RIS-Aided Smart Manufacturing: Information Transmission and Machine Health Monitoring

Hoang, Tiep M.; Dinh-Van, Son; Barn, Balbir; Trestian, Ramona; Nguyen, Huan X.

doi:10.1109/jiot.2022.3187189

Cited by 3 publications

(2 citation statements)

References 25 publications

(51 reference statements)

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“…In particular, Industry 4.0 further emphasizes the importance of digital twins in smart factories that have intelligent production systems [ 9 , 10 , 11 , 12 ]. Additionally, with the increase in the average life expectancy of people, interest in health is increasing.…”

Section: Introductionmentioning

confidence: 99%

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

Lee

Kwon

Park

2023

Sensors

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As the application fields for digital twins have expanded, various studies have been conducted with the objective of optimizing the costs. Among these studies, research on low-power and low-performance embedded devices has been implemented at a low cost by replicating the performance of existing devices. In this study, we attempt to obtain similar particle count results in a single-sensing device replicated from the particle count results in a multi-sensing device without knowledge of the particle count acquisition algorithm of the multi-sensing device. Through filtering, we suppressed the noise and baseline movements of the raw data of the device. In addition, in the process of determining the multi-threshold for obtaining the particle counts, the existing complex particle count determination algorithm was simplified to make it possible to utilize the look-up table. The proposed simplified particle count calculation algorithm reduced the optimal multi-threshold search time by 87% on average and the root mean square error by 58.5% compared to existing method. In addition, it was confirmed that the distribution of particle count from optimal multi-thresholds has a similar shape to that from multi-sensing devices.

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Section: Introductionmentioning

confidence: 99%

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

Lee

Kwon

Park

2023

Sensors

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“…In this context, many manufacturing enterprises around the world have carried out the practice of building intelligent factories, such as the Electronic Works Amberg in Germany, Harley-Davidson in the United States, and the Foshan Haier Drum Washing Machine Factory and the Shangpin Home in China [9][10][11][12]. By connecting the devices in the intelligent factory through the IoT, enterprises can monitor and share information in the cloud system, adjust the process at any time, quickly respond to the individual needs of customers and improve the flexibility of production [13][14][15]. In recent years, 3D printing technology has been further integrated with the intelligent factory [16,17], for it makes product customization more possible and flexible [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory

Jin

Cheng

2023

Sustainability

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Considering the advantages of 3D printing, intelligent factories and distributed manufacturing, the 3D printing distributed intelligent factory has begun to rise in recent years. However, because the supply chain network of this kind of factory is very complex, coupled with the impact of customized scheduling and environmental constraints on the enterprise, the 3D printing distributed intelligent factory is facing the great challenge of realizing green supply chain networks and optimizing production scheduling at the same time, and thus a theoretical gap appears. This paper studies the hybrid optimization of green supply chain networks and scheduling of the distributed 3D printing intelligent factory. Firstly, according to the green supply chain network architecture of the distributed 3D printing intelligent factory, the cost minimization model is constructed. Secondly, mathematical software is used to solve the model, and the scheduling plan can be worked out. Finally, through the simulation analysis, it is concluded that the influencing factors such as demand, factory size and production capacity complicate the production distribution, and it can be observed that the carbon emission cost has gradually become the main factor affecting the total cost. The study has a reference value for the management decision making of the distributed 3D printing intelligent factory under the background of carbon emissions.

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Energy Efficiency Relaying Election Mechanism for 5G Internet of Things: A Deep Reinforcement Learning Technique

Dutriez,

Oubbati,

Gueguen

et al. 2024

2024 IEEE Wireless Communications and Networking Conference (WCNC)

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RIS-Aided Smart Manufacturing: Information Transmission and Machine Health Monitoring

Cited by 3 publications

References 25 publications

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory

Energy Efficiency Relaying Election Mechanism for 5G Internet of Things: A Deep Reinforcement Learning Technique

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