• Journal of The Korean Astronomical Society
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• v.2 no.1
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• pp.23-32
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• 1969

The photometric orbital elements of the eclipsing variable, RZ Pyxidis, are derived by means of Fourier series from photoelectric observations which were made by Michel Breger (1964). Physical dimensions and a model of the system are suggested here.

• CRM연구
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• v.1 no.1
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• pp.49-69
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• 2006

Previous studies, Technology Acceptance Model (TAM) and Post Acceptance Model (PAM) have a little limitation in time series analysis. To solve this limitation, we used system dynamics as research methodology and designed simulation model based on TAM and PAM. Moreover, we designed new simulation model which can analyize time series data in customers' demand change from initial acceptance to post acceptance. This study targeted domestic mobile phone market. The simulation results showed that diffusion graph was similar to real data. That means we validated our simulation model. Since the simulation model offers the graph of customer's demand change by time, so it can be useful as a leaning tool. Therefore, we think this study helps IT companies use the model for forecasting of market demand.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.4
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• pp.95-102
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• 1994

The series of the papers consist of three parts to describe the development, calibration, and applications of the flood forecasting models for the Youngsan Estuarine Dam located at the mouth of the Youngsan river. And this paper discusses the hydrologic model for inflow simulation at Naju station, which constitutes 64 percent of the drainage basin of 3521 .6km$^2$ in area. A simplified TANK model was formulated to simulate hourly runoff from rainfall And the model parameters were optirnized using historical storm data, and validated with the records. The results of this paper were summarized as follows. 1. The simplified TANK model was formulated to conceptualize the hourly rainfall-run-off relationships at a watershed with four tanks in series having five runoff outlets. The runoff from each outlet was assumed to be proportional to the storage exceeding a threshold value. And each tank was linked with a drainage hole from the upper one. 2. Fifteen storm events from four year records from 1984 to 1987 were selected for this study. They varied from 81 to 289rn'm The watershed averaged, hourly rainfall data were determined from those at fifteen raingaging stations using a Thiessen method. Some missing and unrealistic records at a few stations were estimated or replaced with the values determined using a reciprocal distance square method from abjacent ones. 3. An univariate scheme was adopted to calibrate the model parameters using historical records. Some of the calibrated parameters were statistically related to antecedent precipitation. And the model simulated the streamflow close to the observed, with the mean coefficient of determination of 0.94 for all storm events. 4. The simulated streamflow were in good agreement with the historical records for ungaged condition simulation runs. The mean coefficient of determination for the runs was 0.93, nearly the same as calibration runs. This may indicates that the model performs very well in flood forecasting situations for the watershed.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.6
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• pp.91-98
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• 2004

This paper proposes a PCB plane model for generic SPICE circuit simulators. The proposed model reflects two frequency-dependent losses, namely skin and dielectric losses. After power/ground plane pair is divided into arrays of unit-cells, each unit-cell is modeled using a transmission line and two loss models. The loss model is composed of a resistor for DC loss, series HL ladder circuit for skin loss and series RC ladder circuit for dielectric loss. To verify the validity of the proposed model, it is compared with SPICE ac analysis using frequency-dependent resistors. Also, we show that the estimation results using the proposed model have a good correlation with that of VNA measurement for the typical PCB stack-up structure of general desktop PCs. With the proposed model, not only ac analysis but also transient analysis can be easily done for circuits including various non-linear/linear devices since the model consists of passive elements onl.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.41 no.2
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• pp.107-112
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• 2008

To evaluate uncertainty and risk in biological reference points, we applied a bootstrapping method and a Bayesian procedure to estimate the related confidence intervals. Here we provide an example of the maximum sustainable yield (MSY) of turban shell, Batillus cornutus, estimated by the Schaefer and Fox models. Fitting the time series of catch and effort from 1968 to 2006 showed that the Fox model performs better than the Schaefer model. The estimated MSY and its bootstrap percentile confidence interval (CI) at ${\alpha}=0.05$ were 1,680 (1,420-1,950) tons for the Fox model and 2,170 (1,860-2,500) tons for the Schaefer model. The CIs estimated by the Bayesian approach gave similar ranges: 1,710 (1,450-2,000) tons for the Fox model and 2,230 (1,760-2,930) tons for the Schaefer model. Because uncertainty in effort and catch data is believed to be greater for earlier years, we evaluated the influence of sequentially excluding old data points by varying the first year of the time series from 1968 to 1992 to run 'backward' bootstrap resampling. The results showed that the means and upper 2.5% confidence limit (CL) of MSY varied greatly depending on the first year chosen whereas the lower 2.5% CL was robust against the arbitrary selection of data, especially for the Schaefer model. We demonstrated that the bootstrap and Bayesian approach could be useful in precautionary fisheries management, and we advise that the lower 2.5% CL derived by the Fox model is robust and a better biological reference point for the turban shells of Jeju Island.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.268-272
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• 2010

