• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1995년도 가을 학술발표회 논문집
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• pp.17-24
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• 1995

This work proposes an optimality criteria applicable to the optimum design of plane frames. Stress constraints as well as displacement constraints are treated as behavioural constraints and thus the first order approximation of stress constraints is adopted. The design space of practical reinforced concrete frames with discrete design variables has been found to have many local minima, and thus it is desirable to find in advance the mathematical minimum, hopefully global, prior to starting to search a practical optimum design. By using the mathematical minimum as a trial design of any search algorithm, we may not full into a local minimum but apparently costly design. Therefore this work aims at establishing a mathematically rigorous method ⑴ by adopting first-order approximation of constraints, ⑵ by reducing the design space whenever minimum size restrictions become "active" and ⑶ by the of Newton-Raphson Method.

• 전자공학회논문지C
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• 제34C권12호
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• pp.61-69
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• 1997

This paper proposes a new method for improving the performances of the neural network for optimization using a hyubrid of gradient descent method and dynamic tunneling system. The update rule of gradient descent method, which has the fast convergence characteristic, is applied for high-speed optimization. The update rule of dynamic tunneling system, which is the deterministic method with a tunneling phenomenon, is applied for global optimization. Having converged to the for escaping the local minima by applying the dynamic tunneling system. The proposed method has been applied to the travelling salesman problems and the optimal task partition problems to evaluate to that of hopfield model using the update rule of gradient descent method.

• 대한전기학회논문지:전력기술부문A
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• 제48권11호
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• pp.1464-1471
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• 1999

This paper introduces complex-valued competitive learning neural network for spatio-temporal pattern recognition. There have been quite a few neural networks for spatio-temporal pattern recognition. Among them, recurrent neural network, TDNN, and avalanche model are acknowledged as standard neural network paradigms for spatio-temporal pattern recognition. Recurrent neural network has complicated learning rules and does not guarantee convergence to global minima. TDNN requires too many neurons, and can not be regarded to deal with spatio-temporal pattern basically. Grossberg's avalanche model is not able to distinguish long patterns, and has to be indicated which layer is to be used in learning. In order to remedy drawbacks of the above networks, unsupervised competitive learning using complex umber is proposed. Suggested neural network also features simultaneous recognition, time-shift invariant recognition, stable categorizing, and learning rate modulation. The network is evaluated by computer simulation with randomly generated patterns.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 B
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• pp.968-970
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• 1993

In this paper, the loss components of IPMSM(Inteiror Permanent Magnet Synchronous Motor) is derived. To maximize the efficiency of the motor, a design method that optimizes the design variables is proposed Objective function consists of stator winding loss, core loss, and mechanical loss. Simulated annealing is used as the optimization method which is appropriate for finding the global minimum of nonlinear function with many local minima. Through the simulation of the motor characteristics, the prominence of the proposed design method is verified.

• Nuclear Engineering and Technology
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• 제53권11호
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• pp.3772-3783
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• 2021

This work proposes a new efficient meta-heuristic optimization algorithm called Gamma Ray Interactions Based Optimization (GRIBO). The algorithm mimics different energy loss processes of a gamma-ray photon during its passage through a matter. The proposed novel algorithm has been applied to search for the global minima of 30 standard benchmark functions. The paper also considers solving real optimization problem in the field of nuclear engineering, radioisotope identification. The results are compared with those obtained by the Particle Swarm Optimization, Genetic Algorithm, Gravitational Search Algorithm and Grey Wolf Optimizer algorithms. The comparisons indicate that the GRIBO algorithm is able to provide very competitive results compared to other well-known meta-heuristics.

• 한국음향학회지
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• 제13권6호
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• pp.83-90
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• 1994

본 논문에서는 코호넨(Kohonen)의 SOFM (Self-Organizing Feature Map) 알고리즘의 단점을 해결하기 위한 새로운 학습 알고리즘 ASOFM(Adaptive Self-Organized Feature Map)을 제안한다. 코호넨의 학습 알고리즘은 초기화된 연결 벡터에 대하여 극소점에 빠지는 경우도 있다. 그러나 제안된 알고리즘에서는 학습과정중에 네트워크의 상태를 평가할 수 있는 목적함수(object function)을 사용하였고, 이 함수의 출력에 따라 학습의 각 시점에서 적응적으로 학습률의 재조정이 가능하였다. 이 결과, 네트워크의 상태가 최소점에 수렴함이 보증 되고 학습률의 적응성에 의해 임의의 학습패턴에 대한 학습의 일반화 능력이 보장되었다. 또한 제안된 알고리즘은 코호넨의 알고리즘보다 약 $70\%$이상의 학습시간을 단축한다.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.448-457
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• 1998

