• Journal of Korea Water Resources Association
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• v.51 no.7
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• pp.575-584
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• 2018

An increase in heavy rainfall and floods have been observed over South Korea due to recent abnormal weather. In this perspective, the high-resolution weather forecasts have been widely used to facilitate flood management. However, these models are known to be biased due to initial conditions and topographical conditions in the process of model building. Theretofore, a bias correction scheme is largely applied for the practical use of the prediction to flood management. This study introduces a new mean field bias correction (MFBC) approach for the high-resolution numerical rainfall products, which is based on a Bayesian Kriging model to combine an interpolation technique and MFBC approach for spatial representation of the error. The results showed that the proposed method can reliably estimate the bias correction factor over ungauged area with an improvement in the reduction of errors. Moreover, it can be seen that the bias corrected rainfall forecasts could be used up to 72 hours ahead with a relatively high accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.305-312
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• 2018

The crops cultivated and consumed in Korea require specific climate conditions corresponding to their own growth characteristics. This study aims to analyze the relationship between climate change and agricultural productivity. According to growing concern about climate change internationally, many agricultural studies are developing technology to prevent damage from climate change. Before developing technology, we should figure out what kind of crop gets huge damage and how much caused by climate change. In the context of agricultural economics, we can define the reduction of agricultural product yield as a decline in productivity. As a result, this study analyzes the effects of climate change on agricultural productivity using Stochastic Frontier Analysis model. There are several kinds of climate change phenomena that increase the inefficiency of production. In other words, there are several kinds of crops that get negative influence by climate change. The result of this study can be used as basic guideline for producers to prepare for changing weather prior to developing disaster tolerance technology coping actively with special weather report.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.133-144
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• 2017

Recently, weather information is getting closer to our real life, and it is a very important factor especially in the transportation field. Although the damage caused by the abnormal climate changes around the world has been gradually increased and the correlation between the road risk and the possibility of traffic accidents is very high, the domestic research has been performed at the level of basic research. The Purpose of this study is to develop a risk map for the road hazard forecasting service of weather situation by linking real - time weather information and traffic information based on accident analysis data by weather factors. So, we have developed a collection and analysis about related data, processing, applying prediction models in various weather conditions and a method to provide the road hazard map for national highways and provincial roads on a web map. As a result, the road hazard map proposed in this study can be expected to be useful for road managers and users through online and mobile services in the future. In addition, information that can support safe autonomous driving by continuously archiving and providing a risk map database so as to anticipate and preemptively prepare for the risk due to meteorological factors in the autonomous driving vehicle, which is a key factor of the 4th Industrial Revolution, and this map can be expected to be fully utilized.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.3
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• pp.223-228
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• 2019

This study analyzes the propagation of the path losses between Jeju-do and Jin-do transceivers located in the coastal areas of Korea using the Advanced Refractive Prediction System(AREPS) simulation software based on the actual coastal weather database. The simulated data is used to construct a duct map according to the altitude and thickness of the trap. The duct map is then divided into several regions depending on the altitude parameters of Tx and Rx, which can be used to effectively estimate the abnormal wave propagation characteristics due to duct occurrence in the Jeju-do coastal area. To validate the proposed duct map, two representative atmospheric index samples of the weather database in May 2018 are selected, and the simulated path losses using these atmospheric indices are compared with the measured data. The simulated path losses for abnormal conditions at the Rx point at Jeju-do are 167.7 dB and 192.3 dB, respectively, which are in good agreement with the measured data of 164.4 dB and 194.9 dB, respectively.

• Journal of Ocean Engineering and Technology
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• v.32 no.6
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• pp.474-484
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• 2018

In this study, a new numerical modeling system was proposed to predict oil spills, which increasingly occur at sea as a result of abnormal weather conditions such as global warming. The hydrodynamic conditions such as the flow velocity needed to calculate oil dispersion were estimated using a three dimensional hydrodynamic model based on the Navier-Stokes equation, which considered all of the physical variations in the vertical direction. This improved the accuracy compared to those estimated by the conventional shallow water equation. The advection-diffusion model for the spilled oil was combined with the hydrodynamic model to predict the movement and fate of the oil. The effects of absorption, weathering, and wind were also considered in the calculation process. The combined model developed in this study was then applied to various test cases to identify the characteristics of oil dispersion over time. It is expected that the developed model will help to establish initial response and disaster prevention plans in the event of a nearshore oil spill.

