A new -means singular value decomposition method based on self-adaptive matching pursuit and its application in fault diagnosis of rolling bearing weak fault by Hongchao Wang, Wenliao Du

“…Sparse decomposition has excellent adaptability and high flexibility in describing arbitrary complex signals based on redundant and over-complete dictionary, thus having the advantage of being free from the limitations of traditional signal processing methods such as wavelet and fast Fourier transform being imposed by orthogonal basis. …”
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An Efficient Image Compressor for Charge Coupled Devices Camera by Jin Li, Fei Xing, Zheng You

“…Recently, the discrete wavelet transforms- (DWT-) based compressor, such as JPEG2000 and CCSDS-IDC, is widely seen as the state of the art compression scheme for charge coupled devices (CCD) camera. …”
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Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition by Shiyuan Liu, Xiao Yu, Xu Qian, Fei Dong

“…To enhance the performance of the fault diagnosis of bearings under different working conditions, a novel diagnosis framework inspired by feature extraction, transfer learning (TL), and feature dimensionality reduction is proposed in this work, and dual-tree complex wavelet packet transform (DTCWPT) is used for signal processing. …”
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Study on Evaluation Theory of Bridge Damage State and Methodology on Early Warning of Danger by Guofu Jiang, Qianxue Liang, Hua Wang, Yucai Ju, Haihua Wang, Xirui Wang, Longlin Wang, Wensheng Wang

“…In order to solve the above problems, this paper proposes a novel evaluation and judgment theory of bridge damage state and methodology on early warning of danger. The wavelet packet analysis method is used to construct the observation equation and state equation of bridge systems, which could provide a relevant basis for evaluating the damage state of bridges. …”
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A Prediction Model of Structural Settlement Based on EMD-SVR-WNN by Xianglong Luo, Wenjuan Gan, Lixin Wang, Yonghong Chen, Xue Meng

“…Aiming at the problems in the structural deformation prediction model and considering the internal characteristics of deformation monitoring data and the influence of different components in the data on the prediction accuracy, a combined prediction model based on the Empirical Mode Decomposition, Support Vector Regression, and Wavelet Neural Network (EMD-SVR-WNN) is proposed. …”
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Laser Ultrasonic System for Surface Crack Visualization in Dissimilar Welds of Control Rod Drive Mechanism Assembly of Nuclear Power Plant by Yun-Shil Choi, Hyomi Jeong, Jung-Ryul Lee

“…Furthermore, ultrasonic spectral imaging unveiled frequency components of damage-induced waves, while wavelet-transformed ultrasonic propagation imaging enhanced damage visibility by generating a wave propagation video focused on the frequency component of the damage-induced waves. …”
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Classification of Speech Emotion State Based on Feature Map Fusion of TCN and Pretrained CNN Model From Korean Speech Emotion Data by A-Hyeon Jo, Keun-Chang Kwak

“…In the third stage, we perform feature-map fusion using canonical correlation analysis (CCA), stationary wavelet transform (SWT), and fuzzy c-means-based principal component averaging (FCMPCA), respectively. …”
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Research on bearing fault diagnosis based on a multimodal method by Hao Chen, Shengjie Li, Xi Lu, Qiong Zhang, Jixining Zhu, Jiaxin Lu

“…Specifically, the method first employs continuous wavelet transform to generate time-frequency images, capturing local and global features of the signal at different scales. …”
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Safe havens for Bitcoin and Ethereum: evidence from high-frequency data by Fahad Ali, Muhammad Usman Khurram, Ahmet Sensoy

“…To do so, we employ dynamic conditional correlation (DCC), asymmetric DCC, time-varying parameter vector autoregression-based connectedness measures, dynamic correlation-based hedge and safe-haven regression analyses, portfolio optimization and hedging strategies, time- and frequency-based wavelet coherence, and high-frequency 10-min intraday data from January 1, 2018 to January 31, 2023. …”
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Research on Revenue Insurance Premium Ratemaking of Jujube Based on Copula-Stochastic Optimization Model by Li-Mei Qi, Ruo-Yu Yao, Xing-Zhe Zhang, Yu-Jing Zhang, Xiao-Yin Wang, Jian-Ping Tao, Jia-Ming Zhu

