• Journal of the Korean Solar Energy Society
• /
• v.30 no.3
• /
• pp.65-70
• /
• 2010

As for the present wind power industry, most of the computerization for monitoring and control is based on the traditional development methodology, but it is necessary to improve SCADA system since it has a phenomenon of backlog accumulation in the applicable aspect of back-data as well as in the operational aspect in the future. Especially for a system like offshore wind power where a superintendent cannot reside, it is desirable to operate a remote control system. Therefore, it is essential to establish a monitoring system with appropriate control and monitoring inevitably premised on the integrity and independence of data. As a result, a study was carried out on the modeling of offshore wind power data-centered database. In this paper, a logical data modeling method was proposed and designed to establish the database of offshore wind power. In order for designing the logical data modeling of an offshore wind power system, this study carried out an analysis of design elements for the database of offshore wind power and described considerations and problems as well. Through a comparative analysis of the final database of the newly-designed off-shore wind power system against the existing SCADA System, this study proposed a new direction to bring about progress toward a smart wind power system, showing a possibility of a service-oriented smart wind power system, such as future prediction, hindrance-cause examination and fault analyses, through the database integrating various control signals, geographical information and data about surrounding environments.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.1
• /
• pp.171-175
• /
• 2006

In this paper, we have developed a retrodirective active array operating in the 2 GHz LS band. The retrodirective array has the property of redirecting any electromagnetic wave back to the incoming direction without any priory informations. The system is integrated with phase conjugators and antenna array. Microwave phase conjugators can be implemented by microwave mixers. In this research, 2-port gate mixers using pHEMT and $1{\times}4$ monopole array have been used to achieve the retrodirectivity. The measured results have been compared with the theoretical prediction, and it has been shown that there exists a reasonable agreement between them. The monopole array can be used easily in many areas for simplicity and cost-effective property, and the retrodirective array developed in this research can be applied directly in the base station facilities for the wireless mobile communications. indoor wireless LAN and RFID transponders.

• Interaction and multiscale mechanics
• /
• v.5 no.3
• /
• pp.229-265
• /
• 2012

Hygroexpansion of wood is a known and undesired characteristic in civil engineering. When wood is exposed to changing environmental humidity, it adsorbs or desorbs moisture and warps. The resulting distortions or - at restrained conditions - cracks are a major concern in timber engineering. We herein present a multiscale model for prediction of the macroscopic hygroexpansion behavior of individual pieces of softwood from their microstructure, demonstrated for spruce. By applying poromicromechanics, we establish a link between the swelling pressure, driving the hygroexpansion of wood at the nanoscale, and the resulting macroscopic dimensional changes. The model comprises six homogenization steps, which are performed by means of continuum micromechanics, the unit cell method and laminate theory, all formulated in a poromechanical framework. Model predictions for elastic properties of wood as functions of the moisture content closely approach corresponding experimental data. As for the hygroexpansion behavior, the swelling pressure has to be back-calculated from macroscopic hygroexpansion data. The good reproduction of the anisotropy of wood hygroexpansion, based on only a single scalar calibration parameter, underlines the suitability of the model. The multiscale model constitutes a valuable tool for studying the effect of microstructural features on the macroscopic behavior and for assessing the hygroexpansion behavior at smaller length scales, which are inaccessible to experiments. The model predictions deliver input parameters for the analysis of timber at the structural scale, therewith enabling to optimize the use of timber and to prevent moisture-induced damage or failure.

• Journal of the Korean Geotechnical Society
• /
• v.20 no.5
• /
• pp.17-25
• /
• 2004

The flat dilatometer test (DMT) is a geotechnical tool to estimate in-situ properties of various types of ground materials. The undrained shear strength is known to be the most reliable and useful parameter obtained by DMT. However, the existing relationships which were established for other local deposits depend on the regional geotechnical characteristics. In addition, the flat dilatometer test results have been interpreted using three intermediate indices - material index $(I_D)$, horizontal stress index $(K_D)$, and dilatometer modulus (E$_{D}$) and the undrained shear strength has been estimated merely using the horizontal stress index $(K_D)$. In this paper, the applicability of the flat dilatometer to Korean soft clay deposit has been investigated. Then an artificial neural network was developed to evaluate the undrained shear strength by DMT and the ANN, based on the $p_0, p_1, p_2, {\sigma '}_v$ and porewater pressure. The ANN which adopts the back-propagation algorithm was trained based on the DMT data obtained from Korean soft clay. To investigate the feasibility of ANN model, the prediction results obtained from data which were not used to train the ANN and those obtained from existing relationships were compared.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.41 no.4
• /
• pp.261-267
• /
• 2013

