Deep learning based time-frequency domain signal recovery for fiber-connected radar networks

2021 ◽  
Author(s):  
Fangzheng Zhang ◽  
Yuewen Zhou ◽  
Shilong Pan
Keyword(s):  
Deep Learning ◽  
Frequency Domain ◽  
Signal Recovery ◽  
Time Frequency ◽  
Radar Networks

scholarly journals Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hao Chao ◽  
Huilai Zhi ◽  
Liang Dong ◽  
Yongli Liu
Keyword(s):  
Deep Learning ◽  
Frequency Domain ◽  
Time Domain ◽  
Recognition Task ◽  
Frequency Characteristics ◽  
Time Frequency ◽  
Learning Framework ◽  
Valence States ◽  
Correlation Information ◽  
Multiple Domains

Fusing multichannel neurophysiological signals to recognize human emotion states becomes increasingly attractive. The conventional methods ignore the complementarity between time domain characteristics, frequency domain characteristics, and time-frequency characteristics of electroencephalogram (EEG) signals and cannot fully capture the correlation information between different channels. In this paper, an integrated deep learning framework based on improved deep belief networks with glia chains (DBN-GCs) is proposed. In the framework, the member DBN-GCs are employed for extracting intermediate representations of EEG raw features from multiple domains separately, as well as mining interchannel correlation information by glia chains. Then, the higher level features describing time domain characteristics, frequency domain characteristics, and time-frequency characteristics are fused by a discriminative restricted Boltzmann machine (RBM) to implement emotion recognition task. Experiments conducted on the DEAP benchmarking dataset achieve averaged accuracy of 75.92% and 76.83% for arousal and valence states classification, respectively. The results show that the proposed framework outperforms most of the above deep classifiers. Thus, potential of the proposed framework is demonstrated.

scholarly journals Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Peng Lu ◽  
Yabin Zhang ◽  
Bing Zhou ◽  
Hongpo Zhang ◽  
Liwei Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Tree ◽  
Frequency Domain ◽  
Weight Control ◽  
High Reliability ◽  
Low Frequency ◽  
Model Parameters ◽  
Fusion Model ◽  
Tree Model ◽  
Time Frequency

In recent years, deep learning (DNN) based methods have made leapfrogging level breakthroughs in detecting cardiac arrhythmias as the cost effectiveness of arithmetic power, and data size has broken through the tipping point. However, the inability of these methods to provide a basis for modeling decisions limits clinicians’ confidence on such methods. In this paper, a Gate Recurrent Unit (GRU) and decision tree fusion model, referred to as (T-GRU), was designed to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. The fusion model multipathway processing time-frequency domain featured the introduction of decision tree probability analysis of frequency domain features, the regularization of GRU model parameters and weight control to improve the decision tree model output weights. The MIT-BIH arrhythmia database was used for validation. Results showed that the low-frequency band features dominated the model prediction. The fusion model had an accuracy of 98.31%, sensitivity of 96.85%, specificity of 98.81%, and precision of 96.73%, indicating its high reliability and clinical significance.

scholarly journals Data-Driven Bearing Fault Diagnosis of Microgrid Network Power Device Based on a Stacked Denoising Autoencoder in Deep Learning and Clustering by Fast Search without Data Labels

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-29
Author(s):  
Fan Xu ◽  
Xin Shu ◽  
Xin Li ◽  
Xiaodi Zhang
Keyword(s):  
Deep Learning ◽  
Frequency Domain ◽  
Permutation Entropy ◽  
Cluster Center ◽  
Multiple Model ◽  
Denoising Autoencoder ◽  
Time Frequency ◽  
Fast Search ◽  
Center Point ◽  
Clustering Center

