• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.949-963
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• 2017

This study present an efficient way of operating drainage pump station as part of nonstructural measures for reducing urban flood damage. The water level in the drainage pump station was forecast using Neuro-Fuzzy and then operation rule of the drainage pump station was determined applying the genetic algorithm method based on the predicted inner water level. In order to reflect the topographical characteristics of the drainage area when constructing the Neuro-Fuzzy model, the model considering spatial parameters was developed. Also, the model was applied a penalty type of genetic algorithm so as to prevent repeated stops and operations while lowering my highest water level. The applicability of the development model for the five drainage pump stations in the Mapo drainage area was verified. It is considered to be able to effectively manage urban drainage facilities in the development of these operating rules.

• Journal of Power Electronics
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• v.17 no.2
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• pp.476-489
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• 2017

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.

• Journal of Environmental Science International
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• v.14 no.11
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• pp.1015-1026
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• 2005

The southeastern coastal area of the Korean peninsula has a complex terrain including an irregular coastline and moderately high mountains. This implies that mesoscale circulations such as mountain-valley breeze and land-sea breeze can play an important role in wind field and ocean forcing. In this study, to improve the accuracy of complex coastal rind field(surface wind and sea surface wind), we carried out the sensitivity experiments based on PBL schemes in PSU/NCAR Mesoscale Model (MM5), which is being used in the operational system at Korea Meteorological Administration. Four widely used PBL parameterization schemes in sensitivity experiments were chosen: Medium-Range Forecast (MRF), High-resolution Blackadar, Eta, and Gayno-Seaman scheme. Thereafter, case(2004. 8. 26 - 8. 27) of weak-gradient flows was simulated, and the time series and the vertical profiles of the simulated wind speed and wind direction were compared with those of hourly surface observations (AWS, BUOY) and QuikSCAT data. In the simulated results, the strength of rind speed of all schemes was overestimated in complex coastal regions, while that of about four different schemes was underestimated in islands and over the sea. Sea surface wind using the Eta scheme showed the highest wind speed over the sea and its distribution was similar to the observational data. Horizontal distribution of the simulated wind direction was very similar to that of real observational data in case of all schemes. Simulated and observed vertical distribution of wind field was also similar under boundary layer(about 1 km), however the simulated wind speed was underestimated in upper layer.

• Journal of the Korean Society of Costume
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• v.41
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• pp.169-183
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• 1998

The Tartan, the representative check pattern and traditional costume of Scothand, is the most popualr checker which has inherited from the clans of the Scotch Highlands in the ancient times and continued to develop. It is a symbol of the Scotch culture and tadition and widely used in various ways according to status, birthplace purpose and use. Therefore, this study was intended to inquire into Tartan check. The purpose of this study attempted to make a systematic investigation of the characteristic of the Tartan check. the checker using vertical and horizontal lines which was the universal plastic element and inquire into it in terms of era, designers and combined works. By doing so, this study attempted to investigate the phase of the Tartan check in world fashion and further forecast the future of checker design applicable to the 21th-Century fashion. In addition it, attempted to investigate the features of Scottch costume unknown in our academic circles and inquire into the proless in while the Scottch has retained the originality of its own which suppressed by neighboring countries. This study could find out that the checker is the element of infinite applicability in the future. It is expected that the sophisticated and beautiful design using the checker will be presented by many Korean designer through the overall and systanatic study of the checker. On the other hand, to make an empirical study costume of other races, a comprehensive examination should be made of the social and cultural background against which locals are using their costume, through the survey of their real costume and on-spot research. It this respect, this study has some limitation in time and data collection. Besides the analysis of costume through materials and photos in museums as well as the study of cloth material and accurate colors was not con여cted in this in this study. This will remain to be study of in the future.

• Atmosphere
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• v.18 no.4
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• pp.267-277
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• 2008

The occurrence and strength of the Asian Dust over the Korea Peninsular have been increased by the expansion of the desert area. For the continuous monitoring of the Asian Dust event, the geostationary satellites provide useful information by detecting the outbreak of the event as well as the long-range transportation of dust. The Infrared Optical Depth Index (IODI) derived from the MTSAT-1R data, indicating a quantitative index of the dust intensity, has been produced in real-time at Korea Meteorological Administration (KMA) since spring of 2007 for the forecast of Asian dust. The data processing algorithm for IODI consists of mainly two steps. The first step is to detect dust area by using brightness temperature difference between two thermal window channels which are influenced with different extinction coefficients by dust. Here we use dynamic threshold values based on the change of surface temperature. In the second step, the IODI is calculated using the ratio between current IR1 brightness temperature and the maximum brightness temperature of the last 10 days which we assume the clear sky. Validation with AOD retrieved from MODIS shows a good agreement over the ocean. Comparison of IODI with the ground based PM10 observation network in Korea shows distinct characteristics depending on the altitude of dust layer estimated from the Lidar data. In the case that the altitude of dust layer is relatively high, the intensity of IODI is larger than that of PM10. On the other hand, when the altitude of dust layer is lower, IODI seems to be relatively small comparing with PM10 measurement.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.2
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• pp.119-127
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• 2014

