scholarly journals The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8344
Author(s):  
Shih-Lin Lin
Keyword(s):  
Machine Learning ◽  
Optimization Problem ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Sensory Cues ◽  
Original Source ◽  
Noise Interference ◽  
Low Snr ◽  
First Case ◽  
Mode Decomposition

This paper proposes a new method called independent component analysis–variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and data analysis. ICA is a very important method in the field of machine learning. It is an unsupervised learning algorithm that can dig out the independent factors hidden in the observation signal. The VMD method estimates each signal component by solving the frequency domain variational optimization problem, and it is very suitable for mechanical fault diagnosis. The advantage of ICA-VMD is that it requires two sensory cues to distinguish the original source from the unwanted noise. In the three cases studied here, the original source was first contaminated by white Gaussian noise. The three cases in this study are under different SNR conditions. The SNR in the first case is –6.46 dB, the SNR in the second case is –21.3728, and the SNR in the third case is –46.8177. The simulation results show that the ICA-VMD method can effectively recover the original source from the contaminated data. It is hoped that, in the future, there will be new discoveries and advances in science and technology to solve the noise interference problem through this method.

scholarly journals Machine Learning Based Track Classification and Estimation using Kalman Filter

2020 ◽  
Vol 9 (1) ◽  
pp. 1700-1704
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Velocity Model ◽  
Supervised Machine Learning ◽  
Multiple Target ◽  
Low Snr

Classification of target from a mixture of multiple target information is quite challenging. In This paper we have used supervised Machine learning algorithm namely Linear Regression to classify the received data which is a mixture of target-return with the noise and clutter. Target state is estimated from the classified data using Kalman filter. Linear Kalman filter with constant velocity model is used in this paper. Minimum Mean Square Error (MMSE) analysis is used to measure the performance of the estimated track at various Signal to Noise Ratio (SNR) levels. The results state that the error is high for Low SNR, for High SNR the error is Low

scholarly journals Assessment of Micro-Organism Growth Risk on Filters with Machine Learning

2019 ◽  
Vol 111 ◽  
pp. 05017 ◽  
Author(s):  
Andreas Hantsch ◽  
Sabine Döge
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Visual Observation ◽  
Normal Operation ◽  
Health Issues ◽  
Filter Surface ◽  
Test Filter ◽  
Organism Growth ◽  
Electro Magnetic

Modern buildings usually have a practically air-tight envelope. Therefore, mechanical ventilation is very often necessary. A crucial part of the system is the filter which allows to create an atmosphere which is free of dust, aerosols, and pollen. As organic material accumulates on the filter surface, the risk of micro-organism growth rises. This may yield health issues especially for the occupants of buildings in humid regions. For this purpose, a test filter with electrodes has been designed which allowed to measure its electro-magnetic properties, such as resistance, capacitance, and impedance as an indicator for the micro-organism growth risk. After some preliminary tests, electrodes of stainless steel and the electrical capacitance have been selected due to their best durability and signal-to-noise-ratio. The test filter has been implemented in the HVAC system of the institute in order to aggregate data for different abnormal and normal operation data. A machine learning algorithm has been trained successfully to detect anomalies of the filter behaviour and therefore provided more insight than pressure drop measurement alone. Finally, the change intervals of the filter could be adapted to the real degree of pollution without the requirement for visual observation in order to provide best air conditions.

Dictionary learning and shift-invariant sparse coding denoising for CSEM data combined with CEEMD

Geophysics ◽  
2021 ◽  
pp. 1-52
Author(s):  
Guang Li ◽  
Zhushi He ◽  
Jing Tian Tang ◽  
Juzhi Deng ◽  
Xiaoqiong Liu ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Real Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Useful Signal

