• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.799-808
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• 2002

With the enormous advance of communication technology, telephone numbers to be memorized is increased because most people have a lot of telephone numbers. Thus it needs a method for people to easily memorize those telephone numbers. A traditional method is to use Korean nickname by 4 syllable Korean words such as "Pal -li Pal-li," "Yi- sa Yi-sa" whose initial sounds are corresponding to Korean sounds of telephone numbers. However, this method does not apply to all the telephone numbers, and only apply to a few telephone numbers. This paper proposed a Hangul-phone keyboard (TELNICK) considering the reduction of finger's moving distance and the number of strokes, and the generation of Korean nicknames for easy memorizing. This paper also proposes a method to generate many Korean nicknames that can correspond to 4-digit or 3-digit telephone numbers by using TELNICK keyboard and presents the form of Korean nicknames that can correspond to 7-digit or 8-digit telephone numbers.t telephone numbers.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.145-152
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• 2003

In this paper, assuming that the score of speech utterance is the product of HMM log likelihood and HMM weight, we propose a new method that HMM weights are adapted iteratively like the general MCE training. The proposed method adjusts HMM weights for better performance using delta coefficient defined in terms of misclassification measure. Therefore, the parameter estimation and the Viterbi algorithms of conventional 1:.um can be easily applied to the proposed model by constraining the sum of HMM weights to the number of HMMs in an HMM set. Comparing with the general segmental MCE training approach, computing time decreases by reducing the number of parameters to estimate and avoiding gradient calculation through the optimal state sequence. To evaluate the performance of HMM-based speech recognizer by weighting HMM likelihood, we perform Korean isolated digit recognition experiments. The experimental results show better performance than the MCE algorithm with state weighting.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.689-697
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• 2010

In this study, we propose a new procedure for improving the character recognition of text area extracted from video images. The recognition of strings extracted from video, which are mixed with Hangul, English, numbers and special characters, etc., is more difficult than general character recognition because of various fonts and size, graphic forms of letters tilted image, disconnection, miscellaneous videos, tangency, characters of low definition, etc. We improved the recognition rate by taking commonly used letters and leaving out the barely used ones instead of recognizing all of the letters, and then using SVM and RDA character recognition methods. Our numerical results indicate that combining SVM and RDA performs better than other methods.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.12-18
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• 2001

In this paper, we propose the Recurrent Neural Predictive HMM (RNPHMM). The RNPHMM is the hybrid network of the recurrent neural network and HMM. The predictive recurrent neural network trained to predict the future vector based on several last feature vectors, and defined every state of HMM. This method uses the prediction value from the predictive recurrent neural network, which is dynamically changing due to the effects of the previous feature vectors instead of the stable average vectors. The models of the RNPHMM are Elman network prediction HMM and Jordan network prediction HMM. In the experiment, we compared the recognition abilities of the RNPHMM as we increased the state number, prediction order, and number of hidden nodes for the isolated digits. As a result of the experiments, Elman network prediction HMM and Jordan network prediction HMM have good recognition ability as 98.5% for test data, respectively.

• Journal of Chest Surgery
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• v.31 no.7
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• pp.730-734
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• 1998

Background: It is important to know the location, number, size and shape of bullae before thoracotomy or VATS bullectomy. Chest X-ray and chest CT may be used but with some limitation. The purpose of this study was to compare the diagnostic value of thoracography with that of chest X-ray in preoperative detection of bullae. Meterial and Method: Thoracography was performed by injection of non-ionic water-soluble dye into pleural space in 22 primary spontaneous pneumothoraces, which underwent thoracotomy or VATS bullectomy. Chest X-ray and thoracography were compared through operative finding. Results: Sensitivity and accuracy of thoracography(75% and 72.7%) were higher than those of chest X-ray(30% and 36.4%). However, specificity of thoracography(50%) was lower than that of chest X-ray (100%). There were no complications during or after thoracography. Conclusion: Thoracography is a safer and more useful method for preoperative detection of bullae when compared with chest X-ray.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.141-152
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• 2007

Selectivity estimation has been a crucial component in query optimization in relational databases. While extensive researches have been done on this topic for the predicates of numerical data, only little work has been done for substring predicates. We propose novel suffix tree transform algorithms for this problem. Unlike previous approaches where a full suffix tree is pruned and then an estimation algorithm is employed, we transform a suffix tree into a suffix graph systematically. In our approach, nodes with similar counts are merged while structural information in the original suffix tree is preserved in a controlled manner. We present both an error-bound algorithm and a space-bound algorithm. Experimental results with real life data sets show that our algorithms have lower average relative error than that of the previous works as well as good error distribution characteristics.

