• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.53-59
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• 2020

Initial response is important in marine oil spills, such as the Hebei Spirit oil spill, but it is very difficult to predict the movement of oil out of the ocean, where there are many variables. In order to solve this problem, the forecasting of oil spill has been carried out by expanding the particle prediction, which is an existing study that studies the movement of floats on the sea using the data of the float. In the ocean data format HDF5, the current and wind velocity data at a specific location were extracted using bilinear interpolation, and then the movement of numerous points was predicted by particles and the results were visualized using polygons and heat maps. In addition, we propose a spill oil particle matching algorithm to compensate for the lack of data and the difference between the spilled oil and movement. The spilled oil particle matching algorithm is an algorithm that tracks the movement of particles by granulating the appearance of surface oil spilled oil. The problem was segmented using principal component analysis and matched using genetic algorithm to the point where the variance of travel distance of effluent oil is minimized. As a result of verifying the effluent oil visualization data, it was confirmed that the particle matching algorithm using principal component analysis and genetic algorithm showed the best performance, and the mean data error was 3.2%.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.478-484
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• 2018

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.7-13
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• 2021

This study develops an artificial intelligence prediction system for Fine particulate Matter(PM2.5) based on the deep learning algorithm GAN model. The experimental data are closely related to the changes in temperature, humidity, wind speed, and atmospheric pressure generated by the time series axis and the concentration of air pollutants such as SO2, CO, O3, NO2, and PM10. Due to the characteristics of the data, since the concentration at the current time is affected by the concentration at the previous time, a predictive model for recursive supervised learning was applied. For comparative analysis of the accuracy of the existing models, CNN and LSTM, the difference between observation value and prediction value was analyzed and visualized. As a result of performance analysis, it was confirmed that the proposed GAN improved to 15.8%, 10.9%, and 5.5% in the evaluation items RMSE, MAPE, and IOA compared to LSTM, respectively.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.139-146
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• 2009

While air pollutants emission caused by the traffic is one of the major sources, few researches have done. This study investigated the extent to which traffic and road related characteristics such as traffic volumes, speeds and road weather data including wind speed, temperature and humidity, as well as the road geometry affect the air pollutant emission. We collected the real time air pollutant emission data from Seoul automatic stations and real time traffic volume counts as well as the road geometry. The regression air pollutant emission models were estimated. The results show followings. First, the more traffic volume increase, the more pollutant emission increase. The more vehicle speed increase, the more measurement quantity of pollutant decrease. Secondly, as the wind speed, temperature, and humidity increase, the amount of air pollutant is likely to decrease. Thirdly, the figure of intersections affects air pollutant emission. To verify the estimated models, we compared the estimates of the air pollutant emission with the real emission data. The result show the estimated results of Chunggae 4 station has the most reliable data compared with the others. This study is differentiated in the way the model used the real time air pollutant emission data and real time traffic data as well as the road geometry to explain the effects of the traffic and road characteristics on air quality.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.2
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• pp.132-142
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• 2010

Characteristics of meteorological elements were analyzed at Hwagae and Agyang where are the representative areas of Hadong green tea cultivation in Korea. An automatic weather monitoring system (AWS) and a simple data log were employed to measure meteorological data such as temperature, relative humidity, precipitation, and wind direction and speed for 2009. The annual average air temperature of Hwagae and Agyang was 14.5 and 14.2, respectively, showing the warmest month in August ($25.4^{\circ}C$ for Hwagae and $24.9^{\circ}C$ for Agyang) and the coldest month in January ($0.3^{\circ}C$ for Hwagae and $0.2^{\circ}C$ for Agyang). Annual average of daily temperature difference (= daily maximum temperature - daily minimum temperature) was $11.3^{\circ}C$ for Hwagae and $11.1^{\circ}C$ for Agyang. Hwagae and Agyang had 62.7% and 65.3% of the annual average relative humidity, respectively. Annual precipitation was 1387 mm for Hwagae and 1793 mm for Agyang of which were higher of 605mm for Hwagae and 835 mm for Agyang compared to that in 2008. Majority of precipitation occurred between May and August, attributing 77.6% for Hwagae and 76.6% for Agyang to the annual precipitation. The annual total sunshine duration was 2054.3 hrs in Hwagae with the longest monthly sunshine duration in May (235.1 hrs) and the shortest monthly sunshine duration in July (102.5 hrs). Dominant wind direction changed seasonally from northwesterly wind in fall and winter to southeasterly wind in spring and summer. The annual average wind speed was 1.5 m $s^{-1}$ with the highest monthly wind speed of 2.0 m $s^{-1}$ in December and the lowest monthly wind speed of 1.1 m $s^{-1}$ in February. It is expected that continuous observation and assessment of meteorological data will improve our understanding of optimal environmental conditions for green tea cultivation and be used for developing models of green tea cultivation in the Hadong area.

