• KIEAE Journal
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• v.9 no.5
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• pp.39-46
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• 2009

Most Plastic Green Houses in Korea are made according the European weather condition, which lead to have very low solar energy efficiency. Moreover, the function of green houses, as well as the structure of them, has not changed for Korean weather condition. Therefore, the structure and function of them should adopt the regional weather condition in order to improve the energy efficiency. This paper investigates the current plastic green housesin Korea, and presents an alternative for improving the energy efficiency. The elements of green houses were investigated. When using a partial opaque insulation with a thermal storage body, the difference of indoor air temperature became 20C during daytime, and 5C during night, which will save massive fossil fuels.

• International Journal of Air-Conditioning and Refrigeration
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• v.11 no.4
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• pp.199-208
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• 2003

Standard temperature and absolute humidity weather data correlations of Seoul for dynamic energy simulation have been developed regressing the measured data compiled by the Korea Meteorological Adminstration during a l0-year period from 1991 to 2000. The mathematical equations can generate consistent daily and yearly variations of outdoor weather data unlike the measured data which may show abnormal behavior. Considering that each hour of the day follows a certain yearly pattern, 24 correlations are developed for each hour of the day. The derived simple mathematical equations can be used for estimating outdoor temperature and humidity conditions for any arbitrary time of the year.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.273-280
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• 2006

This paper suggests the method that forecasts Dynamic Line Rating (DLR). Thermal Overload Risk Probability (TORP) of the next time is forecasted based on the present weather conditions and DLR value by Monte Carlo Simulation (MCS). To model weather elements of transmission line for MCS process, this paper will propose the use of statistical weather models that time series is applied. Also, through the case study, it is confirmed that the forecasted TORP can be utilized as a criterion that decides DLR of next time. In short, proposed method may be used usefully to keep security and reliability of transmission line by forecasting transmission capacity of the next time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.733-738
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• 2017

In this study, we selected chlorine, a typical toxic material used in many workplaces, as the leakage material, and through the analysis of alternative scenarios based on the meteorological conditions in the summer frequently encountered in accidents, we suggest ways to improve the (method of analysis/accident scenario modeling). The analysis of 296 chemical accidents from January 2014 to December 2016 found that the highest rate of occurrence was in summer, accounting for 35.81% of the total. According to the risk assessment, the influence range and number of inhabitants in the influence area were 712.4 m and 20,090 under the annual mean weather conditions and 796.2 m and 27,143 people under the summer mean weather conditions, respectively. This result implies that, under certain conditions, the range of impacts in the current alternative scenario is incomplete. Therefore, risk assessment systems need to be improved in order to take into consideration the characteristics of each chemical substance.

• Safety and Health at Work
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• v.9 no.1
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• pp.42-52
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• 2018

Background: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man-machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance. Methods: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities. Results: The developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared. Conclusion: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.

• Journal of Environmental Science International
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• v.30 no.12
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• pp.1027-1039
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• 2021

In this study, we investigated the optimal meteorological conditions for cloud seeding using aircraft over the Korean Peninsula. The weather conditions were analyzed using various data sources such as a weather chart, upper air observation, aircraft observation, and a numerical model for cloud seeding experiments conducted from 2018 to 2019 by the National Institute of Meteorological Sciences, Korea Meteorological Administration. Cloud seeding experiments were performed in the seasons of autumn (37.0%) and winter (40.7%) in the West Sea and Gangwon-do. Silver iodide (70.4%) and calcium chloride (29.6%) were used as cloud seeding materials for the experiments. The cloud seeding experiments used silver iodide in cold clouds. Aircraft observation revealed relatively low temperatures, low liquid water content, and strong wind speeds in clouds with a weak updraft. In warm clouds, the cloud seeding experiments used calcium chloride. Observations included relatively high temperatures, high liquid water content, and weak wind speeds in clouds with a weak updraft. Based upon these results, we determined the comprehensive meteorological conditions for cloud seeding experiments using aircraft over the Korean Peninsula. The understanding of optimal weather conditions for cloud seeding gained from this study provide information critical for performing successful cloud seeding and rain enhancement.

