• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.45-48
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• 2006

The system that can generate biological brain information processing mechanism more precisely may have several abilities such as exact cognition, situation decision, learning and inference, and output decision. In this paper, to implement high level information processing and thinking ability in a complex system, the information processing layer based on the biological brain is introduced. The biological brain information processing mechanism, which is analyzed in this paper, provides fundamental information about intelligent engineering system, and the design of the layer that can mimic the functions of a brain through engineering definitions can efficiently introduce an intelligent information processing method having a consistent flow in various engineering systems. The applications proposed in this paper are expected to take several roles as a unified model that generates information process in various areas, such as engineering and medical field, with a dream of implementing humanoid artificial intelligent system.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.125-134
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• 2009

A biological data acquisition based on web has emerged as a powerful tool for allowing scientists to interactively view entries form different databases, and to navigate from one database to another molecular-biology database links. In this paper, the biological conceptual model is constructed hybrid biological data model to represent interesting entities in the data sources to applying navigation rule property for each biological data source based on four biological data integrating layers to control biological data. When some user's requests for application service are occurred, we can get the data from database and data source via web service. In this paper, we propose a query processing model and execution structure based on integrating data layers that can search information on biological data sources.

• Genomics & Informatics
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• v.9 no.1
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• pp.5-11
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• 2011

Treating genomes just as languages raises the possibility of producing concise generalizations about information in biological sequences. Grammars used in this way would constitute a model of underlying biological processes or structures, and that grammars may, in fact, serve as an appropriate tool for theory formation. The increasing number of biological sequences that have been yielded further highlights a growing need for developing grammatical systems in bioinformatics. The intent of this review is therefore to list some bibliographic references regarding the recent progresses in the field of grammatical modeling of biological sequences. This review will also contain some sections to briefly introduce basic knowledge about formal language theory, such as the Chomsky hierarchy, for non-experts in computational linguistics, and to provide some helpful pointers to start a deeper investigation into this field.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1068-1081
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• 2019

Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.48-52
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• 2007

As huge amount of literature including biological data is being generated after post genome era, it becomes difficult for researcher to find useful knowledge from the biological databases. Bio-text mining and related natural language processing technique are the key issues in the intelligent knowledge retrieval from the biological databases. We propose a bio-text mining technique for the biologists who find Knowledge from the huge literature. At first, web robot is used to extract and transform related literature from remote databases. To improve retrieval speed, we generate an inverted file for keywords in the literature. Then, text mining system is used for extracting given knowledge patterns and keywords. Finally, we construct a grid computing environment to guarantee processing speed in the text mining even for huge literature databases. In the real experiment for 10,000 bio-literatures, the system shows 95% precision and 98% recall.

• Journal of Information Processing Systems
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• v.11 no.2
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• pp.280-294
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• 2015

This paper presents the infectious watermarking model (IWM) for the protection of video contents that are based on biological virus modeling by the infectious route and procedure. Our infectious watermarking is designed as a new paradigm protection for video contents, regarding the hidden watermark for video protection as an infectious virus, video content as host, and codec as contagion medium. We used pathogen, mutant, and contagion as the infectious watermark and defined the techniques of infectious watermark generation and authentication, kernel-based infectious watermarking, and content-based infectious watermarking. We experimented with our watermarking model by using existing watermarking methods as kernel-based infectious watermarking and content-based infectious watermarking medium, and verified the practical applications of our model based on these experiments.

• Genomics & Informatics
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• v.2 no.2
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• pp.99-106
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• 2004

In this paper we introduce PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature. PubMiner employs natural language processing techniques and machine learning based data mining techniques for mining useful biological information such as protein­protein interaction from the massive literature. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language processing. The extracted interactions are further analyzed with a set of features of each entity that were collected from the related public databases to infer more interactions from the original interactions. An inferred interaction from the interaction analysis and native interaction are provided to the user with the link of literature sources. The performance of entity and interaction extraction was tested with selected MEDLINE abstracts. The evaluation of inference proceeded using the protein interaction data of S. cerevisiae (bakers yeast) from MIPS and SGD.

• The KIPS Transactions:PartD
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• v.12D no.4 s.100
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• pp.553-568
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• 2005

In order to integrate distributed biological information sources physically or virtually, it is necessary to overcome the heterogeneity of information sources and support a superior generality and adaptation in order to satisfy user's various demands. In this paper, we present a flexible and general XML query engine of a wrapper system in designing the middleware system to integrate data as well as application programs. Since this query engine applies user-defined XML view, it is possible to composite flexible integrated query. The query processing in a wrapper requires view composition, query translation into local sources, and generation of XML documents from local query results. We present a query processing model based on the view tree, where the n views and the XML queries are represented by the view tree. Moreover, to confirm the generality of our query processing model, we present a methodology of query processing for relational databases, web sources, and application programs whose return types of query results are relational tuples or XML documents.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.356-361
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• 1998

Neural Networks, modeled succinctly from the real nervous system of a living body, can be categorized into two folds; artificial neural network(ANN) and biological neural network(BNN). While the former has been developed to solve practical problems using function approximation capability, pattern classification) clustering algorithm, etc, the latter has been focused on verifying the information processing capability to which brain research gives an impetus, by mimicking real biological systems. However, BNN suffers Iron severe nonlinearities dealt with. A bridge between two neural networks is chaotic neural network(CNN), which simply delineate the real nor-vous system and comprises almost all the ANN structures by selecting parameters. Main research theme of this area is to develop an explanation tool to clarify the information processing mechanism in biological systems and its extension to engineering applications. The CNN has a Gaussian-shaped refractory function with hysteresis effect and the chaotic responses of it have been observed fur a wide range of parameter space. Through the examination of the coupling effects of excitatory and inhibitory connections, the secrets of information processing and memory structure will appear.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.299-308
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• 2014

Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human's outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.