• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.3
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• pp.198-204
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• 2014

Ships sailing the seas encounter air resistance. The air resistance depends on the shape of the above-water hull, the ship speed, the wind speed and wind direction. The experimental or statistical methods which are used to predict the air resistance are one of the essential procedures of the calculation of the towing force of the disabled ships. This paper shows simplified air resistance prediction method using the variables of the projected area of the above-water hull, the speed of the ship, the wind speed and its direction. These methods have been applied to the existing computer program which had been set up to predict the towing force of the disabled ships.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of the Korean Solar Energy Society
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• v.38 no.2
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• pp.15-31
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• 2018

A program to design a small capacity wind turbine blade is proposed in this study. The program is based on a matlab GUI environment and designed to perform blade design based on the blade element momentum theory. The program is different from other simulation tools available in a point that it can analyze the side-furling power regulation mechanism and also has an algorithm to find out optimal torque schedule above the rated wind speed region. The side-furling power regulation is used for small-capacity horizontal axis wind turbines because they cannot use active pitch control due to high cost which is commonly used for large-capacity wind turbine. Also, the torque schedule above the rated wind speed region should be different from that of the large capacity wind turbines because active pitching is not used. The program developed in this study was validated with the results with FAST which is the only program that can analyze the performance of side-furled wind turbines. For the validation a commercial 10 kW wind turbine data which is available in the literature was used. From the validation, it was found that the performance prediction from the proposed simple program is close to those from FAST. It was also found that the optimal torque scheduling from the proposed program was found to increase the turbine power substantially. Further experimental validation will be performed as a future work.

• Atmosphere
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• v.31 no.1
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• pp.85-100
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• 2021

The necessity of accurate high-resolution meteorological forecasts becomes increasing in socio-economical applications and disaster risk management. The Korea Meteorological Administration Post-Processing (KMAPP) system has been operated to provide high-resolution meteorological forecasts of 100 m over the South Korea region. This study evaluates and improves the KMAPP performance in simulating wind speeds over complex terrain areas using the ICE-POP 2018 field campaign measurements. The mountainous measurements give a unique opportunity to evaluate the operational wind speed forecasts over the complex terrain area. The one-month wintertime forecasts revealed that the operational Local Data Assimilation and Prediction System (LDAPS) has systematic errors over the complex mountainous area, especially in deep valley areas, due to the orographic smoothing effect. The KMAPP reproduced the orographic height variation over the complex terrain area but failed to reduce the wind speed forecast errors of the LDAPS model. It even showed unreasonable values (~0.1 m s-1) for deep valley sites due to topographic overcorrection. The model's static parameters have been revised and applied to the KMAPP-Wind system, developed newly in this study, to represent the local topographic characteristics better over the region. Besides, sensitivity tests were conducted to investigate the effects of the model's physical correction methods. The KMAPP-Wind system showed better performance in predicting near-surface wind speed during the ICE-POP period than the original KMAPP version, reducing the forecast error by 21.2%. It suggests that a realistic representation of the topographic parameters is a prerequisite for the physical downscaling of near-ground wind speed over complex terrain areas.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.300-302
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• 2008

For the evaluation of wind resources, numerical simulation was carried out as a tool for establishing wind map around the korean peninsula. Initial and boundary condition are given by 3 hourly RDAPS(Regional Data Assimilation and Prediction System) data of KMA(Korea Meteorology Administration) and high resolution terrain elevation land cover(30 seconds) data from USGS(United States Geological Survey). Furthermore, Data assimilation was adopted to improve initial meteorological data with buoy and QuikSCAT seawinds data. The simulation was performed from 2003 to 2006 year. To understand wind data correctly in complex terrain as the korean peninsula, at this research, Wind map was classified 4 categories by distance from coastline and elevation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.7
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• pp.80-86
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• 2015

As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is needed to estimate power outputs of wind generation resources. As a result, geographic information such as latitude and longitude plays a key role to estimate power outputs of spatially distributed wind farms. In this paper, we introduce spatial correlation analysis to estimate the power outputs produced by wind farms that are geographically distributed. We present spatial correlation analysis of empirical power output data for the JEJU Island and ERCOT ISO (Texas) wind farms and propose the Correlation Decay Distance (CoDecDist) model based on geographic correlation analysis to enhance the estimation of wind power outputs.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.487-494
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• 2019

Recently, interest in and use of drones is increasing. In this study, to provide accurate wind prediction at ultra low altitudes of 150 meters or below, the sensitivity of the physical process parameterization and initial conditions was assessed to select the optimal physical process and initial conditions. For this purpose, GFS and LDAPS data were used as initial and boundary conditions, and 7 experiments were constructed using a combination of PBL schemes such as YSU, RUC, ACM2, and LSM such as Noah, RUC, and Pleim. The experiment conducted for 1 month in April 2018. As a result, the RUC-YSU physical process combination using the GFS initial data showed the best performance. This study is meaningful in establishing an optimal modeling method for ultra low altitude wind prediction through experiments using different initial conditions and combination of physical processes.

