• Title/Summary/Keyword: 불완전 데이터

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Markov Chain Analysis of Opportunistic Cognitive Radio with Imperfect Sensing (불완전 센싱 기회적 인지 전파망의 Markov Chain 분석)

  • Ahn, Hong-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.10 no.6
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    • pp.1-8
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    • 2010
  • Wireless multimedia service through the access to mobile telephone network or data network is a vital part of contemporary life, and the demand for frequency spectrum for new services is expected to explode as the ubiquitous computing proliferate. Cognitive radio is a technology, which automatically recognizes and searches for temporally and spatially unused frequency spectrum, then actively determines the communication method, bandwidth, etc. according to the environment, thus utilizing the limited spectrum resources efficiently. In this paper, we investigate the effects of imperfect sensing, misdetection and false alarm, on the primary and secondary users' spectrum usage through the analysis of continuous time Markov Chain. We analyzed the effects of the parameters such as sensing error, offered load on the system performance.

A Comparative Analysis of the Humanities Citation Tools: NAVER Scholar and KCI (인문학 분야의 인용 데이터정보원 비교 분석: 네이버 전문정보, KCI)

  • Park, Sang-Keun
    • Journal of the Korean Society for information Management
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    • v.30 no.1
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    • pp.33-50
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    • 2013
  • The purpose of this study was to identify differences between KCI and Naver Scholar as citation analysis tools. Four subcategories in the humanities category were selected as the subject of study. The recall of Naver Scholar was 64%(2,227 times) and the KCI's was 77%(2,665 times). There were some differences in the results at the individual article level or the subcategory level, but the gaps were not significant. Therefore, researchers who analyze citations are urged to use both databases because neither of them are complete, but supplementary to each other.

Uncertainty Measurement of Incomplete Information System based on Conditional Information Entropy (조건부 정보엔트로피에 의한 불완전 정보시스템의 불확실성 측정)

  • Park, Inkyoo
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.14 no.2
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    • pp.107-113
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    • 2014
  • The derivation of optimal information from decision table is based on the concept of indiscernibility relation and approximation space in rough set. Because decision table is more likely to be susceptible to the superposition or inconsistency in decision table, the reduction of attributes is a important concept in knowledge representation. While complete subsets of the attribute's domain is considered in algebraic definition, incomplete subsets of the attribute's domain is considered in information-theoretic definition. Therefore there is a marked difference between algebraic and information-theoretic definition. This paper proposes a conditional entropy using rough set as information theoretical measures in order to deduct the optimal information which may contain condition attributes and decision attribute of information system and shows its effectiveness.

A Convergence Study on Chest Compression Effects of CPR(Cardio-pulmonary resuscitation)Cube in the Layperson (일반인을 대상으로 한 CPR 큐브의 가슴압박 효과의 융합적 연구)

  • Yang, Hyun-Mo;Kim, Jin-Woo
    • Journal of the Korea Convergence Society
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    • v.10 no.3
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    • pp.221-225
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    • 2019
  • The purpose of this study is to provide the general public with basic data to facilitate the application of Cardio-Pulmonary Resuscitation(CPR). There were two groups using CPR mannequin and CPR cube, and participants were given three days of CPR training and two weeks later evaluated for chest compression. Participants recorded chest compression depth, rate of chest compression, accuracy of chest compression, insufficient recoil and incomplete place. There was a statistically significant difference in insufficient recoil and incomplete place in the study. The use of CPR cube to expand CPR education is also believed to be useful in terms of confidence and quality in implementing CPR.

Storage Techniques Using an Object-Relational Database for XML Documents (객체-관계형 데이터베이스를 이용한 XML 문서 저장 기법)

  • Lee, Wol-Young;Yong, Hwan-Seung
    • The KIPS Transactions:PartD
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    • v.11D no.2
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    • pp.305-316
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    • 2004
  • XML is becoming the de facto standard for data exchange over the Internet as a semistructured data which properties are irregular and incomplete. Therefore, to handle these data efficiently. what we use storage devices and storage techniques are Primary factors. In this paper, we developed storage techniques, which take the virtues of an object-relational database and support various query types needed for XML query languages without regard to the DTD. The techniques are capable with connecting naturally with conventional data and reducing overheads caused by the characteristics of an XML data model.

Characterization Study of Detector Module with Crystal Array for Small Animal PET: Monte Carlo Simulation (소동물 전용 양전자방출단층시스템의 섬광체 배열에 따른 특성 평가: 몬테칼로 시뮬레이션 연구)

  • Baek, Cheol-Ha
    • The Journal of the Korea Contents Association
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    • v.15 no.4
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    • pp.350-356
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    • 2015
  • The aim of this study is to perform simulations to design the detector module with crystal array by Monte Carlo simulation. For this purpose, a small animal PET scanner, employing module with 1~8 crystal array discrimination scheme, was designed. The proposed scanner has an inner diameter of 100 mm with detector modules in crystal array. Each module is composed of a 5.0 mm LSO crystal with a $2.0{\times}2.0mm^2$ sensitive area with a pitch 2.1 mm and 10.0 mm thickness. The LSO crystals are attached to the SiPM which has a dimension of $2.0{\times}2.0mm^2$. The detector module with crystal array of the designed PET detector was simulated using the Monte Carlo code GATE(Geant4 Application for Tomographic Emission). The detector is enough compensation for the loss of data in sinogram due to gaps between modules. The results showed that the high sensitivity and effectively reduced the problem about the missing data were greatly improved by using the detector module with 1 crystal array.