• Journal of the Korean Solar Energy Society
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• v.32 no.6
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• pp.37-43
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• 2012

Long-term variations in wind speed and capacity factor(CF) on Seongsan wind farm of Jeju Island, South Korea were derived statistically. The selected areas for this study were Subji, having a year wind data at 30m above ground level, Sinsan, having 30-year wind data at 10m above ground level and Seongsan wind farm, where long-term CF was predicted. The Measure-Correlate-Predict module of WindPRO was used to predict long-tem wind characteristics at Seongsan wind farm. Eachyear's CF was derived from the estimated 30-year time series wind data by running WAsP module. As a result, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficien to fvariation, CV, and-16.5% ~ 13.2% for the range of variation, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ${\pm}4%$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8608-8615
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• 2015

We developed a system that can collect and transmit real-time environmental data such as temperature, humidity, wind direction, and wind speed, and equipment performing aging tests on fire detectors. This system was installed in three representative sites of railway tunnels in South Korea such as Gumjung, Solan, Seoul Subway Line 4 tunnels. The systems showed a stable performance and collected environmental data for over a year. We analyzed environmental data collected by two of our developed systems installed in the running tunnels of Gwacheon Line of Seoul Subway Line 4. The developed system was capable of safely analyzing tunnel environments for 24 h straight using a wireless communication network, and has potential for use in a variety of fields other than tunnels.

• New & Renewable Energy
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• v.16 no.1
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• pp.15-24
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• 2020

For mechanical load testing of wind turbines, capture matrix is constructed for various range of wind speeds according to the international standard IEC 61400-13. The conventional method wastes considerable amount of data by its invalid data policy -segment data into 10 minutes then remove invalid ones. Previously, we have suggested an alternative way to save the total amount of data to build a capture matrix, but the efficient selection of data has been still under question. The paper introduces optimization algorithms to construct capture matrix with less data. Heuristic algorithm (simple stacking and lowest frequency first), population method (particle swarm optimization) and Q-Learning accompanied with epsilon-greedy exploration are compared. All algorithms show better performance than the conventional way, where the distribution of enhancement was quite diverse. Among the algorithms, the best performance was achieved by heuristic method (lowest frequency first), and similarly by particle swarm optimization: Approximately 28% of data reduction in average and more than 40% in maximum. On the other hand, unexpectedly, the worst performance was achieved by Q-Learning, which was a promising candidate at the beginning. This study is helpful for not only wind turbine evaluation particularly the viewpoint of cost, but also understanding nature of wind speed data.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.569-581
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• 2003

A method of synthetic time series generation was developed and applied to the simulation of homogeneous turbulence in a periodic 3 - D box and the hourly wind data simulation. The method can simulate almost exact sample auto and cross correlations of multiple time series and control non-Gaussian distribution. Using the turbulence simulation, influence of correlations, non-Gaussian distribution, and one-direction anisotropy on homogeneous structure were studied by investigating the spatial distribution of turbulence kinetic energy and enstrophy. An hourly wind data of Typhoon Robin was used to illustrate a capability of the method to simulate sample cross correlations of multiple time series. The simulated typhoon data shows a similar shape of fluctuations and almost exactly the same sample auto and cross correlations of the Robin.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.765-777
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• 2009

Our study aimed to illustrate long term trend in 10 micrometer particular matters excluding confounding effect. Daily 10 micrometer particular matters data were measured in 27 places and meteorological data (maximum temperature, humidity and maximum wind speed, solar radiation) were obtained from the national institute of environmental research for the period from January, 1996 to December 2000. To estimate the increasing and decreasing long term trend in a set of observed data, set up the model. The model included regression spline smooth function on the time and meteorological factors to capture the seasonal time trend and any possible nonlinear relationship. The result was estimated to decrease slightly after adjusting for meteorological factors and seasonal time trend.