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Multi-GPUs nodes are becoming the platform of choice for graph processing. However, in the multiple GPUs environment, there are two main challenges in designing a graph processing system. First, the system suffers from huge communication overhead. GPUs and CPUs are connected through PCIe, whose bandwidth is far smaller than that of GPU memory. Second, the system is developed based on BSP (Bulk Synchronous...
This paper deals with the problem of identifying the mathematical model of a dry-clutch transmission system, from input-output data experimentally collected on a real vehicle. The proposed identification procedure is based on a set-membership identification approach, where a-priori information on the structure of the physical model are taken into account in the selection of the model class. Parameter...
This research focuses on improving business process worked under workflow simulation. In the business process, there are several properties to be considered such as person, cost, time. The current workflow simulation cannot combine these properties to simulate the situation for business decision making. In this paper, the improving business workflow simulation is proposed by adding the business properties...
In this paper we propose an model driven approach for the specification and the execution of flexible workflows composed from cloud services. Flexibility of workflow means fast reactivity to internal and external changes. The basic challenge for workflow now is the ability to respond dynamic changes. To fulfill, we define functional and behavioral views of the flexible workflow. The first view is...
The data locality is significant factor which has a direct impact on the performance of MapReduce framework. Several previous works have proposed alternative scheduling algorithms for improving the performance by increasing data locality. Nevertheless, their studies had focused the data locality on physical MapReduce cluster. As more and more deployment of MapReduce cluster have been on virtual environment,...
The problem of building statistical models of cyber-physical systems using operational data is addressed in this paper, using thecase study of aircraft engines. These models serve as a complementto physics-based models, which may not accurately reflect the operational performance of systems. The accurate modeling of fuelflow rate is an essential aspect of analyzing aircraft engine performance. In...
The integration of an agent-based simulation model as a component of a game engine for serious games targeting prevention and health promotion in the context of infectious diseases is described. It is argued that a combination of agent-based modelling and serious games can help provide a more realistic picture of disease spread than conventional ecoepidemiological models, by facilitating the integration...
The exponential growth of digital data sources has the potential to transform all aspects of society and our lives. However, to achieve this impact, the data has to be processed promptly to extract insights that can drive decision making. Further, traditional approaches that rely on moving data to remote data centers for processing are no longer feasible. Instead, new approaches that effectively leverage...
We present a cyber-physical-human (CPHS) distributed computing framework, AquaSCALE, for gathering, analyzing and localizing anomalous operations of increasingly failure-prone community water services. Today, detection of water pipe leaks takes hours to days. AquaSCALE leverages dynamic data from multiple information sources including IoT (Internet of Things) sensing data, geophysical data, human...
Simulation is a valuable tool for robotics research and development, and various simulation packages have been proposed. However, we are aware of no freely-available packages which implement the required fidelity to accurately model earth-moving robots that manipulate the terrain itself. The software which does exist for this is difficult if not impossible to run in real-time while achieving the desired...
Robotic agents that do everyday manipulation tasks can hugely benefit from being able to predict consequences of their actions just before the execution. However, such a simulation technique is usually computationally-expensive and may not be achieved with agents' self computing power. For this problem, cloud robotics may offer a solution. Cloud robotics is an emerging field in the intersection of...
There is a growing interest to utilize Computer Graphics (CG) renderings to generate large scale annotated data in order to train machine learning systems, such as Deep convolutional neural networks, for Computer Vision (CV). However, there has been a long debate on the usefulness of CG generated data for tuning CV systems (even from the 1980's). Especially, the impact of modeling errors and computational...
Study of flow instability in turbine engine compressors is crucial to understand the inception and evolution of engine stall. Aerodynamics experts have been working on detecting the early signs of stall in order to devise novel stall suppression technologies. A state-of-the-art Navier-Stokes based, time-accurate computational fluid dynamics simulator, TURBO, has been developed in NASA to enhance the...
Cloud computing is a distributed computing paradigm and provides services to the customers through the internet. Cloud computing virtualizes system by pooling and sharing resources. Customers store their data on the cloud using resources and they provide the access of data to the users. Cloud computing has to ensure the security of the data stored in the cloud. Access to the data stored in the cloud...
Forecasting Multiple Time Series (MTS) consists of multiple time series with no relation between them and independent of each other. Predicting each time series independently may lead to increase in time and cost. In this paper, we formalize the problem of predicting the multiple time series together over a MTS database. The proposed framework addresses the following issues. First, it build the initial...
We present Asterism, an open source data-intensive framework, which combines the strengths of traditional workflow management systems with new parallel stream-based dataflow systems to run data-intensive applications across multiple heterogeneous resources, without users having to: re-formulate their methods according to different enactment engines; manage the data distribution across systems; parallelize...
The use of cloud resources for processing and analysing medical data has the potential to revolutionise the treatment of a number of chronic conditions. For example, it has been shown that it is possible to manage conditions such as diabetes, obesity and cardiovascular disease by increasing the right forms of physical activity for the patient. Typically, movement data is collected for a patient over...
Modeling tools and operators help the user / developer to identify the processing field on the top of the sequence and to send into the computing module only the data related to the requested result. The remaining data is not relevant and it will slow down the processing. The biggest challenge nowadays is to get high quality processing results with a reduced computing time and costs. The processing...
The information rate nowadays is expanding very quickly and contains complex and heterogeneous data types (text, images, videos, GPS data, purchase transactions) that require powerful computing engines, able to easily store and process such complex structures. Gartner's definition of the 3Vs (volume, velocity, variety) describing this expansion of data will then lead to extract the unnamed forth V...
In this work, we compare nine methods of experimental identification (polynomial models, artificial neural networks and deterministic methods). We use measured data from the small turbojet engine iSTC-21v to create experimental models through programming environment MATLAB/Simulink. Comparing the output of these calculated models with the real measured data, we get the amount of the mean absolute...
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