“…In the study, firstly, wavelet analysis method was used to stabilize the jujube yield per unit area and the jujube price from 1997 to 2018 in Aksu region, Xinjiang, China. …”
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Transient Electromagnetic Data Noise Suppression Method Based on MPA-VMD-SVD by Yuheng Li, Yang Zhang, Jiwei Shen, Xinze Wen, Jianmei Chen, Wanqiang Zhu

“…The proposed method is compared to stacking-averaging, wavelet threshold denoising, SVD, empirical mode decomposition, and unoptimized VMD. …”
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Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images by Louis Asiedu, Bernard O. Essah, Samuel Iddi, K. Doku-Amponsah, Felix O. Mettle

“…This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. …”
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Cardiac sound classification using a hybrid approach: MFCC-based feature fusion and CNN deep features by Mahbubeh Bahreini, Ramin Barati, Abbas Kamali

“…Additionally, Mel-Frequency Cepstral Coefficients of empirical wavelet subbands serve as handcrafted features. Canonical correlation analysis is employed for feature fusion, effectively combining crucial information from both feature types. …”
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Numerical and Experimental Identification of Seven-Wire Strand Tensions Using Scale Energy Entropy Spectra of Ultrasonic Guided Waves by Ji Qian, Xin Chen, Limin Sun, Guowen Yao, Xu Wang

“…A numerical method was first developed to simulate UGW propagation in a seven-wire strand, employing the wavelet transform to extract UGW time-frequency energy distributions for different loadings. …”
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Investigating surface loading effect on seasonal crustal deformation observed by GNSS in Hong Kong by Hongli Lv, Xiaoxing He, Shunqiang Hu

“…Finally, we carried out cross wavelet transform (XWT) method to quantitatively express the annual phase relationship between GNSS vertical time series and AHNL deformation. …”
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Bearing Fault Classification Based on Conditional Random Field by Guofeng Wang, Xiaoliang Feng, Chang Liu

“…And the CRF and HMM models are built respectively to perform fault classification by taking the sub band energy features of wavelet packet decomposition (WPD) as the observation sequences. …”
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Construction and Analysis of Emotion Computing Model Based on LSTM by Huiping Jiang, Rui Jiao, Zequn Wang, Ting Zhang, Licheng Wu

“…Second, four EEG characteristics were extracted via the wavelet transform (WT) using the LSTM-based sentiment classification network. …”
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Photodiagnosis and photodynamic recognition of cervical cancer with SEM and AFM images. by Sevcan Aytaç, Gürkan Özbey

“…Daubechies (db2, db3, db4, and db5), Coiflet (coif5, coif4, coif3, and coif2), Symlet (sym5, sym4, sym3, and sym2), and Biorthogonal (bior1.3, bior2.8, bior1.5, and bior3.3) 16 discrete wavelet transformation families (DWTF) have been applied to AFM and SEM images. …”
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Analyzing Drought Propagation and Its Influential Factors in the Upper Nan Watershed, Thailand by Muhammad Chrisna Satriagasa, Piyapong Tongdeenok, Naruemol Kaewjampa

“…This study employs an innovative approach to examine the transition from meteorological drought (MD) to hydrological drought (HD) in the Upper Nan Watershed (UNW), Thailand, using the standardized precipitation evapotranspiration index (SPEI) and standardized streamflow index (SSI). Cross-wavelet transform (XWT) and Pearson’s correlation analyses reveal a significant positive correlation between MD and HD, with the drought propagation time (DPT) ranging from 2 to 5 months, notably shorter during the dry season. …”
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Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair by Dan Ma, Yixiang Lu, Yushun Zhang, Hua Bao, Xueming Peng

“…The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. …”
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