• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.340-367
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• 2018

Storage services integration can be done for achieving high availability, improving data access performance and scalability while preventing vendor lock-in. However, multiple services environment management and interoperability have become a critical issue as a result of service architectures and communication interfaces heterogeneity. Storage federation model provides the integration of multiple heterogeneous and self-sufficient storage systems with a single control point and automated decision making about data distribution. In order to integrate diverse heterogeneous storage services into a single storage pool, we are proposing a storage service federation framework named BoxBroker. Moreover, an automated decision model based on a policy-driven data distribution algorithm and a service evaluation method is proposed enabling BoxBroker to make optimal decisions. Finally, a demonstration of our proposal capabilities is presented and discussed.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2005년도 추계학술대회 및 정기총회
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• pp.65-70
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• 2005

Recently, Grid is embossed as a new issue according to the need of cooperation related to distributed resources, data sharing, Interaction and so on. It focuses on sharing of large scale resources, high-performance, applications of new paradigms, which improved more than established distributed computing. Because of the environmental specificity distributed geographically and dynamic, the most important problem in grid environment is to share and to allocate distributed grid resources. This paper proposes supply-driven strategies model that is applicable for resource management in grid environment and presents a optimal resource allocation algorithm based on resource demands. Supply-driven strategies model can offer efficient resource management by transaction allocation based on user demand and provider strategy. This paper implements the supply-driven strategies model on the DEVS modeling and simulation environment and shows the efficiency and excellency of this model by comparing with established models.

• 한국IT서비스학회지
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• 제7권4호
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• pp.231-246
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• 2008

This paper suggests an e-learning system model, a goal-driven personalized e-learning system, which increase the effectiveness of learning. An e-learning system following this model makes the learner choose the learning goal. The learner's choice would lead learning. Therefore, the system enables a personalized adaptive learning, which will raise the effectiveness of learning. Moreover, this paper proposes a SCORM standard, which modifies SCORM 2004 that has been insufficient to implement the "goal driven personalized e-learning system." We add a data model representing the goal that motivates learning, and propose a standard for statistics on learning objects usage. We propose each standard for contents model and sequencing information model which are parts of "goal driven personalized e-learning system." We also propose that manifest file should be added for the standard for contents model, and the file which represents the information of hierarchical structure and general learning paths should be added for the standard for sequencing information model. As a result, the system could sequence and search learning objects. We proposed an e-learning system and modified SCORM standards by considering the many factors of adaptive learning. We expect that the system enables us to optimally design personalized e-learning system.

• Ecology and Resilient Infrastructure
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• 제9권1호
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• pp.24-35
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• 2022

수질분야에서 물재해 안정성 강화를 위해 과거와 현재의 수질을 분석하여 예측하는 기술을 지속적으로 고도화하는 것이 필요하며 데이터 기반의 예측 모형이 하나의 대안으로 대두되고 있다. 데이터 기반 모형은 복잡하고 광범위한 자료의 양을 기반으로 구축되기 때문에 보다 신뢰도 있는 결과를 얻을 수 있는 입력자료의 조합을 위한 상관관계 분석방법의 적용이 필수적이다. 본 연구에서는 보다 신속하고 정확한 데이터 기반의 수질 예측 모형을 구성하기 위한 선행단계로 Gamma Test를 적용하였다. 먼저 팔당댐의 다양한 수문조건에 따른 해당 유역의 복잡성과 정밀성이 재현된 과거와 현재의 일단위 수질을 최대한 확보하고자 물리적 기반 모형 (HSPF, EFDC)을 구동하였다. 팔당댐 수질예측지점과 팔당댐으로 유입되는 주요 하천의 수질을 대상으로 Gamma Test를 수행한 후 해석결과 (Gamma, Gradient, Standar Error, V-Ratio)를 통해 최적의 자료조합을 선정하는 방법을 제시하였다. 본 연구의 결과는 데이터 기반 모형 구축 시 반복적인 수행과정을 생략하여 시간을 단축하면서 보다 효율적으로 최적의 입력자료를 선정할 수 있는 정량적인 기준을 보여준다.

