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_aSpringerLink (Online service) _9106996 |
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| 008 | 181211s2019 gw a o |||| 0|eng d | ||
| 020 | _a9783030032012 | ||
| 024 | 7 |
_a10.1007/978-3-030-03201-2 _2doi |
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| 040 |
_bspa _dES-MaUEC _cES-MaUEC |
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| 050 | 4 |
_aTS183 _b2019 EB |
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| 245 | 0 | 0 |
_aSoft Modeling in Industrial Manufacturing _cedited by Przemyslaw Grzegorzewski, Andrzej Kochanski, Janusz Kacprzyk. |
| 264 | 1 |
_aCham _bImprint: Springer _c2019 |
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| 300 | _a1 recurso en línea (X, 196 páginas) | ||
| 336 |
_2rdacontent _aTexto _btxt |
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| 337 |
_2rdamedia _aelectrónico _bc |
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| 338 |
_2rdacarrier _arecurso electrónico _bcr |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 0 |
_aStudies in Systems Decision and Control _x2198-4182 _v183 |
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| 490 | 0 | _aIntelligent Technologies and Robotics (Springer-42732) | |
| 505 | 0 | _aData and modeling in industrial manufacturing -- From data to reasoning -- Data preprocessing in industrial manufacturing -- Tool condition monitoring in metal cutting -- Assessment of selected tools used for knowledge extraction in industrial manufacturing -- Application of data mining tools in shrink sleeve labels converting process -- Study of thickness variability of the floorboard surface layer -- Applying statistical methods with imprecise data to quality control in cheese manufacturing -- Monitoring series of dependent observations using the sXWAM control chart for residuals -- Diagnosis of out-of-control signals in complex manufacturing processes. | |
| 520 | 3 | _aThis book discusses the problems of complexity in industrial data, including the problems of data sources, causes and types of data uncertainty, and methods of data preparation for further reasoning in engineering practice. Each data source has its own specificity, and a characteristic property of industrial data is its high degree of uncertainty. The book also explores a wide spectrum of soft modeling methods with illustrations pertaining to specific cases from diverse industrial processes. In soft modeling the physical nature of phenomena may not be known and may not be taken into consideration. Soft models usually employ simplified mathematical equations derived directly from the data obtained as observations or measurements of the given system. Although soft models may not explain the nature of the phenomenon or system under study, they usually point to its significant features or properties. | |
| 988 | _aPrimersemestre_2019_Robotics | ||
| 650 | 7 |
_2embne _aProcesos de fabricación _9163432 |
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| 700 | 1 |
_aGrzegorzewski, Przemyslaw. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _998793 |
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| 700 | 1 |
_aKochanski, Andrzej. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 700 | 1 |
_aKacprzyk, Janusz _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _996945 |
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| 776 | 0 | 8 |
_iPrinted edition: _z9783030032005 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030032029 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-03201-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
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_2lcc _cLE |
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| 998 |
_aSI _cm _dz _feng _ggw _h0 _b11/2019 _ek _zSI |
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