two different approaches. In this study, eight different existing RC buildings, constructed before and after Turkish Earthquake Code-1998, are selected. Capacity curves of selected buildings are obtained with and without considering the brittle shear capacities of members. Seismic drift demands occurred in buildings are determined by using both $R-{\mu}-T$ and damping based estimation methods. Results have shown that not only capacity estimation methods but also demand estimation approaches affect the performance of buildings notably. It is concluded that including or excluding the shear capacity of members in nonlinear modeling of existing buildings significantly affects the strength and deformation capacities and hence the performance of buildings.

• Management Science and Financial Engineering
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• v.19 no.1
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• pp.1-10
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• 2013

Hierarchical forecasting strategy does not always outperform direct forecasting strategy. The performance generally depends on demand features. This research guides the use of the alternative forecasting strategies according to demand features. This paper developed and evaluated various classification models such as logistic regression (LR), artificial neural networks (ANN), decision trees (DT), boosted trees (BT), and random forests (RF) for predicting the relative performance of the alternative forecasting strategies for the South Korean navy's spare parts demand which has non-normal characteristics. ANN minimized classification errors and inventory costs, whereas LR minimized the Brier scores and the sum of forecasting errors.

• Structural Engineering and Mechanics
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• v.23 no.2
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• pp.177-193
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• 2006

Determining the hysteretic energy demand and dissipation capacity and level of damage of the structure to a predefined earthquake ground motion is a highly non-linear problem and is one of the questions involved in predicting the structure's response for low-performance levels (life safe, near collapse, collapse) in performance-based earthquake resistant design. Neural Network (NN) analysis offers an alternative approach for investigation of non-linear relationships in engineering problems. The results of NN yield a more realistic and accurate prediction. A NN model can help the engineer to predict the seismic performance of the structure and to design the structural elements, even when there is not adequate information at the early stages of the design process. The principal aim of this study is to develop and test multi-layered feedforward NNs trained with the back-propagation algorithm to model the non-linear relationship between the structural and ground motion parameters and the hysteretic energy demand in steel moment resisting frames. The approach adapted in this study was shown to be capable of providing accurate estimates of hysteretic energy demand by using the six design parameters.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.125-128
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• 2008

Studies for structure design has conducted in many research institutions. A basic concept of Performance-Based Design for structures was presented in seismic fields. Hereafter, Demand were defined to communicate owner's demand to designer by several research institution. Performance-Based Design is guaranteed by an accurate analysis from hazard affected to structures and from social, economical and environmental effects. It is essential to define Performance Level and Performance Objective to grasp accurate demand for structures. In this study, Performance Level and Performance Objective in ATC-40, FEMA-273 and Eurocode were defined to introduce Performance-Based Design.

• Fashion & Textile Research Journal
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• v.11 no.1
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• pp.147-155
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• 2009

This study has been made to examine the basic information, the preferences of material quality, and the demand performance of clothing for underwear materials between atopic patients and non-patients. The measurement tool was a questionnaire. For statistical analysis of data, crosstabs, ${\chi}^2$-test, t-test and ANOVA through SPSS for Windows(version 14.0) were used. The results obtained are as follows. The degree of the basic knowledge about clothing materials indicated that women was higher than men. The material decision method and the most considering part in case of selecting underwear products showed difference between men and women. The preference factors for underwear material quality were drawn with the five factors of sense of weight/pliability, lustering/see-through, tactility, and sense of cold and warmth. The preferred underwear material showed difference according to gender, existence and nonexistence of atopic determatitis, and degree of strength of skin itching caused by clothing materials. The demand performances of clothing in case of selecting underwear were drawn with the four factors of hygiene/practicality, skin protection, quality of materials, and aesthetic attribute. The demand performance of underwear showed difference according to gender, existence and nonexistence of atopic determatitis, degree of strength of skin itching caused by clothing materials.

• Architectural research
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• v.15 no.4
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• pp.201-206
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• 2013

Special attention is required for the design of windows due to their high thermal vulnerability. This paper examines the problems that might arise in the application of the u-value, by reflecting the changes in the u-value of the window, depending on the window-to-wall ratio obtained in an energy demand analysis. Research indicates that the u-value of a window increases with an increase in the difference between the u-values of the frames and the glass. Relative to the changes in the u-value of the windows, the energy demand varied from 1.3% to 9.3%. Windows with a g-value of 0.3 or 0.5 displayed a higher energy demand than windows with a g-value of 0.7. Therefore, when the difference between the performance of the glass and the frame is significant, especially when the g-value is small, a modified heat transmission coefficient should be applied to the window size during the evaluation of the building energy demand.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.2
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• pp.379-398
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• 2014

This pilot study explored the effect of mental demand on a Web searcher's thoughts, emotions, efforts, and performance in Web searches in order to address whether or not there is any difference between searchers exposed to mental demand manipulation and searchers not exposed. Research data were collected via think-aloud protocol (TAP) with a dual-task in experiments and interviews with 10 subjects who participated in this study. For the searcher's thoughts, relevance judgment was found to be hindered by mental demand. For the searcher's emotions, the experimental group was more frustrated than the control group. With respect to the searcher's efforts, searchers for the experimental group with mental demand manipulation were more likely to spend more time, make fewer queries, and visit fewer pages but work harder to find more relevant information that they needed. Lastly, with regard to the searcher's performance, it is likely that performance was highly dependent upon the completion of the search tasks for both groups. The NASA-TLX six components and cognitive load scores of searchers did not make a significant difference in the outcome. The findings support the use of a dual-task methodology as a promising approach for the assessment of cognitive load induced by complex Web searches.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.