• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.81-91
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• 1994

This paper is to build an economic production quantity model for situations, in which, during the stockout period, a fraction .betha.(backorder ratio) of the demand is backordered and remaining fraction (1-.betha.) is lost. This paper develops an objective function representing the average annual cost of a production system by defining a time-weighted backorder cost and a lost sales penalty cost per unit lost under the assumptions of deterministic demand rate and deterministic production rate, and provides an algorithm for its optimal solution. At the extreme .betha.= 1, the presented model reduces to the Fabrycky's model with complete backorders.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.24-27
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• 1996

The storage function method(SFM) is one of hydrologic flood routings which has been used most widely in Korea and Japan. This paper presents a storage function method using multiple model adaptive estimation(MMAE), in which a model set is generated by partitioning storage parameters over feasible range, and each storage function model is estimated, and then the weighted average of them is calculated. Finally, the future runoff is predicted in real time by means of observed data of water level at dam and rainfall. Simulation results applied to actual data show that the proposed method has much better performance than that of conventional SFM.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.915-925
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• 2007

Multivariate GARCH has been useful to model dynamic relationships between volatilities arising from each component series of multivariate time series. Methodologies including EWMA(Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model) models are comparatively reviewed for bivariate time series. In addition, these models are applied to evaluate VaR(Value at Risk) and to construct joint prediction region. To illustrate, bivariate stock prices data consisting of Samsung Electronics and LG Electronics are analysed.

• Journal of Environmental Health Sciences
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• v.27 no.1
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• pp.20-26
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• 2001

personal nitrogen dioxide(NO$_2$) exposures for 31 professional drivers were measured using passive sampler and time activity diary in Asan and Chunan area, and were estimated using time-weighted average model. Mean concentrations of driver’s indoor and outdoor were 24.7$\pm$10.7 ppb and 23.3$\pm$8.3 ppb, respectively with indoor/outdoor of 1.1. Mean personal NO$_2$ exposure was 30.3$\pm$9.7 ppb. Personal NO$_2$ exposures were strongly correlated with indoor car NO$_2$ levels ($R^2$=0.80) rather than residential indoor NO$_2$ level ($R^2$=0.55). and outdoor NO$_2$ level ($R^2$=0.50). The driver’s NO$_2$ exposure using LP-gas with 24.4$\pm$8.0 ppb were statistically different from those using diesel with 36.3$\pm$14.1 ppb(p<0.01). The effect of driver’s smoking for personal NO$_2$ exposure was not found. It was considered that the main NO$_2$in driver is transportation. Since drivers mostly spent their times in indoor and inside car, time-weighted average model could be used to estimated personal NO$_2$ exposure using time activity diary, Though we did not measure all microenvironments, the estimated personal NO$_2$ exposures with 26.9$\pm$10.2 ppb were statistically correlated with measured personal NO $_2$ exposures30.3$\pm$9.7 ppb ($R^2$=0.89). The mean and standard deviation of personal NO$_2$ exposure using Mote-Carlo simulation were 26.6$\pm$7.2 ppb.

• Journal of Environmental Science International
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• v.19 no.8
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• pp.971-981
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• 2010

The objectives of this study were to measure ambient total gaseous mercury (TGM) concentrations in Seoul, to analyze the characteristics of TGM concentration, and to identify of possible source areas for TGM using back-trajectory based hybrid receptor models like PSCF (Potential Source Contribution Function) and RTWC (Residence Time Weighted Concentration). Ambient TGM concentrations were measured at the roof of Graduate School of Public Health building in Seoul for a period of January to October 2004. Average TGM concentration was $3.43{\pm}1.17\;ng/m^3$. TGM had no notable pattern according to season and meteorological phenomena such as rainfall, Asian dust, relative humidity and so on. Hybrid receptor models incorporating backward trajectories including potential source contribution function (PSCF) and residence time weighted concentration (RTWC) were performed to identify source areas of TGM. Before hybrid receptor models were applied for TGM, we analysed sensitivities of starting height for HYSPLIT model and critical value for PSCF. According to result of sensitivity analysis, trajectories were calculated an arrival height of 1000 m was used at the receptor location and PSCF was applied using average concentration as criterion value for TGM. Using PSCF and RTWC, central and eastern Chinese industrial areas and the west coast of Korea were determined as important source areas. Statistical analysis between TGM and GEIA grided emission bolsters the evidence that these models could be effective tools to identify possible source area and source contribution.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.8
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• pp.763-771
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• 2011

