• The KSFM Journal of Fluid Machinery
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• v.18 no.2
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• pp.14-21
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• 2015

Design lifetime of a wind turbine is required to be at least 20 years. The most important step to ensure the deign is to evaluate the loads on the wind turbine as accurately as possible. In this study, extreme design load of a offshore wind turbine using Garrad Hassan (GH) Bladed and National Renewable Energy Laboratory (NREL) FAST codes are calculated considering structural dynamic loads. These wind turbine aeroelastic analysis codes are high efficiency for the rapid numerical analysis scheme. But, these codes are mainly based on the mathematical and semi-empirical theories such as unsteady blade element momentum (UBEM) theory, generalized dynamic wake (GDW), dynamic inflow model, dynamic stall model, and tower influence model. Thus, advanced CFD-dynamic coupling method is also applied to conduct cross verification with FAST and GH Bladed codes. If the unsteady characteristics of wind condition are strong, such as extreme design wind condition, it is possible to occur the error in analysis results. The NREL 5 MW offshore wind turbine model as a benchmark case is practically considered for the comparison of calculated designed loads. Computational analyses for typical design load conditions such as normal turbulence model (NTM), normal wind profile (NWP), extreme operation gust (EOG), and extreme direction change (EDC) have been conducted and those results are quantitatively compared with each other. It is importantly shown that there are somewhat differences as maximum amount of 18% among numerical tools depending on the design load cases.

• Economic and Environmental Geology
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• v.23 no.4
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• pp.383-392
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• 1990

This study found that that dynamics of intensity of use and economic theory of derived demand can both be accommodated through an extensive translog demand model. The basic idea in this recognition is that the skewed life cycle empirical pattern of intensity of use plotted against per capita income is of lognormal form and this lognomal intensity of use model can be mathematically transformed into an eqivalent simple translog intensity of use model. Empirical results showed that this extensive traslog model, which is a flexible function and includes both the classical case of fixed coefficients and the dynamic case of varying coefficients of the explanatory variables, gave better forecasts than the original intensity of use model and other conventional models.

• Environmental and Resource Economics Review
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• v.12 no.2
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• pp.327-346
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• 2003

Unlike previous studies on the causal relationship between energy consumption and economic growth, this paper analyses the dynamic causal relationship between these variables using the dynamic vector using Johansen's multiple cointegration procedure, dynamic vector error-correction model and impulse response function. The empirical results show that while the energy consumption to a shock in income responds positively, the income responds positively to the shocks in energy consumption in the first place and then the responses become negative. We also find that the impact of energy consumption shock on the income is short-lived and causes higher inflationary pressure.

• Korean Chemical Engineering Research
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• v.54 no.3
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• pp.350-359
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• 2016

For accurate estimation over the change of pressure and temperature of the vessel during blowdown period, a new dynamic blowdown model was developed in this work. In particular, heat transfer from the vessel wall to discharge gas at both laminar or turbulent flow in the vessel was embedded to the model to increase the accuracy of blowdown estimation. For thermodynamics, the whole blowdown period was discretized into finite pressure decrement steps, and the step size was adjusted so that the calculation can be more efficiently carried out, while maintaining the model's accuracy. Both Peng-Robinson and Soave-Redlich-Kwong equation of states were applied to the model, and the results were compared each other. Finally, the simulation results was compared with Haque and coworkers' experimental results, and it proved high accuracy of the model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1231-1238
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• 1999

This paper presents an approach for the optimal heating load Identification using Diagonal Recurrent Neural Networks(DRNN). In this paper, the DRNN captures the dynamic nature of a system and since it is not fully connected, training is much faster than a fully connected recurrent neural network. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer. The hidden layer is comprised of self-recurrent neurons, each feeding its output only into itself. In this study, A dynamic backpropagation (DBP) with delta-bar-delta learning method is used to train an optimal heating load identifier. Delta-bar-delta learning method is an empirical method to adapt the learning rate gradually during the training period in order to improve accuracy in a short time. The simulation results based on experimental data show that the proposed model is superior to the other methods in most cases, in regard of not only learning speed but also identification accuracy.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.367-378
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• 2021

This paper develops a dynamic regression model to quantify the contribution of key external factors to operational energy efficiency of ships. On this basis, kernel density estimation is applied to explore distribution patterns of fluctuations in operational performance. An empirical analysis based on these methods show that distribution of fluctuations in Energy Efficiency Operational Indicator (EEOI) is leptokurtic and fat tailed, rather than a normal one. Around 85% of fluctuations in EEOI can be jointly explained by capacity utilization and sailing speed, while the rest depend on other external factors largely beyond control. The variations in capacity utilization and sailing speed cannot be fully passed on to the energy efficiency performance of ships, due to complex interactions between various external factors. The application of the methods is demonstrated, showing a potential approach to develop a rating mechanism for use in the legally binding framework on operational energy efficiency of ships.

