• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.205-213
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• 2021

Due to the advantage of reducing the voltage applied to the switch semiconductor, the input series and output parallel combination is widely used in systems with high input voltage and large output current. On the other hand, the LLC converter is widely used as a high-efficiency power converter, and when connected by ISOP combination, there is a possibility that input voltage imbalance may occur due to a mismatch of passive devices. To avoid damaging the switching device, this study analyzed the DC-link voltage imbalance of a high-capacity supply using an ISOP LLC converter. In addition, the case where DC-link unbalance control was applied and the case not applied was analyzed respectively. Based on this analysis, an initial start-up algorithm was proposed to prevent input power semiconductor device damage due to DC-link over-voltage. The effectiveness of the proposed algorithm has been verified through simulations and experiments.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.182-187
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• 2007

A case study is a beforehand research to develope 3D architectural design guide line for building and construction. For this case study, the research investigates several cases around world such as Building Information Modeling Guide Series of U.S. GSA(General Service Administration), BIM/IFC User Guide of IAI Germany, IDM(Information Delivery Manual) of IAI Norway, CORENet of Singapore the Ministry of National Development with Building and Construction Authority, and Helsinki University of Technology Auditorium Hall 600(HUT-600) of IAI Finland. The common thing of each case is using IFC for sharing information and interoperability in the life cycle of building. Through the case study, it shows the way how the 3D architectural design guide adapted in Korean situation.

• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.329-337
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• 2003

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.96-101
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• 2023

Digital art creation has become an indispensable part of today's society, but traditional digital art production methods have been difficult to meet the growing creative needs of artists. Therefore, this study takes the cloud series works of artist Morten Lasskogen as an example and explores the application value of 3D Max and Unreal Engine in digital art created by analyzing the lighting effects in the works of art. This research aims to form reference materials through actual case analysis and provide artists with more efficient ideas for digital art creation.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.329-332
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• 2007

Annual vegetation growth patterns are determined by the intrinsic phenological characteristics of each land cover types. So, if typical growth patterns of each land cover types are well-estimated, and a NDVI time-series data of a certain area is compared to those estimated patterns, we can implement more advanced analyses such as a land surface-type classification or a land surface type change detection. In this study, we utilized Terra MODIS NDVI 250m data and compressed full annual NDVI time series data into several indices using the Harmonic Analysis of Time Series(HANTS) algorithm which extracts the most significant frequencies expected to be presented in the original NDVI time-series data. Then, we found these frequencies patterns, described by amplitude and phase data, were significantly different from each other according to vegetation types and these could be used for land cover classification. However, in spite of the capabilities of the HANTS algorithm for detecting and interpolating cloud-contaminated NDVI values, some distorted NDVI pixels of June, July and August, as well as the long rainy season in Korea, are not properly corrected. In particular, in the case of two or three successive NDVI time-series data, which are severely affected by clouds, the HANTS algorithm outputted wrong results.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.45-51
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• 2000

This study proposes that analysis and evaluation method of time series ultrasonic signal using the chaotic feature extrac-tion for degradation extent. Features extracted from time series data using the chaotic time series signal analyze quantitatively material degradation extent. For this purpose analysis objective in this study if fractal dimension lyapunov exponent and strange attractor on hyperspace. The lyapunov exponent is a measure of the rate at which nearby trajectories in phase space diverge. Chaotic trajectories have at least one positive lyapunov exponent. The fractal dimension appears as a metric space such as the phase space trajectory of a dynamical syste, In experiment fractal(correlation) dimensions and lyapunov experiments showed values of mean 3.837-4.211 and 0.054-0.078 in case of degradation material The proposed chaotic feature extraction in this study can enhances ultrasonic pattern recognition results from degrada-tion signals.

• Journal of the Korean Society for Railway
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• v.2 no.2
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• pp.21-30
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• 1999

This study proposes the analysis and evaluation method of time series ultrasonic signal using the phase space-frequency domain. Features extracted from time series signal analyze quantitatively characteristics of weld defects. For this purpose, analysis objectives in this study are features of time domain and frequency domain. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics resulting from distance shifts such as parts of head and flange even though the types of defects are identified. These differences in characteristics of weld defects enables the evaluation of unique characteristics of defects in the weld zone. In quantitative fractal feature extraction, feature values of 3.848 in the case of part of head(crack) and 4.102 in the case of part of web(side hole) and 3.711 in the case of part of flange(crack) were proposed on the basis of fractal dimension. Proposed phase space-frequency domain method in this study can integrity evaluation for defect signals of rail weld zone such as side hole and crack.

• Journal of Korean Society of Rural Planning
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• v.25 no.4
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• pp.109-117
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• 2019

The purpose of this study is to present a time series model of onion and garlic prices. After considering the various time series models, we calculated the appropriate time series models for each item and then selected the model with the minimized error rate by reflecting the monthly dummy variables and import data. Also, we examined whether the predictive power improves when we combine the predictions of the Korea Rural Economic Institute with the predictions of time series models. As a result, onion prices were identified as ARMGARCH and garlic prices as ARXM. Monthly dummy variables were statistically significant for onion in May and garlic in June. Garlic imports were statistically significant as a result of adding imports as exogenous variables. This study is expected to help improve the forecasting model by suggesting a method to minimize the price forecasting error rate in the case of the unstable supply and demand of onion and garlic.

• Current Photovoltaic Research
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• v.4 no.4
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• pp.131-139
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• 2016

This paper shows the series power device in the massive roof-top PVs and domestic loads. D-UPFC as the series power device controls the distribution voltage during voltage rise (or fall) condition. D-UPFC consists of the bi-directional ac-ac converter and the transformer. In order to verify the D-UPFC voltage control, the distribution model is used in the case study. D-UPFC enables the voltage control in the distribution voltage range. Dynamic voltage control from voltage rise and voltage fall conditions is performed. Scaled-down experimental test of the D-UPFC is verified the voltage control and it is well performed without high voltage spikes in the inductive load.