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Orthogonal Matrix-Valued Wavelet Packets
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作者 Qingjiang Chen Cuiling Wang Zhengxing Cheng 《Numerical Mathematics A Journal of Chinese Universities(English Series)》 SCIE 2007年第1期45-53,共9页
In this paper, we introduce matrix-valued multiresolution analysis and matrix- valued wavelet packets. A procedure for the construction of the orthogonal matrix-valued wavelet packets is presented. The properties of t... In this paper, we introduce matrix-valued multiresolution analysis and matrix- valued wavelet packets. A procedure for the construction of the orthogonal matrix-valued wavelet packets is presented. The properties of the matrix-valued wavelet packets are investigated. In particular, a new orthonormal basis of L2(R, Cs×s) is obtained from the matrix-valued wavelet packets. 展开更多
关键词 矩阵 多分辨率 正交函数 微分方程
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Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain
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作者 Zengxiang Li Yongchong Wu +3 位作者 Alanoud Al Mazroa Donghua Jiang Jianhua Wu Xishun Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期847-869,共23页
Hidden capacity,concealment,security,and robustness are essential indicators of hiding algorithms.Currently,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robus... Hidden capacity,concealment,security,and robustness are essential indicators of hiding algorithms.Currently,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the algorithms.In practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret image.An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms.In this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet transform.The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding network.During the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret image.These lowresolution secret images are combined using densemodules to obtain a high-quality secret image.The experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various attacks.The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical applications.In conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms.Its ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various applications.Further research and experimentation can explore the algorithm’s performance under different scenarios and expand its potential applications. 展开更多
关键词 Image hiding ROBUSTNESS wavelet transform dynamic region attention
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WT-FCTGN:A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling
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作者 廖志芳 孙轲 +3 位作者 刘文龙 余志武 刘承光 宋禹成 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第7期652-664,共13页
Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produce... Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability. 展开更多
关键词 traffic flow modeling time-series wavelet reconstruction
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Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models
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作者 Mahmood A.Mahmood Khalaf Alsalem 《Computers, Materials & Continua》 SCIE EI 2024年第3期3431-3448,共18页
Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wa... Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases. 展开更多
关键词 Olive leaf diseases wavelet transform deep learning feature fusion
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A lightweight symmetric image encryption cryptosystem in wavelet domain based on an improved sine map
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作者 陈柏池 黄林青 +2 位作者 蔡述庭 熊晓明 张慧 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第3期266-276,共11页
In the era of big data,the number of images transmitted over the public channel increases exponentially.As a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive ... In the era of big data,the number of images transmitted over the public channel increases exponentially.As a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive image.In this paper,an improved sine map(ISM)possessing a larger chaotic region,more complex chaotic behavior and greater unpredictability is proposed and extensively tested.Drawing upon the strengths of ISM,we introduce a lightweight symmetric image encryption cryptosystem in wavelet domain(WDLIC).The WDLIC employs selective encryption to strike a satisfactory balance between security and speed.Initially,only the low-frequency-low-frequency component is chosen to encrypt utilizing classic permutation and diffusion.Then leveraging the statistical properties in wavelet domain,Gaussianization operation which opens the minds of encrypting image information in wavelet domain is first proposed and employed to all sub-bands.Simulations and theoretical analysis demonstrate the high speed and the remarkable effectiveness of WDLIC. 展开更多
