ABSTRACT
The University of New Brunswick (UNB) first developed a virtual reality documentation (VR Doc) system in 2006 and has continued to make improvements. VR Doc has evolved into a web-based interface for organizing and viewing virtual reality panoramas. The main purpose of the VR Doc system is to support virtual site visits, pre-emptively resolve claims, and record photographic as-builts. This project is a collaboration between UNB, Bradley Engineering Ltd, and Bird Construction Inc., and focuses on VR Doc’s virtual site visit capabilities during the COVID-19 pandemic. Physical site visits are more challenging as a result of COIVD-19 and limiting personnel on site has the potential to reduce the spread of the virus. The VR Doc system was implemented on the Fredericton International Airport upgrade site over a five-month period during the COVID-19 pandemic. Representatives of the project owner, the general contractor, and the electrical subcontractor were provided with access to the VR Doc website. These representatives provided valuable feedback that provided insight on how this technology could reduce the number of personnel visiting site. Opportunities to reduce the spread of COVID-19 include: minimizing the site visits by those monitoring project progress, minimizing the site visits by those inspecting completed work, and providing visuals of specific project details to Airport board members and consulting engineers. Impediments to this technology include: introduction of yet another software interface and the need for on-site personnel to capture the images at specific times. Opportunities for improvement are also identified. © 2023, Canadian Society for Civil Engineering.
ABSTRACT
With the current onset of the Coronavirus pandemic social interaction has been deeply affected. However, social media and micro-blogging platforms like Twitter are becoming more of relevance to the masses to express their feelings, opinions, concerns and problems. A large amount of data is generated in this process which may be highly valuable in deriving deep insights about the effects of the pandemic on the people. With modern technological tools like data mining, data processing and APIs, harnessing this sea of data has become more of a possibility. Moreover, with the application of advanced machine learning tools like sentiment analysis, understanding the people’s mindset behind the tweet has also become a possibility. The study oversees the development of a web based interface for connecting two sections of the society: one which is in need of help and the other with the desire to help. In this study, we aim to utilise data analysis and advanced machine learning techniques like RoBERTa(Robustly Optimised BERT pre-training Approach) and CNN-RoBERTa Sentiment Extraction to incorporate methods like sentiment analysis, frequency distribution and comparative analysis on data found from social media for efficient comparison on the effects of COVID-19 in India while also developing a web-based interface for effective exposition and insights over the crisis. © 2021 IEEE
ABSTRACT
Images reproduced for different output devices are known to be limited in the range of colours that can be reproduced. It is accepted that reproductions made with different print processes, and on different substrates, will not match, although the overall reproduction appearance can be optimized using an output rendering. However, the question remains: how different are they visually? This paper reports on a pilot study that tests whether visual difference can be reduced to a single dimensional scale using magnitude estimation. Subject to recent Covid restrictions, the experiment was moved from the lab to an online delivery. We compare the two methods of delivery: in-person under controlled viewing conditions, and online via a web-based interface where viewing conditions are unknown. © 2021 Society for Imaging Science and Technology.