scholarly journals A trend extraction method based on logistic functions and envelopes

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Jingjing Zhang ◽  
Jinglin Luo ◽  
Xuan Zhang
Keyword(s):  
Computation Time ◽  
Extraction Methods ◽  
Logistic Function ◽  
Transform Method ◽  
Starting Position ◽  
Trend Extraction ◽  
Mode Decomposition ◽  
Local Trend ◽  
Logistic Functions ◽  
Step Trend

AbstractA method of step characteristic trend extraction based on logistic functions and envelopes (LFEs) is proposed in this paper. Compared with the existing trend extraction methods, the LFE method can determine the starting position of the step trend using a logistic function and extract the local trend using upper and lower envelopes. This method enhances the extraction accuracy and reduces the computation time. To verify the effectiveness of the LFE method, a simulated signal with a step trend feature was compared with the five-spot triple smoothing method, wavelet transform method and empirical mode decomposition-based method. All of these methods were applied to a real shock signal. The results demonstrate that the LFE method can reliably and accurately extract the trends with step characteristics based on less prior knowledge. Moreover, the proposed technique is suitable for industrial online applications.

TREND EXTRACTION FOR SEASONAL TIME SERIES USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

2011 ◽  
Vol 03 (03) ◽  
pp. 363-383 ◽  
Author(s):  
FAROUK MHAMDI ◽  
JEAN-MICHEL POGGI ◽  
MÉRIEM JAÏDANE
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Extraction Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Energy Ratio ◽  
Trend Extraction ◽  
Seasonal Time Series ◽  
Mode Decomposition ◽  
Ratio Approach ◽  
And Performance

In this paper, we investigate eligibility of trend extraction through the empirical mode decomposition (EMD) and performance improvement of applying the ensemble EMD (EEMD) instead of the EMD for trend extraction from seasonal time series. The proposed method is an approach that can be applied on any time series with any time scales fluctuations. In order to evaluate our algorithm, experimental comparisons with three other trend extraction methods: EMD-energy-ratio approach, EEMD-energy-ratio approach, and the Hodrick–Prescott filter are conducted.

COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS

2007 ◽  
Vol 07 (02) ◽  
pp. 199-214 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Heart Sounds ◽  
Discrete Wavelet ◽  
Clinical Tool ◽  
Prognostic Information ◽  
Transform Method ◽  
Time Frequency

This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

Parallel calibration based on modified trim strategy

2019 ◽  
Vol 40 (2) ◽  
pp. 249-256
Author(s):  
Yaxin Peng ◽  
Naiwu Wen ◽  
Chaomin Shen ◽  
Xiaohuang Zhu ◽  
Shihui Ying
Keyword(s):  
Computation Time ◽  
Extraction Methods ◽  
Intelligent Manufacturing ◽  
Feature Descriptor ◽  
Data Sets ◽  
Rigid Transformation ◽  
Robot Calibration ◽  
Content Type ◽  
Partial Alignment ◽  
Point Set

Purpose Partial alignment for 3 D point sets is a challenging problem for laser calibration and robot calibration due to the unbalance of data sets, especially when the overlap of data sets is low. Geometric features can promote the accuracy of alignment. However, the corresponding feature extraction methods are time consuming. The purpose of this paper is to find a framework for partial alignment by an adaptive trimmed strategy. Design/methodology/approach First, the authors propose an adaptive trimmed strategy based on point feature histograms (PFH) coding. Second, they obtain an initial transformation based on this partition, which improves the accuracy of the normal direction weighted trimmed iterative closest point (ICP) method. Third, they conduct a series of GPU parallel implementations for time efficiency. Findings The initial partition based on PFH feature improves the accuracy of the partial registration significantly. Moreover, the parallel GPU algorithms accelerate the alignment process. Research limitations/implications This study is applicable to rigid transformation so far. It could be extended to non-rigid transformation. Practical implications In practice, point set alignment for calibration is a technique widely used in the fields of aircraft assembly, industry examination, simultaneous localization and mapping and surgery navigation. Social implications Point set calibration is a building block in the field of intelligent manufacturing. Originality/value The contributions are as follows: first, the authors introduce a novel coarse alignment as an initial calibration by PFH descriptor similarity, which can be viewed as a coarse trimmed process by partitioning the data to the almost overlap part and the rest part; second, they reduce the computation time by GPU parallel coding during the acquisition of feature descriptor; finally, they use the weighted trimmed ICP method to refine the transformation.

scholarly journals Biometric Identification Method for Heart Sound Based on Multimodal Multiscale Dispersion Entropy

