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21 papers · ranked by Valyu relevance

01
PPubMed4 Jul 2020
Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case
Ivan Pisa, Antoni Morell, Jose Lopez Vicario, Ramon Vilanova
The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption…
92relevance
02
PPubMed15 Jan 2017
Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection
Erik Marchi, Fabio Vesperini, Stefano Squartini, Björn Schuller
In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these…
84relevance
03
AarXiv2022
Denoising Architecture for Unsupervised Anomaly Detection in Time-Series
Wadie Skaf, Tomáš Horváth
> Abstract. Anomalies in time-series provide insights of critical scenarios across a range of industries, from banking and aerospace to information technology, security, and medicine. However, identifying anomalies in time-series data is particularly challenging due to the imprecise definition of anomalies, the…
83relevance
04
AarXiv2020
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data
Saúl Langarica, Felipe J. Núñez
—In an industrial IoT setting, ensuring the quality of sensor data is a must when data-driven algorithms operate on the upper layers of the control system. Unfortunately, the common place in industrial facilities is to find sensor time series heavily corrupted by noise and outliers. In this work, a purely data-driven…
82relevance
05
PPubMed1 Dec 2022
Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals
Mark R. Anderson, Vasundhara Basu, Ryan D. Martin, Charlotte Z. Reed + 3 more
'Noah J. Rowe' 'Mehdi Shafiee' 'Tianai Ye'] We present a convolutional autoencoder to denoise pulses from a p-type point contact high-purity germanium detector similar to those used in several rare event searches. While we focus on training procedures that rely on detailed detector physics simulations, we also present…
79relevance
06
PPubMed21 Jun 2022
Autoencoders reloaded
Hervé Bourlard, Selen Hande Kabil
In Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called “auto-associative multilayer perceptrons”) were, in the best case, implementing singular value decomposition (SVD) Golub and Reinsch (Linear algebra, Singular value…
77relevance
07
PPubMed24 Mar 2023
Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks
Fatemeh Esmaeili, Erica Cassie, Hong Phan T. Nguyen, Natalie O. V. Plank + 4 more
'Natalie O. V. Plank' 'Charles P. Unsworth' 'Alan Wang' 'Yunfeng Wu' 'Gou-Jen Wang'] Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications. Deep learning algorithms are effective tools for…
75relevance
08
PPubMed29 Aug 2022
Autoencoder-Based Signal Modulation and Demodulation Methods for Sonobuoy Signal Transmission and Reception
Jinuk Park, Jongwon Seok, Jungpyo Hong, Sylvain Girard + 2 more
'Xuebo Zhang'] Sonobuoy is a disposable device that collects underwater acoustic information and is designed to transmit signals collected in a particular area to nearby aircraft or ships and sink to the seabed upon completion of its mission. In a conventional sonobuoy signal transmission and reception system…
75relevance
09
BbioRxiv24 Aug 2019
A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory
Zhengshi Yang, Xiaowei Zhuang, Karthik Sreenivasan, Virendra Mishra + 2 more
In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) layer, one time-distributed fully-connected layer, and one…
74relevance
10
PPubMed1 Apr 2021
Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks
Ebtesam Almazrouei, Gabriele Gianini, Nawaf Almoosa, Ernesto Damiani + 1 more
'Steve Ling'] This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial…
72relevance
11
BbioRxiv9 Mar 2021
Latent representation of the human pan-celltype epigenome through a deep recurrent neural network
Kevin B. Dsouza, Adam Y. Li, Vijay K. Bhargava, Maxwell W. Libbrecht
The availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were celltype specific, applying to a single tissue or cell state. Recently, neural networks have…
71relevance
12
AarXiv2019
Stacked autoencoders based machine learning for noise reduction and signal reconstruction in geophysical data
Debjani Bhowick, Deepak Gupta, Saumen Maiti, Uma Shankar
Autoencoders are neural network formulations where the input and output of the network are identical and the goal is to identify the hidden representation in the provided datasets. Generally, autoencoders project the data nonlinearly onto a lower dimensional hidden space, where the important features get highlighted…
70relevance
13
BbioRxiv29 Oct 2017
Sequence Transfer Learning for Neural Decoding
Venkatesh Elango, Aashish N Patel, Kai J Miller, Vikash Gilja
A fundamental challenge in designing brain-computer interfaces (BCIs) is decoding behavior from time-varying neural oscillations. in typical applications, decoders are constructed for individual subjects and with limited data leading to restrictions on the types of models that can be utilized. currently, the best…
65relevance
14
AarXiv17 Apr 2019
Audio-Noise Power Spectral Density Estimation Using Long Short-Term Memory
Xiaofei Li, Simon Leglaive, Laurent Girin, Radu Horaud
—We propose a method using a long short-term memory (LSTM) network to estimate the noise power spectral density (PSD) of single-channel audio signals represented in the short time Fourier transform (STFT) domain. An LSTM network common to all frequency bands is trained, which processes each frequency band individually…
64relevance
15
CchemRxiv7 Jul 2023
Designing electrodes and electrolytes for batteries by leveraging deep learning
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
64relevance
16
CchemRxiv19 Aug 2025
Self-Supervised Deep-Learning Denoising for X-Ray Fluorescence Microscopy with Multi-Element Detectors
Authors not listed
Scanning emission-based microscopies, such as X-ray fluorescence (XRF) and energy-dispersive X-ray spectroscopy, offer nanometer-scale chemical maps, but suffer from long acquisition times and radiation damage. Lower-flux and shorter dwell time scans mitigate this problem, but the resulting signal loss can only…
63relevance
17
AarXiv2023
Enhancing Continuous Time Series Modelling with a Latent ODE-LSTM Approach
C. Coelho, M. Fernanda P. Costa, L. L. Ferrás
Due to their dynamic properties such as irregular sampling rate and high-frequency sampling, Continuous Time Series (CTS) are found in many applications. Since CTS with irregular sampling rate are difficult to model with standard Recurrent Neural Networks (RNNs), RNNs have been generalised to have continuoustime hidden…
62relevance
18
CchemRxiv16 Apr 2020
Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the ‘Big Data’ Era
Vishal Babu Siramshetty, Dac-Trung Nguyen, Natalia J. Martinez, Anton Simeonov + 2 more
The rise of novel artificial intelligence methods necessitates a comparison of this wave of new approaches with classical machine learning for a typical drug discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by human Ether-à-go-go-Related Gene (hERG), leads to prolonged QT…
61relevance
19
AarXiv2020
Real-time speech enhancement using equilibriated RNN
Daiki Takeuchi, Kohei Yatabe, Yuma Koizumi, Yasuhiro Oikawa + 1 more
'Noboru Harada'] We propose a speech enhancement method using a causal deep neural network (DNN) for real-time applications. DNN has been widely used for estimating a time-frequency (T-F) mask which enhances a speech signal. One popular DNN structure for that is a recurrent neural network (RNN) owing to its capability…
58relevance
20
BbioRxiv21 May 2018
Incorporating Context into Language Encoding Models for fMRI
Shailee Jain, Alexander G Huth
Language encoding models help explain language processing in the human brain by learning functions that predict brain responses from the language stimuli that elicited them. Current word embedding-based approaches treat each stimulus word independently and thus ignore the influence of context on language understanding.…
56relevance
21
CchemRxiv30 Apr 2024
Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design
Jie Lin, Mingyuan Xu, Hongming Chen
Shape-based virtual screening is a widely utilized method in ligand-based de novo drug design, aiming to identify molecules in chemical libraries that share similar 3D shapes but simultaneously possess novel 2D chemical structures compared to the reference compound. As an emerging technology, generative model is an…
51relevance

