Advances in engineering research. Volume 11 / Victoria M. Petrova, editor.

Format:

Book

Contributor:

Petrova, Victoria M., editor.

Series:

Advances in Engineering Research

Advances in Engineering Research ; Volume 11

Language:

English

Subjects (All):

Engineering--Research.

Engineering.

Physical Description:

1 online resource (226 p.)

Place of Publication:

New York : Nova Publishers, 2016.

Summary:

This book focuses on the latest developments in the field of engineering. The first chapter presents a state-of-the-art review on the potential issues to be solved in the design, manufacture and maintenance of the full ocean depth manned cabin. Only in the past 100 years, has the technological capability matched the human desire to deep dive into the ocean and discover its true mysteries. The authors of the second chapter focus on a different type of engineering and proposes a design method of observers used in induction motor systems by utilizing non-singular terminal sliding-mode control. The third chapter is focused on styrene, a high value chemical widely used as feedstock for the commercial manufacture of a large variety of polymers, synthetic rubbers and polymeric resins. The poorest-vanadium catalyst is the most promising for producing styrene without steam, being the most active and selective to styrene. Chapter Four presents a straightforward compromising methodology of dynamic economic and emission active-reactive optimal power flow (AROPF). The remaining chapters of this book provide a review and discussion along with practical guidelines on the application of metamodels in real-world optimization problems; a framework which employs multiple metamodels concurrently, thereby benefiting from the different approximations; a metamodel-assisted framework which adds a dimensionality-reduction component based on variable selection; a new, simple and efficient method for modifying motor fuel through the dynamic cavitation of motor fuel with hydrogen peroxide using a dynamic cavitation reactor; and a discussion on the principles of different Ensemble Methods (EMs) (bagging, boosting, random subspaces, combined approach) by illustrating their applications in the field of feature selection for linear regression.

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (ebrary, viewed September 3, 2016).

ISBN:

1-63483-467-4