• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.12
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• pp.679-685
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• 1999

The purpose of this paper is realizing a rotor-flux-oriented indirect vector controller of an induction motor to compensate the effects of the ironloss generally ignored in the vector controller. Using general ironloss model of induction motor, the vector controller and new decoupling circuit considering ironloss is designed and realized. Also, slip, magnetizing current, stator current and decoupling circuits derived from ironloss model are compared with them from the model not including ironloss and the effects are scrutinized. Finally the total algorithm are realized in 2.2kW servo drive and its effectiveness is verified.

• Journal of Power Electronics
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• v.19 no.1
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• pp.168-178
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• 2019

Discrete space vector modulation (DSVM) is an effective method to improve the steady-state performance of the finite control set predictive control for permanent magnet synchronous motor drive systems. However, it requires complex computations due to the presence of numerous virtual voltage vectors. This paper proposes an improved finite control set model-free predictive control using DSVM to reduce the computational burden. First, model-free deadbeat current control is used to generate the reference voltage vector. Then, based on the principle that the voltage vector closest to the reference voltage vector minimizes the cost function, the optimal voltage vector is obtained in an effective way which avoids evaluation of the cost function. Additionally, in order to implement double-objective control, a two-level decisional cost function is designed to sequentially reduce the stator currents tracking error and the inverter switching frequency. The effectiveness of the proposed control is validated based on experimental tests.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.197-206
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• 2011

The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1187-1193
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• 2013

In this paper, we present realistic 3D head modeling and facial expression systems. For 3D head modeling, we perform generic model fitting to make individual head shape and texture mapping. To calculate the deformation function in the generic model fitting, we determine correspondence between individual heads and the generic model. Then, we reconstruct the feature points to 3D with simultaneously captured images from calibrated stereo camera. For texture mapping, we project the fitted generic model to image and map the texture in the predefined triangle mesh to generic model. To prevent extracting the wrong texture, we propose a simple method using a modified interpolation function. For generating 3D facial expression, we use the vector muscle based algorithm. For more realistic facial expression, we add the deformation of the skin according to the jaw rotation to basic vector muscle model and apply mass spring model. Finally, several 3D facial expression results are shown at the end of the paper.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.821-830
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• 2006

This paper develops support vector based fuzzy linear and nonlinear regression models and applies it to forecasting the exchange rate. We use the result of Tanaka(1982, 1987) for crisp input and output. The model makes it possible to forecast the best and worst possible situation based on fewer than 50 observations. We show that the developed model is good through real data.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.475-478
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• 2015

The objective of this study is to apply a hybrid model for estimating solar radiation and investigate their accuracy. A hybrid model is wavelet-based support vector machines (WSVMs). Wavelet decomposition is employed to decompose the solar radiation time series into approximation and detail components. These decomposed time series are then used as inputs of support vector machines (SVMs) modules in the WSVMs model. Results obtained indicate that WSVMs can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.243-252
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• 2024

Among the Foley sound generation models that have recently begun to be studied, a sound generation technique using the Vector Quantized-Variational AutoEncoder (VQ-VAE) structure and generation model such as Pixelsnail are one of the important research subjects. On the other hand, in the field of deep learning-based acoustic signal compression, residual vector quantization technology is reported to be more suitable than the conventional VQ-VAE structure. Therefore, in this paper, we aim to study whether residual vector quantization technology can be effectively applied to the Foley sound generation. In order to tackle the problem, this paper applies the residual vector quantization technique to the conventional VQ-VAE-based Foley sound generation model, and in particular, derives a model that is compatible with the existing models such as Pixelsnail and does not increase computational resource consumption. In order to evaluate the model, an experiment was conducted using DCASE2023 Task7 data. The results show that the proposed model enhances about 0.3 of the Fréchet audio distance. Unfortunately, the performance enhancement was limited, which is believed to be due to the decrease in the resolution of time-frequency domains in order to do not increase consumption of the computational resources.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.667-670
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• 2000

This paper provides a heuristic theory for modeling and analysis of vector quantization effects in 2-dimensional subband filter banks. This model is used as the basis for optimal filter bank design. The scalar non-linear gain-plus-additive noise quantization model can be used to represent each vector quantizer in 2-band subband codec. The validity and accuracy and of this analytic model is confirmed by comparing the calculated model quantization errors with actual simulation of the optimum LBG vector quantizer. Numerical design examples for the optimum separable paraunitary filter banks are suggested in this paper.

• Korean Management Science Review
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• v.23 no.2
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• pp.75-91
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• 2006

The purpose of this paper is to propose a new software development cost estimation method using SVR(Support Vector Regression) SVR, one of machine learning techniques, has been attracting much attention for its theoretic clearness and food performance over other machine learning techniques. This paper may be the first study in which SVR is applied to the field of software cost estimation. To derive the new method, we analyze historical cost data including both well-known overseas and domestic software projects, and define cost drivers affecting software cost. Then, the SVR model is trained using the historical data and its estimation accuracy is compared with that of the linear regression model. Experimental results show that the SVR model produces more accurate prediction than the linear regression model.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.73-82
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• 2015

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.