• 한국정보과학회논문지:소프트웨어및응용
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• 제36권5호
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• pp.401-410
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• 2009

온톨로지의 논리적 오류와 개념들 간의 포함 관계를 탐지하는 추론 엔진들이 소개되고 있다. 발표된 온톨로지 추론 엔진의 대부분은 태블로 알고리즘을 기반으로 구축되었다. 그러나 대부분의 추론 엔진들은 논리적 오류를 일으키는 원인은 밝히지 않고, 논리적 오류를 갖는 개념만을 탐지한다. 본 논문의 목적은 태블로 알고리즘 전개 과정 중에 발생하는 non-deterministic 상황을 최적화하는 동시에 논리적 오류를 일으키는 원인을 탐지하기 위한 방법을 연구하는 것이다. 따라서 본 논문에서는 논리적 부정 가정기반 진리 유지 시스템(NATMS)을 사용하여 non-deterministic 문제를 해결하고 논리적 오류 원인을 탐지하는 기법을 제안한다. 본 논문에서는 기존에 발표되었던 종속 부호 기반 백트랙킹 기법과 Swoop 프로젝트에 적용된 논리적 오류 원인을 탐지하는 기법을 소개하고, 제안하고자 하는 기법을 설명한다.

• Journal of applied mathematics & informatics
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• 제8권3호
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• pp.693-703
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• 2001

In this paper we consider non-deterministic finite Rabin-Scott’s automata. We use a special structure to descibe all the possible edges of non-determinstic finite automaton defining the given regular language. Such structure can be used for solving various problems of finite automata theory. One of these problems is edge-minimization of non-deterministic automata. As we have not touched this problem before, we obtain here two versions of the algorithm for solving this problem to continue previous series of articles.

• Journal of applied mathematics & informatics
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• 제9권2호
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• pp.645-655
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• 2002

In this Paper we consider non-deterministic finite Rabin-Scott's automata. We define special abstract objects, being pairs of values of states-marking functions. On the basis of these objects as the states of automaton, we define its edges; the obtained structure is considered also as a non-deterministic automaton. We prove, that any edge of any non-deterministic automaton defining the given regular language can be obtained by such techniques. Such structure can be used for solving various problems in the frames of finite automata theory.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.275-278
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• 2001

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• 한국경영과학회지
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• 제8권1호
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• pp.41-59
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• 1983

The purpose of this paper is to present a method of multi-objective, stochastic optimization in water resources system which investigates the development of potential non-normal deterministic equivalents for subsequent use in multiobjective stochastic programming methods, Given probability statement involving a function of several random variables, it is often possible to obtain a deterministic equivalent of it that does not include any orginal random variables. A Stochastic trade-off technique-MSTOT is suggested to help a decision maker achieve satisfactory levels for several objective functions. This makes use of deterministic equivalents to handle random variables in the objective functions. The emphasis is in the development of non-normal deterministic equivalents for use in multiobjective stochastic techniques.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1553-1556
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• 2003

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2003년도 춘계학술대회
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• pp.52-56
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• 2003

In this paper, We evaluate the learning ability of non-monotonic DBM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• 대한전자공학회논문지
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• 제27권4호
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• pp.571-579
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• 1990

A dynamic relocation algorithm for non-deterministic process graph in distributed computer systems is proposed. A method is represented for determining the optimal policy for processing a process tree. A general database query request is modelled by a process tree which represent a set of subprocesses together with their precedence relationship. The process allocation model is based on operating cost which is a function fo selection of site for processing operation, data reduction function and file size. By using expected values of parameters for non-deterministic process tree, the process graph and optimal policy that yield minimum operating cost are determined. As process is relocated according to threshold value and new information of parameters after the execution of low level process for non-deterministic process graph, the assigned state that approximate to optiaml solution is obtained. The proposed algorihtm is heuristic By performing algorithm for sample problems, it is shown that the proposed algorithm is good in obtaining optimal solution.

• 한국지능시스템학회논문지
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• 제11권6호
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• pp.505-509
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• 2001

본 연구에서는 학습기근을 갖는 결정론적 볼츠만 머신의 은닉충 뉴런에 비단조 활성화 함수를 적요한 경위의 학습성능을 XOR 문제와 ADD 학습에 대하여 수지 시뮬레이션을 통하여분석한다. 단조 활성화함수를 사용한 경우와 비교하여 학습 수렴률, 학습안정도, 및 학습 속도에 있어서 성능이 크게 향상됨을 확인하였다. 또한 네트워크의 막전위 분포를 조사함으로서 end-cut-off 타입의 비단조 함수를 이용한 경우에 나타는 다음 층의 뉴런에 영향을 주지 않는 뉴런의 출현, 즉, 신경회로망에 있어서 은닉층 뉴런늬 수을 자율적으로 조정하는것을 확인하였따. 이것은 학습문제에 대하여 네트워크 은닉층 뉴런의 수를 명확하게 결정할수 없는 현재의 상황에 있어서는 새로운 돌파구가 될것으로 기대된다.

• 대한전자공학회논문지
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• 제25권9호
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• pp.1019-1026
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• 1988

A parametric detection scheme for determenistic signals is obtained in a generalized observation model which contains non-additive noise. The model employed in this paper includes several special cases such as those describing purely-additive noise, multiplicative noise, and signal dependent noise and allows the consideration of deterministic and random signals. Locally optimum detectors for known deterministic signals in the model are derived and analyzed for performance. It is shown that the locally optimum detectors are interesting generalizations of those for the purely-additive noise model. Performance of the locally optimum detectors designed for the generalized observation model is compared to that of other common detectors.