• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.212-218
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• 2022

The performance of vision-based intelligent systems, such as self-driving cars and unmanned aerial vehicles, is subject to weather conditions, notably the frequently encountered haze or fog. As a result, studies on haze removal have garnered increasing interest from academia and industry. This paper hereby presents a 4K-capable hardware implementation of an efficient haze removal algorithm with the following two improvements. First, the depth-dependent haze distribution is predicted using a linear model of four haze-relevant features, where the model parameters are obtained through maximum likelihood estimates. Second, the approximated quad-decomposition method is adopted to estimate the atmospheric light. Extensive experimental results then follow to verify the efficacy of the proposed algorithm against well-known benchmark methods. For real-time processing, this paper also presents a pipelined architecture comprised of customized macros, such as split multipliers, parallel dividers, and serial dividers. The implementation results demonstrated that the proposed hardware design can handle DCI 4K videos at 30.8 frames per second.

• Journal of Hydrogen and New Energy
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• v.34 no.6
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• pp.698-711
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• 2023

Consequence analysis using an ALOHA program is conducted to calculate the accidental impact ranges in the cases of hydrogen leakage, explosion, and jet fire in a hydrogen fueled combined cycle power plant. To evaluate the effect of weather conditions and topographic features on the damage range, ALOHA is executed for the power plants located in the inland and coastal regions. The damage range of hydrogen leaked in coastal areas is wider than that of inland areas in all risk factors. The obtained results are expected to be used when designing safety system and establishing safety plans.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• Journal of Korean Society on Water Environment
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• v.29 no.2
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• pp.247-258
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• 2013

A grid-based configuration of Land Surface Models (LSMs) coupled with a climate model can be advantageous in impact assessment of climate change for a large scale area. We assessed the applicability of Common Land Model (CoLM) to runoff and land surface temperature (LST) simulations at the domain that encompasses the Nakdong river basin. To establish a high resolution model configuration of a $1km{\times}1km$ grid size, both surface boundary condition and atmospheric inputs from the observed weather data in 2009 were adjusted to the same resolution. The Leaf Area Index (LAI) was collected from MODerate esolution Imaging Spectroradiometer (MODIS) and the downward short wave flux was produced by a nonstationary multi-site weather state model. Compared with the observed runoffs at the stations on Nakdong river, simulated runoffs properly responded to rainfall. The spatial features and the seasonal variations of the domain fairly well were captured in the simulated LSTs as well. The monthly and seasonal trend of LST were described well compared to the observations, however, the monthly averaged simulated LST exceeded the observed up to $2^{\circ}C$ at the 24 stations. From the results of our study, it is shown that high resolution LSMs can be used to evaluate not only quantity but also quality of water resources as it can capture the geographical features of the area of interest and its rainfall-runoff response.

• Atmosphere
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• v.28 no.1
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• pp.85-97
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• 2018

This study evaluates the performance of the Weather Research and Forecasting (WRF) model in reproducing the present-day (1981~2005) precipitation over Far East Asia and South Korea. The WRF model is configured with 25-km horizontal resolution within the context of the COordinated Regional climate Downscaling Experiment (CORDEX) - East Asia Phase 2. The initial and lateral boundary forcing for the WRF simulation are derived from European Centre for Medium-Range Weather Forecast Interim reanalysis. According to our results, WRF model shows a reasonable performance to reproduce the features of precipitation, such as seasonal climatology, annual and inter-annual variabilities, seasonal march of monsoon rainfall and extreme precipitation. In spite of such model's ability to simulate major features of precipitation, systematic biases are found in the downscaled simulation in some sub-regions and seasons. In particular, the WRF model systematically tends to overestimate (underestimate) precipitation over Far East Asia (South Korea), and relatively large biases are evident during the summer season. In terms of inter-annual variability, WRF shows an overall smaller (larger) standard deviation in the Far East Asia (South Korea) compared to observation. In addition, WRF overestimates the frequency and amount of weak precipitation, but underestimates those of heavy precipitation. Also, the number of wet days, the precipitation intensity above the 95 percentile, and consecutive wet days (consecutive dry days) are overestimated (underestimated) over eastern (western) part of South Korea. The results of this study can be used as reference data when providing information about projections of fine-scale climate change over East Asia.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2B
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• pp.129-146
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• 2011

Characterization the regional impact of the variability of summer extreme precipitation and the rain days over several thresholds (i.e. 10, 20, 30, 40, 50, 60, 70 and 80 mm/day) in South Korea was performed using daily precipitation data of 59 weather stations operated by Korean Meteorological Administration (KMA). To consider the local features of weather stations, we characterized the variability according to the difference of elevations, latitudes, longitudes, river basins, inland or shore area, and the ratio of urbanization. The results showed that the summer extreme precipitation is sensible to the geographical effect which is similar to that of the annual precipitation. Rain days over thresholds have increased during 1973-2009 while the annual rain days have decreased. This indicate that the concentration of precipitation in summer season will be intensified in the future. Increase of summer precipitation amount and number of extreme rain days is higher in inland area, urbanized area, and Han-River basin than that of shore area, unurbanized area, and the other river basins respectively.

