Visual Simulation Application for Hardware In-Loop Simulation (HILS) of Aerospace Vehicles

Jha, Pramod Kumar; Shekhar, Chander; Kumar, L. Sobhan

doi:10.1007/978-3-319-13728-5_53

Cited by 1 publication

(1 citation statement)

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“…With the maturity of computer technology, simulation technology has been further popularized through simulation software. Now, simulation technology has been widely used in stage performance (Bilbao, 2009;Niedenthal et al, 2010;Jaber et al, 2019), event scheduling (Colella, 2011;Arima, 2015), emergency response (Merién et al, 2010), military training (Liang et al, 2001;Machado et al, 2015;Ma et al, 2016), aerospace (Zipfel and Schiehlen, 2001;Jha et al, 2015), industrial manufacturing (Buyya and Murshed, 2010;Yamaguchi et al, 2016;Santipanusopon and Worawattanaparinya, 2019;Taheripour et al, 2019), technology research and development (Boyd and Bruns, 2001;Rapaport et al, 2002;IEEE, 2010;Binder and Heermann, 2014;Moin, 2018;Yingying et al, 2018), and many other fields. The simulation process has many commonalities, and these commonalities can be found in the application of simulation technology in various fields (Bucklew, 2010).…”

Section: Introductionmentioning

confidence: 99%

Quantum-Based Creative Generation Method for a Dancing Robot

et al. 2020

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In this paper, we propose a creative generation process model based on the quantum modeling simulation method. This model is mainly aimed at generating the running trajectory of a dancing robot and the execution plan of the dancing action. First, we used digital twin technology to establish data mapping between the robot and the computer simulation environment to realize intelligent controllability of the robot's trajectory and the dance movements described in this paper. Second, we conducted many experiments and carried out a lot of research into information retrieval, information fidelity, and result evaluation. We constructed a multilevel three-dimensional spatial quantum knowledge map (M-3DQKG) based on the coherence and entangled states of quantum modeling and simulation. Combined with dance videos, we used regions with convolutional neural networks (R-CNNs) to extract character bones and movement features to form a movement library. We used M-3DQKG to quickly retrieve information from the knowledge base, action library, and database, and then the system generated action models through a holistically nested edge detection (HED) network. The system then rendered scenes that matched the actions through generative adversarial networks (GANs). Finally, the scene and dance movements were integrated, and the creative generation process was completed. This paper also proposes the creativity generation coefficient as a means of evaluating the results of the creative process, combined with artificial brain electroenchalographic data to assist in evaluating the degree of agreement between creativity and needs. This paper aims to realize the automation and intelligence of the creative generation process and improve the creative generation effect and usability of dance movements. Experiments show that this paper has significantly improved the efficiency of knowledge retrieval and the accuracy of knowledge acquisition, and can generate unique and practical dance moves. The robot's trajectory is novel and changeable, and can meet the needs of dance performances in different scenes. The creative generation process of dancing robots combined with deep learning and quantum technology is a required field for future development, and could provide a considerable boost to the progress of human society.

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