关键词 image encryption discrete wavelet transform 1D-chaotic system selective encryption Gaussianization operation
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Enhanced Fourier Transform Using Wavelet Packet Decomposition
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作者 Wouladje Cabrel Golden Tendekai Mumanikidzwa +1 位作者 Jianguo Shen Yutong Yan 《Journal of Sensor Technology》 2024年第1期1-15,共15页
Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properti... Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method. 展开更多
关键词 Fourier Transform wavelet Packet Decomposition Time-Frequency Analysis Non-Stationary Signals
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Deep neural network based on multi-level wavelet and attention for structured illumination microscopy
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作者 Yanwei Zhang Song Lang +2 位作者 Xuan Cao Hanqing Zheng Yan Gong 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2024年第2期12-23,共12页
Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior know... Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems. 展开更多
关键词 Super-resolution reconstruction multi-level wavelet packet transform residual channel attention selective kernel attention
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AMicroseismic Signal Denoising Algorithm Combining VMD and Wavelet Threshold Denoising Optimized by BWOA
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作者 Dijun Rao Min Huang +2 位作者 Xiuzhi Shi Zhi Yu Zhengxiang He 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期187-217,共31页
The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized ... The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized VariationalMode Decomposition(VMD)jointWavelet Threshold Denoising(WTD)algorithm(BVW)is proposed.The BVW algorithm integrates VMD and WTD,both of which are optimized by BWOA.Specifically,this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited IntrinsicMode Functions(BLIMFs).Subsequently,these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions,and the effective mode functions are denoised using WTD to filter out the residual low-and intermediate-frequency noise.Finally,the denoised microseismic signal is obtained through reconstruction.The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm.The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities,and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition(EMD)algorithms.The BVW algorithm is more efficient in filtering noise,the waveform after denoising is smoother,the amplitude of the waveform is the closest to the original signal,and the signal-to-noise ratio(SNR)and the root mean square error after denoising are more satisfying.The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site,compared with VMD and EMD,the BVW algorithm ismore efficient in filtering noise,and the SNR after denoising is higher. 展开更多
关键词 Variational mode decomposition microseismic signal DENOISING wavelet threshold denoising black widow optimization algorithm
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Research on the longitudinal protection of a through-type cophase traction direct power supply system based on the empirical wavelet transform
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作者 Lu Li Zeduan Zhang +5 位作者 Wang Cai Qikang Zhuang Guihong Bi Jian Deng Shilong Chen Xiaorui Kan 《Global Energy Interconnection》 EI CSCD 2024年第2期206-216,共11页
This paper proposes a longitudinal protection scheme utilizing empirical wavelet transform(EWT)for a through-type cophase traction direct power supply system,where both sides of a traction network line exhibit a disti... This paper proposes a longitudinal protection scheme utilizing empirical wavelet transform(EWT)for a through-type cophase traction direct power supply system,where both sides of a traction network line exhibit a distinctive boundary structure.This approach capitalizes on the boundary’s capacity to attenuate the high-frequency component of fault signals,resulting in a variation in the high-frequency transient energy ratio when faults occur inside or outside the line.During internal line faults,the high-frequency transient energy at the checkpoints located at both ends surpasses that of its neighboring lines.Conversely,for faults external to the line,the energy is lower compared to adjacent lines.EWT is employed to decompose the collected fault current signals,allowing access to the high-frequency transient energy.The longitudinal protection for the traction network line is established based on disparities between both ends of the traction network line and the high-frequency transient energy on either side of the boundary.Moreover,simulation verification through experimental results demonstrates the effectiveness of the proposed protection scheme across various initial fault angles,distances to faults,and fault transition resistances. 展开更多