• Journal of Digital Contents Society
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• v.17 no.3
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• pp.203-210
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• 2016

Korean is free of omission of sentence elements and modifying scope, so managing it on morphological analyzer is better than parser. In this paper, we propose functional expansion methods of the morphological analyzer to ease the burden of parsing. This method is a longest phrase matching method. When the series of several morpheme have one syntax category by processing of Unknown-words, Compound verbs, Compound nouns, Numbers and Symbols, our method combines them into a syntactic unit. And then, it is to treat by giving them a semantic features as syntax unit. The proposed morphological analysis method removes unnecessary morphological ambiguities and deceases results of morphological analysis, so improves accuracy of tagger and parser. By empirical results, we found that our method deceases 73.4% of Parsing tree and 52.4% of parsing time on average.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.14-20
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• 2003

This paper presents a text extraction method of camera based document image. It is more difficult to recognize camera based document image in comparison with scanner based image because of segmentation problem due to variable lighting condition and versatile fonts. Both document binarization and character extraction are important processes to recognize camera based document image. After converting color image into grey level image, gray level normalization is used to extract character region independent of lighting condition and background image. Local adaptive binarization method is then used to extract character from the background after the removal of noise. In this character extraction step, the information of the horizontal and vertical projection and the connected components is used to extract character line, word region and character region. To evaluate the proposed method, we have experimented with documents mixed Hangul, English, symbols and digits of the ETRI database. An encouraging binarization and character extraction results have been obtained.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.73-81
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• 2007

This paper presents how to recognize the new-type vehicle license plate using multi-link recognizer after extract the features from characters. In order to assist this task, this paper proposed FE-MCBP to recognize each character that got through image preprocess, extract range of vehicle license plate and extract process of each character. FE-MCBP is the recognizer based on the features of the character, The recognizer is employed to identify the new-type vehicle licence plates which have both the hangul and the arabic numeral characters. And its recognition rate is improved 9.7 percent than the back propagation recognizer before. Also it makes use of extract of linear component and region coordinate generation technology to normalize a image of the tilted vehicle license plate. The recognition system of the new-type vehicle license plate make possible recognize a image of the tilted vehicle license plate when using this system. Also, this system can recognize the tilted or imperfect vehicle licence plates.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.183-186
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• 2002

최근 음성인식의 인식 단위로서 문맥의존 음향 모델이 널리 사용되고 있다. 이는 음소의 음향학적 특징, 즉 선행 및 후행음소에 의한 중심 음소의 변이음 모델이 문맥독립 모델보다 좀 더 정확하게 모델링 될 수 있기 때문이다. 하지만 강건한 문맥의존 음향 모델을 작성하기 위해서는 모델 파라미터의 병합(tying)과 미지의 문맥(unseen context)의 처리를 위한 좀더 정교한 해결 방법이 필요하다. 따라서 본 논문에서는 이점을 고려하여 음향학적 특징과 언어학적 특징을 결합하여 상태 분할을 수행할 수 있도록 SSS(Successive State Splitting) 알고리즘의 문맥 방향 상태 분할에 음소결정트리를 접목한 HM-Net(Hidden Markov Network) 구조 결정법을 도입하였다. 또한 HM-Net은 연속적인 상태 분할에 의해 한국어에서 많이 발생하는 변이음들을 효과적으로 모델링 할 수 있다는 점을 고려하여 본 연구실에서 기존에 사용하던 48 유사음소 단위에서 문맥의존 음향 모델 작성에 불필요한 변이음을 제거하여 39 유사음소 단위를 재 정의하였다. 도입한 방법과 새로 정의한 유사음소 단위의 유효성을 확인하기 위해 고립 단어, 4연속 숫자음, 연속 음성인식에 대해 인식 실험을 수행한 결과, 모든 실험에서 재 정의한 39 유사음소 단위가 문맥종속형 HM-Net 음향모델을 이용한 한국어 음성인식에 효과적임을 확인할 수 있었다. 특히 연속 음성인식 실험의 경우, 기존의 48 유사음소 단위보다 평균 $15.08\%$의 인식률 향상이 있었다.