• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.6
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• pp.25-32
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• 1999

The purpose of this study is to analyse the accuracy of weather forecasts of temperature, precipitation probability , and sky condition and to evaluate the applicability of weather forecasts for the estimation of potential evapotranspiration for irrigation scheduling. Five weather station s were selected to compare forecasted and measured climatcal data. The error between forecasted and measured temperature was calculated and discussed. The accuracy of temperature forecast using relative frequency of the error was calculated . The temperature forecasting showed considerably high accuracy. Average sunshine hours for forecasted sky conditions were calculated and showed reasonable quality. From the reliability graphs, the forecasting precipation probabililty was reliable. Potential evapotranspirations were calculated and compared using forecast and measured temperatures. The weather forecast is considered usable for irrigation scheculing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.1990-1997
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• 1998

It is very essential to remove a strong ground clutter and moving clutter for an extraction of accurate information from a Doppler weather radar. Therefore, this paper proposed the use of an adaptive array antenna to overcome the shortages of a conventional weather radar. In the first, a simulation method was suggested for the generation of clutter and weather signals. Using these data, the performance of a weather radar was analyzed under various conditions. It is shown that the quality of pulse-pair estimates was greatly improved from the simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3567-3582
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• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1345-1352
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• 2021

Most of weather radar systems are used to monitor the whole weather situation for the very wide and medium-to-long range area. However, as the likelihood of occurrence of the local weather hazards is increased in recent days, it is very important to detect these wether phenomena with a local weather radar. For this purpose, it is necessary to detect the fast varying low altitude weather conditions and the effect of the ground surface clutter is more evident. Therefore, in this paper, the newly suggested method is explained and analyzed for detection of weather hazards such as the gust and wind shear using the fluctuation of wind velocities and the gradient of wind velocities among range cells. It is shown that the suggested method can be used efficiently in the future for faster detection of weather change through the simple algorithm implementation and also the effect of the ground clutter can be minimized in the detection procedure.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.3
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• pp.146-152
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• 2004

Cost effective simulation schemes for Wind Power Generation Systems (WPGS) considering wind turbine types, generators and load capacities have been strongly investigated by researchers. As an alternative, a true weather condition based simulation method using a real-time digital simulator (RTDS) is experimented in this paper for the online real-time simulation of the WPGS. A stand-alone WPGS is, especially, simulated using the Simulation method for WPGS using Real Weather conditions (SWRW) in this work. The characteristic equation of a wind turbine is implemented in the RTDS and a RTDS model component that can be used to represent any type of wind turbine in the simulations is also established. The actual data related to weather conditions are interfaced directly to the RTDS for the purpose of online real-time simulation of the stand-alone WPGS. The outcomes of the simulation demonstrate the effectiveness of the proposed simulation scheme. The results also signify that the cost effective verification of efficiency and stability for the WPGS is possible by the proposed real-time simulation method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.60-63
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• 2020

With the advent of deep learning, a lot of attempts have been made in computer vision to substitute deep learning models for conventional algorithms. Among them, image classification, object detection, and image restoration have received a lot of attention from researchers. However, most of the contributions were refined in one of the fields only. We propose a new paradigm of model structure. End-to-end model which we will introduce classifies noise of an image and restores accordingly. Through this, the model enhances universality and efficiency. Our proposed model is an 'One-For-All' model which classifies weather condition in an image and returns clean image accordingly. By separating weather conditions, restoration model became more compact as well as effective in reducing raindrops, snowflakes, or haze in an image which degrade the quality of the image.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.10
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• pp.389-398
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• 1985

Recently maximum power demand of our country has become to be under the great in fluence of electric cooling and air conditioning demand which are sensitive to weather conditions. This paper presents the technique and algorithm to forecast the long-term maximum power demand considering the characteristics of electric power and weather variable. By introducing a weather load model for forecasting long-term maximum power demand with the recent statistic data of power demand, annual maximum power demand is separated into two parts such as the base load component, affected little by weather, and the weather sensitive load component by means of multi-regression analysis method. And we derive the growth trend regression equations of above two components and their individual coefficients, the maximum power demand of each forecasting year can be forecasted with the sum of above two components. In this case we use the coincident dry bulb temperature as the weather variable at the occurence of one-day maximum power demand. As the growth trend regression equation we choose an exponential trend curve for the base load component, and real quadratic curve for the weather sensitive load component. The validity of the forecasting technique and algorithm proposed in this paper is proved by the case study for the present Korean power system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.398-402
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• 2011

High resolution windspeed profile measurements are needed in a weather radar to provide the reliable information of rapidly changing weather conditions. However, it is necessary to remove both stationary and moving clutter to obtain the accurate pulse pair estimates. To overcome these problems, a simple adaptive array antenna may be applied to clutter removal. Using the simulated weather and clutter data, the clutter cancellation capability is analyzed for a weather radar with an adaptive antenna. The pulse pair estimates obtained from the adaptive weather radar are compared with those of the raw data.

• Journal of the Korea Institute of Building Construction
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• v.8 no.1
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• pp.37-42
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• 2008

As use of construction equipment has been increasing continuingly, the proportion of equipment expense to the total construction cost has become higher. However, there is a difference between the equipment expenses section in 'Poom-Sam' and practical data, because 'Poom-sam' does not consider non-working days due to weather conditions, legal holidays and management conditions. Therefore, 'Poom-Sam' does not present a reasonable standard for estimating construction equipment expenses. In this study, to estimate realistic construction equipment operating hours, firstly, construction equipment was classified according to work, and weather conditions, in which each work could not be executed, were established. Then, weather data on Seoul and Busan($2004{\sim}2006$) and legal holidays were analyzed to suggest annual standard operating hours. The annual standard operating hours of earthmoving & excavating, compaction, and drilling equipment was estimated to be 1,430 hours, and lifting equipment, concrete paving equipment, asphalt paving equipment, concrete equipment, and crushing & conveying equipment were estimated to be 2,124 hours, 1,156hours, 1,188hours, 1,688hours, and 2,152hours respectively.

• Journal of Environmental Science International
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• v.26 no.12
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• pp.1363-1374
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• 2017

Global climate change caused by industrialization has caused abnormal weather conditions such as urban temperatures and tropical nights, urban heat waves, heat waves, and heavy rains. Therefore, the study tried to analyze climate conditions and weather conditions in the streets and analyze climate factors and meteorological factors that lead to inconvenience to citizens. In the case of trees, the overall temperature, surface temperature, solar irradiance, and net radiation were measured low, and the temperature was lower in the Pedestrian road than in roads. The dry bulb temperature, the black bulb temperature, and the wet bulb temperature for the thermal evaluation showed the same tendency. In the case of thermal evaluation, there was a similar tendency to temperature in WBGT, MRT, and UTCI, and varied differences between types. Although the correlation between the meteorological environment and the thermal environment showed a statistically significant significance, the difference between the measured items was not significant. The study found that the trees were generally pleasant to weather and thermal climate in the form of trees, and the differences were mostly documented.