• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.710-718
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• 2020

Objectives: We analyzed water in waterscape facilities to investigate contamination levels of water-borne pathogens and four test items (pH, turbidity, residual chlorine, and Escherichia coli) at facilities including play fountains, splash parks, and artificial streams from June to October in Suwon City and in the whole of Gyeonggi-do. Methods: A total of 62 waterscape facility samples were collected from 36 sites and tested for pathogenic Escherichia coli and water-borne viruses that cause hand-foot-and-mouth disease, eye disease, and acute enteritis. Results: None of the water-borne pathogens were detected in waterscape facility samples collected from across Gyeonggi-do that were for pre-inspection for facility management. However, the results of samples from Suwon collected in hot weather and during the school vacation period showed five total inconsistencies in turbidity (four cases) and Escherichia coli (one case). Three out of the four inconsistent samples in turbidity were from the same facility which operated a sand filtration system due to its locational factors close to mountains. Conclusion: We suggest that the waterscape facilities in Gyeonggi-do are managed properly in the respect of microbial contamination and water quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.8-14
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• 2020

The human impact of particulate matter are revealed and demand for improved forecast accuracy is increasing. Recently, efforts is made to improve the accuracy of PM10 predictions by using machine learning, but prediction performance is decreasing due to the particulate matter data with a large rate of low concentration occurrence. In this paper, separation prediction model by concentration is proposed to improve the accuracy of PM10 particulate matter forecast. The low and high concentration prediction model was designed using the weather and air pollution factors in Cheonan, and the performance comparison with the prediction models was performed. As a result of experiments with RMSE, MAPE, correlation coefficient, and AQI accuracy, it was confirmed that the predictive performance was improved, and that 20.62% of the AQI high-concentration prediction performance was improved.

• Atmosphere
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• v.32 no.4
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• pp.277-295
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• 2022

The global weather prediction model, Korean Integrated Model (KIM), has been in operation since April 2020 by the Korea Meteorological Administration. This study assessed the performance of heat waves (HWs) in Korea in 2020. Case experiments during 2018-2020 were conducted to support the reliability of assessment, and the factors which affect predictability of the HWs were analyzed. Simulated expansion and retreat of the Tibetan High and North Pacific High during the 2020 HW had a good agreement with the analysis. However, the model showed significant cold biases in the maximum surface temperature. It was found that the temperature bias was highly related to underestimation of downward shortwave radiation at surface, which was linked to cloudiness. KIM tended to overestimate nighttime clouds that delayed the dissipation of cloud in the morning, which affected the shortage of downward solar radiation. The vertical profiles of temperature and moisture showed that cold bias and trapped moisture in the lower atmosphere produce favorable conditions for cloud formation over the Yellow Sea, which affected overestimation of cloud in downwind land. Sensitivity test was performed to reduce model bias, which was done by modulating moisture mixing parameter in the boundary layer scheme. Results indicated that the daytime temperature errors were reduced by increase in surface solar irradiance with enhanced cloud dissipation. This study suggested that not only the synoptic features but also the accuracy of low-level temperature and moisture condition played an important role in predicting the maximum temperature during the HWs in medium-range forecasts.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.1
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• pp.103-116
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• 2021

Analysis of runoff is substantial for effective water management in the watershed. Runoff occurs by reaction of a watershed to the rainfall and has non-linearity and uncertainty due to the complex relation of weather and watershed factors. ANN (Artificial Neural Network), which learns from the data, is one of the machine learning technique known as a proper model to interpret non-linear data. The performance of ANN is affected by the ANN's structure, the number of hidden layer nodes, learning rate, and activation function. Especially, the activation function has a role to deliver the information entered and decides the way of making output. Therefore, It is important to apply appropriate activation functions according to the problem to solve. In this paper, ANN models were constructed to estimate runoff with different activation functions and each model was compared and evaluated. Sigmoid, Hyperbolic tangent, ReLU (Rectified Linear Unit), ELU (Exponential Linear Unit) functions were applied to the hidden layer, and Identity, ReLU, Softplus functions applied to the output layer. The statistical parameters including coefficient of determination, NSE (Nash and Sutcliffe Efficiency), NSEln (modified NSE), and PBIAS (Percent BIAS) were utilized to evaluate the ANN models. From the result, applications of Hyperbolic tangent function and ELU function to the hidden layer and Identity function to the output layer show competent performance rather than other functions which demonstrated the function selection in the ANN structure can affect the performance of ANN.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.39-47
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• 2020

