Digital Twin of Intelligent Small Surface Defect Detection with Cyber-manufacturing Systems

Wu, Yirui; Cao, Hao; Yang, Guoqiang; Lu, Tong; Wan, Shaohua

doi:10.1145/3571734

Cited by 35 publications

(14 citation statements)

References 40 publications

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“…Validation on three datasets confirmed the effectiveness of the model, but the computational burden was heavy [17]. Wu et al fused the features of multimodal data as a rich information source for reliable analysis [18]. Muralitharan et al proposed a neural network-based genetic algorithm (NNGA) and a neural network-based particle swarm optimization (NNPSO) algorithm, in which NNGA had a better performance better in short-term building energy consumption prediction, while the NNPSO algorithm was more suitable for long-term building energy consumption prediction.…”

Section: Related Workmentioning

confidence: 83%

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Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

Wang

Gao³

et al. 2022

Wireless Communications and Mobile Computing

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For the building energy consumption models with complex scale sensitivity, it is difficult to achieve ideal prediction effect with single-granularity prediction model. Therefore, this paper proposed a multigranularity MgHa-LSTM model based on convolutional recurrent neural network, including a multigranularity feature extraction module and a long-term dependency capture module. Multigranularity feature extraction included granularity segmentation, feedback mechanism, and parallel convolutional modules, which can capture short-term scale sensitivity dependencies. Long-term dependency capture consists of a hybrid attention mechanism and long-short term memory layers, which are able to capture long-term dependencies. For building energy consumption patterns with different scale sensitivity, MgHa-LSTM, MLP, CNN, LSTM, and MsC-LSTM models were constructed on the IHEPC building energy consumption dataset used in this paper for comparative experiments. The experimental results showed that on the IHEPC dataset, the MSE of the building energy consumption prediction model is 0.2821 based on the MgHa-LSTM model proposed in this paper, which is equivalent to 93.72% of the MsC-LSTM model with the smallest MSE among other deep learning prediction models. Compared with other deep learning prediction models, the prediction results of the MgHa-LSTM building energy consumption prediction model are more accurate.

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Section: Related Workmentioning

confidence: 83%

“…The predicted value of building energy consumption is defined as Equation (18), where y pred represents the predicted value of building energy consumption of the model, W m and b m represent the weights and bias terms of the fully connected layer, and v lstm represents the output feature…”

Section: Fully Connected Layermentioning

confidence: 99%

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

Wang

Gao³

et al. 2022

Wireless Communications and Mobile Computing

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“…Numerous machine learning [25], [26] and deep learning methods [27], [28]- [31] are applied to capture the spatial-temporal dependence of bicycle-sharing demand. For example, Gated Graph Neural Network (GGNN) was introduced to dynamically predict the bicycle station layout, the number of bicycles, and bicycle dispatching in work [32].…”

Section: A Bicycle-sharing Demand Predictionmentioning

confidence: 99%

An Intelligent Rebalance System for Tidal Phenomenon of Dockless Bicycle-Sharing

Liao

Huang

et al. 2023

IEEE Access

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With the advantages of flexible parking locations and convenient cycling, Dockless Bicyclesharing (DBS) has become increasingly popular worldwide. However, with the massive increase of DBSs and electric fences, DBS systems face several challenges: (1) the hardness of identifying the DBS tidal zones; (2) the difficulty of accurately evaluating and identifying overload fences; (3) the issues of rebalancing DBS in time. To deal with these challenges, we propose a Dockless Bicycle-sharing Dynamic Rebalance (DBSDR) system to dynamically provide the optimal bicycle guidance for the DBS network. The DBSDR system contains three modules: DBS tidal zone identification, evaluation framework of overload fences, and DBS dynamic guidance. For DBS tidal zone identification, tidal zone identification and location from each fence with bicycle flows are provided with the HDBSCAN clustering method. The evaluation framework, covering DBS flows and the parking demand density, is proposed to assess the characteristics of overload fences. Finally, a DBS dynamic guidance method is provided to balance DBS for the tidal phenomenon with guiding users to the optimal target fence. Extensive experiments conducted on real-world DBS datasets show the effectiveness and accuracy of rebalancing the tidal phenomenon in the DBS system.INDEX TERMS Dockless Bicycle-sharing system, bicycle rebalancing, HDBSCAN, user-based guidance.

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“…Our model is designed to be light to process faster, which is of great significance for applications such as implementing on edge computing device in industrial scenarios [40]. Table 2 shows the results of the proposed model and comparison models in terms of model parameters, time complexity, and processing speed.…”

Section: Experiments and Disscussionmentioning

confidence: 99%

DGC‐UWnet: Underwater image enhancement based on computation‐efficient convolution and channel shuffle

Hao

Liu

2023

IET Image Processing

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Underwater image enhancement is receiving increasing attention due to many of the vision research being applied to underwater scenes. To eliminate the impact of complex underwater scenes on imaging, underwater image enhancement algorithm has become an effective solution. However, underwater image enhancement models face a challenge of lightening the model while improving generalizability. Here, DGC‐UWnet is proposed to go for both lightweight and enhancement effect. The proposed model is designed by using depthwise convolution, group convolution and channel shuffle (DGC). Ablation experiment shows that compared with standard convolution, DGC decreases model parameters and computational complexity, and improves the generalizability of the model. Qualitative and quantitative comparative experimental results show that comprehensive performances of the model can catch up with or even surpass state‐of‐the‐art (SOTA) algorithms in terms of processing speed, subjective visual perception and objective evaluation metrics. In addition, application test results prove that DGC‐UWnet can be used as the pre‐processing for underwater applications of other visual algorithms such as improving performance of YOLOv5l.

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Digital Twin of Intelligent Small Surface Defect Detection with Cyber-manufacturing Systems

Cited by 35 publications

References 40 publications

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

An Intelligent Rebalance System for Tidal Phenomenon of Dockless Bicycle-Sharing

DGC‐UWnet: Underwater image enhancement based on computation‐efficient convolution and channel shuffle

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