• Title/Summary/Keyword: Intelligent Techniques

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Development of Automatic Construction System for Steel Frames of High-Rise Buildings (로보틱 크레인 기반 고층건물 구조체 시공 자동화 시스템 개발)

  • Doh, Nak-Ju;Park, Gwi-Tae;Kang, Kyung-In;Im, Myo-Teak;Hong, Dae-Hui;Park, Shin-Suk;Lee, Seoung-Kyou
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2007.04a
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    • pp.84-89
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    • 2007
  • In this paper, we introduce a new technique for automatic construction of steel frames in high-rise buildings. Basically, we combine advanced robotic technologies to building construction techniques. Four main topics will be developed such as: 1) Planning and synthesis of automatic construction system, 2) Development of construction factory system with climbing oil-pressured robot, 3) Core techniques for automatic assembly for steel frames, and 4) Intelligent resource management system. We expect that this new technique will increase the construction efficiency and will alleviate the manpower shortage problem in the aging society.

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Application of Self-Organizing Map and Association Rule Mining for Personalization of Product Recommendations

  • Cho, Yeong-Bin;Cho, Yoon-Ho;Kim, Soung-Hie
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2004.11a
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    • pp.331-339
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    • 2004
  • The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this paper, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.

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Connection location Case-based reasoning teachnique Using indirect data (간접적으로 추출된 데이터를 활용한 사례기반 접속지역 추론기법)

  • 정용진
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2004.11a
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    • pp.189-192
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    • 2004
  • The present much information of internet has to exist for innumerable user so that couldn't satisfy there's a variety of demand. so they have a demerit that search unnecessary information. However Web service is different with other mass media because It is possible that enable Mass Customization for Personalization strategy. In The paper suggest reasoning system that detect user connection location by using indirect abstraction techniques a kind of Case-based reasoning techniques.

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PubMiner: Machine Learning-based Text Mining for Biomedical Information Analysis

  • Eom, Jae-Hong;Zhang, Byoung-Tak
    • 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.

An Intelligent Estimation Method of Robot-location based on Passive RFID Tags in Static Position (정적 Passive RFID 태그를 이용한 지능적인 로봇위치추정기법)

  • Moon Seung-Wuk;Ji Yong-Kwan;Park Jahng-Hyon
    • Journal of Institute of Control, Robotics and Systems
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    • v.12 no.1
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    • pp.9-14
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    • 2006
  • This paper presents methods of robot localization using recent radio frequency identification technology. If the absolute position and orientation of a tag are given in an indoor environment where RFID tags are installed, a robot can estimate its location using the relationship of the identified tag and the robot in a relative coordinate. To derive this relationship, we propose three estimation techniques using a model of a RFID reader, the direction of identification and the detection range. In this algorithm, a suitable estimation method is selected out of the three proposed techniques depending on the situations and trajectory of robot in the detection range. Simulation and experimental results show that the proposed methods can provide good performance for localization.

Web Mining for successful e-Business based on Artificial Intelligence Techniques (성공적인 e-Business를 위한 인공지능 기법 기반 웹 마이닝)

  • 이장희;유성진;박상찬
    • Journal of Intelligence and Information Systems
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    • v.8 no.2
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    • pp.159-175
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    • 2002
  • Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.

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S & P 500 Stock Index' Futures Trading with Neural Networks (신경망을 이용한 S&P 500 주가지수 선물거래)

  • Park, Jae-Hwa
    • Journal of Intelligence and Information Systems
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    • v.2 no.2
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    • pp.43-54
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    • 1996
  • Financial markets are operating 24 hours a day throughout the world and interrelated in increasingly complex ways. Telecommunications and computer networks tie together markets in the from of electronic entities. Financial practitioners are inundated with an ever larger stream of data, produced by the rise of sophisticated database technologies, on the rising number of market instruments. As conventional analytic techniques reach their limit in recognizing data patterns, financial firms and institutions find neural network techniques to solve this complex task. Neural networks have found an important niche in financial a, pp.ications. We a, pp.y neural networks to Standard and Poor's (S&P) 500 stock index futures trading to predict the futures marker behavior. The results through experiments with a commercial neural, network software do su, pp.rt future use of neural networks in S&P 500 stock index futures trading.

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A Hybrid Approach Using Case-based Reasoning and Fuzzy Logic for Corporate Bond Rating

  • Kim, Hyun-jung;Shin, Kyung-shik
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2003.05a
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    • pp.474-483
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    • 2003
  • A number of studies for corporate bond rating classification problems have demonstrated that artificial intelligence approaches such as Case-based reasoning (CBR) can be alternative methodologies to statistical techniques. CBR is a problem solving technique in that the case specific knowledge of past experience is utilized to find a most similar solution to the new problems. To build a successful CBR system to deal with human information processing, the representation of knowledge of each attribute is an important key factor We propose a hybrid approach of using fuzzy sets that describe the approximate phenomena of the real world because it handles inexact knowledge represented by common linguistic terms in a similar way as human reasoning compared to the other existing techniques. Integration of fuzzy sets with CBR is important to develop effective methods for dealing with vague and incomplete knowledge to statistical represent using membership value of fuzzy sets in CBR.

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Data Mining Techniques for Medical Informatics: Application to SNP Analysis

  • Chun, Se-Hak;Kim, Jin;Park, Yoon-Joo;Ham, Ki-Baek;Chun, Se-Chul
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2005.11a
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    • pp.258-263
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    • 2005
  • Haplotype-based analysis using high-density SNP markers have gained a great attention in evaluating genes in gene analysis and various clinical situations. However, there has been no research on disease diagnostic modeling based on SNPs analysis to our knowledge. The purpose of this study is to explore how knowledge discovery techniques are applied in medical informatics area and proposes a Case Based Reasoning (CBR) technique for diagnosis of gastric caner using Single Nucleotide Polymorphism(SNP).

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A New Algorithm of License Plate Location

  • Jin, Dan;Son, Young-Ik;Kim, Kab-Il
    • Proceedings of the KIEE Conference
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    • 2004.05a
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    • pp.108-110
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    • 2004
  • Automatic license plate recognition (LPR) is one of the critical techniques of the intelligent transportation system (ITS), in which license plate location plays an important role. In this paper, through surveying the international existing techniques, a new method for locating license plate is proposed: utilize row scan method to locate up and down boundary of the plate; and based on the location of up and down boundary, take advantage of the feature of plate area to locate left and right boundary of the plate. The tests of using the proposed algorithms have been conducted. The experimental results show that the proposed approaches are reasonable and accurate.

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