Dimension Reduction Analysis of Signal Manifold for Vowels in Time and Frequency Domain

2021 ◽  
Vol 17 ◽  
pp. 69-74
Keyword(s):  
Dimension Reduction ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Domain Analysis ◽  
High Dimensional ◽  
Dimensional Manifold ◽  
Two Dimensional ◽  
Time Domain Simulation ◽  
Time Frequency ◽  
Reduction Methods

In this paper, the LLE and ISOMAP algorithms in manifold learning are applied them to the analysis of vowel signals in time and frequency domain. Time domain simulation results show that the two dimensionality reduction methods can implement two-dimensional visualization of signals while preserving the high-dimensional manifold structure of original signals as much as possible. The time-frequency domain dimension reduction analysis of vowel signal manifold effectively solves the problem that high-dimensional speech signals can’t be intuitively felt, and provides a new potential way for signal classification. The frequency domain analysis is further optimized on the basis of time domain simulation. Because half of the amplitude values in DFT is used in the simulation, the two-dimensional manifold of the signal is roughly linearly distributed, which can effectively reduce redundancy and make the signal more compactly expressed in the frequency domain

Numerical Study on Sea Keeping Performances of Ship by Exact Linear 3-D Potential Method

2007 ◽  
Author(s):  
N. M. Golam Zakaria ◽  
M. S. Baree
Keyword(s):  
Frequency Domain ◽  
Wave Height ◽  
Time Domain ◽  
Domain Analysis ◽  
Potential Method ◽  
Frequency Domain Analysis ◽  
Numerical Calculations ◽  
Irregular Waves ◽  
Linear Potential ◽  
Time Domain Simulation

This paper deals with the numerical calculations of sea-keeping performances of ship in irregular sea condition. Here linear potential theory has been applied for describing the fluid motion and 3-D sink-source technique has been used to determine hydrodynamic forces for surface ship advancing in waves at constant forward speed. Numerical coding based on 3-D potential method has been tested in an extensive manner keeping an eye with the criteria recommended by various ITTC committees [1]. The numerical accuracy of the coding has been examined using some experiment results as well as some other contemporary numerical calculations given by some authors for the case of frequency domain analysis. Taking a typical Panamax Container Vessel and in order to simulate its sea-keeping performances in real sea condition, the frequency domain analysis has been performed. The result is then used for time domain simulation in short crested irregular waves. Unequal frequency spacing has been taken into account to get longer simulation time and also empirical nonlinear roll damping has been taken in the way of time domain simulation. From this time domain simulation, relative wave height has been calculated which could sometimes damage deck equipment as well as posing a risks to personnel in severe sea condition. The effect of speed & wave direction on relative wave height has been considered and finally the numerical results of the maximum and significant values of irregular relative wave heights for these conditions are discussed.

Analysis of Electrocardio Signal Based on Approximate Entropy

2012 ◽  
Vol 429 ◽  
pp. 195-199
Author(s):  
Xiao Lei Zhao ◽  
Ming Rong Ren ◽  
Ya Ting Zhang ◽  
Pu Wang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Approximate Entropy ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Spectrum Estimation ◽  
Transform Method ◽  
Time Frequency ◽  
Relationship Of

The research and detection of heart disease depends on the analysis of the characteristic of electrocardio signal. Current analysis methods mainly include: (1) time domain analysis is a common used approach. With experience learned by observation and calculation, researchers examine errors and interferences to calculate means and variances directly within time domain. Analysis quality of this method demands higher request for researchers’ experience and skill although it’s a direct and significant result. (2) Frequency domain analysis, such as spectrum estimation, is largely applied to electrocardio signal researches and clinical applications. The analysis reflects abundant electrocardio activities, but failed to show details of the characteristics due to lack of time information. (3) time-frequency domain analysis describes energy density under different time and frequency of electrocardio signal at one time. It clarifies the relationship of signal frequency’s changing along with time such as wavelet transform method. (4) Nonlinear analysis is generally applied to biomedicine signal research in recent years. Correlation dimension, kolmogorov entropy, lyapunov component are major research methods to estimate some nonlinear dynamic parameters to represent the characteristic of electrocardio signal.

scholarly journals Targeted Projection Pursuit Tool for Gene Expression Visualisation

2006 ◽  
Vol 3 (2) ◽  
pp. 264-273 ◽  
Author(s):  
Joe Faith ◽  
Michael Brockway
Keyword(s):  
Gene Expression ◽  
Dimension Reduction ◽  
Gene Expression Data ◽  
Projection Pursuit ◽  
High Dimensional ◽  
Data Sets ◽  
Two Dimensional ◽  
Expression Data ◽  
Novel Technique ◽  
Reduction Methods

Summary A tool is introduced that uses a novel technique to enable users to explore two-dimensional views of high dimensional gene expression data sets. Unlike other such tools, the interface is intuitive and efficient, allowing the user to easily select views that meet their requirements. The tool is tested on publicly available gene expression data sets and demonstrated to find views that show the seperation of gene expression data sets into classes more effectively than standard dimension-reduction methods.

The Applied Research of Time-Frequency Analysis Method Employed in Seismic Signal Processing

2014 ◽  
Vol 955-959 ◽  
pp. 1809-1812
Author(s):  
Zuo Ju Wu ◽  
Zhi Jia Wang ◽  
Jun Wei Bi
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Domain ◽  
Seismic Signal ◽  
Frequency Domain Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Seismic Signal Processing

In the traditional processing of seismic signal, the frequency domain analysis method is always available to research some features which always vary with frequency. However, the condition of parameters which vary with time going can’t be considered in this method. So all the information in time domain have been neglected. In this article, time-frequency analysis method called HHT(Hilbert-Huang transform) is applied to analyze the Qingping wave of Wenchuan earthquake meticulously, which is the most advantaged to dissect the change features of the seismic record at different scales. Then we can get the dual properties in time domain and the frequency domain, such as the IMF function of each modal and the instantaneous frequency. For reflecting the time-frequency characteristics exactly and clearly, the Hilbert spectrum has been used to show these messages in the time-frequency plane.

scholarly journals Analysis of Three-Phase Wye-Delta Connected LLC

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3606
Author(s):  
Jing-Yuan Lin ◽  
Chuan-Ting Chen ◽  
Kuan-Hung Chen ◽  
Yi-Feng Lin
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Operation Time ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Complete Analysis ◽  
Plane Analysis ◽  
Analysis Methods ◽  
Three Phase

Three-phase wye–delta LLC topology is suitable for voltage step down and high output current, and has been used in the industry for some time, e.g., for server power and EV charger. However, no comprehensive circuit analysis has been performed for three-phase wye–delta LLC. This paper provides complete analysis methods for three-phase wye–delta LLC. The analysis methods include circuit operation, time domain analysis, frequency domain analysis, and state–plane analysis. Circuit operation helps determine the circuit composition and operation sequence. Time domain analysis helps understand the detail operation, equivalent circuit model, and circuit equation. Frequency domain analysis helps obtain the curve of the transfer function and assists in circuit design. State–plane analysis is used for optimal trajectory control (OTC). These analyses not only can calculate the voltage/current stress, but can also help design three-phase wye-delta connected LLC and provide the OTC control reference. In addition, this paper uses PSIM simulation to verify the correctness of analysis. At the end, a 5-kW three-phase wye–delta LLC prototype is realized. The specification of the prototype is a DC input voltage of 380 V and output voltage/current of 48 V/105 A. The peak efficiency is 96.57%.

scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

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