• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.67-77
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• 2022

The sense of touch by natural wind is one of the most common feels that every person experiences in daily life. However, it has been rarely studied how natural wind can be reproduced in a VR environment and whether the multisensory contents equipped with artificial winds do improve human emotion or not. To address these issues, we first propose a wind reproduction VR system guided by video and wind capture data and also study the effect of the system on positive affect. We collected wind direction and speed data together with a 360-degree video on a yacht. These pieces of data were used to produce a multisensory VR environment by our wind reproduction VR system. 19 college students participated in the experiments, where the Korean version of Positive and Negative Affect Schedule (K-PANAS) was introduced to measure their emotions. Through the K-PANAS, we found that 'inspired' and 'active' emotions increase significantly after experiencing the yacht VR contents with artificial wind. Our experimental results also show that another emotion, 'interested', is most notably affected depending on the presence of the wind. The presented system can be effectively used in various VR applications such as interactive media and experiential contents.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.287-294
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• 2021

The objective of this study was conducted to calculate the damage of whole crop maize in accordance with abnormal climate using the forage yield prediction model through machine learning. The forage yield prediction model was developed through 8 machine learning by processing after collecting whole crop maize and climate data, and the experimental area was selected as Gyeonggi-do. The forage yield prediction model was developed using the DeepCrossing (R²=0.5442, RMSE=0.1769) technique of the highest accuracy among machine learning techniques. The damage was calculated as the difference between the predicted dry matter yield of normal and abnormal climate. In normal climate, the predicted dry matter yield varies depending on the region, it was found in the range of 15,003~17,517 kg/ha. In abnormal temperature, precipitation, and wind speed, the predicted dry matter yield differed according to region and abnormal climate level, and ranged from 14,947 to 17,571, 14,986 to 17,525, and 14,920 to 17,557 kg/ha, respectively. In abnormal temperature, precipitation, and wind speed, the damage was in the range of -68 to 89 kg/ha, -17 to 17 kg/ha, and -112 to 121 kg/ha, respectively, which could not be judged as damage. In order to accurately calculate the damage of whole crop maize need to increase the number of abnormal climate data used in the forage yield prediction model.

• Journal of Wetlands Research
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• v.22 no.4
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• pp.312-320
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• 2020

Various LIDs with natural water circulation function are applied to reduce urban environmental problems and environmental impact of development projects. However, excessive Infiltration and evaporation of LID facilities dry the LID internal soil, thus reducing plant and microbial activity and reducing environmental re duction ability. The purpose of this study was to develop a real-time measurement system with complex sensors to derive the management plan of LID facilities. The test of measurable sensors and Internet of Things (IoT) application was conducted in artificial wetlands shaped in acrylic boxes. The applied sensors were intended to be built at a low cost considering the distributed LID and were based on Arduino and Raspberry Pi, which are relatively inexpensive and commercialized. In addition, the goal was to develop complex sensor measurements to analyze the current state o f LID facilities and the effects of maintenance and abnormal weather conditions. Sensors are required to measure wind direction, wind speed, rainfall, carbon dioxide, Micro-dust, temperature and humidity, acidity, and location information in real time. Data collection devices, storage server programs, and operation programs for PC and mobile devices were developed to collect, transmit and check the results of measured data from applied sensors. The measurements obtained through each sensor are passed through the Wifi module to the management server and stored on the database server in real time. Analysis of the four-month measurement result values conducted in this study confirmed the stability and applicability of ICT technology application to LID facilities. Real-time measured values are found to be able to utilize big data to evaluate the functions of LID facilities and derive maintenance measures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.326-331
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• 2018

Evaporative humidification using a humidifying element is used widely for the humidification of a building or a data center. The performance of a humidifying element is commonly expressed as the humidification efficiency, which is assumed to be independent of the air temperature or humidity. To verify this assumption, a series of tests were conducted under two air conditions - data center ($25^{\circ}C$ DBT, $15^{\circ}C$ WBT) and commercial building ($35^{\circ}C$ DBT, $21^{\circ}C$ WBT) - using humidifying elements made from cellulose/PET and changing the frontal air velocity from 1.0 m/s to 4.5 m/s. Three samples having a 100 mm, 200 mm, or 300 mm depth were tested. The results showed that the humidification efficiency is dependent on the air condition. Indeed, even dehumidification occurred at the inlet of the humidifying element at the air condition of commercial building. This suggests that a proper thermal model should account for the inlet area, where the amount of moisture transfer may be different from the other part of the humidification element. As the depth of the element increased from 100 mm to 200 mm, the humidification efficiency increased by 29%. With further increases to 300 mm, it increased by 42%. On the other hand, the pressure drop also increased by 47% and 86%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.4
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• pp.365-372
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• 2024

The South Korean marine industry is emerging as a significant market, driven by the growing popularity of various water leisure activities, including sailing. This trend suggests a rising demand for sailing yachts. Consequently, since 2022, the design and development of a 28ft sailing yacht have been ongoing, supported by the government and the Ministry of Oceans and Fisheries, to promote yachting culture in South Korea. The Velocity Prediction Program (VPP) analysis was conducted using WinDesign during the preliminary design stage to evaluate performance and determine design parameters. The hydrodynamic model used for this vessel is based on regression methods developed from years of experience in naval architecture and yacht research at the Wolfson Unit, providing reliable estimates for most modern yachts. However, owing to the lack of specific hydrodynamic data from towing tank tests or CFD numerical analysis, verification of the hydrodynamic model has faced some challenges. Additionally, an incomplete weight estimate resulted in variable VCG values, potentially affecting stability and overall performance. The optimal boat speed for this vessel was determined at true wind speeds (TWS) of 4, 8, 12, 16, and 20 knots, using both the jib (up to 120° TWA) and the spinnaker (from 80° TWA). The optimized speed of the yacht was found to be comparable to that of international similar-class yachts.

• Journal of the Korea Institute of Building Construction
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• v.18 no.2
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• pp.161-168
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• 2018

Recently, a prototype of a guide rail type window cleaning robot was developed, and is currently undergoing field testing. The size and the load of the robot have not yet been optimized. In this study, a stress analysis was performed to derive quantitative data to improve the current window cleaning robot and secure its structural safety. Through the analysis of its own weight, resistance to wind speed, and other factors, it was found that the robot can be improved in terms of the drooping caused by its own weight and the drag force against wind pressure. The analysis results obtained will be directly applied to improve the design of the window cleaning robot, and it is expected that this will advance the completeness of the robot's design.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1085-1092
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• 2009

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model and Neural Networks (NN) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Paju monitoring site in Korea. In the both ARE model and NN model, seven meteorological variables and four pollution variables are used as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide ($SO_2$), Nitrogen dioxide ($NO_2$), Cobalt (CO), and Promethium 10 (PM10). The result showed that the NN model is generally better suited for describing the ozone concentration than the ARE model. However, the ARE model will be expected also good when we add the explanatory variables in the model.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1593-1600
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• 2016

This paper suggests a temperature guidance for requirements which must be addressed in the preparation of specifications for military equipment used in land applications in the Korean peninsula. In general, the equipment should be designed to operate during all but a certain small percentage of the time. Daegu and Yangpyeong are the hottest and coldest regions by month, respectively, based on surface weather observations over 132 regions from 1904 to 2014. The 1-percent high and low temperatures for land environment in the South Korea are $38.7^{\circ}C$, and -$29.0^{\circ}C$, respectively. This paper also presents the temperature values occurring for specified frequencies of occurrence during the most severe month. Diurnal cycles associated with the hottest and coldest top one-percent temperatures, including associated solar radiation, relative humidity, and wind-speed are provided.