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• Add a differential sequence to reduce the impact of noise in ECG signal. • Add a variable nonlinear layer to enhance the non-linear capability of the model. • Adaptive Learning rate is used to converge the training process. • The performance of proposed CNN is better than the other widely used algorithms. • The proposed algorithm can serve as an adjunct tool to assist clinicians in confirming their diagnosis. In this paper, an improved convolutional neural network (CNN) named Feature Selection CNN (FS-CNN) and Synthetic Minority Over-sampling Technique (SMOTE) named Multiply Stochastic SMOTE (MS-SMOTE) are proposed for imbalanced ECG multi-factor classification. FS-CNN includes convolutional expression layer, multi-residual block, and decision layer. The primary purpose of FS-CNN is to find the most influential features during automatic model optimization dynamically. MS-SMOTE combines the advantage of SMOTE and borderline SMOTE to balance the number of different categories. By dynamically selecting SMOTE and borderline SMOTE based on the proportion of data, the synthetic data can have a better category property. Comparing to other wide-used algorithms, our method is better than most other algorithms in many indicators. And in simulation 2, our method can deal with a large number of ECG multi-factor data classifications. Also, two self-controlled experiments are designed to examine how different parameters affect the result of the above problem. [ABSTRACT FROM AUTHOR]

• Classification of roughness to unmeasured surfaces is firstly proposed. • No need to artificially design any indicators. • The method used in this article is robust to the light source environment. • This method is combined with machine vision system. At present, machine vision roughness detection mostly needs to design roughness related indexes based on images, and the index design has human intervention and is heavily dependent on the light source environment. To solve this problem, the paper classifies the surface roughness based on the deep convolutional neural network method, which can realize the roughness detection without index design. The most important thing is that the detection method has good light source robustness under different light source environments. The study adopts an end-to-end image analysis method, by means of image enhancement pre-processing of a small number of source images, after multi-layer convolution and pooling operations, as well as comprehensive processing of fully connected and classification layers, the convolutional kernel can automatically extract the features of the image, and finally the surface roughness obtained by vertical disc cutter milling can be classified and predicted. In addition, based on the experimental light source environment with two different luminance of night and day, by comparing with the technically mature ResNet50 and DenseNet121 convolutional neural network models, the deep convolutional neural network Xception model not only has high roughness classification accuracy, but also has more light source environment robustness. This method makes the online measurement of visual roughness possible. [ABSTRACT FROM AUTHOR]