Fracture Images Classification Based on Fractional Cosine Transform and Markov Mode

2011 ◽  
Vol 311-313 ◽  
pp. 970-973
Author(s):  
Yong Liang Zhang ◽  
Li Xin Gao ◽  
Ling Li
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Analysis Method ◽  
Cosine Transform ◽  
Time Frequency ◽  
Fracture Failure ◽  
Metal Fracture ◽  
Correct Recognition Rate ◽  
Better Than

Fracture images automatic classification and recognition is an important one of fracture failure intelligent diagnosis, and in which feature extraction is a key issue. In this paper, fractional cosine transform, which is a useful time-frequency analysis method, is used in feature extraction of fracture images, and then the classification of fatigue, dimples, intergranular and cleavage is performed by Hidden markov model (HMM). For metal fracture images classification, experiment shows that fractional cosine transform is better than the cosine transform in fracture images feature description, and the correct recognition rate can be achieved 98.8% based on HMM classification mode

scholarly journals SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4457 ◽  
Author(s):  
She ◽  
Zhu ◽  
Tian ◽  
Wang ◽  
Yokoi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Hand Movement ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Movement Recognition ◽  
S Transform

Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.

scholarly journals Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image

2019 ◽  
Vol 11 (3) ◽  
pp. 243 ◽  
Author(s):  
Bangyan Zhu ◽  
Xiao Wang ◽  
Zhengwei Chu ◽  
Yi Yang ◽  
Juan Shi
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Recognition Rate ◽  
Correct Recognition ◽  
Matching Model ◽  
Side Scan Sonar ◽  
Recognition Model ◽  
Adaboost Algorithm ◽  
Recognition Process ◽  
Correct Recognition Rate

In order to realize the automatic and accurate recognition of shipwreck targets in side-scan sonar (SSS) waterfall images, a pipeline that contains feature extraction, selection, and shipwreck recognition, an AdaBoost model was constructed by sample images. Shipwreck targets are detected quickly by a nonlinear matching model, and a shipwreck recognition in SSS waterfall images are given, and according to a wide set of combinations of different types of these individual procedures, the model is able to recognize the shipwrecks accurately. Firstly, two feature-extraction methods suitable for recognizing SSS shipwreck targets from natural sea bottom images were studied. In addition to these two typical features, some commonly used features were extracted and combined as comprehensive features to characterize shipwrecks from various feature spaces. Based on Independent Component Analysis (ICA), the preferred features were selected from the comprehensive features, which avoid dimension disaster and improved the correct recognition rate. Then, the Gentle AdaBoost algorithm was studied and used for constructing the shipwreck target recognition model using sample images. Finally, a shipwreck target recognition process for the SSS waterfall image was given, and the process contains shipwreck target fast detection by a nonlinear matching model and accurate recognition by the Gentle AdaBoost recognition model. The results show that the correct recognition rate of the model for the sample image is 97.44%, while the false positive rate is 3.13% and the missing detection rate is 0. This study of a measured SSS waterfall image confirms the correctness of the recognition process and model.

Feature Extraction by Decryption to Target

2014 ◽  
Vol 1008-1009 ◽  
pp. 1509-1512
Author(s):  
Qing E Wu ◽  
Hong Wang ◽  
Li Fen Ding
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Correct Recognition ◽  
Palmprint Recognition ◽  
Feature Extraction Method ◽  
Energy Feature ◽  
Correct Recognition Rate

To carry out an effective classification and recognition for target, this paper studied the target owned characteristics, discussed a decryption algorithm, gave a feature extraction method based on the decryption process, and extracted the feature of palmprint in region of interest. Moreover, this paper used the wavelet transform to extract the energy feature of target, gave an approach on matching and recognition to improve the correctness and efficiency of existing recognition approaches, and compared it with existing approaches of palmprint recognition by experiments. The experiment results show that the correct recognition rate of the approach in this paper is improved averagely by 2.34% than that of the existing recognition approaches.

