Dictionary Learning Research Articles

A novel weighted sparse classification framework with extended discriminative dictionary for data-driven bearing fault diagnosis

Dictionary learning has emerged as an effective approach for data-driven fault diagnosis due to its strong sparse representation ability. Nevertheless, the gathered vibration signals always exhibit a time-shift phenomenon, greatly diminishing the representation capability of dictionary learning methods. Besides, in real-world industrial scenarios, where heavy noise as common features inevitably cover the discriminative features, the decline in the recognition performance of data-driven fault diagnosis methods is a critical challenge. In this paper, a novel weighted sparse classification framework with extended discriminative dictionary (WSC-EDD) is proposed. First, an extended discriminative dictionary design strategy is developed to construct generalized-domain extended dictionary model with strong representation and discrimination ability, in which a novel generalized-domain dictionary fusion method is developed to reduce the effect of time-shift phenomenon and enhance the representation ability of the dictionary. Further, generalized-domain discriminative sub-dictionaries are optimized by the K-singular value decomposition in a data-driven fashion and then the whole extension dictionary are designed adaptively. Second, a weighted optimization method is developed to highlight the contribution of non-zero elements in the correct projection region in sparse codes, in which weighted diagonal matrices are designed. Finally, a sparse recognition method is established for bearing fault classification. The experiment results on three challenging bearing datasets demonstrate that the proposed framework yields average diagnosis accuracies of 99.75%, 99.93% and 100%, respectively. Moreover, the superiority and robustness of WSC-EDD are further confirmed by comparing with several competing methods.

High-resolution seismic inversion method based on joint data-driven in the time-frequency domain

Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains. Time-domain inversion has stronger stability and noise resistance compared to frequency-domain inversion. Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution. Therefore, the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution, stability, and noise resistance. The introduction of prior information constraints can effectively reduce ambiguity in the inversion process. However, the existing model-driven time-frequency joint inversion assumes a specific prior distribution of the reservoir. These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features. Therefore, this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain. The method is based on the impedance and reflectivity samples from logging, using joint dictionary learning to obtain adaptive feature information of the reservoir, and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity. The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation. We have finally achieved an inversion method that combines constraints on time-domain features and frequency features. By testing the model data and field data, the method has higher resolution in the inversion results and good noise resistance.

Brain tumor detection in combined 3D MRI and CT images using Dictionary learning based Segmentation and Spearman Regression

Brain tumor detection in combined 3D MRI and CT images using Dictionary learning based Segmentation and Spearman Regression

Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial applications

Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation, and colour integrity in low-light enhancement tasks. This study presents a novel image enhancement framework using a detailed-based dictionary learning and camera response model (CRM). It combines dictionary learning with edge-aware filter-based detail enhancement. It assumes each small detail patch could be sparsely characterised in the over-complete detail dictionary that was learned from many training detail patches using iterative ℓ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\ell}}_{1}$$\\end{document}-norm minimization. Dictionary learning will effectively address several enhancement concerns in the progression of detail enhancement if we remove the visibility limit of training detail patches in the enhanced detail patches. We apply illumination estimation schemes to the selected CRM and the subsequent exposure ratio maps, which recover a novel enhanced detail layer and generate a high-quality output with detailed visibility when there is a training set of higher-quality images. We estimate the exposure ratio of each pixel using illumination estimation techniques. The selected camera response model adjusts each pixel to the desired exposure based on the computed exposure ratio map. Extensive experimental analysis shows an advantage of the proposed method that it can obtain enhanced results with acceptable distortions. The proposed research article can be generalised to address numerous other similar problems, such as image enhancement for remote sensing or underwater applications, medical imaging, and foggy or dusty conditions.

Technique for Kernel Matching Pursuit Based on Intuitionistic Fuzzy c-Means Clustering

Kernel matching pursuit (KMP) requires every step of the searching process to be global optimal searching in the redundant dictionary of functions in order to select the best matching signal structure. Namely, the dictionary learning time of KMP is too long. To solve the above drawbacks, a rough dataset was divided into some small-sized dictionaries to substitute local searching for global searching by using the property superiority of dynamic clustering performance, which is also superior in the intuitionistic fuzzy c-means (IFCM) algorithm. Then, we proposed a novel technique for KMP based on IFCM (IFCM-KMP). Subsequently, three tests including classification, effectiveness, and time complexity were carried out on four practical sample datasets, the conclusions of which fully demonstrate that the IFCM-KMP algorithm is superior to FCM and KMP.

