Search Results - deep stacked denoising autoencoder

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance

Authors: Sanjay Roka Manoj Diwakar

Source: Journal of Electronic Imaging. 32

Deep learning framework designed for high-performance lithium-ion batteries state monitoring

Authors: Paul Takyi-Aninakwa Shunli Wang Guangchen Liu et al.

Source: Renewable and Sustainable Energy Reviews. 218:115803

Subject Terms: Lithium-ion batteries, State of charge estimation, Secondary scale feature extraction, Long short-term memory, Deep-stacked denoising autoencoder

Access URL: https://rgu-repository.worktribe.com/output/2830428

Improved deep reinforcement learning for car-following decision-making

Authors: Yang, Xiaoxue Zou, Yajie Zhang, Hao et al.

Source: Physica A. 624

Subject Terms: Car-following model, Deep reinforcement learning, Driving behavior imitation, Stacked denoising autoencoders, Behavioral research, Decision making, Learning systems, Recurrent neural networks, Auto encoders, Car-following modeling, De-noising, Deterministics, Driving behaviour, Policy gradient, Reinforcement learnings, Stacked denoising autoencoder, Reinforcement learning

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-65535

An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive Workload Estimation with Physiological Signals

Authors: Zheng, Zhanpeng Yin, Zhong Zhang, Jianhua

Source: Chinese Control Conference (CCC)
2020 39th Chinese Control Conference (CCC)

Subject Terms: 0209 industrial biotechnology, Cognitive workloads, Ensemble learning, Stacked denoising autoencoders, 0202 electrical engineering, electronic engineering, information engineering, Extreme learning machines, 02 engineering and technology, Electroencephalograms

File Description: application/pdf

Access URL: https://ieeexplore.ieee.org/document/9188806
https://oda.oslomet.no/oda-xmlui/handle/11250/2772135
https://hdl.handle.net/11250/2772135

Stacked Denoising Autoencoder-based Deep Collaborative Filtering using the change of similarity / 積層自己符号化器による類似性の変化に着目した協調フィルタリングの提案

Authors: Tomonobu OZAKI Yosuke SUZUKI 尾崎 知伸 et al.

Source: JSAI Technical Report, SIG-KBS. 2016, :02

A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis.

Authors: Yu, Jianbo¹ jianboyu.bob@gmail.com

Source: Computers in Industry. Jun2019, Vol. 108, p62-72. 11p.

Subject Terms: *STATISTICAL correlation, DEEP learning, SIGNAL denoising, DEBUGGING, GEARBOXES

Reconstructing echoes completely submerged in background noise by a stacked denoising autoencoder method for low-power EMAT testing

Authors: Jinjie Zhou Dianrui Yu Xiang Li et al.

Source: Measurement Science and Technology. 34:125910

Subject Terms: 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Authors: Yang, Shuo Yin, Zhong Wang, Yagang et al.

Availability: https://hdl.handle.net/10642/8300

Stacked denoising autoencoders for sentiment analysis: a review.

Authors: Sagha, Hesam Cummins, Nicholas Schuller, Björn

Source: WIREs: Data Mining & Knowledge Discovery. Sep/Oct2017, Vol. 7 Issue 5, pn/a-N.PAG. 9p.

Subject Terms: *Sentiment analysis, *Algorithms, Deep learning, Signal denoising, Support vector machines

Enhanced Stacked Denoising Autoencoder-Based Feature Learning for Recognition of Wafer Map Defects.

Authors: Yu, Jianbo¹ (AUTHOR) jianboyu.bob@gmail.com

Source: IEEE Transactions on Semiconductor Manufacturing. Nov2019, Vol. 32 Issue 4, p613-624. 12p.

Subject Terms: *SEMICONDUCTOR manufacturing, *MANUFACTURING processes, DEEP learning, PATTERN recognition systems, TEXTURE mapping

Fault diagnosis method of rotating machinery based on stacked denoising autoencoder.

Authors: Chen, Zhouliang Li, Zhinong Li, Chuan et al.

Source: Journal of Intelligent & Fuzzy Systems. 2018, Vol. 34 Issue 6, p3443-3449. 7p.

