• Korean Journal of Metals and Materials
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• v.47 no.10
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• pp.613-620
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• 2009

The Kachanov-Rabotnov (K-R) creep model was proposed to accurately model the long-term creep curves above $10^5$ hours of Alloy 617. To this end, a series of creep data was obtained from creep tests conducted under different stress levels at $950^{\circ}C$. Using these data, the creep constants used in the K-R model and the modified K-R model were determined by a nonlinear least square fitting (NLSF) method, respectively. The K-R model yielded poor correspondence with the experimental curves, but the modified K-R model provided good agreement with the curves. Log-log plots of ${\varepsilon}^{\ast}$-stress and ${\varepsilon}^{\ast}$-time to rupture showed good linear relationships. Constants in the modified K-R model were obtained as ${\lambda}$=2.78, and $k=1.24$, and they showed behavior close to stress independency. Using these constants, long-term creep curves above $10^5$ hours obtained from short-term creep data can be modeled by implementing the modified K-R model.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.433-449
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• 2021

In this study, a newly enhanced Fluid-Structure Interaction (FSI) model which incorporates mooring lines was used to simulate a floating structure. The model has two parts: a Computational Fluid Dynamics (CFD) model and a mooring model. The open-source CFD OpenFOAM® v1712 toolbox was used in the present study, and the convergence criteria and relaxation method were added to the computational procedure used for the OpenFOAM multiphase flow solver, interDyMFoam. A newly enhanced, tightly coupled solver, CoupledinterDyMFoam, was used to decrease the artificial added mass effect, and the results were validated through a series of benchmark cases. The mooring model, based on the finite element method, was established in MATLAB® and was validated against a benchmark analytical elastic catenary solution and numerical results. Finally, a model which simulates a floating structure with mooring lines was successfully constructed by connecting the mooring model to CoupledinterDyMFoam.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• Journal of KIISE:Information Networking
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• v.32 no.4
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• pp.433-442
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• 2005

An increase In traffic has a large Influence on the performance of a total network. Therefore, traffic management has become an important issue of network management. In this paper, we propose a new routing algorithm that attempts to analyze network conditions using time series prediction models and to propose predictive optimal routing decisions. Traffic congestion is assumed when the predicting result is bigger than the permitted bandwidth. By collecting traffic in real network, the predictable model is obtained when it minimizes statistical errors. In order to predict network traffic based on time series models, we assume that models satisfy a stationary assumption. The stationary assumption can be evaluated by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). We can obtain the result of these two functions when it satisfies the stationary assumption. We modify routing oaths by predicting traffic in order to avoid traffic congestion through experiments. As a result, Predicting traffic and balancing load by modifying paths allows us to avoid path congestion and increase network performance.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.597-607
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• 2011

Value at Risk(VaR) is being widely used as a simple tool for measuring financial risk. Although VaR has a few weak points, it is used as a basic risk measure due to its simplicity and easiness of understanding. However, it becomes very difficult to estimate the volatility of the portfolio (essential to compute its VaR) when the number of assets in the portfolio is large. In this case, we can consider the application of a dimension reduction technique; however, the ordinary factor analysis cannot be applied directly to financial data due to autocorrelation. In this paper, we suggest a dimension reduction method that uses the time-series factor analysis and DCC(Dynamic Conditional Correlation) GARCH model. We also compare the method using time-series factor analysis with the existing method using ordinary factor analysis by backtesting the VaR of real data from the Korean stock market.

• Journal of Power Electronics
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• v.11 no.6
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• pp.870-879
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• 2011

Regulated peak power tracking (RPPT) systems such as the series structure and the series-parallel structures are commonly used in satellite space power systems. However, these structures process the solar array power or the battery power to the load through two cascaded regulators during one orbit cycle, which reduces the energy transfer efficiency. Also the battery charging time is increased due to placement of converter between the battery and the solar array. In this paper a parallel structure has been proposed which can improve the energy transfer efficiency and the battery charging time for satellite space power RPPT systems. An analogue controller is used to control all of the required functions, such as load voltage regulation and solar array stabilization with maximum power point tracking (MPPT). In order to compare the system efficiency and the battery charging efficiency of the proposed structure with those of a series (conventional) structure and a simplified series-parallel structure, simulations are performed and the results are analyzed using a loss analysis model. The proposed structure charges the battery more quickly when compared to the other two structures. Also the efficiency of the proposed structure has been improved under different modes of solar array operation when compared with the other two structures. To verify the system, experiments are carried out under different modes of solar array operation, including PPT charge, battery discharge, and eclipse and trickle charge.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.281-293
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• 2021