Vortex rope identification in Francis turbine based on cyclostationary extended dictionary learning

Francis turbine is one of the most commonly used hydraulic machinery for hydroelectric power generation, but its stability is frequently jeopardized by vortex rope. It is crucial to promptly and accurately detect vortex rope for the reliable operation of turbine unit. To address this challenge, the Cyclostationary Extended Dictionary Learning (CEDL) approach is proposed to identify vortex rope in Francis turbine. CEDL utilizes the Fisher discrimination criterion and low-rank constraints through the preprocessing and extending strategies to achieve accurate identification. Preprocessing specifically refers to the construction of filter for raw data based on cyclostationarity Then, the trained dictionary is used to extract features, and a linear classifier is developed based on these features. To evaluate the proposed method, vortex rope experiments are conducted in a Francis turbine under five conditions, and every condition includes four states: normal state, initial vortex rope, stable vortex rope and severe vortex rope. The results reveal three main findings. Firstly, the trained atoms in each sub-dictionary exhibit unique characteristics such as tonal frequency at 7 kHz and broadband frequency during 12–20 kHz, which are linked to the evolution stages of vortex rope. Secondly, the dictionary trained for a specific condition can achieve a classification accuracy of up to 90%, and even exceed 97% in some cases. Finally, the dictionary trained in a specific working condition can achieve relatively high classification accuracy in the scenarios of other working conditions.

Multi-task learning mixture density network for interval estimation of the remaining useful life of rolling element bearings

Existing remaining useful life (RUL) predictions of rolling element bearings have the following shortcomings. 1) Model-driven methods typically employ a sole model for processing the data of an individual, making it challenging to accommodate the variety of degradation behaviors and susceptible to abnormal interference. 2) Data-driven methods place greater emphasis on training data, and in reality, it can be challenging to acquire comprehensive data covering the lifecycle. 3) Many studies fail to give adequate attention to the assessment of RUL uncertainty. This paper proposes a multi-task learning mixture density network (MTL-MDN) method to address these issues. Firstly, the peak-of-Histogram (PoHG) is extracted and served as the novel health indicators. Secondly, multi-task learning dictionaries are constructed based on the evolution law of PoHG, thus combining both model-driven and data-driven strategies. Finally, a multi-task learning strategy is proposed with mixture density networks. It effectively accomplishes the collaborative learning objective of numerous degradation samples in the regression problem and accomplishes the uncertainty assessment of RUL. After analyzing the experimental and real-world degradation data of rolling element bearings throughout their lifecycle, and comparing it to other modern RUL prediction methods, it becomes evident that the proposed MTL-MDN method offers superior prediction accuracy and robustness.

OptiLoc: Integrating SCSO and DV-Hop for wireless sensor network localization with application to disease forecasting in cattle farm monitoring