Subject Terms: *ARTIFICIAL neural networks, ROTATING machinery, DEEP learning, MACHINERY vibration, DEBUGGING

Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments

Authors: Mohammadamini, Mohammad Matrouf, Driss Laboratoire Informatique d'Avignon (LIA) et al.

Contributors: Mohammadamini, Mohammad Matrouf, Driss Laboratoire Informatique d'Avignon (LIA) et al.

Source: EUSIPCO ; https://hal.science/hal-02882342 ; EUSIPCO, Jan 2021, Amsterdam, Netherlands

Subject Terms: speaker recognition, x-vector, data augmentation, noise compensation, denoising autoencoder, deep stacked denoising autoencoder, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

Subject Geographic: Amsterdam, Netherlands

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2006.15903; ARXIV: 2006.15903

Availability: https://hal.science/hal-02882342
https://hal.science/hal-02882342v1/document
https://hal.science/hal-02882342v1/file/Data%20augmentation%20versus%20noise%20compensation%20for%20xvector%20speaker%20recognition%20systems%20in%20noisy.pdf

Segmentation‐enhanced gamma spectrum denoising based on deep learning.

Authors: Lu, Xiangqun Zheng, Hongzhi Liu, Yaqiong et al.

Source: IET Communications (Wiley-Blackwell). Jan2024, Vol. 18 Issue 1, p63-80. 18p.

Subject Terms: DEEP learning, HILBERT-Huang transform, GAMMA-ray scattering, ELECTRONIC noise, FEATURE extraction

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance.

Authors: Roka, Sanjay Diwakar, Manoj

Source: Journal of Electronic Imaging; May/Jun2023, Vol. 32 Issue 3, p33015-033015-23, 1p

Subject Terms: VIDEO surveillance, ANOMALY detection (Computer security), IMAGE denoising, OPTICAL flow, CONVOLUTIONAL neural networks, DEEP learning

Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments

Authors: Mohammadamini, Mohammad Matrouf, Driss

Contributors: Mohammadamini, Mohammad Matrouf, Driss

Subject Terms: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Computation and Language, deep stacked denoising autoencoder, speaker recognition, 02 engineering and technology, Computer Science - Sound, x-vector, noise compensation, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Audio and Speech Processing (eess.AS), denoising autoencoder, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Computation and Language (cs.CL), data augmentation, Electrical Engineering and Systems Science - Audio and Speech Processing

File Description: application/pdf

Access URL: http://arxiv.org/abs/2006.15903
https://hal.science/hal-02882342v1
https://hal.science/hal-02882342v1/document

Deep Learning for Computer-Aided Diagnosis in Brain Images / 頭部コンピューター支援診断におけるディープラーニング

Authors: Mitsutaka NEMOTO 根本 充貴

Source: Medical Imaging Technology. 2017, 35(4):200

Monitoring of complex profiles based on deep stacked denoising autoencoders.

Authors: Chen, Shumei Yu, Jianbo Wang, Shijin

Source: Computers & Industrial Engineering. May2020, Vol. 143, pN.PAG-N.PAG. 1p.

Subject Terms: *STATISTICAL process control, VISUAL learning, DEEP learning, ACOUSTIC field, FALSE alarms, AUTOCORRELATION (Statistics)

Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder.

Authors: Xia, Min Li, Teng Liu, Lizhi et al.

Source: IET Science, Measurement & Technology (Wiley-Blackwell). Sep2017, Vol. 11 Issue 6, p687-695. 9p.

Feature Analysis of Marginalized Stacked Denoising Autoenconder for Unsupervised Domain Adaptation.

Authors: Wei, Pengfei Ke, Yiping Goh, Chi Keong

Source: IEEE Transactions on Neural Networks & Learning Systems. May2019, Vol. 30 Issue 5, p1321-1334. 14p.

Subject Terms: RANDOM noise theory, SOCIAL marginality, LEARNING, PHYSIOLOGICAL adaptation, NOISE control

Short-Term Electricity Price Forecasting With Stacked Denoising Autoencoders.

Authors: Wang, Long Zhang, Zijun Chen, Jieqiu

Source: IEEE Transactions on Power Systems. Jul2017, Vol. 32 Issue 4, p2673-2681. 9p.

Subject Terms: *ELECTRIC rates, *FORECASTING, *ARTIFICIAL neural networks, SIGNAL denoising, SUPPORT vector machines