• Journal of Fashion Business
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• v.1 no.4
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• pp.10-18
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• 1997

Sleeping is important for human being in that it has a great effect on their daily activities. Considering that an average adult sleeps seven to eight hours a third of our life is spent in bed. The present study intends to make consumers aware of the important of the bedding science and help the manufactures develop efficient, up-to-date beds. For these two purposes, consumers' recognitions and opinions on the features of the bedding were surveyed. In the study, a questionnaire consisting of various items on the features of the bedding was designed and distributed to consumers in 8 regions & Taegu in Korea to survey the patterns in which they buy, use, and maintain their bedding. The result of the study are as follows; Air conditioner is widely used popular because of hot weather in Taegu. People in Taegu mostly purchase their bedding from market, in every 5~6 years. The important point to be considered for bedding purchases were humidity absorption, air permeability, light weight in summer comforters, thermal insulation, flexibility, color figure design in winter comforters, humidity absorption, flexibility, color figure design in mats. As summer bedding, a sheet of single-layer quilt and rush mat were most popular which indicates climate of hot and humid weather in Taegu. Bedding uses in winter were in order a sheet of cotton, silk, wool quilt, and silk quilt showed high level in contrast to other areas. Dissatisfactions with summer quilts were humidity absorption, air permeability, heaviness and in winter quilts they were dissatisfied with thermal insulation, heaviness, flattening. In details about bedding managements, people in Taegu most frequently disinfected their bedding by sunlight in one month, and refinished every 5 years.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.135-142
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• 2022

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.

• Proceedings of the Korean Quaternary Association Conference
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• 2004.06a
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• pp.24-25
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• 2004

It is well known that there is an inverse relationship between the strength of Indian summer monsoon Rainfall (ISMR) and extent of Eurasian snow cover/depth in the preceding season. Tibetan snow cover/depth also affects the Asian monsoon rainy season largely. The positive correlation between Tibetan sensible heat flux and southeast Asian rainfall suggest an inverse relationship between Tibetan snow cover and southeast Asian rainfall. Developments in Regional Climate Models suggest that the effect of Tibetan snow on the ISMR can be well studied by Limited Area Models (LAMs). LAMs are used for regional climate studies and operational weather forecast of several hours to 3 days in future. The Eta model developed by the National Center for Environmental Prediction (NCEP), the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Regional Climate Model (RegCM) have been used for weather prediction as well as for the study of present-day climate and variability over different parts of the world. Regional Climate Model (RegCM3) has been widely . used for various mesoscale studies. However, it has not been tested to study the characteristics of circulation features and associated rainfall over India so far. In the present study, Regional Climate Model (RegCM-3) has been integrated from 1 st April to 30th September for the years 1993-1996 and monthly mean monsoon circulation features and rainfall simulated by the model at 55km resolution have been studied for the Indian summer monsoon season. Characteristics of wind at 850hPa and 200hPa, temperature at 500hPa, surface pressure and rainfall simulated by the model have been examined for two convective schemes such as Kuo and Grell with Arakawa-Schubert as the closure scheme, Model simulated monsoon circulation features have been compared with those of NCEP/NCAR reanalyzed fields and the rainfall with those of India Meteorological Department (IMD) observational rainfall datasets, Comparisons of wind and temperature fields show that Grell scheme is closer to the NCEP/NCAR reanalysis, The influence of Tibetan snowdepth in spring season on the summer monsoon circulation features and subsequent rainfall over India have been examined. For such sensitivity experiment, NIMBUS-7 SMMR snowdepth data have been used as a boundary condition in the RegCM3, Model simulation indicates that ISMR is reduced by 30% when 10cm of snow has been introduced over Tibetan region in the month of previous April. The existence of Tibetan snow in RegCM3 also indicates weak lower level monsoon westerlies and upper level easterlies.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.185-194
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• 2016

The effect of artificial changes in geographical features on local wind was analyzed at the construction site of bridge and fill-up bank in the southern part of Haui-do. Geographic Information System (GIS) data and Computational Fluid Dynamics (CFD) model were used in this study. Three-dimensional numerical topography based on the GIS data for the target area was constructed for the surface boundary input data of the CFD model. The wind observations at an Automatic Weather Station (AWS) located in Haui-do were used to set-up the model inflows. The seasonal simulations were conducted. The differences in surface wind speed between after and before artificial changes in geographical features were analyzed. The surface wind speed decreases 5 to 20% at the south-western part and below 2% of the spatial average for salt field. There was also marked the effect of artificial changes in geographical features on local wind in the westerly wind case for the target area.