• Smart Structures and Systems
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• 제5권2호
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• pp.119-137
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• 2009

Smart sensors densely distributed over structures can use their computational and wireless communication capabilities to provide rich information for structural health monitoring (SHM). Though smart sensor technology has seen substantial advances during recent years, implementation of smart sensors on full-scale structures has been limited. Hardware resources available on smart sensors restrict data acquisition capabilities; intrinsic to these wireless systems are packet loss, data synchronization errors, and relatively slow communication speeds. This paper addresses these issues under the hardware limitation by developing corresponding middleware services. The reliable communication service requires only a few acknowledgement packets to compensate for packet loss. The synchronized sensing service employs a resampling approach leaving the need for strict control of sensing timing. The data aggregation service makes use of application specific knowledge and distributed computing to suppress data transfer requirements. These middleware services are implemented on the Imote2 smart sensor platform, and their efficacy demonstrated experimentally.

• 한국화재소방학회논문지
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• 제17권4호
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• pp.98-104
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• 2003

대구 지하철역 제연의 문제점과 그 대책을 조사하기위해 지하철역 승강장 계단 주위 10 m${\times}$3 m${\times}$5.4 m 의 공간에서의 제연을 FDS로 모사하였다. 200 ㎾의 폴리우레탄 화재와 각 급기구와 배기구의 공기유량 0.9 ㎥/s에 대한 급기방식의 제연성능을 온도와 연기입자의 분포로 조사한 결과, 급기방식은 제연효과가 거의 없어 연기를 신속하게 배출하지 못함을 알 수 있었다. 또 세가지 기계제연방식 중에서 배기방식의 제연성능이 가장 우수하므로, 급기방식을 단순히 배기방식으로 전환함으로써 제연성능을 대폭 개선할 수 있음을 확인하였다.

• Structural Engineering and Mechanics
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• 제37권1호
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• pp.17-37
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• 2011

This paper presents a new Internet-based computational framework for the realistic simulation of multi-scale response of structural systems. Two levels of parallel processing are involved in this frame work: multiple local distributed computing environments connected by the Internet to form a cluster-to-cluster distributed computing environment. To utilize such a computing environment for a realistic simulation, the simulation task of a structural system has been separated into a simulation of a simplified global model in association with several detailed component models using various scales. These related multi-scale simulation tasks are distributed amongst clusters and connected to form a multi-level hierarchy. The Internet is used to coordinate geographically distributed simulation tasks. This paper also presents the development of a software framework that can support the multi-level hierarchical simulation approach, in a cluster-to-cluster distributed computing environment. The architectural design of the program also allows the integration of several multi-scale models to be clients and servers under a single platform. Such integration can combine geographically distributed computing resources to produce realistic simulations of structural systems.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.14-14
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• 2011

Groundwater in the Waikatoregion is a valuable resource for agriculture, water supply, forestry and industries. The 434,000 ha study area comprises the upper Waikato River catchment from the outflow of Lake Taupo (New Zealand's largest lake) through to Lake Karapiro (a man-made hydro lake with high recreational value) (Figure 1). Water quality in the area is naturally high. However, there are indications that this quality is deteriorating as a result of land use intensification and deforestation. Compounding this concern for decision makers is the lag time between land use changes and the realisation of effects on groundwater and surface water quality. It is expected that the effects of land use changes have not yet fully manifested, and additional intensification may take decadesto fully develop, further compounding the deterioration. Consequently, Environment Waikato (EW) have proposed a programme of work to develop a groundwater model to assist managing water quality and appropriate policy development within the catchment. One of the most important and critical decisions of any modelling exercise is the choice of the modelling platform to be used. It must not inhibit future decision making and scenario exploration and needs to allow as accurate representation of reality as feasible. With this in mind, EW requested that two modelling platforms, MODFLOW/MT3DMS and FEFLOW, be assessed for their ability to deliver the long-term modelling objectives for this project. The two platforms were compared alongside various selection criteria including complexity of model set-up and development, computational burden, ease and accuracy of representing surface water-groundwater interactions, precision in predictive scenarios and ease with which the model input and output files could be interrogated. This latter criteria is essential for the thorough assessment of predictive uncertainty with third-party software, such as PEST. This paper will focus on the attributes of each modelling platform and the comparison of the two approaches against the key criteria in the selection process. Primarily due to the ease of handling and developing input files and interrogating output files, MODFLOW/MT3DMS was selected as the preferred platform. Other advantages and disadvantages of the two modelling platforms were somewhat balanced. A preliminary regional groundwater numerical model of the study area was subsequently constructed. The model simulates steady state groundwater and surface water flows using MODFLOW and transient contaminant transport with MT3DMS, focussing on nitrate nitrogen (as a conservative solute). Geological information for this project was provided by GNS Science. Professional peer review was completed by Dr. Vince Bidwell (of Lincoln Environmental).