• 대한의용생체공학회:의공학회지
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• 제35권5호
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• pp.142-150
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• 2014

The key to model-based iterative reconstruction (MBIR) algorithms for transmission computed tomography lies in the ability to accurately model the data formation process from the emitted photons produced in the transmission source to the measured photons at the detector. Therefore, accurately modeling the system matrix that accounts for the data formation process is a prerequisite for MBIR-based algorithms. In this work we compared quantitative performance of the three representative ray-driven methods for calculating the system matrix; the ray-tracing method (RTM), the distance-driven method (DDM), and the strip-area based method (SAM). We implemented the ordered-subsets separable surrogates (OS-SPS) algorithm using the three different models and performed simulation studies using a digital phantom. Our experimental results show that, in spite of the more advanced features in the SAM and DDM, the traditional RTM implemented in the OS-SPS algorithm with an edge-preserving regularizer out-performs the SAM and DDM in restoring complex edges in the underlying object. The performance of the RTM in smooth regions was also comparable to that of the SAM or DDM.

• 산업경영시스템학회지
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• 제41권3호
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• pp.90-96
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• 2018

This paper proposed data driven techniques to forecast the time point of water management of the water reservoir without measuring manganese concentration with the empirical data as Juam Dam of years of 2015 and 2016. When the manganese concentration near the surface of water goes over the criteria of 0.3mg/l, the water management should be taken. But, it is economically inefficient to measure manganese concentration frequently and regularly. The water turnover by the difference of water temperature make manganese on the floor of water reservoir rise up to surface and increase the manganese concentration near the surface. Manganese concentration and water temperature from the surface to depth of 20m by 5m have been time plotted and exploratory analyzed to show that the water turnover could be used instead of measuring manganese concentration to know the time point of water management. Two models for forecasting the time point of water turnover were proposed and compared as follow: The regression model of CR20, the consistency ratio of water temperature, between the surface and the depth of 20m on the lagged variables of CR20 and the first lag variable of max temperature. And, the Box-Jenkins model of CR20 as ARIMA (2, 1, 2).

• 한국해양학회지:바다
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• 제28권4호
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• pp.133-142
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• 2023

이 논문에서는 RNN (Recurrent Neural Networks)-LSTM (Long Short-Term Memory) 을 적용하여 Lorenz 시스템을 예측하는 자료 기반 인공지능 모델을 구축하고, 이 모델이 미분방정식을 차분화하여 해를 구하는 역학 모델을 대체할 수 있는지 가능성을 진단하였다. 구축된 자료기반 모델이 초기 조건의 작은 교란이 근본적으로 다른 결과를 만들어내는 Lorenz 시스템의 카오스적인 특성을 반영한다는 것과, 시스템의 안정적인 두 개의 닻을 중심으로 운동하면서 전이 과정을 반복하는 특성, "결정론적 불규칙 흐름"의 특성, 분기 현상을 모사한다는 것을 확인하였다. 또한, 적분 시간 간격을 조절함으로써 전산자원을 절감할 수 있는 자료기반 모델의 장점을 보였다. 향후 자료기반 모델의 정교화와 자료기반 모델을 위한 자료동화 기법의 연구를 통해 자료기반 인공지능 모델의 활용성을 확대할 수 있을 것으로 기대한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.666-668
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• 1998

In this paper we illustrate the design of a node label data flow machine based on self-timed paradigm. Data flow machines differ from most other parallel architectures, they are based on the concept of the data-driven computation model instead of the program store computation model. Since the data-driven computation model provides the excution of instructions asynchronously, it is natural to implement a data flow machine using self timed circuits.

• Smart Structures and Systems
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• 제15권3호
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• pp.897-911
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• 2015

This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.

• KEPCO Journal on Electric Power and Energy
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• 제4권2호
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• pp.101-105
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• 2018

본 논문에서는 가스 터빈 축 진동 신호 비정상 상태 분석의 사례 연구를 위해 커널 회귀 모델을 적용한다. 원격으로 전송되는 발전소 가스터빈의 진동데이터에 커널 회귀 모델을 적용하여 설비를 실시간으로 감시 및 분석 외에도, 축진동 신호의 비정상 상태를 분석하기 위하여 활용될 수 있다. 정상운전 중에 측정한 가스터빈의 정상적인 축진동 데이터 기반의 훈련데이터를 사용하여 생성한 자동연관커널회귀의 경험적 모델을 생성하고 적용할 수 있다. 이 데이터 기반 모델의 예측치를 실시간 데이터와 비교하여 신호의 상태를 분석하고 잔차를 감시하여 이상상태에 대한 분석 정보를 제공할 수 있다. 이상상태에서 발생하는 잔차는 비정상적으로 변화됨으로서 비정상 상태를 분석 할 수 있다. 본 논문에서 커널회귀모델은 축진동 센서의 신호 이상의 원인 분석 사례에서 고장을 구분할 수 있는 정보를 제공한다.