• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1725-1732
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• 2024

In nuclear power plants, reactor coolant leakage can occur due to various reasons. Early detection of leaks is crucial for maintaining the safety of nuclear power plants. Currently, a detection system is being developed in Korea to identify reactor coolant system (RCS) leakage of less than 0.5 gpm. Typically, RCS leaks are detected by monitoring temperature, humidity, and radioactivity in the containment, and a water level in the sump. However, detecting small leaks proves challenging because the resulting changes in the containment humidity and temperature, and the sump water level are minimal. To address these issues and improve leak detection speed, it is necessary to quantify the leaks and develop an artificial intelligence-based leak detection system. In this study, we employed bidirectional long short-term memory, which are types of neural networks used in artificial intelligence, to predict the relative humidity in the leakage area for leak quantification. Additionally, an optimization technique was implemented to reduce learning time and enhance prediction performance. Through evaluation of the developed artificial intelligence model's prediction accuracy, we expect it to be valuable for future leak detection systems by accurately predicting the relative humidity in a leakage area.

• Journal of Forest and Environmental Science
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• v.33 no.4
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• pp.305-314
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• 2017

This study aimed to predict changes in forest area using a probability density function, in order to promote effective forest management in the area north of the civilian control line (known as the Minbuk area) in Korea. Time series analysis (2010 and 2016) of forest area using land cover maps and accessibility expressed by distance covariates (distance from buildings, roads, and civilian control line) was applied to a probability density function. In order to estimate the probability density function, mean and variance were calculated using three methods: area weight (AW), area rate weight (ARW), and sample area change rate weight (SRW). Forest area increases in regions with lower accessibility (i.e., greater distance) from buildings and roads, but no relationship with accessibility from the civilian control line was found. Estimation of forest area change using different distance covariates shows that SRW using distance from buildings provides the most accurate estimation, with around 0.98-fold difference from actual forest area change, and performs well in a Chi-Square test. Furthermore, estimation of forest area until 2028 using SRW and distance from buildings most closely replicates patterns of actual forest area changes, suggesting that estimation of future change could be possible using this method. The method allows investigation of the current status of land cover in the Minbuk area, as well as predictions of future changes in forest area that could be utilized in forest management planning and policymaking in the northern area.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.851-856
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• 2016

Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. The Most of previous MOS has used a linear regression model for weather prediction, but it is hard to manage an irregular nature of prediction of wind speed. In order to solve the problem, a nonlinear regression method using SVR (Support Vector Regression) is introduced for a development of MOS for wind speed prediction. Experiments are performed for KLAPS (Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea. The MLR and SVR based linear and nonlinear methods are compared to each other for prediction accuracy of wind speed. Also, the comparison experiments are executed for the variation in the number of UM elements.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.42-42
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• 2017

Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed $L^*a^*b^*$ color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to $L^*a^*b^*$ color space. All color information contain in both $a^*$ and $b^*$ layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.8
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• pp.788-792
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• 2008

In this paper, we propose a method that uses the constrained inter-view prediction for multi-view video coding. In the multi-view video, there exists occluded area because of the locations and angles of cameras. This increases the computational complexity, as it still uses both reference pictures for predicting the area which is not shown in the current frame. In this paper, we propose a method that does not use the inter-view prediction in cases of the occluded macroblocks. Experimental results show that benefits about 4% can be achieved compared with the conventional approaches.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.3
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• pp.177-190
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• 1998

