• International Area Studies Review
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• v.13 no.2
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• pp.255-276
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• 2009

This article has examined the international transmission of returns among S&P500, Nikkei225 and SENSEX stock index cash markets using the daily closing prices covered from January 4, 2002 to February 6, 2009. For this purpose we employed dynamic time series models such as the Granger causality analysis and variance decomposition analysis based on VAR model. The main empirical results are as follows; First, according to Granger causality tests we find that S&P500 stock index has a significant prediction power on the changes of SENSEX and Nikkei225 stock index market and vice versa. However, US stock market's influence is dominant to the other stock markets at a significant level statistically. Second, according to variance decomposition, SENSEX stock index is more sensitive to the movement of S&P500 than that of Nikkei225 stock index. These kinds of empirical results shows that the three stock markets are integrated over times and these results will be informative for the international investors to build the world-wide investment portfolio and risk management strategies, etc.

• Korean Journal of Metals and Materials
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• v.48 no.3
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• pp.194-202
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• 2010

Application of a stronger and more durable material for reactor pressure vessels (RPVs) might be an effective way to insure the integrity and increase the efficiency of nuclear power plants. A series of research projects to apply the SA508 Gr.4 steel in ASME code to RPVs are in progress because of its excellent strength and durability compared to commercial RPV steel (SA508 Gr.3 steel). In this study, the microstructural characteristics and mechanical properties of SA508 Gr.3 Mn-Mo-Ni low alloy steel and SA508 Gr.4N Ni-Mo-Cr low alloy steel were investigated. The differences in the stable phases between these two low alloy steels were evaluated by means of a thermodynamic calculation using ThermoCalc. They were then compared to microstructural features and correlated with mechanical properties. Mn-Mo-Ni low alloy steel shows the upper bainite structure that has coarse cementite in the lath boundaries. However, Ni-Mo-Cr low alloy steel shows the mixture of lower bainite and tempered martensite structure that homogeneously precipitates the small carbides such as $M_{23}C_6$ and $M_7C_3$ due to an increase of hardenability and Cr addition. In the mechanical properties, Ni-Mo-Cr low alloy steel has higher strength and toughness than Mn-Mo-Ni low alloy steel. Ni and Cr additions increase the strength by solid solution hardening. In addition, microstructural changes from upper bainite to tempered martensite improve the strength of the low alloy steel by grain refining effect, and the changes in the precipitation behavior by Cr addition improve the ductile-brittle transition behavior along with a toughening effect of Ni addition.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.1-11
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• 2021

Thermal protection system structures such as double-panel structures are used on the skin of the fuselage and wings to prevent the transfer of high heat into the interior of an high supersonic/hypersonic aircraft. The thin-walled double-panel skin can be exposed to acoustic loads by high power engine noise and jet flow noise, which can cause sonic fatigue damage. In order to predict the fatigue life of the skin, the octave bandwidth SPL should be calculated as narrow bandwidth PSD or acoustic load history using interpolation method. In this paper, a method of converting the octave bandwidth SPL acoustic load into a narrow bandwidth PSD and reconstructed acoustic load history was investigated. The octave bandwidth SPL was converted to the narrow bandwidth PSD using various interpolation methods such as flat, log and linear scale, and the probabilistic characteristics and fatigue damage results were compared. It was found that average error of fatigue damage index by the log scale interpolation method was relatively small among three methods.

