Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans
The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming increasingly popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, r...
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| Định dạng: | Online |
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| Ngôn ngữ: | Tiếng Anh |
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MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Truy cập trực tuyến: | ONIX_20220111_9783036523576_844 |
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| _version_ | 1869531455501107200 |
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| collection | Directory of Open Access Books |
| description | The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming increasingly popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors. While, in many cases, control-oriented models, which are generally simple, are the best choice, multibody models, which can be much more detailed, may be better suited to some applications, such as during the design stage of a new product. |
| format | Online |
| id | doab-20.500.12854ir-77012 |
| 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-770122024-04-09T23:15:52Z Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans Cuadrado, Javier Naya, Miguel Kalman filter motion capture gait analysis inertial sensor rail vehicles track irregularities multibody dynamics inertial sensors computer vision singular configuration parallel robot motion control 3D tracking screw theory Kalman filtering coupled states-inputs estimation virtual sensors slider-crank mechanism virtual sensoring physical sensors smart/intelligent sensors sensor technology and applications sensing principles signal processing in sensor systems symbolic generation real-time computation human-in-the-loop haptic devices parameter estimation curve fitting method hydraulic system predictive maintenance characteristic curve product life cycle digital twin adaptive Kalman filter nonlinear models virtual sensing multibody based observers vehicle dynamics estimation sideslip angle estimation factor graph graphical models movable repetitive lander fault-tolerant soft-landing landing configuration stability optimization n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming increasingly popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors. While, in many cases, control-oriented models, which are generally simple, are the best choice, multibody models, which can be much more detailed, may be better suited to some applications, such as during the design stage of a new product. 2022-01-11T13:49:16Z 2022-01-11T13:49:16Z 2021 book ONIX_20220111_9783036523576_844 9783036523576 9783036523583 https://directory.doabooks.org/handle/20.500.12854/77012 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4622 https://mdpi.com/books/pdfview/book/4622 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-2358-3 10.3390/books978-3-0365-2358-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036523576 9783036523583 256 Basel, Switzerland open access |
| spellingShingle | Kalman filter motion capture gait analysis inertial sensor rail vehicles track irregularities multibody dynamics inertial sensors computer vision singular configuration parallel robot motion control 3D tracking screw theory Kalman filtering coupled states-inputs estimation virtual sensors slider-crank mechanism virtual sensoring physical sensors smart/intelligent sensors sensor technology and applications sensing principles signal processing in sensor systems symbolic generation real-time computation human-in-the-loop haptic devices parameter estimation curve fitting method hydraulic system predictive maintenance characteristic curve product life cycle digital twin adaptive Kalman filter nonlinear models virtual sensing multibody based observers vehicle dynamics estimation sideslip angle estimation factor graph graphical models movable repetitive lander fault-tolerant soft-landing landing configuration stability optimization n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans |
| title | Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans |
| title_full | Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans |
| title_fullStr | Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans |
| title_full_unstemmed | Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans |
| title_short | Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans |
| title_sort | combining sensors and multibody models for applications in vehicles machines robots and humans |
| topic | Kalman filter motion capture gait analysis inertial sensor rail vehicles track irregularities multibody dynamics inertial sensors computer vision singular configuration parallel robot motion control 3D tracking screw theory Kalman filtering coupled states-inputs estimation virtual sensors slider-crank mechanism virtual sensoring physical sensors smart/intelligent sensors sensor technology and applications sensing principles signal processing in sensor systems symbolic generation real-time computation human-in-the-loop haptic devices parameter estimation curve fitting method hydraulic system predictive maintenance characteristic curve product life cycle digital twin adaptive Kalman filter nonlinear models virtual sensing multibody based observers vehicle dynamics estimation sideslip angle estimation factor graph graphical models movable repetitive lander fault-tolerant soft-landing landing configuration stability optimization n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | Kalman filter motion capture gait analysis inertial sensor rail vehicles track irregularities multibody dynamics inertial sensors computer vision singular configuration parallel robot motion control 3D tracking screw theory Kalman filtering coupled states-inputs estimation virtual sensors slider-crank mechanism virtual sensoring physical sensors smart/intelligent sensors sensor technology and applications sensing principles signal processing in sensor systems symbolic generation real-time computation human-in-the-loop haptic devices parameter estimation curve fitting method hydraulic system predictive maintenance characteristic curve product life cycle digital twin adaptive Kalman filter nonlinear models virtual sensing multibody based observers vehicle dynamics estimation sideslip angle estimation factor graph graphical models movable repetitive lander fault-tolerant soft-landing landing configuration stability optimization n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20220111_9783036523576_844 |