Výsledky vyhledávání - Reduced Conventional Autoencoder

Selection methodology of representative meteorological days for assessment of renewable energy systems Autor Hassan, Muhammed A., Khalil, Adel, Abubakr, Mohamed

“…) instead of the full year for reduced costs of the data itself and the system simulations…”
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Multi-Task Autoencoder for Noise-Robust Speech Recognition Autor Zhang, Haoyi, Liu, Conggui, Inoue, Nakamasa, Shinoda, Koichi

“…). We evaluate it using the Aurora 2 dataset and CHIME 3 dataset. It reduced WER by 15.7% from the conventional denoising autoencoder in the Aurora 2 test set A…”
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Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system Autor Teutsch, Philipp, Pfeffer, Philipp, Sharifi Ghazijahani, Mohammad, Cierpka, Christian, Schumacher, Jörg, Mäder, Patrick

“… Within the context of reduced-order models, convolutional autoencoders (CAEs) pose a universally applicable alternative to conventional approaches…”
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Robust reduced-order machine learning modeling of high-dimensional nonlinear processes using noisy data Autor Tan, Wallace Gian Yion, Xiao, Ming, Wu, Zhe

“…Autoencoder-based reduced-order machine learning models have been developed for modeling and predictive control of nonlinear chemical processes with high dimensionality such as discretization of reaction–diffusion processes…”
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Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling Autor Park, Yeonjin, Hong, Jungpyo

“…% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations…”
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An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling Autor Cheng, M., Fang, F., Pain, C.C., Navon, I.M.

“…Considering the high computation cost required in conventional computation fluid dynamic simulations, machine learning methods have been introduced to flow dynamic simulations in years, aiming on reducing CPU time…”
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Reduced-order modeling via convolutional autoencoder for emulating combustion of hydrogen/methane fuel blends Autor Ding, Siyu, Ni, Chenxu, Chu, Xu, Lu, Qingzhou, Wang, Xingjian

“… This study presents parametric reduced order models (ROMs) that leverage deep neural network-based dimension reduction through a convolutional autoencoder (AE…”
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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder Autor Liu, Junxiu, Wu, Guopei, Luo, Yuling, Qiu, Senhui, Yang, Su, Li, Wei, Bi, Yifei

“…), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding…”
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Health indicator for machine condition monitoring built in the latent space of a deep autoencoder Autor González-Muñiz, Ana, Díaz, Ignacio, Cuadrado, Abel A., García-Pérez, Diego

“… such as reduced maintenance costs, improved productivity or increased machine availability. The reconstruction error of deep autoencoders has been widely used…”
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Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages Autor Hu, Dan, Zhang, Han, Wu, Zhengwang, Wang, Fan, Wang, Li, Smith, J. Keith, Lin, Weili, Li, Gang, Shen, Dinggang

“… analysis extremely challenging. With the conventional multimodal fusion strategies, adding fMRI data for age prediction has a high risk of introducing more noises than useful features, which would lead to reduced accuracy…”
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Model order reduction of building energy simulation models using a convolutional neural network autoencoder Autor Banihashemi, Farzan, Weber, Manuel, Lang, Werner

“… This paper introduces a novel approach to model order reduction (MOR) of BES models. The approach utilizes a deep learning-based unsupervised convolutional neural network autoencoder (CNN-AE…”
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Data Imputation in Electricity Consumption Profiles through Shape Modeling with Autoencoders Autor Duarte, Oscar, Duarte, Javier E., Rosero-Garcia, Javier

“… Conventional data imputation methods are not suitable for these datasets, because they are time series with special characteristics and because, for some applications, it is quite important…”
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A novel wasserstein autoencoder-enhanced thermo-mechanical coupled reduced-order model for high pressure turbine blades life monitoring Autor Wang, Rongqiao, Chen, Ruoqi, Zhao, Yan, Shen, Tianbao, Chen, Gaoxiang, Hu, Dianyin, Jiang, Zhimin, Wang, Xuemin

“…) blades, a wasserstein autoencoder (WAE)-enhanced thermodynamically coupled reduced-order model (ROM…”
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Pixel-Wise Ensembled Masked Autoencoder for Multispectral Pansharpening Autor Cui, Yongchuan, Liu, Peng, Ma, Yan, Chen, Lajiao, Xu, Mengzhen, Guo, Xingyan

“… However, most existing methods rely on the conventional observation model, which treats LRMS as a blurred and downsampled version of HRMS…”
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Autoencoder for Position-Assisted Beam Prediction in mmWave ISAC Systems Autor Aziz El-Banna, Ahmad A., Dobre, Octavia A.

“… This overhead can be reduced by incorporating the position information with beam adjustments. This letter proposes a lightweight autorencoder (LAE…”
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Deep convolutional autoencoders for the time–space reconstruction of liquid rocket engine flames Autor Zapata Usandivaras, José F., Bauerheim, Michael, Cuenot, Bénédicte, Urbano, Annafederica

“… Although still being intractable in the optimization of most of combustion devices, their potential can be harnessed via appropriate reduced order models (ROMs…”
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A Robust Autoencoder HBC Transceiver With CGAN-Based Channel Modeling Autor Ali, Abdelhay, Abdelrahman, Amr N., Celik, Abdulkadir, Fouda, Mohammed E., Eltawil, Ahmed M.

“… Using the conductive properties of the human body, HBC offers significant advantages over conventional radio frequency wireless communication methods, including ultralow power consumption and minimal interference…”
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Energy load time-series forecast using decomposition and autoencoder integrated memory network Autor Bedi, Jatin, Toshniwal, Durga

“… Recent advancements in the field of electricity load forecasting provide powerful tools to capture non-linear energy demand trends and outperform conventional load prediction models…”
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Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network Autor Sahani, Mrutyunjaya, Choudhury, Sasmita, Siddique, Marif Daula, Parida, Tanmoy, Dash, Pradipta Kishore, Panda, Sanjib Kumar

“… To address this, we have developed a novel hybrid model: a reduced deep convolutional stack autoencoder with a minimum variance multikernel random vector functional link network (RDCSAE-MVMRVFLN…”
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Autoencoder for Design of Mitigation Model for DDOS Attacks via M-DBNN Autor Agrawal, Ankit, Singh, Rajiv, Khari, Manju, Vimal, S., Lim, Sangsoon

“… Conventional machine learning methodologies are being harmed by reduced recognition rates and greater false-positive rates due to the emergence of new threats…”
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