• Journal of Information Processing Systems
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• v.7 no.3
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• pp.459-472
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• 2011

The vast number of biomedical literature is an important source of biomedical interaction information discovery. However, it is complicated to obtain interaction information from them because most of them are not easily readable by machine. In this paper, we present a method for extracting biomedical interaction information assuming that the biomedical Named Entities (NEs) are already identified. The proposed method labels all possible pairs of given biomedical NEs as INTERACTION or NO-INTERACTION by using a Maximum Entropy (ME) classifier. The features used for the classifier are obtained by applying various NLP techniques such as POS tagging, base phrase recognition, parsing and predicate-argument recognition. Especially, specific verb predicates (activate, inhibit, diminish and etc.) and their biomedical NE arguments are very useful features for identifying interactive NE pairs. Based on this, we devised a twostep method: 1) an interaction verb extraction step to find biomedically salient verbs, and 2) an argument relation identification step to generate partial predicate-argument structures between extracted interaction verbs and their NE arguments. In the experiments, we analyzed how much each applied NLP technique improves the performance. The proposed method can be completely improved by more than 2% compared to the baseline method. The use of external contextual features, which are obtained from outside of NEs, is crucial for the performance improvement. We also compare the performance of the proposed method against the co-occurrence-based and the rule-based methods. The result demonstrates that the proposed method considerably improves the performance.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.529-539
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• 2012

Testing is an important part of quality control in the software life cycle. One of the most important issues in the software testing is to generate the appropriate test cases. Generally, the software can be tested based on product understanding. However, it is not easy to create test cases that can ensure the quality of the software according to the clients' request. Especially, it is difficult to create test cases for abnormal or exceptional situations. In this paper, we present a novel approach to generate exceptional test cases at the design level of UML model. Furthermore, we describe the results of actual experiment where DAB(Digital Audio Broadcasting) parsing program is tested with previous method and also with the proposed method. The results implies that our proposed method of generating test cases for exceptional situations detect more faults than that of previous method by 7.08%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.791-798
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• 2002

In this thesis. we present a new method for inducing a probabilistic dependency grammar (PDG) from text corpus. As words in Korean are composed of a set of more basic morphemes, there exist various dependency relations in a word. So, if the induction process does not take into account of these in-word dependency relations, the accuracy of the resulting grammar nay be poor. In comparison with previous PDG induction methods. the main difference of the proposed method lies in the fact that the method takes into account in-word dependency relations as well as inter-word dependency relations. To access the performance of the proposed method, we conducted an experiment using a manually-tagged corpus of 25,000 sentences which is complied by Korean Advanced Institute of Science and Technology (KAIST). The grammar induction produced 2,349 dependency rules. The parser with these dependency rules shoved 69.77% accuracy in terms of the number of correct dependency relations relative to the total number dependency relations for best-1 parse trees of sample sentences. The result shows that taking into account in-word dependency relations in the course of grammar induction results in a more accurate dependency grammar.

• Journal of KIISE:Information Networking
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• v.30 no.4
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• pp.504-512
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• 2003

In this paper, we propose a new adaptive dissolve detection method based on the analysis of a dissolve modeling error which is the difference between an ideally modeled dissolve curve with no correlation and an actual dissolve curve including a correlation. The proposed dissolve detection method consists of two steps. First, candidate dissolve regions are extracted using the characteristics of a downward convex parabola, then each candidate region is verified based oil the dissolve modeling error. If the dissolve modeling error for a candidate region is less than a threshold defined by the target modeling error with a target correlation, the candidate region is determined as a resolve region with a lower correlation than the target correlation. The threshold is adaptively determined based on the variances between the candidate regions and the target correlation. By considering the correlation between neighbor scenes, the proposed method is able to be a semantic scene-change detector. The proposed method was tested on various types of data and its performance proved to be more accurate and reliable regardless of variation of variance of test sequences when compared with other commonly use methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.855-870
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• 2020

Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.199-202
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• 2001

ISO/IEC 15938-1(MPEG-7 Systems)에서는 멀티미디어 컨텐츠에 대한 메타데이터의 효율적인 전송과 저장을 위한 이진 표현 방법인 BiM(binary format for MPEC-7)을 제공한다. 멀티미디어 컨텐츠를 기술(description)하는 메타데이터의 텍스트 표현은 대체로 많은 저장 용량과 전송 리소스를 요구하기 때문에 효율적인 압축을 위해서는 이진 형식으로의 변환이 요구된다. 또한 텍스트 형식은 방송 환경과 같은 스트리밍 전송에는 적절하지 못한 단점이 있다. BiM은 컨텐츠에 대한 기술을 전체 또는 2개 이상의 AU(access units) 단위로 분할하며 부호화하는 방법을 지원함으로써 스트리밍 전송을 가능하게 한다. 이러한 구조는 이진 포맷 형태로 표현되는 헤더를 가지는 패킷 기반 형태이며, 융통성이 있는 전송 순서를 제공한다. 또한, 비트 스트림의 전체를 해석(parsing)하지 않고 랜덤 엑세스 기능을 제공하는 장점이 있다. BiM이 지닌 이러한 장점들로 인하여 현재 방송산업계를 중심으로 메타데이터를 방송에 활용하기 위한 기술을 표준화하는 국제 민간 표준화 기구인 TV-Anytime 포럼에서는 방송 컨텐츠에 대한 메타데이터의 압축에 관한 요구사항을 만족하는 하나의 방법으로 BiM을 고려하고 있다 본 논문에서는 이러한 MPEG-7 시스템의 BiM을 소개하고, 이를 이용하여 TV-Anytime 포럼의 메타데이터를 이진 포맷으로 부호화한 실험과 그 결과를 기술한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.319-324
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• 2011

Recently, development of app for smartphone is much and many apps provide information by reproduced with achieved information from internet site. There is a need of method dependent on reproduced data by app. For solving these requirements, this paper first identifies problems such as lower hardware performance and limited bandwidth when legacy web pages are accessed by smart phones. This paper suggests three methods, "real time," "cache," and "static" to develop application programs for smart phones by considering identified problems.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.90-97
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• 2020

Recently, with the growth of mixed reality industrial infrastructure, relevant convergence research has been proposed. For real-time mixed reality services such as remote video conferencing, the research on real-time acquisition-process-transfer methods is required. This paper aims to implement an AR remote rendering method of volumetric 3D video data. We have proposed and implemented two modules; one, the parsing module of the volumetric 3D video to a game engine, and two, the server rendering module. The result of the experiment showed that the volumetric 3D video sequence data of about 15 MB was compressed by 6-7%. The remote module was streamed at 27 fps at a 1200 by 1200 resolution. The results of this paper are expected to be applied to an AR cloud service.

• Journal of the Korean Institute of Telematics and Electronics
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• v.14 no.5
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• pp.15-21
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• 1977

The syntactic pattern recognition system of Korean characters is composed of three main functional parts; Preprocessing, Graph-representation, and Segmentation. In preprocessing routine, the input pattern has been thinned using the Hilditch's thinning algorithm. The graph-representation is the detection of a number of nodes over the input pattern and codification of branches between nodes by 8 directional components. Next, segmentation routine which has been implemented by top down nondeterministic parsing under the control of tree grammar identifies parts of the graph-represented Pattern as basic components of Korean characters. The authors have made sure that this system is effective for recognizing Korean characters through the recognition simulations by digital computer.