• Journal of Korean Society of Disaster and Security
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• v.13 no.2
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• pp.39-51
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• 2020

Recently, flood damage has been increasing in Korea due to frequent local torrential rains caused by abnormal weather conditions. According to the calculation of the recurrence period of torrential rain that occurred in North Chungcheong Province on July 16, 2017, it was estimated that the rainfall frequency in the upper are of Goessan Dam was around 1,524 years, and the highest level of Goesan Dam rose to EL.137.60 meters, leaving only 5 cm of margin until the height of the dam floor (EL.137.65 meters). The Goesan Dam, which operated for 62 years since 1957, needs to be prepared to cope with the increase of floodgate volume in the basin, the development of a single purpose dam for power generation only, and there are no measurement facilities for flood control, so efficient operation methods are needed to secure the safety of residents in upper and lower regions. In this study, a method of dam operation was proposed by constructing a rain matrix for quick decision making in flood prediction, calculating the highest level of dam for each condition in advance, and preparing a survey table, and quickly finding the level corresponding to the conditions in case of a situation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.145-152
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• 2020

In this paper, we propose the Crop Classification Management System (CCMS) that detects changes in growth environment to improve crop production rate. The CCMS consists of two modules. First, the Crop Classification Module (CCM) classifies crops through CNN. Second, the Farm Anomaly Detection Module (FADM) detects abnormal crops by comparing accumulated data of farms. The CCM recognizes crops currently grown on farms and sends them to the FADM, and the FADM picks up the weather data from the past to the present day of the farm growing the crops and applies them to the Nelson rules. The FADM uses the Nelson rules to find out weather data that has occurred and adjust farm conditions through IoT devices. The performance analysis of CCMS showed that the CCM had a crop classification accuracy of about 90%, and the FADM improved the estimated yield by up to about 30%. In other words, managing farms through the CCMS can help increase the yield of smart farms.

• Convergence Security Journal
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• v.11 no.5
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• pp.109-120
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• 2011

How meteorological situations have affected human life for survival have been an important element of living or military strategy throughout history. In modern society, overcrowding of cities has brought about many problems. Moreover, high-rise buildings and land cover have been causing abnormal weather conditions. The wind corridor, especially in urban areas has been flowing differently from the dominant weather condition of the surroundings. Therefore, the wind corridor in urban areas can be a main component in protecting major national facilities in urban areas from damage. Especially the wind corridor is a main factor to derive harm from poisonous substances in air. This paper seeks to find out the wind corridor in urban areas and the efficiency of that. In addition to that, it studies how to use the direction to protect major national facilities and areas from damage. It is considered that this study will be useful to make defence project, not only for preventing CBR(chemical, biological, and radiological) terrorism and violent assembly, but also for evacuation of people in case of big accidents or natural disasters.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.3
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• pp.213-224
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• 2019

There is an increasing frequency in the occurrence of abnormal weather phenomena such as sharp increases and decreases in temperature. Under these weather conditions, the heading time of rice changes unexpectedly, which poses problems in agriculture. Therefore, we investigated the effect of temperature on the heading response at different growth stages in rice. During the period from transplanting to heading, the plants were subjected to different temperature treatments, each for a 9-day period, to observe the heading response. For the heading date analysis, "heading date" was defined as the number of days from transplanting to the appearance of the first spikelet. We found that the influence of temperature increased in the order of rooting stage, followed by meiosis, early tillering, spikelet differentiation, and panicle initiation stage in all ecological types and cultivars. In particular, unlike the results reported previously, the effect of temperature on heading during the photo-sensitive period was very small. Meanwhile, the influence of temperature on vegetative growth response at different growth stages was not consistent with heading response. These results can be used as basic data for predicting the variation in heading date owing to temperature variation at each growth stage. In addition, we propose that the concept of day length should be included in determining the influence of temperature on the photo-sensitive period.

• Journal of Platform Technology
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• v.12 no.3
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• pp.62-70
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• 2024

Recent surveillance systems employ multiple sensors, such as cameras and radars, to enhance the accuracy of intrusion detection. However, object recognition through camera (RGB, Thermal) sensors may not always be accurate during nighttime, in adverse weather conditions, or when the intruder is camouflaged. In such situations, it is possible to detect intruders by utilizing the trajectories of objects extracted from camera or radar sensors. This paper proposes a method to detect intruders using only trajectory information in environments where object recognition is challenging. The proposed method involves training an LSTM-Attention based trajectory classification model using normal and abnormal (intrusion, loitering) trajectory data of animals and humans. This model is then used to identify abnormal human trajectories and perform intrusion detection. Finally, the validity of the proposed method is demonstrated through experiments using real data.