• Korean Journal of Computational Design and Engineering
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• v.6 no.2
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• pp.133-139
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• 2001

Since a great number of 2D engineering drawings are being used in industry and at the same time 3D CAD becomes popular in recent years, we need to reconstruct 3D CAD models from 2D legacy drawings. In this thesis, a combination of a feature recognition method and an expert system is suggested for the 3D solid model reconstruction. Modeling primitives of 3D CAD systems are recognized and constructed by using the pattern matching technique of the features modeling. Additional information for the 3D model reconstruction can be generated by extracting symbols or text entities which are related to form entities. For complex and indefinite cases which cannot be solved by the process of feature recognition, an expert system with a rule base has been used for decision-making. A 3D reconstruction system which recognizes 2D DXF drawing files has been implemented where models composed with protrusions, holes, and cutouts can be handled.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.28-30
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• 2006

텍스트마이닝(text mining) 기법을 통해 생물학 문헌으로부터 단백질 이름과 그들 간의 상호 관계를 추출하는 시스템이 제안된 바 있다[1]. 이 시스템에서 단백질 이름을 추출하는 과정을 패턴 일치 오토마타(PMA: Pattern Matching Automata)라는 방법을 이용하여 좀 더 유연하고 높은 성능을 가지도록 개선할 수 있었다. 본 논문은 예제를 통해 PMA의 학습, 테스트 과정과 결과를 설명함으로써 단백질 이름 추출작업에서의 PMA의 가능성과 성능 향상을 위한 앞으로의 방안을 제시한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.656-662
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• 2018

Pattern matching algorithm is widely used in many application fields such as bio-informatics, intrusion detection, etc. Among many string matching algorithms, KMP (Knuth-Morris-Pratt) algorithm is commonly used because of its fast execution time when using large texts. However, the processing speed of KMP algorithm is also limited when the text size increases significantly. In this paper, we propose a high throughput parallel KMP algorithm considering CPU-GPU memory hierarchy based on OpenCL in GPGPU (General Purpose computing on Graphic Processing Unit). We focus on the optimization for the allocation of work-times and work-groups, the local memory copy of the pattern data and the failure table, and the overlapping of the data transfer with the string matching operations. The experimental results show that the execution time of the optimized parallel KMP algorithm is about 3.6 times faster than that of the non-optimized parallel KMP algorithm.

• Journal of KIISE:Software and Applications
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• v.29 no.10
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• pp.684-693
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• 2002

Electronic Ink is a stored data in the form of the handwritten text or the script without converting it into ASCII by handwritten recognition on the pen-based computers and Personal Digital Assistants(PDAs) for supporting natural and convenient data input. One of the most Important issue is to search the electronic ink in order to use it. We proposed and implemented a script matching algorithm for the electronic ink. Proposed matching algorithm separated the input stroke into a set of primitive stroke using the curvature of the stroke curve. After determining the type of separated strokes, it produced a stroke feature vector. And then it calculated the distance between the stroke feature vector of input strokes and one of strokes in the database using the dynamic programming technique. We did various experiments and our algorithm showed high matching rate over 97.7% for only the Korean script and 94% for the data mixed Korean with the Chinese character.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.295-302
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• 2010

Example-based super resolution(EBSR) is a method to reconstruct high-resolution images by learning patch-wise correspondence between high-resolution and low-resolution images. It can reconstruct a high-resolution from just a single low-resolution image. However, when it is applied to a text image whose font type and size are different from those of training images, it often produces lots of noise. The primary reason is that, in the patch matching step of the reconstruction process, input patches can be inappropriately matched to the high-resolution patches in the patch dictionary. In this paper, we propose a new patch matching method to overcome this problem. Using an image observation model, it preserves the correlation between the input and the output images. Therefore, it effectively suppresses spurious noise caused by inappropriately matched patches. This does not only improve the quality of the output image but also allows the system to use a huge dictionary containing a variety of font types and sizes, which significantly improves the adaptability to variation in font type and size. In experiments, the proposed method outperformed conventional methods in reconstruction of multi-font and multi-size images. Moreover, it improved recognition performance from 88.58% to 93.54%, which confirms the practical effect of the proposed method on recognition performance.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.323-331
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• 2014

