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

Linux container technology has more than proved itself useful in cloud computing as a lightweight alternative to virtualisation, whilst still offering good enough resource isolation. Docker is emerging as a popular runtime for managing Linux containers, providing both management tools and a simple file format. Research into the performance of containers compared to traditional Virtual Machines and bare metal shows that containers can achieve near native speeds in processing, memory and network throughput. A technology born in the cloud, it is making inroads into scientific computing both as a format for sharing experimental applications and as a paradigm for cloud based execution. However, it has unexplored uses in traditional cluster and grid computing. It provides a run time environment in which there is an opportunity for typical cluster and parallel applications to execute at native speeds, whilst being bundled with their own specific (or legacy) library versions and support software. This offers a solution to the Achilles heel of cluster and grid computing that requires the user to hold intimate knowledge of the local software infrastructure. Using Docker brings us a step closer to more effective job and resource management within the cluster by providing both a common definition format and a repeatable execution environment. In this paper we present the results of our work in deploying Docker containers in the cluster environment and an evaluation of its suitability as a runtime for high performance parallel execution. Our findings suggest that containers can be used to tailor the run time environment for an MPI application without compromising performance, and would provide better Quality of Service for users of scientific computing.

Orchestrating Docker Containers in the HPC Environment.

This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) system For a given set of working conditions, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation LabVIEW software Furthermore, a third-order polynomial function is used to generate two detection limits (high and low limits) for the VR and PR ratios The high and low detection limits are compared with real-time long-term data measurements from a 11 kWp GCPV system installed at the University of Huddersfield, United Kingdom Furthermore, samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system The maximum detection accuracy (DA) of the proposed algorithm before considering the fuzzy logic system is equal to 9527%; however, the fault DA is increased up to a minimum value of 988% after considering the fuzzy logic system

Multi-layer photovoltaic fault detection algorithm

Orchestrating Docker Containers in the HPC Environment

The impact of photovoltaic (PV) hot-spots is assessed through the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. PV hot-spots were categorized into eight different groups using the percentage of power loss. All hot-spots groups were modeled using the cumulative density function, state-of-the-art geographical mapping, and performance ratio (PR) analysis. Significantly, it was found that 92.15% of the PV modules affected by hot-spotted PV string are located in northern U.K., where the effect of low-temperature levels, heavy snow, and hoarfrost are more significant. Finally, it was found that the distribution of PV modules affected by only one hot-spotted solar cell are likely (82.41%) located in coastal locations. Hence, coastal locations expect to have lower risks for causing multiple hot-spotted solar cells in PV modules, compared to central and colder locations. The PR of all examined PV modules was analyzed. It was evident that the mean PR is significantly reduced due to the existence of hot-spots in the PV modules. The least difference in the PR between healthy and hot-spotted PV modules is equal to −0.83%, whereas the most difference is calculated at −15.47%.

/pdf/evaluating-power-loss-and-performance-ratio-of-hot-spotted-ixuw6ns0e1.pdf

Evaluating Power Loss and Performance Ratio of Hot-Spotted Photovoltaic Modules

The document text similarity measurement and analysis is a growing application of Natural Language Processing. This paper presents the results of using different techniques for semantic text similarity measurements in documents used for safety-critical systems. The research objective of this work is to measure the degree of semantic equivalence of multi-word sentences for rules and procedures contained in the documents on railway safety. These documents, with unstructured data and different formats, need to be preprocessed and cleaned before the set of Natural Language Processing toolkits, and Jaccard and Cosine similarity metrics are applied. The results demonstrate that it is feasible to automate the process of identifying equivalent rules and procedures and measure similarity of disparate safety-critical documents using Natural language processing and similarity measurement techniques.

Violeta Holmes

Papers

Orchestrating Docker Containers in the HPC Environment.

Multi-layer photovoltaic fault detection algorithm

Orchestrating Docker Containers in the HPC Environment

Evaluating Power Loss and Performance Ratio of Hot-Spotted Photovoltaic Modules

Document Processing: Methods for Semantic Text Similarity Analysis