Blockchain Based Data-Preserving AI Learning Environment Model for Cyber Security System (AI 사이버보안 체계를 위한 블록체인 기반의 Data-Preserving AI 학습환경 모델)

  • Kim, Inkyung;Park, Namje
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.12
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    • pp.125-134
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    • 2019
  • As the limitations of the passive recognition domain, which is not guaranteed transparency of the operation process, AI technology has a vulnerability that depends on the data. Human error is inherent because raw data for artificial intelligence learning must be processed and inspected manually to secure data quality for the advancement of AI learning. In this study, we examine the necessity of learning data management before machine learning by analyzing inaccurate cases of AI learning data and cyber security attack method through the approach from cyber security perspective. In order to verify the learning data integrity, this paper presents the direction of data-preserving artificial intelligence system, a blockchain-based learning data environment model. The proposed method is expected to prevent the threats such as cyber attack and data corruption in providing and using data in the open network for data processing and raw data collection.

신경망모형을 이용한 아파트 가격 모형에 관한 연구

  • Hong, Han-Kook
    • Proceedings of the Korea Society for Industrial Systems Conference
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    • 2009.05a
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    • pp.220-226
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    • 2009
  • 본 연구는 회귀모형을 부정하기보다는 새로운 모형을 도입하여, 회귀모형의 문제점을 극복하고 회귀모형과 상호보완적인 모형을 소개하고자 본 연구를 수행하였다. 현재까지 인공지능 분야에서 널리 이용되어 왔던 신경망모형(Neural Network Model)은 입력변수가 불완전하고 변동폭이 넓은 경우에도 해석이 가능하며, 데이터 수가 적거나 불규칙한 경우라도 사례의 반복학습을 통해 오차를 줄여나가기 때문에, 데이터 수에 민감한 영향을 받는 회귀모형보다 정밀한 산정이 가능하다(박우열, 차정환, 강경인, 2002). 이러한 신경망모형에 아파트 특성들을 도입하여 아파트 가격을 정밀하고 유효하게 예측하는 것은 아파트 가격에 대한 연구 분야에 큰 의미가 있다. 그리고 주택에 관한 기존 연구와 신규 연구에 신경망모형이 활용될 수 있으리라 판단된다.

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신경망모형을 이용한 아파트 가격 모형에 관한 연구

  • Hong, Han-Kook
    • Proceedings of the Korean Society for Quality Management Conference
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    • 2010.04a
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    • pp.379-385
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    • 2010
  • 본 연구는 회귀모형을 부정하기보다는 새로운 모형을 도입하여, 회귀모형의 문제점을 극복하고 회귀모형과 상호보완적인 모형을 소개하고자 본 연구를 수행하였다. 현재까지 인공지능 분야에서 멀리 이용되어 왔던 신경망모형 (Neural Network Model)은 입력변수가 불완전하고 변동 폭이 넓은 경우에도 해석이 가능하며, 데이터 수가 적거나 불규칙한 경우라도 사례의 반복학습을 통해 오차를 줄여나가기 때문에, 데이터 수에 민감한 영향을 받는 회귀모형보다 정밀한 산정이 가능하다(박우열, 차정환, 강경인, 2002). 이러한 신경망모형에 아파트 특성들을 도입하여 아파트 가격을 정말하고 유효하게 예측하는 것은 아파트 가격에 대한 연구 분야에 큰 의미가 있다. 그리고 주택에 관한 기존 연구와 신규 연구에 신경망모형이 활용될 수 있으리라 판단된다.

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Implement of CRDI Engine Diagnostic System using the OBD-II (OBD-II를 이용한 CRDI 엔진 진단 시스템 구현)

  • Kim, Hwa-seon;Jang, Seong-jin;Jang, Jong-wook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2013.10a
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    • pp.459-462
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    • 2013
  • CRDI 시스템에서의 ECU는 센서의 정보를 분석하여 최적의 조건으로 엔진이 동작하도록 한다. 이러한 ECU의 프로그램 부분과 데이터 부분은 제작자에서만 변경할 수 있어 엔진을 진단하는 진단기의 경우 전문가가 아니면 사용하거나 내용을 이해하기가 쉽지 않다. 본 연구에서는 산업용 차량의 엔진 데이터 값을 OBD-II표준을 사용하여 입력받아 사용자 중심의 진단기를 PC 및 모바일용으로 개발하였다. 본 연구의 진단기는 운전자 중심의 진단 서비스를 제공하며, 자동차 고장진단 신호 및 센서 출력 신호를 유선시스템과 무선 시스템인 블루투스 모듈을 이용하여 실시간 통신이 제공되도록 함으로써 엔진이상으로 인한 사고의 예방이 가능하고, 최적의 조건으로 엔진이 동작하므로 과도한 배기가스 배출이나 불완전 연소가스 배출과 같은 대기환경오염을 예방할 수 있어 최근 대두되고 있는 에코산업에도 이바지 할 수 있을 것이다.

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