In this study, crash situations of representative bus crash types were elicited by analyzing a total of 1,416 bus repair record which were collected in 2018~2019. K-means clustering was used as a methodology for this study. Bus repair record contain the information of repair term, type of bus operation, responsibility of accident, weather condition, road surface condition, type of accident, other party, type of road and type of location for each data. Also, by checking collision parts of each bus repair record, each record was classified by types of collision regions. From this, 760 record are classified to frontal type, 363 record are classified to middle-frontal type, 374 record are classified to middle-rear type and 331 record are classified to rear type. As mentioned, k-means clustering was performed on each type of collision parts. As a result, this study analyzed the severity of bus crash based on actual bus accident data which are based on bus repair record not the crash data from the TAAS. Also, this study presented crash situation of representative bus crash types. It is expected that this study can be expanded to analyzing hydrogen bus crash and defining indicators of hydrogen bus safety.

• Journal of Animal Science and Technology
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• v.63 no.2
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• pp.339-353
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• 2021

This study aimed to assess the causality of different climate variables on the production of whole crop maize (Zea mays L.; WCM) in the central inland region of the Korea. Furthermore, the effect of these climate variables was also determined by looking at direct and indirect pathways during the stages before and after silking. The WCM metadata (n = 640) were collected from the Rural Development Administration's reports of new variety adaptability from 1985-2011 (27 years). The climate data was collected based on year and location from the Korean Meteorology Administration's weather information system. Causality, in this study, was defined by various cause-and-effect relationships between climatic factors, such as temperature, rainfall amount, sunshine duration, wind speed and relative humidity in the seeding to silking stage and the silking to harvesting stage. All climate variables except wind speed were different before and after the silking stage, which indicates the silking occurred during the period when the Korean season changed from spring to summer. Therefore, the structure of causality was constructed by taking account of the climate variables that were divided by the silking stage. In particular, the indirect effect of rainfall through the appropriate temperature range was different before and after the silking stage. The damage caused by heat-humidity was having effect before the silking stage while the damage caused by night-heat was not affecting WCM production. There was a large variation in soil surface temperature and rainfall before and after the silking stage. Over 350 mm of rainfall affected dry matter yield (DMY) when soil surface temperatures were less than 22℃ before the silking stage. Over 900 mm of rainfall also affected DMY when soil surface temperatures were over 27℃ after the silking stage. For the longitudinal effects of soil surface temperature and rainfall amount, less than 22℃ soil surface temperature and over 300 mm of rainfall before the silking stage affected yield through over 26℃ soil surface temperature and less than 900 mm rainfall after the silking stage, respectively.

• Convergence Security Journal
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• v.20 no.4
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• pp.61-70
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• 2020

The unmanned aerial vehicle that emerged with the 4th Industrial Revolution attracts attention not only from Korea but also from around the world, and its utilization and market size are gradually expanding. For the first time, it was used for military purposes, but it is currently used for transportation, investigation, surveillance, and agriculture. China, along with the US and Europe, is emerging as a leader in the commercial unmanned aerial vehicle market, and Korea, which has the world's seventh-largest technology in related fields, is striving to promote various technology development policies and system improvement related to unmanned aerial vehicles. Military drones will revolutionize the means of war by using a means of war called an unmanned system based on theories such as network-oriented warfare and effect-oriented warfare. Mobile equipment, including drones, is greatly affected by environmental factors such as terrain and weather, as well as technological developments and interests in the field. Now, drones are being used actively in many fields, and especially in the military field, the use of advanced drones is expected to create a new defense environment and provide a new paradigm for war.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.52-59
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• 2021

