A shell space constrained approach for curve design on surface meshes

Jin, Y.; Song, Dan; Wang, Tongtong; Huang, Jin; Song, Ying; He, Le

doi:10.1016/j.cad.2019.03.001

Cited by 10 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Health and safety were cited as the top advantages of remote user research by Ratcliffe J. et al [34], who also mentioned more general concerns including air quality in enclosed lab areas and the possibility of COVID-19 transmission through shared HMDs and controllers. Recent approaches for curve design [35] and attribute transmission among similar triangle meshes [36] have also been used to offset volumes or shells around triangle meshes. Table 1 summarizes the outcomes of some studies representing the I4.0 type, software, hardware, limitations, and challenges.…”

Section: Manufacturing Data and Strategies For Visualization Enhancem...mentioning

confidence: 99%

Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer

Alatawi,

Albalawi,

Shahata

et al. 2023

Sensors

View full text Add to dashboard Cite

The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of 1.000 and a standard deviation of 0.000. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is 0.000 shows that there is no variability in the outcomes.

show abstract

Section: Manufacturing Data and Strategies For Visualization Enhancem...mentioning

confidence: 99%

Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer

Alatawi,

Albalawi,

Shahata

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…[Noakes et al 1989] and [Camarinha et al 1995] provide the basic variational theory of splines on manifolds. This approach is adopted in several other papers [Arnould et al 2015;Gousenbourger et al 2018Gousenbourger et al , 2014Hofer and Pottmann 2004;Jin et al 2019;Pottmann and Hofer 2005;Samir et al 2011]. While most such works do not address implementation and performance, [Hofer and Pottmann 2004] and [Jin et al 2019] eventually resort to projection methods.…”

Section: Related Workmentioning

confidence: 99%

“…This approach is adopted in several other papers [Arnould et al 2015;Gousenbourger et al 2018Gousenbourger et al , 2014Hofer and Pottmann 2004;Jin et al 2019;Pottmann and Hofer 2005;Samir et al 2011]. While most such works do not address implementation and performance, [Hofer and Pottmann 2004] and [Jin et al 2019] eventually resort to projection methods. Overall, the variational approach is too computationally expensive to support user interaction on large meshes.…”

Section: Related Workmentioning

confidence: 99%