• 한국정보처리학회논문지
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• 제5권6호
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• pp.1390-1403
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• 1998

데이터베이스 공유 시스템(Database Sharing System : DSS)은 고성능 트랜잭션 처리를 위해 제안된 시스템이다. DSS에서 고속의 통신망으로 연결된 노드들은 별도의 메모리와 운영체제, 그리고 DBMS를 가지며, 데이터베이스를 저장하고 있는 디스크는 모든 노드에 의해 공유된다. 빈번한 디스크 액세스를 피하기 위해 각 노드는 자신의 메모리 버퍼에 최근에 액세스한 페이지들을 캐싱한다. 그러나 동일한 페이지가 여러 노드에 의해 동시에 캐싱될 수 있으므로, 각 노드가 최신의 내용을 항상 엑세스하기 위해서는 캐싱된 데이터의 일관성이 유지되어야 한다. 본 논문에서는 로킹 오버헤드를 줄이기 위해 주사본 권한을 이용하여 데이터베이스를 논리적으로 분할한 DSS 구조에서 필요한 캐쉬 일관성 기법들을 제안한다. 제안된 기법들은 캐쉬 일관성을 위해 소요되는 메시지 전송량과 디스크 입출력 오버헤드를 줄임으로써 성능을 향상시키며, 데이터베이스 부하가 동적으로 변하는 경우에도 효율적으로 동작한다는 장점을 갖는다.

• 산업경영시스템학회지
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• 제45권4호
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• pp.180-186
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• 2022

Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., soil moisture sensors) and micro-climate monitoring sensors (e.g., thermometers and irradiance sensors) is installed in the APV system. This study aims at introducing a decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and performance estimation. Particularly, the real-time monitoring data is used as an input of the DSS system for performance estimation of an APV system in terms of production yields of crops and monetary benefit so that a data-driven function is implemented in the proposed system. The proposed DSS is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the smart farming technology in the field of agriculture.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.214-217
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• 1996

Early decision support systems (DSS) were the "passive" decision support systems in the sense that the systems only able to do what the users explicitly direct them to do. But some researchers such as Raghav Rao et al. [51 showed architectures to suggest general idea of the innovative DSS systems which offer active form of decision support, say, "active Intelligent Decision Support Systems(active IDSS)". The system can perform not only what the users want to do but some voluntary (or involuntary) intelligent works. This paper presents the issues in the design of the active IDSS in the domain of investment evaluation, a domain area where few researchers have suggested frameworks or architectures to discriminate good investment from bad one. We propose a new paradigm, by utilizing historical investment results using neural network and Multivariate Discriminant Analysis(MDA), to identify goodness of investment. A new active IDSS architecture which consists of neural network, expert system and three components of the traditional passive DSS is suggested with some scenario based results.nario based results.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2007년도 춘계학술대회
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• pp.139-151
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• 2007

Decision-making in the crisis management happens in dynamic, rapidly changing, and often unpredictable distributed environments. Crisis management Decision Support Systems (DSSs) in E-Government are challenged by the need to use it availably at anytime, from anywhere, and even under any-situation. In this paper the reasons of developing distributed architecture for crisis management Decision Support Systems (DSSs) in E-Government are analyzed. Consequently, a distributed architecture for crisis management Decision Support System (DSS) is proposed in this paper. Finally it is implemented by Web Services. If crisis management Decision Support System (DSS) based on distributed architecture is implemented by Web Service, then it can provide decision support for decision-makers to deal with crisis at anytime, from anywhere, and even under any-situation.

