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

The cloud computing paradigm promises to deliver hardware to the end user at a low cost with an easy to use interface via the internet. This paper outlines an effort at the University of Huddersfield to deploy a private Infrastructure as a Service cloud to enhance the student learning experience. The paper covers the deployment methods and configurations for OpenStack along with the security provisions that were taken to deliver computer hardware. The rationale behind the provisions of virtual hardware and OS configurations have been defined in great detail supported by examples. This paper also covers how the resource has been used within the taught courses as a Virtual Laboratory, and in the research projects. A second use case of the cloud for Automated Formative Assesment (AFA) by using JClouds and Chef for Continuous Integration is presented. The AFA deployment is an example of a Software as a Service offering that has been added on to the IaaS cloud. This development has led to an increase in freedom for the student.

Using OpenStack to improve student experience in an H.E. environment

In this paper, the development of fault detection method for PV modules defective bypass diodes is presented. Bypass diodes are nowadays used in PV modules in order to enhance the output power production during partial shading conditions. However, there is lack of scientific research which demonstrates the detection of defective bypass diodes in PV systems. Thus, this paper propose a PV bypass diode fault detection classification based on Mamdani fuzzy logic system, which depends on the analysis of Vdrop, Voc , and Isc obtained from the I-V curve of the examined PV module. The fuzzy logic system depends on three inputs, namely percentage of voltage drop (PVD), percentage of open circuit voltage (POCV), and the percentage of short circuit current (PSCC). The proposed fuzzy system can detect up to 13 different faults associated with defective and non-defective bypass diodes. In addition, the proposed system was evaluated using two different PV modules under various defective bypass conditions. Finally, in order to investigate the variations of the PV module temperature during defective bypass diodes and partial shading conditions, i5 FLIR thermal camera was used.

/pdf/detecting-defective-bypass-diodes-in-photovoltaic-modules-4gwbzbsu97.pdf

Detecting Defective Bypass Diodes in Photovoltaic Modules using Mamdani Fuzzy Logic System

The developers of path planning algorithms and localization have significantly improved the usability of the robot those days. By using software such as a Gmapping, the robot will be able to create a map of the surrounding area. This research utilizes the 3D sensor Asus_xtion_ pro to create an indoor map using SLAM and create 3D models for surrounding objects with a Turtlebot robot. In the first case, we used the Turtlebot to generate an indoor map of the robotic lab room using the Gmapping ROS packet. In the second case, we used the robot to create 3D models for the surrounding objects in the room. We used the Raspberry Pi 3 as a replacement of the laptop that was used to control the Turtlebot. The same implementation of the first and second tasks have been repeated to compare the performance. The Raspberry Pi accomplishes the given tasks successfully; however, there is some delay due to the different on the CPU power. Finally, the low cost proposed solution is capable of running ROS based SLAM algorithm and using the point on cloud to create 3D models. In addition, the use of Raspberry Pi allows the robot save considerable amount of power in contrast with the use of a normal laptop.

An Asus_xtion_probased indoor MAPPING using a Raspberry Pi with Turtlebot robot Turtlebot robot

Development of 3D graph-based model to examine photovoltaic micro cracks

In this paper, a novel photovoltaic (PV) hot-spotting fault detection algorithm is presented. The algorithm is implemented using the analysis of 2580 polycrystalline silicon PV modules distributed across the U.K. The evaluation of the hot-spots is analyzed based on the cumulative density function (CDF) modeling technique, whereas the percentage of power loss (PPL) and PV degradation rate are used to categorize the hot-spots into eight different categories. Next, the implemented CDF models are used to predict possible PV hot-spots affecting the PV modules. The developed algorithm is evaluated using three different PV modules affected by three different hot-spots. Remarkably, the proposed CDF models precisely categorize the PV hot-spots with a high rate of accuracy of almost above 80%.

/pdf/novel-photovoltaic-hot-spotting-fault-detection-algorithm-jwktf37wji.pdf

Violeta Holmes

Papers

Using OpenStack to improve student experience in an H.E. environment

Detecting Defective Bypass Diodes in Photovoltaic Modules using Mamdani Fuzzy Logic System

An Asus_xtion_probased indoor MAPPING using a Raspberry Pi with Turtlebot robot Turtlebot robot

Development of 3D graph-based model to examine photovoltaic micro cracks

Novel Photovoltaic Hot-Spotting Fault Detection Algorithm