• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.108-114
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• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.488-494
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• 2008

This paper proposes an effective approach based on binary coding Particle Swarm Optimization (PSO) to identify the switching operation plan for feeder reconfiguration. The proposed method considers the advantages and disadvantages of existing particle swarm optimization method and redefined the operators of PSO algorithm to fit the application field of distribution systems. Shift operator is proposed to construct the binary coding particle swarm optimization for feeder reconfiguration. A typical distribution system of Taiwan Power Company is used in this paper to demonstrate the effectiveness of the proposed method. The test results show that the proposed method can apply to feeder reconfiguration problems more effectively and stably than existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.1
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• pp.43-50
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• 1990

Using the trace mapping, we suggest the generating algorithm of non-binary GMW code sequences, to expand the ground field GF(2) into GF(p), p>2. And constructing non-binary GMW code sequences over GF(3) and GF(5), respectively, it is shown that they have the Hamming autocorrelation functions identical to m-sequences, non-linearity to improve the disadvantages of linearity, and balance properties.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.27-40
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• 2022

In this paper, the focus on the removal noise in the binary image based on the variational Bayesian method with the Ising model. The observation and the latent variable are the degraded image and the original image, respectively. The posterior distribution is built using the Markov random field and the Ising model. Estimating the posterior distribution is the same as reconstructing a degraded image. MCMC and variational Bayesian inference are two methods for estimating the posterior distribution. However, for the sake of computing efficiency, we adapt the variational technique. When the image is restored, the iterative method is used to solve the recursive problem. Since there are three model parameters in this paper, restoration is implemented using the VECM algorithm to find appropriate parameters in the current state. Finally, the restoration results are shown which have maximum peak signal-to-noise ratio (PSNR) and evidence lower bound (ELBO).

• Kyungpook Mathematical Journal
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• v.57 no.1
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• pp.99-108
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• 2017

In this paper we construct quadratic ${\beta}-algebras$ on a field, and we discuss both linear-quadratic ${\beta}-algebras$ and quadratic-linear ${\beta}-algebras$ in a field. Moreover, we discuss some relations of binary operations in ${\beta}-algebras$.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3458-3481
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• 2021

Existing methods for blind identification of linear block codes without a candidate set are mainly built on the Gauss elimination process. However, the fault tolerance will fall short when the intercepted bit error rate (BER) is too high. To address this issue, we apply the reverse algebra approach and propose a novel "two-step-screening" algorithm by solving the linear error equations on the binary Galois field, or GF(2). In the first step, a recursive matrix partition is implemented to solve the system linear error equations where the coefficient matrix is constructed by the full codewords which come from the intercepted noisy bitstream. This process is repeated to derive all those possible parity-checks. In the second step, a check matrix constructed by the intercepted codewords is applied to find the correct parity-checks out of all possible parity-checks solutions. This novel "two-step-screening" algorithm can be used in different codes like Hamming codes, BCH codes, LDPC codes, and quasi-cyclic LDPC codes. The simulation results have shown that it can highly improve the fault tolerance ability compared to the existing Gauss elimination process-based algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1172-1179
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• 2022

This paper describes a design of point scalar multiplier for public-key cryptography based on binary Edwards curves (BEdC). For efficient implementation of point addition (PA) and point doubling (PD) on BEdC, projective coordinate was adopted for finite field arithmetic, and computational performance was improved because only one inversion was involved in point scalar multiplication (PSM). By applying optimizations to hardware design, the storage and arithmetic steps for finite field arithmetic in PA and PD were reduced by approximately 40%. We designed two types of point scalar multipliers for BEdC, Type-I uses one 257-b×257-b binary multiplier and Type-II uses eight 32-b×32-b binary multipliers. Type-II design uses 65% less LUTs compared to Type-I, but it was evaluated that it took about 3.5 times the PSM computation time when operating with 240 MHz. Therefore, the BEdC crypto core of Type-I is suitable for applications requiring high-performance, and Type-II structure is suitable for applications with limited resources.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.8
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• pp.382-389
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• 2002

We present experimental results on the multiplexed and stack-wise recording of near-field holograms. Experiments on angular multiplexing show that the angular selectivity of near-field hologram is better than that of the conventional hologram. Experiments on stack-wise recording prove that near-fields originated from sub-diffraction-limit-size objects could be stored in a photorefractive crystal at 2mm apart from the crystal surface. In addition, to improve the data access and transfer time, a silicon nano-aperture array was introduced and applied to the recording of near-field holograms.

• Journal of the Korean Geographical Society
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• v.38 no.5
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• pp.822-834
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• 2003

It is difficult to analyse geomorphic landscape changes effectively by a field survey. A satellite image interpretation may play an important role to overcome such a problem. The purpose of this study is to assess the accuracy of different method to identify geomorphic landscape change by using satellite imagery. The study area is the Yongjong coastal wetland in which a huge man-made environmental change occured to build a new international airport in the past decade. These changes may be summarized that a coastal landscape has been changed to a terrestrial landscape, and also a natural landscape to a man-made landscape. In order to detect these changes, we applied three different satellite interpreting methods, including a binary change mask using band difference. a binary change mask using normalized band difference. and a binary change mask using NDVI. We concluded that a binary change mask using NDVI is the best method among three different methods analysing the coastal geomorphic-landscape changes.