• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.64-68
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• 2022

Herein, we propose a LoRaWAN-based small draft system that can measure the ocean current flow (speed, direction, and distance) in real time at the request of the Coast Guard to develop a device that can promptly find survivors at sea. This system has been implemented and verified in the early stages of rescue after maritime vessel accidents, which are frequent. GPS signals often transmit considerable errors, so correction algorithms using the improved triangulation method algorithm are required to accurately indicate the direction of currents in real time. This paper is structured in the following manner. The introduction section elucidates rescue activities in the case of a maritime accident. Chapter 2 explains the characteristics and main parameters of the GPS surveying technique and LoRaWAN communication, which are related studies. It explains and expands on the critical distance error correction algorithm for GPS signals and its improvement. Chapter 3 discusses the design and analysis of small draft buoys. Chapter 4 presents the testing and validation of the implemented system in both onshore and offshore environments. Finally, Section 5 concludes the study with the expected impact and effects in the future.

• Journal of the Korean Statistical Society
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• v.28 no.1
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• pp.93-106
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• 1999

In this paper we consider estimation of a real valued parameter in the drift coefficient of a Hilbert space valued Ito stochastic differential equation. First we consider observation of the corresponding diffusion in a fixed time interval [0, T] and prove the Bernstein - von Mises theorem concerning the convergence of posterior distribution of the parameter given the observation, suitably normalised and centered at the MLE, to the normal distribution as Tlongrightarrow$\infty$. As a consequence, the Bayes estimator of the drift parameter becomes asymptotically efficient and asymptotically equivalent to the MLE as Tlongrightarrow$\infty$. Next, we consider observation in a random time interval where the random time is determined by a predetermined level of precision. We show that the sequential MLE is better than the ordinary MLE in the sense that the former is unbiased, uniformly normally distributed and efficient but is latter is not so.

• Journal of Korea Foundry Society
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• v.26 no.3
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• pp.129-132
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• 2006

In the present study the new method was proposed by using the real-time X-ray observation and metal die in order to evaluate fluidity and viscosity of the molten metal during pouring into the mold. The special mold for the present experiment was introduced since X-ray could not transmit thick mold wall and scatter the image of the molten metal during pouring. The present study also discussed for evaluation of viscosities by using the flow data from radioscopy images, and the viscosities of six commercial aluminum alloys were evaluated and compared.

• Journal of the Korean earth science society
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• v.30 no.4
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• pp.478-484
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• 2009

A web page was developed to enhance students' learning experience in studying meteorological phenomena. After collecting the meteorological elements observed with automatic weather observation system (AWS), it serve real-time meteorological information on demand. Past meteorological information as well as real-time current information can be retrieved because the web page can save and accumulate observed information in its data base. The completed web page was successfully applied in school settings in teaching students meteorology research sections of earth science. The results show that students experienced authentic and meaningful learning through the real-time meteorological information from the web page. In addition, large scale of time was required to observe meteorological phenomena and it hindered practical meteorological research in earth science classes. However, it is expected that the time limitation can be overcome by utilizing accumulated meteorological information of the web page.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.5
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• pp.682-689
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• 2011

Recently, there are frequent disasters by Swell-like Wave in the coastal area, Korea peninsula. This phenomenon (Swell-like Wave) has long period above 10 seconds compared with wind wave. To prevent these disasters by the long-period wave in advance, it's necessary to observe it in real time. But existing instruments for wave observation can not observe long-period wave because they mainly are aimed to measure the short-period wind wave. Therefore, in this research it is tried to develop the GPS based Long Period Wave Observation System which real time operation can be realzied in the sea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.5 no.1
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• pp.9-18
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• 1999

To predict the oil spill dispersion phenomena in the ocean, the oil spill response model, which can be used for strategic purpose on the oil spill site, based on Lagrangian particle-tracking method was formulated and applied to the neighboring area with Pusan port where the oil spill incident occurred when the tanker ship No.1 Youil struck on a small rock near the Namhyungjeto on September 21, 1995. The real-time tidal currents to be required as input data of the oil spill model were obtained by the two-dimensional hydrodynamic model and the tide prediction model. Evaluation of tidal currents using observation data was successful. For wind data, other input data of oil spill model, observed data on the spot were used. To verify the oil spill model, the oil spill modelling results were compared with the field data obtained from the spill site. Compared the modelling results with the observation data, there exist some discrepancies but the general pattern of modelling results was similar to that of field observation. The modelling results on 7 days after spill occurred showed that the 40% of spilled oil is in floating, 36% in evaporated, 23% at shore, and 1% in out of boundary, respectively. According to the evaluation of weighting curves of effective components to the dispersion of oil, the winds make a 37% of contribution to the dispersion of oil, turbulent diffusion 39.5%, and tidal currents 23.5%, respectively. Provided the more accurate wind data are supported, more favorable results might be obtained.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.117-127
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• 2020

The Early Warning System for agrometeorological hazard of the Rural Development Administration (Korea) forecasts detailed weather for each farm based on the meteorological information provided by the Korea Meteorological Administration, and estimates the growth of crops and predicts a meteorological hazard that can occur during the growing period by using the estimated detailed meteorological information. For verification of early warning system, automated weather observation network was constructed in the study area. Moreover, a real-time web display system was built to deliver near real-time weather data collected from the observation network. The meteorological observation system collected diverse meteorological variables including temperature, humidity, solar radiation, rainfall, soil moisture, sunshine duration, wind velocity, and wind direction. These elements were collected every minute and transmitted to the server every ten minutes. The data display system is composed of three phases: the first phase builds a database of meteorological data collected from the meteorological observation system every minute; the second phase statistically analyzes the collected meteorological data at ten-minutes, one-hour, or one-day time step; and the third phase displays the collected and analyzed meteorological data on the web. The meteorological data collected in the database can be inquired through the webpage for all data points or one data point in the unit of one minute, ten minutes, one hour, or one day. Moreover, the data can be downloaded in CSV format.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.488-491
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• 1988

In this work, we developed a real-time versatile advanced control and supervisory software for a personal computer control. This software, basically, has background and foreground tasks which are performed in parallel at real time. First, background tasks are composed of controls of various kinds, reports and input-ouput of signals etc, which are performed every sampling time. Second, foreground tasks are observation of operation conditions, data search, regulation of controllers and graphical design and display of processes, which are performed by users request. Additionally, this software has the functions of transporting data and composing distributed control systems, and all background tasks are composed of combination of unit function blocks.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1419-1424
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• 2018

Very short-term electric load forecasting is essential for real-time power system operation. In this paper, a very short-term electric load forecasting technique applying the Kalman filter algorithm is proposed. In order to apply the Kalman filter algorithm to electric load forecasting, an electrical load forecasting algorithm is defined as an observation model and a state space model in a time domain. In addition, in order to precisely reflect the noise characteristics of the Kalman filter algorithm, the optimal error covariance matrixes Q and R are selected from several experiments. The proposed algorithm is expected to contribute to stable real-time power system operation by providing a precise electric load forecasting result in the next six hours.