The concentrations of air pollutants In large cities such as Pusan area have been increased every year due to the increasing of fuels consumption at factories and by vehicles as well as the gravitation of the population. In addition to the pollution sources, time and spatial variation of air pollutants concentration and meteorological factors have a great influence on the air pollution problem. Especially , its concentration is governed by wind direction, wind speed, precipitation, solar radiation, temperature, humidity and cloud amounts, etc. In this study, we have analyzed various data of meteorological factors using typical patterns of the air pressure to investigate how the concentration of air pollutants is varied with meteorological condition. Using the relationship between meteorological factors (air temperature, relative humidity, wind speed and solar radiation) and the concentration of air pollutants (SO2, O3) , experimental prediction formulas for their concentration were obtained. Therefore, these prediction formulas at each meteorological factor in a pressure pattern may be roughly used to predict the air pollutants concentration and contributed to estimate the variation of its value according to the weather condition in Pusan city.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.4
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• pp.55-62
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• 2018

Problems on vibration due to blasting for infrastructure development are getting important because of a civil appeal. Blasting-induced vibration is representative construction pollution, hence, it is possible that a number of environmental damages occur. In this study, borehole test blasting was conducted at Sintanjin area, Daejeon and square root equation with 95% confidence level was proposed for prediction of blasting-induced vibration. The vibration value predicted from this equation was more conservatively evaluated than the values predicted from U.S. Department of Interior, Bureau of Mines (USBM) and Nippon Oil & Fats Co., Ltd. (NOF) equations. Therefore, the proposed equation in this study seems to contribute for safety blast design. However, for optimal blast design, inducing equation for prediction of blasting-induced vibration through the identical test blasting with field construction such as rock slope blasting would be required.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.43-51
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• 2008

In this paper, we are implemented low-power structure for Inter prediction, Intra prediction, Deblocking filter, Transform and Quantization blocks in H.264/AVC Encoder. The proposed Inter/Intra prediction blocks are shown 60.2% cell area reduction by adder reduction through Distributed Arithmetic, 44.3% add operation reduction using MUX for hardware share in Deblocking filter block. Furthermore we applied CSD and CSS process to reduce the cell area instead of multipliers that take a lot of area. The FPGA(Field Programmable Gate Array) and ARM Process based H.264/AVC encoder is implemented using proposed low power IPs. The proposed structure Platforms are implemented to interlock with FPGA and ARM processors. H.264/AVC Encoder implementation using Platforms shows that proposed low-power IPs can use H.264/AVC Encoder SoC effectively.

• Journal of Korea Water Resources Association
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• v.40 no.6 s.179
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• pp.447-458
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• 2007

In this study, the coastal urban flood prediction and warning system based on HEC-RAS and SWMM were investigated to evaluate a watershed of On-Cheon stream in Busan which has characteristics of costal area cased by flooding of coastal urban areas. The basis of this study is a selection of various geological data from the numerical map that is a watershed of On-Cheon stream and computation of hydrologic GIS data. Thiessen method was used for analyzing of rainfall on the On-Cheon stream and 6th regression equation, which is Huff's Type II was time-distribution of rainfall. To evaluate the deployment of flood prediction and warning system, risk depth was used on the 3 selected areas. To find the threshold runoff for hydraulic analysis of stream, HEC-RAS was used and flood depth and threshold runoff was considered with the effect of tidal water level. To estimate urban flash flood trigger rainfall, PCSWMM 2002 was introduced for hydrologic analysis. Consequently, not only were the criteria of coastal urban flood prediction and warning system decided on the watershed of On-Cheon stream, but also the deployment flow charts of flood prediction and warning system and operation system was evaluated. This study indicates the criteria of flood prediction and warning system on the coastal areas and modeling methods with application of ArcView GIS, HEC-RAS and SWMM on the basin. For the future, flood prediction and warning system should be considered and developed to various basin cases to reduce natural flood disasters in coastal urban area.

• The Journal of Engineering Geology
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• v.18 no.1
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• pp.45-54
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• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model, which is one of the statistical analysis methods. The slope hazards data of Seoul and Kyonggi Province, which were induced by heavy rainfall in 1998, were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. Among these data, the number of data occurred slope hazards was 34 sections and the number of data non-occurred slope hazards was 27 sections. The statistical analyses using the decision tree model were applied to chi-square statistics, gini index and entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320 m, respectively.