Výsledky vyhledávání - Quantified Autoencoder

Interpreting denoising autoencoders with complex perturbation approach Autor Arumugam, Dharanidharan, Kiran, Ravi

“…•importance of input pixel features for image reconstruction using autoencoders is quantified…”
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Quantifying particle size and size distribution of mine tailings through deep learning approach of autoencoders Autor Xiao, Bolin, Miao, Shengjun, Gao, Qian

“…A new tailings particle size and size distribution (PSD) quantifying method using Autoencoders is presented to transform multidimensional PSD information into a 1-dimensional code…”
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A dual linear autoencoder approach for vessel trajectory prediction using historical AIS data Autor Murray, Brian, Perera, Lokukaluge Prasad

“… In this study, a novel dual linear autoencoder approach is suggested to predict the future trajectory of a selected vessel…”
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HFLGuard: clustering and autoencoder-based defense against heterogeneous federated poisoning Autor LI Yongfei, FANG Chen, CHANG Chaowen, GUO Yuanbo, FU Chunhui

“…”. Subsequently, the K-means clustering algorithm provided an initial classification of gradients, followed by a Transformer-based autoencoder that detected malicious gradients and quantified their deviation…”
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Autoencoder-based image processing framework for object appearance modifications Autor Ślot, Krzysztof, Kapusta, Paweł, Kucharski, Jacek

“… Qualitative visual object properties, quantified using appropriately derived visual attribute descriptors, are subject to alterations…”
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Performance enhancement of ACO-OFDM-based VLC systems using a hybrid autoencoder scheme Autor Hao, Lili, Wang, Dongyi, Cheng, Wenyong, Li, Jing, Ma, Anfan

“… A novel PAPR reduction scheme is applied based on weighted autoencoder and amplitude clipping methods to address the high PAPR and LED nonlinearity problems…”
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Online quantitative monitoring of milling cutter health condition based on deep convolutional autoencoder Autor Lei, Yuncong, Li, Changgen, Guo, Liang, Gao, Hongli, Liang, Junhua, Sun, Yi, He, Jigang

“… However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM…”
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Deep autoencoder architecture for bridge damage assessment using responses from several vehicles Autor Sarwar, Muhammad Zohaib, Cantero, Daniel

“…•Vehicle-assisted method for bridge damage assessment.•Deep autoencoder and Kullback-Leibler divergence based damage index is applied…”
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Infomax-based deep autoencoder network for recognition of multi-element geochemical anomalies linked to mineralization Autor Esmaeiloghli, Saeid, Tabatabaei, Seyed Hassan, Carranza, Emmanuel John M.

“…In recent years, deep autoencoder networks (DANs) have shown enormous potential to achieve state-of-the-art performance for recognizing multi-element geochemical anomalies related to mineralization…”
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A Bayesian Augmented-Learning framework for spectral uncertainty quantification of incomplete records of stochastic processes Autor Chen, Yu, Patelli, Edoardo, Edwards, Benjamin, Beer, Michael

“… Models are characterized by the posterior distribution of model parameters, whereby uncertainties over learned models, reconstructions and spectral representations are all quantified…”
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Impact of VAEformer Compression Algorithm Precision Loss on the Tropospheric Delays for Microwave Remote Sensing Autor Ding, Junsheng, Xu, Cancan, Chen, Wu, Chen, Junping, Wang, Jungang, Zhang, Yize, Bai, Lei, Han, Tao, Xiong, Yuhao

“…Ray-tracing through numerical weather models (NWMs) is one of the most accurate methods for determining slant tropospheric delays (STDs) in microwave remote…”
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Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder Autor Kurmi, Yashwant, Viswanathan, Malvika, Zu, Zhongliang

“…Purpose To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE…”
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Robust Damage Detection and Localization Under Varying Environmental Conditions Using Neural Networks and Input‐Residual Correlations Autor Römgens, Niklas, Abbassi, Abderrahim, Fürll, Florian, Grießmann, Tanja, Rolfes, Raimund, Marx, Steffen

“…This study aims to evaluate sequences of raw time series using an autoencoder structure for unsupervised damage detection and localization under varying environmental conditions (ECs…”
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A deep autoencoder network connected to geographical random forest for spatially aware geochemical anomaly detection Autor Soltani, Zeinab, Hassani, Hossein, Esmaeiloghli, Saeid

“…-stationarities in support of spatially aware anomaly detection. A deep autoencoder network (DAN) is trained to abstract deep feature codings…”
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Adversarial relationship graph learning soft sensor via negative information exclusion Autor Jia, Mingwei, Yang, Chao, Pan, Zhouxin, Liu, Qiang, Liu, Yi

“… Irrelevant and false variable relationships caused by noise are treated as negative information, quantified through mutual information loss…”
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Ball bearing multiple failure diagnosis using feature-selected autoencoder model Autor Cheng, Ren-Chi, Chen, Kuo-Shen

“… Furthermore, for unsupervised machine learning schemes, although it could avoid the concern of labeling in training, it lacks a quantified evaluation of the prediction results…”
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Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration Autor Xing, Le, Casson, Alexander J.

“…: We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite…”
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Anomaly Detection and Objective Security Evaluation Using Autoencoder, Isolation Forest, and Multi-Criteria Decision Methods Autor Zhang, Hongbin, Zhang, Haibin

“… The method first employs an autoencoder (AE) to perform dimensionality reduction on the raw data collected from a testbed environment, followed by anomaly detection using the Isolation Forest (IF) algorithm…”
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Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders Autor Chen, Junsheng, Li, Jian, Chen, Weigen, Wang, Youyuan, Jiang, Tianyan

“… Firstly, the stacked denoising autoencoders (SDAE) model with moving window and multiple noise levels is developed to reconstruct the normal operating data…”
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Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network Autor Li, Sheng, Jin, Liang, Jiang, Jinpeng, Wang, Honghai, Nan, Qiuming, Sun, Lizhi

“… This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array…”
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