• Journal of Internet Computing and Services
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• v.13 no.2
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• pp.99-108
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• 2012

Many methods had been developed to improve the accuracy of extracting information from a vast amount of data. This paper combined a number of natural language processing methods such as NER (named entity recognition), sentence extraction, and part of speech tagging to carry out text analysis. The data source is comprised of texts obtained from the web using a domain-specific data extraction agent. A framework for the extraction of information from unstructured data was developed using the aforementioned natural language processing methods. We simulated the performance of our work in the extraction and analysis of texts for the detection of organizational structures. Simulation shows that our study outperformed other NER classifiers such as MUC and CoNLL on information extraction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.123-144
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• 2014

Wireless sensor networks are generally deployed for specific applications to accomplish certain objectives over a period of time. To fulfill these objectives, it is crucial that the sensor network continues to function for a long time, even if some of its nodes become faulty. Energy efficiency and fault tolerance are undoubtedly the most crucial requirements for the design of an information extraction protocol for any sensor network application. However, most existing software agent based information extraction protocols are incapable of satisfying these requirements because of static agent itineraries and large agent sizes. This paper proposes an Information Extraction protocol based on Multiple software Agents with Dynamic Itineraries (IEMADI), where multiple software agents are dispatched in parallel to perform tasks based on the query assigned to them. IEMADI decides the itinerary for an agent dynamically at each hop using local information. Through mathematical analysis and simulation, we compare the performance of IEMADI with a well known static itinerary based protocol with respect to energy consumption and response time. The results show that IEMADI provides better performance than the static itinerary based protocols.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1338-1343
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• 2003

Data is the expression of the language or numerical values that show some characteristics. And information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns and make decisions. Today, knowledge extraction and application of the knowledge are broadly accomplished to improve the comprehension and to elevate the performance of systems in several industrial fields. The knowledge extraction could be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge can be drawn by rules. Clustering (CU, input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for expression the knowledge by rules. In this paper, the various approaches of the knowledge extraction are examined by categories that separate the methods by the applied industrial fields. Also, the several test data and the experimental results are compared and analysed based upon the applied techniques that include CL, ISP, NF, NN, EM, and so on.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.77-84
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• 2023

Given the growing volume of biomedical papers, the ability to efficiently extract keywords has become crucial for accessing and responding to important information in the literature. In this study, we conduct a comprehensive evaluation of different unsupervised learning-based models and BERT-based models for keyword extraction in the biomedical field. Our experimental findings reveal that the BioBERT model, trained on biomedical-specific data, achieves the highest performance. This study offers precise and dependable insights to guide forthcoming research in biomedical keyword extraction. By establishing a well-suited experimental framework and conducting thorough comparisons and analyses of diverse models, we have furnished essential information. Furthermore, we anticipate extending our contributions to other domains by providing comparative experiments and practical guidelines for effective keyword extraction.

• Journal of the Korea Computer Graphics Society
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• v.19 no.2
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• pp.9-18
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• 2013

We propose a novel method to extract keyframes from home videos based on the 5W and 1H information. Keyframe extraction is a kind of video summarization which selects only specific frames containing important information of a video. As a home video may have content with a variety of topics, we cannot make specific assumptions for information extraction. In addition, to summarize a home video we must analyze human behaviors, because people are important subjects in home videos. In this paper, we extract 5W and 1H information by analyzing human faces, human behaviors, and the global information of background. Experimental results demonstrate that our technique extract more similar keyframes to human selections than previous methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.444-453
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• 1997

This paper presents the prototype design of hardware/software cosynthesis system for pipelined application-specific instruction processors. Taking application programs in VHDL as inputs, the proposed system generates a pipelined instruction-set processor and the instruction sequences running on the generated machine. The design space of datapath and controller is defined by the architectural templates embedded in the system. Generating the intyermediate code adequate for parallelism analysis and extraction, the system converts it into assembly codes. Experimental results show the effectiveness of the proposed system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.363-371
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• 2015

Feature extraction is one of the main goals in many remote sensing analyses. After high-resolution imagery became more available, it became possible to extract more detailed and specific features. Thus, considerable image segmentation algorithms have been developed, because traditional pixel-based analysis proved insufficient for high-resolution imagery due to its inability to handle the internal variability of complex scenes. However, the individual segmentation method, which simply uses color layers, is limited in its ability to extract various target features with different spectral and shape characteristics. Spectral indices can be used to support effective feature extraction by helping to identify abundant surface materials. This study aims to evaluate a feature extraction method based on a segmentation technique with spectral indices. We tested the extraction of diverse target features-such as buildings, vegetation, water, and shadows from eight band WorldView-2 satellite image using decision tree classification and used the result to draw the appropriate spectral indices for each specific feature extraction. From the results, We identified that spectral band ratios can be applied to distinguish feature classes simply and effectively.

• Journal of Plant Biotechnology
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• v.6 no.1
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• pp.63-67
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• 2004

A simple procedure for the extraction of the lipolytic enzyme from rice bran has been developed. High activity of lipolytic enzyme was obtained by first defatting the rice bran to remove lipid components with various extraction conditions. Then, after rove cycles of aqueous extraction, rice bran lipolytic enzyme was purified using micro- and ultrafiltration apparatus. Lipolytic enzyme activity was estimated by its hydrolytic action of tributyrin. The result indicated that the standard activity curve of butyric acid showed that the potential rice bran enzyme is a hydrolytic lipase enzyme. In addition, it showed higher lipolytic activity and specific enzyme activity with further purification by micro- and ultrafiltration. The size of rice bran lipase enzyme was identified through 15 ％ SDS-PAGE. The molecular weight of the rice bran lipase enzyme was 41 kDa.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.258-261
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• 2011

Living in the age of information people face problems related to information overload. Information is easy to produce, store and distribute through various communication channels, one of which is emails. With the appearance of the mobile devices, such as smart phones and tabs, people can have access to email inbox at any moment of time from everywhere. In this paper we present information extraction system with a specific goal of extracting meeting location from the announcement of seminar or conference. We apply a machine learning method (conditional random fields, CRF), train the system using annotated corpus of seminar and conference announcements and validate results by applying various extracted correction rules and patterns. Furthermore, we normalize extracted location, and reference using geo-coding databases, OpenStreetMap and Wikipedia resources to determine real geographical coordinates.

• Proceedings of the KAIS Fall Conference
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• 2004.11a
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• pp.299-301
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• 2004

A simple procedure for the extraction of the lipolytic enzyme from rice bran has been developed. High activity of lipolytic enzyme was obtained by first defatting the rice bran to remove lipid components with various extraction conditions. Then, after five cycles of aqueous extraction, rice bran lipolytic enzyme was purified using micro- and ultrafiltration apparatus. Lipolytic enzyme activity was estimated by its hydrolytic action of tributyrin. The result indicated that the standard activity curve of butyric acid showed that the potential rice bran enzyme is a hydrolytic lipase enzyme. In addition, it showed higher lipolytic activity and specific enzyme activity with further purification by micro- and ultrafiltration.