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 238 ◽  
Author(s):  
Xiefeng Cheng ◽  
Pengfei Wang ◽  
Chenjun She
Keyword(s):  
Markov Models ◽  
Heart Sound ◽  
Recognition Rate ◽  
Ensemble Empirical Mode Decomposition ◽  
Heart Sounds ◽  
Feature Representation ◽  
Intrinsic Mode Functions ◽  
Single Cycle ◽  
Starting Position ◽  
Mode Decomposition

In this paper, a new method of biometric characterization of heart sounds based on multimodal multiscale dispersion entropy is proposed. Firstly, the heart sound is periodically segmented, and then each single-cycle heart sound is decomposed into a group of intrinsic mode functions (IMFs) by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). These IMFs are then segmented to a series of frames, which is used to calculate the refine composite multiscale dispersion entropy (RCMDE) as the characteristic representation of heart sound. In the simulation experiments I, carried out on the open heart sounds database Michigan, Washington and Littman, the feature representation method was combined with the heart sound segmentation method based on logistic regression (LR) and hidden semi-Markov models (HSMM), and feature selection was performed through the Fisher ratio (FR). Finally, the Euclidean distance (ED) and the close principle are used for matching and identification, and the recognition accuracy rate was 96.08%. To improve the practical application value of this method, the proposed method was applied to 80 heart sounds database constructed by 40 volunteer heart sounds to discuss the effect of single-cycle heart sounds with different starting positions on performance in experiment II. The experimental results show that the single-cycle heart sound with the starting position of the start of the first heart sound (S1) has the highest recognition rate of 97.5%. In summary, the proposed method is effective for heart sound biometric recognition.

scholarly journals Fitting Logistic IRT Models: Small Wonder

1999 ◽  
Vol 2 ◽  
pp. 74-94 ◽  
Author(s):  
Miguel A. García-Pérez
Keyword(s):  
Item Response ◽  
State Of The Art ◽  
Logistic Function ◽  
Parameter Estimates ◽  
Data Set ◽  
Parameter Spaces ◽  
Irt Models ◽  
Best Fit ◽  
Logistic Functions ◽  
Metaphorical Interpretation

State-of-the-art item response theory (IRT) models use logistic functions exclusively as their item response functions (IRFs). Logistic functions meet the requirements that their range is the unit interval and that they are monotonically increasing, but they impose a parameter space whose dimensions can only be assigned a metaphorical interpretation in the context of testing. Applications of IRT models require obtaining the set of values for logistic function parameters that best fit an empirical data set. However, success in obtaining such set of values does not guarantee that the constructs they represent actually exist, for the adequacy of a model is not sustained by the possibility of estimating parameters. This article illustrates how mechanical adoption of off-the-shelf logistic functions as IRFs for IRT models can result in off-the-shelf parameter estimates and fits to data. The results of a simulation study are presented, which show that logistic IRT models can fit a set of data generated by IRFs other than logistic functions just as well as they fit logistic data, even though the response processes and parameter spaces involved in each case are substantially different. An explanation of why logistic functions work as they do is offered, the theoretical and practical consequences of their behavior are discussed, and a testable alternative to logistic IRFs is commented upon.

Seismic waves generated by an impulsive point source in a solid/fluid configuration with a plane boundary

Geophysics ◽  
1985 ◽  
Vol 50 (7) ◽  
pp. 1083-1090 ◽  
Author(s):  
Adrianus T. de Hoop ◽  
Jos H. M. T. van der Hijden
Keyword(s):  
Point Source ◽  
Seismic Waves ◽  
Integral Transform ◽  
Vertical Plane ◽  
Computation Time ◽  
Plane Boundary ◽  
Head Wave ◽  
Transform Method ◽  
Expansion Model ◽  
Fluid Configuration

The space‐time acoustic wave motion generated by an impulsive point source in a solid/fluid configuration with a vertical plane boundary is calculated with the aid of the modified Cagniard method. Two types of sources are considered in detail, viz. (1) a point source of expansion (model for an explosive source), and (2) a point force parallel to the vertical interface (model for a mechanical vibrator). Numerical results are presented for the transmitted scalar traction in the fluid in those regions of space where head wave contributions occur. There is a marked difference in the time response observed for the two types of sources and for the different positions of the receiver in the fluid with respect to the position of the source in the solid. These waveform differences are important when the transmitted wave in the fluid is used to determine experimentally the elastic properties of the solid. Scholte waves are observed only when the source is close to the fluid/solid interface. As compared with the traditional Fourier‐Bessel integral transform method of handling this problem, the computation time with the method presented here is considerably less.

scholarly journals Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 112
Author(s):  
Hamada Esmaiel ◽  
Dongri Xie ◽  
Zeyad A. H. Qasem ◽  
Haixin Sun ◽  
Jie Qi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Extraction Methods ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Radiated Noise ◽  
Passive Sonar ◽  
Multi Stage ◽  
Mode Decomposition

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

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