Disease prediction and management in cattle farming present significant challenges due to the complex interplay of environmental factors, disease transmission dynamics and the need for accurate localization of disease-carrying cattle. Traditional approaches often fall short in optimizing anchor node selection and accurately forecasting disease occurrences. To address these limitations, we propose OptiLoc, a novel framework that integrates advanced optimization techniques and disease forecasting algorithms. The proposed OptiLoc framework leverages Enhanced Sand Cat Swarm Optimization (SCSO) for optimizing anchor node selection within the farm area network. By dynamically adjusting the exploration rate and incorporating an opposition-based memory mechanism, OptiLoc ensures thorough exploration of the search space while preventing premature convergence. Furthermore, the multi-objective optimization aspect of SCSO considers factors such as coverage, energy efficiency, and connectivity, resulting in optimal anchor node placement. Additionally, OptiLoc integrates disease forecasting capabilities using logistic regression classifiers with dictionary learning. By extracting meaningful features from sensor data and training linear classifiers, OptiLoc predicts disease outbreaks among the cattle population. These predictions are seamlessly integrated into the optimization process through an expanded fitness function, which considers disease-related parameters alongside traditional optimization objectives. Furthermore, OptiLoc enhances disease management and control efforts by localizing sensor nodes using the DV-Hop method. By estimating sensor node positions based on hop distances to anchor nodes and dynamically calibrating hop distances based on real-time data on disease prevalence and environmental factors, OptiLoc achieves precise localization results. The proposed OptiLoc framework offers several advantages over traditional approaches. It optimizes anchor node placement, accurately forecasts disease outbreaks, and enhances localization accuracy, thereby empowering farmers with actionable insights to safeguard the health and well-being of their livestock. Performance evaluation reveals for the anchor nodes, it measures a mean localization error of 3.2% to 9.1% with a mean error of 5.91%. Similarly, for the sensor nodes, the mean localization error is 3.45% to 8.25% with a mean error of 5.445%. The AUC of the logistic regression classifier with dictionary learning is 0.97. Through its comprehensive approach, OptiLoc represents a significant advancement in optimizing WSNs in agricultural environments.

Fault diagnosis of driving gear in a battery swapping system based on audio features and SRC-Adaboost

Abstract Because of electrification conditions, key components of battery swapping systems (BSSs) for electric heavy trucks are always damaged by electric erosion, which presents challenges to the safety and efficiency of high-intensity transportation. Due to the special working conditions of a BSS, the fault diagnosis of its driving gear encounters several issues, including reciprocation motion, low and fluctuating speed, and complicated noises. To solve these problems, audio features, including Mel-frequency cepstral coefficients and Gammatone cepstral coefficients, are extracted from the vibration signals. Then, these features are utilized to construct an original dictionary. Next, based on data augmentation and dictionary learning, a robust dictionary is generated from the original dictionary. Finally, with the robust dictionary, sparse representation-based classification is integrated into AdaBoost to achieve accurate fault diagnosis for the driving gear in BSS. The effectiveness of the fault diagnosis scheme is validated based on the monitoring data of the BSS, and the accuracy of fault diagnosis is 99.17%.

Tensor Ring Decomposition Guided Dictionary Learning for OCT Image Denoising.

Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e., three-dimensional low-rank and sparsity priors, under a unified framework. Specifically, to effectively use the strong correlation between nearby OCT frames, we construct the OCT group tensors by extracting cubic patches from OCT images and clustering similar patches. Then, since each created OCT group tensor has a low-rank structure, to exploit spatial, non-local, and its temporal correlations in a balanced way, we enforce the TR decomposition model on each OCT group tensor. Next, to use the beneficial three-dimensional inter-group sparsity, we learn shared dictionaries in both spatial and temporal dimensions from all of the stacked OCT group tensors. Furthermore, we develop an effective algorithm to solve the resulting optimization problem by using two efficient optimization approaches, including proximal alternating minimization and the alternative direction method of multipliers. Finally, extensive experiments on OCT datasets from various imaging devices are conducted to prove the generality and usefulness of the proposed TRGDL model. Experimental simulation results show that the suggested TRGDL model outperforms state-of-the-art approaches for OCT image denoising both qualitatively and quantitatively.

Emotion Dictionary Learning With Modality Attentions for Mixed Emotion Exploration

Emotion Dictionary Learning With Modality Attentions for Mixed Emotion Exploration

Phase noise cancellation for digital holographic microscopy based on compressed sensing iterative adaptive sparse dictionary

Digital holographic microscopy (DHM), like other digital imaging techniques, will introduce severe coherent noise, which is called random noise, due to the high coherence of the required light source and the measurement experimental environment. Random noise seriously affects the accuracy of DHM surface topography measurements. To overcome this problem, this paper proposes a DHM phase noise cancellation method based on compressed sensing (CS) iterative adaptive sparse dictionary. The holographic image is first acquired using the proposed recording method. Then a sparse dictionary more suitable for the sample is trained by dictionary learning to sparsely encode the holographic image. Finally, reconstruction is performed using the improved orthogonal matching pursuit (IOMP), which can obtain almost noise-free holographic images. The experimental results show that the reconstructed phase peak signal-to-noise ratio (PSNR) is 33.2871, which is improved by 65.01 %. The sample feature similarity is 0.9553, which is improved by 45.41 %. The proposed method can also be used with different learning modes and learning objects. On top of the original results, the reconstructed phase PSNR was able to continue to improve by 5.99 % and its reconstruction time was also accelerated by 29.57 %.

