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

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Creativity: Flow and the Psychology of Discovery and Invention

This paper presents a design and development of a Grid-Connected Photo Voltaic Virtual Instrumentation System (GCPV-VIS) which is intended to facilitate monitoring and failure detection of a grid-connected photovoltaic plant using statistical methods. The approach has been validated using an experimental database of environment and electrical parameters from a 1.98 kip plant installed at the University of Huddersfield, United Kingdom. There are few instances of statistical tools being deployed in the analysis of PV measured data. The main focus of this research is, therefore, to devise a Virtual Instrument capable of simulating theoretical performances of PV systems and deploying statistical analysis of PV real-time data. The fault detection is based on the comparison between measured and theoretical output power using t-test statistical analysis. The obtained results indicate that the proposed method can detect the faults of the grid-connected PV system, and can be used for continuous monitoring of PV system status.

Grid-connected PV virtual instrument system (GCPV-VIS) for detecting photovoltaic failure

This paper presents a novel detection technique for inspecting solar cells’ micro cracks. Initially, the solar cell is captured using the electroluminescence (EL) method, then processed by the proposed technique. The technique consists of three stages: the first stage combines two images, the first image is the crack-free (healthy) solar cell, whereas the second is the cracked solar-cell image. Both output images are processed into a bit-by-bit gridding technique, which enables the detection of all bits in the considered area of the cracked solar cell. The second stage uses an OR gate between each examined bit for both healthy and cracked solar cells. The final calibrated image presents a high-quality and low-noise structure, thus making it easier to identify the micro crack size, location, and its orientation. In order to examine the effectiveness of the proposed technique, three different cracked photovoltaic (PV) solar cells have been examined. The results show that the micro cracks’ size, orientation, and location are more visible using the proposed technique. In addition, the developed technique has been validated using a full-scale PV module, and compared with up-to-date available PV micro crack detection methods.

/pdf/novel-photovoltaic-micro-crack-detection-technique-1dnle4t08s.pdf

Novel Photovoltaic Micro Crack Detection Technique

High-Performance Computing (HPC) and the ability to process large amounts of data are of paramount importance for UK business and economy as outlined by Rt Hon David Willetts MP at the HPC and Big Data conference in February 2014. However there is a shortage of skills and available training in HPC to prepare and expand the workforce for the HPC and Big Data research and development. Currently, HPC skills are acquired mainly by students and staff taking part in HPC-related research projects, MSc courses, and at the dedicated training centres such as Edinburgh Universitys EPCC. There are few UK universities teaching the HPC, Clusters and Grid Computing courses at the undergraduate level. To address the issue of skills shortages in the HPC it is essential to provide teaching and training as part of both postgraduate and undergraduate courses. The design and development of such courses is challenging since the technologies and software in the fields of large scale distributed systems such as Cluster, Cloud and Grid computing are undergoing continuous change. The students completing the HPC courses should be proficient in these evolving technologies and equipped with practical and theoretical skills for future jobs in this fast developing area. In this paper we present our experience in developing the HPC, Cluster and Grid modules including a review of existing HPC courses offered at the UK universities. The topics covered in the modules are described, as well as the coursework project based on practical laboratory work. We conclude with an evaluation based on our experience over the last ten years in developing and delivering the HPC modules on the undergraduate courses, with suggestions for future work.

Developing High Performance Computing Resources for Teaching Cluster and Grid Computing Courses

Broadband log-periodic antenna optimization is a very challenging problem for antenna design. However, up to now, the universal method for log-periodic antenna design is Carrel's method dating from the 1960s. This paper compares five antenna design optimization algorithms (Differential Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive Invasive Weed) as solutions to the broadband antenna design problem. The algorithms compared are evolutionary algorithms which use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. The focus of the comparison is given to the algorithm with the best results, nevertheless, it becomes obvious that the algorithm which produces the best fitness values (Invasive Weed Optimization) requires very substantial computational resources due to its random search nature. Log-periodic antennas (LPDA: Log-Periodic Dipole Arrays) are frequently preferred for broadband applications due to their very good directivity characteristics and flat gain curve. The purpose of this study is, in the first place, the accurate modelling of the log-periodic type of antennas, the detailed calculation of the important characteristics of the antennas under test (gain, vswr, front-to-back ratio) and the confrontation with accurate measurements results. In the second place, various evolutionary optimization algorithms are used, and notably the relatively new (2006) Invasive Weed Optimization (IWO) algorithm of Mehrabian & Lucas, for optimizing the performance of a log-periodic antenna with respect to maximum gain, Side-Lobe-Levels (SLL) and matching to 50 Ohms, VSWR. The multi-objective optimization algorithm is minimising a so-called fitness function including all the above requirements and leads to the optimum dipole lengths, spacing between the dipoles, and dipole wire diameters. In some optimization cases, a constant dipole wire radius is used in order to simplify the construction of the antenna. Fig. 1 is depicting the main antenna geometrical characteristics.

/pdf/comparison-of-evolutionary-algorithms-for-lpda-antenna-25kp5jzllq.pdf

Comparison of evolutionary algorithms for LPDA antenna optimization

In our research, we aim to evaluate the effectiveness of games-based learning within a computer aided design and manufacture undergraduate module. Although widely used in a selection of subject are...

/pdf/game-based-learning-in-mechanical-engineering-education-case-hycgadeq45.pdf

Violeta Holmes

Papers

Grid-connected PV virtual instrument system (GCPV-VIS) for detecting photovoltaic failure

Novel Photovoltaic Micro Crack Detection Technique

Developing High Performance Computing Resources for Teaching Cluster and Grid Computing Courses

Comparison of evolutionary algorithms for LPDA antenna optimization

Game-based learning in mechanical engineering education: Case study of games-based learning application in computer aided design assembly