• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2216-2219
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• 2003

Accurate speech region detection and automatic syllable segmentation is important part of speech recognition system. In automatic speech recognition system, they are needed for the purpose of accurate recognition and less computational complexity, In this paper, we Propose improved syllable segmentation method using ramp edge detection method and residual signal Peak energy. These methods were used to ensure accuracy and robustness for endpoint detection and syllable segmentation system. They have almost invariant response to various background noise levels. As experimental results, we obtained the rate of 90.7％ accuracy in syllable segmentation in a condition of accurate endpoint detection environments.

• 대한전자공학회논문지
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• 제24권5호
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• pp.769-776
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• 1987

A method of on-line recognition of Korean characters (Hangeul) by augmented conterxt free grammar is described in this paper. Syntactic analysis with context free grammar oftern has ambiguity. Insufficient description of relations among Hangrul sub-patterns causes this ambiguity can be determined through repetition of experiments. Flexible syntactic analysis is executed by adapting the condition to the (advice)part of augmented context free grammar. The ratio of correct recognition of this method is more than 99%.

• 동아시아식생활학회지
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• 제16권2호
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• pp.128-135
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• 2006

This study was conducted to determine the relationship between intake of food unconformable to each Sasang constitution and the recognition of irregular symptoms of health condition. The study subjects, 362 university students nationwide, were classified according to their Sasang constitution. Regarding the correlation between intake of constitutionally unconformable food and health recognition, the Soeum type showed a positive correlation between unconformable food intake and multiple subjective symptoms. According to the detailed food type, the Soyang type showed a positive correlation between chicken intake and the symptoms of eyes and skin, and between pepper intake and multiple subjective symptoms. The Soeum type showed a negative correlation between mackerel intake and the symptoms of mouth and anus. A negative correlation was observed between mung-bean intake and multiple subjective symptoms, and this tendency persisted in the correlations between nonglutinous millet intake and multiple subjective symptoms, between wheat powder intake and multiple subjective symptoms, and not only the symptoms of the respiratory system but also eyes and skin. Positive correlations were found between banana intake and multiple subjective symptoms, and between mile intake and the symptoms of the respiratory system. However, the Taeum type didn't display any significant correlation with any food type.

• 한국가구학회지
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• 제23권2호
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• pp.207-213
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• 2012

It was investigated present condition and enhancement of wooden playground. Wood material is the concept of eco-friendly materials. Wood is the best material which helps children to improve their emotional and physical quality. Recently, the number of wooden playgrounds was reduced but synthetic resin material has greatly increased. It was because of the high construction cost of wooden playground. Therefore, to increase the number of wooden playgrounds, it is the most important to remind people of positive recognition about wood material.

• Tribology and Lubricants
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• 제11권3호
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• pp.24-30
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• 1995

This paper was undertaken to recognize the pattern of the wear debris by neural network as a link for the development of diagnosis system for movable condition of the lubricated machine surface. The wear test was carried out under different experimental conditions using the wear test device was made in laboratory and wear testing specimen of the pin-on-disk type were rubbed in paraffine series base oil, by varying applied load, sliding distance and mating material. The neural network has been used to pattern recognition of four parameter (diameter, elongation, complex and contrast) of the wear debris and learned the friction condition of five values (material 3, applied load 1, sliding distance 1). The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by the neural network. The characteristic parameter of the large wear debris over a few micron size enlarged recognition ability.

• 한국정밀공학회지
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• 제17권7호
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• pp.98-105
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• 2000

In this study, we developed rotating machine vibration condition monitoring system based upon Windows NT and DSP Board. Developed system includes signal analysis module, trend monitoring and simple diagnosis using threshold value. Trend analysis and report generation are offered with database management tool which was developed in MS-ACCESS environment. Post-processor, based upon Matlab, is developed for vibration signal analysis and fault detection using statistical pattern recognition scheme based upon Bayes discrimination rule and neural networks. Concerning to Bayes discrimination rule, the developed system contains the linear discrimination rule with common covariance matrices and the quadratic discrimination rule under different covariance matrices. Also the system contains k-nearest neighbor method to directly estimate a posterior probability of each class. The result of case studies with the data acquired from Pyung-tak LNG pump and experimental setup show that the system developed in this research is very effective and useful.

• 한국인지과학회:학술대회논문집
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• 한국인지과학회 2006년도 춘계학술대회
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• pp.53-58
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• 2006

We examined the cerebral activation of the emotional and linguistic attributes during the visual word recognition. This research investigated the affective priming effect preserving the behavioral paradigm. We used the primed-evaluation task in which the participants classify the target as positive or negative, and manipulated the emtional attributes by emtional relations of the prime-target word pairs(PP, PN, NP, NN). ROIs analyses for the semantic processing and emotional processing were performed. The results showed that the semantic processing areas including the IPL, SMG, and aSTS were activated differently according to the experimental condition. The activations of the IPL were increased only on the NN condition, whereas the activation of the SMG was decreased only on the PP condition. Furthmore, the activation of the emotional processing areas including the mPFC and ACC, was different according to the emotional realtions of word pairs. Similar to the SMG, the BOLD signal of the mPFC was decreaed only on the PP condition, whereas the activation of ACC was Increased only on the NN condition. These results were seemed to show the interact ive cerebral activations for processing the emtoional and linguistic attributes in a word, during visual word recognition.

• 디지털산업정보학회논문지
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• 제15권4호
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• pp.149-157
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• 2019

In case of reading passport using a smart phone in contrast with a dedicated passport reading system, MRZ(Machine Readable Zone) character recognition can be hard when the character strokes were broken, touched or blurred according to the lighting condition, and the position and size of MRZ character lines were varied due to the camera distance and angle. In this paper, the effective recognition algorithm of the passport MRZ information using a combined neural network recognizer of CNN(Convolutional Neural Network) and ANN( Artificial Neural Network), is proposed under the various sized and skewed passport images. The MRZ line detection using connected component analysis algorithm and the skew correction using perspective transform algorithm are also designed in order to achieve effective character segmentation results. Each of the MRZ field recognition results is verified by using five check digits for deciding whether retrying the recognition process of passport MRZ information or not. After we implement the proposed recognition algorithm of passport MRZ information, the excellent recognition performance of the passport MRZ information was obtained in the experimental results for PC off-line mode and smart phone on-line mode.

• 한국통신학회논문지
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• 제17권11호
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• pp.1199-1205
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• 1992

본 논문에서는 HMM(Hidden Markov Models)에서 문제점이 되는 개인차에의한 변동을 흡수하고, 적은 학습 데이타로서 인식률을 향상시키기 위하여 신경회로망을 이용한 NN-HMM(Neural Network Hidden Makov Models)에 의해 한국어 인식에 관하여 연구하였다. 이 방법은 HMM과 신경회로망의 출력을 각각 독립적인 인식값으로 가정하여 두 시스템의 확률곱으로 서로 보정되어 최대 인식확률의 음성모델을 인식하는 음성인식 시스템이다. 본 방법의 타당성을 평가하기 위하여 남, 여화자가 28개의 DDD 지역명을 발성한 음성데이타로 실험한 결과, 이산분포 HMM에 의한 방법에서는 91[%], 신경회로망에 의한 방법에서는 89[%], 제안된 방법에서는 95[%]의 향상된 인식률을 얻으므로써 인식성능의 우수함을 확인하였다.

• 한국소음진동공학회논문집
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• 제21권2호
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.