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An incremental machine learning algorithm for nuclear forensics
No full textThis paper presents an incremental machine learning algorithm that identifies the origin, or provenance, of samples of nuclear material. This is part of work being undertaken by the Canadian National Nuclear Forensics Library development program, which seeks to build a comprehensive database of signatures for radioactive and nuclear materials under Canadian regulatory control. One difficulty with this application is the small ratio of the number of examples over the number of classes. So, we introduce variants to a basic generative algorithm, based on ideas from the robust statistics literature and elsewhere, to address this issue and to improve robustness to attribute noise. We show experimentally the effectiveness of the approach, and the problems that arise, when adding new examples and classes.Peer reviewed: YesNRC publication: Ye
Real-time spectral characterization of a photon pair source using a chirped supercontinuum seed
No full textWe perform joint spectral intensity measurements by studying stimulated four wave mixing in a birefringent fiber photon pair source. Seeding the process with a chirped supercontinuum beam, measurements are acquired in as little as 5 s.Peer reviewed: YesNRC publication: Ye
Structure of the LPS O-chain from Fusobacterium nucleatum strain MJR 7757\u202fB
No full textFusobacterium nucleatum is an anaerobic bacterium found in the human mouth where it causes periodontitis. It was also found in colorectal cancer tissues and is linked with pregnancy complications, including pre-term and still births. Cell surface structures of the bacterium could be implicated in pathogenesis. Here we report the following structure of the lipopolysaccharide O-chain of F. nucleatum strain MJR 7757\u202fB:where Lac is (R)-1-carboxyethyl (lactic acid residue); all monosaccharides are in the pyranose form. ManNAc4Lac, analogue of N-acetylmuramic acid, is found for the first time in natural sources.Peer reviewed: YesNRC publication: Ye
Municipal solid waste supply chain mapping analysis
No full textThe objective of this study is to estimate the municipal solid waste generation and diversion using models and evaluate the costs and GHG emissions associated with the waste supply chain.Peer reviewed: NoNRC publication: Ye
Data analytics to improve building performance: a critical review
No full textThe data inherent in building automation systems, computerized maintenance management systems, security and access control systems, and IT networks represent an untapped opportunity to improve the operation and maintenance (O&M) of buildings. This paper reports the findings of a critical review of the literature regarding the use of data analytics in building O&M applications, and a two-day stakeholder's workshop titled Big Data in Building Operations. Building on the discussions at the workshop and the literature survey, the current state of the O&M related decision-making process was identified: the data availability in existing buildings was discussed; the challenges related with accessing and processing these datasets were examined; and emerging sensing technologies were presented. Further, the research fields applying data analytics in O&M were introduced, the barriers to their widespread use in practice were discussed, future work recommendations were developed; and the need for semantic models of O&M data and comprehensive open O&M datasets was identified for the development and assessment of data analytics-driven energy and comfort management algorithms.Peer reviewed: YesNRC publication: Ye
SNAP RTM: a cost-effective compression RTM variant to manufacture composite component for transportation applications
No full textThe Corporate Fuel Average Efficiency (CAF\uc9) regulation requires average fuel consumption of cars to increase from 37.8 mpg (6.2 L/100 km) to 54.5 mpg (4.3 L/100 km) by 2025. One solution to help reach this target is vehicle lightweighting. As a reference, a 10% reduction in vehicle weight can result in a 5 \u2013 8% reduction of fuel consumption. Among the lightweight material alternatives, fibre reinforced composite materials are believed to enable car body-weight reductions of 25% to 50%, weight reduction that cannot be obtained with lightweight metals alone. From an industrial point of view, process cycle time and cost are the main barriers to a wider use of fibre reinforced composites in mass produced vehicles, as current high performance composite manufacturing processes do not meet the 2 to 5 minutes cycle time desired by the transportation industry for the production of large series components. In order to benefit from the performance of advanced composites needed to achieve significant reductions in vehicle weight, it is then necessary to develop rapid and cost-effective processing techniques adapted to these materials. In recent years, material suppliers have been reducing the cure time required for thermoset resins that have helped to shorten cycle times targeted by the automotive industry. Among the manufacturing technologies available to produce high performance composites parts, liquid moulding technologies appear to have some potential to successfully introduce those rapid cure resin systems at faster production rates. In this study, a cost effective variant of the Compression-RTM process has been developed to manufacture high performance composite plaques. The injection and compression parameters were studied and compared to traditional RTM moulding: cycle time, part quality and part mechanical performance. Results validated the use of low cost / low pressure injection equipment combined with a static mix head and innovative tool design to manufacture composite plaques within cycle times targeted by the transportation industry.Peer reviewed: YesNRC publication: Ye
The role of FEA in the design of advanced electric machines
No full text*Importance of FEA in electric machine design *Variable flux motor *Reduced rare-earth permanent magnet motor *Electric motor development using additive manufacturingPeer reviewed: NoNRC publication: Ye
