• International Journal of Highway Engineering
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• v.9 no.4
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• pp.1-9
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• 2007

To decide the number of road lane is very important and related to the 30th design hourly factor in the design of transportation facilities. But, as the quantitative division of road types is difficult, most planner and designer for deciding the 30th design hourly factors have used the fixed values in our country. In this study, we have analyzed the time series property of the design hourly factors in national highways and developed the model capable of estimating the 30th design hourly factors using real data. The presented model is a simple regression model(DHV = K*AADT), which is applied to the division of road lanes(2 or 4 lanes) and the level of AADT(3 levels). As a results, the simple regression model have better performance than the existing method with respect to MAPE and $R^2$. Also, the variations of the 30th design hourly factors are small. The more traffic volume increase, the more the factors decrease. But, the limitation of this study is to use the exiting method estimating the values of the factors, it is subject to study hereafter.

• Journal of Bio-Environment Control
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• v.23 no.3
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• pp.174-180
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• 2014

Standard weather data available to greenhouse environmental design are limited in most regions of the country. So, instead of using standard weather data, in order to find the method to build design weather data for greenhouse heating and cooling, design outdoor weather conditions were analyzed and compared by TAC method and frequency analysis using climatological normal and thirty years from 1981 to 2010 hourly weather data provided by KMA and standard weather data provided by KSES. Average TAC values of outdoor temperature, relative humidity and insolation using thirty years hourly weather data showed a good agreement with them using standard weather data. Therefore, in regions which are not available standard weather data, we suggest that design outdoor weather conditions should be analyzed using thirty years hourly weather data. Average of TAC values derived from every year hourly weather data during the whole period can be established as environmental design standards, and also minimum and maximum of them can be used as reference data.

• Journal of the Korean Wood Science and Technology
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• v.46 no.6
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• pp.627-636
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• 2018

This research was performed to supplement the previous research about the driving rain index (DRI) for Korea determined by using daily weather data for 30 years. The average annual driving rain index (AADRI) was calculated from the hourly weather data, and the magnitude of DRI was investigated according to wind directions. The hourly climate data were obtained from the Korea Meteorological Administration (KMA) for the period 2009 to 2017. Of 82 locations investigated, seven were classified into regions where the level of exposure of walls to rain was high. The result showed quite a difference from the previous results, in which no high exposure regions were observed. Since the hourly-based and the daily-based annual driving rain index (ADRI) values showed only a slight difference, the result may be explained by the length of the periods used in both studies. The change of DRI according to wind directions showed that there was a certain range of wind directions in which driving rain easily approached building walls. It suggests that the consideration of wind directions with high DRI would be useful to develop a good design of wooden buildings from the point of wood preservation and maintenance.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.6
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• pp.539-549
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• 2009

The purpose of this study is to estimate hourly Asian dust emission flux in springtime by using the optimized Weather Research Forecasting model (WRF) in order to accurately predict the horizontal flux of Asian dusts. Asian dust emission flux using 5 empirical formulas such as US EPA, Park and Inn, Wang, The Goddard Chemistry Aerosol Radiation and Transport (GOCART) and Dust Entrainment and Deposition (DEAD) were calculated and compared by using classified land-use types and size distribution at various locations in China and Mongolia together with the hourly meteorological elements of the WRF model. As a result, the empirical formula in US EPA among them, which was considered the various conditions such as vegetation, soil type and terrain, was better than the other 4 empirical formulas. However, these formulas were adjusted hourly and vertically in time and space because there was different order and time resolution of dust emissions from original empirical formulas.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.8-14
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• 1999

This paper presents the methods of short-term load forecasting Kohonen neural networks and back-propagation neural networks. First, historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Next day hourly load of weekdays and weekend except holidays are forecasted. For load forecasting in summer, max-temperature and min-temperature data as well as historical hourly load date are used as inputs of load forecasting neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation(1994-95).

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.337-341
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• 2016

Purpose: Evaluation of atmospheric conditions for proper timing of spray application is important to prevent off-target movement of crop protection materials. Susceptible crops can be damaged downwind if proper application procedure is not followed. In our previous study, hourly data indicated unfavorable conditions, primarily between evening 18:00 hrs in the evening and 6:00 hrs next morning, during clear conditions in the hot summer months in the Mississippi delta. With the requirement of timely farm operations, sub-hourly data are required to provide better guidelines for pilots, as conditions of atmospheric stability can change rapidly. Although hourly data can be interpolated to some degree, finer resolution for data acquisition of the order of 15 min would provide pilots with more accurate recommendations to match the data recording frequency of local weather stations. Methods: In the present study, temperature and wind speed data obtained at a meteorological tower were re-sampled to calculate the atmospheric stability ratio for sub-hour and hourly recommendations. High-precision evaluation of temperature inversion periods influencing atmospheric stability was made considering strength, time of occurrence, and duration of temperature inversion. Results and Discussion: The results indicated that atmospheric stability could be determined at different time intervals providing consistent recommendations to aerial applicators, thereby avoiding temperature inversion with minimal off-target drift of the sprayed liquid.

• 한국해양학회지
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• v.7 no.1
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• pp.15-18
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• 1972

The observations of hourly change of salinity and temperature were made in the Korea Strait from August 1968 to July 1969. The largest hourly change of salinity and temperature was shown in August and the smallest in April. The range of hourly change of temperature (5.67-15.75$^{\circ}C$ on the depth of 125m) and salinity (32.1-34.3 on 20m layer) were significantly wide in August. These changes are correlated with the movement of water masses vertically and horizontally caused by changing direction and force of the current.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.167-183
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• 1995

The objective of study is to evaluate highway capaicty estimation alternative and to develop capacity from statistical distribution of observed traffic flow. Speed-Volume relation is analyzed from vehicle's headway distribution eliminating the long headway by confidence intervals 99%, 95%, 90%. Capacity estimate alternatives were evaluated from 95% , 90%, 85% level of cummulative distribution of observed hourly traffic flow adjusted to confidence intervals. The result of investigation revealed that maximum hourly rate of flow is 2, 130pcu at confidence interval of 995, 2, 233pcu at 95%, 2, 315pcu at 90% respectively. Compared to the capacity of 2, 200pcu per hour per lane used in HCM and KHCM(Korea Highway Capacity Manual), capa챠y appears to correspond to confidence interval of 95%. Using the traffic flow rate at confidence interval of 95% the maximum hourly flow rate is 2, 187pcu at 95% of cummulative volume distribution, 2, 153pcu at 90%, 2, 215pcu at 85%. The study suggests that raional capacity esimation alternative is to take the 95% of cummulative distribution of observed hourly traffic flow at 95% confidence headway interval eliminating 5% long headway.(i.e. 95-95 rule)

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.165-174
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• 2018

The model using time series data can be considered as a flood forecasting model of a small river due to its efficiency for model development and the advantage of rapid simulation for securing predicted time when reliable data are obtained. Transfer Function Noise (TFN) model has been applied hourly flood forecast in Italy, and UK since 1970s, while it has mainly been used for long-term simulations in daily or monthly basis in Korea. Recently, accumulating hydrological data with good quality have made it possible to simulate hourly flood prediction. The purpose of this study is to assess the TFN model applicability that can reflect exogenous variables by combining dynamic system and error term to reduce prediction error for tributary rivers. TFN model with hourly data had better results than result from Storage Function Model (SFM), according to the flood events. And it is expected to expand to similar sized streams in the future.