The traditional health indicator (HI) construction method of electric equipment devices in microgrid networks, such as bearings that require different time-frequency domain indicators, needs several models to combine. Therefore, it is necessary to manually select appropriate and sensitive models, such as time-frequency domain indicators and multimodel fusion, to build HIs in multiple steps, which is more complicated because sensitivity characteristics and suitable models are more representatives of bearing degradation trends. In this paper, we use the stacked denoising autoencoder (SDAE) model in deep learning to construct HI directly from the microgrid power equipment of raw signals in bearings. With this model, the HI can be constructed without multiple model combinations or the need for manual experience in selecting the sensitive indicators. The SDAE can extract the representative degradation information adaptively from the original data through several nonlinear hidden layers automatically and approximate complicated nonlinear functions with a small reconstruction error. After the SDAE extracts the preliminary HI, a model is needed to divide the wear state of the HI constructed by the SDAE. A cluster model is commonly used for this, and unlike most clustering methods such as k-means, k-medoids, and fuzzy c-means (FCM), in which the clustering center point must be preset, cluster by fast search (CFS) can automatically find available cluster center points automatically according to the distance and local density between each point and its clustering center point. Thus, the selected cluster center points are used to divide the wear state of the bearing. The root mean square (RMS), kurtosis, Shannon entropy (SHE), approximate entropy (AE), permutation entropy (PE), and principal component analysis (PCA) are also used to construct the HI. Finally, the results show that the performance of the method (SDAE-CFS) presented is superior to other combination HI models, such as EEMD-SVD-FCM/k-means/k-medoids, stacked autoencoder-CFS (SAE-CFS), RMS, kurtosis, SHE, AE, PE, and PCA.

Health condition identification for rolling bearing using a multi-domain indicator-based optimized stacked denoising autoencoder

2019 ◽  
Vol 19 (5) ◽  
pp. 1602-1626 ◽  
Author(s):  
Xiaoan Yan ◽  
Ying Liu ◽  
Minping Jia
Keyword(s):  
Deep Learning ◽  
Frequency Domain ◽  
Rolling Bearing ◽  
Health Condition ◽  
Feature Representation ◽  
Model Parameters ◽  
Denoising Autoencoder ◽  
Time Frequency ◽  
Grasshopper Optimization Algorithm ◽  
Nature Inspired Algorithm

Stacked denoising autoencoder is one of the most classic models of deep learning. However, there are two problems in the traditional stacked denoising autoencoder: (1) the parameter selection of stacked denoising autoencoder mainly depends on expert experience and (2) stacked denoising autoencoder is mainly restricted to learn automatically single-domain features from raw vibration signals while identifying the fault type, which implies that no linear mapping relationship located in other domains of vibration data is neglected, which may lead to the imperfect diagnostic results. Consequently, to address these issues, learn the well-rounded feature representation, and improve recognition accuracy, this article presents a novel approach called multi-domain indicator-based optimized stacked denoising autoencoder for automatic fault identification of rolling bearing. First, multi-domain indicator of the original vibration signal is constructed through calculating the expression of different domains (e.g. time frequency domain, and time frequency domain). Second, the constructed multi-domain indicator is regarded as the input dataset to train stacked denoising autoencoder architecture containing three hidden layers, and a recently reported nature-inspired algorithm named grasshopper optimization algorithm is employed to synchronously determine the model parameters of stacked denoising autoencoder, which is aimed at learning more robust and reliable feature representation. Finally, the feature representation learned in the testing set is fed into the trained stacked denoising autoencoder model containing softmax classifier for identifying bearing health conditions. The presented method is evaluated using two bearing vibration datasets. Experimental results indicate that our approach can provide high-accuracy recognition over 99% for bearing health condition, and it achieves more decent and precise classification results compared with some shallow learning model and standard deep learning architecture.

scholarly journals Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram

2018 ◽  
Vol 39 (12) ◽  
pp. 124005 ◽  
Author(s):  
Qiao Li ◽  
Qichen Li ◽  
Chengyu Liu ◽  
Supreeth P Shashikumar ◽  
Shamim Nemati ◽  
...  
Keyword(s):  
Deep Learning ◽  
Frequency Domain ◽  
Sleep Staging ◽  
Time Frequency ◽  
The Cross

scholarly journals Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

2021 ◽  
Vol 38 (5) ◽  
pp. 1541-1548
Author(s):  
Chang Liu ◽  
Ruslan Antypenko ◽  
Iryna Sushko ◽  
Oksana Zakharchenko ◽  
Ji Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Frequency Domain ◽  
Detection Algorithm ◽  
Radar Signal ◽  
Signal Identification ◽  
Time Frequency ◽  
Weight Calculation ◽  
Radar Signals ◽  
Frequency Features

Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.

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