one-dimensional convolutional autoencoderssurrogate modelThis paper proposes a one‐dimensional convolutional autoencoders (1D‐CAE) surrogate‐based electromagnetic (EM) optimization technique exploiting particle swarm optimization (PSO) algorithm for microwave filter design. The 1D‐CAE is a flexible ...
In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN), which uses a convolution autoencoder to pre-train the images and uses an attention-based DenseNet to ...
In this paper, a Convolution Autoencoder (CAE) is used to obtain high-level feature representations from the input signature and these high level features are fused with handcrafted features to constitute a hybrid feature set. The hybrid set of features is presented as an input to an Online ...
Convolutional auto-encoderLong short term memoryRemaining useful life predictionTo optimize the operation and maintenance of nuclear power systems, this study presents a remaining useful life (RUL) prediction method for electric valves by combining convolutional auto-encoder (CAE) and long short term ...
In this paper, a convolutional autoencoder (CAE) is designed for spectral feature extraction and classification, which is composed of coding network, decoding network, and classification network. The encoder network consists of convolutional layers and maximum pooling layers, the decoder network consists...
Keywords: multi-scale representation learning (MSRL); pyramid pooling module (PPM); compact depth-wise separable convolution (CSeConv); convolution auto-encoder (CAE); object classification; synthetic aperture radar (SAR)1. Introduction As the vital task of object classification with synthetic aperture...
Convolutional autoencoders (CAE) [14] and generative adversarial networks (GAN) [15] are the commonly used reconstruction models. Li et al. [16] first introduced deep learning technology into field of fabric defect detection by proposed an autoencoder model. Even with some success, such ...
Common methods used are auto-encoder (AE) [6], long-short term memory network (LSTM) [7] and Convolutional Neural Network (CNN) [8]. CNN is an artificial neural network based on the connection of multiple convolutional layers. Because the design of its frame structure uses the convolution ...
[19] proposed a neural network that has a multi-scale 3D deep convolution for HSI classification that can learn 2D multi-scale spatial features and 1D spectral features from the HSI data end-to-end. S. Mei et al. [20] proposed an unsupervised 3D convolutional auto-encoder (3D-CAE) that...
Medical image denoising using convolutional denoising autoencoders. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, Spain, 15 December 2016. 48. Tian, C.; Xu, Y.; Li, Z.; Zuo, W.; Fei, L.; Liu, H. Attention-guided CNN for ...