• Journal of the Society of Naval Architects of Korea
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• v.52 no.2
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• pp.110-118
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• 2015

Weather routing method is one of the best practices of SEEMP (Ship Energy Efficiency Management Plan) for fuel-efficient operation of ship. KR is carrying out a basic research for development of the weather routing algorithm and making a monitoring system by FOC (Fuel Oil Consumption) analysis compared to the reference, which is the great circle route. The added resistances applied global sea/weather data can be calculated using ship data, and the results can be corrected to ship motions. The global sea/weather data such as significant wave height, ocean current and wind data can be used to calculate the added resistances. The reference route in a usual navigation is the great circle route, which is the shortest distance route. The global sea/weather data can be divided into grids, and the nearest grid data from a ship's position can be used to apply a ocean going vessel's sea conditions. Powell method is used as an optimized routing technique to minimize FOC considered sea/weather conditions, and FOC result can be compared with the great circle route result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.151-152
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• 2010

In this paper, a weather field server is designed and developed for local area weather condition monitoring system. Weather field server consists of an integrated sensor board and wireless sensor node to enable quick response of changing weather conditions in the valley region. A server program is established to monitor and analyze real-time weather condition in order to detect and prevent weather damages occurring in the locality.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• Journal of Ocean Engineering and Technology
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• v.34 no.6
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• pp.377-386
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• 2020

This paper discusses the effectiveness of onboard measurements and data extracted from weather information for speed-power analysis. Furthermore, validation results of hull and propeller cleaning and painting during dry-docking are discussed. Wind and wave information can be obtained from onboard measurements or weather information from the National Oceanic and Atmospheric Administration (NOAA). The weather information of a specified position and time is extracted from NOAA weather data and compared with onboard measurements. In addition, to validate the effects of hull cleaning and painting during dry-docking, speed-power analysis results of before and after dry-docking are compared. The results show that both onboard measurements and weather information show acceptable reliability when added resistance and speed-power analysis results are compared with each other. Moreover, the ship performance analysis (SPA) software clearly shows the effects of hull cleaning and painting, and it can provide reliable analysis results with either onboard measurements or weather information. In conclusion, it is confirmed that the analysis method and SPA software used in this study are effective in analyzing the ship's speed-power performance.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.222-232
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• 2021

The power output of solar panels highly depends on environmental situations like weather and air pollution. Due to bad weather or air pollution, it is difficult for solar panels to operate at their full potential. Knowing the power output of solar panels in advance helps set up the solar panels correctly and work their possible potential. This paper presents an approach to predict the power output of solar panels based on weather and air pollution features using machine learning methods. We create machine learning models with three kinds set of features, such as weather, air pollution, and weather and air pollution. Our datasets are collected from the area of Seoul, South Korea, between 2017 and 2019. The experimental results show that the weather and air pollution features can be efficient factors to predict the power output of solar panels.

• Atmosphere
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• v.34 no.3
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• pp.273-281
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• 2024

Analyzing the information about climate change on Korean Peninsula is essential for the national defense. In this study, we used HadGEM3-RA model output (a member of CORDEX-EA) and analyzed the 3 operational weather factors (VMC, runway temperature, WBGT), which affect the aircraft field. The number of future limited days was quantitatively calculated based on the model outputs applying SSP1-2.6 and SSP5-8.5 and the operational limits of the previous three factors, and the spatial distribution, time series, and correlation of each result were analyzed. In conclusion, it was analyzed that the number of limited days by VMC would decrease, resulting from the rise in temperature and the drop in relative humidity. This means the operational environment in VMC will improve. On the other hand, the number of limited days by the runway temperature and WBGT would increase, resulting from the rise in temperature. This means the operational environment in runway temperature and WBGT will worsen.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1641-1648
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• 2016

A weather radar estimates Doppler frequency and width of Doppler spectrum from the received weather signal which represents the return echoes from rain or dust particles in a corresponding area. These estimates are very important parameters since they are directly related to precipitation, wind velocity and degree of turbulence. Therefore, these estimated values should be highly reliable to obtain accurate weather information. However, the echoes of a weather radar include both the weather signal and the clutter which occurred from ground reflection or moving objects, etc. The existence of the clutter in the echoes may cause serious errors in the estimation of weather-related parameters. Therefore, in this paper, models are developed to represent the weather signal and the clutter for the purpose of analyzing estimation errors caused by the strong clutter echoes. Using these models, various return echoes according to the weather signal and clutter power are simulated to analyze the effects of the clutter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1737-1743
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• 2008

A weather radar extracts the weather information from the return echoes which consist of scattered electromagnetic wave signals from rain, cloud and dust particles, etc. The characteristics of Doppler weather signal and ground clutter should be analyzed to extract the accurate weather information. However, the conventional symmetric weather Doppler model is somewhat inappropriate in representing various weather situations. Therefore, the improved model is suggested to describe the skewness in the Doppler spectrum model. Using the suggested model, many various weather signals can be simulated efficiently in time and spectral domain according to weather situations, operation environment and system characteristics. This simulation method may be very helpful in verifying the accuracy of the weather information extraction algorithms and developing the new system for further performance improvement.

• Atmosphere
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• v.31 no.5
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• pp.539-551
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• 2021

In this paper, the weather records in 'Nanjung Ilgi' were investigated and the weather characteristics of the southern coast of Korea (SC_Korea) was discussed. The Nanjung Ilgi is a personal diary written by admiral Yi Sun-sin from January 1592 to November 1598 during the 7-year war caused by the Japanese invasion. He is a respected great leader in the history of world naval warfare, winning all 23 battles against the Japanese. Of the 1593 days of diaries currently preserved, only 42 days have no weather records. Weather was recorded in detail, including sky conditions, precipitation, wind characteristics and others. Weather records were extracted from the diary, converted to the solar calendar, and compared with the meteorological data of Yeosu. The average annual precipitation day is about 90 days, which is similar to the current 95~100 days. As in the current climate, precipitation frequently occurs for about 30 days in summer, but less than 15 days in other seasons, and the rainy season starts from June 14 to 21 and ends from July 6 to 17. It seems that the abnormal cold and heat phenomena, which deviate significantly from the seasonal average climate, occurred on 6 and 21 days, respectively, over 7 years. This means that the weather records of Nanjung Ilgi can be used as valuable data on the climate of SC_Korea in the late 16th century. The fact that he recorded the weather even in such extreme battle conditions shows that he clearly recognized the importance of weather in warfare.

• Journal of Environmental Science International
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• v.31 no.4
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• pp.329-339
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• 2022

Better understanding the mechanism of black ice occurrence on the road in winter is necessary to reduce the socio-economic damage it causes. In this study, intensive observations of road weather elements and surface information under the influence of synoptic high-pressure patterns (22^nd December, 2020 and 29^th January, and 25^th February, 2021) were carried out using a mobile observation vehicle. We found that temperature and road surface temperature change is significantly influenced by observation time, altitude and structure of the road, surrounding terrain, and traffic volume, especially in tunnels and bridges. In addition, even if the spatial distribution of temperature and road surface temperature for the entire observation route is similar, there is a difference between air and road surface temperatures due to the influence of current weather conditions. The observed road temperature, air temperature and air pressure in Nongong Bridge were significantly different to other fixed road weather observation points.