Earticle

As a further development of the simulation grid, the cloud simulation platform is a new kind of network modeling as well as simulation platforms, and one of the hottest research directions in the cloud computing. While brings about the users with a lot of convenience, the cloud simulation shows also many severe security issues with its own characteristics, which can’t be solved effectively by the traditional access control strategies. According to the traditional role based access control(RBAC) model, this paper proposed a tree proxy-based and service-oriented access control(TPSAC) model. In the TPSAC model, a multilevel inherited meta permission and a multi-tree child-sibling linked list were adopted to separate the permission loading function and the permission distribution function to achieve a multi-granularity and quantized access control with the cloud simulation. A verification experiment on the CloudSim simulation platform was conducted then to demonstrate that the TPSAC model achieved the desired result.

When Cloud Platform for Computer Aided Engineering Simulation (CPCAES) are developed to use Service-Oriented Architecture (SOA), on the one hand, it’s necessary to accurately express the service requirements of users, on the other hand it’s need to semantic of the existing services in the platform, and then to match the ontologies. The existing Ontology Web Language for Services / Universal Description Discovery and Integration (OWL-S / UDDI) algorithm has weak matching precision, and not meet the Computer Aided Engineering (CAE) simulation applications. The other semantic similarity algorithms also have weakness in judgment factor, or do not meet the requirements of CAE simulation applications for matching to service requirement ontology in cloud platform. Such that, the authors present a kind of ontology of service requirement for CAE simulation, model the content of the ontology, including resource requirements, computing requirements, computing job requirements, input, output, etc. The authors give the ontology mapping relationship between Ontology Web Language for Services (OWL-S) and the ontology, give the matching decision rules for the ontologies, propose a matching algorithm for matching the ontologies, and compare with the classic OWL-S / UDDI matching algorithm and a similarity matching algorithm proposed in other paper in the ability for measure the similarity quantity of services, the ability to suit the applications, as well as algorithms recall rate and precision rate. The results show the matching algorithm proposed in this paper is more suitable for service requirement ontology of CPCAES, can use the quantify value for the similarity analysis to the ontologies, and has higher precision rate and higher recall rate, which can reach about 90%. The research work in this paper is used in the second prototype of CPCAES, and the research team is developing the third prototype based on semantic Web Services and SOA framework.

As the absence of effective depictive expression of uncertain information effects the effectively selection of optimum state, the selection method of optimum state of qualitative modeling and reasoning process in complex system based on QSIM and cloud model is proposed. Firstly the calculation method of similarity degree using the distance based on cloud model is given. Then the selection methods of optimum state of qualitative modeling using similarity degree based on cloud model are studied. The selection method of optimum state using the distance based on cloud model is used in constraint filter. Lastly the proposed method is applied to the case of modeling in the agile supply chain management for verifying the proposed methods. The methods proposed in this paper have the merit of objective expression the uncertain information. And the fusion of qualitative and quantitative information can be used in complex system simulation

Modeling method to render cloud on the simulation is divided into two schemes, static and dynamic, depending on the accessibility of the cloud. Two schemes show the complete dissimilar properties in the aspects of realism and real-time processing. Thus, in this paper, we propose a new scheme that merges two modeling methods of the cloud for flight simulation by using the L-system. Since the proposed scheme considers both realism and rendering speed, it is possible to be implemented the formation and growth of the cloud in real-time manner. Finally, the proposed method is evaluated by the experimental results of real implementation in the flight simulation software based on OpenSceneGraph(OSG).

균일한 정상유동을 하는 유체 안에 가로놓인 원주로부터 일어나는 vortex유출을 이산 vortex 방법을 써서 vortex cloud모형으로 simulation했다. 원주표면에서 생겨나는 와도를 매 시간 간격마다 미리 정해진 많은 갯수의 이산화된 초생 vortex로 나타낸 후 기 유출된 vortex들에 합류시켜 누적된 vortex들의 운동을 탐색함으로서 vortex 분포 상태의 진화를 알아낸다. vortex들의 이동은 cloud-in-cell 기법을 써서 추적 하였고 항력 계수와 양력 계수는 Sarpkaya의 식과 Lee의 식을 써서 계산하여 실험치와 비교하였다. 계산 인수 사이의 상호 연관 관계를 논고하였으며 부분적이나마 적정값 선정 원칙을 제시하였다.

