• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.402-410
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• 2004

In this paper, an optimal design method of clonal selection based Fuzzy-Neural Networks (FNN) model for complex and nonlinear systems is presented. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. Also Advanced Clonal Selection (ACS) is proposed to find the parameters such as parameters of membership functions, learning rates and momentum coefficients. The proposed method is based on an Immune Algorithm (IA) using biological Immune System and The performance is improved by control of differentiation rate. Through that procedure, the antibodies are producted variously and the parameter of FNN are optimized by selecting method of antibody with the best affinity against antigens such as object function and limitation condition. To evaluate the performance of the proposed method, we use the time series data for gas furnace and traffic route choice process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.1044-1051
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• 2003

This paper surveys the EBP(Error Back Propagation) learning, the Cross Entropy function and the LBL(Layer By Layer) learning, which are used for learning the MLP(Multi Layer Perceptrons). We compare the merits and demerits of each learning method in the handwritten digit recognition. Although the speed of EBP learning is slower than other learning methods in the initial learning process, its generalization capability is better. Also, the speed of Cross Entropy function that makes up for the weak points of EBP learning is faster than that of EBP learning. But its generalization capability is worse because the error signal of the output layer trains the target vector linearly. The speed of LBL learning is the fastest speed among the other learning methods in the initial learning process. However, it can't train for more after a certain time, it has the lowest generalization capability. Therefore, this paper proposes the standard of selecting the learning method when we apply the MLP.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.7
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• pp.1468-1476
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• 1997

In mobile communication systems, the Reduced State Sequence Estimation(RSSE) receiver must be able to track changes in the channel. This is carried out by the adaptive channel estimator. However, when the tentative decisions are used in the channel estimator, incorrect decisions can cause error propagation. This paper presents a new channel estimator using the path history in the Viterbi decoder for preventing error propagation. The selection of the path history in the Viterbi decoder for preventing error propagation. The selection of the path history for the channel estimator depends on the path metric as in the decoding of the Viterbi decoder in RSSE. And a discussion on the channel estimator with different adaptation algorithms such as Least Mean Square(LMS) algorithm and Recursive Least Square(RLS) algorithm is provided. Results from computer simulations show that the RSSE receivers using the proposed channel estimator have better performance than the other conventional RSSE receiver, and that the channel estimator with RLS algorithm is adequate for multipath fading channel.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.843-850
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• 2003

Script viruses are easy to make a variation because they can be built easily and be spread in text format. Thus signature-based method has a limitation in detecting script viruses. In a consequence, many researches suggest simple heuristic methods, but high false-positive error is always being an obstacle. In order to overcome this problem, our previous study concentrated on analyzing data flow of codes and has low-false positive error, but still could not detect a polymorphic virus because polymorphic virus loads self body and changes it before make a descendent. We suggest a heuristic detection method which expands the detection range of previous method to include polymorphic script viruses. Expanded data flow analysis heuristic has an expanded grammar to detect Polymorphic copy Propagation. Finally, we will show the experimental result for the effectiveness of suggested method.

• Korean Journal of Optics and Photonics
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• v.34 no.5
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• pp.192-201
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• 2023

The band-limited angular spectrum method (BL-ASM) causes aliasing errors due to spatial frequency control problems. In this paper, a sampling interval adjustment technique for phase holograms and a technique for reducing speckle noise and improving image quality using a deep-learningbased U-net model are proposed. With the proposed technique, speckle noise is reduced by first calculating the sampling factor and controlling the spatial frequency by adjusting the sampling interval so that aliasing errors can be removed in a wide range of propagation. The next step is to improve the quality of the reconstructed image by learning the phase hologram to which the deep learning model is applied. In the S/W simulation of various sample images, it was confirmed that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were improved by 5% and 0.14% on average, compared with the existing BL-ASM.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.471-476
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• 2010