This research is based on real-time service model that uses lock screen of smart devices which is mostly exposed to device users. The potential for lock screen space is immense due to their exposing time for user. The effect can be maximized by offering useful information contents on lock screen. This service model offers real-time keyword with abridged sentence. They match real-time keyword with news by using text matching algorithm and extracts kernel sentence from news to provide short sentence to user. News from the lock screen to match real-time query sentence, and then only to the original core of the ability to move a user evaluation was conducted after adding. The report provided a key statement users feel the lack of original Not if you go to an average of 5.71%. Most algorithms allow only real-time zoom key sentence extracted keywords can accurately determine the reason for that was confirmed.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.1-7
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• 2024

Comparing text features involves evaluating the "similarity" between texts. It is crucial to use appropriate similarity measures when comparing similarities. This study utilized various techniques to assess the similarities between newspaper articles, including deep learning and a previously proposed method: a combination of Pointwise Mutual Information (PMI) and Word Pair Matching (WPM), denoted as PMI+WPM. For performance comparison, law data from medical research in Japan were utilized as validation data in evaluating the PMI+WPM method. The distribution of similarities in text data varies depending on the evaluation technique and genre, as revealed by the comparative analysis. For newspaper data, non-deep learning methods demonstrated better similarity evaluation accuracy than deep learning methods. Additionally, evaluating similarities in law data is more challenging than in newspaper articles. Despite deep learning being the prevalent method for evaluating textual similarities, this study demonstrates that non-deep learning methods can be effective regarding Japanese-based texts.

• Language and Information
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• v.14 no.2
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• pp.159-181
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• 2010

This paper presents a hybrid approach to the sentiment analysis of online texts. The sentiment of a text refers to the feelings that the author of a text has towards a certain topic. Many existing approaches employ either a pattern-based approach or a machine learning based approach. The former shows relatively high precision in classifying the sentiments, but suffers from the data sparseness problem, i.e. the lack of patterns. The latter approach shows relatively lower precision, but 100% recall. The approach presented in the current work adopts the merits of both approaches. It combines the pattern-based approach with the machine learning based approach, so that the relatively high precision and high recall can be maintained. Our experiment shows that the hybrid approach improves the F-measure score for more than 50% in comparison with the pattern-based approach and for around 1% comparing with the machine learning based approach. The numerical improvement from the machine learning based approach might not seem to be quite encouraging, but the fact that in the current approach not only the sentiment or the polarity information of sentences but also the additional information such as target of sentiments can be classified makes the current approach promising.

• The Magazine of the IEIE
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• v.15 no.2
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• pp.75-87
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• 1988

본 논문에서는 음성에 의한 man-machine communication의 핵심기술인 음성인식 및 합성의 전반적인 기술에 관하여 그 현황을 알아본다. 먼저 음성인식에서 해결되어야 할 문제점들을 고찰하고 격리단어 인식, 연결단어 인식, 그리고 연속언어 인식의 기술현황을 기술한다. 격리단어 인식에서는 pattern matching 방법에서 사용되는 입력어휘의 특징 추출, reference와의 유사도 측정, 유사도 측정 결과에 의한 인식결정에 관해서 논한다. 연결단어 및 연속언어 인식에서는 현재 연구가 되고 있는 "bottom-up approach"와 "top-down approach"에 관해서 설명하고 이들 방법의 어려운 점들을 고찰한다. 다음 음성 합성에서는 기존의 여러 가지 합성 방식을 검토하고 이들의 장단점을 기술한다. 마지막으로 한 예로서 한국어 text-to-speech 변환 시스템에 관하여 기술한다.