• Information and Communications Magazine
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• v.15 no.9
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• pp.97-106
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• 1998

It is crucial to predict the variabilities of the near-earth space environment associated with the solar activity, which cause enormous socio-economic impacts on mankind. The geomagnetic storm prediction scheme adopted in this study is designed to predict such variabilities in terms of the geomagnetic indices, AE and Dst, the cross-polar cap potential difference, the energy dissipation rate over the polar ionosphere and associated temperature increase in the thermosphere. The prediction code consists of two parts; prediction of the solar wind and interplanetary magnetic field based upon actual flare observations and estimation of various electrodynamic quantities mentioned above from the solar wind-magnetosphere coupling function 'epsilon' which is derivable through the predicted solar wind parameters. As a test run, the magnetic storm that occurred in early November, 1993, is simulated and the results are compared with the solar wind and the interplanetary magnetic field measured by the Japanese satellite, Geotail, and the geomagnetic indices obtained from ground magnetic observatories. Although numerous aspects of the code are to be further improved, the comparison between the simulated results and the actual measurements encourages us to use this prediction scheme as the first appoximation in forecasting the disturbances of the near-earth space environment associated with solar flares.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.3
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• pp.207-215
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• 1993

In order to statistically predict $O_3$ levels in Seoul, the study used the TMS (telemeted air monitoring system) data from the Department of Environment, which have monitored at 20 sites in 1989 and 1990. Each data in each site was characterized by 6 major criteria pollutants ($SO_2, TSP, CO, NO_2, THC, and O_3$) and 2 meteorological parameters, such as wind speed and wind direction. To select proper variables and to determine each pollutant's behavior, univariate statistical analyses were extensively studied in the beginning, and then various applied statistical techniques like cluster analysis, regression analysis, and expert system have been intensively examined. For the initial study of high level $O_3$ prediction, the raw data set in each site was separated into 2 group based on 60 ppb $O_3$ level. A hierarchical cluster analysis was applied to classify the group based on 60 ppb $O_3$ into small calsses. Each class in each site has its own pattern. Next, multiple regression for each class was repeatedly applied to determine an $O_3$ prediction submodel and to determine outliers in each class based on a certain level of standardized redisual. Thus, a prediction submodel for each homogeneous class could be obtained. The study was extended to model $O_3$ prediction for both on-time basis and 1-hr after basis. Finally, an expect system was used to build a unified classification rule based on examples of the homogenous classes for all of sites. Thus, a concept of high level $O_3$ prediction model was developed for one of $O_3$ alert systems.

• Structural Engineering and Mechanics
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• v.45 no.1
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• pp.1-18
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• 2013

The typhoon wind characteristics designing for buildings or bridges located in complex terrain and typhoon prone region normally cannot be achieved by the very often few field measurement data, or by physical simulation in wind tunnel. This study proposes a numerical simulation procedure for predicting directional typhoon design wind speeds and profiles for sites over complex terrain by integrating typhoon wind field model, Monte Carlo simulation technique, CFD simulation and artificial neural networks (ANN). The site of Stonecutters Bridge in Hong Kong is chosen as a case study to examine the feasibility of the proposed numerical simulation procedure. Directional typhoon wind fields on the upstream of complex terrain are first generated by using typhoon wind field model together with Monte Carlo simulation method. Then, ANN for predicting directional typhoon wind field at the site are trained using representative directional typhoon wind fields for upstream and these at the site obtained from CFD simulation. Finally, based on the trained ANN model, thousands of directional typhoon wind fields for the site can be generated, and the directional design wind speeds by using extreme wind speed analysis and the directional averaged mean wind profiles can be produced for the site. The case study demonstrated that the proposed procedure is feasible and applicable, and that the effects of complex terrain on design typhoon wind speeds and wind profiles are significant.