Researchers from China's Shandong University have developed a novel method for fault diagnosis in PV arrays, using feature engineering and one-dimensional convolutional neural networks (1D-CNN).

According to the pair approximation approach there are three possibilities concerning the kind of stability: marginal stability, asymptotic stability and instability, shown in a 1D and 2D phase ...

The algorithm utilizes a deep convolutional neural network and a non-local triple attention mechanism for feature extraction and multi-scale fusion of renal dynamic imaging images to achieve accurate ...

1D Convolutional Neural Network,Class Activation Maps,Classification Task,Convolutional Block,Convolutional Layers,Convolutional Neural Network,Deep Learning,Diagnostic Process,Dimensionality ...

The multi-channel neighboring feature convolutional network establishes a strong coupling relationship between features and fault labels in nonlinear data, enabling high-precision fault diagnosis. (ii ...

Raman spectroscopy in biological applications faces challenges due to complex spectra, characterized by peaks of varying widths and significant biological background noise. Convolutional neural ...

Sun, Y., Xue, B., Zhang, M. and Yen, G.G. (2019) A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification. IEEE Transactions on ...

We proposed a convolutional autoencoder with sequential and channel attention (CAE-SCA) to address this issue. Sequential attention (SA) is based on long short-term memory (LSTM), which captures ...

Fig. 2: Convolutional Autoencoder (CAE) anomaly scores with time-domain MSE loss Fig. 3: WaveNet Autoencoder anomaly scores with time-domain MSE loss Fig. 4: Attention Autoencoder anomaly scores with ...