Results for LSTM Denoising Autoencoder

Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

Denoising Architecture for Unsupervised Anomaly Detection in Time-Series

Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data

Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals

Autoencoders reloaded

Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks

Autoencoder-Based Signal Modulation and Demodulation Methods for Sonobuoy Signal Transmission and Reception

A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

Latent representation of the human pan-celltype epigenome through a deep recurrent neural network

Stacked autoencoders based machine learning for noise reduction and signal reconstruction in geophysical data

Sequence Transfer Learning for Neural Decoding

Audio-Noise Power Spectral Density Estimation Using Long Short-Term Memory

Designing electrodes and electrolytes for batteries by leveraging deep learning

Self-Supervised Deep-Learning Denoising for X-Ray Fluorescence Microscopy with Multi-Element Detectors

Enhancing Continuous Time Series Modelling with a Latent ODE-LSTM Approach

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the ‘Big Data’ Era

Real-time speech enhancement using equilibriated RNN

Incorporating Context into Language Encoding Models for fMRI

Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design

Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

Denoising Architecture for Unsupervised Anomaly Detection in Time-Series

Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data

Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals

Autoencoders reloaded

Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks

Autoencoder-Based Signal Modulation and Demodulation Methods for Sonobuoy Signal Transmission and Reception

A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

Latent representation of the human pan-celltype epigenome through a deep recurrent neural network

Stacked autoencoders based machine learning for noise reduction and signal reconstruction in geophysical data

Sequence Transfer Learning for Neural Decoding

Audio-Noise Power Spectral Density Estimation Using Long Short-Term Memory

Designing electrodes and electrolytes for batteries by leveraging deep learning

Self-Supervised Deep-Learning Denoising for X-Ray Fluorescence Microscopy with Multi-Element Detectors

Enhancing Continuous Time Series Modelling with a Latent ODE-LSTM Approach

Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the ‘Big Data’ Era

Real-time speech enhancement using equilibriated RNN

Incorporating Context into Language Encoding Models for fMRI

Diff-Shape: A Novel Constrained Diffusion Model for Shape based De Novo Drug Design