Suchergebnisse - deep convolutional variational autoencoder (dcvae)~

Unsupervised deep learning approach for structural anomaly detection using probabilistic features.

Autoren: Wan, Hua-Ping Zhu, Yi-Kai Luo, Yaozhi et al.

Quelle: Structural Health Monitoring; Jan2025, Vol. 24 Issue 1, p3-33, 31p

Schlagwörter: MACHINE learning, STRUCTURAL health monitoring, DISTRIBUTION (Probability theory), AUTOENCODERS, VECTOR data, DEEP learning

Inverse design of reflective metasurface antennas using deep learning from small‐scale statistically random pico‐cells.

Autoren: You, Xi Chong Lin, Feng Han

Quelle: Microwave & Optical Technology Letters. Feb2024, Vol. 66 Issue 2, p1-11. 11p.

Schlagwörter: *DEEP learning, *ANTENNAS (Electronics), *UNIT cell, *TISSUE arrays, *GRID cells

An efficient epileptic seizure detection based on tunable Q-wavelet transform and DCVAE-stacked Bi-LSTM model using electroencephalogram.

Autoren: Sivasaravanababu, S. Prabhu, V. Parthasarathy, V. et al.

Quelle: European Physical Journal: Special Topics; 2022, Vol. 231 Issue 11/12, p2425-2437, 13p

Schlagwörter: EPILEPSY, ELECTRIC measurements, EXTRACTION techniques, FEATURE extraction, DEEP learning, ELECTROENCEPHALOGRAPHY

Unsupervised deep learning approach for structural anomaly detection using probabilistic features

Autoren: Hua-Ping Wan Yi-Kai Zhu Yaozhi Luo et al.

Quelle: Structural Health Monitoring. 24:3-33

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0201 civil engineering

Deep Learning Approach for Epileptic Focus Localization.

Autoren: Daoud, Hisham Bayoumi, Magdy

Quelle: IEEE Transactions on Biomedical Circuits & Systems; Apr2020, Vol. 14 Issue 2, p209-220, 12p

Stochastic electrical resistivity tomography with ensemble smoother and deep convolutional autoencoders.

Autoren: Aleardi, Mattia Vinciguerra, Alessandro Stucchi, Eusebio et al.

Quelle: Near Surface Geophysics; Apr2022, Vol. 20 Issue 2, p160-177, 18p

Schlagwörter: ELECTRICAL resistance tomography, GEOPHYSICS, DEEP learning, MATHEMATICAL optimization, ITERATIVE methods (Mathematics)

Wood-species identification based on terahertz spectral data augmentation and pseudo-label guided deep clustering.

Autoren: Wang, Yuan Wang, Zhi-Gang He, Yi-Hao et al.

Quelle: Wood Material Science & Engineering; Oct2024, Vol. 19 Issue 5, p1004-1014, 11p

Schlagwörter: WOOD, DATA augmentation, MACHINE learning, PROBLEM solving, TERAHERTZ spectroscopy, GRISELINIA littoralis

Dynamic Soft Sensor Model for Endpoint Carbon Content and Temperature in BOF Steelmaking Based on Adaptive Feature Matching Variational Autoencoder.

Autoren: Liu, Zhaoxiang Liu, Hui Chen, Fugang et al.

Quelle: Processes; Sep2024, Vol. 12 Issue 9, p1807, 33p

Schlagwörter: BASIC oxygen furnaces, FEATURE extraction, MARGINAL distributions, DEEP learning, STEEL manufacture

DeepComBat: A statistically motivated, hyperparameter‐robust, deep learning approach to harmonization of neuroimaging data.

Autoren: Hu, Fengling Lucas, Alfredo Chen, Andrew A. et al.

Quelle: Human Brain Mapping; Aug2024, Vol. 45 Issue 11, p1-19, 19p

Schlagwörter: DATA structures, DEEP learning, STATISTICAL learning, DATA harmonization, THICKNESS measurement

Unsupervised varistor surface defect detection based on variational autoencoder. (English)

Autoren: TANG Shancheng CHEN Ming WANG Hanbo et al.

