• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.607-612
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• 2008

This paper proposes a robot visual servo approach based on image appearance and a wavelet function neural network. The inputs of the wavelet neural network are changes of image features or the elements of image appearance vector, and the outputs are changes of robot joint angles. Image appearance vector is calculated by using eigen subspace transform algorithm. The proposed approach does not need a priori knowledge of the robot kinematics, hand-eye geometry and camera models. The experiment results on a real robot system show that the proposed method is practical and simple.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.6
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• pp.509-516
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• 2015

The overmodulation characteristics of a carrier-based medium vector pulse width modulation (CBMVPWM) are examined in this study. CBMVPWM can completely eliminate leakage currents in a three-phase, three-level inverter using only the switching states with the same common mode voltage even in an overmodulation operation. The analytic equations for the magnitude of the output voltage and the switching frequency are derived for overmodulation operation, and the effect of dead time on the leakage current is demonstrated. This study presents the operating principle of CBMVPWM, basic overmodulation features, and simulations and experiments for operating verification.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1105-1107
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• 2000

This paper proposes the new reduced-order extended Luenberger observer and presents the application to sensorless vector control of induction motor. The main features of the proposed observer are discussed and compared with the other observers.

• Journal of applied mathematics & informatics
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• v.11 no.1_2
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• pp.1-57
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• 2003

Vector fields in three-space admit bundles of internal variables such as a Heisenberg algebra bundle. Information transmission along field lines of vector fields is described by a wave linked to the Schrodinger representation in the realm of time-frequency analysis. The preservation of local information causes geometric optics and a quantization scheme. A natural circle bundle models quantum information visualized by holographic methods. Features of this setting are applied to magnetic resonance imaging.

• Speech Sciences
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• v.8 no.3
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• pp.133-142
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• 2001

In the area of speech processing, raw signals used to be presented in 2D format and different kinds of algorithms use the format to solve their problems. However, such kinds of presentation methods have limitations to extract characteristics from the signal, even though the algorithms are quiet good. The basic reason is that not much information can be detected from the 2D signal. Strange attractor in the field of chaos theory provides the 3D presentation method. In the area of the recognition problem, signal construction method is very important because good features can be detected from a good shape of attractors. This paper discusses a new presentation method that can be used to construct strange attractor in a different way. Normal strange attractor uses time-delay idea while the new method uses time-delay and vector average. This method provides us good information to be applied to speaker recognition problem.

• Korean Journal of Computational Design and Engineering
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• v.14 no.4
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• pp.217-224
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• 2009

Most divide-and-conquer implementations for Delaunay triangulation utilize quad-edge or winged-edge data structure since triangles are frequently deleted and created during the merge process. How-ever, the proposed divide-and-conquer algorithm utilizes the array based data structure that is much simpler than the quad-edge data structure and requires less memory allocation. The proposed algorithm has two important features. Firstly, the information of space partitioning is represented as a permutation vector sequence in a vertices array, thus no additional data is required for the space partitioning. The permutation vector represents adaptively divided regions in two dimensions. The two-dimensional partitioning of the space is more efficient than one-dimensional partitioning in the merge process. Secondly, there is no deletion of edge in merge process and thus no bookkeeping of complex intermediate state for topology change is necessary. The algorithm is described in a compact manner with the proposed data structures and operators so that it can be easily implemented with computational efficiency.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1715-1723
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• 2018

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.78-81
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• 2003

본 논문은 SVM(support vector machine)을 이용한 음성인식기에 대해 효과적인 특징 파라메터를 제안한다. SVM은 특징 공간에서 비선형 경계를 찾아 분류하는 방법으로 적은 학습 데이터에서도 좋은 분류 성능을 나타낸다고 알려져 있으며 최적의 특징 파라메터를 선택하기 위해 본 논문에서는 SVM을 이용한 음성인식기를 사용하여 PCA(principal component analysis), ICA(independent component analysis) 알고리즘을 적용하여 MFCC(met frequency cepstrum coefficient)의 특징 공간을 변화시키면서 각각의 인식 성능을 비교 검토하였다. 실험 결과 ICA에 의한 특징 파라메터가 가장 우수한 성능을 나타내었으며 특징 공간에서 각 클래스의 분포도 또한 ICA가 가장 높은 선형 분별성을 나타내었다.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.308-310
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• 1996

Recently, as a result of the progress in power electronics and microelectronics, the inverter technology is quickly developing. Also, by using the fast microprocessor and small-sized switching devices, such as IPM, the Inverter becomes more compact and cheap. This paper proposes an inexpensive and small-sized vector controller for induction motors using 87C196MC and IPM. The proposed inverter contributes further space-saving, and high performance features to motor drives system.