• Journal of Drive and Control
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• v.21 no.4
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• pp.37-45
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• 2024

Currently, many studies have applied a deep learning-based image recognition technology for solving labor shortages and other issues caused by rural aging. This study aimed to determine if thermal imaging could be used in a deep learning model to recognize oriental melon grown in a hydroponic system. To recognize oriental melon using thermal imaging, time-series thermal imaging was performed under sunny and cloudy weather conditions. Temperatures of oriental melon and canopy were extracted from thermal images. Differences between extracted temperatures according to weather conditions were then determined. It was found that thermal images acquired after 14:00 were suitable for stable recognition of oriental melon regardless of weather conditions. Based on this result, additional thermal images were acquired to train YOLO v5 and Faster R-CNN models. Acquired thermal images were trained with original and augmented data. Recognition performances of training models were compared with the best mAP (mean Average Precision). As a result, it was confirmed that both YOLO v5 and Faster R-CNN models achieved the best mAP@0.5 of 92% or more regardless of data augmentation. Data augmentation did not significantly affect the accuracy of either model. This might be because thermal images used to train models were acquired under restrictive conditions in a hydroponic greenhouse, which affected model generalization. Therefore, additional experiments under various conditions are necessary to improve generalization of the model in the future.

• Atmosphere
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• v.30 no.2
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• pp.169-181
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• 2020

We conducted a study on the impact of observation station density; this was done in order to enable the accurate estimation of spatial meteorological variables. The purpose of this study is to help operate an efficient observation network by examining distributions of temperature, relative humidity, and wind speed in a test area of a three-dimensional meteorological observation project in the Yeongdong region in 2019. For our analysis, we grouped the observation stations as follows: 41 stations (for Step 4), 34 stations (for Step 3), 17 stations (for Step 2), and 10 stations (for Step 1). Grid values were interpolated using the kriging method. We compared the spatial accuracy of the estimated meteorological grid by using station density. The effect of increased observation network density varied and was dependent on meteorological variables and weather conditions. The temperature is sufficient for the current weather observation network (featuring an average distance about 9.30 km between stations), and the relative humidity is sufficient when the average distance between stations is about 5.04 km. However, it is recommended that all observation networks, with an average distance of approximately 4.59 km between stations, be utilized for monitoring wind speed. In addition, this also enables the operation of an effective observation network through the classification of outliers.

• Journal of the Korean Solar Energy Society
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• v.33 no.5
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• pp.9-17
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• 2013

Hourly horizontal global solar radiation has been used as one of significant parameters in a weather file for building energy simulations, which determines the quality of building thermal performance. However, as about twenty two weather stations in Korea have actually measured the horizontal global sola radiation, the weather files collected in other stations requires solar data simulation from the other meteorological parameters. Thus, finding the reliable complicated method that can be used in various weather conditions in Korea is critically important. In this paper, three solar simulation models were selected and evaluated through the reliability test with the simulated hourly horizontal global solar radiation against the actually measured solar data to find the most suitable model for the south east area of Korea. Three selected simulation models were CRM, ZHM, and MRM. The first two models are regression type models using site-fitted coefficients which are derived from the correlation between measured solar data and local meteorological parameters from the previous years, and the last model is a mechanistic type model using the meteorological data to calculate conditions of atmospheric constituents that cause absorption and scattering of the extraterrestrial radiation on the way to the surface on the Earth. The evaluation results show that ZHM is the most reliable model in this area, yet a complicated hybrid simulation methods applying the advantages of each simulation method with the monthly-based weather data is needed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.111-122
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• 2012

There has been a strong demand for providing diverse services to drivers utilizing existing ITS infrastructure. To this end, this study is aiming at improving the accuracy of a variable speed limit system by determining recommended speeds for the system utilizing the information from Urban Traffic Information System(UTIS) and Weather Information System(WIS). In order to determine appropriate speed limits under inclement weather conditions for the variable speed limit system, this study examined three methods: i) the method utilizing the information from WIS, ii) the method utilizing the information from UTIS, and iii) the method which combines the information from WIS and UTIS using different weights for diverse weather conditions. Finally, this study selected the third method which determines an appropriate speed limit using the relationship between the vehicle operating speed and the minimum stopping distance which is estimated using the existing speed limit, surface coefficient of friction and superelevation.