Surface velocity reconstruction of a vibrating structure based on dictionary learning and sparse sampling

The sparse regularization has been successfully applied to near-field acoustic holography to provide the reconstruction accuracy of sound field with limited number of measurements. However, most of the applications are concentrated on the reconstruction of the sound pressure and the corresponding sparse bases are designed for the sound pressure. In this study, dictionary learning is introduced and K-SVD is utilized to generate a sparse basis for the velocity. Then the reconstruction of surface velocity of a vibrating structure can be realized in a sparse framework to improve the reconstruction accuracy with limited number of measurements. In the process of data sample selection, the equivalent source method is used to generate the velocity samples according to the feature of the sound field and samples can be obtained by numerical simulations. The results of numerical simulations and experiment demonstrate the validity of the learned dictionary and the advantage of the proposed method.

An efficient multivariate approach to dictionary learning for portfolio selection

Financial market noise greatly limits portfolio optimization by concealing key patterns and resulting in inaccurate asset selection. In this paper, we propose a novel approach that leverages dictionary learning, specifically the modified K-SVD algorithm, for denoising financial time series in the context of multivariate portfolio selection. Our method, which considers highly correlated short datasets, trains the dictionary simultaneously for multiple assets, resulting in more robust and adaptive denoising. We next use a vector auto-regressive process on the denoised data to estimate covariance matrices and build optimal portfolios using the minimum variance approach. Extensive computer simulations are conducted to assess the impact of our denoising method on portfolio performance in terms of several metrics, such as cumulative returns, Sharpe ratio, and model accuracy. The findings indicate how dictionary learning can improve the robustness of investment portfolios in the face of market noise and volatility.

Deep discriminative sparse representation learning for machinery fault diagnosis

The high complexity of actual machinery vibration environments introduces various interferences into vibration signals, making it challenging to eliminate redundant information and extract discriminative features of mechanical faults. Shallow-structured sparse classification models enable intelligent fault diagnosis but struggle with deeper feature extraction, especially in small-sample cases. In this paper, we introduce a deep discriminative sparse representation learning (DDSRL) framework with a deep architecture for this issue. In DDSRL, the first-layer dictionary atoms are automatically learned from raw signals and borrowed for sparse coding to enhance sparsity and interclass discriminability. Then, the weight matrix is defined to screen high-energy atoms from the upper dictionary as new training samples for the next dictionary learning and sparse coding layers, thereby eliminating interclass redundant atoms and mining deeper discriminative representations of cyclostationary impulses or meshing harmonics from the shallow dictionary. Finally, DDSRL extracts feature sequences sensitive to fault-induced waveforms via the learned dictionaries and coding coefficients for intelligent fault identification. Experimental verifications on two datasets with high-speed bearing and compound gear-bearing faults demonstrate that DDSRL outperforms six popular sparse classification models, sparse autoencoder, convolutional neural network, and principal component analysis network. Furthermore, the layer-by-layer feature extraction process of DDSRL and feature visualization analysis provide more credible fault diagnosis results.

Enhanced Image Denoising with Diffusion Probability and Dictionary Learning Adaptation

Enhanced Image Denoising with Diffusion Probability and Dictionary Learning Adaptation

Multi-modality multi-label ocular abnormalities detection with transformer-based semantic dictionary learning.