Quelle: Computer Integrated Manufacturing Systems; May2022, Vol. 28 Issue 5, p1337-1351, 15p

基于复杂样本的安全态势要素分类架构. (Chinese)

Alternate Title: Classification framework of security situation elements based on complex samples. (English)

Autoren: 何春蓉 朱江 张欣

Quelle: Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) / Chongqing Youdian Daxue Xuebao (Ziran Kexue Ban); Aug2022, Vol. 34 Issue 4, p719-727, 9p

Schlagwörter: ARTIFICIAL neural networks, SECURITY classification (Government documents), LINEAR network coding, PROBLEM solving, COMPUTER network security

Causal Flow-Based Variational Auto-Encoder for Disentangled Causal Representation Learning.

Autoren: DI FAN YANNIAN KOU CHUANHOU GAO

Quelle: ACM Transactions on Intelligent Systems & Technology; Oct2025, Vol. 16 Issue 5, p1-26, 26p

Schlagwörter: CAUSAL models, CAUSAL inference, PROBABILISTIC generative models, SUPERVISED learning, CAUSATION (Philosophy)

深度生成式故障诊断模型研究. (Chinese)

Alternate Title: Research on Deep Generative Fault Diagnosis Models. (English)

Autoren: 黄汉坤 岑健 赵必创 et al.

Quelle: Machine Tool & Hydraulics; Feb2025, Vol. 53 Issue 4, p205-213, 9p

Schlagwörter: GENERATIVE adversarial networks, BOLTZMANN machine, FAULT diagnosis, GENERALIZATION

Supervised Multi-Layer Conditional Variational Auto-Encoder for Process Modeling and Soft Sensor.

Autoren: Tang, Xiaochu Yan, Jiawei Li, Yuan

Quelle: Sensors (14248220); Nov2023, Vol. 23 Issue 22, p9175, 17p

Schlagwörter: FEATURE extraction, DETECTORS, MANUFACTURING processes, DEEP learning, ORDER picking systems

Epileptic Seizure Detection Using Machine Learning: A Systematic Review and Meta-Analysis.

Autoren: Bai, Lin Litscher, Gerhard Li, Xiaoning

Quelle: Brain Sciences (2076-3425); Jun2025, Vol. 15 Issue 6, p634, 16p

Schlagwörter: MACHINE learning, EPILEPSY, DEEP learning, ARTIFICIAL intelligence, DIAGNOSIS of epilepsy

Stochastic Scenario Generation Methods for Uncertainty in Wind and Photovoltaic Power Outputs: A Comprehensive Review.

Autoren: Zheng, Kun Sun, Zhiyuan Song, Yi et al.

Quelle: Energies (19961073); Feb2025, Vol. 18 Issue 3, p503, 31p

Schlagwörter: WIND power, ROBUST optimization, RENEWABLE energy sources, DEEP learning, PARAMETER estimation

Health indicator construction based on normal states through FFT‐graph embedding.

Autoren: Kim, GwanPil Jung, Jason J. Camacho, David

Quelle: Expert Systems; Nov2024, Vol. 41 Issue 11, p1-14, 14p

Schlagwörter: FAST Fourier transforms, REPRESENTATIONS of graphs, HEALTH status indicators, ROTATING machinery

Inverse design of a novel multiport power divider based on hybrid neural network.

Autoren: Sun, Siyue Zhu, Ma Qi, Baojun et al.

Quelle: Concurrency & Computation: Practice & Experience; 11/30/2024, Vol. 36 Issue 26, p1-20, 20p

Schlagwörter: ARTIFICIAL neural networks, PHYSICAL mobility, MULTIPORT networks, MAP design, 5G networks

An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein.

Autoren: Menon, Sneha Adhikari, Subinoy Mondal, Jagannath

Quelle: eLife; 12/18/2024, p1-30, 30p

Unsupervised wood species identification based on multiobjective optimal clustering and feature fusion.

Autoren: Wang, Yuan Ma, Wen-Jin Yang, Meng et al.

Quelle: Wood Science & Technology; Mar2025, Vol. 59 Issue 2, p1-25, 25p