비만아 부모의 돌봄 경험: q 방법론

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

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Flow The Psychology Of Optimal Experience

Fast Fourier Transform

Creativity: Flow and the Psychology of Discovery and Invention

In this paper we present a cluster middleware, designed to implement a Linux-Windows Hybrid HPC Cluster, which not only holds the characteristics of both operating system, but also accepts and schedules jobs in both environments. Beowulf Clusters have become an economical and practical choice for small-and-medium-sized institutions to provide High Performance Computing (HPC)resources. The HPC resources are required for running simulations, image rendering and other calculations, and to support the software requiring a specific operating system. To support the software, small-scale computer clusters would have to be divided in two or more clusters if they are to run on a single operating system. The x86 virtualisation technology would help running multiple operating systems on one computer, but only with the latest hardware which many legacy Beowulf clusters do not have. To aid the institutions, who rely on legacy non-virtualisation-supported facilities rather than high-end HPC resources, we have developed and deployed a bi-stable hybrid system built around Linux CentOS 5.5 with the improved OSCAR middleware, and Windows Server 2008 and Windows HPC 2008 R2. This hybrid cluster is utilised as part of the University of Hudders field campus grid.

/pdf/hybrid-computer-cluster-with-high-flexibility-37ebsajm5l.pdf

Hybrid Computer Cluster with High Flexibility

Linux containers and Docker have gained immense popularity as a lightweight alternative to hypervisor based Virtual Machines (VMs). In the context of High Performance Computing and the scientific community, it is clear that containers can serve many useful purposes from system administration, to improved cluster resource management and as a format for sharing reproducible research. However, when compared to VMs, containers seem to trade isolation for performance and ease of use, which poses unique security challenges. In this paper we review how Docker is being used in science, highlight easy to perform exploits, and evaluate the impact of these on HPC deployments. We also summarise a number of strategies for hardening such a system to reduce the vulnerability of hosting User Defined Containers. Based on these, an original solution to enforce default options and container ownership for nonadministrative users in the HPC use case is presented, in addition to the experience of implementing such a system on a cluster at the University of Huddersfield.

Securing user defined containers for scientific computing

This paper investigates the impact of video streaming quality on bandwidth consumption during the transfer of video data from a humanoid robot `NAO' to computing devices, used to perform face recognition tasks, and to the cloud. It presents the results of profiling the network performance of connecting NAO with an edge controller, and discusses the effect of using different qualities of video streaming on the consumed up-link bandwidth. This study considers the limitation of the up-link bandwidth in the Wi-Fi network. It compares the performances of Wi-Fi and Ethernet connections between the NAO robot and a computer. In addition, it examines the accuracy of the face recognition tasks using various streaming scenarios, such as colored video and black & white video. It investigates real-time video streaming using a wide range of frame rates, and video qualities, and their impact on the bandwidth, and accuracy of face identification. The results of our investigations are used to determine the acceptable video quality, frame rate, buffering and bandwidth that would give optimal results in face recognition using NAO robot, and enable efficient data transfer to the cloud.

/pdf/impact-of-video-streaming-quality-on-bandwidth-in-humanoid-1btvilrvz8.pdf

Impact of video streaming quality on bandwidth in humanoid robot NAO connected to the cloud

Many physical situations involve chaotic systems implemented in hardware. Among them one-dimensional piecewise linear maps are popular candidates for such applications because of their property of generating robust chaos. In physical implementations, the control parameter of these maps may deviate from its ideal value due to hardware imprecision. Since the dynamics of a chaotic map is completely defined by its control parameter, one needs to know the value of the parameter in a hardware realisation. In this paper, we show that it is possible to determine the parameter, through the realisation of the unstable fixed point of the map, by utilising noise that is always present in the system. We present this in the form of an algorithm and demonstrate its efficacy through simulated results. We also determine the bounds on the signal-to-noise ratio required for successful parameter estimation. The proposed approach is expected to be beneficial to the existing noise reduction techniques and time series recovery algorithms that require a reasonably accurate knowledge of the map.

/pdf/parameter-estimation-for-1d-pwl-chaotic-maps-using-noisy-42217lsybx.pdf

Parameter estimation for 1D PWL chaotic maps using noisy dynamics

To optimize the energy production and usage, of a Renewable Energy System (RES), it is essential that an effective monitoring and control strategy is employed. In order to develop new control algorithms the designer needs to evaluate the RES parameters and at the same time simulate real test condition load profiles. The developed system is composed of a monitoring tool that measures and displays, in real-time, the parameters of Photo- Voltaic (PV) solar panels, and of an electronic load that can simulate load profiles such as the averaged domestic or stepped power consumption. The monitoring system is implemented using a µcontroller and the electronic load is composed of a MOSFET, driven using a Pulse Width Modulated (PWM) output of a PIC µcontroller. Furthermore, the power consumption profiles are stored in the internal memory of the PIC µcontroller. The obtained results demonstrate the ability of the system to monitor the current, voltage and temperature of PV panels as well as the ability to simulate different electronic load profiles.

/pdf/design-of-a-monitoring-and-test-system-for-pv-based-2gpu0mavnm.pdf

Violeta Holmes

Papers

Hybrid Computer Cluster with High Flexibility

Securing user defined containers for scientific computing

Impact of video streaming quality on bandwidth in humanoid robot NAO connected to the cloud

Parameter estimation for 1D PWL chaotic maps using noisy dynamics

Design of a Monitoring and Test system for PV based Renewable Energy Systems