• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.716-721
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• 2003

This paper deals with the iris recognition as one of biometric techniques which are applied to identify a person using his/her behavior or congenital characteristics. The iris of a human eye has a texture that is unique and time invariant for each individual. First, we obtain the feature vector from the 2D iris pattern having a property of size invariant and divide it into 24 sectors which are further through three types of 2D Gabor filters. At the recognition process, we compute the similarity measure based on the correlation values. Here, since we use three different matching values obtained from three different directional Gabor filters and select the maximum value among them, it is possible to minimize the recognition error rate. To show the usefulness of the proposed algorithm, we applied it to a biometric database consisting of 50 iris patterns extracted from 10 subjects and finally get more higher than 90% recognition rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.653-659
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• 2011

In general, electrocardiogram(ECG) signals are sampled with a frequency over 200Hz and stored for a long time. It is required to compress data efficiently for storing and transmitting them. In this paper, a method for compression of ECG data is proposed, using by Non Uniform B-spline approximation, which has been widely used to approximation theory of applied mathematics and geometric modeling. ECG signals are compressed and reconstructed using B-spline basis function which curve has local controllability and control a shape and curve in part. The proposed method selected additional knot with each step for minimizing reconstruction error and reduced time complexity. It is established that the proposed method using B-spline approximation has good compression ratio and reconstruct besides preserving all feature point of ECG signals, through the experimental results from MIT-BIH Arrhythmia database.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.645-652
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• 2011

The present study investigated the tracking system of a reflector to trace the movement of sun. The system was designed to minimize the error between the vertical vector of reflector and the position of sun. The proposed system was able to collect the sun lights at a point as a useful source of light energy and transmit the collected light to a remote area through optical fibers. Also the study successfully solved the controller design problem due to the complexity of modeling of the sun tracking system using a fuzzy logic controller which mimics human reasoning.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.77-82
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• 2005

This paper is proposed fuzzy neural network(FNN) and artificial neural network(ANN) based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed control and estimation of speed of induction motor using fuzzy and neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the experimental results to verify the effectiveness of the new method.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.41-51
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• 2013

In the Information age, internet addiction has been a big issue in a modern society. The adverse effects of the internet addiction have been increasing at an exponential speed. Along with a great variety of internet-connected device supplies, K-scale diagnostic criteria have been used for the internet addiction self-diagnose tests in the high-speed wireless Internet service, netbooks, and smart phones, etc. The K-scale diagnostic criteria needed to be changed to meet the changing times, and the diagnostic criteria of K-scale was changed in March, 2012. In this paper, we analyze the internet addiction and K-scale features on the actual condition of Gyeongbuk collegiate areas using the revised K-scale diagnostic criteria in 2012. The diagnostic method on internet addiction is measured by the respondents' subjective estimation. Willful error of the respondents can be occurred to hide their truth. In this paper, we add the survey response to the trusted reliability values to reduce response errors on the K-scale on the K-scale, and enhance the reliability of the analysis.

• Journal of electromagnetic engineering and science
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• v.17 no.1
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• pp.20-28
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• 2017

A fully integrated dual-band CMOS power amplifier (PA) is developed for 802.11n WLAN applications using wafer-level package (WLP) technology. This paper presents a detailed design for the optimal impedance of dual-band PA (2 GHz/5 GHz PA) output transformers with low loss, which is provided by using 2:2 and 2:1 output transformers for the 2 GHz PA and the 5 GHz PA, respectively. In addition, several design issues in the dual-band PA design using WLP technology are addressed, and a design method is proposed. All considerations for the design of dual-band WLP PA are fully reflected in the design procedure. The 2 GHz WLP CMOS PA produces a saturated power of 26.3 dBm with a peak power-added efficiency (PAE) of 32.9%. The 5 GHz WLP CMOS PA produces a saturated power of 24.7 dBm with a PAE of 22.2%. The PA is tested using an 802.11n signal, which satisfies the stringent error vector magnitude (EVM) and mask requirements. It achieved an EVM of -28 dB at an output power of 19.5 dBm with a PAE of 13.1% at 2.45 GHz and an EVM of -28 dB at an output power of 18.1 dBm with a PAE of 8.9% at 5.8 GHz.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.721-727
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• 2012

Since a conventional multi-cell transmit diversity scheme depends on the feedback from the user for the channel gain information, its performance gets to severely degrade when the channel varies fast due to the high mobility of the user. Also, transmit power of the base station cannot be fully used in the conventional scheme because only one transmit antenna is used for data transmission. In this paper, we propose a multi-cell transmit diversity scheme appropriate for fast fading channel. In the proposed scheme, channel-independent precoding vector is applied over all transmit antennas and different precoding vectors are applied for neighboring subcarriers so that the received signal is avoided to experience deep fading over multiple neighboring subcarriers. Simulation results show that the proposed scheme has better detector output signal-to-noise ratio (SNR) and bit error rate (BER) performances than the conventional scheme.

• The Journal of the Acoustical Society of Korea
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• v.37 no.3
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• pp.156-162
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• 2018

The LMF (Least Mean Fourth) algorithm is well known for its fast convergence and low steady-state error especially in non-Gaussian noise environments. Recently, there has been increasing interest in the LMS (Least Mean Square) algorithms with self-adjusted step size. It is because the self-adjusted step-size LMS algorithms have shown to outperform the conventional fixed step-size LMS in the various situations. In this paper, a self-adjusted step-size LMF algorithm is proposed, which adopts an averaged gradient based step size as a self-adjusted step size. It is expected that the proposed algorithm also outperforms the conventional fixed step-size LMF. The superiority of the proposed algorithm is confirmed by the simulations in the time invariant and time variant channels.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.328-333
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• 2003

NGE (Nested Generalized Exemplars) proposed by Salzberg improved the storage requirement and classification rate of the Memory Based Reasoning. It constructs hyperrectangles during training and performs classification tasks. It worked not bad in many area, however, the major drawback of NGE is constructing hyperrectangles because its hyperrectangle is extended so as to cover the error data and the way of maintaining the feature weight vector. We proposed the OH (Optimizing Hyperrectangle) algorithm which use the feature weight vectors and the ED(Exemplar Densimeter) to optimize resulting Hyperrectangles. The proposed algorithm, as well as the EACH, required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the EACH. Also, by reducing the number of stored patterns, it showed excellent results in terms of classification when we compare it to the k-NN and the EACH.

• Management & Information Systems Review
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• v.35 no.2
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• pp.1-12
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• 2016

In this study, price discovery between the KOSPI200 spot, and leveraged ETFs(Leveraged KODEX, Leveraged TIGER, Leveraged KStar) is investigated using the vector error correction model(VECM). The main findings are as follows. Leveraged KODEX(Leveraged TIGER, Leveraged KStar) and KOSPI200 spot are cointegrated in most cases. There is no interrelations between the movement of Leveraged KODEX(Leveraged TIGER, Leveraged KStar) and KOSPI200 spot markets in case of daily data. Namely, in daily data, Leveraged KODEX(Leveraged TIGER, Leveraged KStar) doesn't plays more dominant role in price discovery than the KOSPI200 spot.