This study explores the possibility to use the one-dimensional convolutional neural network (CNN) to extract the damage sensitive features automatically from the raw strain response data of a structure under a certain excitation without requiring any hand-crafted feature extraction. The validity of the...
In this paper, a new deep neural network (DNN), multichannel one-dimensional convolutional neural network (MC1-DCNN), is proposed to investigate feature learning from high-dimensional process signals. Wavelet transform is used to extract multiscale components with fault features from process signals....
This paper proposes a novel approach for driving chemometric analyses from spectroscopic data and based on a convolutional neural network (CNN) architecture. For such purpose, the well-known 2-D CNN is adapted to the monodimensional nature of spectroscopic data. In particular, filtering and pooling...
One-dimensional convolutional neural network-based active feature extraction for fault detection and diagnosis of industrial processes and its understanding via ... S Chen,J Yu,S Wang - 《Isa Transactions》 被引量: 0发表: 2022年 One-dimensional convolutional neural network-based active feature extrac...
(x1.shape[1]//2))(x1)x3=layers.Dense(units=int(x1.shape[1]))(x2)x4=tf.expand_dims(x3,axis=1)output_signal=layers.Multiply()([x4,x])returnoutput_signaldefDCNNRC(class_number):'''one-dimensional dilated convolutional neural network with residual connection method'''weight_coef=0.2#...
The proposed method utilizes two-dimensional convolutional neural network (2D-CNN) architecture as a DL algorithm for damage detection. Here, a computer-... T Das,S Guchhait - 《International Journal of Structural Stability & Dynamics》 被引量: 0发表: 2024年 加载更多来源...
Our method treats very limited part of raw APK (Android application package) file of the target as a short string and analyzes it with one-dimensional convolutional neural network (1-D CNN). We used two different datasets each consisting of 5,000 malwares and 2,000 goodwares. We confirmed...
an enhanced method for estimating tsunami time series using a one-dimensional convolutional neural network model (1D CNN) is considered. While the use of deep learning for this problem is not new, most of existing research has focused on assessing the capability of a network to reproduce inundati...
In this study, we propose a Signal-Based One-Dimensional Convolutional Neural Network (SB 1D CNN) model that is customized for seizure signals. The SB 1D CNN model replaces traditional ReLU and Pooling layers with counterparts that are better suited to negative signal fluctuations and adjusts ...
achieve convergence. We provide empirical evidence for the mitigation of bad initial conditioning in PINNs for solving one-dimensional consolidation problems of porous media through the introduction of affine transformations after the classical output layer of artificial neural network architectures, ...