• Journal of Astronomy and Space Sciences
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• v.28 no.4
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• pp.333-344
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• 2011

An optical tracking system has advantages for observing geostationary earth orbit (GEO) satellites relatively over other types of observation system. Regular surveying for unidentified space objects with the optical tracking system can be an early warning tool for the safety of five Korean active GEO satellites. Two strategies of positioning on the observed image of Communication, Ocean and Meteorological Satellite 1 are tested and compared. Photometric method has a half root mean square error against streak method. Also eccentricity method for initial orbit determination (IOD) is tested with simulation data and real observation data. Under 10 minutes observation time interval, eccentricity method shows relatively better IOD results than the other time interval. For follow-up observation of unidentified space objects, at least two consecutive observations are needed in 5 minutes to determine orbit for geosynchronous orbit space objects.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.2
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• pp.307-316
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• 1994

The rainfall forecasting model of the short term is improved at the point where meterological data is not gaged. In this study, the adopted model is based on the assumptions for simulation model of rainfall process, meteorological homogeneousness, prediction and estimation of meteorological data. A Kalman Filter technique is used for rainfall forecasting. In the existing models, the equation of the model is non-linear type with regard to rainfall rate, because hydrometer size distribution (HSD) depends on rainfall intensity. The equation is linearized about rainfall rate as HSD is formulated by the function of the water storage in the cloud. And meteorological input variables are predicted by emprical model. It is applied to the storm events over Taech'ong Dam area. The results show that root mean square error between the forecasted and the observed rainfall intensity is varing from 0.3 to 1.01 mm/hr. It is suggested that the assumptions of this study be reasonable and our model is useful for the short term rainfall forecasting at the ungaged point of the meteorological data.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.97-104
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• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.54 no.1
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• pp.38-53
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• 2018

The purpose of this study is to estimate potential yield (PY) for Korean west coast fisheries using the holistic production method (HPM). HPM involves the use of surplus production models to apply input data of catch and standardized fishing efforts. HPM compared the estimated parameters of the surplus production from four different models: the Fox model, CYP model, ASPIC model, and maximum entropy model. The PY estimates ranged from 174,232 metric tons (mt) using the CYP model to 238,088 mt using the maximum entropy model. The highest coefficient of determination ($R^2$), the lowest root mean square error (RMSE), and the lowest Theil's U statistic (U) for Korean west coast fisheries were obtained from the maximum entropy model. The maximum entropy model showed relatively better fits of data, indicating that the maximum entropy model is statistically more stable and accurate than other models. The estimate from the maximum entropy model is regarded as a more reasonable estimate of PY. The quality of input data should be improved for the future study of PY to obtain more reliable estimates.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.117-126
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• 2018

Due to intelligent transport systems, location-based applications, and augmented reality, demand for image maps and 3D (Three-Dimensional) maps is increasing. As a result, data acquisition using UAV (Unmanned Aerial Vehicles) has flourished in recent years. However, even though orthophoto map production and research using UAVs are flourishing, few studies on 3D modeling have been conducted. In this study, orthophoto and 3D modeling research was performed using various angle images acquired by a UAV. For orthophotos, accuracy was evaluated using a GPS (Global Positioning System) survey that employed VRS (Virtual Reference Station) acquired checkpoints. 3D modeling was evaluated by calculating the RMSE (Root Mean Square Error) of the difference between the outline height values of buildings obtained from the GPS survey to the corresponding 3D modeling height values. The orthophotos satisfied the acceptable accuracy of NGII (National Geographic Information Institute) for a 1/500 scale map from all angles. In the case of 3D modeling, models based on images taken at 45 degrees revealed better accuracy of building outlines than models based on images taken at 30, 60, or 75 degrees. To summarize, it was shown that for orthophotos, the accuracy for 1/500 maps was satisfied at all angles; for 3D modeling, images taken at 45 degrees produced the most accurate models.