According to the International Air Transport Association (IATA), 61% of the accidents occurred during the approach and landing phase of the flight, with 16% of the accidents caused by unstable access of the commercial aircraft. It was identified that the pilot's unstable approach and poor manipulation of correction led to accidents by continuing the excessive approach without go-around manuever. The causes of unstable access may vary, including airport approach procedures, pilot error, misplanning, workload, ATC (Air Traffic Contol) congestion, etc. In this study, we use the flight data analysis system to select domestic case airports and aircraft type where unstable approach events occur repeatedly. Through flight data analysis, including main events, airport approach procedures, pilot operations, as well as various environmental factors such as weather and geographical conditions at the airport. It aims to identify and eliminate the tendency of unstable approach events and the causes and risks of them to derive implications for mitigating unstable approach events and for developing navigation safety measures.

• Wind and Structures
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• v.35 no.1
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• pp.21-34
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• 2022

Gust factor is an important parameter for the conversion between peak gust wind and mean wind speed used for the structural design and wind-related hazard mitigation. The gust factor of typhoon wind is observed to show a significant dispersion and some differences with large-scale weather systems, e.g., monsoons and extratropical cyclones. In this study, insitu measurement data captured by 13 meteorological towers during a strong typhoon Morakot are collected to investigate the statistical characteristics, height and wind speed dependency of the gust factor. Onshore off-sea and off-land winds are comparatively studied, respectively to characterize the underlying terrain effects on the gust factor. The theoretical method of peak factor based on Gaussian assumption is then introduced to compare the gust factor profiles observed in this study and given in some building codes and standards. The results show that the probability distributions of gust factor for both off-sea winds and off-land winds can be well described using the generalized extreme value (GEV) distribution model. Compared with the off-land winds, the off-sea gust factors are relatively smaller, and the probability distribution is more leptokurtic with longer tails. With the increase of height, especially for off-sea winds, the probability distributions of gust factor are more peaked and right-tailed. The scatters of gust factor decrease with the mean wind speed and height. AS/NZ's suggestions are nearly parallel with the measured gust factor profiles below 80m, while the fitting curve of off-sea data below 120m is more similar to AIJ, ASCE and EU.

• Korean Journal of Veterinary Service
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• v.45 no.2
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• pp.101-110
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• 2022

This study was performed to investigate infectious gastrointestinal diseases in 115 Korean cats (83 indoors and 32 outdoors) with digestive signs such as diarrhea, anorexia or abdominal distention. Detection of infectious pathogens was analyzed using real-time PCR. As a result, 85 of 115 Korean cats were detected with feline corona virus (FCoV), feline parvo virus, Group A rotavirus, Clostridium perfringens (C. perfringens), Campylobacter coli (C. coli), Campylobacter jejuni, enterotoxigenic Escherichia coli, enteropathogenic Escherichia coli, Salmonella spp., Tritrichomonas foetus, Cyclospora cayetanensis, and Giardia lamblia. The most frequently detected pathogen was C. perfringens (52 cats, 61.2%), followed by FCoV (43 cats, 50.6%) and C. coli (16 cats, 18.8%). Also, single infection was the most common (43 cats), followed by double infection in 31 cats, triple infection in 7 cats, and quadruple infection in 4 cats. There was no significant relationship between pathogen detection and age, gender, living environment, weather, and diarrhea. However, there was a significant difference between the age group under 1 year and the age group 1~7 (P value<0.05). In this study, cats with suspected gastrointestinal infection were randomly evaluated, and other factors that could affect pathogen detection were insufficiently considered. For this reason, additional epidemiological investigations with a larger number of cats and sufficient consideration of the causes that may affect the results are needed. Nevertheless, it is thought that this study can also provide valuable information on gastrointestinal pathogens in Korean cats.