Healing Seismic Data with Phase Corrections for Processing of Single-Sensor Data in the Desert Environment

2021 ◽  
Author(s):  
Andrey Bakulin ◽  
Ilya Silvestrov ◽  
Dmitry Neklyudov
Keyword(s):  
Speech Processing ◽  
Original Data ◽  
Sensor Data ◽  
Desert Environment ◽  
Time Frequency ◽  
Weak Reflection ◽  
Phase Corrections ◽  
Single Sensor ◽  
Efficient Processing ◽  
Novel Method

Abstract Acquiring data with single sensors or small arrays in a desert environment may lead to challenging data quality for subsequent processing. We present a new approach to effectively "heal" such data and allow efficient processing and imaging without requiring any additional acquisition. A novel method combines the power of seismic beamforming and time-frequency masking originating from speech processing. First, we create an enhanced version of the data with beamforming or local stacking. Beamforming effectively suppresses scattered noise and finds weak reflection signals, albeit sacrificing some higher frequencies. Next, we employ a seismic time-frequency masking procedure to fix the original data while using beamformed data as a guide. Time-frequency masking effectively fixes corrupt and broken phase of the original data. After such data-driven healing, prestack data can be effectively processed and imaged, while maintaining the higher frequencies lost during beamforming.

scholarly journals Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1168 ◽  
Author(s):  
Sebin Park ◽  
Myeong-Seon Gil ◽  
Hyeonseung Im ◽  
Yang-Sae Moon
Keyword(s):  
Kalman Filtering ◽  
Input Parameter ◽  
Original Data ◽  
Measurement Noise ◽  
Sensor Data ◽  
Noise Variance ◽  
Noise Parameters ◽  
Input Noise ◽  
Novel Method ◽  
Filtering Technique

To effectively maintain and analyze a large amount of real-time sensor data, one often uses a filtering technique that reflects characteristics of original data well. This paper proposes a novel method for recommending the measurement noise for Kalman filtering, which is one of the most representative filtering techniques. Kalman filtering corrects inaccurate values of input sensor data, and its filtering performance varies depending on the input noise parameters. In particular, if the noise parameters determined based on the user’s experience are incorrect, the accuracy of Kalman filtering may be reduced significantly. Based on this observation, this paper addresses how to determine the measurement noise variance, a major input parameter of Kalman filtering, by analyzing past sensor data and how to use the estimated noise to improve the filtering accuracy. More specifically, to estimate the measurement noise variance, two analytical methods are proposed: one a transform-based method using a wavelet transform and the other a learning-based method using a denoising autoencoder. Experimental results show that the proposed methods estimated the measurement noise variance accurately and were superior to the experience-based method in the filtering accuracy.

scholarly journals Fusion Method and Application of Several Source Vibration Fault Signal Spatio-Temporal Multi-Correlation

2021 ◽  
Vol 11 (10) ◽  
pp. 4318
Author(s):  
Longhuan Cheng ◽  
Jiantao Lu ◽  
Shunming Li ◽  
Rui Ding ◽  
Kun Xu ◽  
...  
Keyword(s):  
Vibration Signal ◽  
Original Data ◽  
Sensor Data ◽  
Fusion Method ◽  
Vibration Sensors ◽  
Fusion Data ◽  
Single Sensor ◽  
Spatio Temporal ◽  
Signal Processing Methods ◽  
Spatial Locations

Combined with other signal processing methods, related algorithms are widely used in the diagnosis and identification of rotor faults. In order to solve the problem that the vibration signal of a single sensor is too single, a new multi-source vibration signal fusion method is proposed. This method explores the correlation between vibration sensors at different locations by using multiple cross-correlations of spatial locations. First, wavelet noise reduction and linear normalization are used to process the original data. Then, the signal energy correlation function between the sensors is established, and the adaptive weight is obtained. Finally, the data fusion result is obtained. Taking rotor bearing and gear failures at different speeds as an example, the data of three vibration sensors at different positions are fused using the spatio-temporal multiple correlation fusion method (STMF). Through the intelligent fault diagnosis method stacked auto encoder (SAE), compared with single sensor data, average weighted fusion data and neural network fusion data, STMF method can reach a diagnosis accuracy of more than 94% at 700 rpm, 900 rpm and 1100 rpm. It is concluded that the result of the STMF method is more effective and superior.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

Automatic Recognition of the Lighting Whistler waves from the Wave Data of SCM Boarded on ZH-1 satellite

2021 ◽  
Author(s):  
Jing Yuan ◽  
Zijie Wang ◽  
Dehe Yang ◽  
Qiao Wang ◽  
Zeren Zima ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Satellite Observation ◽  
Original Data ◽  
Electromagnetic Environment ◽  
Mel Frequency Cepstral Coefficients ◽  
Frequency Profile ◽  
Time Frequency ◽  
Wave Data ◽  
Audio Clip ◽  
Long Short Term Memory

