The Effectiveness of a Digital Twin Learning System in Assisting Engineering Education Courses: A Case of Landscape Architecture

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(15), P. 6484 - 6484

Published: July 25, 2024

In conventional engineering education, issues such as the discrepancy between virtual and real environments, rigid practical operations, lack of reflective support, a disconnect online offline learning prevail. Digital twin technology, with its high fidelity real-time interaction features, presents an innovative instructional aid for education. This study developed digital system to assist instructors in implementing project-based teaching models landscaping technology courses. To assess effectiveness this system, quasi-experiment was designed. Seventy students from vocational school majoring China were recruited participants. These divided into two groups, each consisting 35 students, same pace. The experimental group utilized supplement instructor’s courses, while control received instruction through traditional methods. experiment lasted eight weeks, comprising total 16 classes. Ultimately, results indicated that significantly outperformed those critical thinking, cognitive load, experience, academic performance. Additionally, research examined acceptance learners toward using influencing factors based on Technology Acceptance Model, aiming provide insights enhancing education courses targeted technological development.

Language: Английский

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Real-Time Analysis of Industrial Data Using the Unsupervised Hierarchical Density-Based Spatial Clustering of Applications with Noise Method in Monitoring the Welding Process in a Robotic Cell

Information, Journal Year: 2025, Volume and Issue: 16(2), P. 79 - 79

Published: Jan. 22, 2025

The application of modern machine learning methods in industrial settings is a relatively new challenge and remains the early stages development. Current computational power enables processing vast numbers production parameters real time. This article presents practical analysis welding process robotic cell using unsupervised HDBSCAN algorithm, highlighting its advantages over classical k-means algorithm. paper also addresses problem predicting monitoring undesirable situations proposes use real-time graphical representation noisy data as particularly effective solution for managing such issues.

Language: Английский

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Human-Machine Dialogue: Chabots Revolutionizing Maintenance in Industry 5.0

Published: Jan. 1, 2025

Language: Английский

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Opportunities and Barriers for Implementing Human-Centric Manufacturing in SMEs: Results from Focus Group Workshops in Argentina

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 253, P. 1452 - 1461

Published: Jan. 1, 2025

Language: Английский

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Factories of the future in industry 5.0—Softwarization, Servitization, and Industrialization

Internet of Things, Journal Year: 2024, Volume and Issue: 28, P. 101431 - 101431

Published: Nov. 21, 2024

Language: Английский

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Gemelos Digitales en la Industria 5.0 – una Revisión Sistemática de Literatura

European Public & Social Innovation Review, Journal Year: 2024, Volume and Issue: 9, P. 1 - 21

Published: Aug. 30, 2024

Introducción: La Industria 5.0 integra tecnologías avanzadas con enfoques centrados en el ser humano para mejorar la seguridad fabricación, colaboración humano-robot y eficiencia. Los gemelos digitales, réplicas virtuales de sistemas físicos, son centrales esta iniciativa laboral eficiencia operativa. Metodología: Esta SLR utilizó una estrategia búsqueda exhaustiva cinco bibliotecas digitales: IEEE Explore, Scopus, Taylor & Francis Online, ACM Digital Library Web of Science. Resultados: hallazgos destacan las contribuciones los digitales a trabajadores mediante monitoreo tiempo real, detección inteligente gestión proactiva riesgos. se logra través integración datos real. también mejoran fabricación al permitir producción más inteligentes adaptativos. Discusión: A pesar su potencial, deben abordar desafíos como calidad datos, complejidad computacional, ciberseguridad, factores humanos impactos socioeconómicos. Conclusiones: subraya papel avance 5.0, promoviendo entornos industriales seguros, eficientes humano.

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AI-Powered Obstacle Detection for Safer Human-Machine Collaboration

Acta Electrotechnica et Informatica, Journal Year: 2024, Volume and Issue: 24(3), P. 23 - 27

Published: Sept. 1, 2024

Abstract This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal research was to design implement an artificial intelligence application that recognizes obstacles, including humans, increases safety. resulting Android uses a MiDaS model generate depth map environment from drone’s camera approximate distance all obstacles avoid collision. Besides, this work introduced us DJI Mobile SDK neural network optimizations for their use on smartphones.

Language: Английский

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