Controlled-source electromagnetic (CSEM) data recorded in industrialized areas are inevitably contaminated by strong cultural noise. Traditional noise attenuation methods are often ineffective for intricate aperiodic noise. To address the problem mentioned above, we propose a novel noise isolation method based on fast Fourier transform (FFT), complementary ensemble empirical mode decomposition (CEEMD) and shift-invariant sparse coding (SISC, an unsupervised machine learning algorithm under a data-driven framework). First, large powerline noise is accurately subtracted in the frequency domain. Then, the CEEMD based algorithm is used to correct the large baseline drift. Finally, taking advantage of the sparsity of periodic signals, SISC is applied to autonomously learn a feature atom (the useful signal with a length of one period) from the detrended signal and recover CSEM signal with high accuracy. We demonstrate the performance of the SISC by comparing with other three promising signal processing methods, including the mathematic morphology filtering (MMF), soft-threshold wavelet filtering, and K-SVD (another dictionary learning method) sparse decomposition. Experimental results illustrate that SISC provides the best performance. Robustness test results show that SISC can increase the signal-to-noise ratio (SNR) of noisy signal from 0 dB to more than 15 dB. Case studies of synthetic and real data collected in the Chinese Provinces Sichuan and Yunnan show that the proposed method is capable of effectively recovering the useful signal from the observed data contaminated with different kinds of strong ambient noise. The curves of U/I and apparent resistivity after applying the proposed method improved greatly. Moreover, the proposed method performs better than the robust estimation method based on correlation analysis.

Marine seismic signal denoising using VMD with Hausdorff distance and wavelet transform

2021 ◽  
pp. 154851292110360
Author(s):  
Abhishek Kesharwani ◽  
Vaibhav Aggarwal ◽  
Shubham Singh ◽  
Rahul B R ◽  
Arvind Kumar
Keyword(s):  
Wavelet Transform ◽  
Hausdorff Distance ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Signal Interference ◽  
Signal Denoising ◽  
Low Snr ◽  
Synthetic Signal ◽  
Mode Decomposition ◽  
Marine Seismic

In marine seismic acquisitions, signal interference remains a major menace. In this paper, a denoising approach based on the Variational Mode Decomposition (VMD) combined with the Hausdorff distance (HD) and Wavelet transform (WT) is proposed. There has been substantial research in this field over the years. However, traditional denoising methods fall short of achieving satisfactory results in an extremely low signal to noise ratio (SNR) environment. The feasibility, and stability of the proposed method was validated by performing simulations in MATLAB on both a synthetic signal and a seismic signal generated using real dataset. It was found that the proposed method does well in preserving marine signals in low SNR environments, and has a superior output SNR.

scholarly journals Fault Detection in Multi-Core C&I Cable via Machine Learning Based Time-Frequency Domain Reflectometry

2019 ◽  
Vol 10 (1) ◽  
pp. 158 ◽  
Author(s):  
Chun-Kwon Lee ◽  
Seung Jin Chang
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Frequency Domain ◽  
Nuclear Power ◽  
Power Plants ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Noise Interference ◽  
Time Frequency ◽  
Frequency Domain Reflectometry

The integrity and functionality of the control and instrumentation (C&I) cable systems are essential when it comes to ensuring the reliability and safety of system operations, especially in vehicles or power plants. Whenever a fault occurs in a multi-core cable, it not only affects signals of the individual faulty line but inflicts the rest through crosstalk and noise interference. Thus, it is imperative that cable diagnostic technologies are eligible of detecting the fault and further differentiating the faulty line to prevent the original fault from jeopardizing the entire system operation. We propose here a diagnostic method which detects the presence and the location of a fault, and further differentiates the faulty line within the multi-core C&I cables using a machine learning algorithm based on the time-frequency domain reflectometry results. Neural networks and the hierarchy clustering algorithm are used for fault detection and the identification of the faulty line. The proposed clustering algorithm is verified via experiments with four possible fault scenarios using automotive wires and C&I cables for nuclear power plants. Hence, the proposed algorithm allows a fault in multi-core cables to be accurately detected and estimated when given the location and the reflection coefficient of a fault.

scholarly journals Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation

2021 ◽  
Vol 11 (4) ◽  
pp. 1942
Author(s):  
Yunseong Lee ◽  
Chanhong Park ◽  
Taeyoung Kim ◽  
Yeongyoon Choi ◽  
Kiseon Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Poor Performance ◽  
Direction Of Arrival ◽  
Estimation Accuracy ◽  
High Signal ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Low Snr

Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.

scholarly journals Classification of Aggressive Behaviors Based on sEMG Feature Extraction and Machine Learning Algorithm

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 656-656
Author(s):  
Youngjun Kim ◽  
Uchechukuwu David ◽  
Yeonsik Noh
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Learning Machine

