Search Results - "stacked pruning sparse denoising autoencoder"
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Authors: et al.
Source: Machines, Vol 9, Iss 12, p 360 (2021)
Subject Terms: intelligent fault diagnosis, stacked pruning sparse denoising autoencoder, convolutional neural network, anti-noise, Mechanical engineering and machinery, TJ1-1570
File Description: electronic resource
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Authors: et al.
Source: Applied Soft Computing. 88:106060
Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 16. Peace & justice
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3
Authors: et al.
Source: Energy Reports, Vol 7, Iss, Pp 2201-2213 (2021)
Subject Terms: Electricity Price Forecasting, Sufficient Dimension Reduction, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Deep Neural Network, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Stacked pruning sparse denoising autoencoder, Maximum Separation Subspace, 7. Clean energy, Tensor Canonical Correlation Analysis, TK1-9971
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Authors: et al.
Source: Applied Soft Computing. Mar2020, Vol. 88, pN.PAG-N.PAG. 1p.
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Contributors: et al.
Subject Terms: process monitoring, dynamics, variable time lag, dynamic autoregressive latent variables model, sintering process, hammerstein output-error systems, auxiliary model, multi-innovation identification theory, fractional-order calculus theory, canonical variate analysis, disturbance detection, power transmission system, k-nearest neighbor analysis, statistical local analysis, intelligent fault diagnosis, stacked pruning sparse denoising autoencoder, convolutional neural network, anti-noise, flywheel fault diagnosis, belief rule base, fuzzy fault tree analysis, Bayesian network, evidential reasoning, aluminum reduction process, alumina concentration, subspace identification, distributed predictive control, spatiotemporal feature fusion, gated recurrent unit, attention mechanism
File Description: application/octet-stream
Relation: ONIX_20221025_9783036551739_23; https://mdpi.com/books/pdfview/book/6065
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6
Authors: et al.
Source: Machines; Dec2021, Vol. 9 Issue 12, p360-360, 1p
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7
Authors:
Source: Sensors (14248220); Oct2025, Vol. 25 Issue 20, p6439, 22p
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Authors: et al.
Source: Global Energy Interconnection; Oct2025, Vol. 8 Issue 5, p874-890, 17p
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Authors: et al.
Source: Structural Durability & Health Monitoring (SDHM); 2025, Vol. 19 Issue 5, p1183-1201, 19p
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10
Authors: et al.
Source: Machines; Aug2025, Vol. 13 Issue 8, p697, 26p
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Authors: et al.
Source: International Journal of Precision Engineering & Manufacturing-Green Technology; Jul2025, Vol. 12 Issue 4, p1091-1116, 26p
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12
Authors:
Source: Applied Sciences (2076-3417); Jun2025, Vol. 15 Issue 12, p6455, 32p
Subject Terms: COST functions, AUTOENCODERS, DEEP learning, DYNAMIC models, ORIGINALITY
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Authors: et al.
Source: Structural Durability & Health Monitoring (SDHM); 2025, Vol. 19 Issue 2, p365-383, 19p
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14
Authors: et al.
Source: Machines; Jan2025, Vol. 13 Issue 1, p71, 25p
Subject Terms: GENERATIVE adversarial networks, FAULT diagnosis, DIAGNOSIS, PROBLEM solving, DATA modeling
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15
Authors: et al.
Source: Symmetry (20738994); Nov2024, Vol. 16 Issue 11, p1461, 19p
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Authors:
Source: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering; Oct2024, Vol. 238 Issue 13, p1269-1282, 14p
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Authors: et al.
Source: Quality & Reliability Engineering International; Oct2024, Vol. 40 Issue 6, p3517-3536, 20p
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Authors: et al.
Source: CEAS Aeronautical Journal; Oct2024, Vol. 15 Issue 4, p881-893, 13p
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Authors: et al.
Source: Journal of Physics: Conference Series; 2024, Vol. 2822 Issue 1, p1-12, 12p
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Authors: et al.
Source: Scientific Reports; 8/10/2024, Vol. 14 Issue 1, p1-16, 16p
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