The vortex shedding from a circular cylinder placed in a steady uniform stream is simulated by the vortex cloud model of the discrete vortex method. The vorticity created at the cylinder surface is discretely represented by a number of nascent vortices at each time step and the motion of these cumulative vortices is monitored to produce the evolution of the vortex distribution pattern. Convection of vortices was traced by the vortex-in-cell technique and the force coefficients were calculated by both Sarpkaya's formulae and Lee's formulae for comparison. Discussions concerning the interrelation between the computational parameters and some principles for choosing the suitable values are included.

Radiative transfer modeling of ice clouds is developed. Ice clouds located near tropopause reflect most of sunlight, thus atmospheric and surface effects can be minimized. Cloud properties such as cloud optical thickness (COT) and effective radius are important parameters to determine the magnitude of reflectance, while atmospheric and surface parameters rarely affect reflectance value. For selected homogeneous cloud pixels of MODerate Resolution Imaging Spectroradiometer (MODIS) observation, reflectances are calculated using MODIS cloud products as inputs of radiative transfer model (RTM). For three types of phase function (Henyey-Greenstein, Garcia-Siewert, Baum) calculated reflectances are compared with observations for validation. All cases show linear relationship between simulated values and measured values, however each represent different bias and slope. The result shows that phase function determine angular distribution of reflectance.

The study evaluates how smart nanoparticles affect beam structural performance while using computational resources hosted on remote servers. An enhanced advanced adaptive harmony search algorithm (AAHS) serves to boost optimization efficiency levels. The algorithm makes two sequential parameter adjustment stages which start by adapting harmony memory through variable bandwidth methods and proceed with adaptive step-size implementations. The research investigates the best design parameters for ZnO nanoparticle reinforced nanocomposite sinusoidal beams under different axial force and foundation property conditions and applied voltage levels. Results show that the proposed AAHS method outperforms alternative optimization methods according to comparative research. Under 50 GPa buckling force and 100 V applied voltage the optimal beam should have L/h ratio of 4.425 together with 118 GPa foundation spring constant, 29 Pa shearing constant and 0.055 ZnO nanoparticle volume fraction. The study demonstrates that all three factors namely applied voltage, buckling force and foundation stiffness critically affect optimization of beams.

This paper describes a fast circuit simulation technique using the latency insertion method (LIM) with a parallel and distributed leapfrog algorithm. The numerical simulation results on the PC cluster system that uses the cloud computing system are shown. As a result, it is confirmed that our method is very useful and practical.

Interference and collisions often occur in the loading process at shipyards. Existing simulation methods focus primarily on resource processes and schedules, and there is a lack of real-time reflection in the complex and highly variable loading process. This study aims to develop a spatial environment incorporating real-time product data, such as hulls, and confirms its effectiveness by simulating various construction scenarios. As a method, a near real-time spatial environment based on broadband laser scanning was established, with the situation of loading heavy cargo assumed when converting an existing ship into an LNG dual-fuel propulsion ship. A case study simulation of near-real-time cargo loading processes was then conducted using Unity 3D to confirm the interference and collision risks within the spatial environment. The results indicated that interference occurred in structures previously not identified in the design data, and a collision occurred during the loading object erection phase. The simulation confirmed that the identification of interference and collision risks during the erection phase highlights the need for a relocation or removal process of potential hazards before erection takes place. An improved erection simulation that integrates near real-time data could effectively prevent interference and collision risks.

