• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.1-8
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• 2014

This paper proposes an emotion classifier from EEG signals based on Bayes' theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.172-177
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• 2013

With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.497-500
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• 2000

In this paper, we compare the bit-error-rate performance of the fast-frequency-hopped spread-spectrum multiple-access (FFH/SSMA) and direct-sequence code-division multiple access (DS/CDMA) systems in a Rician fading channel. Each system has a same data rate, bandwidth and transmits over a Rician fading channel. The results illustrate tradoffs in performance between the FFH/SSMA and DS/CDMA systems as a function of the parameters such as average signal to noise ratio and processing gain. The performance of the FFH/SSMA system is shown to be less sensitive to the change of fading environments, while the change of processing gain and average signal to noise ratio gives considerable affect to the FFH/SSMA system compared with the DS/CDMA system. Without respect to the change of system parameters, for most of Rician fading channels (except non-fading channel), FFH/SSMA system gives better performance than DS/CDMA system in BBR < 10$\^$-3/.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2452-2459
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• 2020

Neutron spectrum is essential to the safe operation of reactors. Traditional online neutron spectrum measurement methods still have room to improve accuracy for the application cases of wide energy range. From the application of artificial neural network (ANN) algorithm in spectrum unfolding, its accuracy is difficult to be improved for lacking of enough effective training data. In this paper, an adaptive deviation-resistant neutron spectrum unfolding method based on transfer learning was developed. The model of ANN was trained with thousands of neutron spectra generated with Monte Carlo transport calculation to construct a coarse-grained unfolded spectrum. In order to improve the accuracy of the unfolded spectrum, results of the previous ANN model combined with some specific eigenvalues of the current system were put into the dataset for training the deeper ANN model, and fine-grained unfolded spectrum could be achieved through the deeper ANN model. The method could realize accurate spectrum unfolding while maintaining universality, combined with detectors covering wide energy range, it could improve the accuracy of spectrum measurement methods for wide energy range. This method was verified with a fast neutron reactor BN-600. The mean square error (MSE), average relative deviation (ARD) and spectrum quality (Qs) were selected to evaluate the final results and they all demonstrated that the developed method was much more precise than traditional spectrum unfolding methods.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.8
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• pp.953-961
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• 2013

In this study, the analyzed turbine is a 5MW upwind-type wind turbine. This conceptual model was made to compare the results of the numerical analysis program in the IEA Annex23 Subtask2 OC3 project. The numerical analysis program used in this study is FAST developed by NREL and AQWA of ANSYS. Motion characteristics, such as RAO, average motion, significant motion and average amplitude of 1/10 highest motion were obtained through the numerical analysis. The results of the numerical analysis were compared with the results of other numerical analyses and the experimental results, and all the results agreed with one another. The results will help resolve the fundamental design trade-offs between basic floating system concepts.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.195-198
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• 2005

Besides strong customer orientation, today's companies should respond increasingly to a market getting worse for securing competitiveness with a diversity of product spectrum. In these new conditions in the surrounding field of manufacturing, the changeability of manufacturing system develops more and more to the key success factor. The following contributions introduce a fast and cost-efficient concept for changeover which bases on a modular system's architecture. The modular structure of manufacturing system allows the required changing processes with limited side effects on other modules. Based on a modular concept, manufacturing systems can be easily configured and reconfigured to specific requirements.

• Journal of Power Electronics
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• v.7 no.4
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• pp.271-277
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• 2007

In this paper, an induction motor rotor fault diagnosis system using current signals, which are measured using the axis-transformation method is presented. Inverter-fed motor drives, unlike line-driven motor drives, have stator currents which are rich in harmonics and therefore fault diagnosis using stator current is not trivial. The current signals for rotor fault diagnosis need precise and high resolution information, which means the diagnosis system demands additional hardware such as a low pass filter, high resolution ADC, an encoder and additional hardware. Therefore, the proposed axis-transformation method is expected to contribute to a low cost fault diagnosis system in inverter-fed motor drives without the need for any additional hardware. In order to confirm the validity of the developed algorithms, various experiments for rotor faults are tested and the line current spectrum of each faulty situation, using the Park transformation, is compared with the results obtained from the FFT(Fast Fourier Transform).

• Journal of the Optical Society of Korea
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• v.19 no.4
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• pp.334-339
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• 2015

Terabit-per-second (Tb/s) transmission capacity for the next generation of long-haul communication networks can be achieved using multicarrier optical super-channel technology. In an elastic orthogonal frequency division multiplexing (OFDM) super-channel transmission system, demultiplexing a portion of an entire spectrum in the form of a subband with minimum power is critically required. A major obstacle to achieving this goal is the analog-to-digital converter (ADC), which is power-hungry and extremely expensive. Without a proper ADC that can work with low power, it is unrealistic to design a 100G coherent receiver suitable for a commercially deployable optical network. Discrete Fourier transform (DFT) is often seen as a primary technique for understanding partial demultiplexing, which can be attained either optically or electronically. If fairly comparable performance can be achieved with an all-optical DFT circuit, then a solution independent of data rate and modulation format can be obtained. In this paper, we investigate two distinct OFDM super-channel receiver models, based on electronic and all-optical DFT-technologies, for partial carrier demultiplexing in a multi-Tb/s transmission system. The performance comparison of the receivers is discussed in terms of bit-error-rate (BER) performance.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.6
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• pp.589-601
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• 2016

The hybrid grid was adopted and numerical prediction analysis of propeller unsteady bearing force considering free surface was performed for mode and full-scale KCS hull-propeller-rudder system by employing RANS method and VOF model. In order to obtain the propeller velocity under self-propulsion point, firstly, the numerical simulation for self-propulsion test of full-scale ship is carried out. The results show that the scale effect of velocity at self-propulsion point and wake fraction is obvious. Then, the transient two-phase flow calculations are performed for model and full-scale KCS hull-propeller-rudder systems. According to the monitoring data, it is found that the propeller unsteady bearing force is fluctuating periodically over time and full-scale propeller's time-average value is smaller than model-scale's. The frequency spectrum curves are also provided after fast Fourier transform. By analyzing the frequency spectrum data, it is easy to summarize that each component of the propeller bearing force have the same fluctuation frequency and the peak in BFP is maximum. What's more, each component of full-scale bearing force's fluctuation value is bigger than model-scale's except the bending moment coefficient about the Y-axis.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.6
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• pp.909-921
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• 1995

The objective of this paper is to develop an algorithm for a real-time automatic fingerprint recognition system. The algorithm employs the Fast Fourier Transform (FFT) in determining the directions of ridges in fingerprint images, and utilizes statistical information in recognizing the fingerprints. The information used in fingerprint recognition is based on the dircetions along ridge curves and characteristic points such as core points and delta points. In order to find ridge directions, the algorithm applies the FFT to a small block of the size 8x8 pixels, and decides the directions by interpreting the resulted Fourier spectrum. By using the FFT, the algorithm does not require conventional preprocessing procedures such as smoothing, binarization, thinning, and restorationl. Finally, in matching two fingerprint images, the algorithm searches and compares two kinds of feature blocks, one as the blocks where the dircetions cannot be defined from the Fourier spectrum, and the other as the blocks where the changes of directions become abrupt. The proposed algorithm has been implemented on a SunSparc-2 workstation under the Open Window environment. In the experiment, the proposed algorithm has been applied to a set of fingerprint images obtained by a prism system. The result has shown that while the rate of Type II error - Incorrect recognition of two different fingerprints as the identical fingerprints - is held at 0.0%, the rate of Type I error - Incorrect recognition of two identical fingerprints as the different ones - is 2.2%.