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Artificial Intelligence Generated Content (AIGC) technology aligns seamlessly with the design requirements of non-genetic heritage, offering a viable pathway for its modernization. This paper delineates the specific design needs of non-genetic heritage and utilizes a diffusion model to create themed images and animations related to this heritage. Additionally, AIGC is employed to enhance the creation of virtual reality interactive imagery. The Long Short-Term Memory (LSTM) network is deployed to classify time-series gesture data, facilitating the training and categorization of Chinese Sign Language (CSL) gestures for virtual interactive engagement with non-heritage themes. We have integrated the AIGC operation process into the theme of non-genetic inheritance, thereby constructing a robust development trajectory for AIGC-enhanced non-genetic heritage. The experimental setup is crafted to ascertain the optimal number of iterations and training durations through the control variable method. We evaluate the efficacy of the diffusion model for anti-implicit writing analysis and the performance of the speech recognition, text dialogue, and text response modules within the non-heritage multimodal interactive framework using Word Error Rate (WER) and Mean Opinion Score (MOS). A descriptive analysis of users’ interactive experiences with non-heritage content is also conducted. The results indicate that the speech recognition module achieved a WER of 0.365, while the text response module garnered an MOS of 4.49 with a standard deviation of 0.56. This multimodal, non-heritage virtual interaction, leveraging multiple modalities, enriches users’ experiences and deepens their understanding and appreciation of non-heritage content. Consequently, this enhances the high-quality development of non-genetic heritage.

Aiming at the complex and changeable flight operation environment, a dynamic prediction model of flight chain delay based on digital twin is proposed to improve the accuracy and adaptability of the traditional prediction methods. The model is constructed based on the digital twin flight chain system. Through the model, the unit-level delay prediction is accomplished by the multi-channel feature modeling method under the sliding windows, and the parameter optimization is completed by a hybrid optimization strategy. Moreover, the whole chain’s delays are analyzed and corrected with the help of the twin data-driven strategy. Flight data in China are used to conduct the study. The mean absolute error (MAE) of flight delay prediction is 11.79 min, which is lower than those of other baseline models or static models, and the forecast error of subsequent flight delay can be reduced by 6.44% after twin data-driven analysis is implemented. The results show that the model is beneficial for the digital twin system to realize the virtual reality interaction, and has excellent prediction accuracy and adaptability. [ABSTRACT FROM AUTHOR]

Digital twin technology is a key technology to realize cyber-physical system. Owing to the problems of low visual monitoring of the blade-rotor test rig and poor equipment monitoring capabilities, this paper proposes a framework based on the digital twin technology. The digital-twin based architecture and major function implementation have been carried out form five dimensions, i.e. Physical layer, Virtual layer, Data layer, Application layer and User layer. Three key technologies utilized to create the system including underlying equipment real-time communication, virtual space building and virtual reality interaction have been demonstrated in this paper. Based on RS-485 and other communication protocols, the data acquisition of the underlying devices have been successfully implemented, and then the real-time data reading has been achieved. Finally, the rationality of the system has been validated by taking the blade-rotor test rig as the application object, which provides a reference for the monitoring and evaluation of equipment involved in manufacturing and experiment. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The test of mine unmanned vehicle has problems of great danger, long test time, high test cost and narrow test coverage. In order to solve the above problems, the simulation system of unmanned mine vehicle based on parallel control theory is studied. The system adopts key technologies such as mine vehicle dynamics modeling, high-fidelity scene reconstruction, and virtual sensor modeling. The system realizes the functions of comprehensive deduction of the unmanned driving algorithm, system integration reliability test, mining area production prediction simulation, and virtual and actual interactive parallel deduction. The main step of dynamic modeling of the mine vehicle is divided into two parts: vehicle model building and visual scene creation. The vehicle dynamic model is associated with the virtual scene. The simulation data generated by the vehicle model is used for driving the vehicle in the virtual scene to move in real-time. In view of the complex and irregular characteristics of the large-scale open-pit mine scene, the high-precision 3D model data of the mine is obtained by means of UAV aerial mapping and laser radar 3D scanning. Based on the virtual micro polygon geometry technology, high pixel virtual texture technology, and 3D scene real-time rendering technology, a high-fidelity virtual 3D scene is constructed. The virtual sensor mainly comprises virtual laser radar, virtual millimeter wave radar, virtual inertial navigation device and virtual vision camera. The virtual sensor is carried on the virtual mine car. It is responsible for generating virtual data information in a simulated mining area scene, and sending the data to the automatic driving controller for processing. Based on the simulation system, single-vehicle test, multi-vehicle scheduling test and intelligent scheduling algorithm test can be carried out. The dynamic virtual-reality interaction between on-site vehicles and virtual vehicles can be tested. The system is used to provide a verification platform for stable transportation of the whole mining area, deduction simulation of complex intersections and optimal decision of intelligent scheduling algorithm. The system ensures the efficiency and safety of unmanned driving test and accelerates the upgrading of unmanned driving technology in the mining area.

The development of intellectualization trend in online education has been characterized by constructing a multi-terminal immersive learning environment. Virtual reality (VR) technology has been increasingly used in online education to create multisensory interactive learning. However, the technical features of this technology, including high immersion and strong interactions, have not been entirely played substantially. Consequently, improvements in the perceptual learning effect have been hindered. To address these issues, this study built a novel VR interaction model for perceptual learning by introducing reflective thinking variables and individual participation factors from the task-technology fit perspective. Furthermore, the deployment strategy of this model used to build a VR education system was proposed. The usability evaluation results of the proposed model show that the path hypothesis of the novel model is verified. Particularly, the path coefficients of reflective thinking, learner participation, and instructor participation factors on the perceptual learning effect were 0.238 (p < 0.01), 0.398 (p < 0.001), and 0.348 (p < 0.001), respectively. Compared to the traditional VR education system, the immersion and interaction of the VR education system using the proposed deployment strategy were enhanced by 4.9% and 10.7%, respectively. Further, learners' perceptual learning effect improved by 5.3%. [ABSTRACT FROM AUTHOR]