scholarly journals Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial Sensors

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Zhiqiang Peng ◽  
Yue Zhang
Keyword(s):  
Feature Extraction ◽  
Angular Velocity ◽  
Frequency Domain ◽  
Time Domain ◽  
Inertial Sensors ◽  
Extraction Methods ◽  
Feature Extraction Method ◽  
The Difference ◽  
The Time Domain ◽  
Almost All

Correctly identifying human activities is very significant in modern life. Almost all feature extraction methods are based directly on acceleration and angular velocity. However, we found that some activities have no difference in acceleration and angular velocity. Therefore, we believe that for these activities, any feature extraction method based on acceleration and angular velocity is difficult to achieve good results. After analyzing the difference of these indistinguishable movements, we propose several new features to improve accuracy of recognition. We compare the traditional features and our custom features. In addition, we examined whether the time-domain features and frequency-domain features based on acceleration and angular velocity are different. The results show that (1) our custom features significantly improve the precision of the activities that have no difference in acceleration and angular velocity; and (2) the combination of time-domain features and frequency-domain features does not significantly improve the recognition of different activities.

scholarly journals A Vibration Feature Extraction Method Based on Time-Domain Dimensional Parameters and Mahalanobis Distance

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Junjun Chen ◽  
Bing Xu ◽  
Xin Zhang
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Time Domain ◽  
Mahalanobis Distance ◽  
Extraction Method ◽  
Vibration Signal ◽  
Feature Extraction Method ◽  
Feature Parameters ◽  
Dimensional Parameters ◽  
Failure Types

To accurately describe the characteristics of a signal, the feature parameters in time domain and frequency domain are usually extracted for characterization. However, the total number of feature parameters in time domain and frequency domain exceeds twenty, and all of the feature parameters are used for feature extraction, which will result in a large amount of data processing. For the purpose of using fewer feature parameters to accurately reflect the characteristics of the vibration signal, a simple but effective vibration feature extraction method combining time-domain dimensional parameters (TDDP) and Mahalanobis distance (MD) is proposed, i.e., TDDP-MD. In this method, ten time-domain dimensional parameters are selected to extract fault features, and the distance evaluation technique based on Mahalanobis distance criterion function is also introduced to calculate the feature vector, which can be used to classify different failure types. Finally, the proposed method is applied to fault diagnosis of rolling element bearings, and experimental analysis results show that the proposed method can recognize different failure types accurately and effectively with only ten time-domain dimensional parameters and a small quantity of training samples.

scholarly journals ANALYSIS OF TIME DOMAIN MCSEM DATA IN THE PRESENCE OF RESISTIVE LAYERS

2012 ◽  
Vol 30 (3) ◽  
Author(s):  
Diego Da Costa Miranda ◽  
Cícero Roberto Teixeira Régis
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Theoretical Formulation ◽  
Resistive Layer ◽  
Hydrocarbon Reservoir ◽  
Time Domain Data ◽  
Electric Structure ◽  
The Time Domain ◽  
Almost All ◽  
Model Frequency

Modeling of mCSEM data is usually done in the frequency domain, from its theoretical formulation to the analysis of the results. However, the time domain approach is, in principle, capable of providing equivalent information about the geo-electric structure of the subsurface and has the advantage of recording only the secondary fields, so the signal is not dominated by the primary field. In this work, we model frequency domain mCSEM data in 1-D environments, thenwe perform the discrete Fourier transform to obtain time domain results. We simulated marine geological environments with and without the resistive layer that represents the hydrocarbon reservoir. We verified that, in almost all model configurations, the time domain data are significantly different when calculated for models withand without hydrocarbons. We calculated the results considering variations in the position of the receivers and in the sea depth. We observed the influence of the airwave, even at large sea depths, and although a simple separation of this influence on data is not possible, the time domain results allowed us to do a qualitative analysis of its effects on the survey.

scholarly journals Analysis and Implementation of Brain Waves Feature Extraction and Classification to Control Robotic Hand

2019 ◽  
Vol 1 (3) ◽  
pp. 31-41 ◽  
Author(s):  
Mohammed Z. Al-Faiz ◽  
Ammar A. Al-hamadani
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Frequency Domain ◽  
Error Rate ◽  
Time Domain ◽  
Channel Selection ◽  
Extraction Methods ◽  
Robotic Hand ◽  
Selection Algorithm ◽  
Absolute Value

In this paper, (i) time domain, frequency domain and spatial domain feature extraction methods were investigated. (ii) Two dimensionality reduction methods were proposed, implemented and compared. (iii) The method pair (feature extraction + dimensionality reduction) that owns the lowest classification error rate will be used to learn a machine learning algorithm to control robotic hand in offline mode. Two classes EEG dataset of three bipolar channels was used. The extracted feature vectors were fed into Support Vector Machine with Radial Basis Function kernel (SVM-RBF) to train the classifier. The experimented time domain feature extraction methods were: Mean Absolute Value (MAV), integrated Absolute Value (IAV), Zero Crossing (ZC), Root Mean Square (RMS), Waveform Length (WL) and Slope Sign Change (SSC). Frequency domain feature was the Autoregressive Feature (AR). Finally, the spatial domain feature was the Common Spatial Patterns (CSP). Matlab codes for Principal Component Analysis (PCA) and channel selection algorithm were designed and used to reduce the dimensionality of the features vector. Results showed that CSP features got the lowest error rate for both dimensionality reduction technique with 2.14%. Results recommends to use channel selection algorithm over PCA since it owns the lowest processing time of 8.2s over 8.5s for PCA.

