• International Journal of Advanced Culture Technology
• /
• v.7 no.4
• /
• pp.327-333
• /
• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• Journal of the korean Society of Automotive Engineers
• /
• v.15 no.1
• /
• pp.107-119
• /
• 1993

The objective of this study is to analyze the static and dynamic characteristics of automotive rubber components by computer simulation. Bush / rectangular type engine mounts and wind shield weather strip are analyzed by using the commercial code ABAQUS and the results are verified by experiments. Large deformation static response is analyzed in order to get the information about the deformation pattern and static stiffness of engine mounts, and about the seperation force of wind shield weather strip from body. The isothermal steady-state dynamic response of components which have been subjected to an initial static pre-load is analyzed for the dynamic stiffness of engine mount rubber components. There are good agreements between simulation and experiments. So it is possible to apply the computer simulation to the design of automotive rubber components.

• Journal of Korea Multimedia Society
• /
• v.25 no.9
• /
• pp.1328-1337
• /
• 2022

Recently, research on various font designs has been actively conducted to deliver effective emotional information in a digital environment. In this study, we propose a Hangul font outline vector modification algorithm that effectively conveys sensitivity according to weather information and can be transformed immediately. The algorithm performs a series of transformations: sets outlines according to design pattern templates, calculates the glyph's position to reflect physical rules, splits outline segments into smaller sizes and deforms the outlines. Through this, we could create several vector font designs such as humidity, cloud, wind, and snow. The usability evaluation was close to good, so it can be used in diverse ways if we improve readability and effective design expression.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
• /
• v.10 no.S_1
• /
• pp.23-28
• /
• 2001

A new numerical weather prediction and dispersion model, the Operational Multi-scale Environment model with Grid Adaptivity(OMEGA) including an embedded Atmospheric Dispersion Model(ADM), is introduced as a next generation atmospheric simulation system for real-time hazard predictions, such as severe weather or the transport of hazardous release. OMEGA is based on an unstructured grid that can facilitate a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from 20 -30 meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning and time. In particular, the unstructured grid cells in the horizontal dimension can increase the local resolution to better capture the topography or important physical features of the atmospheric circulation and cloud dynamics. This means the OMEGA can readily adapt its grid to a stationary surface, terrain features, or dynamic features in an evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with observed data.

• 한국태양에너지학회:학술대회논문집
• /
• 2009.11a
• /
• pp.142-147
• /
• 2009

In order to achieve performance assessment and optimal control of a double-skin system, an accurate simulation model is required. In the previous study, a lumped simulation model of such system was developed. As a follow-up of the previous research, the first objective of this paper is to investigate how the mathematical model should be calibrated according to system configuration(cavity width, depth, height, airflow pattern, local environment, etc.). And the second objective of this study is to discuss the effect of local weather information. In conclusion, this paper describes that the model should be recalibrated according to configuration. And it is necessary to have local weather information for accurate prediction and optimal control of the system.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1071-1081
• /
• 2021

In addition to the current quality control procedures for the weather observation performed by the Korea Meteorological Administration (KMA), this study proposes quality inspection standards for Internet of Things (IoT) urban weather observed data based on machine learning that can be used in smart cities of the future. To this end, in order to confirm whether the standards currently set based on ASOS (Automated Synoptic Observing System) and AWS (Automatic Weather System) are suitable for urban weather, usability was verified based on SKT AWS data installed in Seoul, and a machine learning-based quality control algorithm was finally proposed in consideration of the IoT's own data's features. As for the quality control algorithm, missing value test, value pattern test, sufficient data test, statistical range abnormality test, time value abnormality test, spatial value abnormality test were performed first. After that, physical limit test, stage test, climate range test, and internal consistency test, which are QC for suggested by the KMA, were performed. To verify the proposed algorithm, it was applied to the actual IoT urban weather observed data to the weather station located in Songdo, Incheon. Through this, it is possible to identify defects that IoT devices can have that could not be identified by the existing KMA's QC and a quality control algorithm for IoT weather observation devices to be installed in smart cities of future is proposed.

