Combination of Variational Mode Decomposition for Feature Extraction and Deep Belief Network for Feature Classification in Motor Imagery Electroencephalogram Recognition

Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).

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Operation feature extraction of flood discharge structure based on improved variational mode decomposition and variance dedication rate

Journal of Vibration and Control ◽

10.1177/1077546319878542 ◽

2019 ◽

Vol 26 (3-4) ◽

pp. 229-240

Author(s):

Jianwei Zhang ◽

Ge Hou ◽

Han Wang ◽

Yu Zhao ◽

Jinlin Huang

Keyword(s):

Feature Extraction ◽

Empirical Mode Decomposition ◽

Variational Mode Decomposition ◽

Denoising Method ◽

Flood Discharge ◽

Mode Decomposition ◽

Empirical Wavelet Transform ◽

Discharge Structure ◽

Operation Characteristic ◽

Rate Method

Operation feature extraction of flood discharge structures under ambient excitation has attracted increasing attention in recent years. However, the vibration signal of flood discharge structures is a nonstationary random signal with low signal-to-noise ratio, which is mixed with lots of low-frequency water flow noise and high-frequency white noise. It is difficult to excavate the hidden vibration characteristic information accurately. To solve the problem, we propose a novel denoising method called improved variational mode decomposition. As an improved method of variational mode decomposition, improved variational mode decomposition can effectively determine the decomposition mode number of variational mode decomposition by using the mutual information method. Furthermore, improved variational mode decomposition is combined with a variance dedication rate to extract the overall operation characteristic information of the structure. In order to evaluate the applicability and effectiveness of the proposed improved variational mode decomposition–variance dedication rate method, we compare the denoising results of simulation signals produced by an improved variational mode decomposition–variance dedication rate with those produced by digital filter, wavelet threshold, empirical mode decomposition, empirical wavelet transform, complete ensemble empirical mode decomposition with adaptive noise, and improved variational mode decomposition methods and find a better performance of the improved variational mode decomposition–variance dedication rate method. In addition, the proposed method is applied to the Three Gorges Dam, and the results show that the improved variational mode decomposition–variance dedication rate method can effectively remove heavy background noises and extract the operation characteristic information of the flood discharge structure, which contributes to health monitoring and damage identification of the flood discharge structure.

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Fault feature extraction for rolling bearings based on parameter-adaptive variational mode decomposition and multi-point optimal minimum entropy deconvolution

Measurement ◽

10.1016/j.measurement.2020.108469 ◽

2020 ◽

pp. 108469

Author(s):

Xiangyu Zhou ◽

Yibing Li ◽

Li Jiang ◽

Li Zhou

Keyword(s):

Feature Extraction ◽

Variational Mode Decomposition ◽

Minimum Entropy ◽

Rolling Bearings ◽

Mode Decomposition ◽

Fault Feature Extraction ◽

Parameter Adaptive

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The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

Energies ◽

10.3390/en12030359 ◽

2019 ◽

Vol 12 (3) ◽

pp. 359 ◽

Cited By ~ 14

Author(s):

Yuxing Li ◽

Xiao Chen ◽

Jing Yu ◽

Xiaohui Yang ◽

Huijun Yang

Keyword(s):

Feature Extraction ◽

Optimization Method ◽

Sample Entropy ◽

Maximum Energy ◽

Data Driven ◽

Center Frequency ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Radiated Noise ◽

Mode Decomposition

The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.

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Application of Multiacoustic Data in Feature Extraction of Anemometer

Complexity ◽

10.1155/2021/7955909 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Dawei Chen ◽

Xu Guo

Keyword(s):

Feature Extraction ◽

Wind Power ◽

Evaluation Method ◽

Training Model ◽

Classification Algorithm ◽

Deep Belief Network ◽

Evaluation Function ◽

Trust Network ◽

Belief Network ◽

Acoustic Data

The acoustic characteristics of wind instruments are a major feature in the field of vocal music. This paper studies the application effect of wind power instrument feature extraction based on multiacoustic data. Combined with the acoustic data training model, the classification algorithm based on deep trust network is used to process multiple acoustic data. Using multiple acoustic data for feature extraction, the recognition and matching between multiple acoustic data and wind measuring instrument are realized. The experiment not only evaluates the error of the network classification algorithm but also describes the evaluation function of the deep belief network classification algorithm in the system. The traditional SNR evaluation method is used to improve the deficiency of evaluation function. Through the deep belief network classification algorithm for self-learning, the instrument recognition method with strong applicability is established. Finally, the effectiveness of multiacoustic data in wind power instrument feature extraction is verified.

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