EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 12 (2021) Int. J. Metrol. Qual. Eng., 12 (2021) 11 References
Issue
Int. J. Metrol. Qual. Eng.
Volume 12, 2021
Topical Issue - Advances in Metrology and Quality Engineering
Article Number 11
Number of page(s) 8
DOI https://doi.org/10.1051/ijmqe/2021009
Published online 26 May 2021
  1. L. Alessi, C. Detken, ‘Real time' early warning indicators for costly asset price boom/bust cycles: a role for global liquidity, 2009 [Google Scholar]
  2. D. Gerdesmeier, H.-E. Reimers, B. Roa et al., Early warning indicators for asset price booms, Rev. Econ. Finance 3, 1–20 (2011) [Google Scholar]
  3. J. Wen, H. Gao, Degradation assessment for the ball screw with variational autoencoder and kernel density estimation, Adv. Mech. Eng. 10, 1687814018797261 (2018) [Google Scholar]
  4. V.N. Livina, T.M. Lenton, A modified method for detecting incipient bifurcations in a dynamical system, Geophys. Res. Lett. 34 (2007) [CrossRef] [Google Scholar]
  5. M. Dey, S.P. Rana, S. Dudley, A case study based approach for remote fault detection using multi-level machine learning in a smart building, Smart Cities 3, 401–419 (2020) [Google Scholar]
  6. V. Livina, A. Lewis, M. Wickham, Tipping point analysis of electrical resistance data with early warning signals of failure for predictive maintenance, arXiv:1904.04636 (2019) [Google Scholar]
  7. D.P. Kingma, M. Welling, Auto-encoding variational bayes, arXiv:1312.6114 (2013) [Google Scholar]
  8. K. Pearson, Principal components analysis, London Edinburgh Dublin Philos, Mag. J. Sci. 6, 559 (1901) [Google Scholar]
  9. J. Schein, S.T. Bushby, N.S. Castro, J.M. House, A rule-based fault detection method for air handling units, Energy Build. 38, 1485–1492 (2006) [CrossRef] [Google Scholar]
  10. P. Bak, C. Tang, K. Wiesenfeld, Self-organized criticality: an explanation of the 1/f noise, Phys. Rev. Lett. 59, 381 (1987) [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  11. F. Titikpina, A. Caucheteux, A. Charki, D. Bigaud, Uncertainty assessment in building energy performance with a simplied model, Int. J. Metrol. Quality Eng. 6, 308 (2015) [CrossRef] [Google Scholar]
  12. M.G. Kendall, Rank correlation methods (1948) [Google Scholar]
  13. D.P. Kingma, M. Welling et al., An introduction to variational autoencoders, Found. Trends Mach. Learn. 12, 307–392 (2019) [CrossRef] [Google Scholar]
  14. J.M. Drake, B.D. Grien, Early warning signals of extinction in deteriorating environments, Nature 467, 456–459 (2010) [CrossRef] [PubMed] [Google Scholar]
  15. B.M. Rogers, K. Solvik, E.H. Hogg, J. Ju, J.G. Masek, M. Michaelian, L.T. Berner, S.J. Goetz, Detecting early warning signals of tree mortality in boreal north America using multiscale satellite data, Glob. Change Biol. 24, 2284–2304 (2018) [CrossRef] [Google Scholar]
  16. J. Broderick, R. Burchill, H. Clark, A system for early warning of bearing failure (1972) [Google Scholar]
  17. B. Schwaab, S.J. Koopman, A. Lucas, Systemic risk diagnostics: coincident indicators and early warning signals (2011) [Google Scholar]
  18. G.E. Box, G.M. Jenkins, G.C. Reinsel, G.M. Ljung, Time series analysis: forecasting and control, John Wiley & Sons (2015) [Google Scholar]
  19. Y. Zhou, C. Lork, W.-T. Li, C. Yuen, Y.M. Keow, Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques, Appl. Energy 253, 113548 (2019) [CrossRef] [Google Scholar]
  20. V.N. Livina, F. Kwasniok, T.M. Lenton, Potential analysis reveals changing number of climate states during the last 60 kyr, Climate Past 6 (2010) [Google Scholar]
  21. J.T. Andrews, E.J. Morton, L.D. Grin, Detecting anomalous data using auto-encoders, Int. J. Mach. Learn. Comput. 6, 21 (2016) [Google Scholar]
  22. D. Bigaud, A. Charki, A. Caucheteux, F. Titikpina, T. Tiplica, Detection of faults and drifts in the energy performance of a building using Bayesian networks, J. Dyn. Syst. Measur. Control 141 (2019) [CrossRef] [Google Scholar]
  23. W.-Y. Lee, J.M. House, N.-H. Kyong, Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks, Appl. Energy 77, 153–170 (2004) [CrossRef] [Google Scholar]