In spite of considerable progress in the recent decades, there still remain large uncertainties in numerical cloud models. In this study, effects of uncertainty in terminal velocity of graupel on cloud simulation are investigated. For this, a two-dimensional bin microphysics cloud model is employed, and deep convective clouds are simulated under idealized environmental conditions. In the sensitivity experiments, the terminal velocity of graupel is changed to twice and half the velocity in the control experiment. In the experiment with fast graupel terminal velocity, a large amount of graupel mass is present in the lower layer. On the other hand, in the experiment with slow graupel terminal velocity, almost all graupel mass remains in the upper layer. The graupel size distribution exhibits that as graupel terminal velocity increases, in the lower layer, the number of graupel particles increases and the peak radius in the graupel mass size distribution decreases. In the experiment with fast graupel terminal velocity, the vertical velocity is decreased mainly due to a decrease in riming that leads to a decrease in latent heat release and an increase in evaporative cooling via evaporation, sublimation, and melting that leads to more stable atmosphere. This decrease in vertical velocity causes graupel particles to fall toward the ground easier. By the changes in graupel terminal velocity, the accumulated surface precipitation amount differs up to about two times. This study reveals that the terminal velocity of graupel should be estimated more accurately than it is now.

Cloud droplet activation process is well described by $K{\ddot{o}}hler$ theory and several parameterizations based on $K{\ddot{o}}hler$ theory are used in a wide range of models to represent this process. Here, we test the two different method of calculating the solute effect in the $K{\ddot{o}}hler$ equation, i.e., osmotic coefficient method (OSM) and ${\kappa}-K{\ddot{o}}hler$ method (KK). To do that, each method is implemented in the cloud droplet activation parameterization module of WRF-CHEM (Weather Research and Forecasting model coupled with Chemistry) model. It is assumed that aerosols are composed of five major components (i.e., sulfate, organic matter, black carbon, mineral dust, and sea salt). Both methods calculate similar representative hygroscopicity parameter values of 0.2~0.3 over the land, and 0.6~0.7 over the ocean, which are close to estimated values in previous studies. Simulated precipitation, and meteorological variables (i.e., specific heat and temperature) show good agreement with reanalysis. Spatial patterns of precipitation and liquid water path from model results and satellite data show similarity in general, but on regional scale spatial patterns and intensity show some discrepancy. However, meteorological variables, precipitation, and liquid water path do not show significant differences between OSM and KK simulations. So we suggest that the relatively simple KK method can be a good alternative to the OSM method that requires various information of density, molecular weight and dissociation number of each individual species in calculating the solute effect.

클라우드 컴퓨팅은 정보의 다양성과 빅데이터를 IT자원을 이용하여 처리할 수 있는 컴퓨팅 개념이다. 정부는 신재생에너지를 활용한 전력생산을 장려하기 위해 RPS를 시행하였고 시스템을 구축하여 지리적으로 분산되어 있는 빅데이터를 수집하여 운영하고 있다. RPS제도를 이행하는 발전사업자들은 의무할당량 중 REC 부족분을 타 발전사업자들로부터 REC를 구매하여 조달해야 한다. REC는 자율시장에 근거하여 거래되고 있고, 매매가격의 편차가 크기 때문에 RPS 빅데이터를 통해 형평성있는 REC가격을 예측할 필요가 있다. 본 연구에서는 부정확한 가격추이와 규칙을 정량적으로 표현하여, 클라우드 환경에서 퍼지기반으로 REC가격을 예측하는 방법을 제안한다. 클라우드 환경에서 RPS 빅데이터를 통한 상호연관성과 가격결정에 영향을 주는 변수들에 대한 분석이 가능하고 시뮬레이션을 통해 REC 가격을 예측할 수 있다. 클라우드 환경에서 퍼지로직은 매물수량과 매매가격을 이용하여 투명성있는 REC 가격을 예측하고 장기적으로 수렴된 가격을 제시할 것이다.

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

최근 코로나19로 인한 사회적 거리두기의 지속과 팬데믹의 재발가능성은 고위험군으로 분류되는 65세 이상 고령자뿐 아니라 모든 세대에서의 사회적 단절 증가로 심리적 안녕감을 위축시키고 있다. 본 논문에서는 문헌 연구를 기반으로, 클라우드 서비스 API 활용 관점에서의 반려로봇 서비스 아키텍처를 AWS RoboMaker, AWS IoT Greengrass, Amazon SageMaker, AWS AI 서비스를 중심으로 제시하고 고령자의 심리적 안녕감을 개선하는 대화 기반 핵심 시나리오를 도출한다.