In this paper, wind tunnel test and analytical prediction are compared for result of flapping motion in helicopter forward flight condition. Tests were performed at low speed wind tunnel at Chungnam National University, test section of wind tunnel has 1.8 by 1.8 meter open-jet test section area. According to the results of measured data for aerodynamic performance of model rotor in forward flight. It has to observed the difference of analytical and measured results of power coefficient for fixed thrust coefficient. And calculated and measured data of helicopter rotor flapping angles in forward flight are compared for a model rotor in a wind tunnel. A test was conducted to verify the measured data of coning and lateral/longitudinal flapping angle with predicted values.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.31 no.3
• /
• pp.182-187
• /
• 2018

In this study, 20.8% of a p-type Si bifacial solar cell was used to develop a photovoltaic (PV) module to obtain the maximum power under a limited installation area. The transparent back sheet material was replaced during fabrication with a white one, which is opaque in commercial products. This is very beneficial for the generation of more electricity, owing to the additional power generation via absorption of light from the rear side. A new model is suggested herein to predict the power of the bifacial PV module by considering the backside reflections from the roof and/or environment. This model considers not only the frontside reflection, but also the nonuniformity of the backside light sources. Theoretical predictions were compared to experimental data to prove the validity of this model, the error range for which ranged from 0.32% to 8.49%. Especially, under $700W/m^2$, the error rate was as low as 2.25%. This work could provide theoretical and experimental bases for application to a distributed and microgrid network.

• The Korean Journal of Applied Statistics
• /
• v.22 no.5
• /
• pp.995-1005
• /
• 2009

Conditional correlation between financial time series plays an important role in risk management, asset allocation and portfolio selection and therefore diverse efforts for modeling conditional correlations in multivariate-GARCH processes have been made in last two decades. In particular, CCC (cf. Bollerslev, 1990) and DCC(dynamic conditional correlation, cf. Engle, 2002) models have been commonly used since they are relatively parsimonious in the number of parameters involved. This article is concerned with DCC modeling for multivariate GARCH processes in comparison with CCC specification. Various multivariate financial time series are analysed to illustrate possible advantages of DCC over CCC modeling.

• Journal of Korean Society for Atmospheric Environment
• /
• v.25 no.1
• /
• pp.15-25
• /
• 2009

Donghae city is one of the most representative cement industrial city in Korea. The area is faced with the East Sea to the East and with high montane region of Tae-Back mountain range to the West. Many pollutant sources of air pollution are located near the coast, but the largest point sources of the region are located at the bottom of the mountain area in Donghae city. The local wind is highly affected by local topography and plays an important role in transport and dispersion of contaminants from the pollution sources. This study was designed to evaluate enhancement of MM5 predictions by using Four Dimensional Data Assimilation (FDDA), the SONDE data and the national meteorological station, data only. The alternative meteorological fields predicted with and without FDDA were used to simulate spatial and temporal variations of NOx in combined with Atmospheric Dispersion Models (CALPUFF). For the modeling domain, the alternative meteorological fields with 1.1 km spatial resolution were interpolated to the CALMET with 0.5 km resolution. The vertical layers set to have 35 and 12 layers for MM5 and CALPUFF, respectively. MM5 with the FDDA did not resulted in significant improvement of meteorological field prediction in Donghae region, which is primarily because of complex geography and wind scheme. The result of CALPUFF, however, showed reduction of uncertainty errors by using the interpolation scheme of the actual measurement data.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.3 no.4 s.11
• /
• pp.119-129
• /
• 2003

Concrete and steel are essential structural materials in the construction. But, concrete, different from steel, consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructors. Concrete have two kinds of properties, immediately knowing properties such as slump, air contents and time dependent one like strength. Therefore, concrete mixes depend on experiences of experts. However, at point of time using High Performance Concrete, new method is wanted because of more ingredients like mineral and chemical admixtures and lack of data. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network ate used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength, slump, and air contents are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.7
• /
• pp.378-388
• /
• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.