• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.71-74
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• 1996

본 연구에서는 반복 학습제어 이론을 기초로 하는 하이브리드 신경망 제어기를 제안한다. 신경망으로는 백프로퍼게이션(backpropagation) 신경망을 사용하고, 기존의 반복 학습 제어 이론의 단점을 보안한 제어 알고리즘을 제안한다. 백프로퍼게이션 신경망의 맵핑(mapping)의 특징으로 원하는 목표 패턴에 추종할 수 있는 출력 패턴을 생성하고 반복 학습에 소요되는 학습시간을 줄일 수 있다. 실험결과에서 보듯이 제안된 제어 알고리즘은 목표패턴에 수렴함을 알 수 있다. 제시한 알고리즘은 CD-ROM 드라이브와 같은 광디스크 드라이브류의 초점 제어 등에 응용할 수 있다.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 1999.04a
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• pp.59-60
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• 1999

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.11a
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• pp.602-605
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• 2000

Recently, a study of intellectual smell recognition system is applied for the various fields such as control of food processing and survey of decay. A basic gas recognition system was implemented gases using four metal oxides semiconductor sensors as inputs. A CPLD chip of twenty thousand gates level was used for this purpose. The CPLD chip was designed and the availability of the one chip smell recognition system was tested.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.132-137
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• 2004

In this paper, the adaptive noise canceler using neural network with backpropagation is designed. The adaptive noise canceler using the least mean square algorithm has the large correlativity of the reference signal. The performance of the adaptive noise canceler shows the limitation when the information signal is relatively small to the noise. The system proposed in this paper plays an important role in denoising these signals. In addition, the experiments are carried out to analyze the effects of the number of hidden layers and nodes about the system. The performance of the proposed adaptive noise canceler is compared with that of the system which is used the least mean square algorithm.

• Annual Conference on Human and Language Technology
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• 1996.10a
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• pp.479-483
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• 1996

본 논문은 구운 분석의 검색 영역 축소를 통한 구문 분석기의 성능 향상을 목적으로 connectionism을 이용한 부분 구문 인식기의 설계와 구현을 기술한다. 본 부분 구문 인식기는 형태소 분석된 문장으로부터 명사-주어부와 술어부를 인식함으로써 전체 검색 영역을 여러 부분으로 나누어 구문 분석문제를 축소시키는 것을 목적으로 하고 있다. Connectionist 모델은 입력층과 출력층으로 구성된 개선된 퍼셉트론 구조이며, 입/출력층 사이의 노드들을, 입력층 사이의 노드들을 연결하는 연결 강도(weight)가 존재한다. 명사-주어부 및 술어부 구문 태그를 connectionist 모델에 적용하며, 학습 알고리즘으로는 개선된 백프로퍼게이션 학습 알고리즘을 사용한다. 부분 구문 인식 실험은 112개 문장의 학습 코퍼스와 46개 문장의 실험 코퍼스에 대하여 85.7%와 80.4%의 정확한 명사-주어부 및 술어부 인식을, 94.6%와 95.7%의 명사-주어부와 술어부 사이의 올바른 경계 인식을 보여준다.

• The Journal of Engineering Geology
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• v.15 no.1
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• pp.67-76
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• 2005

This paper presents the application of a neural network for prediction of the unconfined compressive strength from physical properties and schmidt hardness number on rock samples. To investigate the suitability of this approach, the results of analysis using a neural network are compared to predictions obtained by statistical relations. The data sets containing 55 rock sample records which are composed of sandstone and shale were assembled in Daegu area. They were used to learn the neural network model with the back-propagation teaming algorithm. The rock characteristics as the teaming input of the neural network are: schmidt hardness number, specific gravity, absorption, porosity, p-wave velocity and S-wave velocity, while the corresponding unconfined compressive strength value functions as the teaming output of the neural network. A data set containing 45 test results was used to train the networks with the back-propagation teaming algorithm. Another data set of 10 test results was used to validate the generalization and prediction capabilities of the neural network.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.211-224
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• 1996

In the research area of estimation of the software development efforts, a number of researches have been accomplished in order to control the costs and to make software more competitive. However, most of them were restricted to the functional algorithm models or the statistic models. Moreover, since they are dealing with the cases of foreign countries, the results are hard to apply directly to the domestic environment for the efficient project management because of lack of accuracy, fitness, flexibility and portability. Therefore, it is appropriate to suggest and propose a new approach supported by artificial neural network which is composed of back propagation and feel-forward algorithms to improve the exactness of the efforts estimation and to advance practical uses. In this study, the artificial neural network approach is used to model the software cost estimation and the results are compared with the revised COCOMO and the multiregression model in order to validate the superiority of the model.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.150-159
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• 1998

This paper described the recognition of the Off-line handwritten Hangul based on neural network using a feature extraction method. Features of Hangul can be extracted by a $5{\times}5$ window method which is the modified $3{\times}3$ mask method. These features are coded to binary patterns in order to use neural network's inputs efficiently. Hangul character is recognized by the consonant, the vertical vowel, and the horizontal vowel, separately. In order to verify the recognition rate, three different coding methods were used for neural networks. Three methods were the fixed-code method, the learned-code I method, and the learned-code II method. The result was shown that the learned-code II method was the best among three methods. The result of the learned-code II method was shown 100% recognition rate for the vertical vowel, 100% for the horizontal vowel, and 98.33% for the learned consonants and 93.75% for the new consonants.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.30-36
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• 2000

The hidden node plays a role of the functional units that classifies the features of input pattern in the given question. Therefore, a neural network that consists of the number of a suitable optimum hidden node has be on the rise as a factor that has an important effect upon a result. However there is a problem that decides the number of hidden nodes based on back-propagation learning algorithm. If the number of hidden nodes is designated very small perfect learning is not done because the input pattern given cannot be classified enough. On the other hand, if designated a lot, overfitting occurs due to the unnecessary execution of operation and extravagance of memory point. So, the recognition rate is been law and the generality is fallen. Therefore, this paper suggests a method that decides the number of neural network node with feature information consisted of the parameter of learning algorithm. It excludes a node in the Pruning target, that has a maximum value among the feature value obtained and compares the average of the rest of hidden node feature value with the feature value of each hidden node, and then would like to improve the learning speed of neural network deciding the optimum structure of the multi-layer neural network as pruning the hidden node that has the feature value smaller than the average.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.4
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• pp.398-405
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• 2001

The rivet joint has typical structural feature that can be initiation site for the fatigue crack due to the combination of local stress concentration around rivet hole and the moisture trapping. From a viewpoint of structural assurance, it is crucial to evaluate the size of crack around the rivet holes by appropriate nondestructive evaluation techniques. Lamb wave that is one of guided waves, offers a more efficient tool for nondestructive inspection of plates. The neural network that is considered to be the most suitable for pattern recognition has been used by researchers in NDE field to classify different types of flaws and flaw sizes. In this study, clack size evaluation around the rivet hole using the neural network based on the back-propagation algorithm has been tarried out by extracting some features from the ultrasonic Lamb wave for A12024-T3 skin panel of aircraft. Special attention was paid to reduce the coupling effect between the transducer and the specimen by extracting some features related to time md frequency component data in ultrasonic waveform. It was demonstrated clearly that features extracted from the time and frequency domain data of Lamb wave signal were very useful to determine crack size initiated from rivet hole through neural network.