• 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 the Institute of Electronics Engineers of Korea SC
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• v.47 no.4
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• pp.7-14
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• 2010

Stable-state behaviors of asynchronous sequential machines represented as finite state machines can be corrected by feedback control schemes. In this paper, we propose a state feedback control scheme for input/state asynchronous machines with uncertain transitions. The considered asynchronous machine is deterministic, but its state transition function is partially known due to model uncertainty or inner logic errors. The control objective is to compensate the behavior of the closed-loop system so that it matches a sub-behavior of a prescribed model despite uncertain transitions. Furthermore, during the execution of corrective action, the controller reflects the exact knowledge of transitions into the next step, i.e., the range of the behavior of the closed-loop system can be enlarged through learning. The design procedure for the proposed controller is described in a case study.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.39-48
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• 2009

Asynchronous sequential machines, or clockless logic circuits, have several advantages over synchronous machines such as fast operation speed, low power consumption, etc. In this paper, we propose a novel robust controller for input/output asynchronous sequential machines with uncertain state transitions. Due to model uncertainties or inner failures, the state transition function of the considered asynchronous machine is not completely known. In this study, we present a formulation to model this kind of asynchronous machines ana using generalized reachability matrices, we address the condition for the existence of an appropriate controller such that the closed-loop behavior matches that of a prescribed model. Based on the previous research results, we sketch design procedure of the proposed controller and analyze the stable-state operation of the closed-loop system.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.127-129
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• 2001

입력된 문장을 분석하여 미리 정해진 범주에 따라 그 문장의 감정 상태의 천이를 출력해 주는 감정인식 시스템을 제안한다. Naive Bayes 알고리즘을 사용했던 이전 방법과 달리 새로 연구된 시스템은 Hidden Markov Model(HMM)을 사용한다. HMM은 특정 분포로 발생하는 현상에서 그 현상의 원인이 되는 상태의 천이를 찾아내는데 적합한 방법으로서, 하나의 문장에 여러 가지 감정이 표현된다는 가정 하에 감정인식에 관한 이상적인 알고리즘이라 할 수 있다. 본 논문에서는 HMM을 사용한 감정인식 시스템에 관한 개요를 설명하고 이전 버전에 비해 보다 향상된 실험결과를 보여준다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.12
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• pp.1257-1263
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• 2015

A correlation between the transient regime and steady state regime on the creep crack growth (CCG) for Grade 91 steel, which is used as the structural material for the Gen-IV reactor systems, was investigated. A series of CCG tests were performed using 1/2" CT specimens under a constant applied load and at a constant temperature of $600^{\circ}C$. The CCG rates for the transient and steady state regimes were obtained in terms of $C^*$ parameter. The transient CCG rate had a close correlation with the steady-state CCG rate, as the slope of the transient CCG data was very similar to that of the steady state data. The transient rate was slower by 5.6 times as compared to the steady state rate. It can be inferred that the steady state CCG rate, which is required for long-time tests, can be predicted from the transient CCG rate obtained from short-time tests.

• Journal of the Korean Society of Propulsion Engineers
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• v.2 no.2
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• pp.47-55
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• 1998

Steady-state and transient performance for the medium scale civil aircraft turbofan engine was analyzed. Steady-state performance was analyzed on maximum take-off condition, maximum climb condition, and cruise condition. At 90％RPM of the low pressure compressor, the partload performance was economized. The transient performance was analyzed with cases of the step increase, the ramp increase, the ramp decrease, and the step increase and ramp decrease for the input fuel flow. For the transient performance analysis, work matching between compressor and turbine was needed. Modified Euler method was used the integration of residual torque in work matching equation. At all flight condition, the overshoot of the high pressure turbine inlet temperature was appeared in the step and ramp increase case, and the surge of high pressure compressor was appeared in the step increase case and the ramp increase case within 5.5 seconds of maximum climb condition.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.4
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• pp.575-584
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• 2017

It is suggested the circuit network to analyze the time response of control system. And it is analyzed the interrelation for classical control and modern control by the transfer function and the state equation. Without complicated integration of state transition equation, it is suggested to possible time response by combining the state transition matrix and the transfer function. A source program is coded to display the time response according to the unit-step and the sinusoidal input. Transient response is analyzed in the unit-step input and phase difference between current and voltage is analyzed in sinusoidal input. As writing the suggested contents in transient response or state-space analysis, it is improved the understanding for control engineering and ability for system design.

• Water for future
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• v.27 no.2
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• pp.129-137
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• 1994

The surface hydrology of large land areas is susceptible to several preferred stable states with transitions between stable states induced y stochastic fluctuation. This comes about due to the close coupling of land surface and atmospheric interaction. An interesting and important issue is the duration of residence in each mode. Mean transtion times between the stable modes are analyzed for different model parameters or climatic types. In an example situation of this differential equation exhibits a bimodal probability distribution of soil moisture states. Uncertainty analysis regarding the model parameters is performed using a Monte-Carlo simulation method. The method developed in this research may reveal some important characteristics of soil moisture or precipitation over a large area, in particular, those relating to abrupt changes in soil moisture or precipitation having extremely variable duration.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.910-913
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• 2021

Successor representation (SR) 은 두뇌 내 해마의 공간 세포가 인지맵을 구성하여 환경을 학습하고, 이를 활용하여 변화하는 환경에서 유연하게 최적 전략을 수립하는 기전을 모사한 강화학습 방법이다. 특히, 학습한 환경 정보를 활용, 환경 구조 안에서 목표가 변화할 때 강인하게 대응하여 일반 model-free 강화학습에 비해 빠르게 보상 변화에 적응하고 최적 전략을 찾는 것으로 알려져 있다. 본 논문에서는 SR 기반 강화학습 알고리즘이 보상의 변화와 더불어 환경 구조, 특히 환경의 상태 천이 확률이 변화하여 보상의 변화를 유발하는 상황에서 어떠한 성능을 보이는 지 확인하였다. 벤치마크 알고리즘으로 SR 의 특성을 목적 기반 강화학습으로 통합한 SR-Dyna 를 사용하였고, 환경 상태 천이 불확실성과 보상 변화가 동시에 나타나는 2-stage 마르코프 의사결정 과제를 실험 환경으로 사용하였다. 시뮬레이션 결과, SR-Dyna 는 환경 내 상태 천이 확률 변화에 따른 보상 변화에는 적절히 대응하지 못하는 결과를 보였다. 본 결과를 통해 두뇌의 강화학습과 알고리즘 강화학습의 차이를 이해하여, 환경 변화에 강인한 강화학습 알고리즘 설계를 기대할 수 있다.