7,698 research outputs found

    A study of publish/subscribe systems for real-time grid monitoring

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    Monitoring and controlling a large number of geographically distributed scientific instruments is a challenging task. Some operations on these instruments require real-time (or quasi real-time) response which make it even more difficult. In this paper, we describe the requirements of distributed monitoring for a possible future electrical power grid based on real-time extensions to grid computing. We examine several standards and publish/subscribe middleware candidates, some of which were specially designed and developed for grid monitoring. We analyze their architecture and functionality, and discuss the advantages and disadvantages. We report on a series of tests to measure their real-time performance and scalability

    Distributed monitoring and control of future power systems via grid computing

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    It is now widely accepted within the electrical power supply industry that future power systems operates with significantly larger numbers of small-scale highly dispersed generation units that use renewable energy sources and also reduce carbon dioxide emissions. In order to operate such future power systems securely and efficiently it will be necessary to monitor and control output levels and scheduling when connecting such generation to a power system especially when it is typically embedded at the distribution level. Traditional monitoring and control technology that is currently employed at the transmission level is highly centralized and not scalable to include such significant increases in distributed and embedded generation. However, this paper proposes and demonstrates the adoption of a relatively new technology 'grid computing' that can provide both a scalable and universally adoptable solution to the problems associated with the distributed monitoring and control of future power systems

    A Tool to Assist in the Analysis of Gaze Patterns in Upper Limb Prosthetic Use

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    Gaze tracking, where the point of regard of a subject is mapped onto the image of the scene the subject sees, can be employed to study the visual attention of the users of prosthetic hands. It can show whether the user is pays greater attention to the actions of their prosthetic hand as they use it to perform manipulation tasks, compared with the general population. Conventional analysis of the video data requires a human operator to identify the key areas of interest in every frame of the video data. Computer vision techniques can assist with this process, but a fully automatic systems requires large training sets. Prosthetic investigations tend to be limited in numbers. However, if the assessment task is well controlled, it is possible to make a much simpler system that uses initial input from an operator to identify the areas of interest and then the computer tracks the objects throughout the task. The tool described here, employs colour separation and edge detection on images of the visual field to identify the objects to be tracked. To simplify the computer's task further, this test uses the Southampton Hand Assessment Procedure (SHAP), to define the activity spatially and temporarily, reducing the search space for the computer. The work reported here is the development a software tool capable of identifying and tracking the Points of Regard and Areas of Interest, throughout an activity with minimum human operator input. Gaze was successfully tracked for fourteen unimpaired subjects, which was compared with the gaze of four users of myoelectric hands. The SHAP cutting task is described and the differences in attention observed with a greater number of shorter fixations by the prosthesis users compared to unimpaired subjects. There was less looking ahead to the next phase of the task by the prosthesis users

    A strengthened and sensorised custom silicone glove for use with an intelligent prosthetic hand

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    External gloves for anthropomorphic prosthetic hands protect the mechanisms from damage and ingress of contaminants and can be used to create a pleasing, life-like appearance. The properties of the glove material are the result of a compromise between the resistance to damage and flexibility. Silicone gloves are easier to flex and keep clean, but also more easily damaged. This paper details the use of nanoclay fillers to enhance the properties of silicone, successfully increasing strength whilst maintaining flexibility. The performance of the enhanced silicone is as robust and resistant to tear and puncture as commercial gloves, while being more flexible. This flexibility makes the incorporation of a piezo-electric pressure sensor based on the EEonyx conductive fabric, practical. A sandwich of the cloth and copper fabric creates the sensor, which decreases in resistance with increasing pressure. The sensors are characterised and production variability within the silicone are tested. Three sensors are incorporated into a glove made to fit around a Southampton Intelligent Hand. The hand adapts its grip shape and force depending on the object held. The technology is adaptable and it can be incorporated in a glove produced to fit any prosthetic hand. <br/

    Scalability tests of R-GMA-based grid job monitoring system for CMS Monte Carlo data production

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    Copyright @ 2004 IEEEHigh-energy physics experiments, such as the compact muon solenoid (CMS) at the large hadron collider (LHC), have large-scale data processing computing requirements. The grid has been chosen as the solution. One important challenge when using the grid for large-scale data processing is the ability to monitor the large numbers of jobs that are being executed simultaneously at multiple remote sites. The relational grid monitoring architecture (R-GMA) is a monitoring and information management service for distributed resources based on the GMA of the Global Grid Forum. We report on the first measurements of R-GMA as part of a monitoring architecture to be used for batch submission of multiple Monte Carlo simulation jobs running on a CMS-specific LHC computing grid test bed. Monitoring information was transferred in real time from remote execution nodes back to the submitting host and stored in a database. In scalability tests, the job submission rates supported by successive releases of R-GMA improved significantly, approaching that expected in full-scale production

    Performance of R-GMA for monitoring grid jobs for CMS data production

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    High energy physics experiments, such as the Compact Muon Solenoid (CMS) at the CERN laboratory in Geneva, have large-scale data processing requirements, with data accumulating at a rate of 1 Gbyte/s. This load comfortably exceeds any previous processing requirements and we believe it may be most efficiently satisfied through grid computing. Furthermore the production of large quantities of Monte Carlo simulated data provides an ideal test bed for grid technologies and will drive their development. One important challenge when using the grid for data analysis is the ability to monitor transparently the large number of jobs that are being executed simultaneously at multiple remote sites. R-GMA is a monitoring and information management service for distributed resources based on the grid monitoring architecture of the Global Grid Forum. We have previously developed a system allowing us to test its performance under a heavy load while using few real grid resources. We present the latest results on this system running on the LCG 2 grid test bed using the LCG 2.6.0 middleware release. For a sustained load equivalent to 7 generations of 1000 simultaneous jobs, R-GMA was able to transfer all published messages and store them in a database for 98% of the individual jobs. The failures experienced were at the remote sites, rather than at the archiver's MON box as had been expected
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