<abstract>
<p>Classifying and identifying surface defects is essential during the production and use of aluminum profiles. Recently, the dual-convolutional neural network(CNN) model fusion framework has shown promising performance for defects classification and recognition. Spurred by this trend, this paper proposes an improved dual-CNN model fusion framework to classify and identify defects in aluminum profiles. Compared with traditional dual-CNN model fusion frameworks, the proposed architecture involves an improved fusion layer, fusion strategy, and classifier block. Specifically, the suggested method extracts the feature map of the aluminum profile RGB image from the pre-trained VGG16 model's <italic>pool5</italic> layer and the feature map of the maximum pooling layer of the suggested A4 network, which is added after the Alexnet model. then, weighted bilinear interpolation unsamples the feature maps extracted from the maximum pooling layer of the A4 part. The network layer and upsampling schemes ensure equal feature map dimensions ensuring feature map merging utilizing an improved wavelet transform. Finally, global average pooling is employed in the classifier block instead of dense layers to reduce the model's parameters and avoid overfitting. The fused feature map is then input into the classifier block for classification. The experimental setup involves data augmentation and transfer learning to prevent overfitting due to the small-sized data sets exploited, while the K cross-validation method is employed to evaluate the model's performance during the training process. The experimental results demonstrate that the proposed dual-CNN model fusion framework attains a classification accuracy higher than current techniques, and specifically 4.3% higher than Alexnet, 2.5% for VGG16, 2.9% for Inception v3, 2.2% for VGG19, 3.6% for Resnet50, 3% for Resnet101, and 0.7% and 1.2% than the conventional dual-CNN fusion framework 1 and 2, respectively, proving the effectiveness of the proposed strategy.</p>
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In this paper, vibration signal multifractal method was researched, multifractal on time-frenquency domain was an energy fractal and the signal feature extraction was based on the analysis of its energy distributing. The method analysed signal on timefrequency domain to characterize the distributing of its frequency or energy, and the signal's feature was extracted by fractal dimension. After the signal was changed to time-frequency domain by Hilbert-Huang transform, general dimension Dq would be calculated from the signal in time-frequency domain by least square method. In the end, examples of emulator and practical application proved that this integrated method was feasible.
Yacht amusement industry has gradually become a fashion industry in the amusement places in coastal or other waters. Along with the industry's development, security issues also need to address. In this paper, the yacht intelligent monitoring system has been proposed. And it is constituted of the hardware system including an MMA7260 acceleration sensor, a GPS module, a battery capacity monitor and two wireless transmission modules and the visual monitoring software system set in the monitoring room, with the MC9S12 single-chip as MCU.
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