• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2010년도 추계학술발표논문집 1부
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• pp.344-348
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• 2010

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2010년도 추계학술발표논문집 1부
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• pp.332-336
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• 2010

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2009년도 춘계학술발표논문집
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• pp.454-457
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• 2009

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2006년도 추계학술발표논문집
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• pp.122-128
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• 2006

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• 한국경영과학회지
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• 제7권2호
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• pp.5-29
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• 1982

This paper presents the network models for general dynamic conveyor systems which are characterized by transporting and storing materials between work stations over time. With an appropriate choice of time-slice the conveyor system can be represented exactly as a dynamic flow network which can be solved by an efficient pure network algorithm.

• 한국컴퓨터정보학회논문지
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• 제13권4호
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• pp.169-175
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• 2008

현재의 블록 암호화 알고리즘은 암호화키 값을 변환하지 않고 설계되며, 각각의 블록의 라운드 함수들을 적용하며 암호화한다. 그러므로, 반복적인 라운드 구조의 블록암호화 기법을 위한 가장 강력한 방법들인 차분 암호 분석법 또는 선형 암호 분석법에 의해 평문이나 암호화키는 쉽게 노출된다는 취약점을 가지고 있다. 다이내믹 암호는 키의 크기, 라운드의 수, 그리고 평문의 길이가 동시에 측정될 수 있는 특성을 가지고 있다. 다이내믹 네트워크는 대칭적 블록 암호들에 대한 네트워크들 속에서 이러한 특성들을 만족시키는 독특한 네트워크이다. 우리는 중간 결과에 의한 공격, 선형 암호 분석법, 그리고 차분 암호 분석법에 대한 다이내믹 네트워크의 강력함을 분석한다. 또한, 본 논문에서 대칭적인 블록 암호를 위한 다이내믹 네트워크라 불리는 새 네트워크 방식을 제안한다.

• 한국정보통신학회논문지
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• 제15권7호
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• pp.1495-1500
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• 2011

동적 암호는 키의 크기, 라운드의 수 그리고 평문의 크기가 동시에 측정될 수 있는 특성을 갖고 있다. 본 논문에서는 동적 네트워크에 기반한 대칭적인 블록 암호 알고리즘을 제안한다. 제안하는 동적 암호는 중간충돌공격과 선형 암호해독법에 대해서 안전하다. 또한 동적 암호에 대한 미분 분석이 힘든 것을 보여준다.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권9호
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Journal of Mechanical Science and Technology
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• 제19권spc1호
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• pp.255-264
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• 2005

In this paper, a practical bushing model is proposed to improve the accuracy of the vehicle dynamic analysis. The results of the rubber bushing are used to develop an empirical bushing model with an artificial neural network. A back propagation algorithm is used to obtain the weighting factor of the neural network. Since the output for a dynamic system depends on the histories of inputs and outputs, Narendra algorithm of 'NARMAX' form is employed to consider these effects. A numerical example is carried out to verify the developed bushing model. Then, a full car dynamic model with artificial neural network bushings is simulated to show the feasibility of the proposed bushing model.

• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.285-291
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• 2008

Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.