• 한국통신학회논문지
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• 제22권5호
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• pp.881-888
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• 1997

The existing method s for curvature estimation have a common problem in determining a unique smoothong factor. we previously proposed two approaches to overcome that problem: a constrained regularization approach and a mean field annealing approach. We consistently detected corners from the perprocessed smooth boundary obtained by either the constrained eglarization approach or the mean field annealing approach. Moreover, we defined corner sharpness to increase the robustness of both approaches. We evaluate the performance of those methods proposed in this paper. In addition, we show some matching results using a two-dimensional Hopfield neural network in the presence of occlusion as a demonstration of the power of our proposed methods.

• 충청수학회지
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• 제35권1호
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• pp.39-52
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• 2022

We introduce Clifford-valued neural networks with leakage delays. Furthermore, we study the uniqueness and existence of Clifford-valued Hopfield artificial neural networks having the Stepanov weighted pseudo almost periodic forcing terms on leakage delay terms. However the noncommutativity of the Clifford numbers' multiplication made our investigation diffcult, so our results are obtained by decomposing Clifford-valued neural networks into real-valued neural networks. Our analysis is based on the differential inequality techniques and the Banach contraction mapping principle.

• 한국통신학회논문지
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• 제16권6호
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• pp.545-555
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• 1991

본 논문에서는 단층 퍼센트론 모델의 학습기능과 신경회로망 형성메모리의 오류정정 능력이 서로 보완적으로 결합된 새로운 적응 패턴인식 시스템의 광학적구현을 실현하였다. 여기서, 단층 퍼센트론 모델은 2차원 LCTV 공간 광변조기를 이용하여 편광인코딩방법과 비전형 양자화 방법으로 구현하였으며, Hopfield 연장메모리는 2차원 모델로 황장하고multifocus holoens를 이용하여 광학적으로 구현하였다. 아리비아 숫자 짝.홀수 판별에 고나한 광학적 실험 결과, 오류 및 부분 입력에 대한 정확한 패턴 분류가 됨을 확인함으로서, 본 논문에서 제시한 새로운 적응 광 패턴인식 시스템이 실제로 영상처리, 패턴인식 등의 분야에서 그 응용 가능성을 제시하였다.

• 전자공학회논문지CI
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• 제37권4호
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• pp.26-36
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• 2000

본 논문은 실시간 멀티프로세서 스케줄링 문제를 효과적으로 해결하는 신경망 알고리즘을 제안한다. 제안된 알고리즘은 대표적인 신경망 모델인 홉 필드 네트워크를 근간으로 태스크의 처리요구에 대해 지정된 시간이내에 처리할 수 있는 실시간 시스템을 신경망의 장점인 병렬처리가 가능하도록 구현하였다. 본 알고리즘의 성능을 비교하기 위하여 기존에 실시간 멀티프로세서 스케줄링을 위해 연구되는 EDA와 LLA의 두 알고리즘과 비교한다. 제안된 알고리즘은 VHDL을 이용하여 하드웨어로 설계한다.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.825-831
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• 2007

In this paper, an interface between a human and a computer is presented. The human and computer interface(HCI) serves as another area of human and machine interfaces. Methods for the HCI we used are voice recognition and image recognition for detecting human's emotional feelings. The idea is that the computer can recognize the present emotional state of the human operator, and amuses him/her in various ways such as turning on musics, searching webs, and talking. For the image recognition process, the human face is captured, and eye and mouth are selected from the facial image for recognition. To train images of the mouth, we use the Hopfield Net. The results show 88%$\sim$92% recognition of the emotion. For the vocal recognition, neural network shows 80%$\sim$98% recognition of voice.

• 전자공학회논문지S
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• 제35S권6호
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• pp.101-108
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• 1998

물체를 인식하기 위한 효율적인 방법 중의 하나는 물체의 경계선에서 가장 적절한 특징들을 추출해 내어 인식에 사용하는 것이다. 본 논문에서는 경계선 위의 각 화소에서 주변 화소들과의 관계를 이용해 코너점, 접점, 변곡점을 추출하여 물체의 특징점으로 사용하였다. 기존에 주로 사용되던 중요한 특징점의 하나인 코너점은 곡률 함수상에서 찾고, 또한 물체가 직선과 곡선으로 이루어져 있을 경우 코너점만으로 물체를 표현하기에 부족하므로 곡률 함수를 미디안 필터링하여 양자화 잡음을 제거함으로써 접점과 변곡점을 찾는 새로운 방법을 제안하였다. 그리고 이 세 가지 특징점을 물체 정합의 요소로 사용하여 물체를 정합하였다. 정합 방법으로는 Discrete Hopfield Neural Network을 사용하였으며, 성능 분석 결과 곡선이 섞인 물체에서 코너점만으로 물체를 정합한 경우보다 특징점으로 물체를 정합한 경우 우수한 정합 성능을 나타내었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.254-257
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• 2002

In previous study about combinatorial optimization problem solver by using neural network, since Hopfield method, to converge into the optimum solution sooner and certainer is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, the dynamical system has lately attracted attention. Then we propose the "dynamical" combinatorial optimization problem solver using hysteresis neural network. In this article, the proposal system is evaluated by the N-Queen problem.

• 대한전기학회논문지:전력기술부문A
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• 제49권1호
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• pp.26-30
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• 2000

Switching of the transmission lines(T/L) is one of the methods for wuppressing the short circuit capacity. This paper presents the T/L switching operation by using the Hopfield neural network(HNN). The switching of T/L can make the line powers and the bus voltages deteriorated, as well as the fault current decreased. Such an insecure state should be avoided when the T/L is operated to be open. In this studies, the inequality constraints are formulated into the objective function to be incorporated with the HNN. Test results show that the convergence characteristics of HNN lead to the adequate solution of T/L switching.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2000년도 특별강연 및 춘계학술발표대회 개요집
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• pp.303-306
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• 2000

Quality estimation of the weld has been one of the important issues in RSW which is a main process of the sheep metal fabrication in auto-body industry, It was well known that among the various welding process variables, dynamic resistance has a close relation with nugget formation. With this variable, it is possible to estimate the weld quality in real time. In this study, a new quality estimation algorithm is developed with the primary dynamic resistance measured at welding machine timer. For this, feature recognition method of Hopfield neural network is used. Primary resistance patterns are vectorized and classified with five patterns. The network trained by these patterns recognizes the dynamic resistance pattern and estimates the weld quality Because the process variable monitored at the primary circuit is used, it is possible to apply this system to real time application without any consideration of electrode wear or shunt effect.

• 대한산업공학회지
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• 제19권1호
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• pp.1-17
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• 1993

It is well known that an assembly operation is usually constrained by the geometric interference between parts. These constraints are normally presented as AND/OR precedence relationships. To find a feasible assembly sequence which satisfies the geometric constraints is not an easy task because of the TSP(Traveling Salesman Problem) nature with precedence constraints. In this paper, we developed an automated system based on Neural Network for generating feasible assembly sequences. Modified Hopfield and Tank network is used to solve the problem of AND/OR precedence-constrained assembly sequences. An economic assembly sequence can be also obtained by applying the cost matrix that contains cost-reducing factors. To evaluate the performance and effectiveness of the developed system, a case of automobile generator is tested. The results show that the developed system can provide a "good" planning tool for an assembly planner within a reasonable computation time period.