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008			210204s2021 gw | s |||| 0|eng d
020			_a9783030693671
024	7		_a10.1007/978-3-030-69367-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aQ342 _b2021 EB
245	0	0	_aImpact and Opportunities of Artificial Intelligence Techniques in the Steel Industry : _bOngoing Applications, Perspectives and Future Trends _cedited by Valentina Colla, Costanzo Pietrosanti.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XIV, 152 páginas) _b67 ilustraciones, 59 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2
490	0		_aAdvances in Intelligent Systems and Computing _x2194-5357 _v1338
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aChallenges and frontiers in implementing artificial intelligence in process industry -- Data Pre-processing for effective design of Machine Learning-based models in the steel sector -- Quantifying uncertainty in physics-informed variational autoencoders for anomaly detection -- Mapping of Standardized State Machines to Utilize Machine Learning Models in Process Control Environments -- Quality4.0 - Transparent product quality supervision in the age of Industry 4.0 -- Artificial Intelligence and Machine Learning techniques for generation and assessment of products properties data -- The use of advanced data analytics to monitor process-induced changes to the microstructure and mechanical properties in flat steel strip -- Unsupervised Deep Learning for Detection of Non-uniform Surface Defect Distributions in Flat Steel Production -- Machine Learning-based models for supporting optimal exploitation of process off-gases in integrated steelworks -- Industrial Cyber Security at the Network Edge: the BRAINE Project approach -- Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery -- TSorage: A Modern and Resilient Platform for Time Series Management at Scale.
520	3		_aThis book collects perceptions and needs expectations and experiences concerning the application of Artificial Intelligence (AI) and Machine Learning in the steel sector. It contains a selection of themes discussed within the Workshop entitled "Impact and Opportunities of Artificial Intelligence in the Steel Industry" organized by the European Steel Technology Platform as an online event from October 15 until November 5, 2020. The event aimed at analyzing the diffusion of AI technologies in steelworks and at providing indications for future research, development and innovation actions addressing the sector demands. The chapters treat general analyses on transversal themes and applications for process optimization, product quality enhancement, yield increase, optimal exploitation of resources and smart data handling. The book is devoted to researchers and technicians in the steel or AI fields as well as for managers and policymakers exploring the opportunities provided by AI in industry.
988			_aSpringer_Robotics_2021
650		7	_aInteligencia artificial _2embne _9413115
650		7	_aIndustria manufacturera _2embne _9668140
650		7	_aProcesos de fabricación _2embne _9163432
700	1		_aColla, Valentina _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aPietrosanti, Costanzo _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030693664
776	0	8	_iPrinted edition: _z9783030693688
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-69367-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2021 _dz _eo _zSI