http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : G. Manson (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Vibration-based structural health monitoring using large sensor networks
A. Deraemaeker,A. Preumont,E. Reynders,G. De Roeck,J. Kullaa,V. Lämsä,K. Worden,G. Manson,R. Barthorpe,E. Papatheou,P. Kudela,P. Malinowski,W. Ostachowicz,T. Wandowski 국제구조공학회 2010 Smart Structures and Systems, An International Jou Vol.6 No.3
Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project Smart Sensing For Structural Health Monitoring(S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.
-
Vibration-based structural health monitoring using large sensor networks
Deraemaeker, A.,Preumont, A.,Reynders, E.,De Roeck, G.,Kullaa, J.,Lamsa, V.,Worden, K.,Manson, G.,Barthorpe, R.,Papatheou, E.,Kudela, P.,Malinowski, P.,Ostachowicz, W.,Wandowski, T. Techno-Press 2010 Smart Structures and Systems, An International Jou Vol.6 No.3
Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.
-
Multimarker Prediction of Coronary Heart Disease Risk
Kim, H.C.,Greenland, P.,Rossouw, J.E.,Manson, J.E.,Cochrane, B.B.,Lasser, N.L.,Limacher, M.C.,Lloyd-Jones, D.M.,Margolis, K.L.,Robinson, J.G. Elsevier Biomedical 2010 JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY - Vol.55 No.19
Objectives: The aim of this study was to investigate whether multiple biomarkers contribute to improved coronary heart disease (CHD) risk prediction in post-menopausal women compared with assessment using traditional risk factors (TRFs) only. Background: The utility of newer biomarkers remains uncertain when added to predictive models using only TRFs for CHD risk assessment. Methods: The Women's Health Initiative Hormone Trials enrolled 27,347 post-menopausal women ages 50 to 79 years. Associations of TRFs and 18 biomarkers were assessed in a nested case-control study including 321 patients with CHD and 743 controls. Four prediction equations for 5-year CHD risk were compared: 2 Framingham risk score covariate models; a TRF model including statin treatment, hormone treatment, and cardiovascular disease history as well as the Framingham risk score covariates; and an additional biomarker model that additionally included the 5 significantly associated markers of the 18 tested (interleukin-6, d-dimer, coagulation factor VIII, von Willebrand factor, and homocysteine). Results: The TRF model showed an improved C-statistic (0.729 vs. 0.699, p = 0.001) and net reclassification improvement (6.42%) compared with the Framingham risk score model. The additional biomarker model showed additional improvement in the C-statistic (0.751 vs. 0.729, p = 0.001) and net reclassification improvement (6.45%) compared with the TRF model. Predicted CHD risks on a continuous scale showed high agreement between the TRF and additional biomarker models (Spearman's coefficient = 0.918). Among the 18 biomarkers measured, C-reactive protein level did not significantly improve CHD prediction either alone or in combination with other biomarkers. Conclusions: Moderate improvement in CHD risk prediction was found when an 18-biomarker panel was added to predictive models using TRFs in post-menopausal women.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
