• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2017.10a
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• pp.145-146
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• 2017

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.29-37
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• 1996

계절성을 갖는 승법 계절 혼합 시계열 모형들의 동질성 검정을 위하여 Wald 검정 통계량을 구하고 그 극한 분포가 ${\chi}^2$-분포함을 보였으며 시뮬레이션 연구를 통하여 뒷받침하였다. 도시 규모가 비슷한 우리나라 지역별 평균 온도자료를 가지고 이 동질성 검정을 수행하여 시계열을 지역별로 모형화하여 예측한 것과 동질성이 있는 것을 묶고 모형화하여 예측한 것에 대한 예측 오차를 비교하였다.

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2015.11a
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• pp.51-52
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• 2015

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.252-252
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• 2019

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.9-18
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• 2019

The intensity and the number of days of high temperature occurrence are also high and record heat occurred. In addition, the global warming phenomenon is intensifying globally, and especially in South Korea, the urban heat island phenomenon is also occurring due to rapid urbanization due to rapid industrial development. As the temperature of the city rises, it causes problems such as the comfort of the residential living and the cooling load. In this study, the cool roof performance is evaluated according to the roof color to reduce these problems. Unlike previous studies, UAV(Unmanned Aerial Vehicle) thermal infrared camera was used to obtain the surface temperature (white, grey, green, blue, brown, black) according to the rooftop color by remote sensing technique. As a result, the surface temperature of white color was $11{\sim}20^{\circ}C$ lower than other colors. Also air conditioning temperature of white color was $1.5{\sim}4.4^{\circ}C$ lower than other colors and the digital thermometer of white color was about $1.5{\sim}3.5^{\circ}C$ lower than other colors. It was confirmed that the white cool roof performance is the best, and the UAV and the thermal infrared camera can confirm the cool roof performa.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.5
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• pp.907-918
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• 2000

The pyrolysis characteristics of celluloses and plastics, which are the principal materials contributing to the municipal solid wastes(MSWs), was investigated with a thermal gravimetric analysis reaction system. The experiments were carried out in a nitrogen atmosphere in the temperature range of 400~900K at various experimental conditions. Also, a modified pyrolysis model for compressed MSWs has been proposed. Varing the heating rate to 20, 30, 40K/min, reaction orders of MSWs' main component were around 1.1~1.9, activation energies were 117~166kJ/mol for celluloses and 187~239kJ/mol for plastics. Char yield was proportional to the heating rate, particle size, and compressed ratio. The model proposed in this study, which is applying Arrhenius equation and thermodynamics, is closer to the experimental results than the conventional model.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.52-58
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• 2019

In this study, we have developed a forecasting model for city- gas acceptance. City-gas corporations have to report about city-gas sale volume next year to KOGAS. So it is a important thing to them. Factors influenced city-gas have differences corresponding to usage classification, however, in city-gas acceptence, it is hard to classificate. So we have considered tha outside temperature as factor that influence regardless of usage classification and the model development was carried out. ARIMA, one of the traditional time series analysis, and LSTM, a deep running technique, were used to construct forecasting models, and various Ensemble techniques were used to minimize the disadvantages of these two methods.Experiments and validation were conducted using data from JB Corp. from 2008 to 2018 for 11 years.The average of the error rate of the daily forecast was 0.48% for Ensemble LSTM, the average of the error rate of the monthly forecast was 2.46% for Ensemble LSTM, And the absolute value of the error rate is 5.24% for Ensemble LSTM.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• Ecology and Resilient Infrastructure
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• v.3 no.1
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• pp.46-53
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• 2016

This study was conducted to analyze the effect of the different land covers of an urban park (Hyowon park) in downtown Suwon on the urban thermal variations during a hot summer. The effect of the air temperature reduction in the urban park was 4.4%-4.5% for the downtown residence (Maetan-dong). This value was about 0.8% lower than that of the outskirts residence (Sanggwanggyo-dong). The daily mean temperature, daily maximum temperature, summer day and heat wave frequency were measured under the different land covers (cement-block, grass, pine-grass, shading area and mixed forest) showed these values generally decreased under natural land cover types. Daily minimum temperature and tropical night frequency didn't seem to correlate with the land cover types. Means of thermal comfort indices (wet bulb globe temperature, heat index and discomfort index) in the shading area, mixed forest and the pine-grass types were lower than those of cement block and grass types. However the levels of those indices were equal to 'very high' or 'caution' levels in the afternoon (13:00-15:00). In the morning (06:00-08:00), thermal comfort indices of the urban park didn't correlate with land cover types. Therefore, to reduce heat stress and to improve the thermal comfort in urban parks, an increase in the area of natural land cover such as grass, forest and open spaces is required.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.144-158
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• 2012

A higher spatial resolution is preferable to support the accuracy of detailed climate analysis in urban areas. Airborne LiDAR (Light Detection And Ranging) and satellite (KOMPSAT-2, Korea Multi-Purpose Satellite-2) images at 1 to 4 m resolution were utilized to produce digital elevation and building surface models as well as land cover maps at very high(5m) resolution. The Climate Analysis Seoul(CAS) was used to calculate the fractional coverage of land cover classes in built-up areas and thermal capacity of the buildings from their areal volumes. It then produced analyzed maps of local-scale temperature based on the old and new input data. For the verification of the accuracy improvement by the precise input data, the analyzed maps were compared to the surface temperature derived from the ASTER satellite image and to the ground observation at our detailed study region. After the enhancement, the ASTER temperature was highly correlated with the analyzed temperature at building (BS) areas (R=0.76) whereas there observed no correlation with the old input data. The difference of the air temperature deviation was reduced from 1.27 to 0.70K by the enhancement. The enhanced precision of the input data yielded reasonable and more accurate local-scale temperature analysis based on realistic surface models in built-up areas. The improved analysis tools can help urban planners evaluating their design scenarios to be prepared for the urban climate.