  24. Y. Qin, C. Yuen, S. Adams, Invariant learning based multi-stage identification for lithium-ion battery performance degradation, arXiv:2008.05123 (2020) [Google Scholar]
  25. L. Alessi, C. Detken, Quasi real time early warning indicators for costly asset price boom/bust cycles: a role for global liquidity, Eur. J. Political Econ. 27, 520–533 (2011) [CrossRef] [Google Scholar]
  26. C. Matt, T. Hess, A. Benlian, Digital transformation strategies, Bus. Inf. Syst. Eng. 57, 339–343 (2015) [Google Scholar]
  27. D. Paschek, C.T. Luminosu, A. Draghici, Automated business process management in times of digital transformation using machine learning or articial intelligence, in: MATEC Web of Conferences, Vol. 121, EDP Sciences, 2017, p. 04007 [EDP Sciences] [Google Scholar]
  28. S. Yang, M.P. Wan, W. Chen, B.F. Ng, S. Dubey, Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization, Appl. Energy 271 (2020) [Google Scholar]
  29. J. Qiu, W. Liang, L. Zhang, X. Yu, M. Zhang, The early-warning model of equipment chain in gas pipeline based on DNN-hmm, J. Natur. Gas Sci. Eng. 27, 1710–1722 (2015) [Google Scholar]
  30. V. Livina, G. Lohmann, M. Mudelsee, T.M. Lenton, Forecasting the underlying potential governing the time series of a dynamical system, Physica A 392, 3891–3902 (2013) [Google Scholar]
  31. H. Stopps, M. Touchie, Reduction of HVAC system runtime due to occupancy-controlled smart thermostats in contemporary multi-unit residential building suites, in IOP Conference Series: Materials Science and Engineering, Vol. 609, IOP Publishing, 2019 [Google Scholar]
  32. W.-T. Li, N.U. Hassan, F. Khan, C. Yuen, Y.M. Keow, Data driven model for performance evaluation and anomaly detection in integrated air source heat pump operation, in ICIT, 2019, pp. 1280–1285 [Google Scholar]
  33. A. Ari, R. Dagtekin, Early warning signals of the 2000/2001 Turkish Nancial crisis (2007) [Google Scholar]
  34. Y.-C. Lu, C.-H. Shen, Y.-C. Wei, Revisiting early warning signals of corporate credit default using linguistic analysis, Pacic-Basin Finance J. 24, 1–21 (2013) [Google Scholar]
  35. E.P. Davis, D. Karim, Comparing early warning systems for banking crises, J. Financial Stab. 4, 89–120 (2008) [Google Scholar]
  36. U.B. Singh, A. Ahluwalia, C. Sharma, R. Jindal, R. Thakur, Planktonic indicators: a promising tool for monitoring water quality (early-warning signals), Ecol. Environ. Conserv. 19, 793–800 (2013) [Google Scholar]
  37. T. Squartini, I. Van Lelyveld, D. Garlaschelli, Early-warning signals of topological collapse in interbank networks, Sci. Rep. 3, 3357 (2013) [Google Scholar]
  38. W.-Y. Lee, J.M. House, C. Park, G.E. Kelly, Fault diagnosis of an air-handling unit using articial neural networks, Trans. Am. Soc. Heating Refriger. Air Condition. Eng. 102, 540–549 (1996) [Google Scholar]
  39. J. Prettyman, T. Kuna, V. Livina, A novel scaling indicator of early warning signals helps anticipate tropical cyclones, Europhys. Lett. 121, 10002 (2018) [Google Scholar]
  40. M. Dey, S.P. Rana, S. Dudley, Smart building creation in large scale hvac environments through automated fault detection and diagnosis, Fut. Gener. Comput. Syst. 108, 950–966 (2020) [Google Scholar]
  41. Y. Yu, D. Woradechjumroen, D. Yu, A review of fault detection and diagnosis methodologies on air-handling units, Energy Build. 82, 550–562 (2014) [Google Scholar]
  42. T. Lenton, V. Livina, V. Dakos, E. Van Nes, M. Scheer, Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness, Philos. Trans. Royal Soc. A 370, 1185–1204 (2012) [Google Scholar]
  43. J. Prettyman, T. Kuna, V. Livina, Generalized early warning signals in multivariate and gridded data with an application to tropical cyclones, Chaos 29, 073105 (2019) [Google Scholar]
  44. M. Li, T. Zeng, R. Liu, L. Chen, Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis, Briengs Bioinform. 15, 229–243 (2014) [Google Scholar]
  45. M.J. Zaki, W. Meira, Data mining and analysis: fundamental concepts and algorithms, Cambridge University Press (2014) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.