Smart adaptive big data analysis with advanced deep learning
|Author:||Juuso, Esko K.1|
1Control Engineering, Faculty of Technology, FI-90014 University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019062521765
|Publish Date:|| 2019-06-25
Increasing volumes of data, referred as big data, require massive scale and complex computing. Artificial intelligence, deep learning, internet of things and cloud computing are proposed for heterogeneous datasets in hierarchical analytics to manage with the volume, variety, velocity and value of the big data. These solutions are not sufficient in technical systems where measurements, waveform signals, spectral data, images and sparse performance indicators require specific methods for the feature extraction before interactions can be properly analysed. In practical applications, the data analysis, knowledge-based methodologies and optimization need to be combined. The solutions require compact calculation units which can be adaptively modified. The artificial intelligence is extended with various methodologies of computational intelligence. The advanced deep learning approach proposed in this paper uses generalized norms in feature generation, nonlinear scaling in developing compact indicators and linear interactions in model-based systems. The intelligent temporal analysis is available for all indices, including for stress, condition and quality indicators. The service and automation solutions combine these data-driven solutions with the domain expertise by using fuzzy logic for case-based systems. The applications are developed gradually in connections, conversion, cyber, cognition and configuration layers. The advanced methodology is based on the integration of features, scaling functions and interaction models specified by parameters. All the sub-systems and different combinations of them can be recursively updated and optimized with evolutionary computing. The systems adapt to the changing operating conditions and provide situation awareness for the risk analysis. The approach supports different levels of the smart adaptive systems.
|Pages:||403 - 416|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The author would like to thank the research program “Measurement, Monitoring and Environmental Efficiency Assesment (MMEA)” funded by the TEKES (the Finnish Funding Agency for Technology and Innovation) and the Artemis Innovation Pilot project “Production and energy system automation and Intelligent-Built (Arrowhead)”.
© 2018 E. K. Juuso. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0