An optimization procedure for the design of a ventilation axial-flow fan is presented in this paper. Flow analyses of the preliminary fan are performed by solving three-dimensional Reynolds-averaged Navier-Stokes equations via a finite-volume solver with the shear-stress transport turbulence model as a turbulence closure. Three variables, the hub-to-tip ratio and the stagger angles at the mid and tip spans, are selected for the optimization. The Latin-hypercube sampling method as a design-of-experiments technique is used to generate twenty-five design points within the design space. and the weighted average surrogate models, WTA1, WTA2, and WTA3, are applied for find optimal designs. The results show that the efficiency is considerably enhanced.

• Korean Journal of Agricultural Science
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• v.41 no.3
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• pp.169-175
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• 2014

Objective of this study was to investigate effects of pre-processing method and number of sampling leaves on stability of the reflectance measurement for Chinese cabbage and kale leaves. Chinese cabbage and kale were transplanted and cultivated in a plant factory. Leaf samples of the kale and cabbage were collected at 4 weeks after transplanting of the seedlings. Spectra data were collected with an UV/VIS/NIR spectrometer in the wavelength region from 190 to 1130 nm. All leaves (mature and young leaves) were measured on 9 and 12 points in the blade part in the upper area for kale and cabbage leaves, respectively. To reduce the spectral noise, the raw spectral data were preprocessed by different methods: i) moving average, ii) Savitzky-Golay filter, iii) local regression using weighted linear least squares and a $1^{st}$ degree polynomial model (lowess), iv) local regression using weighted linear least squares and a $2^{nd}$ degree polynomial model (loess), v) a robust version of 'lowess', vi) a robust version of 'loess', with 7, 11, 15 smoothing points. Effects of number of sampling leaves were investigated by reflectance difference (RD) and cross-correlation (CC) methods. Results indicated that the contribution of the spectral data collected at 4 sampling leaves were good for both of the crops for reflectance measurement that does not change stability of measurement much. Furthermore, moving average method with 11 smoothing points was believed to provide reliable pre-processed data for further analysis.

• Design & Manufacturing
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• v.13 no.2
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• pp.1-5
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• 2019

In this study, micro cellular injection molding of automobile head lamp housing with uneven thickness structure was performed to obtain improvement on deformation and light-weight of the part. The thickness of the presented model was uniformly modified to control the deformation of the molded part. In order to maximize the lightweight ratio, the model having an average thickness of 2.0 mm were thinly molded to an average thickness of 1.6 mm. GFM(Gas Free Molding) and CBM(Core Back Molding) technology were applied to improve the problems of the conventional foam molding method. Equal Heat & Cool system was also applied by 3D cooling core and individual flow control system. Warpage of the molded parts with even cooling was minimized. To improve the mechanical properties of foamed products, complex resin containing nano-filler was used and variation of mechanical properties was evaluated. It was shown that the weight reduction ratio of products with light-weighted injection molding was 8.9 % and the deformation of the products was improved from the maximum of 3.6 mm to 2.0 mm by applying Equal Heat & Cool mold cooling system. Also the mechanical strength reduction of foamed product was less than 12% at maximum.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.1-10
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• 2023

Recently, NAND flash memories have replaced magnetic disks due to non-volatility, high capacity and high resistance, in various computer systems but it has disadvantages which are the limited lifespan and imbalanced operation latency. Therefore, many page replacement policies have been studied to overcome the disadvantages of NAND flash memories. Although it is clear that these policies reflect execution characteristics of various environments and applications, researches on the foundation-policy decision for disk buffer management are insufficient. Thus, in this paper, we propose a foundation-policy recommendation model, called FRM for effectively utilizing NAND flash memories. FRM proposes a suitable page replacement policy by classifying and analyzing characteristics of workloads through machine learning. As an implementation case, we introduce FRM with a disk buffer management policy and in experiment results, prediction accuracy and weighted average of FRM shows 92.85% and 88.97%, by training dataset and validation dataset for foundation disk buffer management policy, respectively.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.17-26
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• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.