关键词 Through-type Cophase traction direct power supply system Traction network Empirical wavelet transform(EWT) Longitudinal protection
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Wavelet Multi-Resolution Interpolation Galerkin Method for Linear Singularly Perturbed Boundary Value Problems
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作者 Jiaqun Wang Guanxu Pan +1 位作者 Youhe Zhou Xiaojing Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期297-318,共22页
In this study,a wavelet multi-resolution interpolation Galerkin method(WMIGM)is proposed to solve linear singularly perturbed boundary value problems.Unlike conventional wavelet schemes,the proposed algorithm can be r... In this study,a wavelet multi-resolution interpolation Galerkin method(WMIGM)is proposed to solve linear singularly perturbed boundary value problems.Unlike conventional wavelet schemes,the proposed algorithm can be readily extended to special node generation techniques,such as the Shishkin node.Such a wavelet method allows a high degree of local refinement of the nodal distribution to efficiently capture localized steep gradients.All the shape functions possess the Kronecker delta property,making the imposition of boundary conditions as easy as that in the finite element method.Four numerical examples are studied to demonstrate the validity and accuracy of the proposedwavelet method.The results showthat the use ofmodified Shishkin nodes can significantly reduce numerical oscillation near the boundary layer.Compared with many other methods,the proposed method possesses satisfactory accuracy and efficiency.The theoretical and numerical results demonstrate that the order of theε-uniform convergence of this wavelet method can reach 5. 展开更多
关键词 wavelet multi-resolution interpolation Galerkin singularly perturbed boundary value problems mesh-free method Shishkin node boundary layer
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Performance of Continuous Wavelet Transform over Fourier Transform in Features Resolutions
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作者 Michael K. Appiah Sylvester K. Danuor Alfred K. Bienibuor 《International Journal of Geosciences》 CAS 2024年第2期87-105,共19页
This study presents a comparative analysis of two image enhancement techniques, Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), in the context of improving the clarity of high-quality 3D seismic d... This study presents a comparative analysis of two image enhancement techniques, Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), in the context of improving the clarity of high-quality 3D seismic data obtained from the Tano Basin in West Africa, Ghana. The research focuses on a comparative analysis of image clarity in seismic attribute analysis to facilitate the identification of reservoir features within the subsurface structures. The findings of the study indicate that CWT has a significant advantage over FFT in terms of image quality and identifying subsurface structures. The results demonstrate the superior performance of CWT in providing a better representation, making it more effective for seismic attribute analysis. The study highlights the importance of choosing the appropriate image enhancement technique based on the specific application needs and the broader context of the study. While CWT provides high-quality images and superior performance in identifying subsurface structures, the selection between these methods should be made judiciously, taking into account the objectives of the study and the characteristics of the signals being analyzed. The research provides valuable insights into the decision-making process for selecting image enhancement techniques in seismic data analysis, helping researchers and practitioners make informed choices that cater to the unique requirements of their studies. Ultimately, this study contributes to the advancement of the field of subsurface imaging and geological feature identification. 展开更多
关键词 Continuous wavelet Transform (CWT) Fast Fourier Transform (FFT) Reservoir Characterization Tano Basin Seismic Data Spectral Decomposition
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Weak Fault Feature Extraction of the Rotating Machinery Using Flexible Analytic Wavelet Transform and Nonlinear Quantum Permutation Entropy
