Sense and Respond
Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufa...
保存先:
| フォーマット: | Online |
|---|---|
| 言語: | 英語 |
| 出版事項: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
|
| 主題: | |
| オンライン･アクセス: | ONIX_20220506_9783036538143_44 |
| タグ: |
タグなし, このレコードへの初めてのタグを付けませんか!
|
| _version_ | 1869518689417560064 |
|---|---|
| collection | Directory of Open Access Books |
| description | Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization); and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively). The next natural evolutionary step is to provide value by creating industrial cyber-physical assets with human-like intelligence. This will only be possible by further integrating strategic smart sensor technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards. This reprint of “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensor technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes. |
| format | Online |
| id | doab-20.500.12854ir-80978 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-809782024-04-09T23:16:01Z Sense and Respond Villalba-Diez, Javier Ordieres Meré, Joaquin EEG sensors manufacturing systems problem-solving deep learning TDOA sensor networks hyperboloids node distribution genetic algorithms asynchronous Cramér–Rao lower bound heteroscedasticity soft sensors industrial optical quality inspection artificial vision long-term monitoring benefits indoor air quality low cost occupational safety and health industry 4.0 IOTA tangle Industry 4.0 IIoT geometric deep learning lean management cramer rao lower bound localization LPS multi-objective optimization sensor failure wireless sensor networks conceptual framework sensors approaches tools data application project engineering LCA SDG 9 SDG 11 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization); and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively). The next natural evolutionary step is to provide value by creating industrial cyber-physical assets with human-like intelligence. This will only be possible by further integrating strategic smart sensor technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards. This reprint of “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensor technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes. 2022-05-06T11:19:30Z 2022-05-06T11:19:30Z 2022 book ONIX_20220506_9783036538143_44 9783036538143 9783036538136 https://directory.doabooks.org/handle/20.500.12854/80978 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/5319 https://mdpi.com/books/pdfview/book/5319 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3813-6 10.3390/books978-3-0365-3813-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036538143 9783036538136 168 Basel open access |
| spellingShingle | EEG sensors manufacturing systems problem-solving deep learning TDOA sensor networks hyperboloids node distribution genetic algorithms asynchronous Cramér–Rao lower bound heteroscedasticity soft sensors industrial optical quality inspection artificial vision long-term monitoring benefits indoor air quality low cost occupational safety and health industry 4.0 IOTA tangle Industry 4.0 IIoT geometric deep learning lean management cramer rao lower bound localization LPS multi-objective optimization sensor failure wireless sensor networks conceptual framework sensors approaches tools data application project engineering LCA SDG 9 SDG 11 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Sense and Respond |
| title | Sense and Respond |
| title_full | Sense and Respond |
| title_fullStr | Sense and Respond |
| title_full_unstemmed | Sense and Respond |
| title_short | Sense and Respond |
| title_sort | sense and respond |
| topic | EEG sensors manufacturing systems problem-solving deep learning TDOA sensor networks hyperboloids node distribution genetic algorithms asynchronous Cramér–Rao lower bound heteroscedasticity soft sensors industrial optical quality inspection artificial vision long-term monitoring benefits indoor air quality low cost occupational safety and health industry 4.0 IOTA tangle Industry 4.0 IIoT geometric deep learning lean management cramer rao lower bound localization LPS multi-objective optimization sensor failure wireless sensor networks conceptual framework sensors approaches tools data application project engineering LCA SDG 9 SDG 11 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | EEG sensors manufacturing systems problem-solving deep learning TDOA sensor networks hyperboloids node distribution genetic algorithms asynchronous Cramér–Rao lower bound heteroscedasticity soft sensors industrial optical quality inspection artificial vision long-term monitoring benefits indoor air quality low cost occupational safety and health industry 4.0 IOTA tangle Industry 4.0 IIoT geometric deep learning lean management cramer rao lower bound localization LPS multi-objective optimization sensor failure wireless sensor networks conceptual framework sensors approaches tools data application project engineering LCA SDG 9 SDG 11 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20220506_9783036538143_44 |