The Role of Virtual Reality Simulation in Manufacturing in Industry 4.0

Akpan, Ikpe Justice; Offodile, Onyebuchi Felix

doi:10.3390/systems12010026

Cited by 7 publications

(2 citation statements)

References 79 publications

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“…Kyriakidou et al [34] uses structural equation modeling (SEM), a multivariate technique, to evaluate the maturity levels of information and communications technology (ICT) with consideration of contextual characteristics in terms of ICT access, use and skills. Online blogs [35], academic publications [79,80] and patents [36] have been exploited for technology maturity assessments, and the results show a good agreement with expert opinions [35].…”

Section: Technological Feature Evaluationmentioning

confidence: 69%

Feature Distributions of Technologies

Zhu,

Deng,

Pan

et al. 2024

Preprint

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The development of one technology can be portrayed by common methods like Gartner’s hype cycle or S-curve, however, there is no method to characterize the feature distributions of multiple technologies within a specific domain. This study proposes a big data-based method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or causing potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach via comparing historical trends with literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.

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Section: Technological Feature Evaluationmentioning

confidence: 69%

Feature Distributions of Technologies

Zhu,

Deng,

Pan

et al. 2024

Preprint

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“…Digital modelling and simulation technology in digital virtual simulation technology represents an important integrated application within the context of the intelligent era of Industry 4.0 [1]. In this context, the digital clothing design method applied to the fashion market has emerged as a nascent field.…”

Section: Introductionmentioning

confidence: 99%

Digital Virtual Simulation for Cultural Clothing Restoration: Case Study of Tang Dynasty Mural ‘Diplomatic Envoys’ from Crown Prince Zhang Huai’s Tomb

Liu,

Cui,

Wang

2024

JTAER

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The advent of 3D virtual presentation technology for clothing has led to the gradual popularisation of digital virtual clothing in the modern fashion industry. However, there remains a gap between the application of this technology and the integration of cultural attributes in the field of digital communication of traditional cultural clothing. Consequently, the objective of this paper is to propose the establishment of a fusion system integrating archaeological research on traditional culture with emerging virtual presentation technology. This paper draws inspiration from the replicability and easy dissemination of digital products to combine cultural archaeology and digital technology. The aim is to provide ideas for the diversity of dissemination of cultural heritage. The research object is Diplomatic Envoys, a Chinese mural painting of the Tang Dynasty that depicts friendly exchanges between countries. The research is divided into two research stages. A CLO3D software-based digital restoration test was conducted to reproduce the costumes of officials and foreign envoys depicted in the Tang Dynasty mural. The FAHP model was employed to verify the accuracy of the restoration results. The experiment demonstrated that the digitally reconstructed clothing exhibited a high degree of similarity to the unearthed mural figure clothing object. Furthermore, the restoration result passed the credibility verification, resulting in a ‘credible’ outcome. The application of digital virtual simulation clothing restoration methods offers two key advantages. Firstly, in comparison with traditional clothing restoration methods, digital restoration enables the rapid assessment of the resulting clothing effect, thereby reducing the likelihood of secondary damage to cultural relics due to manual errors. Secondly, the benefits of digital technology facilitate the convenient storage, replication, and dissemination of clothing data information. Data can not only be extended to online exhibition halls but also to game animation, clothing production, and other fields for the purposes of creative redesign and information dissemination. Furthermore, these benefits can penetrate the education industry to disseminate information to the public through all-round display models and explanations.

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Conversational and generative artificial intelligence and human–chatbot interaction in education and research

Akpan,

Kobara,

Owolabi

et al. 2024

Int Trans Operational Res

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Artificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human‐like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self‐diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline‐based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

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The Role of Virtual Reality Simulation in Manufacturing in Industry 4.0

Cited by 7 publications

References 79 publications

Feature Distributions of Technologies

Feature Distributions of Technologies

Digital Virtual Simulation for Cultural Clothing Restoration: Case Study of Tang Dynasty Mural ‘Diplomatic Envoys’ from Crown Prince Zhang Huai’s Tomb

Conversational and generative artificial intelligence and human–chatbot interaction in education and research

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