Abstract New surface electromyography (sEMG) feature extraction approach combined with Empirical Mode Decomposition (EMD) and Dispersion Entropy (DisEn) is proposed for classifying aggressive and normal behaviors from sEMG data. In this study, we used the sEMG physical action dataset from the UC Irvine Machine Learning repository. The raw sEMG was decomposed with EMD to obtain a set of Intrinsic Mode Functions (IMF). The IMF, which includes the most discriminant feature for each action, was selected based on the analysis by Hibert Transform (HT) in the time-frequency domain. Next, the DisEn of the selected IMF was calculated as a corresponding feature. Finally, the DisEn value was tested using five different classifiers, such as LDA, Quadratic DA, k-NN, SVM, and Extreme Learning Machine (ELM) for the classification task. Among these ML algorithms, we achieved classification accuracy, sensitivity, and specificity with ELM as 98.44%, 100%, and 96.72%, respectively.

Machine Learning-Based Robust Watermarking Technique for Medical Image Transmitted Over LTE Network

2018 ◽  
Vol 27 (1) ◽  
pp. 105-114 ◽  
Author(s):  
Ankur Rai ◽  
Harsh Vikram Singh
Keyword(s):  
Machine Learning ◽  
Orthogonal Frequency Division Multiplexing ◽  
Medical Image ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Region Of Interest ◽  
Physical Layer ◽  
Support Vector ◽  
Hybrid Watermarking ◽  
3Gpp Long Term Evolution

Abstract This paper discusses a safe and secure watermarking technique using a machine learning algorithm. In this paper, the propagation of a watermarked image is simulated over the third-generation partnership project (3GPP)/long-term evolution (LTE) downlink physical layer. The watermark data are scrambled and a transform domain-based hybrid watermarking technique is used to embed this watermark into the transform coefficients of the host image and transmitted over the orthogonal frequency division multiplexing (OFDM) downlink physical layer. Support vector machine (SVM) is used as a classifier for the classification of non-region of interest (NROI) and region of interest (ROI) in a medical image. The result achieved in this experiment revealed that a 10−6 bit error rate (BER) value is realizable for a greater value of signal-to-noise ratio (SNR; i.e. more than 10.4 dB of SNR). The peak SNR (PSNR) of the received cover image is more than 35 dB, which is acceptable for clinical applications.

scholarly journals Development, Implementation, and Evaluation of a Personalized Machine Learning Algorithm for Clinical Decision Support: Case Study With Shingles Vaccination (Preprint)

2019 ◽  
Author(s):  
Ji Chen ◽  
Sara Chokshi ◽  
Roshini Hegde ◽  
Javier Gonzalez ◽  
Eduardo Iturrate ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Clinical Decision ◽  
Control Group ◽  
Signal To Noise ◽  
Noise Optimization ◽  
Foundational Work

BACKGROUND Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)–integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician’s interaction with these alerts in general. OBJECTIVE This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the <i>signal</i> of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts. METHODS We built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician’s interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients. RESULTS During the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; <i>P</i>=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; <i>P</i>=.20). CONCLUSIONS All key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.

scholarly journals Enhanced Characteristic Vibration Signal Detection of Generator Based on Time-Wavelet Energy Spectrum and Multipoint Optimal Minimum Entropy Deconvolution Adjusted Method

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yu-Ling He ◽  
Tao Wang ◽  
Kai Sun ◽  
Xiao-Long Wang ◽  
Bo Peng ◽  
...  
Keyword(s):  
Energy Spectrum ◽  
Signal To Noise Ratio ◽  
Short Circuit ◽  
Vibration Signal ◽  
Minimum Entropy ◽  
Low Snr ◽  
Wavelet Energy ◽  
Mode Decomposition ◽  
Before And After ◽  
Characteristic Signal

To overcome the shortage of low SNR (signal to noise ratio) of the multipole generator vibration signal which brings rigid difficulty to the fault diagnosis, a new method which combines the Time-Wavelet Energy Spectrum (TWES) with the Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) algorithm is proposed. This method uses TWES to extract and enhance the characteristic signal, while employing MOMEDA to optimize the spectrum structure and filter the noise. The application of this method to the simulating signal as well as the test stator vibration signal in a 6-pole generator before and after rotor interturn short circuit fault validates the effectiveness of the method. Moreover, the comparison among the proposed method and some other general methods such as the Empirical Mode Decomposition (EMD) and the maximum correlative kurtosis deconvolution (MCKD) suggests that the proposed method is superior to these methods.

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