• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.786-791
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• 2011

An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student's-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.4
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• pp.251-258
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• 1986

There is much redundancy in image data such as TV signals and many techniques to redice it have been studied. In this paper, Hadamard transform is studied through computer simulation and experimental model. Each element of hadamard matrix is either +1 or -1, and the row vectors are orthogonal to another. Its hardware implementation is the simplest of the usual orthogonal transforms because addition and sulbraction are necessary to calculate transformed signals, while not only addition but multiplication are necessary in digital Fourier transform, etc. Linclon data (64$ imes$64) are simulated using 8th-order and 16th-order Hadamard transform, and 8th-order is implemented to hardware. Theoretical calculation and experimental result of 8th-order show that 2.0 bits/sample are required for good quality.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.299-306
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• 2010

This paper proposes a new doubly-fed induction generator (DFIG) system using a matrix converter controlled by direct duty ratio pulse-width modulation (DDPWM) scheme. DDPWM is a recently proposed carrier based modulation strategy for matrix converters which employs a triangular carrier and voltage references in a voltage source inverter. By using DDPWM, the matrix converter can directly and effectively generate rotor voltages following the voltage references within the closed control loop. The operation of the proposed DFIG system was verified through computer simulation and experimental works with a hardware simulator of a wind power turbine, which was built using a motor-generator set with vector drive. The simulation and experimental results confirm that a matrix converter with a DDPWM modulation scheme can be effectively applied for a DFIG wind power system.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.226-228
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• 2005

Predicting the cellular location of an unknown protein gives valuable information for inferring the possible function of the protein. For more accurate Prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting . The overall prediction accuracy evaluated by the 5-fold cross-validation reached $88.53\%$ for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful forpredicting subcellular localization of proteins.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.15-18
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• 2008

This paper proposes a method to reconstruct the 3-D face using structured light image. First of all, we suppose that each sight vector of a projector and camera are parallel. We project the structured light in the shape of lattice on the background to acquire the reference-structured light image. This image is used to calibrate the projector and camera. Since then, we acquire the face-structured light image which is projected the same structured light on the face. These two structured light images are used to reconstruct the 3-D face through the variation which is measured from the positional difference of feature vectors. In our experiment result, we could reconstruct the 3-D face image as recognize through these simple devices.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.116-125
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• 2002

This paper presented a method of extracting shape information from a clipart image and then measured the similarity between clipart images using the extracted shape information. The results indicated that the outlines of the extracted clipart images were clearer that those of the original images. Previous methods of extracting shape information could be classified into outline-based methods and region-based methods. Included in the former category, the proposed method expressed the convex and concave aspects of an outline using the ratio of a rectangle. Accordingly, the proposed method was superior in expressing shape information than previous outline-based feature methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.11
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• pp.1564-1570
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• 2015

This paper intends to suggest a sensing circuit of PWM voltage for a motor emulator operated in the inverter. In the emulation of the motor using a power converter, it is necessary to measure instantaneous voltage at the PWM voltage loaded from the inverter. Using a filter can generate instantaneous voltage, while it is difficult to follow the rapidly changing inverter voltage caused by the propagation delay and signal attenuation. The method of measuring the duty of PWM using FPGA can generate output voltage from the one-cycle delay of PWM, while the cost of hardware is increasing in order to acquire high precision. This paper suggests a PWM voltage sensing circuit using the analogue system that shows high precision, one-cycle delay of PWM and low-cost hardware. The PWM voltage sensing circuit works in the process of integrating input voltage for valid time by comparing levels of three-phase PWM input voltage, and produce the output value integrated at zero vector. As a result of PSIM simulation and the experiment with the produced hardware, it was verified that the suggested circuit in this paper is valid.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.141-156
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• 2011

In this paper we develop a link cost function for data delivery in sensor network. Usually most conventional methods determine the optimal coefficients in the cost function without considering the surrounding environment of the node such as the wireless propagation environment or the topological environment. Due to this reason, there are limitations to improve the quality of data delivery such as data delivery ratio and delay of data delivery. To solve this problem, we derive a new cost function using the concept of Partially Connected Neural Network (PCNN) which is modeled according to the input types whether inputs are correlated or uncorrelated. The correlated inputs are connected to the hidden layer of the PCNN in a coupled fashion but the uncoupled inputs are in an uncoupled fashion. We also propose the training technique for finding an optimal weight vector in the link cost function. The link cost function is trained to the direction that the packet transmission success ratio of each node maximizes. In the experimental section, we show that our method outperforms other conventional methods in terms of the quality of data delivery and the energy efficiency.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1277-1298
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• 2017

Related to the maximum vector problem, a skyline query is to discover dominating tuples from a set of tuples, where each defines an object (such as a hotel) in several dimensions (such as the price and the distance to the beach). A tuple, an instance of an object, dominates another tuple if it is equally good or better in all dimensions and better in at least one dimension. Traditionally, skyline queries are defined upon single-instance data or upon objects each of which is associated with an instance. However, in some cases, an object is not associated with a single instance but rather by multiple instances. For example, on a review website, many users assign scores to a product or a service, and a user's score is an instance of the object representing the product or the service. Such data is an example of multi-instance data. Unlike most (if not all) others considering the traditional setting, we consider skyline queries defined upon multi-instance data. We define the dominance calculation and propose an algorithm to reduce its computational cost. We use synthetic and real data to evaluate the proposed methods, and the results demonstrate their utility.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.793-798
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• 2012

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.