Copy CodeCopy Command This example shows how to analyze and compress a 1-D convolutional neural network used to estimate the frequency of complex-valued waveforms. The network used in this example is a sequence-to-one regression network using the Complex Waveform data set, which contains 500 syn...
下面是实现“Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks”所需的步骤: 步骤详解 1. 收集数据集 首先,你需要收集包含正常运行和故障运行的电机数据集。确保数据集具有充足的样本数量和多样性,以便训练和测试模型。 2. 数据预处理 对收集到的数据进行预处理是非常重要的,下面是一些可能的...
convolutional layer is2(k−1)and the stride is 1, then the receptive field size of such a network can be computed asR=(f−1)(2K−1)+1, wherefis the filter size andKis the number of convolutional layers. Change the filter size and number of layers to easily adjust the receptive...
Convolutional Neural Networks Geometry 1. Introduction When we hear about convolutions in machine learning and deep neural networks, we typically think about 2-D convolutions used for image recognition tasks. Indeed, convolutional neural networks (CNNs) revolutionized the field of computer vision by ...
Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by...
Good convolutional codes for the precoded (1-D)(1+D)n partial-response channels 来自 dx.doi.org 喜欢 0 阅读量: 48 作者: BFU Filho 摘要: than the MSN code. However, with slightly higher decoding complexity, the second new channel code outperforms the MSN code关键词:...
Analytic expressions for the exact bit error probabilities of rate R=1/2, memory m=2 convolutional encoders are derived for a maximum-likelihood (ML) decod... M Lentmaier,DV Truhachev,KS Zigangirov - 《IEEE Transactions on Information Theory》 被引量: 38发表: 2004年 About the Efficiency...
Convolutional neural networks (CNNs), on the other hand, can fuse and simultaneously optimize two major sets of an assessment task (feature extraction and classification) into a single learning block during the training phase. This ability not only provides an improved classification performance but ...
The proposed 1D-CNN model is compact and has only one convolutional layer, which can reduce the processing time immensely. Recent studies [33–36] showed that the majority of 1D-CNN applications have employed a shallow structure that has one or two CNN layers and the number of parameters is...
9. Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks[J].Serkan Kiranyaz;Turker Ince;Moncef Gabbouj,IEEE Transactions on Biomedical Engineering.2015,第3期 10. Recognition and management of atrial fibrillation[J].Sharon Brewer;Shelly Seth,Nursing Made Incredibly E...