• Journal of KIISE
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• v.44 no.7
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• pp.668-673
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• 2017

Deep neural networks have shown remarkable performance in various fields of pattern recognition such as voice recognition, image recognition and object detection. However, underlying mechanisms of the network have not been fully revealed. In this paper, we focused on empirical analysis of the network parameters. The Faster R-CNN(region-based convolutional neural network) was used as a baseline network of our work and three important parameters were analyzed: the dropout ratio which prevents the overfitting of the neural network, the size of the anchor boxes and the activation function. We also compared the performance of dropout and batch normalization. The network performed favorably when the dropout ratio was 0.3 and the size of the anchor box had not shown notable relation to the performance of the network. The result showed that batch normalization can't entirely substitute the dropout method. The used leaky ReLU(rectified linear unit) with a negative domain slope of 0.02 showed comparably good performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.9-15
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• 2009

This paper proposes a cancellation algorithm of impulse noise using a rank-order method. The proposed method is a fast and simple algorithm that is composed of two parts. The first part involves noise detection using a fuzzy technique, where an image is divided into RGB color channels. Then every pixel in each color channel is investigated and assigned a probability indicating its chances of being a noise pixel. At this time, the rank order method using a noise-detection mask is utilized for accurate noise detection. Thereafter, the second part involves noise-cancellation, where each noise-pixel value in an image is replaced in proportion to its fuzzy probability. Through the experiments, both the conventional and proposed methods were simulated and compared. As a result, it is shown that proposed method is able to detect noisy pixels more accurately, and produce resulting images with high PSNR values.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.410-414
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• 2006

In the present, Orthogonal Frequency Division Multiple Access (OFDMA) that wireless access scheme for high speed data transmission is noticed in mobile communication market and OFDMA/TDD scheme will be used combining Time Division Duplex (TDD) scheme based on OFDMA. The Base Station's receiver synchronizes the symbol timing to anyone user's symbol and the other user's symbols have some Symbol Timing Offset (STO). Linear phase shift is occurred by each user's STO in an OFDMA symbol and the Multiple Access Interference (MAI) caused by the summation of each user's linear phase shift degrades the performance of ranging code detection. In this paper, we analyze the ranging code detection performance for each users STO in OFDMA/TDD system. Simulation results show that the more users access and mobile speed increase, the more ranging code detection performance degrades.

• The Journal of the Korea Contents Association
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• v.7 no.7
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• pp.60-67
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• 2007

The spectrum sensing is important for decision of using frequency band. It checks the frequency band for cognitive radio system. In this paper, we apply autocorrelation function to the energy detection method. We use the autocorrelation function to improve the performance of spectrum sensing method based on the energy detection method. This method is different from cyclostationary process method where parameters such as the mean or the autocorrelation function are time-varying periodically. And we propose improved method that is robust in poor radio environment. If the proposed method applies for sensing in the cognitive radio system, it will have the structural simplicity and the fast computation of spectrum sensing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4A
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• pp.356-363
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• 2002

Video scene segmentation is fundamental role for content based video analysis and many kinds of scene segmentation schemes have been proposed in previous researches. However, there is a problem, which is to find optimal threshold value according to various kinds of movies and its content because only fixed single threshold value usually used for cut detection. In this paper, we proposed the variable dynamic threshold method, which change the threshold value by a probability distribution of cut detection interval and information of frame feature differences and cut detection interval in previous cut detection is used to determine the next cut detection. For this, we present a cut detection algorithm and a parameter generation method to change the threshold value in runtime. We also show the proposed method, which can minimize fault alarm rate than the existing methods efficiently by experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3B
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• pp.188-195
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• 2002

In this paper, we consider efficient multiuser receiver structures using Hopfield neural network algorithm focused to construct a synchronous multi-rate code division multiple access (CDMA) system. Although the optimum receiver for multiuser detection can be realized attaining the best BER performance, it is too complex for practical implementation. Therefore, we propose near-optimal receivers of relatively low computationally complex multiuser detection structures for realizing multi-rate CDMA system and their performances are compared with conventional matched filter and other prominent multi-rate multiuser detectors, Computer simulations show that the Hopfield neural network based multiuser receiver achieves substantially better BER performance in Rayleigh fading environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.997-1003
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• 2007

In this paper, we propose a global approach for automatic inspection of defects in the polarizer film image. The proposed method does not rely on local feature of the defect. It is based on a global image reconstruction scheme using the singular value decomposition(SVD). SVD is used to decompose the image and then obtain a diagonal matrix of the singular values. Among the singular values, the first singular value is used to reconstruct a image. In reconstructed image, the normal pixels in background region have a different characteristics from the pixels in defect region. It is obtained the ratio of pixels in the reconstructed image to ones in the original image and then the defects are detected based on the the statistical process of the ratio. The experiment results show that the proposed method is efficient for defect inspection of polarizer lam image.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.2
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• pp.207-217
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• 1993

In this paper, knowing a priori probability of phase offset between transmitted codes and reference codes in the receiver, we construct the state diagram of acquisition process of the direct sequence spread-spectrum communication system using the expanding window search. The scannings are performed from the cell with higher probability code epoch synchronization to that with lower one. We expand window size from initial value by r times of its previous size in each search, construct the corresponding the state diagrams, and derive average synchronization time using the Markov process and Mason's gain formula. Average synchronization times versus number of search n and detection probability $P_d$ are calculated.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.94-101
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• 2012

This paper proposes an improved image steganalysis scheme to raise the detection rate of stego images out of cover images. To improve the detection rate of stego image in the steganalysis, tiny variation caused by data hiding should be amplified. For this, we extract feature vectors of cover image and stego image by two steps. First, we separate image into upper 4 bit subimage and lower 4 bit subimage. As a result, stego noise is expanded more than two times. We decompose separated subimages into twelve subbands by applying 3-level Haar wavelet transform and calculate co-occurrence probabilities of two different subbands in the same scale. Since co-occurrence probability of the two wavelet subbands is affected by data hiding, it can be used as a feature to differentiate cover images and stego images. The extracted feature vectors are used as the input to the multilayer perceptron(MLP) classifier to distinguish between cover and stego images. We test the performance of the proposed scheme over various embedding rates by the LSB, S-tool, COX's SS, and F5 embedding method. The proposed scheme outperforms the previous schemes in detection rate to existence of hidden message as well as exactness of discrimination.