• Environmental Analysis Health and Toxicology
• /
• v.22 no.2 s.57
• /
• pp.111-118
• /
• 2007

This study was performed to measure gaseous Organochlorine Pesticides (OCPs : heptachlor epoxide, ${\alpha}/{\gamma}-chlordane$, trans-nonachlor, endosulfan, ${\gamma}-HCH$ and p, p'-DDE) concentration using PUF high volume sampler from June, 2000 to June, 2002 in the semi-rural atmosphere. Using monitoring data for two years, we tried to investigate the annual cycles of gaseous OCPs. We considered three functions to describe the annual cycle: Gaussian, Lorentzian and sinusoidal functions. These functions accounted for $54{\sim}91%$ of the variability in concentration for each gaseous OCPs, and the sinusoidal function gave the best fits. It was seen that the gaseous OCPs concentration increased during the warmer weather while decreased during colder weather. The variation of the gaseous OCPs concentration was closely similar to the variations of ambient temperature. The annual cycle of endosulfan was strongly higher than in comparison with other gaseous OCPs, while for ${\gamma}-HCH$, the cycle was weakly high and did not show apparent seasonal variation. The position of the annual maximum exists generally late July to early August. The period that showed levels more than a half maximum was from late June to early September.

• KCID journal
• /
• v.21 no.1
• /
• pp.32-44
• /
• 2014

In Korea, global warming caused by the climate changes impacted on weather system with increase in frequency and intensity of precipitation, and the rainfall pattern changes significantly by regional groups. Furthermore, it is expected that the regional and annual fluctuation ranges of the rainfall in the future would be more severe. Nowadays, agricultural drainage system designed by the existing standard of 20-year return period and 2 days of fixation time cannot deal with the increment rainfall such as localized heavy rain and local torrential rainfalls. Therefore, it is required to reinforce the standard of the drainage system in order to reduce the agricultural flood damage brought by unusual weather. In addition, it is needed to improve the standard of agricultural drainage design in order to cultivate farm products in paddy fields as facility vegetable cultivation and up-land field crop have been damaged by the moisture injury and flooding. In order to prepare for the changes of rainfall pattern due to climate changes and improve the agricultural drainage design standards by the increase of cultivating farm products, the purpose of this study is to examine the impact of climate changes, the changes of relative design standard, and the analytic situation of agricultural flood damages, to consider the drainage design standard revision, and finally to prepare for enhanced agricultural drainage design standards.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.5
• /
• pp.676-682
• /
• 2014

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

• Atmosphere
• /
• v.25 no.4
• /
• pp.611-622
• /
• 2015

Heavy rainfall ($>30mm\;hr^{-1}$) over the Korean Peninsula is examined in order to understand thermo-dynamic characteristics of the atmosphere, using radiosonde observational data from seven upper-air observation stations during the last 17 years (1997~2013). A total of 82 heavy rainfall cases during the summer season (June-August) were selected for this study. The average values of thermo-dynamic indices of heavy rainfall events are Total Precipitable Water (TPW) = 60 mm, Convective Available Potential Energy (CAPE) = $850J\;kg^{-1}$, Convective Inhibition (CIN) = $15J\;kg^{-1}$, Storm Relative Helicity (SRH) = $160m^2s^{-2}$, and 0~3 km bulk wind shear = $5s^{-1}$. About 34% of the cases were associated with a Changma front; this pattern is more significant than other synoptic pressure patterns such as troughs (22%), migratory cyclones (15%), edges of high-pressure (12%), typhoons (11%), and low-pressure originating from Changma fronts (6%). The spatial distribution of thermo-dynamic conditions (CAPE and SRH) is similar to the range of thunderstorms over the United States, but extreme conditions (supercell thunderstorms and tornadoes) did not appear in the Korean Peninsula. Synoptic conditions, vertical buoyancy (CAPE, CIN), and wind parameters (SRH, shear) are shown to discriminate among the environments of the three types. The first type occurred with high CAPE and low wind shear by the edge of the high pressure pattern, but Second type is related to Changma front and typhoon, exhibiting low CAPE and high wind shear. The last type exhibited characteristics intermediate between the first and second types, such as moderate CAPE and wind shear near the migratory cyclone and trough.