• 한국태양에너지학회 논문집
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• 제22권4호
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• pp.68-76
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• 2002

This paper discusses thermal and ventilation performance which might be caused by the adoption of one of specific building facade techniques, Double Skin System(DSS). One building with a prototypical DSS was selected and systematically investigated through field monitoring and computer simulation techniques. A network model of ventilation was successfully made using COMIS to evaluate ventilation performance of the system which can hardly be done by field measurements. Various operating conditions of air conditioning on/off and window opening were implemented in this type of building. Through the appropriate operation of the DSS in summer, simulation-based and experimental results implicate that it can lead to cooling energy savings.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.601-604
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• 2008

가속도분석 시스템은 Quanterra 기록계에서 지원하는 DSS(Data Subscription Service)를 이용하여 전국의 가속도계로 부터 수신된 DSS 값을 가속도 값으로 계산한다. 지진발생시 지도상에 관측된 지점의 가속도 값을 표출하고 가속도계의 설치 장소에 따른 가속도 값 특성을 분석하여 분리 적용하는 방안과 가속도계의 비정상 작동에 대한 대책을 연구한다.

• 경영과학
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• 제2권
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• pp.72-77
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• 1985

최근 선진국에서는 S/W 개발의 낮은 생산성에 따른 개발정체현상 (Backlog), 전산운영(System Management)과 관련한 현업의 불만점증과 개 개발된 S/W의 보전업무(Maintenance)의 과다현상등이 심각하게 논의되고 있는데 이는 S/W의 위기라 불리워지며 다음과 같이 설명이 되고 있다.(중략)

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1998년도 추계학술대회 논문집
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• pp.133-136
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• 1998

Supporting decision-makers involves not only functions of conventional DSS such as problem identification, alternative generation and selection but also education and business processing. The purpose of this paper is to propose Decision-Maker Support System (DMSS) that comprehensively supports decision-makers who should enhance their decision quality. The DMSS consists of three core subsystem: distance learning system, conventional DSS and ERP system.

• 천문학회보
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• 제41권1호
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• pp.54.3-55
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• 2016

DEEP-South Scheduling and Data reduction System (DS SDS) consists of two separate software subsystems: Headquarters (HQ) at Korea Astronomy and Space Science Institute (KASI), and SDS Data Reduction (DR) at Korea Institute of Science and Technology Information (KISTI). HQ runs the DS Scheduling System (DSS), DS database (DB), and Control and Monitoring (C&M) designed to monitor and manage overall SDS actions. DR hosts the Moving Object Detection Program (MODP), Asteroid Spin Analysis Package (ASAP) and Data Reduction Control & Monitor (DRCM). MODP and ASAP conduct data analysis while DRCM checks if they are working properly. The functions of SDS is three-fold: (1) DSS plans schedules for three KMTNet stations, (2) DR performs data analysis, and (3) C&M checks whether DSS and DR function properly. DSS prepares a list of targets, aids users in deciding observation priority, calculates exposure time, schedules nightly runs, and archives data using Database Management System (DBMS). MODP is designed to discover moving objects on CCD images, while ASAP performs photometry and reconstructs their lightcurves. Based on ASAP lightcurve analysis and/or MODP astrometry, DSS schedules follow-up runs to be conducted with a part of, or three KMTNet telescopes.

• Environmental Engineering Research
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• 제14권2호
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• pp.111-119
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• 2009

A prototype GIS-based decision support system (DSS) was developed by using a database management system (DBMS), a model management system (MMS), a knowledge-based system (KBS), a graphical user interface (GUI), and a geographical information system (GIS). The method of selecting a dispersion model or a modeling scheme, originally devised by Park and Seok, was developed using our GIS-based DSS. The performances of candidate models or modeling schemes were evaluated by using a single index(statistical score) derived by applying fuzzy inference to statistical measures between the measured and predicted concentrations. The fumigation dispersion model performed better than the models such as industrial source complex short term model(ISCST) and atmospheric dispersion model system(ADMS) for the prediction of the ground level $SO_2$ (1 hr) concentration in a coastal area. However, its coincidence level between actual and calculated values was poor. The neural network models were found to improve the accuracy of predicted ground level $SO_2$ concentration significantly, compared to the fumigation models. The GIS-based DSS may serve as a useful tool for selecting the best prediction model, even for complex terrains.