2 research outputs found
People mover project (control system part)
No full textThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2011.Cataloged from PDF version of thesis report.Includes bibliographical references (page 69).People-Mover is an electrical self-motivated medium. The term is generally used only to describe systems serving relatively small areas such as airports, downtown districts or theme parks, but is sometimes applied to considerably more complex automated systems. Normally, the train runs autonomously form terminals to terminals. Its speed and movement can be controlled wirelessly.
Practically, people movers typically consist of driverless trains with up to about four cars each capable of carrying 20 to 100 passengers who are mostly standing. They have been successfully used for surface transportation in airports for over thirty years. A new category of automated people mover called personal rapid transit (PRT) is being implemented at London's Heathrow InternationalAirport.
In this project we designed and implemented this independent rail system which is planned for people transportation from system specifications down to fully working system as well as hardware and software. Main goal of this project is to convert the theoretical knowledge into practical system. On that aspect this project is perfect, because here all features of Electrical Engineering (control system, circuit designing and implementation, wireless communication, micro electronics etc.) as well as some mechanical engineering features are being used.Shayla Azad BhuyanChoudhury Tanzia SiddquiSazia Afrin ChowdhuryMD Kazi ZaffrullahB. Electrical and Electronic Engineerin
Visual representation of bug report assignment recommendations
No full textSoftware development projects typically use an issue tracking system where the project members and users can either report faults or request additional features. Each of these reports needs to be triaged to determine such things as the priority of the report or which developers should be assigned to resolve the report. To assist a triager with report assigning, an assignment recommender has been suggested as a means of improving the process. However, proposed assignment recommenders typically present a list of developer names, without an explanation of the rationale. This work focuses on providing visual explanations for bug report assignment recommendations. We examine the use of a supervised and unsupervised machine learning algorithm for the assignment recommendation from which we can provide recommendation rationale. We explore the use of three types of graphs for the presentation of the rationale and validate their use-cases and usability through a small user study
