| 1. |
- Shaoyang, Zhang, et al.
(författare)
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Image enhancement on fractional differential for road traffic and aerial images under bad weather and complicated situations
- 2014
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Ingår i: Transportation letters. - 1942-7867 .- 1942-7875. ; 6:4, s. 197-205
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Tidskriftsartikel (refereegranskat)abstract
- To enhance road traffic images and aerial images in bad weather, a new fractional differential operator is studied, and it is different from the traditional Tiansi operator. In the operator, for any sized kernel, the coefficient at the center position is not a constant, and it is the function of the fractional order and the kernel size; for the other pixels in the kernel, there is no zero coefficient value, and the values of the coefficients are calculated according to the distances from the center position on the basis of fractional differential. In experiments, a number of images are tested, and the results show that the studied operator is suitable for the vague images of the low contrast and rich textures, with less color changes. When compared with other newly proposed algorithms (e.g. Dark channel prior and Multiple scale Retinex, etc.) it performs better in some cases.
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| 2. |
- Li, Xuebing, et al.
(författare)
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A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion
- 2021
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Ingår i: Measurement. - : Elsevier BV. - 0263-2241 .- 1873-412X. ; 185
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Tidskriftsartikel (refereegranskat)abstract
- Tool wear monitoring during the cutting process is crucial for ensuring part quality and productivity. A datadriven monitoring approach based on radar map feature fusion is proposed for tool wear recognition and quantitative prediction, aiming at tracking the evolution of tool wear comprehensively. Specifically, the sensitive features from multi-source signals are fused by a radar map, and health indicators capable of characterizing the tool wear evolution are obtained. For the recognition of tool wear state and the quantitative prediction of tool wear values, the Adaboost Decision Tree (Adaboost-DT) ensemble learning model and stacked bi-directional long short-term memory (SBiLSTM) deep learning network are established, respectively. Experimental results demonstrated that the proposed approach could recognize the current wear state quickly and accurately whilst predicting wear values based on limited historical data available. Combining tool wear recognition and prediction results contributes to making a more flexible tool replacement decision in intelligent manufacturing processes.
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| 3. |
- Zhang, Bowen, et al.
(författare)
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An imbalanced data learning approach for tool wear monitoring based on data augmentation
- 2023
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Ingår i: Journal of Intelligent Manufacturing. - : Springer Nature. - 0956-5515 .- 1572-8145.
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Tidskriftsartikel (refereegranskat)abstract
- During cutting operations, tool condition monitoring (TCM) is essential for maintaining safety and cost optimization, especially in the accelerated tool wear phase. Due to the safety constraints of the actual production environment and the tool's properties, the data for each wear stage is usually unbalanced, and these unbalances lead to difficulties in failure monitoring. To this end, a novel TCM method based on data augmentation is proposed, which uses generative adversarial networks (GANs) to generate valuable artificial samples for a few classes to balance the data distribution. Unlike the traditional GANs, the proposed Conditional Wasserstein GAN-Gradient Penalty (CWGAN-GP) avoids pattern collapse and training instability and simultaneously generates more realistic data and signal samples with labels for different wear states. To evaluate the quality of the generated data, an evaluation index is proposed to evaluate the generated data while further screening the samples to achieve effective oversampling. Finally, the continuous wavelet transform (CWT) is combined with the convolutional neural network (CNN) architecture of Inception-ResNet-v2 for TCM, and it is demonstrated that data augmentation can effectively improve the performance of training classification models for unbalanced data by comparing three classification methods with two data augmentation experiments, and the proposed method has a better monitoring performance.
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