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- Action Recognition (1)
- Critical Magnetic Field (1)
- Deep Learning (1)
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- Ferromagnetic Properties (1)
- Ferromagnetism (1)
- Magnetic Bubbles (1)
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- Manufacturing Assembly (1)
- Multimodal Sensor System (1)
- Paramagnetism (1)
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- Shear Flow, Critical Field Strength (1)
- Three-Dimensional Rotation (1)
- Uniform Magnetic Fields, Rotation (1)
Articles 1 - 2 of 2
Full-Text Articles in Mechanical Engineering
Three-Dimensional Rotation Of Paramagnetic And Ferromagnetic Prolate Spheroids In Simple Shear And Uniform Magnetic Field, Christopher A. Sobecki, Yanzhi Zhang, Cheng Wang
Three-Dimensional Rotation Of Paramagnetic And Ferromagnetic Prolate Spheroids In Simple Shear And Uniform Magnetic Field, Christopher A. Sobecki, Yanzhi Zhang, Cheng Wang
Mathematics and Statistics Faculty Research & Creative Works
We examine a time-dependent, three-dimensional rotation of magnetic ellipsoidal particles in a two-dimensional, simple shear flow and a uniform magnetic field. We consider that the particles have paramagnetic and ferromagnetic properties, and we compare their rotational dynamics due to the strengths and directions of the applied uniform magnetic field. We determine the critical magnetic field strength that can pin the particles' rotations. Above the critical field strength, the particles' stable steady angles were determined. In a weak magnetic regime (below the critical field strength), a paramagnetic particle's polar angle will oscillate toward the magnetic field plane while its azimuthal angle …
Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin
Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin
Computer Science Faculty Research & Creative Works
Production innovations are occurring faster than ever. Manufacturing workers thus need to frequently learn new methods and skills. In fast changing, largely uncertain production systems, manufacturers with the ability to comprehend workers' behavior and assess their operation performance in near real-time will achieve better performance than peers. Action recognition can serve this purpose. Despite that human action recognition has been an active field of study in machine learning, limited work has been done for recognizing worker actions in performing manufacturing tasks that involve complex, intricate operations. Using data captured by one sensor or a single type of sensor to recognize …