<p>Lightning whistlers, found frequently in electromagnetic satellite observation, are the important tool to study electromagnetic environment of the earth space. With the increasing data from electromagnetic satellites, a considerable amount of time and human efforts are needed to detect lightning whistlers from these tremendous data. In recent years, algorithms for lightning whistlers automatic detection have been conducted. However, these methods can only work in the time-frequency profile (image) of the electromagnetic satellites data with two major limitations: vast storage memory for the time-frequency profile (image) and expensive computation for employing the methods to detect automatically the whistler from the time-frequency profile. These limitations hinder the methods work efficiently on ZH-1 satellite. To overcome the limitations and realize the real-time whistler detection automatically on board satellite, we propose a novel algorithm for detecting lightning whistler from the original observed data without transforming it to the time-frequency profile (image).</p><p>The motivation is that the frequency of lightning whistler is in the audio frequency range. It encourages us to utilize the speech recognition techniques to recognize the whistler in the original data \of SCM VLF Boarded on ZH-1. Firstly, we averagely move a 0.16 seconds window on the original data to obtain the patch data as the audio clip. Secondly, we extract the Mel-frequency cepstral coefficients (MFCCs) of the patch data as a type of cepstral representation of the audio clip. Thirdly, the MFCCs are input to the Long Short-Term Memory (LSTM) recurrent neutral networks to classification. To evaluate the proposed method, we construct the dataset composed of 10000 segments of SCM wave data observed from ZH-1 satellite(5000 segments which involving whistler and 5000 segments without any whistler). The proposed method can achieve 84% accuracy, 87% in recall, 85.6% in F1score.Furthermore, it can save more than 126.7MB and 0.82 seconds compared to the method employing the YOLOv3 neutral network for detecting whistler on each time-frequency profile.</p><p> </p><p>Key words: ZH-1 satellite, SCM,lightning whistler, MFCC, LSTM</p>

A Novel Method for Gender and Age Detection Based on EEG Brain Signals

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Haitham Issa ◽  
Sali Issa ◽  
Wahab Shah
Keyword(s):  
Cross Validation ◽  
Image Feature ◽  
Emotional States ◽  
Time Frequency ◽  
Brain Signals ◽  
Average Accuracy ◽  
Gender And Age ◽  
Novel Method ◽  
Fold Cross Validation ◽  
Validation Strategy

This paper presents a new gender and age classification system based on Electroencephalography (EEG) brain signals. First, Continuous Wavelet Transform (CWT) technique is used to get the time-frequency information of only one EEG electrode for eight distinct emotional states instead of the ordinary neutral or relax states. Then, sequential steps are implemented to extract the improved grayscale image feature. For system evaluation, a three-fold-cross validation strategy is applied to construct four different classifiers. The experimental test shows that the proposed extracted feature with Convolutional Neural Network (CNN) classifier improves the performance of both gender and age classification, and achieves an average accuracy of 96.3% and 89% for gender and age classification, respectively. Moreover, the ability to predict human gender and age during the mood of different emotional states is practically approved.

scholarly journals Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

2018 ◽  
Vol 14 (04) ◽  
pp. 4
Author(s):  
Xuemei Yao ◽  
Shaobo Li ◽  
Yong Yao ◽  
Xiaoting Xie
Keyword(s):  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Fusion Algorithm ◽  
Monitoring And Diagnosis ◽  
Multi Sensor Data Fusion ◽  
Basic Probability ◽  
Optimal Fusion ◽  
Single Sensor ◽  
Mechanical Devices

As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.

Improved EEMD in the Application of Signal Singularity Detection

2012 ◽  
Vol 518-523 ◽  
pp. 3847-3851
Author(s):  
Mei Jun Zhang ◽  
Chuang Wang ◽  
Hao Chen ◽  
Qun Zhang Tu
Keyword(s):  
Frequency Spectrum ◽  
Original Data ◽  
Singularity Detection ◽  
Time Frequency ◽  
Local Characteristics ◽  
Cross Terms ◽  
Marginal Spectrum ◽  
Frequency Components ◽  
Detection Signal ◽  
Improved Eemd

In order to solve the endpoint effect and modal aliasing phenomenon in EMD and EEMD,Improved EEMD is put forward, and the application in signal singularity detection is researched in this paper. The improved EEMD will signal drops down into a series of different IMF to highlight the different local characteristics of original data, and then calculate Hilbert marginal spectrum and time-frequency spectrum to determine the frequency of these mutations and mutations position. To compared with FT, STFT, WVD,WT, EMD and EEMD etc, No cross-terms and no false IMF components are produced in the Hilbert time-frequency spectrum of the improved EEMD. Different frequency components and frequency mutations position are clearly distinguished at the same time. The Hilbert time-frequency spectrum of the improved EEMD has more superior detection signal singularity ability.

scholarly journals Embedding Undersampling Rotation Forest for Imbalanced Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Huaping Guo ◽  
Xiaoyu Diao ◽  
Hongbing Liu
Keyword(s):  
Imbalanced Data ◽  
Feature Space ◽  
Original Data ◽  
Training Set ◽  
Data Set ◽  
Minority Class ◽  
Rotation Forest ◽  
Novel Method ◽  
Individual Classifier ◽  
The Cost

Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work well on the imbalanced data which are characterized as having much less examples of one class (minority class) than the other (majority class), and the cost of misclassifying minority class examples is often much more expensive than the contrary cases. This paper proposes a novel method called Embedding Undersampling Rotation Forest (EURF) to handle this problem (1) sampling subsets from the majority class and learning a projection matrix from each subset and (2) obtaining training sets by projecting re-undersampling subsets of the original data set to new spaces defined by the matrices and constructing an individual classifier from each training set. For the first method, undersampling is to force the rotation matrix to better capture the features of the minority class without harming the diversity between individual classifiers. With respect to the second method, the undersampling technique aims to improve the performance of individual classifiers on the minority class. The experimental results show that EURF achieves significantly better performance comparing to other state-of-the-art methods.

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