Search Results - "Convolutional adversarial autoencoder"

Reducing variations in multi-center Alzheimer’s disease classification with convolutional adversarial autoencoder by Cobbinah, Bernard M., Sorg, Christian, Yang, Qinli, Ternblom, Arvid, Zheng, Changgang, Han, Wei, Che, Liwei, Shao, Junming

“…Based on brain magnetic resonance imaging (MRI), multiple variations ranging from MRI scanners to center-specific parameter settings, imaging protocols, and…”
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Unsupervised anomaly detection of nuclear power plants under noise background based on convolutional adversarial autoencoder combining self-attention mechanism by Sun, Xiang, Guo, Shunsheng, Liu, Shiqiao, Guo, Jun, Du, Baigang

“…•This study involves an anomaly detection problem of NPPs under a noisy background, which is rarely studied in previous research.•A novel unsupervised anomaly…”
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Parameter Space Compression and Random Structure Automatic Generation for the Inverse Design of Photonic Crystal Fibers Based on Convolutional Adversarial Autoencoder by Yu, Haomiao, Huang, Wei, Xiao, Fangxin, Song, Binbin, Chen, Shengyong

“…) is proposed based on convolutional adversarial autoencoder (CAAE) and forward prediction convolutional neural network (PCNN…”
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Deep feature representation with online convolutional adversarial autoencoder for nonlinear process monitoring by Yang, Xu, Xiao, Jieshi, Huang, Jian, Peng, Kaixiang

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A Novel Fault Detection Method Based on One-Dimension Convolutional Adversarial Autoencoder (1DAAE) by Wang, Jian, Li, Yakun, Han, Zhiyan

“… To deal with this problem, this paper proposes a novel unsupervised fault detection method named one-dimension convolutional adversarial autoencoder (1DAAE…”
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Anomaly Detection Using Convolutional Adversarial Autoencoder and One-class SVM for Landslide Area Detection from Synthetic Aperture Radar Images by Mabu, Shingo, Hirata, Soichiro, Kuremoto, Takashi

“… for the training, where the proposed model combines a convolutional adversarial autoencoder, principal component analysis, and one-class support vector machine…”
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A data-driven methodology for bridge indirect health monitoring using unsupervised computer vision by Hurtado, A. Calderon, Alamdari, M. Makki, Atroshchenko, E., Chang, K.C., Kim, C.W.

“…In recent years, researchers have extensively explored the application of drive-by inspection technology for bridge damage assessment. This approach involves…”
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Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things by Zhang, Weijie, Zhang, Lanping, Zhang, Xixi, Wang, Yu, Liu, Pengfei, Gui, Guan

“…Network traffic classification (NTC) has attracted great attention in many applications such as secure communications, intrusion detection systems. The…”
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Detecting in-vehicle intrusion via semi-supervised learning-based convolutional adversarial autoencoders by Hoang, Thien-Nu, Kim, Daehee

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Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities by Mo, Shaoxing, Zabaras, Nicholas, Shi, Xiaoqing, Wu, Jichun

“… In this study, we develop a convolutional adversarial autoencoder (CAAE) to parameterize non‐Gaussian conductivity fields with heterogeneous conductivity within each facies using a low…”
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Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties by Zhou, Zitong, Zabaras, Nicholas, Tartakovsky, Daniel M.

“… We use a convolutional adversarial autoencoder (CAAE) to parameterize a heterogeneous non…”
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Detecting In-vehicle Intrusion via Semi-supervised Learning-based Convolutional Adversarial Autoencoders by Hoang, Thien-Nu, Kim, Daehee

“…With the development of autonomous vehicle technology, the controller area network (CAN) bus has become the de facto standard for an in-vehicle communication…”
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Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders by Bulten, Wouter, Litjens, Geert

“…We propose an unsupervised method using self-clustering convolutional adversarial autoencoders to classify prostate tissue as tumor or non-tumor without any labeled training data…”
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An adversarial model for electromechanical actuator fault diagnosis under nonideal data conditions by Wang, Chao, Tao, Laifa, Ding, Yu, Lu, Chen, Ma, Jian

“…; this severely limits the applications of intelligent data-driven diagnosis approaches. Therefore, this paper provides an extended convolutional adversarial autoencoder (ECAAE…”
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A Novel Fault Detection Method Based on Reconstruction Error and Clustering of Latent Variables by Wang, Jian, Xu, Jing, Li, Yakun

“… In this paper, we propose a novel unsupervised fault detection method named One Dimension Convolutional Adversarial AutoEncoder (1DAAE…”
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Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties by Zhou, Zitong, Zabaras, Nicholas, Tartakovsky, Daniel M

“… We use a convolutional adversarial autoencoder (CAAE) for the parameterization of the heterogeneous non-Gaussian conductivity field with a low-dimensional latent representation…”
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A Fault Diagnosis Method for Mechanical Rotating Components Based on Automatic Learning of Pseudo Labels by Yin, Shuangyan, Yang, Jingli, Yang, Cheng

“…). First, Self-normalizing Convolutional Adversarial Autoencoder (SCAAE) is designed to obtain deep representation feature sets with labeled and unlabeled samples in unsupervised learning mode…”
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Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities by Mo, Shaoxing, Zabaras, Nicholas, Shi, Xiaoqing, Wu, Jichun

“… In this study, we develop a convolutional adversarial autoencoder (CAAE) to parameterize non-Gaussian conductivity fields with heterogeneous conductivity within each facies using a low-dimensional latent representation…”
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Development of an Advanced Data-Driven Methodology for Bridge Structural Health Monitoring Based on Drive-By Inspection Technology by Hurtado, Andres Felipe Calderon

“…Bridges are susceptible to structural damage over time due to various conditions, such as variations in load conditions, environmental factors and material…”
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Deep Neural Network Surrogates for Inverse Problems by Zhou, Zitong

“…Inverse problems in subsurface flow are generally challenging due to the need for a large number of expensive numerical solutions to partial di↵erential…”
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