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作者 Lili Bai Wenhui Li +3 位作者 He Ren Feng Li TaoYan Lirong Chen 《Computers, Materials & Continua》 SCIE EI 2024年第6期4513-4531,共19页
Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extrac... Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform(FAWT)with Nonlinear Quantum Permutation Entropy.FAWT,leveraging fractional orders and arbitrary scaling and translation factors,exhibits superior translational invariance and adjustable fundamental oscillatory characteristics.This flexibility enables FAWT to provide well-suited wavelet shapes,effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults.In our approach,gearbox vibration signals undergo FAWT to obtain sub-bands.Quantum theory is then introduced into permutation entropy to propose Nonlinear Quantum Permutation Entropy,a feature that more accurately characterizes the operational state of vibration simulation signals.The nonlinear quantum permutation entropy extracted from sub-bands is utilized to characterize the operating state of rotating machinery.A comprehensive analysis of vibration signals from rolling bearings and gearboxes validates the feasibility of the proposed method.Comparative assessments with parameters derived from traditional permutation entropy,sample entropy,wavelet transform(WT),and empirical mode decomposition(EMD)underscore the superior effectiveness of this approach in fault detection and classification for rotating machinery. 展开更多
关键词 Rotating machinery quantum theory nonlinear quantum permutation entropy Flexible Analytic wavelet Transform(FAWT) feature extraction
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Efficient simulation of spatially correlated non-stationary ground motions by wavelet-packet algorithm and spectral representation method
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作者 Ji Kun Cao Xuyang +1 位作者 Wang Suyang Wen Ruizhi 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2024年第4期799-814,共16页
Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic ... Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice.The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function.Another difficulty is the estimation of evolutionary power spectral density(EPSD)with quite a few samples.To tackle these problems,the wavelet packet transform(WPT)algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain.The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process.For the simulation of spatially varying ground motions,the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models.Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques.In addition to a good match with the target coherency model,two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording,including waveform,temporal/spectral non-stationarity,normalized energy buildup,and significant duration. 展开更多
关键词 non-stationarity time-varying spectrum wavelet packet transform(WPT) spectral representation method(SRM) response spectrum spatially varying recordings
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People Recognition by RGB and NIR Analysis from Digital Image Database Using Cross-Correlation and Wavelets
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作者 David Martínez-Martínez Yedid Erandini Niño-Membrillo +3 位作者 José Francisco Solís-Villarreal Oscar Espinoza-Ortega Lizbeth Sandoval-Juárez Francisco Javier Núñez-García 《Engineering(科研)》 2024年第10期353-359,共7页
This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features ... This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software). 展开更多
关键词 Palm Vein Recognition CROSS-CORRELATION Haar wavelets Multilayer Perceptron
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Variational Mode Decomposition-Informed Empirical Wavelet Transform for Electric Vibrator Noise Analysis
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作者 Zhenyu Xu Zhangwei Chen 《Journal of Applied Mathematics and Physics》 2024年第6期2320-2332,共13页
Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition... Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method. 展开更多
关键词 Electric Vibrator Noise Analysis Signal Decomposing Variational Mode Decomposition Empirical wavelet Transform
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Wavelet Multiview-Based Hybrid Deep Learning Model for Forecasting El Niño-Southern Oscillation Cycles
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作者 Winston Zhou Xiaodi Wang 《Atmospheric and Climate Sciences》 2024年第4期450-473,共24页
