• 제어로봇시스템학회논문지
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• 제2권2호
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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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• 제6권4호
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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.

• 한국통신학회논문지
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• 제15권1호
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• pp.43-50
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• 1990

Trace 사상을 이용하여 GF(2)에서 GF(p), P>2로 기본장을 확장한 非二元 GMW 부호계열을 발생시킬 수 있는 알고리즘을 제시하고 GF(3)와 GF(5)에서 부호계열을 각각 발생시켜 이들 부호계열은 m-계열과 같은 해밍자기상관함수 특성을 갖고 선형성에 대한 단점을 보완하며 평형특성을 갖게 됨을 보였다.

• Communications for Statistical Applications and Methods
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• 제29권1호
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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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• 제57권1호
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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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• 제12권1호
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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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• 제15권9호
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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.

• 한국정보통신학회논문지
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• 제26권8호
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• pp.1172-1179
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• 2022

이진 에드워즈 곡선 (Binary Edwards Curves; BEdC) 기반의 공개키 암호 시스템을 위한 점 스칼라 곱셈기 설계에 대해 기술한다. BEdC 상의 점 덧셈 (Point Addition; PA)과 점 두배 (Point Doubling; PD) 연산의 효율적인 구현을 위해 유한체 연산에 투영 좌표계를 적용하였으며, 이에 의해 점 스칼라 곱셈 (Point Scalar Multiplication; PSM)에 단지 1회의 유한체 역원 연산만 포함되어 연산성능이 향상되었다. 하드웨어 설계에 최적화를 적용하여 PA와 PD의 유한체 연산을 위한 저장 공간과 연산 단계를 약 40% 감소시켰다. BEdC를 위한 점 스칼라 곱셈기를 두 가지 유형으로 설계했으며, Type-I은 257-b×257-b 이진 곱셈기 1개를 사용하고, Type-II는 32-b×32-b 이진 곱셈기 8개를 사용한다. Type-II 설계는 Type-I 구조에 비해 LUT를 65% 적게 사용하나, 240 MHz로 동작할 때 약 3.5배의 PSM 연산시간이 소요되는 것으로 평가되었다. 따라서 Type-I의 BEdC 크립토 코어는 고성능이 필요한 경우에 적합하고, Type-II 구조는 저면적이 필요한 분야에 적합하다.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제51권8호
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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.

• 대한지리학회지
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• 제38권5호
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• pp.822-834
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• 2003

오늘날의 지형경관변화 분석은 그 규모와 시공간적 제한으로 말미암아 과거와 같이 연구자의 직접적인 참여관찰에 의해 취득된 정보만으로는 많은 한계를 가지게 된다. 이에 본 연구에서는 위성영상을 이용하여 직접적인 참여관찰이 어려운 해안습지 지역의 대규모 지형경관변화를 분석하는 방법들을 비교, 평가하는 것을 주목적으로 하였다. 연구지역은 지난 10년간 국제공항의 건설로 대규모 지형경관변화를 경험한 영종도 북부 해안습지를 사례지역으로 하였다. 분석방법은 기존의 마스크 기법을 보완한 화소차이 마스크 기법. 화소표준화 마스크 기법, NDVI 마스크 기법을 이용하였다. 분석결과 해안습지의 지형경관변화를 구체적이고 정확하게 반영하는 기법은 NDVI 마스크 기법인 것으로 판단되었다.