The research presented here represents a collaborative effort with the SFWMD on developing scenarios for future climate for the SFWMD area. The project focuses on developing methodology for simulating precipitation representing both natural quasi-oscillatory modes of variability in these climate variables and also the secular trends projected by the IPCC scenarios that are publicly available. This study specifically provides the results for precipitation modeling. The starting point for the modeling was the work of Tebaldi et al that is considered one of the benchmarks for bias correction and model combination in this context. This model was extended in the framework of a Hierarchical Bayesian Model (HBM) to formally and simultaneously consider biases between the models and observations over the historical period and trends in the observations and models out to the end of the 21st century in line with the different ensemble model simulations from the IPCC scenarios. The low frequency variability is modeled using the previously developed Wavelet Autoregressive Model (WARM), with a correction to preserve the variance associated with the full series from the HBM projections. The assumption here is that there is no useful information in the IPCC models as to the change in the low frequency variability of the regional, seasonal precipitation. This assumption is based on a preliminary analysis of these models historical and future output. Thus, preserving the low frequency structure from the historical series into the future emerges as a pragmatic goal. We find that there are significant biases between the observations and the base case scenarios for precipitation. The biases vary across models, and are shrunk using posterior maximum likelihood to allow some models to depart from the central tendency while allowing others to cluster and reduce biases by averaging. The projected changes in the future precipitation are small compared to the bias between model base run and observations and also relative to the inter-annual and decadal variability in the precipitation.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.146-153
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• 2000

The rubber crawler system for farm machine is composed of driving units such as track rollers, driving sprockets and rubber crawlers. Vibration characteristics of the rubber crawler system varies by driving speed, center of gravity, mass□moment of inertial□location arrangement of track rollers and dynamic parameters such as dynamic spring constant (k) and viscous damping coefficient (c) of a rubber crawler. In general, vibration of the rubber crawler system occurs by reason for mechanical interaction between the rubber crawler and track rollers. Because the dynamic spring constant and viscous damping coefficient vary periodically by mechanical characteristics(deformation characteristics) of the rubber crawler when track rollers drive on the between lugs of the rubber crawler. Therefore, both dynamic parameters k and c were expressed as Fourier series by authors through the shaking test of the rubber crawler and further, vibration characteristics of the rubber crawler system could be simulated analytically. However, actual values of dynamic parameters k and c are different from those obtained by the shaking test because dynamic characteristics of the rubber crawler vary by the effect of variable tension and driving resistance of track rollers. So, actual values of k and c should be identified in the condition of actual driving test. In this study, dynamic parameters such as k and c of the rubber crawler system, which are expressed as Fourier series, were identified using the Gauss-Newton Method. Therefore, validity of identified parameters k and c was discussed through the simulation using experimental data of actual driving test. As a result, in the Fourier series of dynamic parameters of spring constant k and viscous damping coefficient c, excellent parameter convergence and simulation were observed using the Fourier series' zero order and first term of the dynamic model. Furthermore, it was clarified that identification for model parameters which are fitted to actual dynamic motion (vibration) wave of the crawler system was possible by using the time series data observed in vertical and pitching motion of the crawler system.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.69-78
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• 1999

In this paper, a neural network approach to forecast Korean regional precipitation is presented. We first analyze the characteristics of the conventional models for time series prediction, and then propose a new model and its learning method for the precipitation forecast. The proposed model is a layered network in which the outputs of a layer are buffered within a given period time and then fed fully connected to the upper layer. This study adopted the dual connections between two layers for the model. The network behavior and learning algorithm for the model are also described. The dual connection structure plays the role of the bias of the ordinary Multi-Layer Perceptron(MLP), and reflects the relationships among the features effectively. From these advantageous features, the model provides the learning efficiency in comparison with the FIR network, which is the most popular model for time series prediction. We have applied the model to the monthly and seasonal forecast of precipitation. The precipitation data and SST(Sea Surface Temperature) data for several decades are used as the learning pattern for the neural network predictor. The experimental results have shown the validity of the proposed model.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1083-1093
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• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.