Cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative artificial intelligence and cyber–physical manufacturing systems in the immersive industrial metaverse

Equilibrium Quarterly Journal of Economics and Economic Policy, Год журнала: 2024, Номер 19(3), С. 719 - 748

Опубликована: Сен. 27, 2024

Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data real-time event analytics, machine deep learning algorithms articulate digital twin smart factories in relation to learning-assisted planning, (IoT)-based production logistics, enterprise resource coordination. cooperative behaviors 3D assembly operations collaborative environments require ambient environment geospatial simulation tools, computer vision spatial mapping algorithms, generative artificial intelligence (AI) planning software. Flexible cloud computing necessitate sensing actuation capabilities, cognitive visualization sensor fusion image recognition technologies so as lead tangible business outcomes. Purpose the article: We show that AI cyber–physical manufacturing fog edge task scheduling are instrumental interactive economics metaverse. Generative AI-based metaverse develops on IoRT management multi-sensory extended reality modeling technologies, for data-driven decision-making processes. Virtual reinforcement autonomous virtual equipment learning-based object detection can be leveraged networked immersive processing. Methods: Evidence appraisal checklists citation software deployed justifying inclusion or exclusion reasons collection analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, Systematic Review Toolbox. Findings & value added: Modal actuators sensors, robot trajectory computational systems enable scalable computation processes environments. Ambient remote cloud-based robotic cooperation improve IoT-based logistics multi-agent controls factories. Context acquisition shape path lines, collision-free motion coordinated unpredictable perception tasks, increasing economic performance. This collective writing cumulates debates upon most recent relevant literature twin-based Things, by use evidence

Язык: Английский

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Tensor-based ranking-hiding privacy-preserving scheme for cloud-fog-edge cooperative cyber-physical-social systems

Journal of Network and Computer Applications, Год журнала: 2025, Номер unknown, С. 104167 - 104167

Опубликована: Март 1, 2025

Язык: Английский

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Integrating Relationship Path and Entity Neighbourhood Information for Knowledge Graph Intelligence of Social Things

Chinese journal of information fusion., Год журнала: 2025, Номер 2(1), С. 27 - 37

Опубликована: Март 20, 2025

In the evolving framework of Intelligence Social Things (IoST), which amalgamates social networks and IoT ecosystems, knowledge graphs are essential for facilitating networked systems to efficiently process leverage intricate relational data. Knowledge offer technical assistance various artificial intelligence applications, such as e-commerce, intelligent navigation, healthcare, media. Nonetheless, current frequently lack completeness, harboring a considerable quantity implicit that remains be revealed. Consequently, tackling difficulty finalising has emerged pressing research priority. Most contemporary methods separately analyse entity neighbourhood information or connection routes, neglecting significance in investigation relationship paths. A novel approach, RPEN-KGC (Relationship Path Entity Neighbourhood Graph Completion), is suggested enable fusion paths graph completion. comprises sampler an inferencer. The conducts random walks between pairs furnish dependable inference utilises contrastive method grounded similarity steer walks, hence enhancing sampling efficiency augmenting strategies. inferencer derives semantic characteristics deduces greater variety within domain. Experiments performed on public NELL-995 FB15K-237 datasets link prediction task indicate significantly enhances most metrics relative baseline approaches. These findings demonstrate proficiently forecasts absent graphs.

Язык: Английский

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Artificial Intelligence-Enhanced Digital Twin Systems Engineering Towards the Industrial Metaverse in the Era of Industry 5.0

Chinese Journal of Mechanical Engineering, Год журнала: 2025, Номер 38(1)

Опубликована: Март 27, 2025

Abstract With the continuous advancement and maturation of technologies such as big data, artificial intelligence, virtual reality, robotics, human-machine collaboration, augmented many enterprises are finding new avenues for digital transformation intelligent upgrading. Industry 5.0, a further extension development 4.0, has become an important trend in industry with more emphasis on human-centered sustainability flexibility. Accordingly, both industrial metaverse twins have attracted much attention this era. However, relationship between them is not clear enough. In paper, comparison made firstly. Then, we propose concept framework Digital Twin Systems Engineering (DTSE) to demonstrate how support era 5.0 by integrating systems engineering principles. Furthermore, discuss key challenges DTSE, particular intelligence enhances application DTSE. Finally, specific scenario aviation field presented illustrate prospects

Язык: Английский

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Network extension planning towards resilient urban critical infrastructures using deep reinforcement learning

Applied Soft Computing, Год журнала: 2025, Номер 176, С. 113163 - 113163

Опубликована: Апрель 16, 2025

Язык: Английский

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Society 5.0: Scope, Opportunities, and Challenges in Achieving Super Smart Society

Опубликована: Янв. 1, 2025

Язык: Английский

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Advanced Vibration Analysis in Smart Factories

International Journal of Latest Technology in Engineering Management & Applied Science, Год журнала: 2025, Номер 14(4), С. 507 - 518

Опубликована: Май 10, 2025

Abstract: In the development of smart factories, advanced vibration analysis is essential since it allows for real-time machinery and equipment monitoring diagnostics. Smart sensor integration signifies continuous collection, processing, data to identify early indications mechanical failures maintain maximum performance in intricate industrial systems. With a focus on how artificial intelligence (AI) revolutionizing conventional diagnostic approaches, this chapter examines most recent developments techniques. Machine learning deep algorithms are used AI-driven diagnostics complex defect patterns that traditional techniques could overlook. On top that, combine from several sources improves precision resilience, making possible problems big, networked By integrating with signal processing like wavelet transformation Fast Fourier Transform (FFT), complete image system health produced, allowing predictive maintenance minimizing downtime. This shows as we go toward Industry 5.0, AI, technology, fusion work together improve factory operations, increase overall reliability, facilitate long-term growth manufacturing sectors.

Язык: Английский

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Decentralized Security for Multi-User Vr Applications Using Multi-Agent Reinforcement Learning

Опубликована: Янв. 1, 2025

Язык: Английский

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TransRAG for parallel transportation: toward reliable and trustworthy transportation systems via retrieval-augmented generation

Frontiers of Information Technology & Electronic Engineering, Год журнала: 2024, Номер unknown

Опубликована: Дек. 26, 2024

Язык: Английский

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