• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 추계학술대회 논문집 학회본부
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• pp.432-435
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• 1990

Neural networks are a class of systems that have many simple processors (neurons) which are highly interconnected. The function of each neuron is simple, and the behavior is determined predominately by the set of interconnections. Thus, a neural network is a special form of parallel computer. Although major impetus for using neural networks is that they may be able to "learn" the solution to the problem that they are to solve, we argue that another, perhaps even stronger, impetus is that they provide a framework for designing massively parallel machines. The highly interconnected architecture of switching networks suggests similarities to neural networks. Here, we present switching applications in which neural networks can solve the problems efficiently. We also show that a computational advantage can be gained by using nonuniform time delays in the network.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.213-217
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• 1990

Neural networks are a class of systems that have many simple processors (neurons) which are highly interconnected. The function of each neuron is simple, and the behavior is determined predominately by the set of interconnections. Thus, a neural network is a special form of parallel computer. Although a major impetus for using neural networks is that they may be able to "learn" the solution to the problem that they are to solve, we argue that another, perhaps even stronger, impetus is that they provide a framework for designing massively parallel machines. The highly interconnected architecture of switching networks suggests similarities to neural networks. Here, we present two switching applications in which neural networks can solve the problems efficiently. We also show that a computational advantage can be gained by using nonuniform time delays in the network.e network.

• 제어로봇시스템학회논문지
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• 제7권3호
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• pp.229-237
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• 2001

In this paper, we propose two design methods of neural networks controller for the height control of helicopter, one is the design of neural network controller having learning capability and the other is the design of more improved neural network controller. Through the simulation results, we show that the proposed controllers have controllers have enhanced control performance(rapid response, effectiveness and safety) than the typical neural networks controller in the height control of helicopter.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2815-2817
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• 2002

Classification method of cancers using cDNA microarrays data was developed using genetic algorithms and neural networks. For gene selection, 2308 genes were ranked using genetic algorithms, and selected by frequency number of selection from 1000 of genetic iterative runs. To calculate fitness values, artificial neural networks are used as classifier. The small, round blue cell tumors (SRBCTs) which is difficult to distinguish via pathological single test was used as test diseases for classification, and the test results showed the 96% of exact classification capability for 25 test samples.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.234-238
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• 1996

In this paper, Plasma-Enhanced Chemical Vapor Deposition (PECVD) modeling using Polynomial Neural Networks (PNN) has been introduced. The deposition of SiO2 was characterized via a 25-1 fractional factorial experiment, was used to train PNNs using predicted squared error (PSE). The optimal neural network structure and learning parameters were determined by means of a second fractional factorial experiment. The optimized networks minimized both learning and prediction error. From these PNN process models, the effect of deposition conditions on film properties has been studied. The deposition experiments were carried out in a Plasma Therm 700 series PECVD system. The models obtained will ultimately be used for several other manufacturing applications, including recipe synthesis and process control.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• 한국컴퓨터정보학회논문지
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• 제17권3호
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• pp.11-25
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• 2012

이 논문은 이진 삼차 재귀 신경망(Binary Third-order Recurrent Neural Networks: BTRNN)에 유전자 알고리즘을 적용하여 문맥-자유 문법을 추론하는 방법을 제안한다. BTRNN은 각 입력심볼에 대응되는 재귀 신경망들의 다층적 구조이고 외부의 스택과 결합된다. BTRNN의 매개변수들은 모두 이진수로 표현되며 상태 전이와 동시에 스택의 한 동작이 실행된다. 염색체로 표현된 BTRNN들에 유전자 알고리즘을 적용하여 긍정과 부정의 입력 패턴들의 문맥-자유 문법을 추론하는 최적의 BTRNN를 얻는다. 이 방법은 기존의 신경망 이용방법보다 적은 학습량과 적은 학습회수로 작거나 같은 상태 수를 갖는 BTRNN을 추론한다. 또한 문법 표현의 염색체 이용방법보다 parsing과정에서 결정적인 상태전이와 스택동작이 실행되므로 입력 패턴에 대한 인식처리 시간복잡도가 우수하다. 문맥-자유 문법의 비단말 심볼의 개수 p, 단말 심볼의 개수 q, 그리고 길이가 k인 문자열이 입력이 될 때, BTRNN의 최대 상태수가 m이라고 하면, BTRNN의 인식처리 병렬처리 시간은 O(k)이고 순차처리 시간은 O(km)이다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (C)
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• pp.399-402
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• 2007

뇌 속 뉴런들의 네트워크는 근본적으로 recurrent neural networks(RNNs)의 형태를 지닌다. 이 논문에서는 반복되는 뉴런 반응 패턴들 사이의 관계를 네트워크에 저장함으로써 생물의 기억이 생성된다는 가정하에, 이를 표현할 수 있는 RNN 모델을 제안하였고, evolutionary algorithm을 통해 이러한 여러 쌍의 기억들이 저장된 네트워크가 존재할 수 있음을 보였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.667-668
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• 2008

This paper demonstrates that the recurrent neural networks can be used successfully for the identification of finite automata (FAs). A new type of recurrent neural network (RNN) is proposed and the offline training algorithm, regulated Levenberg-Marquadt (LM) algorithm, for the network is developed. Simulation result shows that the identification and the extraction of FAs are practically achievable.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제63차 동계학술대회논문집 29권1호
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• pp.17-19
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• 2021

본 논문에서는 코로나19 사태에 대비하여 실시간으로 마스크를 제대로 쓴 사람과 제대로 쓰지 않은 사람을 구분하는 시스템을 제안한다. 이 시스템을 사용하기 위하여 모델 학습 시에 합성곱 신경망(CNN : Convolutional Neural Networks)를 사용한다. 학습된 모델을 토대로 영상에 적용 시 하르 특징 분류기(Haar Cascade Classifier)로 얼굴을 탐지하여 마스크 여부를 판단한다.