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Design of a See-through Off-Axis Head-Mounted-Display Optical System with an Ellipsoidal Surface
Junhua Wang,Qing Zhou,Jie Chen,Lexin Hou,Min Xu 한국광학회 2018 Current Optics and Photonics Vol.2 No.3
A new method to design a see-through off-axis head-mounted-display (OA-HMD) optical system with an ellipsoidal surface is proposed, in which a tilted ellipsoidal surface is used as the combiner, which yields the benefits of easier fabrication and testing compared to a freeform surface. Moreover, we realize a coaxial structure in the relay lens group, which is simple and has looser tolerance requirements, thus making assembly easier. The OA-HMD optical system we realize has a simple structure and consists of a combiner and 7 pieces of coaxial relay lenses. It has a 48° × 36° field of view (FOV) and 12-mm exit pupil diameter.
Jing Jin Shen,Jia Ming Zhou,Shan Lu,Yue Yang Hou,Rong Qing Xu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.12
Instrumented indentation is a versatile method of extracting hyper-elastic material parameters, particularly useful for applications where stress-strain data are difficult to be insitu measured. Because the analytical force-displacement relation is still unavailable for the indentation of hyper-elastic materials, identifying hyper-elastic parameters often requires an iterative optimization strategy that fits finite element simulations with experimental data. However, the optimization strategy is burdened by heavy computation and its prediction accuracy is greatly influenced by the choice of optimization algorithm. To address these challenges in this study, a bidirectional long short-term memory (BLSTM) neural network is presented that directly predicts hyper-elastic material parameters from indentation load-displacement data, focusing on Mooney-Rivlin hyper-elasticity as an example. To improve the predication accuracy, the condition numbers for the inverse identification of the hyper-elastic parameters are investigated. And, a normalization procedure is proposed to treat the input data, which can guarantee the BLSTM network is well-conditioned. During evaluation, the trained BLSTM network significantly outperforms the iterative optimization strategy using a genetic algorithm. Furthermore, the effect of the normalization procedure is demonstrated.