In this paper, we propose an effective method that recognizes the vehicle license plate using RGB color information and back-propagation neural network algorithm. First, the image of the vehicle license plate is adjusted by the Mean of Blue values in the vehicle plate and two candidate areas of Red and Green region are classified by calculating the differences of pixel values and the final Green area is searched by back-propagation algorithm. Second, our method detects the area of the vehicle plate using the frequence of the horizontal and the vertical histogram. Finally, each of codes are detected by an edge detection algorithm and are recognized by error back-propagation algorithm. In order to evaluate the performance of our proposed extraction and recognition method, we have run experiments on a new car plates. Experimental results showed that the proposed license plate extraction is better than that of existing HSI information model and the overall recognition was effective.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.13 no.3
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• pp.254-261
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• 2001

It is well known that when long waves propagate from deep ocean onto a continental shelf with a very steep continental slope, the waves reflected from the shore can not propagate offshore and are re-reflected from the continental slope so that large water level fluctuations are induced near the shore. Liu(1986) has analyzed this phenomenon by assuming a topography which has a depth discontinuity along a semicircular offshore boundary, but his solution is erroneous. In the present paper, we correct his analytical solutions for a straight shoreline and a rectangular harbor. The corrected solution is then compared with the numerical results of the Galerkin finite element model of Jeong et al.(1998), which is based on the extended mild-slope equation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1419-1424
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• 2005

This paper suggests a new cipher mode of operation which can recover cryptographic synchronization. First, we study the typical cipher modes of operation, especially focused on cryptographic synchronization problems. Then, we suggest a statistical cipher-feedback mode of operation. We define the error sources mathmatically and simulate propagation errors caused by a bit insertion or bit deletion. In the simulation, we compare the effects of changing the synchronization pattern length and feedback key length. After that, we analyze the simulation results with the calculated propagation errors. finally. we evaluate the performance of the statistical cipher-feedback mode of operation and recommand the implementation considerations.

• Journal of the Korea Society of Computer and Information
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• v.15 no.4
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• pp.57-63
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• 2010

The damping parameter of Levenberg-Marquardt algorithm switches between error backpropagation and Gauss-Newton learning and affects learning speed. Fixing the damping parameter induces some oscillation of error and decreases learning speed. Therefore, we propose the way of a variable damping parameter with referring to the alternation of error. The proposed method makes the damping parameter increase if error rate is large and makes it decrease if error rate is small. This method so plays the role of momentum that it can improve learning speed. We tested both iris recognition and wine recognition for this paper. We found out that this method improved learning speed in 67% cases on iris recognition and in 78% cases on wine recognition. It was also showed that the oscillation of error by the proposed way was less than those of other algorithms.

• Journal of KIISE:Software and Applications
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• v.28 no.1
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• pp.1-13
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• 2001

RMO(Region Monitor Object)는 결함전파나 객체군에 주어진 요구사항의 위반에 의해 발생하는 오류를 처리하는 실시간객체로써 여러 실시간객체의 상태를 감시 및 분석하여 오류를 감지하고, 증상을 진단한 후 알맞은 복구 및 재구성을 실행하다, 이를 위하여 RMO는 응용 실시간객체를 감시할 수 있는 권한을 갖는다. RMO의 권한을 지원해주는 구조는 결함허용 중개자를 이용한다. 결함허용 중개자(FTB 또는 Fault Tolerance Broker)는 RMO가 응용 실시간객체를 감시할 때에 응용의 설계와 응용의 위치에 투명하게 수행될 수 있게 중개자 역할을 한다. 제안하는 감시 구조에는 결함허용 중개자가 응용 실사간객체마다 스터브로 붙는 스터브 방식과 각 노드의 커널에 모듈로 존재하는 커널 모듈 방식이 있다. 본 논문은 스터브 방식에서 RMO가 응용 실시간객체를 감시하는 구조를 제시하고 구현한다. 결함허용 중개자 스터브는 응용 실시간객체와 같은 주소 공간에 존재하면서 응용 실시간객체에서 발생하는 메세지를 가로채고 소속자료에 접근한다. RMO는 결함허용 중개자 스터브가 제공하는 인터페이스를 통해서 응용 실시간객체에 대한 감시 정보를 얻는다. 제안한 감시 구조는 실시간객체 모델인 dRTO(dependable RTO) 모델에 기반하여 설계하였고 실시간 커널인 dKernel 상에서 구현 및 실험하였으나 다른 모델이나 커널에도 적용될 수 있다.