• Electric Engineers Magazine
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• v.214 no.6
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• pp.27-31
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• 2000

일반적으로 차단기의 차단 용량은 대칭 실효치로 표시하고 있다. 그러나 실제 전력 계통의 고장 전류는 고장 최초 몇 사이클 동안에는 상당한 직류분 전류가 포함되어 있어 비대칭 전류가 흐르게 마련이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.609-611
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• 2012

논문에서는 로우듀티사이클 환경을 고려하여 목적지까지 데이터 전송의 신뢰성뿐만 아니라 낮은 데이터 지연도 보장하는 DIGF (Delay Improvement Greedy Forwarding) 기법을 제안한다. 초기에 제안된 그리디 포워텅 기법들은 무선링크가 갖는 비신뢰성 및 비대칭성의 문제점을 해결하기 위해 데이터 전송 성공률과 에너지 효율을 높이는 기법이 제안되었다. 하지만 많은 그리디 포워텅 기법들은 노드들이 데이터를 송수신하기 위해 대기하고 있는 수신대기상태로 인한 많은 에너지 소모를 고려하지 않아 네트워크 라이프타임을 감소시킨다. 이러한 문제점을 해결하고자 제안기법인 DIGF는 무선링크의 비신뢰성과 비대칭성을 고려할 뿐만 아니라 로우듀티사이클 환경을 고려한다. 또한 로우듀티사이클 환경을 고려할 때 발생되는 높은 수면지연성 (Sleep latency) 을 해결하기 위한 알고리즘을 제안하여 낮은 전송지연과 신뢰성 있는 데이터 전송을 보장한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.199-202
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• 2002

신경회로망을 동적 정보처리에 응용하기 위해서는 비대칭 결합 신경회로망에서 생성되는 동적 상태천이에 관한 직관적 이해가 필요하다. 자기결합을 갖고 결합하중치가 비대칭인 순환결합형 신경회로망은 복수 개의 리미트사이클이 기억 가능하다는 것이 알려져 있다. 현재까지 이산시간 모델의 네트워크에 대한 상태천이 해석은 상세하게 이루어져 왔다. 그러나 연속시간 모델에 대한 해석은 네트워크 규모의 증가에 따른 급격한 계산량의 증가 때문에 연구가 그다지 활발하게 이루어지지 않고 있다. 본 논문에서는 각 뉴런이 최근접 뉴런에만 이진화된 결합하중 +1 및 -1로 연결된 연속시간모델 순환결합형 신경회로망의 동적인 상태천이 특성을 해석하여 이산시간 모델에서 기억 가능한 리미트사이클과의 차이점을 분석한다. 또한 연속시간 네트워크 모델에 카오스 신호를 인가하여 리미트사이클간의 천이를 제어할 수 있는 가능성을 분석하여 동적정보처리에 네트워크를 응용할 수 있는 가능성을 검토한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.189-194
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• 2017

The reversibility of a quantum logic circuit can be realized when two reversible conditions of information reversible and energy reversible circuits are satisfied. In this paper, we have modeled the computation cycle required to recover the information reversibility from the multivalued quantum logic to the original state. For modeling, we used a function embedding method that uses a unitary switch as an arithmetic exponentiation switch. In the quantum logic circuit, if the adjoint gate pair is symmetric, the unitary switch function shows the balance function characteristic, and it takes 1 cycle operation to recover the original information reversibility. Conversely, if it is an asymmetric structure, it takes two cycle operations by the constant function. In this paper, we show that the problem of 2-cycle restoration according to the asymmetric structure when the hybrid MCT gate is realized with the ternary M-S gate can be solved by equivalent conversion of the asymmetric gate to the gate of the symmetric structure.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.361-366
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• 2002

It has been reported that neural network with cyclic connections generates limit cycles. The dynamics of discrete time network with cyclic connections has been analyzed. But the dynamics of cyclic network in continuous time has not been known well due to its huge calculation complexity. In this paper, we study the dynamics of the continuous time network with cyclic connections and the effect of chaotic signal in the network for transitions between limit cycles.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.179-184
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• 2006

An intuitive understanding of the dynamic pattern generation in asymmetric networks may be considered an essential component in developing models for the dynamic information processing. It has been reported that the neural network with cyclic connections generates multiple limit cycles. The dynamics of discrete time network with cyclic connections has been investigated intensively. However, the dynamics of a cyclic connection neural network in continuous-time has not been well-known due to the considerable complexity involved in its calculation. In this paper, the dynamic behavior of a continuous-time cyclic connection neural network, in which each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$, has been investigated. Furthermore, the dynamics and stability of the network have been analyzed using a piece-wise linear approximation.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.4
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• pp.22-28
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• 2002

It has been reported that neural network with cyclic connections generates limit cycles. The dynamics of discrete time network with cyclic connections has been analyzed. But the dynamics of cyclic network in continuous time has not been known well due to its huge calculation complexity. In this paper, we study the dynamics of the continuous time network with cyclic connections and the effect of chaotic signal in the network for transitions between limit cycles.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.5
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• pp.76-85
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• 1999

The intuitive understanding of the dynamic pattern generation in asymmetric networks may be useful for developing models of dynamic information processing. In this paper, dynamic behavior of the cyclic connection neural network, in which each neuron is connected only to its nearest neurons with binary synaptic weights of $\pm$ 1, has been investigated. Simulation results show that dynamic behavior of the network can be classified into only three categories: fixed points, limit cycles with basin and limit cycles with no basin. Furthermore, the number and the type of limit cycles generated by the networks have been derived through analytical method. The sufficient conditions for a state vector of $n$-neuron network to produce a limit cycle of $n$- or 2$n$-period are also given. The results show that the estimated number of limit cycles is an exponential function of $n$. On the basis of this study, cyclic connection neural network may be capable of storing a large number of dynamic information.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.68-76
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• 1999

The intuitive understanding of the dynamic pattern generation in asymmetric networks may be useful for developing models of dynamic information processing. In this paper, dynamic behavior of the ring connection neural network in which each neuron is only to its nearest neurons with binary synaptic weights of ±1, has been inconnected vestigated Simulation results show that dynamic behavior of the network can be classified into only three categories: fixed points, limit cycles with basin and limit cycles with no basin. Furthermore, the number and the type of limit cycles generated by the networks have been derived through analytical method. The sufficient conditions for a state vector of n-neuron network to produce a limit cycle of n- or 2n-period are also given The results show that the estimated number of limit cycle is an exponential function of n. On the basis of this study, cyclic connection neural network may be capable of storing a large number of dynamic information.

• Composites Research
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• v.18 no.6
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• pp.1-8
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• 2005

The fiber-reinforced composite materials have been advanced for various applications because of their excellent mechanical and electromagnetic properties. On their manufacturing processes, however, thermo-curing inherently produces the undesired thermal deformation mainly from temperature drop from the process temperature to the room temperature, so called spring-back. The spring-back must be understood especially in the hybrid composites in order to design and fabricate desired shape. In this research, (glass fiber / epoxy) + (carbon fiber / epoxy) unsymmetric hybrid composites were fabricated under various conditions such as cure cycle, laminate thickness, stacking sequence and curing sequence. Coupons were made and spring-back were measured using coordinate measuring machine (CMM). Using the Classical Lamination Theory (CLT) and finite element analysis (ANSYS), the behavior of spring-back were predicted and compared with the experimental data. The results from CLT and FEA agreed well with the experimental data. Although, the spring-back could be reduced by lowering curing temperature, at any case, the spring-back could not be removed completely.