scholarly journals An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE

2018 ◽  
Vol 25 (s2) ◽  
pp. 98-106 ◽  
Author(s):  
Hu Zhang ◽  
Lei Zhao ◽  
Quan Liu ◽  
Jingjing Luo ◽  
Qin Wei ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Frequency Band ◽  
Extraction Method ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Permutation Entropy ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Bearing Vibration

Abstract The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing.

scholarly journals A Comparative Study of Multiscale Sample Entropy and Hierarchical Entropy and Its Application in Feature Extraction for Ship-Radiated Noise

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 793 ◽  
Author(s):  
Weijia Li ◽  
Xiaohong Shen ◽  
Yaan Li
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Probabilistic Neural Network ◽  
Extraction Methods ◽  
Sample Entropy ◽  
Real World Data ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Frequency Components ◽  
The Difference

The presence of marine ambient noise makes it difficult to extract effective features from ship-radiated noise. Traditional feature extraction methods based on the Fourier transform or wavelets are limited in such a complex ocean environment. Recently, entropy-based methods have been proven to have many advantages compared with traditional methods. In this paper, we propose a novel feature extraction method for ship-radiated noise based on hierarchical entropy (HE). Compared with the traditional entropy, namely multiscale sample entropy (MSE), which only considers information carried in the lower frequency components, HE takes into account both lower and higher frequency components of signals. We illustrate the different properties of HE and MSE by testing them on simulation signals. The results show that HE has better performance than MSE, especially when the difference in signals is mainly focused on higher frequency components. Furthermore, experiments on real-world data of five types of ship-radiated noise are conducted. A probabilistic neural network is employed to evaluate the performance of the obtained features. Results show that HE has a higher classification accuracy for the five types of ship-radiated noise compared with MSE. This indicates that the HE-based feature extraction method could be used to identify ships in the field of underwater acoustic signal processing.

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

scholarly journals Time and Frequency Domain Dynamic Analysis of Offshore Mooring

2021 ◽  
Vol 9 (7) ◽  
pp. 781
Author(s):  
Shi He ◽  
Aijun Wang
Keyword(s):  
Dynamic Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Linearization Method ◽  
Euler Method ◽  
Single Component ◽  
Hydrodynamic Drag ◽  
Lumped Mass ◽  
Mooring Lines ◽  
The Time Domain

The numerical procedures for dynamic analysis of mooring lines in the time domain and frequency domain were developed in this work. The lumped mass method was used to model the mooring lines. In the time domain dynamic analysis, the modified Euler method was used to solve the motion equation of mooring lines. The dynamic analyses of mooring lines under horizontal, vertical, and combined harmonic excitations were carried out. The cases of single-component and multicomponent mooring lines under these excitations were studied, respectively. The case considering the seabed contact was also included. The program was validated by comparing with the results from commercial software, Orcaflex. For the frequency domain dynamic analysis, an improved frame invariant stochastic linearization method was applied to the nonlinear hydrodynamic drag term. The cases of single-component and multicomponent mooring lines were studied. The comparison of results shows that frequency domain results agree well with nonlinear time domain results.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

A Multiple Harmonic Open-Loop Controller for Hydro/Aerodynamic Force Measurements in Rotating Machinery Using Magnetic Bearings

2002 ◽  
Vol 124 (4) ◽  
pp. 827-834 ◽  
Author(s):  
D. O. Baun ◽  
E. H. Maslen ◽  
C. R. Knospe ◽  
R. D. Flack
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Open Loop ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Rotor Vibration ◽  
Aerodynamic Forces ◽  
Analog Input ◽  
Input And Output ◽  
The Time Domain

Inherent in the construction of many experimental apparatus designed to measure the hydro/aerodynamic forces of rotating machinery are features that contribute undesirable parasitic forces to the measured or test forces. Typically, these parasitic forces are due to seals, drive couplings, and hydraulic and/or inertial unbalance. To obtain accurate and sensitive measurement of the hydro/aerodynamic forces in these situations, it is necessary to subtract the parasitic forces from the test forces. In general, both the test forces and the parasitic forces will be dependent on the system operating conditions including the specific motion of the rotor. Therefore, to properly remove the parasitic forces the vibration orbits and operating conditions must be the same in tests for determining the hydro/aerodynamic forces and tests for determining the parasitic forces. This, in turn, necessitates a means by which the test rotor’s motion can be accurately controlled to an arbitrarily defined trajectory. Here in, an interrupt-driven multiple harmonic open-loop controller was developed and implemented on a laboratory centrifugal pump rotor supported in magnetic bearings (active load cells) for this purpose. This allowed the simultaneous control of subharmonic, synchronous, and superharmonic rotor vibration frequencies with each frequency independently forced to some user defined orbital path. The open-loop controller was implemented on a standard PC using commercially available analog input and output cards. All analog input and output functions, transformation of the position signals from the time domain to the frequency domain, and transformation of the open-loop control signals from the frequency domain to the time domain were performed in an interrupt service routine. Rotor vibration was attenuated to the noise floor, vibration amplitude ≈0.2 μm, or forced to a user specified orbital trajectory. Between the whirl frequencies of 14 and 2 times running speed, the orbit semi-major and semi-minor axis magnitudes were controlled to within 0.5% of the requested axis magnitudes. The ellipse angles and amplitude phase angles of the imposed orbits were within 0.3 deg and 1.0 deg, respectively, of their requested counterparts.

Sign in / Sign up

Export Citation Format

Share Document