The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Ex... The El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon with far-reaching impacts on global weather patterns, ecosystems, and economies. This study aims to enhance ENSO forecasting with the Extended Reconstruction Sea Surface Temperature v5 (ERSSTv5) climate model. The M-band discrete wavelet transforms (DWT) are utilized to capture multi-scale temporal and spatial features effectively. Long-short term memory (LSTM) autoencoders are also used to capture significant spatial and temporal patterns in sea surface temperature (SST) anomaly data. Deep learning techniques such as the convolutional neural networks (CNN) are used with non-image and image time series data. We also employ parallel computing in a various support vector regression (SVR) approximators to enhance accuracy. Preliminary results indicate that this hybrid model effectively identifies key precursors and patterns associated with El Niño events, surpassing traditional forecasting methods. Results of the hybrid model produce a correlation of 0.93 in 4-month lagged forecasting of the Oceanic Niño Index (ONI)—indicative of high success rate of the model. Future work will focus on evaluating the model’s performance using additional reanalysis datasets and other methods of deep learning to further refine its robustness and applicability. We propose wavelet-based deep learning models which have potential to shine a light on achieving United Nations’ 2030 Agenda for Sustainable Development’s goal 13: “Climate Action”, as an innovation with potential in improving time series image forecasting in all fields. 展开更多
关键词 El Niño-Southern Oscillation (ENSO) Autoencoders Discrete wavelet Transform (DWT) Convolutional Neural Network (CNN) Support Vector Regression (SVR)
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基于ICEEMDAN-SSA-Wavelet的声发射信号降噪研究
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作者 姚慧栋 金永 +1 位作者 王江 李玉珠 《现代电子技术》 北大核心 2024年第5期93-97,共5页
针对粘接件声发射(AE)信号含有噪声分量难以滤除的问题,提出一种改进ICEEMDAN的方法。该方法首先使用ICEEMDAN分解原始AE信号,并通过相关系数和能量差值的方法筛选出低频分量和高频分量;运用麻雀优化算法(SSA)优化后的改进小波阈值去噪... 针对粘接件声发射(AE)信号含有噪声分量难以滤除的问题,提出一种改进ICEEMDAN的方法。该方法首先使用ICEEMDAN分解原始AE信号,并通过相关系数和能量差值的方法筛选出低频分量和高频分量;运用麻雀优化算法(SSA)优化后的改进小波阈值去噪算法对其进行去噪;最后将保留的低频分量和去噪后的高频分量重构成一个新的信号,通过实验数据对比和分析评估降噪效果。实验结果表明,相较于改进小波阈值去噪和ICEEMDAN去噪,文中提出的方法对金属与非金属粘接件AE信号的降噪效果更好,能够保护原始信号的频域信息,进而提高脱粘检测精度。 展开更多
关键词 ICEEMDAN去噪 小波阈值去噪 声发射信号 金属与非金属粘接件 SSA 信号降噪
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基于Mann-Kendall和Wavelet分析的唐山市近60年来降水量时空变化研究
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作者 郑苗 《水利科技与经济》 2024年第3期74-77,共4页
降水量时空变化研究,对了解和预测气候变化趋势、合理规划水资源,以及应对极端天气事件具有重要意义。基于唐山市12个标准气象站点的1961-2020年观测资料,采用Mann-Kendall和Wavelet方法相结合的方式,对唐山市降水量时变性进行分析。结... 降水量时空变化研究,对了解和预测气候变化趋势、合理规划水资源,以及应对极端天气事件具有重要意义。基于唐山市12个标准气象站点的1961-2020年观测资料,采用Mann-Kendall和Wavelet方法相结合的方式,对唐山市降水量时变性进行分析。结果表明,近60年来研究区降水量变化斜率为-1.59mm/a,经Mann-Kendall检测的趋势值Sen’slo值为-1.23mm/a;年际降水量于2014年发生突变,但并不显著;利用Wavelet分析发现,区域降水量存在1~8、7~10、14~16年的变化周期。 展开更多
关键词 Mann-Kendall非参数检验 wavelet分析 降水量 唐山市
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The Construction of a Class of Compactly Supported Orthogonal Matrix-valued 被引量:1
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作者 CHEN Qing-jiang CHENG Chuan-rui CHENG Zheng-xing 《Chinese Quarterly Journal of Mathematics》 CSCD 北大核心 2006年第4期608-616,共9页
In this paper,we introduce matrix-valued multiresolution analysis and orthogonal matrix-valued wavelets.We obtain a necessary and sufficient condition on the existence of orthogonal matrix-valued wavelets by means of ... In this paper,we introduce matrix-valued multiresolution analysis and orthogonal matrix-valued wavelets.We obtain a necessary and sufficient condition on the existence of orthogonal matrix-valued wavelets by means of paraunitary vector filter bank theory.A method for constructing a class of compactly supported orthogonal matrix-valued wavelets is proposed by using multiresolution analysis method and matrix theory. 展开更多
关键词 ORTHOGONAL matrix-valued multiresolution analysis matrix-valued scal-ing fun-nctions matrix-valued wavelets Hermitian matrix
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基于深度学习和小波分析的LSTM-Wavelet模型股价预测
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作者 李梦 黄章杰 徐健晖 《重庆工商大学学报(自然科学版)》 2023年第2期99-105,共7页
针对股价数据具有高噪声、非线性和非平稳性等特征,使得股价精确预测非常困难的问题,提出小波-长短记忆网络(LSTM-Wavelet)模型应用于股价预测。首先,利用小波(Wavelet)分解降低金融时间序列的不稳定性,并分析小波系数的细节特征;接着,... 针对股价数据具有高噪声、非线性和非平稳性等特征,使得股价精确预测非常困难的问题,提出小波-长短记忆网络(LSTM-Wavelet)模型应用于股价预测。首先,利用小波(Wavelet)分解降低金融时间序列的不稳定性,并分析小波系数的细节特征;接着,发挥长短记忆网络(LSTM)模型的优势,深层挖掘小波系数中的长期依赖关系,对分解后的各层小波系数分别建模预测;最后进行预测小波系数的数据重构。使用中石油近两年的股价数据进行实证分析,以每个交易日的开盘价、最高价、最低价、交易量为特征输入,预测当日中石油的收盘价。结果表明:相较于标准LSTM模型和小波-ARIMA(ARIMA-Wavelet)模型,提出的LSTM-Wavelet模型有更好的预测效果;通过小波分析将复杂股票数据,分解为长短记忆网络(LSTM)容易识别的小波系数,根据各层小波系数不同的数据特征进行分层预测,提高了预测精度。 展开更多
关键词 股价预测 小波分解 LSTM模型 LSTM-wavelet模型
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