Graph convolutional networkSemi-supervised classificationMulti-view data significantly improves the accuracy of machine learning algorithms by providing a holistic representation of object features. However, previous research on the use of Graph Convolutional Networks (GCNs) for processing node connectivity and...
A binary classification model (spray – rope) and a ternary classification model (spray – transition – rope) were developed with a 1D Convolutional neural network architecture capable of taking 5 secs of tri-axial (3-channel) vibration signal data as input, and outputting the predicted ...
The study uses 1D convolutional neural network techniques, training, and testing to predict low- and high-pressure compressors (N1 and N2), exhaust gas temperature (EGT), fuel flow (FF), and vibration (VIB) for selected engines. To prove the study approach is accurate and efficient, ...
Network structure description The proposed One-Dimensional Two-Dimensional dual-channel Information Fusion Convolutional Neural Network fault diagnosis architecture is illustrated in Fig. 4. The network consists of two parallel parts: a one-dimensional CNN channel and a two-dimensional CNN channel, which...
参考:A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012. 首次成功应用ReLU作为CNN的激活函数 使用Dropout丢弃部分神元,避免了过拟合 使用重叠MaxPooling(让池化层的步长小于池化核的大小), 一定程度上提升了特征的丰富性 ...
subsampling/pooling:下采样层,先将 2 x 2 单元的值求和,然后乘以参数 w,然后加偏置,最后取 sigmoid。每层的w,b相同,即每层参数个数为 2 AlexNet(2012) 论文:ImageNet Classification with Deep Convolutional Neural Networks 结构: 要点: Rectified Linear Unit(ReLU) ...
Fig1Convolutionalneuralnetworkarchitecture 在故障诊断方面,鉴于轴承振动信号的海量数 11卷积层 据,基于深度学习的故障诊断方法已成为目前研究的 [11]卷积层为CNN的核心组成部分,主要通过卷积 一个热点。余志锋等提出一种变分模态分解与连 运算进行特征提取。由于卷积操作会降低数据的维 续小波变换和CNN相结合的方法,完...
The neurons at the output layer use softmax loss of the network predictions for 1000 classes. 2.2. 1D convolutional neural networks The conventional deep CNNs presented in the previous section are designed to operate exclusively on 2D data such as images and videos. This is why they are often...
[18] used a convolutional neural network (CNN) inspired by a U-net architecture to predict subgrid-scale flame surface density for a premixed turbulent flame on an under-resolved mesh typical of large eddy simulation. Chung et al. [20] developed a data-assisted modeling approach that uses ...
The convolutional neural network (CNN) model with an AUC of 82% surpassed long short-term memory in [19]. For a private dataset, the CNN classifier achieved an accuracy of 93.24% [20]. The features were created using multivariate intrinsic mode functions to extract nonlinear and nonstationary...