• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.224-234
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• 2008

Multi-phase machines can be used in variable speed drives. Their applications include electric ship propulsion, 'more-electric aircraft' and traction applications, electric vehicles, and hybrid electric vehicles. Multi-phase machines enable independent control of a few numbers of machines that are connected in series in a particular manner with their supply being fed from a single voltage source inverter(VSI). The idea was first implemented for a five-phase series-connected two-motor drive system, but is now applicable to any number of phases more than or equal to five-phase. The number of series-connected machines is a function of the phase number of VSI. Theoretical and simulation studies have already been reported for number of multi-phase multi-motor drive configurations of series-connection type. Variable speed induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the sensor, information concerning the rotor speed is extracted from measured stator currents and voltages at motor terminals. Open-loop estimators or closed-loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. This paper analyses operation of an MRAS estimator based sensorless control of a vector controlled series-connected two-motor five-phase drive system with current control in the stationary reference frame. Results, obtained with fixed-voltage, fixed-frequency supply, and hysteresis current control are presented for various operating conditions on the basis of simulation results. The purpose of this paper is to report the first ever simulation results on a sensorless control of a five-phase two-motor series-connected drive system. The operating principle is given followed by a description of the sensorless technique.

• School Mathematics
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• 제16권2호
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• pp.317-334
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• 2014

The purpose of this study is to grasp pre-service secondary teachers' understanding-realities for Taylor series convergence conceptions and to examine a teaching way using GeoGebra based on the understanding-realities. In this study, most pre-service teachers have abilities to calculate the Taylor series and radius of convergence, but they are vulnerable to conceptual problems which give meaning of the equality between a given function and its Taylor series at any point. Also they have some weakness in determining the change of radius of convergence according to the change of Taylor series' center. To improve their weakness, we explore a teaching way using dynamic and CAS functionality of GeoGebra. This study is expected to improve the pedagogical content knowledge of pre-service secondary mathematics teachers for infinite series treated in high school mathematics.

• Journal of Korea Water Resources Association
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• 제41권10호
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• pp.1067-1077
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• 2008

An understanding of temporal trends of stream flows can help in the river management and the water resources planning for natural circumstances and human communities. Changes in temperature, precipitation, flow, and land use (agriculture, flood prevention activities, reservoir operation, interbasin diversion, etc.) are all eventually reflected in the flow pattern of the river. An assumption that the stationarity of the hydrologic series implying time-invariant characteristics of the time series accepted in water structure designs can no longer be valid if the flow changes as a result of the climate change or the stream flow use. Therefore, the identification and description of the characteristics of changes in hydrologic time series is a very important task in the river basin management. In this study, the statistical tests on the flow change forced by excess water diversions in the Sumjin River basin were performed by ways of single variable and time series variable comparisons. The tests showed that currently the Sumjin River basin statistically keeps its homogeneity in annual streamflow series, but the changed situation has been appeared in dry season streamflow series.

• Journal of the Korea Convergence Society
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• 제12권10호
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• pp.9-17
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• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• 제23권2호
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• pp.103-111
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• 2023

This paper proposes a new data correction technique that transforms anomalies in time series data into normal values. With the recent development of IT technology, a vast amount of time-series data is being collected through sensors. However, due to sensor failures and abnormal environments, most of time-series data contain a lot of anomalies. If we build a predictive model using original data containing anomalies as it is, we cannot expect highly reliable predictive performance. Therefore, we utilizes the LSTM-GAN model to detect anomalies in the original time series data, and combines DTW (Dynamic Time Warping) and GAN techniques to replace the anomaly data with normal data in partitioned window units. The basic idea is to construct a GAN model serially by applying the statistical information of the window with normal distribution data adjacent to the window containing the detected anomalies to the DTW so as to generate normal time-series data. Through experiments using open NAB data, we empirically prove that our proposed method outperforms the conventional two correction methods.

• Journal of the Korea Society of Computer and Information
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• 제28권8호
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• pp.67-75
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• 2023

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.

• KSCE Journal of Civil and Environmental Engineering Research
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• 제26권2B호
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• pp.131-137
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• 2006

We have investigated the properties of the Singular Spectrum Analysis (SSA) coupled with the Linear Recurrent Formula which made it possible to complement the parametric time series model. The SSA has been applied to extract the underlying properties of the principal component of hydrologic time series, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, the prediction by the SSA method can be applied to hydrologic time series governed (may be approximately) by the linear recurrent formulae. This study has examined the forecasting ability of the SSA-LRF model. These methods were applied to monthly discharge and water surface level data. These models indicated that two of the time series have good abilities of forecasting, particularly showing promising results during the period of one year. Thus, the method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제46권3호
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• Journal of the Korean Mathematical Society
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• 제15권2호
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• pp.167-176
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• 1979

• Kyungpook Mathematical Journal
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• 제21권1호
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• pp.87-90
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• 1981