Using a high fidelity CCGT simulator for building prognostic systems

Pressure to reduce maintenance costs in power utilities has resulted in growing interest in prognostic monitoring systems. Accurate prediction of the occurrence of faults and failures would result not only in improved system maintenance schedules but also in improved availability and system efficiency. The desire for such a system has driven research into the emerging field of prognostics for complex systems. At the same time there is a general move towards implementing high fidelity simulators of complex systems especially within the power generation field, with the nuclear power industry taking the lead. Whilst the simulators mainly function in a training capacity, the high fidelity of the simulations can also allow representative data to be gathered. Using simulators in this way enables systems and components to be damaged, run to failure and reset all without cost or danger to personnel as well as allowing fault scenarios to be run faster than real time. Consequently, this allows failure data to be gathered which is normally otherwise unavailable or limited, enabling analysis and research of fault progression in critical and high value systems. This paper presents a case study of utilising a high fidelity industrial Combined Cycle Gas Turbine (CCGT) simulator to generate fault data, and shows how this can be employed to build a prognostic system. Advantages and disadvantages of this approach are discussed

The Use of Crowd-sourced Cycling Data for Cycling Analyses (Strava)

This presentation includes several research outcomes from the UBDC transport work package. Specifically, we validate the usefulness of Strava data for cycling behaviour analysis and provide two examples (i.e., commuting cycling behaviour and evaluation of new cycling infra)

The Use of Crowd-sourced Cycling Data for Cycling Analyses (Strava)

This presentation includes several research outcomes from the UBDC transport work package. Specifically, we validate the usefulness of Strava data for cycling behaviour analysis and provide two examples (i.e., commuting cycling behaviour and evaluation of new cycling infra)

The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data

The benefits of cycling have been well established for several decades. It can improve public health and make cities more active and environmentally friendly. Due to the significant net benefits, many local governments in Scotland have promoted cycling. Glasgow City Council constructed four significant pieces of cycling infrastructure between 2013 and 2015, partly in preparation for the 2014 Commonwealth Games and partly to encourage cycling more generally. This required substantial capital investment. However, the effectiveness of these big new infrastructure investments has not been well examined, mostly due to data limitations. In this study, we utilised data from the activity tracking app Strava for the years 2013–2016 and fixed effects panel data regression models to examine whether the new cycling infrastructure has increased cycling volumes on these routes. Our results show that three of the infrastructure projects have a positive effect on the monthly total volume of cycling trips made by users of the app, with flows up by around 12% to 18%. Although this result is promising, it needs to be interpreted with care due to the characteristics of the data

Can accessing the Internet while travelling encourage commuters to use public transport regardless of their attitude?

Due to advances in technology (in particular the Internet), people have become less restricted by space and time, and can use travel time more productively by using their Internet-connected mobile devices on the move. Some operators provided Internet access on public transport to increase ridership. This has been shown to increase ridership, however it is not clear if it can induce people who prefer private cars to public transport to consider using public transport. In this paper, we examine the relationship between the frequency of using the Internet while commuting or travelling, and commuting mode choice, and how this relationship varies for people who have different attitudes toward public transport. Our results show that commuters who use the Internet frequently on the move tend to use public transport more. In addition, this association is significant for those who prefer private cars to public transport, showing the potential effectiveness of new technology in generating new riders

UNLV New Horizons Band & Red Rock Saxophone Quartet & UNLV Symphonic Winds

Program listing performers and works performed

Tipping out the Boot Grit:the use of on-going feedback devices to enhance feedback dialogue

This project is premised on a belief in the importance of understanding feedback as a dialogue between students and teachers. In addition it considers the importance of feedback as an ongoing and multi-faceted part of students’ engagement with a course, rather than a singular process that occurs at only one point. On this basis the project looks in-depth at two different moments in the learning cycle where feedback can be encouraged in a more dialogical way. Insights into these feedback approaches, at these moments, can then be used to inform the development of other forms of dialogue through the learning cycle

Predicting cycling volumes using crowdsourced activity data

Planning for cycling is often made difficult by the lack of detailed information about when and where cycling takes place. Many have seen the arrival of new forms of data such as crowdsourced data as a potential saviour. One of the key challenges posed by these data forms is understanding how representative they are of the population. To address this challenge, a limited number of studies have compared crowdsourced cycling data to ground truth counts. In general, they have found a high correlation over the long run but with limited geographic coverage, and with counters placed on routes already known to be popular with cyclists. Little is known about the relationship between cyclists present in crowdsourced data and cyclists in manual counts over shorter periods of time and on non-arterial routes. We fill this gap by comparing multi-year crowdsourced data to manual cyclist counts from a cordon count in Scotland’s largest city, Glasgow. Using regression techniques, we estimate models that can be used to adjust the crowdsourced data to predict total cycling volumes. We find that the order of magnitude can be predicted but that the predictions lack the precision that may be required for some applications

Exploring the Relationship Between Strava Cyclists and All Cyclists

In this presentation we discuss our work in comparing counts of cyclists derived from the Strava activity tracking app to manual counts in Glasgow. The preliminary results indicate that there is a high correlation between these two counts. This suggests that data from the Strava app may be useful for tracking the activity of cyclists