The continuation of social distancing due to the recent COVID-19 and the possibility of a recurrence of the pandemic are reducing psychological well-being by increasing social disconnection in all generations, not just the elderly over 65 who are classified as high-risk groups. In this paper, based on literature research, a service architecture of companion robots is proposed from the viewpoint of cloud service API utilization focusing on AWS RoboMaker, AWS IoT Greengrass, Amazon SageMaker, and AWS AI services and also presented dialogue-based core scenarios for improving the psychological well-being of the elderly

Surface cracks in buildings are critical indicators of structural safety, and even minor defects may require immediate repair if they exceed regulatory thresholds. Such cracks can signify underlying structural flaws or construction deficiencies, and may also cause psychological discomfort and practical inconvenience for occupants. In particular, comprehensive inspection of apartment buildings is challenging due to the large number of units and extensive surface area. Traditional visual inspections conducted by skilled personnel rely heavily on human experience and are inherently subjective. To address this issue, this study proposes a parametric method for measuring crack widths and determining whether they meet the criteria for critical defects. A quantitative standard for crack evaluation is presented, with a focus on identifying major cracks classified as critical. A parametric mock-up was developed for algorithm creation and validation. To identify cracks exceeding the critical threshold of 0.3 mm, specifications for 3D scanners and point cloud density were derived, and wall surfaces with simulated cracks were generated accordingly. Crack-related points were extracted, and the distances between them were measured using a parametric algorithm to determine the width of the crack. The algorithm was then validated by comparing the results against the critical defect threshold. Preprocessing steps in the point cloud acquisition process were excluded in this study, and non-orthogonal architectural forms were not considered. Future research will aim to automate initial crack detection and preprocessing using deep learning techniques, enhancing the practical application of this approach in post-construction building maintenance.

한국형차세대원자로 APR-1400의 안전감압계통이 작동하면 물, 공기 및 증기가 sparger를 통해 격납건물 내 핵연료재장전 수조로 차례로 방출된다. 방출 과정 중 생기는 여러 현상 중에서 수조 내의 공기 기포군은 저주파, 고진폭의 진동 하중을 발생하며, 주파수가 침수 구조물의 고유 주파수와 거의 같은 경우에는 구조물에 심각한 영향을 줄 수 있다. 이러한 현상은 복잡하기 때문에 주파수와 하중에 대한 규명은 주로 실험에 의존해 왔으며 수치해석적 연구는 이루어지지 않았다. 본 연구에서는 sparger를 통해 수조 내로 방출되는 공기 기포군의 거동에 대한 수치해석을 상용 열수력 해석 코드인 FLUENT Version 4.5를 사용하여 수행하였다. 다상유동 해석모델중 VOF(Volume Of Fluid)모델을 사용하여 물, 공기 및 증기 등의 다상유동을 모의하였다. 해석결과를 sparger 개발을 위해 ABB-Atom이 수행하였던 실험결과와 비교하여 만족할만한 결과를 얻었다.

If the safety depressurization system of APR-1400, the Korean next generation reactor, is in operation, water, air and steam are successively discharging into a in-containment refueling water storage tank through spargers. Among the phenomena occurring during the discharging processes, the air bubble clouds produce a low-frequency and high-amplitude oscillatory loading, which may result in the most significant damages to the submerged structures if the oscillation frequency is the same or close to the natural frequency of the structures. The involved phenomena are so complicated that most of the prediction of frequency and pressure loads has been resorted to experimental work and computational approach has been precluded. This study deals with a numerical simulation on the behavior of air bubble clouds discharging into a water pool through a sparger, by using a commercial thermal hydraulic analysis code, FLUENT, version 4.5. Among the multiphase flow models, the VOF (Volume Of Fluid) model was selected to simulate the water, air and steam flows. A satisfactory result was obtained comparing the analysis results with the ABB-Atom test results which had been performed for the development of sparser.