A neural network based control algorithm with fuzzy compensation is proposed for the automated adjustment in the production of electronic end-products. The process of adjustment is to tune the variable devices in order to examine the specified performances of the products ready prior to packing. Camcorder is considered as a target product. The required test and adjustment system is developed. The adjustment system consists of a NNC(neural network controller), a sub-NNC, and an auxiliary algorithm utilizing the fuzzy logic. The neural network is trained by means of errors between the outputs of the real system and the network, as well as on the errors between the changing rate of the outputs. Control algorithm is derived to speed up the learning dynamics and to avoid the local minima at higher energy level, and is able to converge to the global minimum at lower energy level. Many unexpected problems in the application of the real system are resolved by the auxiliary algorithms. As the adjustments of multiple items are related to each other, but the significant effect of performance by any specific item is not observed. The experimental result shows that the proposed method performs very effectively and are advantageous in simple architecture, extracting easily the training data without expertise, adapting to the unstable system that the input-output properties of each products are slightly different, with a wide application to other similar adjustment processes.

• Asian Journal of Atmospheric Environment
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• 제9권2호
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• pp.158-164
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• 2015

The estimation of the concentration of hydroxyl radical (OH) in the atmosphere is essential to build atmospheric models and to understand the mechanisms of the reactions involved in OH. Although water vapor is one of the most abundant species in the troposphere, only a few studies have been performed for the reaction of OH and water vapor. Here I demonstrate an ab initio study on the complex forming reation of OH with $H_2O$ in the gas phase performed based on density functional theory to calculate the reaction rate and the energy states of the reactant and the OH-$H_2O$ complex. The structure of the complex, which belongs to the Cs point group, was optimized at global minima. The transition state was not found at the B3LYP and MP2 levels of theory. Rate constants of the forward and the reverse reactions were calculated as $1.1{\times}10^{-16}cm^3\;molecule^{-1}\;s^{-1}$ and $5.3{\times}10^9\;s^{-1}$, respectively. The extremely slow rates of complex forming reaction and the resulting hydrogen atom exchange reaction of OH and $H_2O$, which are consistent with experimentally determined values, imply a negligible possibility of a change in OH reactivity through the title reaction.

• 한국지능시스템학회논문지
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• 제10권3호
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• pp.270-279
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• 2000

본 논문은 확률 펄스연산을 이용한 신경회로망이 on-Chip학습 알고리즘에 대해 기술하였다. 확률 펄스 연산은 임이의 펄스열에서 1과 0이 발생할 확률을 통해 표현된 수를 사용하여 계산하는 것을 일컫는다. 이러한 확률연산을 신경회로망에 적용하면 하드웨어 구현먼적을 줄일 수 있다는 것과 확률적인 특징으로 인해 지역 최소값으로부터 빠져 나와 광역 최적해에 도달할 수 있다는 장점을 갖고 있다. 또한 본 연구에서는 칩 냅에 학습할 수 있는 on-chip학습 알고리즘을 역전파 학습 알고리즘으로부터 유도하였다. 이렇게 유도된 알고리즘을 검증하기 위하여 비선형 패턴분리문제를 모의실험 하였다. 도한 활자체 및 필기체 숫자 인식에도 적용하여 좋은 결과를 얻었다.

• 전자공학회논문지B
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• 제28B권10호
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• pp.831-841
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• 1991

A combined model of probabilistic and MLP(multi layer perceptron) model is proposed for the pattern classification of EMG( electromyogram) signals. The MLP model has a problem of not guaranteeing the global minima of error and different quality of approximations to Bayesian probabilities. The probabilistic model is, however, closely related to the estimation error of model parameters and the fidelity of assumptions. A proper combination of these will reduce the effects of the problems and be robust to input variations. Proposed model is able to get the MAP(maximum a posteriori probability) in the probabilistic model by estimating a priori probability distribution using the MLP model adaptively. This method minimize the error probability of the probabilistic model as long as the realization of the MLP model is optimal, and this is a good combination of the probabilistic model and the MLP model for the usage of MLP model reliability. Simulation results show the benefit of the proposed model compared to use the Mlp and the probabilistic model seperately and the average calculation time fro classification is about 50ms in the case of combined motion using an IBM PC 25 MHz 386model.