Feature Extraction of Vibration of Centrifugal Fan Based on LLE

2014 ◽  
Vol 1070-1072 ◽  
pp. 1941-1944
Author(s):  
Yong Hao Liao ◽  
Bo Liu
Keyword(s):  
Feature Extraction ◽  
Diagnostic Accuracy ◽  
Wavelet Packet ◽  
Centrifugal Fan ◽  
Analysis Method ◽  
Feature Extraction Method ◽  
Different Types ◽  
Feature Information ◽  
New Feature ◽  
Better Than

In order to improve classification ability and diagnostic accuracy of centrifugal fan signals, a new feature extraction method from fault signals of centrifugal fan vibration based on manifold learning method (MLM) that is a kind of reduction method of data dimension is proposed in this paper.The MLM is able to remain nonlinear information of original signal, to improve the classification and diagnostic ability of fault better than traditional reducing dimension methods. The results in this paper show that, fault feature information of centrifugal fan vibration is extracted effectively by the MLM and the fault feature information of different types are separated effectively in themselves areas. The diagnostic accuracy by feature extracted by the MLM is significantly higher than by the wavelet packet analysis method.

scholarly journals A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5714
Author(s):  
Xun Zhang ◽  
Guanghua Xu ◽  
Jiachen Kuang ◽  
Lin Suo ◽  
Sicong Zhang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Speed Estimation ◽  
Analysis Method ◽  
Variable Speed ◽  
Extraction Technology ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Phase Current ◽  
Order Analysis ◽  
Three Phase

Planetary gearboxes are the key components of large equipment, such as wind turbines, shield machines, etc. The operating state of the planetary gearbox is related to the safety of the equipment as a whole, and its feature extraction technology is essential. In assessing the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under a tacholess condition, a three-phase current, variable-speed tacholess envelope order analysis method is proposed. Firstly, a tacholess rotation speed estimation is completed by extracting the trend term of the instantaneous frequency of the asynchronous motor’s three-phase currents. The motor slip rate is assumed to be constant. Then, the envelope order analysis signal is obtained by re-sampling in the angular domain. Finally, the features of the envelope order signal are extracted, and a linear discriminant analysis (LDA) algorithm is used to fuse multiple indexes to generate a comprehensive feature reflecting the operating status of the planetary gearbox. The results of the simulation analysis and experimental verification show that the proposed method is effective in evaluating the operating state of the planetary gearbox under variable speed conditions. Compared with the traditional time–frequency ridge extraction method, the tacholess speed estimation method can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order completes the planetary gearbox state identification process, and a 95% classification accuracy rate is achieved.

Voice Recognition from Spectrograms: A Wavelet Based Approach

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 165-172 ◽  
Author(s):  
G. van de Wouwer ◽  
P. Scheunders ◽  
D. van Dyck ◽  
M. de Bodt ◽  
F. Wuyts ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Recognition Accuracy ◽  
Voice Recognition ◽  
Original Signal ◽  
Pattern Recognition Technique ◽  
Time Frequency ◽  
Novel Method ◽  
Recognition Technique

The performance of a pattern recognition technique is usually determined by the ability of extracting useful features from the available data so as to effectively characterize and discriminate between patterns. We describe a novel method for feature extraction from speech signals. For this purpose, we generate spectrograms, which are time-frequency representations of the original signal. We show that, by considering this spectrogram as a textured image, a wavelet transform can be applied to generate useful features for recognizing the speech signal. This method is used for the classification of voice dysphonia. Its performance is compared with another technique taken from the literature. A recognition accuracy of 98% is achieved for the classification between normal an dysphonic voices.

A New Kind of FRFT Analysis Method for Multi-Component LFM Signals

2011 ◽  
Vol 204-210 ◽  
pp. 973-978
Author(s):  
Qiang Guo ◽  
Ya Jun Li ◽  
Chang Hong Wang
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Fractional Fourier Transform ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Low Snr ◽  
The Time Domain ◽  
Signal Analysis Method

To effectively detect and recognize multi-component Linear Frequency-Modulated (LFM) emitter signals, a multi-component LFM emitter signal analysis method based on the complex Independent Component Analysis(ICA) which was combined with the Fractional Fourier Transform(FRFT) was proposed. The idea which was adopted to this method was the time-domain separation and then time-frequency analysis, and in the low SNR cases, the problem which is generally plagued by noised of feature extraction of multi-component LFM signal based on FRFT is overcame. Compared to the traditional method of time-frequency analysis, the computer simulation results show that the proposed method for the multi-component LFM signal separation and feature extraction was better.

Signal Feature Extraction about the Oil Whirl and Oil Oscillation Based on EMD

2011 ◽  
Vol 141 ◽  
pp. 483-487
Author(s):  
Bao Jie Xu ◽  
Ran Liu
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Oil Whirl ◽  
Adaptive Time

The article discusses the EMD and adaptive time-frequency analysis method based on EMD, and explains the characteristics about oil whirl, oil oscillation. Apply EMD and Hilbert-Huang t transformation to extract characteristics, which verifies applying EMD into feature extraction is effective.

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