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This book explains how to perform data de-noising, in large scale,
with a satisfactory level of accuracy. Three main issues are
considered. Firstly, how to eliminate the error propagation from
one stage to next stages while developing a filtered model.
Secondly, how to maintain the positional importance of data whilst
purifying it. Finally, preservation of memory in the data is
crucial to extract smart data from noisy big data. If, after the
application of any form of smoothing or filtering, the memory of
the corresponding data changes heavily, then the final data may
lose some important information. This may lead to wrong or
erroneous conclusions. But, when anticipating any loss of
information due to smoothing or filtering, one cannot avoid the
process of denoising as on the other hand any kind of analysis of
big data in the presence of noise can be misleading. So, the entire
process demands very careful execution with efficient and smart
models in order to effectively deal with it.
This book presents the selected peer-reviewed proceedings of the
International Conference on Thermal Engineering and Management
Advances (ICTEMA 2020). The contents discuss latest research in the
areas of thermal engineering, manufacturing engineering, and
production management. Some of the topics covered include
multiphase fluid flow, turbulent flows, reactive flows, atmospheric
flows, combustion and propulsion, computational methods for
thermo-fluid arena, micro and nanofluidics, renewable energy and
environment sustainability, non-conventional energy resources,
energy principles and management, machine dynamics and
manufacturing, casting and forming, green manufacturing, production
planning and management, quality control and management, and
traditional and non-traditional manufacturing. The contents of this
book will be useful for students, researchers as well as
professionals working in the area of mechanical engineering and
allied fields.
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