• Journal of the Korean Institute of Telematics and Electronics A
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• 제31A권7호
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• pp.47-54
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• 1994

The propagation prediction within a cell coverage in land mobile radio service is very important. The propagation loss is presented in a A+B logS110TR form, where both A and B are the parameter as function of the frequency and the antenna height and R is the distance of between base and mobile station. The propagation prediction is Cheju area is not easy, because a great number of peaks are found here and there at the foot of the Hanla Mt. The characteristics of radio propagation in Cheju area are measured for the Seorum transmitter site. The formular of correction which is regard to the configuration of the ground is presented, and the predicted values are compared with the measured one.

• Journal of Environmental Science International
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• 제11권4호
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• pp.383-389
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• 2002

Odor sources of a chemical plant in Ulsan were surveyed and temperatures, humidities and flow rates of each exhaust gas were measured. The air samples collected from each source were transferred to the laboratory for sensory test and their odor concentrations were investigated. The odor emission rate of each source was estimated from the recorded results and assigned the sources expected to be needed for the odor prevention policy using the simple prediction equation of the affection by malodor to the nearest residential area. From the total odor emission rate of the examined plant and the relation table for expectable affection area it was concluded that total odor emission of this plant might be decreased for the prevention of residential complaint.

• Journal of The Korean Astronomical Society
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• 제53권6호
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• pp.139-147
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• 2020

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

• Korean Journal of Remote Sensing
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• 제17권3호
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• pp.199-210
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• 2001

The purpose of this study is to compare the landslide prediction models quantitatively using prediction rate curve. A case study from the Jangheung area was used to illustrate the methodologies. The landslide locations were detected from remote sensing data and field survey, and geospatial information related to landslide occurrences were built as a spatial database in GIS. As prediction models, joint conditional probability model and certainty factor model were applied. For cross-validation approach, landslide locations were partitioned into two groups randomly. One group was used to construct prediction models, and the other group was used to validate prediction results. From the cross-validation analysis, it is possible to compare two models to each other in this study area. It is expected that these approaches will be used effectively to compare other prediction models and to analyze the causal factors in prediction models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제33권6호
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• pp.589-594
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• 2015

In reflection of road expansion and increasing use rates, interest has blossomed in predicting driving environment. In addition, a gigantic scale of big data is applied to almost every area around the world. Recently, technology development is being promoted in the area of road traffic particularly for traffic information service and analysis system in utilization of big data. This study examines actual cases of road management systems and road information analysis technologies, home and abroad. Based on the result, the limitations of existing technologies and road management systems are analyzed. In this study, a development direction and expected effort of the prediction of road information are presented. This study also examines regression analysis about relationship between guide name and traffic volume. According to the development of driving environment prediction platform, it will be possible to serve more reliable road information and also it will make safe and smart road infrastructures.

• The Journal of the Korea institute of electronic communication sciences
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• 제6권2호
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• pp.157-162
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• 2011

International Telecommunication Union(ITU), recommended a propagation prediction models that can be applied to a various propagation environments that many services have been established in the field of broadcasting and telecommunications using ITU-R. Each propagation prediction models are revised with the complement procedures of an expected difference of channel environment and prepared for a standard of a propagation prediction. In this research, it is possible to realized a practical propagation prediction in each transmitting station for a broadcasting environments of ITU-R Rec. P.1546-3 model, so called the point-to-area, using supplementary parameters of the transmitting station specification.

• Journal of Positioning, Navigation, and Timing
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• 제3권2호
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• pp.83-90
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• 2014

In this study, to improve an existing ground-wave based time broadcasting system, a study that predicts the field distribution and field strength of the transmitted signal of a new ground-wave based time broadcasting system was performed. The prediction area was assumed to be the Korean peninsula; and to reflect the mountainous terrain of the Korean peninsula in the prediction of the variations of field distribution and field strength, a new prediction method based on the Monteath model was proposed and utilized. As field distribution changes depending on the position of a transmitter station, potential sites for the transmitter station were selected considering the geographical characteristics. In this regard, the ground conductivity information of North Korea cannot be obtained, and thus, the ground conductivity of the North Korean region was reflected considering the geological characteristics of South Korea and North Korea. Based on this, the variations of field distribution and field strength were predicted by setting the Korean peninsula as the prediction area, and the prediction results depending on the position of the transmitter station were discussed.

• Journal of Biosystems Engineering
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• 제40권1호
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• pp.47-60
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• 2015

Purpose: The purpose of this study was to non-destructively and quickly predict the capsaicinoid content of domestic red pepper powders from various areas of Korea using a pungency measurement system in combination with visible and near-infrared (VNIR) spectroscopic techniques. Methods: The reflectance spectra of 149 red pepper powder samples from 14 areas of Korea were obtained in the wavelength range of 450-950 nm and partial least squares regression (PLSR) models for the prediction of capsaicinoid content were developed using area models. Results: The determination coefficient of validation (RV2), standard error of prediction (SEP), and residual prediction deviation (RPD) for the capsaicinoid content prediction model for the Namyoungyang area were 0.985, ${\pm}2.17mg/100g$, and 7.94, respectively. Conclusions: These results show the possibility of VNIR spectroscopy combined with PLSR models in the non-destructive and facile prediction of capsaicinoid content of red pepper powders from Korea.

• Journal of Korean Society for Atmospheric Environment
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• 제24권E2호
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• pp.63-73
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• 2008

In this study, neural network models (NNMs) were examined as alternatives to dispersion models in predicting the short-term $SO_2$ concentrations in a coastal area because the performances of dispersion models in coastal areas have been found to be unsatisfactory. The NNMs were constructed for various combinations of averaging time and prediction time in advance by using the historical data of meteorological parameters and $SO_2$ concentrations in 2002 in the coastal area of Boryeung, Korea. The NNMs were able to make much more accurate predictions of 1 hr $SO_2$ concentrations at ground level in the morning in coastal area than the atmospheric dispersion models such as fumigation models, ADMS3 and ISCST3 for identical conditions of atmospheric stability, area, and weather. Even when predictions of 24-h $SO_2$ concentrations were made 24 hours in advance, the predictions and measurements were in good accordance(correlation coefficient=0.65 for n=216). This accordance level could be improved by appropriate expansion of training parameters. Thus it may be concluded that the NNMs can be successfully used to predict short-term ground level concentrations averaged over time less than 24 hours even in complex terrain. The prediction performance of ANN models tends to improve as the prediction lagging time approaches the concentration averaging time, but to become worse as the lagging time departs from the averaging time.

• Journal of the Ergonomics Society of Korea
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• 제27권2호
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• pp.1-7
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• 2008

This study quantified 7 trunk muscles' physiological cross-sectional areas (PCSAs) and developed prediction equations for the physiological cross-sectional area as a function of anthropometic variables for Korean people. Nine females and nine males were participated in the magnetic resonance imaging (MRI) scans approximately from S1 through T8. Muscle fiber angle corrected cross-sectional areas (anatomical cross sectional areas: ACSAs) were recorded at each vertebral level and maximum value of ACSAs were determined as physiological cross sectional area (PCSA). There was a significant gender difference in PCSAs of all muscles (p<0.05). Stepwise linear regression techniques using anthropometric measures (e.g., height, weight, trunk depths and widths) as independent variables were conducted to develop prediction equations for the PCSA for each muscle. For males, six muscles' significant prediction equations (p<0.05) were developed except quadratus lumborum. For females, three prediction equations were developed for psoas, quadratus lumborum, and erector spinae muscles (p<0.05).