• Korean Journal of Hydrosciences
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• v.3
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• pp.31-44
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• 1992

Precise run-off forecasting depends on the ability to predict quantitative rainfall intensity. The purpose of this study is to develop a stochastic model for the shori-term rainfall prediction. It is required for the model to predict rainfall intensities at all the telemetered rain-gauge locations simultaneously. All the model parameters, which are used in this work ; velocity and direction of storm movement, radial spectrum, and dimensionless time distribution of rainfall, are the results of the previous study. We formulated the model and operated it, so that in this study was analyzed particulary the influence of 4 dimensionless time distributions on the prediction and the influence of the model on run-off.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.69-82
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• 2020

In many technologies, artificial intelligence (AI) is becoming an important topic for areas based on the field of big data. However, applied AI cases and the research status of radio wave technology are not widely known to the public. The spread of AI to other areas is being followed by radio wave technologies, and much effort is being taken to evolve it into intelligent radio wave technologies in the future. This paper presents the recent areas of interest in radio wave technology, such as spectral sharing, illegal spectrum monitoring, radar detection, radio wave medical imaging, and channel modeling; examines the requirements for applying AI; and describes the applied cases, research trends, and standardization efforts that apply AI technology to them. On this basis, we will discuss the prospects of AI application to the expected radio wave technology of the future.

• Earthquakes and Structures
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• v.11 no.5
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• pp.861-871
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• 2016

The Groningen gas field shows exponential growth in earthquake event counts around a magnitude M1 with a doubling time of 6-9 years since 2001. This behavior is identified with dimensionless curvature in land subsidence, which has been evolving at a constant rate over the last few decades essentially uncorrelated to gas production. We demonstrate our mechanism by a tabletop crack formation experiment. The observed skewed distribution of event magnitudes is matched by that of maxima of event clusters with a normal distribution. It predicts about one event < M5 per day in 2025, pointing to increasing stress to human living conditions.

• Water for future
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• v.23 no.3
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• pp.341-350
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• 1990

Precise run-off forecasting depends on the ability to predict quantitative rainfall intensity. This study suggests a stochastic model for 1 hour order rainfall prediction. The model simultaneously predicts rainfall intensity at all telemetered rain-gauge locations. All model parameters, velocity and direction of storm movement, radial spectrum, dimensionless time distribution of rainfall, are estimated from telemetered and historical data for the basin being predicted. Also the estimated parameters are based on the previous study. The results are the influence of dimensionless time distributions on the prediction and the model on run-off.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.553-556
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• 2012

The interference among the rain radars and interference in the adjacent wireless station due to the spurious signals from the rain radar were analyzed in this paper. The rain radar measures the rain intensity using S-band signal. The measured data are utilized in forecasting the rainfall. The interference among the rain radars or in the adjacent wireless stations may be caused by the high output power of rain radar. Based on the propagation analysis of S band signal and the deduced interference protection ratio of rain radar, the interference due to the rain radar are analyzed. Also, the radiation spectrum characteristics of a rain radar are deduced from the caused interference effects by the spurious signals of the rain radar.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.280.2-286
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• 1998

Flood is the main natural disaster mostly in the world. It is a care problem to prevent flood disaster generally. The frequency of flood disaster is high and the distributing field is wide, the 50 percent population and 70 percent properties distribute at the threaten field of flood disaster in China. Flood disaster has caused a huge amount of economical losses and these losses have an increasing trend. Along with the development of reducing natural disaster action, it has become one of the most attentive problems for monitoring flood, preventing flood and forecasting flood efficiently. Remote sensing has the characteristics of large spatial observing areas, wide spectrum ranges, and imaging far away from the targets, imaging capabilities all weather. Spatial remote sensing information, which records the full, processes of the disaster's occurrence and development in real-time. It is a scientific basis for management, planning and decision-making. Through systemic analyzing the RS monitoring theory, based on compounding RS information, the technology and method of monitoring flood disaster are studied.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1021-1023
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• 2003

Atmospheric water vapor is a key variable in numerical weather prediction (NWP) models, but it is a crucial factor to limit the accuracy of high-precision GPS positioning technique. For both issues, knowledge about the amount of water vapor is extremely important. In this study, we perform a simulation study to utilize GPS signals through a developed tomographic scheme to retrieve 3D structure of atmospheric wet refractivity, which may be assimilated into NWP models for advancing forecasting or position calculation for improving GPS positioning accuracy. For the purpose of knowing the absolute accuracy of the developed tomographic method, a well-defined temporal and spatial varying state of atmospheric profile is utilized. Under such circumstance, several factors that may influence the retrievals can be easily examined and their impacts may be clearly quantified. They include the values of the positional dilution of precision (PDOP) factors of the GPS signals, ... etc. Based upon the use of a variety spectrum of adjustable factors, many interesting findings are obtained. For example, the more is the number of the observed GPS signals the better becomes the retrievals as expected. Also, the smaller is the PDOP value the better becomes the retrievals.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.367-367
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• 2012

기후변화로 인해 기온, 강수량, 습도 등의 기후를 예측하고 변화하는 환경에 적응해가며 생활하고 있다. 또한 여러 가지 외부적인 요인들의 영향을 받아 상수도 시설에서의 에너지 사용량도 영향을 많이 받는다. 하지만 이러한 상수도 시설의 사용량 변화로 인해 상수도 수요량의 변화량을 예측하는데 있어서 국내 연구 및 방법이 많이 부족한 상황이다. 이에 본 연구에서는 다중스케일을 기반으로 하는 비선형 예측 모형을 개발하고자 한다. 다중스케일 분석에서도 가장 우수한 분해 능력을 가지는 Wavelet Transform을 적용하여 시계열을 분해한 후 패턴인식 기반의 비선형 예측모형인 Support Vector Machine(SVM)을 적용하였다. 상수도 수요량의 예측 과정은 다음과 같다. 첫째, 상수도 수요량 자료를 Wavelet Transform 기법을 통하여 단순화 시킨다. 둘째, Global Wavelet Spectrum을 통하여 통계적으로 의미 있는 성분만을 추출하고 이를 해석 대상으로 한다. 셋째, 특정 주기를 갖는 유의한 독립성분들에 대해서 최적 지체시간을 결정한 후 SVM모형을 통해 예측 모형을 구축한다. 넷째, 나머지 성분에 대해서도 SVM 모형을 적용하여 예측을 실시한 후 앞서 예측된 성분과 모두 결합하여 최종적으로 예측시계열을 구성한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.116-118
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• 2023

In recent years, the need for economical and sustainable ship routing has emerged due to the enforced regulations on environmental issues. Despite the development of weather forecasting technology, maritime accidents by rough waves have continued to occur due to incorrect weather forecasts. In this study, onboard measurements are conducted to observe the acutal situation on merchant ships in operation encountering rough waves. The types of measured data include information related to navigation (Ship's position, speed, bearing, rudder angle) and engine (engine revolutions, power, shaft thrust, fuel consumption), weather conditions (wind, waves), and ship motions (roll, pitch, and yaw). These ship experiments was conducted to 28,000 DWT bulk carrier, 63,000 DWT bulk carrier, 20,000 TEU container ship, and 12,000 TEU container ship. The actual ship experiment of each ship is intended to acquire various types of data and utilize them for multi-objective studies related to ship operation. Additionally, in order to confirm the sea conditions, the directional wave spectrum was reproduced using a wave simulation model. Through data collection from ship experiments and wave simulations, various studies could be proceeding such as the measurement for accurate wave information by marine radar and analysis for cargo collapse accidents. In addition, it is expected to be utilized in various themes from the perspective of safety and efficiency in ship operation.