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RISS 인기검색어
- 검색키워드
- 저자 : Jesus S. Martinez (검색결과 2 건)
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Darragh Lydon,S.E. Taylor,Myra Lydon,Jesus Martinez del Rincon,David Hester 국제구조공학회 2019 Smart Structures and Systems, An International Jou Vol.24 No.6
Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.
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Integral nuclear data validation using experimental spent nuclear fuel compositions
Ian C. Gauld,Mark L. Williams,Franco Michel-Sendis,Jesus S. Martinez 한국원자력학회 2017 Nuclear Engineering and Technology Vol.49 No.6
Measurements of the isotopic contents of spent nuclear fuel provide experimental data that are a prerequisitefor validating computer codes and nuclear data for many spent fuel applications. Under theauspices of the Organisation for Economic Co-operation and Development (OECD) Nuclear EnergyAgency (NEA) and guidance of the Expert Group on Assay Data of Spent Nuclear Fuel of the NEAWorkingParty on Nuclear Criticality Safety, a new database of expanded spent fuel isotopic compositions has beencompiled. The database, Spent Fuel Compositions (SFCOMPO) 2.0, includes measured data for more than750 fuel samples acquired from 44 different reactors and representing eight different reactor technologies. Measurements for more than 90 isotopes are included. This new database provides data essentialfor establishing the reliability of code systems for inventory predictions, but it also has broader potentialapplication to nuclear data evaluation. The database, together with adjoint based sensitivity and uncertaintytools for transmutation systems developed to quantify the importance of nuclear data onnuclide concentrations, are described.
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