• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1371-1388
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• 2023

Floods are becoming more severe and frequent due to global warming-induced climate change. Water disasters are rising in Korea due to severe rainfall and wet seasons. This makes preventive climate change measures and efficient water catastrophe responses crucial, and synthetic aperture radar satellite imagery can help. This research created 1,423 water body learning datasets for individual water body regions along the Han and Nakdong waterways to reflect domestic water body properties discovered by Sentinel-1 satellite radar imagery. We created a document with exact data annotation criteria for many situations. After the dataset was processed, U-Net, a deep learning model, analyzed water body detection results. The results from applying the learned model to water body locations not involved in the learning process were studied to validate soil water body monitoring on a national scale. The analysis showed that the created water body area detected water bodies accurately (F1-Score: 0.987, Intersection over Union [IoU]: 0.955). Other domestic water body regions not used for training and evaluation showed similar accuracy (F1-Score: 0.941, IoU: 0.89). Both outcomes showed that the computer accurately spotted water bodies in most areas, however tiny streams and gloomy areas had problems. This work should improve water resource change and disaster damage surveillance. Future studies will likely include more water body attribute datasets. Such databases could help manage and monitor water bodies nationwide and shed light on misclassified regions.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1446-1453
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• 2021

In the past, several experimental investigations aiming at characterizing the natural circulation (NC) behavior in test facilities were carried out. They showed a variety of flow patterns characterized by an inverted U-shape of the NC flow curve versus primary mass inventory. On the other hand, attempts to reproduce such curves using thermal-hydraulic system codes, showed 10-30% differences between the measured and calculated NC mass flow rate. Actually, the used computer codes are generally based upon nodalization using single U-tube representation. Such model may not allow getting accurate simulation of most of the NC phenomena occurring during such tests (like flow redistribution and flow reversal in some SG U-tubes). Simulations based on multi-U-tubes model, showed better agreement with the overall behavior, but remain unable to predict NC phenomena taking place in the steam generator (SG) during the experiment. In the current study, the CATHARE code is considered in order to assess a NC characterization test performed in the four loops PKL facility. For this purpose, four different SG nodalizations including, single and multi-U-tubes, 1D and 3D SG inlet/outlet zones are considered. In general, it is shown that the 1D and 3D models exhibit similar prediction results up to a certain point of the rising part of the inverted U-shape of the NC flow curve. After that, the results bifurcate with, on the one hand, a tendency of the 1D models to over-predict the measured NC mass flow rate and on the other hand, a tendency of the 3D models to under-predict the NC flow rate.

• Spatial Information Research
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• v.22 no.6
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• pp.45-54
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• 2014

Establishment of safety net for the socially disadvantaged attracts large attention because of the recent crime increasing against vulnerable groups. For the successful establishment of social safety net, the test-bed for evaluation and realization of crime-related research results is required. However, previous R&D test-bed projects such as The Korean Land Specialization Program or U-Eco City project remains only to the stage of verification. Therefore, there are limitedness for realization of result technologies or sustainable operation & management of test-bed after projects finished. So, sustainable operation & management system and guideline of test-bed are necessary. Therefore, this study reviews the strengths and weaknesses of existing test-bed cases and intelligent security researches. After reviewing, the concept of a Intelligent Security Test-bed is established and appropriate test-bed selection criteria is also suggested. Based on objective criteria, selected test-bed can achieve sustainable management even after finishing the project and contribute the construction of standard model for citizen's safety.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.225-236
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• 2020

This paper examines empirically the net impact of residual state ownership on privatized firm efficiency in the transitional context of Vietnam. Vietnamese privatization has its own characteristics. Instead of mass and full privatization, Vietnam has chosen a partial and gradual path. Thus, it is important to assess the net impact of residual state ownership on privatized firms during the post-privatization period. This study employs stochastic frontier analysis to investigate the association between residual state ownership and the efficiency of privatized firms, using a sample of all privatized firms that are listed on the Vietnamese stock exchanges over the period from 2007 to 2017. Also, two-stage least squares regression is incorporated into the model to deal with potential endogeneity issues. Our study provides evidence that state ownership should not be considered as a pure source of agency problems. Indeed, the net impact of residual state ownership on privatized firm efficiency is non-monotonic, and the relationship between residual state ownership and privatized firm efficiency is under an inverted U-shape. A moderate level (less than 50%) of residual state ownership might be beneficial to privatized firm efficiency whereas too much state ownership is detrimental to the efficiency of privatized firms.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.17-22
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• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.547-557
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• 2019