• 정보과학회 컴퓨팅의 실제 논문지
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• 제21권1호
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• pp.13-18
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• 2015

최근 시뮬레이션이 다양한 계산과학 및 산업 분야에서 널리 활용되면서, 컴퓨팅 자원에 대한 그 요구사항 또한 점점 다양해지고 있다. 특히 이러한 요구는 기존 슈퍼컴퓨터와 같은 CPU 중심의 자원에서 벗어나, 사용자 별 설정 및 활용이 쉬운 유연하고 효율적인 고성능 클라우드 컴퓨팅의 필요성이 커지고 있다. 클라우드 컴퓨팅을 이용해 시뮬레이션을 수행하기 위해서는 다수의 가상머신으로 이루어진 대규모의 가상 클러스터의 실시간 구축이 필연적이다. 이러한 대규모의 가상 클러스터 생성은 동시 다발적인 가상머신 요청을 야기시키고, 이 요청들에 의해 대기 시간이 매우 길어지는 문제가 발생할 수 있다. 이런 문제의 주요 원인은 각각의 가상머신에서 사용되는 가상 이미지를 생성, 복사하는 작업들간에 병목 현상 때문이다. 본 논문에서는 가상머신 이미지들의 생성 시간을 최소화하고, 가상 클러스터의 I/O 성능을 향상시킬 수 있는 방법을 제안한다. 또한 다양한 실험을 통해 제안한 방법의 우수성을 검증한다.

• 한국전산구조공학회논문집
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• 제35권2호
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• pp.109-117
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• 2022

기존 화석 연료의 고갈 및 환경오염의 문제와 대용량 발전을 위하여 해양환경 및 자원을 이용한 친환경에너지 발전에 대한 연구 및 개발이 증가하고 있으며, 이 중 높은 발전 효율을 가진 해상태양광 발전에 대한 연구가 크게 증가하고 있다. 환경하중이 비교적 약한 내수조건과 달리, 환경하중이 강한 해양에서의 태양광 발전을 위해서는 더 강한 강성의 구조재를 사용해야 한다. 하지만, 구조재의 생산 가능성, 무게를 포함한 구조물 특성 및 경제적 효율성 등의 제약조건이 발생할 수 있다. 따라서, 본 연구에서는 부유식 방파제를 설치함으로써 태양광구조물에 작용하는 파랑하중을 감소시켜 구조재의 강성 강화를 최소화하고자 하였다. 부유식 방파제의 크기 및 구조물로부터의 거리를 변화하여 이에 따른 파랑하중 및 구조재 응력의 감소 정도를 확인하였다. 다수 부력체의 상호간섭을 고려한 파랑하중의 경우, 고차경계요소법(Higher-Order Boundary Element Emthod)을 이용해 산정하였으며, 구조재에 작용하는 응력은 유한요소법(Finite Element Method)을 통해 평가하였다. 각 조건에서의 최대응력을 분석 및 비교함으로써 해상태양광 발전 시스템에 대한 부유식 방파제의 영향을 확인하였으며, 부유식 방파제의 크기가 파랑하중 및 구조재 응력 감소에 큰 영향을 미침을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제10권4호
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• pp.165-171
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• 2005