• Korean Journal of Oriental Medicine
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• v.14 no.3
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• pp.103-111
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• 2008

Recently, skin diagnosis has been suggested as a promising tool for discrimination of Sasang Constitution, reported by examining the skin characteristics such as thickness, stiffness, slip, and skin textures like wrinkles and furrows. However, the works had a limitation in that clinical decision on the skin characteristics was made by relying upon oriental medicine doctors' subjective sense of touch. In order to objectify the skin diagnosis and claim its efficacy on the discrimination of the Sasang Constitutions, it is necessary to demonstrate its discrimination capability by providing numerical values in terms of physical quantities obtained from measurements using today's sensors and equipment technologies, which motivated this work as a priliminary step towards objectification of skin diagnosis. The skin characteristics focused in this work is the slip property of the back-hand skin that has been exploited using the dynamic friction measurement system. First, curved geometric effects of the back-hand skin on the measured lateral/vertical force signals were estimated using the artificially designed silicon coated structures, which led to a suggestion on a quality controlled experimental design based upon a empirical analysis model. Second, the experimental design thus suggested has been applied to the measurement of dynamic friction coefficients for two healthy male subjects of Taeumin (TE) and Soyangin (SY), respectively. The result shows that the dynamic friction coefficient is less for the SY subject than for the TE subject around the area of the skin used for diagnosis by the oriental medicine doctor, implying the TE subject's skin is more slippery than the SE subject's that is consistent with the oriental medicine doctor's diagnosis. Hopefully, this work can provide guidelines for obtaining quality data in friction measurement to be collected for discussion on the efficacy of the skin diagnosis and its objectification through statistical analysis.

• Atmosphere
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• v.31 no.5
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• pp.637-656
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• 2021

The United States has been known as the world's major producer of crops such as wheat, corn, and soybeans. Therefore, using meteorological long-term forecast data to project reliable crop yields in the United States is important for planning domestic food policies. The current study is part of an effort to improve the seasonal predictability of regional-scale precipitation across the United States for estimating crop production in the country. For the purpose, a dynamic downscaling method using Weather Research and Forecasting (WRF) model is utilized. The WRF simulation covers the crop-growing period (March to October) during 2000-2020. The initial and lateral boundary conditions of WRF are derived from the Pusan National University Coupled General Circulation Model (PNU CGCM), a participant model of Asia-Pacific Economic Cooperation Climate Center (APCC) Long-Term Multi-Model Ensemble Prediction System. For bias correction of downscaled daily precipitation, empirical quantile mapping (EQM) is applied. The downscaled data set without and with correction are called WRF_UC and WRF_C, respectively. In terms of mean precipitation, the EQM effectively reduces the wet biases over most of the United States and improves the spatial correlation coefficient with observation. The daily precipitation of WRF_C shows the better performance in terms of frequency and extreme precipitation intensity compared to WRF_UC. In addition, WRF_C shows a more reasonable performance in predicting drought frequency according to intensity than WRF_UC.

• International Area Studies Review
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• v.13 no.3
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• pp.213-238
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• 2009

This paper empirically examines effects of exporting on employment over the period 2000-2007 for Korean listed and non-listed manufacturing firms. The paper employs the dynamic panel model of labor demand and controls for simultaneity of the exports and real wages using a two step random effect Tobit-DPD (Dynamic Panel Data) procedure. Our empirical results suggest that surprisingly, there is no robust evidence for employment effects of exporting of Korea's large firms and small-medium sized firms during the sample period after Korean financial crisis. This implies that Korean exporting patterns have been changed in a way that exporting highly capital intensive goods leads to importing more intermediate goods and thus to countervailing the employment effects of exporting. This suggests that expansion of exporting by lowering exporting prices through the bilateral FTA might not be helpful to enhancing employment in Korea.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.1-22
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• 2022

We investigate the dynamic relationships among digital piracy activities, TV ratings, and online buzz for Korean TV dramas using a panel vector autoregression model. Our main findings include 1) TV ratings are negatively affected by digital piracy activities but positively affected by google buzz, 2) digital piracy activities are negatively affected by TV ratings and social buzz, and 3) social buzz and google buzz are positively influenced by each other. While many empirical studies were conducted to reveal the effects of music or movie piracy, our understanding of drama piracy is limited. We provide empirical evidence of the dynamic relationships between drama piracy, TV ratings, and online buzz. Our findings show the presence of indirect piracy effects on TV ratings through online buzz. Further, we reveal that social buzz and google trends play different roles in promoting TV ratings and piracy activities. We discuss the implications of our findings for theory and practitioners.