To extract the depth map from a single image, a number of CNN-based deep learning methods have been performed in recent research. In this study, the GAN structure of Pix2Pix is maintained. this model allows to converge well, because it has the structure of the generator and the discriminator. But the convolution in this model takes a long time to compute. So we change the convolution form in the generator to a depthwise convolution to improve the speed while preserving the result. Thus, the seven down-sizing convolutional hidden layers in the generator U-Net are changed to depthwise convolution. This type of convolution decreases the number of parameters, and also speeds up computation time. The proposed model shows similar depth map prediction results as in the case of the existing structure, and the computation time in case of a inference is decreased by 64%.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.633-640
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• 2014

In this research, the surface roughness affecting the pressure drop in a pipe used as the steam generator of a PWR was studied. Based on the CFD (Computational Fluid Dynamics) technique using a commercial code named ANSYS-FLUENT, a straight pipe was modeled to obtain the Darcy frictional coefficient, changed with a range of various surface roughness ratios as well as Reynolds numbers. The result is validated by the comparison with a Moody chart to set the appropriate size of grids at the wall for the correct consideration of surface roughness. The pressure drop in a full-scale U-shaped pipe is measured with the same code, correlated with the surface roughness ratio. In the next stage, we studied a reduced scale model of a U-shaped heat pipe with experiment and analysis of the investigation into fluid-structure interaction (FSI). The material of the pipe was cut from the real heat pipe of a material named Inconel 690 alloy, now used in steam generators. The accelerations at the fixed stations on the outer surface of the pipe model are measured in the series of time history, and Fourier transformed to the frequency domain. The natural frequency of three leading modes were traced from the FFT data, and compared with the result of a numerical analysis for unsteady, incompressible flow. The corresponding mode shapes and maximum displacement are obtained numerically from the FSI simulation with the coupling of the commercial codes, ANSYS-FLUENT and TRANSIENT_STRUCTURAL. The primary frequencies for the model system consist of three parts: structural vibration, BPF(blade pass frequency) of pump, and fluid-structure interaction.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.859-873
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• 2023

Wildfires cause a lot of environmental and economic damage to the Earth over time. Various experiments have examined the harmful effects of wildfires. Also, studies for detecting wildfires and pollutant emissions using satellite remote sensing have been conducted for many years. The wildfire product for the Geostationary Environmental Monitoring Spectrometer (GEMS), Korea's first environmental satellite sensor, has not been provided yet. In this study, a false-color composite for better expression of wildfire smoke was created from GEMS and used in a U-Net model for wildfire detection. Then, a classification model was constructed to distinguish yellow dust from the wildfire smoke candidate pixels. The proposed method can contribute to disaster monitoring using GEMS images.

• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.351-359
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• 2016

The Korean Prototype Gen-IV sodium-cooled fast reactor (PGSFR) is supposed to be loaded with a relatively-costly low-enriched U fuel, while its envisaged transuranic fuels are not available for transmutation. In this work, the U-enrichment reduction by improving the neutron economy is pursued to save the fuel cost. To improve the neutron economy of the core, a new reflector material, PbO, has been introduced to replace the conventional HT9 reflector in the current PGSFR core. Two types of PbO reflectors are considered: one is the conventional pin-type and the other one is an inverted configuration. The inverted PbO reflector design is intended to maximize the PbO volume fraction in the reflector assembly. In addition, the core radial configuration is also modified to maximize the performance of the PbO reflector. For the baseline PGSFR core with several reflector options, the U enrichment requirement has been analyzed and the fuel depletion analysis is performed to derive the equilibrium cycle parameters. The linear reactivity model is used to determine the equilibrium cycle performances of the core. Impacts of the new PbO reflectors are characterized in terms of the cycle length, neutron leakage, radial power distribution, and operational fuel cost.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.724-733
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• 1999

An effective method of system modeling and dynamic scheduling for the back-end line of semiconductor manufacturing is proposed. The virtual factory, describing semiconductor manufacturing line, is designed in detail, and then a Petri net model simulator is developed for operation and control of the modular cells of the virtual factory. The petri net model is a colored timed Petri nets (CTPNs). The simulator will be utilized to analyze and evaluate various dynamic status and operatons of manufacturing environments. The dynamic schedulaer has a hierarchical structure with the higher for planning level and the lower for dynamic scheduling level. The genetic algorithm is applied to extract optimal conditions of the scheduling algorithm. The proposed dynamic scheduling is able to realize the semiconductor manufacturing environments for the diversity of products, the variety of orders by many customers, the flexibility of order change by changing market conditions, the complexity of manufacturing processes, and the uncertainty of manufacturing resources. The proposed method of dynamic scheduling is more effective and useful in dealing with such recent pressing requirements including on-time delivery, quick response, and flexibility.