고속의 컴퓨터, 대용량 저장장치, 초고속 네트워크는 현재 우리가 쉽게 접근할 수 있는 컴퓨팅 인프라이다. 하지만 분자 시뮬레이션과 같은 자연과학 및 응용과학 분야에의 시뮬레이션에서는 여전히 더 많은 컴퓨팅 파워, 더 커다란 저장장치를 필요로 한다. 이러한 요구는 그리드 컴퓨팅(1)이라는 차세대 분산 컴퓨팅 환경을 우리에게 제시하였다. 하지만 현재까지 제안된 그리드 컴퓨팅 기술은 통신 인터페이스와 프로토콜 등의 글로버스 툴킷(2, 3)과 같은 미들웨어 수준에 대한 연구만이 중심이 되고 있다. 이러한 환경은 응용 플랫폼에 대한 연구의 부족과 어플리케이션의 부족을 가져왔으며, 그 결과 사용자는 그리드 컴퓨팅 기술에 대한 이용을 미비하게 만들었다. 따라서 본 연구에서는 분자 시뮬레이션 그리드 (MGrid: Molecular Simulation Grid System) 에서 적용을 목적으로 고효율(High Throughput)의 시뮬레이션 실험을 위한 사용자 환경(User Environment)을 정의하고, 사용자에게 친근한 추상화된 작업 모델을 제안함으로써 보다 효율적이고 안정적인 그리드 자원 이용을 가능하게 한다.(4, 5)

• Structural Engineering and Mechanics
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• 제39권6호
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• pp.767-793
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• 2011

An efficient edge-based smoothed finite element method (ES-FEM) has been recently developed for solving solid mechanics problems. The ES-FEM uses triangular elements that can be generated easily for complicated domains. In this paper, the complexity study of the ES-FEM based on triangular elements is conducted in detail, which confirms the ES-FEM produces higher computational efficiency compared to the FEM. Therefore, the ES-FEM offers an excellent platform for adaptive analysis, and this paper presents an efficient adaptive procedure based on the ES-FEM. A smoothing domain based energy (SDE) error estimate is first devised making use of the features of the ES-FEM. The present error estimate differs from the conventional approaches and evaluates error based on smoothing domains used in the ES-FEM. A local refinement technique based on the Delaunay algorithm is then implemented to achieve high efficiency in the mesh refinement. In this refinement technique, each node is assigned a scaling factor to control the local nodal density, and refinement of the neighborhood of a node is accomplished simply by adjusting its scaling factor. Intensive numerical studies, including an actual engineering problem of an automobile part, show that the proposed adaptive procedure is effective and efficient in producing solutions of desired accuracy.

• 센서학회지
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• 제29권1호
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• pp.19-26
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• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• Advances in Computational Design
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• 제2권3호
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• pp.165-194
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• 2017

The present work proposes the thermo mechanically induced statistics of nonlinear transverse central deflection of elastically supported functionally graded (FG) plate subjected to static loadings with random system properties. The FG plate is supported on two parameters Pasternak foundation with Winkler cubic nonlinearity. The random system properties such as material properties of FG material, external loading and foundation parameters are assumed as uncorrelated random variables. The material properties are assumed as non-uniform temperature distribution with temperature dependent (TD) material properties. The basic formulation for static is based on higher order shear deformation theory (HSDT) with von-Karman nonlinear strain kinematics through Newton-Raphson method. A second order perturbation technique (SOPT) and direct Monte Carlo simulation (MCS) are used to compute the nonlinear governing equation. The effects of load parameters, plate thickness ratios, aspect ratios, volume fraction, exponent, foundation parameters, and boundary conditions with random system properties are examined through parametric studies. The results of present approaches are compared with those results available in the literature and by employing direct Monte Carlo simulation (MCS).