• 한국멀티미디어학회논문지
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• 제21권4호
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• pp.441-449
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• 2018

Deep learning has been used for various processing centered on image recognition. One core algorithms of the deep learning, convolutional neural network is an deep neural network that specialized in image recognition. In this paper, we use a convolutional neural network to classify forest insects and propose an optimization method. Experiments were carried out by combining two weight initialization and six parameter update methods. As a result, the Xavier-SGD method showed the highest performance with an accuracy of 82.53% in the 12 different combinations of experiments. Through this, the latest learning algorithms, which complement the disadvantages of the previous parameter update method, we conclude that it can not lead to higher performance than existing methods in all application environments.

• 전자공학회논문지B
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• 제33B권9호
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• pp.117-123
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• 1996

In this paper, we present a new learning method for the fuzzy ARTMAP which is effective for the noisy input patterns. Conventional fuzzy ARTMAP employs only fuzzy AND operation between input vector and weight vector in learning both top-down and bottom-up weight vectors. This fuzzy AND operation causes excessive update of the weight vector in the noisy input environment. As a result, the number of spurious categories are increased and the recognition ratio is reduced. To solve these problems, we propose a new method in updating the weight vectors: the top-down weight vectors of the fuzzy ART system are updated using weighted average of the input vector and the weight vector itself, and the bottom-up weight vectors are updated using fuzzy AND operation between the updated top-down weitht vector and bottom-up weight vector itself. The weighted average prevents the excessive update of the weight vectors and the fuzzy AND operation renders the learning fast and stble. Simulation results show that the proposed method reduces the generation of spurious categories and increases the recognition ratio in the noisy input environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2723-2740
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• 2017

Steganographic schemes which are based on minimizing an additive distortion function defined the overall impacts after embedding as the sum of embedding costs for individual image element. However, mutual impacts during embedding are often ignored. In this paper, an adaptive JPEG steganographic method based on weight distribution for embedding costs is proposed. The method takes mutual impacts during embedding in consideration. Firstly, an analysis is made about the factors that affect embedding fluctuations among JPEG coefficients. Then the Distortion Update Strategy (DUS) of updating the distortion costs is proposed, enabling to dynamically update the embedding costs group by group. At last, a kind of adaptive JPEG steganographic algorithm is designed combining with the update strategy and well-known additive distortion function. The experimental result illustrates that the proposed algorithm gains a superior performance in the fight against the current state-of-the-art steganalyzers with high-dimensional features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1377-1389
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• 2016

This paper presents a target model update scheme for the mean-shift tracking with background weighted histogram. In the scheme, the target candidate histogram is corrected by considering the back-projection weight of each pixel in the kernel after the best target candidate in the current frame image is chosen. In each frame, the target model is updated by the weighted average of the current target model and the corrected target candidate. We compared our target model update scheme with the previous ones by applying several test sequences. The experimental results showed that the object tracking accuracy was greatly improved by using the proposed scheme.

• Journal of Communications and Networks
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• 제9권4호
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• pp.499-510
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• 2007

Shortest path tree(SPT) construction is essential in high performance routing in an interior network using link state protocols. When some links have new state values, SPTs may be rebuilt, but the total rebuilding of the SPT in a static way for a large computer network is not only computationally expensive, unnecessary modifications can cause routing table instability. This paper presents a new update algorithm, dynamic shortest path tree(DSPT) that is computationally economical and that maintains the unmodified nodes mostly from an old SPT to a new SPT. The proposed algorithm reduces redundancy using a dynamic update approach where an edge becomes the significant edge when it is extracted from a built edge list Q. The average number of significant edges are identified through probability analysis based on an arbitrary tree structure. An update derived from significant edges is more efficient because the DSPT algorithm neglect most other redundant edges that do not participate in the construction of a new SPT. Our complexity analysis and experimental results show that DSPT is faster than other known methods. It can also be extended to solve the SPT updating problem in a graph with negative weight edges.

• 한국통신학회논문지
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• 제36권3C호
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• pp.157-162
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• 2011

동시 등화는 통신 채널을 블라인드 등화 시 수렵 특성 개선에 유용하다. 그러나 동시 등화는 등화기가 정상상태에 수렴한 후에도 동시 적응을 계속함으로써 성능 개선이 제한적이다. 본 논문에서는 동시 등화의 수렴 특성과 함께 정상상태 성능을 개선하기 위해, 가변 수렴상수와 가중치기반의 탭 계수 갱신을 적용하는 새로운 동시 등화 기법을 제안한다. 제안하는 동시 vsCMA+DD 등화는 가변 수렴상수 CMA(variable step-size CMA: vsCMA)와 판정의거 (decision-directed: DD) 알고리즘의 오차 신호를 사용하여 가중치를 산출하고, 이를 이용하여 두 등화기를 각각 가중 갱신한다. 제안 방법은 vsCMA에 의해 CMA의 오차 성능을 개선하고, 가중치기반의 탭 계수 갱신에 의해 수렴 속도와 정상상대 성능을 개선하였다. 모의실험을 통해 제안 방식의 성능 개선을 검증하였다.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.142-147
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• 1996

In this paper, we describe the algorithm which controls an unknown nonlinear system with multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate unknown nonlinear function on the nonlinear system by using of multilayer neural network. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. The whole control system constitutes controller using feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• 전자공학회논문지S
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• 제34S권9호
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• pp.41-49
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• 1997

In this paper, we describe the algorithm which controls an unknown nonlinear system with disturbance a using multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate an unknown nonlinear system by using of multilayer neural netowrk. WE include a disturbance among the modelling error, and the weight-update rule of multilayer neural network is derived to satisfy Laypunov stability. The whole control system constitutes controller using the feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2002년도 춘계종합학술대회
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• pp.509-513
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• 2002

This paper presents a semi-blind Minimum Mean Square Error (MMSE) beamforming adaptive algorithm used far OFDM/CDMA combined system. The proposed algorithm exploits the transmitting pilot signal in the initial period of the transmission to update the weight vector. Then it applies the blind adaptive period to update the weight vector, in which the pilot signal is no longer used. The derivation of the algorithm based on the Mean Square Error (MSE) criterion is also presented. Computer simulation is carried out to verify the performance of the proposed approach.

• 한국정보통신학회논문지
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• 제17권9호
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• pp.2041-2047
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• 2013

본 논문에서는 DVB-S2 기반 고속 LDPC 복호를 위한 알고리즘을 제안하였다. 체크 노드 연산중에 비트 노드 연산을 수행하여 기존의 LDPC 복호 알고리즘에 비해 반복횟수를 줄일 수 있는 horizontal shuffle scheduling 알고리즘을 기반으로 하여 복호 속도를 보다 고속화 할 수 있는 알고리즘을 제안하였다. 기존의 체크 노드 연산은 하나의 메모리에서 값을 가져오기 때문에 체크 노드 연산과정에서 많은 지연이 발생 하는데 이를 row weight의 개수인 dc개의 병렬구조로 설계함으로써 체크 노드 연산과정의 지연을 줄일 수 있고 따라서 고속 복호가 가능하다. 이를 DVB-S2에 제시되고 있는 다양한 부호화율에서 dc개의 분리 할 수 있는 최대의 메모리를 제시하고 전송률을 제시하였다.