Digital Twin-based manufacturing system: a survey based on a novel reference model

Liu, Shimin; Zheng, Pai; Bao, Jinsong

doi:10.1007/s10845-023-02172-7

Cited by 11 publications

(8 citation statements)

References 196 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the proposed Hybrid multi-phase heuristic approach algorithm has certain limitations. In many diverse research areas, a significant number of scholars have applied advanced optimization algorithms to solve challenging decision-making problems, such as those in online learning [49], scheduling, multi-objective optimization [50], transportation, medicine, and data classification [51], [52], [53]. Numerous advanced optimization algorithms have been employed as solution methods and have proven to be efficient in solving complex decision-making problems.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Heterogeneous Vehicle Routes for Urban Distribution Considering the Three-Dimensional Bin Packing Problem of Electric Meters

He,

Zhang,

Lin

et al. 2024

IEEE Access

View full text Add to dashboard Cite

With the establishment of smart grids, there is a growing demand for metering electric meters in urban areas. Given the diverse sizes and delicate nature of these meters, traditional scheduling solutions are unable to cater to the multifaceted requirements of urban environments, meters loading, and subsequent logistics scheduling. This study presents an intelligent scheduling model for electric meters in an urban context, taking into account various constraints such as urban traffic congestion, three-dimensional packing of metering devices, delivery time windows, and heterogeneous vehicles. To solve this, we design an improved whale optimization algorithm using a hybrid multi-phase heuristic approach (IWOA-HMOHA). Simulation results show that compared with the traditional meter logistics scheduling strategy, the IWOA-HMOHA algorithm reduces the objective function by 5.4%~ 26.1% compared with other similar algorithms. In addition, compared with the traditional first-in-last-out cargo packing method, the vehicle space utilization rate is improved by 12.62%. The proposed models and algorithms demonstrate excellent adaptability to a range of urban constraints, offering valuable insights and a robust framework essential for the development of logistics solutions in urban.INDEX TERMS logistics applications; the three-dimensional bin packing problem; hybrid multi-phase heuristic approach; evolutionary algorithms; difference strategy

show abstract

Section: Discussionmentioning

confidence: 99%

Optimizing Heterogeneous Vehicle Routes for Urban Distribution Considering the Three-Dimensional Bin Packing Problem of Electric Meters

He,

Zhang,

Lin

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In recent years, significant research progress has been made in the field of intelligent industrial applications, with numerous scholars providing substantial contributions [8][9][10][11][12][13][14][15][16][17][18][19]. For instance, Aristova et al [8] have extensively investigated the widespread application of industrial robots and automation systems in the manufacturing sector, resulting in enhanced production efficiency and quality.…”

Section: Intelligent Industrial Applicationsmentioning

confidence: 99%

Digital-Twin-Based Monitoring System for Slab Production Process

Fu,

Li,

Shi

et al. 2024

Future Internet

View full text Add to dashboard Cite

The growing demand for high-quality steel across various industries has led to an increasing need for superior-grade steel. The quality of slab ingots is a pivotal factor influencing the final quality of steel production. However, the current level of intelligence in the steelmaking industry’s processes is relatively insufficient. Consequently, slab ingot quality inspection is characterized by high-temperature risks and imprecision. The positional accuracy of quality detection is inadequate, and the precise quantification of slab ingot production and quality remains challenging. This paper proposes a digital twin (DT)-based monitoring system for the slab ingot production process that integrates DT technology with slab ingot process detection. A neural network is introduced for defect identification to ensure precise defect localization and efficient recognition. Concurrently, environmental production factors are considered, leading to the introduction of a defect prediction module. The effectiveness of this system is validated through experimental verification.

show abstract

“…Music, as one of the greatest inventions in human history, not only serves as a medium for cultural expression but also represents a cultural industry with tremendous potential for growth [1]. In recent years, with the rapid development of digital technology [2] [3] [4], the music industry is undergoing profound changes. As society's demand for music continues to expand, diversified music creation has become an inevitable trend.…”

Section: Introductionmentioning

confidence: 99%

Exploring Music Style Transfer and Innovative Composition using Deep Learning Algorithms

2024

ijacsa

View full text Add to dashboard Cite

Automatic music generation represents a challenging task within the field of artificial intelligence, aiming to harness machine learning techniques to compose music that is appreciable by humans. In this context, we introduce a text-based music data representation method that bridges the gap for the application of large text-generation models in music creation. Addressing the characteristics of music such as smaller note dimensionality and longer length, we employed a deep generative adversarial network model based on music measures (MT-CHSE-GAN). This model integrates paragraph text generation methods, improves the quality and efficiency of music melody generation through measure-wise processing and channel attention mechanisms. The MT-CHSE-GAN model provides a novel framework for music data processing and generation, offering an effective solution to the problem of long-sequence music generation. To comprehensively evaluate the quality of the generated music, we used accuracy, loss rate, and music theory knowledge as evaluation metrics and compared our model with other music generation models. Experimental results demonstrate our method's significant advantages in music generation quality. Despite progress in the field of automatic music generation, its application still faces challenges, particularly in terms of quantitative evaluation metrics and the breadth of model applications. Future research will continue to explore expanding the model's application scope, enriching evaluation methods, and further improving the quality and expressiveness of the generated music. This study not only advances the development of music generation technology but also provides valuable experience and insights for research in related fields.

show abstract

Digital Twin-based manufacturing system: a survey based on a novel reference model

Cited by 11 publications

References 196 publications

Optimizing Heterogeneous Vehicle Routes for Urban Distribution Considering the Three-Dimensional Bin Packing Problem of Electric Meters

Optimizing Heterogeneous Vehicle Routes for Urban Distribution Considering the Three-Dimensional Bin Packing Problem of Electric Meters

Digital-Twin-Based Monitoring System for Slab Production Process

Exploring Music Style Transfer and Innovative Composition using Deep Learning Algorithms

Contact Info

Product

Resources

About