A very high resolution weather analysis system (VHRAS) of 50 m horizontal resolution is established based on LAPS. VHRAS utilizes the 3 hourly forecast data of the Unified Model (UM) of the Korea Meteorological Administration (KMA) with the horizontal resolution of 12 km as initial guess fields. The analysis system ingests the automatic weather station (AWS) data as input observations. The analysis system operates every hour for Seoul, Korea region in real time basis. It takes less than 10 minutes for one analysis cycle. The size of grid of the analysis domain is $800{\times}660$, respectively. The analysis results from December 2010 to February 2011 showed that the mean biases of temperature, maximum and minimum temperature were -0.07, 1.6, $0.2^{\circ}C$, respectively. The temperature in the central part of the city revealed relatively higher value than that of the surrounding mountainous areas, which showed a heat island feature. The heat island appears in zonal direction since the central city region is developed along a large river. Along the heat island, the eastern region was warmer than the western region. The warmer temperature in the western part of the heat island was caused by anthropogenic heat change in conjunction with the change of land use. This system will provide more reliable weather data and information in Seoul.

• Journal of Korean Society of Rural Planning
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• v.19 no.4
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• pp.137-147
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• 2013

Most of livestock houses are concentrated in certain area with mass rearing system resulting in rapid spread of infectious diseases such as HPAI (highly pathogenic avian influenza). The livestock-related vehicles which frequently travel between farms could be a major factor for disease spread by means of transmission of airborne aerosol including pathogens. This study was focused on the quantitative measurement of aerosol concentration by field experiment while vehicles were passing through the road. The TSP (total suspended particle) and PM10 (particle matter) were measured using air sampler with teflon filter installed downward the road with consideration of weather forecast and the direction of road. And aerosol spectrometer and video recorders were also used to measure the real-time distribution of aerosol concentration by its size. The results showed that PM2.5 was not considerable for transmission of airborne aerosol from the livestock-related vehicle. The mass generated from the road during the vehicle movement was measured and calculated to 241.4 ${\mu}g/m^3$ by means of the difference between TSP and PM2.5. The dispersion distance was predicted by 79.6 m from the trend curve.

• Applied Chemistry for Engineering
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• v.23 no.1
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• pp.35-41
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• 2012

This paper proposes a new methodology for the real-time on-line quality monitoring of biofuel processes through the integration of infrared spectroscopy and chemometrics. A method of Partial Least Squares (PLS) in Chemometrics is employed for quantitative analysis of key components in bioethanol products. After a number of preprocessing methods and variable importance in projection (VIP) are used, Savitzky-Golay method showed the best performance in terms of spectrum correction, noise reduction, and model maintenance. The proposed method allows us to economically forecast the concentration of multiple impurities encountered with the production of bioethanol. The proposed system is also accurate enough ($R^2$ > 0.99) to replace the laboratory analysis.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.963-972
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• 2009

The objective of this study is to evaluate an application of stochastic continuous storage function model with ensemble Kalman filter technique. The case study is performed at the upstream basin of Jibo streamflow gauge including Andong and Imha dam. Test period is for the rainy season during 2006 and 2007. Long term runoff analysis is feasible in the case of using deterministic model. Ensemble members for input data and parameters are generated using Monte Carlo simulation for the purpose of applying ensemble Kalman filter technique. The cumulative absolute errors of stochastic model to the deterministic one are improved for the amount of 17.5 %, 18.3 % and more than 40.0 % for Andong dam, Imha dam and Jibo station, respectively. The results indicate that the stochastic model improves the accuracy of the simulated discharge considerably.

• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.25-39
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• 2008

The objective of this study is to forecast the operational continuous ability using Artificial Neural Networks in battalion defensive operation for the commander decision making support. The forecasting of the combat result is one of the most complex issue in military science. However, it is difficult to formulate a mathematical model to evaluate the combat power of a battalion in defensive operation since there are so many parameters and high temporal and spatial variability among variables. So in this study, we used company combat power level data in Battalion Command in Battle Training as input data and used Feed-Forward Multilayer Perceptrons(MLP) and General Regression Neural Network (GRNN) to evaluate operational continuous ability. The results show 82.62%, 85.48% of forecasting ability in spite of non-linear interactions among variables. We think that GRNN is a suitable technique for real-time commander's decision making and evaluation of the commitment priority of troops in reserve.