• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.209-222
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• 2015

Time series are used in many studies for model building and analysis. We must be very careful to understand the kind of time series data used in the analysis. In this review article, we will begin with some issues related to the use of aggregate and systematic sampling time series. Since several time series are often used in a study of the relationship of variables, we will also consider vector time series modeling and analysis. Although the basic procedures of model building between univariate time series and vector time series are the same, there are some important phenomena which are unique to vector time series. Therefore, we will also discuss some issues related to vector time models. Understanding these issues is important when we use time series data in modeling and analysis, regardless of whether it is a univariate or multivariate time series.

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.167-167
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• 1999

The majority of text categorization systems use the term-based classification method. However, because of too many terms, this method is not effective to classify the documents in areal-time environment. This paper presents a real-time concept-based text categorization system,which classifies texts using thesaurus. The system consists of a Korean morphological analyzer, athesaurus tool, and a probability-vector similarity measurer. The thesaurus tool acquires the meaningsof input terms and represents the text with not the term-vector but the concept-vector. Because theconcept-vector consists of semantic units with the small size, it makes the system enable to analyzethe text with real-time. As representing the meanings of the text, the vector supports theconcept-based classification. The probability-vector similarity measurer decides the subject of the textby calculating the vector similarity between the input text and each subject. In the experimentalresults, we show that the proposed system can effectively analyze texts with real-time and do aconcept-based classification. Moreover, the experiment informs that we must expand the thesaurustool for the better system.

• Journal of electromagnetic engineering and science
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• v.18 no.3
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• pp.206-211
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• 2018

In this paper, we proposed a two-level feature vector fusion technique to improve the performance of target classification. The proposed method combines feature vectors of the early-time region and late-time region in the first-level fusion. In the second-level fusion, we combine the monostatic and bistatic features obtained in the first level. The radar cross section (RCS) of the 3D full-scale model is obtained using the electromagnetic analysis tool FEKO, and then, the feature vector of the target is extracted from it. The feature vector based on the waveform structure is used as the feature vector of the early-time region, while the resonance frequency extracted using the evolutionary programming-based CLEAN algorithm is used as the feature vector of the late-time region. The study results show that the two-level fusion method is better than the one-level fusion method.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.17 no.2
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• pp.137-144
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• 2021

The mode superposition time history analysis method is commonly used in a seismic analysis. The maximum response in the time history analysis can be derived by combining the responses of individual modes. The residual mode response is the response of the modes which are not considered in the time history analysis. In this paper, the residual vector method to consider the residual mode response in the time history analysis is introduced and evaluated. Seismic analyses for a sample structure model and a reactor vessel model are performed to evaluate the residual vector method. The analysis results show that residual mode response is well calculated when the residual vector method is used. It is confirmed that the residual vector method is useful and acceptable to consider the residual mode response in a seismic analysis of the nuclear power plant equipment.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.195-198
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• 1997

This paper proposes the effective compensation method of the rotor time constant of induction motor. An indirect vector control method is highly dependent on the motor parameters. To solve the problem of performance degradation due to parameter variation in an indirect vector control of induction motor, we compensate the rotor time constant by current error feedback. The proposed method is a simple on-line rotor time constant compensation method using the information from terminal voltages and currents. As the current error, difference between current command and estimated current, approaches to zero, the value of rotor time constant in an indirect vector controller follows the real value of induction motor. This scheme is valid transient region as well as steady state region regardless of low or high speed. This method is verified by computer simulation. For this, we constructed the simulation model of induction motor, indirect vector controller and current regulated PWM (CRPWM) voltage source inverter (VSI) using SIMULINK in MATLAB.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.335-348
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• 2009

Fuzzy regression is used as a complement or an alternative to represent the relation between variables among the forecasting models especially when the data is insufficient to evaluate the relation. Such phenomenon often occurs in seasonal time series data which require large amount of data to describe the underlying pattern. Semiparametric model is useful tool in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. In this paper we propose fuzzy semiparametric support vector regression so that it can provide good performance on forecasting of the seasonal time series by incorporating into fuzzy support vector regression the basis functions which indicate the seasonal variation of time series. In order to indicate the performance of this method, we present two examples of predicting the seasonal time series. Experimental results show that the proposed method is very attractive for the seasonal time series in fuzzy environments.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.12
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• pp.1001-1008
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• 2003

A time-delay compensation method for vector control system is proposed that can compensate for voltage and current distortions resulting from a time delay in the overall system due to the low pass filter, hysteresis control inverter, microprocessor program computation time, and so on. The proposed scheme estimates the time delay using the difference between the Q-axis stator current command and the time-delayed actual Q-axis stator current in a synchronous reference frame, then compensates the time delay in the voltage and current using the angular displacement of a DQ transformation. Accordingly, the proposed scheme can accurately compensate for the time delay related to the overall system, thereby significantly improving the performance of the vector control system, as verified by simulation and experiment.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.265-269
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• 1994

The switching dead time avoiding a bridge leg short circuit in PWM voltage source inverter produces distortions of the controlling inverter output performance such as current waveform, voltage vector, and torque. In this paper, the influence of dead time is investigated. The on-line space voltage vector modulation method is used for current controller. It is possible to compensate dead time by space voltage vector modulation which generates additional pulse compensating voltage distortion caused by dead time. In addition, narrow pulse which is generally neglected can be compensated. All the algorithms, including field-oriented control are performed by one chip microprocessor 80C196MC and DSP TMS320C31. Experimental results probe that the proposed scheme provides a good inverter output performance.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.12
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• pp.1933-1939
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• 1990

In this paper, we proposed an algorithm which used backtracking method to get time information, and it be modelled DMS (Dynamic Multisection) by feature vectors and time information whic are represented to similiar feature in word patterns spoken during continuous time domain, for Korean Speech recognition by independent speaker using DMS. Each state of model is represented time sequence, and have time information and feature vector. Typical feature vector is determined as the feature vector of each state to minimize the distance between word patterns. DDD Area names are selected as recognition wcabulary and 12th LPC cepstrum coefficients are used as the feature parameter. State of model is made 8 multisection and is used 0.2 as weight for time information. Through the experiment result, recognition rate by DMS model is 94.8%, and it is shown that this is better than recognition rate (89.3%) by MSVQ(Multisection Vector Quantization) method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.119-119
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• 2000

In this research an induction motor vector control system was developed using Simulink and RTW(Real Time Workshop). On the Simulink window, control system is designed in the form of block diagrams, program codes are produced automatically with the RTW, then c compiler compiles the program codes. With this automatic real time program producing mechanism rapid prototyping is realized without the time-consuming manual program coding procedures. To show effectiveness of the proposed designing scheme a DSP-based induction motor vector control system was constructed and implemented