• Journal of the Korean Wood Science and Technology
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• v.45 no.4
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• pp.399-408
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• 2017

Research on the rapid and accurate prediction of physical properties of wood using near-infrared (NIR) spectroscopy has attracted recent attention. In this study, partial least squares analysis was performed between NIR spectra and air-dried specific gravity of five domestic conifer species including larch (Larix kaempferi), Korean pine (Pinus koraiensis), red pine (Pinus densiflora), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa). Fifty different lumbers per species were purchased from the five National Forestry Cooperative Federations of Korea. The air-dried specific gravity of 100 knot- and defect-free specimens of each species was determined by NIR spectroscopy in the range of 680-2500 nm. Spectral data preprocessing including standard normal variate, detrend and forward first derivative (gap size = 8, smoothing = 8) were applied to all the NIR spectra of the specimens. Partial least squares analysis including cross-validation (five groups) was performed with the air-dried specific gravity and NIR spectra. When the performance of the regression model was expressed as $R^2$ (coefficient of determination) and root mean square error of calibration (RMSEC), $R^2$ and RMSEC were 0.63 and 0.027 for larch, 0.68 and 0.033 for Korean pine, 0.62 and 0.033 for red pine, 0.76 and 0.022 for cedar, and 0.79 and 0.027 for cypress, respectively. For the calibration model, which contained all species in this study, the $R^2$ was 0.75 and the RMSEC was 0.37.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.447-461
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• 2018

Domestic film industry sales are increasing every year. Theaters are the primary sales channels for movies and the number of audiences using the theater affects additional selling rights. Therefore, the number of audiences using the theater is an important factor directly linked to movie industry sales. In this paper we consider a hybrid model that combines a multiple linear regression model and the Bass model to predict the audience numbers for a specific day. By combining the two models, the predictive value of the regression analysis was corrected to that of the Bass model. In the analysis, three films with different release dates were used. All subset regression method is used to generate all possible combinations and 5-fold cross validation to estimate the model 5 times. In this case, the predicted value is obtained from the model with the smallest root mean square error and then combined with the predicted value of the Bass model to obtain the final predicted value. With the existence of past data, it was confirmed that the weight of the Bass model increases and the compensation is added to the predicted value.

• Journal of Korean Academy of Nursing
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• v.39 no.5
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• pp.700-708
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• 2009

Purpose: This study was done to test the validity and reliability of the Adolescent Mental Problem Questionnaire (AMPQ) for Korean high school students. Methods: The AMPQ was designed to assess adolescents', mental health status and problem behavior (Ahn, 2006). A methodological study design was used with exploratory factor analysis, Pearson's correlation coefficients, and a fitness of the modified model for validity. Also, Cronbach's alpha coefficients and alternative-form method for reliability were used. AMPQ was tested with a sample of 36,313 high school students. The participants consisted of 18,701 males and 17,612 females. Results: Seven factors were extracted through factor analysis: 'Psychiatric problems', 'Delinquency', 'Academic troubles', 'Family problems', 'Hazardous behavior', 'Harmful circumstance', 'Eating problems'. These factors explained 51.1% of the total variance. The fitness of the modified model was good ($X^2$=38,413.76, Goodness of Fit Index [GFI]=.94, Adjusted Goodness of Fit Index [AGFI]=.93, Comparative Fit Index [CFI]=.95, Root Mean Square Error of Approximation [RMSEA]=.05), and concurrent validity with Korea-Youth Self-Report [K-YSR] was .63. Cronbach's alpha coefficient of the 31 items was .85. Conclusion: The results of present study suggest that the modified AMPQ instrument may be useful for efficiently assessing mental health status and problem behavior in late adolescent, high school students.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.102-110
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• 2015

The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.132-140
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• 2008

Conventional rectal catheters which are used for the measurement of abdominal pressure can cause erroneous result affecting detrusor pressure, and the catheter itself is not comfortable to the patients. To reduce these problems, we invented a new method for measuring abdominal pressure in non invasive manner using surface electromyography (EMG) signals of the rectus abdominis muscle. Our results showed that the correlation coefficient and root mean square error (RMSE) between the measured abdominal pressures by the conventional rectal catheters and the estimated values by our proposed algorithm were $0.79{\pm}0.06$ and $0.10{\pm}0.07$, respectively. These findings suggest that the surface EMG of rectus abdominis muscle might be used indirectly for more convenient measurement of abdominal pressure on ambulatory urodynamic study.