• The Journal of the Acoustical Society of Korea
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• v.40 no.1
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• pp.31-37
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• 2021

In this paper, we design an acoustic meta-material silencer that operates at low frequency to reduce noise in duct. A high refractive index meta-material silencer is demonstrated with a combination of zigzag structured thin waveguide and helmholtz resonator, which reduces the speed of sound. Finite Element Method (FEM) analysis via thermo-viscous acoustic mesh is performed in order to calculate thermo-viscous dissipation in sub-wavelength waveguide. Sound power reflection, transmission and absorption coefficients are obtained utilizing 4-Microphone Method. The results show that cut-off frequency and transmission loss can be controlled through adjusting intervals of the zigzag structures. A wide-band acoustic silencer is also suggested by connecting meta-materials in series or parallel.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• Informatization Policy
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• v.29 no.2
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• pp.66-90
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• 2022

The radical digital transformation and development of the contactless digital economy in the wake of the COVID-19 pandemic are increasing the need to solve various problems such as conflicts of interest among market participants and delays in related laws and regulations. This study investigates the concept and characteristics of the contactless digital economy and identifies the related regulatory issues and resolutions through literature review, news article analysis, and expert interviews. From the literature review, it is identified that the contactless digital economy has eight hyper-innovation characteristics: hyper-intelligence, hyper-connectivity, hyper-convergence, hyper-personalization, hyper-automation, hyper-precision, hyper-diversity, and hyper-trust. From news article analyses and expert interviews, this study identifies various regulatory issues, such as competition between incumbents and new entrants, the collision of constitutional rights, collision of social values, conflict between market participants, absence of laws and regulations, and existence of excessive market power, and then proposes a series of resolutions.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.1-14
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• 2022

This study aims to identify topics in newspaper articles related to fine particle matter and to investigate the characteristics and time series trend of each topic. Related national newspaper articles during 1990 and 2021 were collected from Bigkinds. A total of 18 topics have been discovered using LDA, and 11 clusters deduced from clustering. Hot topics include related products/residence, overseas cause(China), power plant as a domestic cause, nationwide emergency reduction measures, international cooperation, political issues, current situation & countermeasure in other countries, and consumption patterns. Cold topics include the concentration standard and indoor air quality improvement. These findings would be useful in inferring the political direction and strategies. In particular, the consumer protection policy should be expanded as the related market is growing. It will also be necessary to pursue policies that will promote public safety and health, and that will enhance public consensus and international cooperation.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.631-644
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• 2022

This paper considers robust estimation of the sparse vector autoregressive model (sVAR) useful in high-dimensional time series analysis. First, we generalize the result of Xu et al. (2008) that the adaptive lasso indeed has robustness in sVAR as well. However, adaptive lasso method in sVAR performs poorly as the number and sizes of outliers increases. Therefore, we propose new robust estimation methods for sVAR based on least absolute deviation (LAD) and Huber estimation. Our simulation results show that our proposed methods provide more accurate estimation in turn showed better forecasting performance when outliers exist. In addition, we applied our proposed methods to power usage data and confirmed that there are unignorable outliers and robust estimation taking such outliers into account improves forecasting.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Korean Journal of Agricultural Science
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• v.49 no.1
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• pp.61-75
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• 2022

The first purpose of this study is to evaluate the usefulness of pork traceability data, which is monthly time-series data, and to draw implications with regard to its usefulness. The second purpose is to construct a dynamic ecological equation model (DEEM) that reflects the biological characteristics at each growth stage, such as pregnancy, birth and growth, and the slaughter of pigs, using traceability data. With the monthly pig model devised in this study, it is expected that the number of slaughtered animals (supply) that can be shipped in the future is predictable and that policy simulations are possible. However, this study was limited to traceability data and focused only on building a supply-side model. As a result of verifying the traceability data, it was found that approximately 6% of farms produce by mixing great grand parent (GGP), grand parent (GP), parent stock (PS), and artificial insemination (AI), meaning that it is necessary to separate them by business type. However, the analysis also showed that the coefficient values estimated by constructing an equation for each growth stage were consistent with the pig growth outcomes. Also, the model predictive power test was excellent. For this reason, it is judged that the model design and traceability data constructed with the cohort and the dynamic ecological equation model system considering biological growth and shipment times are excellent. Finally, the model constructed in this study is expected to be used as basic data to inform producers in their decision-making activities and to help with governmental policy directions with regard to supply and demand. Research on the demand side is left for future researchers.