• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1361-1364
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• 1990

In order to tackle global environmental problems such as destruction of the ozone layer or climatic changes due to atmospheric temperature increase, the acquisition of plentiful and precise data is necessary. Therefore, a means of conducting long-lasting high-resolution measurements over broad areas is required. A feasibility study has been made on a high altitude (20km), super-pressured helium-filled PLTA (Powered Ligher-than-Air) vehicle as an ideal platform for environmental observation. It has a long service life and carries a larger payload than an artificial satellite. This PLTA platform uses an electric propulsion system to maintain position in space against wind currents. The thruster is driven by solar power acquired from solar cells. For night use, solar energy is stored in regenerative fuel cells. This study focuses on energy balance and structural analysis of the hull and platform. The platform is capable of conducting high resolution remote sensing as well as having the capability to serve as a telecommunications relay. The platform could replace a number of ground-based telecommunications relay facilities, guaranteeing sufficient radio frequency intensity to secure good quality telecommunication transmittal. The altitude at which the platform resides has the lowest wind flow in the lower stratosphere, and permits viewing from the ground within a 1,000km range. Because this altitude is much lower than that required of an artificial satellite, the measuring resolution is a couple of thousand times higher than with artificial satellites. The platform can also be used to chase typhoons and observe them from their sources in tropical regions.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.329-338
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• 2021

This study predicts the amount of wind power generation for rational operation plan of wind power generation and capacity calculation of ESS. For forecasting, we present a method of predicting wind power generation by combining a physical approach and a statistical approach. The factors of wind power generation are analyzed and variables are selected. By collecting historical data of the selected variables, the amount of wind power generation is predicted using deep learning. The model used is a hybrid model that combines a bidirectional long short term memory (LSTM) and a convolution neural network (CNN) algorithm. To compare the prediction performance, this model is compared with the model and the error which consist of the MLP(:Multi Layer Perceptron) algorithm, The results is presented to evaluate the prediction performance.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.68-68
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• 2017

Measurements of solar wind speed near the Sun (< 0.1 AU) are important for understanding acceleration mechanism of solar wind as well as space weather predictions, but hard to directly measure them. For the first time, we provide 2D solar wind speed maps in the LASCO field of view using three consecutive days data. By applying the Fourier convolution and inverse Fourier transform, we decompose the 3D intensity data (r, PA, t) into the 4D one (r, PA, t, v). Then, we take the weighted mean along speed to determine the solar wind speeds that gives V(r, PA, t) in every 30 min. The estimated radial speeds are consistent with those given by an artificial flow and plasma blobs. We find that the estimated speeds are moderately correlated with those from slow CMEs and those from IPS observations. A comparison of yearly solar wind speed maps in 2000 and 2009 shows that they have very remarkable differences: azimuthally uniform distribution in 2000 and bi-modal distribution (high speed near the poles and low speed near the equator) in 2009.

• Wind and Structures
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• v.18 no.3
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• pp.253-265
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• 2014

The platform and floating structure of spar type offshore wind turbine systems should be designed in order for the 6-DOF motions to be minimized, considering diverse loading environments such as the ocean wave, wind, and current conditions. The objective of this study is to optimally design the platform and substructure of a 3MW spar type wind turbine system with the maximum postural stability in 6-DOF motions as well as the minimum material cost. Therefore, design variables of the platform and substructure were first determined and then optimized by a hydrodynamic analysis. For the hydrodynamic analysis, the body weight of the system was considered, and the ocean wave conditions were quantified to the wave forces using the Morison's equation. Moreover, the minimal number of computation analysis models was generated by the Design of Experiments (DOE), and the design variables of the platform and substructure were finally optimized by using a genetic algorithm with a neural network approximation.

• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.33.2-33.2
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• 2010

고층건물의 최상층 수평변위는 해당 건물의 안전성 및 사용성 평가에 중요한 지표가 된다 이러한 건물의 수평변위는 주로 풍하중에 기인한다 본 논문에서는 이러한 구조반응을 풍하중에 기인한 풍속데이터로부터 직접 추정하기 위해서 인공신경망(Artificial Neural Network, ANN)을 도입하였다 이에 대한 적용성을 판단하기 위해서 고층건물을 형상화한 모형테스트를 실시하고 풍향, 풍속, 변위 값을 얻었다. 이후 인공신경망에 적용시켜 실제 실험 데이터와의 비교를 통해 타당성을 검토하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.78-83
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• 2014

Recently, a rapid growth of wind power system as a leading renewable energy source has compelled a number of companies to develop intelligent monitoring and diagnostic system. Such systems can detect early mechanical faults, which prevents from costly repairs. Generally, fault diagnostic system for wind turbines is based on vibration and process signal analysis. In this work, different type of mechanical faults such as mass unbalance and shaft misalignment which can always happen in wind turbine system is considered. The proposed intelligent fault diagnostic algorithm utilizes artificial neural network and Wavelet transform. In order to verify the feasibility of the proposed algorithm, mechanical fault generation experimental system manufactured by Gaon corporation is utilized.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.3
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• pp.177-186
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• 2019

Wind power tower has been used to augment the performance of VAWT (Vertical Axis Wind Turbine). However, inappropriately designed wind power tower could reduce the performance of VAWT. Hence, an optimization study was conducted on a wind power tower. Six design variables were selected, such as the outer radius and the inner radius of the guide wall, the adoption of the splitter, the inner radius of the splitter, the number of the guide wall and the circumferential angle. For the objective function, the periodic averaged torque obtained at the VAWT was selected. In the optimization, Design of Experiment (DOE), Genetic Algorithm (GA), and Artificial Neural Network (ANN) have been applied in order to avoid a localized optimized result. The ANN has been continuously improved after finishing the optimization process at each generation. The performance of the VAWT was improved more than twice when it operated within the optimized wind power tower compared to that obtained at a standalone.

• Journal of Applied Reliability
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• v.14 no.4
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• pp.208-212
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• 2014

An experimental gear-box was set-up to simulate the real situation of the wind-turbine. Artificial cracks of different sizes were machined into the gear. Vibration signals were acquired to diagnose the different crack fault conditions. Time-domain features such as root mean square, variance, kurtosis, normalized 6th central moments were used to capture the characteristics of different crack conditions. Normal condition, 1 mm crack condition, 2mm crack condition, 6mm crack condition, and tooth fault condition were compared using ANFIS and DAG-SVM methods, and three different DAG-SVM models were compared. High-pass filtering improved the success rates remarkably in the case of DAG-SVM.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.05a
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• pp.113-122
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• 2004

Cable domes deform very largely because of the characteristics of flexible hybrid system and pre-tension, and include geometrical non-linearity in those structural behavior. Especially wind load is more dominant than seismic load, because cable domes are flexible structures whose bending stiffness is very small and self-weight is very light. Therefore, in this paper, the Modified Stiffly Stable Method is applied to analyze the nonlinear dynamic behavior of cable domes and compared these results with ones of the $Newmark-{\beta}$ Method which is generally used. The Seoul Olympic Gymnastic Arena is taken as an numerical example and three kinds of models with giving each different intensity of pre-tension are selected. And dynamic nonlinear behavior of cable domes are analyzed by artificial spectrum of wind velocity wave which is similar to actual wind loads.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.61-68
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• 2003

Cable domes deform very largely because of the characteristics of flexible hybrid system and pre-tension, and include geometrical non-linearity in those structural behavior. Especially wind load is more dominant than seismic loads, because cable domes are flexible structures whose stiffness is very small and self-weight is very light. Therefore, in this paper, Modified Stiffly Stable Method is applied to analyze the nonlinear dynamic behavior of cable domes and compared these results with ones of Newmark-β Method which is generally used. The Seoul Olympic Gymnastic Arena is taken as an numerical example and three kinds of models with giving each different intensity of pre-tension are selected. And dynamic nonlinear behavior of cable domes are analyzed by artificial spectrum of wind velocity wave which is similar to actual wind loads.