Zirui Zhao

Gongjin Lan, Qiangqiang Lai, Bing Bai et al.

Automotive engine assembly and disassembly are common and crucial programs in the automotive industry. Traditional education trains students to learn automotive engine assembly and disassembly in lecture courses and then to operate with physical engines, which are generally low effectiveness and high cost. In this work, we developed a multi-layer structured Virtual Reality (VR) system to provide students with training in automotive engine (Buick Verano) assembly and disassembly. We designed the VR training system with The VR training system is designed to have several major features, including replaceable engine parts and reusable tools, friendly user interfaces and guidance, and bottom-up designed multi-layer architecture, which can be extended to various engine models. The VR system is evaluated with controlled experiments of two groups of students. The results demonstrate that our VR training system provides remarkable usability in terms of effectiveness and efficiency. Currently, our VR system has been demonstrated and employed in the courses of Chinese colleges to train students in automotive engine assembly and disassembly. A free-to-use executable file (Microsoft Windows) and open-source code are available at https://github.com/LadissonLai/SUSTech_VREngine for facilitating the development of VR systems in the automotive industry. Finally, a video describing the operations in our VR training system is available at https://www.youtube.com/watch?v=yZe4YTwwAC4

Guimin Hu, Zirui Zhao, Lukas Heilmann et al.

Designing and displaying haptic signals with sensory and emotional attributes can improve the user experience in various applications. Free-form user language provides rich sensory and emotional information for haptic design (e.g., ``This signal feels smooth and exciting''), but little work exists on linking user descriptions to haptic signals (i.e., language grounding). To address this gap, we conducted a study where 12 users described the feel of 32 signals perceived on a surface haptics (i.e., electrovibration) display. We developed a computational pipeline using natural language processing (NLP) techniques, such as GPT-3.5 Turbo and word embedding methods, to extract sensory and emotional keywords and group them into semantic clusters (i.e., concepts). We linked the keyword clusters to haptic signal features (e.g., pulse count) using correlation analysis. The proposed pipeline demonstrates the viability of a computational approach to analyzing haptic experiences. We discuss our future plans for creating a predictive model of haptic experience.