| 000 | 06099naaaa2201525uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/54583 | ||
| 005 | 20220220080101.0 | ||
| 020 | _abooks978-3-03928-291-3 | ||
| 020 | _a9783039282906 | ||
| 020 | _a9783039282913 | ||
| 024 | 7 |
_a10.3390/books978-3-03928-291-3 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 100 | 1 |
_aPosada, Jorge _4auth |
|
| 700 | 1 |
_aLópez de Lacalle, Luis Norberto _4auth |
|
| 245 | 1 | 0 | _aNew Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes |
| 260 |
_bMDPI - Multidisciplinary Digital Publishing Institute _c2020 |
||
| 300 | _a1 electronic resource (428 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aModern factories are experiencing rapid digital transformation supported by emerging technologies, such as the Industrial Internet of things (IIOT), industrial big data and cloud technologies, deep learning and deep analytics, AI, intelligent robotics, cyber-physical systems and digital twins, complemented by visual computing (including new forms of artificial vision with machine learning, novel HMI, simulation, and visualization). This is evident in the global trend of Industry 4.0. The impact of these technologies is clear in the context of high-performance manufacturing. Important improvements can be achieved in productivity, systems reliability, quality verification, etc. Manufacturing processes, based on advanced mechanical principles, are enhanced by big data analytics on industrial sensor data. In current machine tools and systems, complex sensors gather useful data, which is captured, stored, and processed with edge, fog, or cloud computing. These processes improve with digital monitoring, visual data analytics, AI, and computer vision to achieve a more productive and reliable smart factory. New value chains are also emerging from these technological changes. This book addresses these topics, including contributions deployed in production, as well as general aspects of Industry 4.0. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by-nc-nd/4.0/ _2cc _4https://creativecommons.org/licenses/by-nc-nd/4.0/ |
||
| 546 | _aEnglish | ||
| 653 | _alocalization | ||
| 653 | _asmart system | ||
| 653 | _an/a | ||
| 653 | _aconnected enterprise | ||
| 653 | _adigital manufacturing | ||
| 653 | _aAHP | ||
| 653 | _aYOLOv3 | ||
| 653 | _adecision support | ||
| 653 | _aneural network | ||
| 653 | _avertex distance | ||
| 653 | _adepthwise separable convolution | ||
| 653 | _acutting insert selection | ||
| 653 | _asmart service | ||
| 653 | _acontour detection | ||
| 653 | _aconvolutional neural networks | ||
| 653 | _aplatform-based ecosystem | ||
| 653 | _ain-line dimensional inspection | ||
| 653 | _adilated convolutions | ||
| 653 | _afabric defect detection | ||
| 653 | _aclassification | ||
| 653 | _aFCM | ||
| 653 | _aLGM | ||
| 653 | _adigital information flow | ||
| 653 | _aturning | ||
| 653 | _acomputer vision | ||
| 653 | _acontrol service | ||
| 653 | _ablister defect | ||
| 653 | _aRMTs | ||
| 653 | _afeature pyramid | ||
| 653 | _aresearch and development indicators | ||
| 653 | _amaintenance expert | ||
| 653 | _apolymer lithium-ion battery | ||
| 653 | _aIT concept | ||
| 653 | _aIndustry 4.0 | ||
| 653 | _amatching | ||
| 653 | _adata reduction | ||
| 653 | _acompetence | ||
| 653 | _afibre of preserved Szechuan pickle | ||
| 653 | _aelliptical paraboloid array | ||
| 653 | _arelative angle | ||
| 653 | _ageometric relationship | ||
| 653 | _aoptical system | ||
| 653 | _aconfigure-to-order | ||
| 653 | _aaircraft structure crack detection | ||
| 653 | _adigital twins | ||
| 653 | _asmart factory | ||
| 653 | _aD-VGG16 | ||
| 653 | _aoptical slope sensor | ||
| 653 | _asmart manufacturing | ||
| 653 | _aself-calibration method | ||
| 653 | _aconvolutional neural network | ||
| 653 | _aindustry 4.0 | ||
| 653 | _askyline queries | ||
| 653 | _amachine learning | ||
| 653 | _ascalability test | ||
| 653 | _acyber-physical production systems | ||
| 653 | _aCyber-Physical Systems (CPS) | ||
| 653 | _ademand-side response | ||
| 653 | _acutting parameter optimization | ||
| 653 | _aimage smoothing | ||
| 653 | _amarketing innovations | ||
| 653 | _agenetic algorithm | ||
| 653 | _aautomation system | ||
| 653 | _adefect detection | ||
| 653 | _ascheduling | ||
| 653 | _ajob shop systems | ||
| 653 | _abig data | ||
| 653 | _aoperator theory | ||
| 653 | _amicro-armature | ||
| 653 | _atrain wheel | ||
| 653 | _aindustrial knowledge graph | ||
| 653 | _aindustrial load management | ||
| 653 | _abilinear model | ||
| 653 | _aartificial neural networks | ||
| 653 | _a4th industrial revolution | ||
| 653 | _aINDUSTRY 4.0 | ||
| 653 | _aconstruction equipment | ||
| 653 | _alean assembly | ||
| 653 | _acapacity control | ||
| 653 | _aGrad-CAM | ||
| 653 | _arevolution workpiece | ||
| 653 | _achatter | ||
| 653 | _aanomaly detection | ||
| 653 | _aQFD | ||
| 653 | _asocial network | ||
| 653 | _adeep learning | ||
| 653 | _acontrol as a service | ||
| 653 | _awarm forming | ||
| 653 | _aautomated surface inspection | ||
| 653 | _acloud-based control system | ||
| 653 | _ainnovative marketing tools | ||
| 653 | _aInternet of Things (IoT) | ||
| 653 | _aflower pollination algorithm | ||
| 653 | _aHED | ||
| 653 | _aedge computing | ||
| 653 | _apredictive analytics | ||
| 653 | _aBIM | ||
| 653 | _adigital platforms | ||
| 653 | _aindustrial big data | ||
| 653 | _aenergy flexibility | ||
| 653 | _aimpacts marketing innovations | ||
| 653 | _aintellectualization of industrial information | ||
| 653 | _aeconomic recession | ||
| 653 | _a3D mesh reconstruction | ||
| 653 | _ademand-side management | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/2109 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/54583 _70 _zDOAB: description of the publication |
| 999 |
_c74738 _d74738 |
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