Oil and steel prices, which are major pricescosts in the shipbuilding industry, were predicted. Firstly, the error of the moving average line (N=3-5) was examined, and in all three error analyses, the moving average line (N=3) was small. Secondly, in the linear prediction of data through existing theory, oil prices rise slightly, and steel prices rise sharply, but in reality, linear prediction using existing data was not satisfactory. Thirdly, we identified the limitations of linear prediction methods and confirmed that oil and steel price prediction was somewhat similar to actual moving average line prediction methods. Due to the high volatility of major price flows, large errors were inevitable in the forecast section. Through the time series analysis method at the end of this paper, we were able to achieve not bad results in all analysis items relative to artificial intelligence (Prophet). Predictive data through predictive analysis using eight predictive models are expected to serve as a good research foundation for developing unique tools or establishing evaluation systems in the future. This study compares the basic settings of artificial intelligence programs with the results of core price prediction in the shipbuilding industry through time series prediction theory, and further studies the various hyper-parameters and event effects of Prophet in the future, leaving room for improvement of predictability.

• The Journal of Information Systems
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• v.30 no.2
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• pp.147-163
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• 2021

Purpose The study aims to predict real estate prices by utilizing regional characteristics. Since real estate has the characteristic of immobility, the characteristics of a region have a great influence on the price of real estate. In addition, real estate prices are closely related to economic development and are a major concern for policy makers and investors. Accurate house price forecasting is necessary to prepare for the impact of house price fluctuations. To improve the performance of our predictive models, we applied LSTM, a widely used deep learning technique for predicting time series data. Design/methodology/approach This study used time series data on real estate prices provided by the Ministry of Land, Infrastructure and Transport. For time series data preprocessing, HP filters were applied to decompose trends and SOM was used to cluster regions with similar price directions. To build a real estate price prediction model, SVR and LSTM were applied, and the prices of regions classified into similar clusters by SOM were used as input variables. Findings The clustering results showed that the region of the same cluster was geographically close, and it was possible to confirm the characteristics of being classified as the same cluster even if there was a price level and a similar industry group. As a result of predicting real estate prices in 1, 2, and 3 months, LSTM showed better predictive performance than SVR, and LSTM showed better predictive performance in long-term forecasting 3 months later than in 1-month short-term forecasting.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.342-342
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• 2023

메콩강을 공유하는 6개국은 주로 강의 개발과 수자원의 활용을 통해 경제성장을 이룩하고 있다. 하지만, 각국의 산업화나 경제성장의 수준, 메콩강에 대한 의존도와 관심, 전략 등이 서로 달라 개발에 따른 국가 간 수자원 공유, 환경피해, 지역보존 등의 문제들이 발생하고 있다. 메콩지역의 국가 중 베트남은 하천유역의 많은 부분이 국가공유하천으로 인접국가의 유역개발에 따라 다양한 물 분쟁이 발생할 수 있으며, 잦은 홍수피해가 발생하고, 낙후된 사회인프라로 인해 이수 및 수질오염과 관련된 물 문제 역시 지역적으로 발생하고 있다. 해당지역의 물 문제해결을 위한 정책결정의 지원을 위해서는 수리·수문학적 기초 또는 상세 분석이 필요하며, 본 연구에서는 매년 홍수와 대규모 범람, 비효율적 댐운영으로 인한 가뭄, 염수침입 등의 물 문제가 발생하는 Ca River 유역을 대상유역으로 선정하고 K-series SW 기반의 홍수범람 해석을 수행하였다. K-water에서 개발된 다양한 K-Series SW 중 연구대상유역인 Ca River 하류 유역에 대한 적용에 적합한 모형을 기존 현황조사 등을 바탕으로 1차원 하천흐름해석을 위한 K-River, 2차원 홍수범람해석을 위한 K-Flood 모형을 선정하고 분석을 수행하였다. 2010년과 2013년의 홍수기를 대상으로 K-River모형을 이용하여 Ca river 하류의 수리학적 현상을 해석하였으며, 해당 결과를 기반으로 K-Flood 모형을 이용한 2차원 홍수범람해석을 수행하고 실제 범람지도와의 비교를 수행하였다. 그리고 결과검토를 통해 모의 결과가 수위에 대해 높은 재현성을 보이고 있으며 범람면적과 침수심의 모의결과가 실제 침수양상과 비슷한 양상을 보임을 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.518-520
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• 2021

In this paper, we propose a hand gesture recognition method by modifying the textNAS used for text classification so that it can be applied to multivariate time series data. It can be applied to various fields such as behavior recognition, emotion recognition, and hand gesture recognition through multivariate time series data classification. In addition, it automatically finds a deep learning model suitable for classification through training, thereby reducing the burden on users and obtaining high-performance class classification accuracy. By applying the proposed method to the DHG-14/28 and Shrec'17 datasets, which are hand gesture recognition datasets, it was possible to obtain higher class classification accuracy than the existing models. The classification accuracy was 98.72% and 98.16% for DHG-14/28, and 97.82% and 98.39% for Shrec'17 14 class/28 class.