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008			210731s2021 sz | o |||| 0|eng d
020			_a9783030728229
024	7		_a10.1007/978-3-030-72822-9 _2doi
040			_aES-VaU _bspa _cES-VaU _dES-VaU
050		4	_aT57.6-.97 _b2021 EB
100	1		_aPark, Chiwoo. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aData Science for Nano Image Analysis _cby Chiwoo Park, Yu Ding
250			_a1st ed. 2021.
264		1	_aCham _c2021 _bSpringer International Publishing
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aInternational Series in Operations Research & Management Science _x2214-7934 _v308
505	0		_aChapter 1. Introduction -- Chapter 2. Image Representation -- Chapter 3. Segmentation -- Chapter 4. Shape Analysis -- Chapter 5. Location and Dispersion Analysis -- Chapter 6. Lattice Pattern Analysis -- Chapter 7. Change Point Detection -- Chapter 8. State Space Modeling for Size Changes -- Chapter 9. Shape Change Tracking -- Chapter 10. Tracking Nucleation, Growth and Aggregation -- Chapter 11. Further Issues and Discussions.
520			_aThis book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization. This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, graphical methods, and real-time dynamic modeling and object tracking. The data science methods are described in the context of nano image applications, with specific material science case studies.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-72822-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
988			_aSpringer_Business_2021
999			_c239508 _d239508