Blindness is preventable by early detection of ocular abnormalities. Computer-aided diagnosis for ocular abnormalities is built by analyzing retinal imaging modalities, for instance, Color Fundus Photography (CFP). This research aims to propose a multi-label detection of 28 ocular abnormalities consisting of frequent and rare abnormalities from a single CFP by using transformer-based semantic dictionary learning. Rare labels are usually ignored because of a lack of features. We tackle this condition by adding the co-occurrence dependency factor to the model from the linguistic features of the labels. The model learns the relation between spatial features and linguistic features represented as a semantic dictionary. The proposed method treats the semantic dictionary as one of the main important parts of the model. It acts as the query while the spatial features are the key and value. The experiments are conducted on the RFMiD dataset. The results show that the proposed method achieves the top 30% in Evaluation Set on the RFMiD dataset challenge. It also shows that treating the semantic dictionary as one of the strong factors in model detection increases the performance when compared with the method that treats the semantic dictionary as a weak factor.

Fisher discrimination multiple kernel dictionary learning for robust identification of nonlinear features in machinery health monitoring

In sparse representation-based classification, Fisher discrimination dictionary learning (FDDL) has attracted widespread attention due to its advantages such as fewer parameters and good dictionary discriminability. However, due to its linear nature, it is difficult to handle nonlinear features. Kernel-based nonlinear transformation enables dictionary learning to capture nonlinear features embedded in samples, but traditional single-kernel methods have limited performance. In this paper, a Fisher discrimination multiple kernel dictionary learning (FDMKDL) method is proposed, in which Fisher discriminative criterion is imposed on both high-dimensional samples and coding coefficients. Specifically, we derive the kernel version of FDDL, i.e., Fisher discrimination kernel dictionary learning (FDKDL) to learn nonlinear features and promote dictionary discriminability. Meanwhile, to avoid the problem of poor adaptability and possible manual selection caused by single-kernel methods, we further derive a flexible multiple kernel learning (MKL) framework, which utilizes the complementary information of multiple kernel functions to adaptively obtain the optimal weights and synthesize a discriminative kernel space. Finally, FDMKDL combines FDKDL with this MKL framework to obtain a more discriminative dictionary. Experimental evaluations conducted on two datasets demonstrate the effectiveness and robustness of the proposed FDMKDL method in learning nonlinear features for machinery health monitoring when compared to several state-of-the-art methods.

Feature Extraction Based on Sparse Coding Approach for Hand Grasp Type Classification

The kinematics of the human hand exhibit complex and diverse characteristics unique to each individual. Various techniques such as vision-based, ultrasonic-based, and data-glove-based approaches have been employed to analyze human hand movements. However, a critical challenge remains in efficiently analyzing and classifying hand grasp types based on time-series kinematic data. In this paper, we propose a novel sparse coding feature extraction technique based on dictionary learning to address this challenge. Our method enhances model accuracy, reduces training time, and minimizes overfitting risk. We benchmarked our approach against principal component analysis (PCA) and sparse coding based on a Gaussian random dictionary. Our results demonstrate a significant improvement in classification accuracy: achieving 81.78% with our method compared to 31.43% for PCA and 77.27% for the Gaussian random dictionary. Furthermore, our technique outperforms in terms of macro-average F1-score and average area under the curve (AUC) while also significantly reducing the number of features required.

Fourier Domain Robust Denoising Decomposition and Adaptive Patch MRI Reconstruction.

The sparsity of the Fourier transform domain has been applied to magnetic resonance imaging (MRI) reconstruction in k -space. Although unsupervised adaptive patch optimization methods have shown promise compared to data-driven-based supervised methods, the following challenges exist in MRI reconstruction: 1) in previous k -space MRI reconstruction tasks, MRI with noise interference in the acquisition process is rarely considered. 2) Differences in transform domains should be resolved to achieve the high-quality reconstruction of low undersampled MRI data. 3) Robust patch dictionary learning problems are usually nonconvex and NP-hard, and alternate minimization methods are often computationally expensive. In this article, we propose a method for Fourier domain robust denoising decomposition and adaptive patch MRI reconstruction (DDAPR). DDAPR is a two-step optimization method for MRI reconstruction in the presence of noise and low undersampled data. It includes the low-rank and sparse denoising reconstruction model (LSDRM) and the robust dictionary learning reconstruction model (RDLRM). In the first step, we propose LSDRM for different domains. For the optimization solution, the proximal gradient method is used to optimize LSDRM by singular value decomposition and soft threshold algorithms. In the second step, we propose RDLRM, which is an effective adaptive patch method by introducing a low-rank and sparse penalty adaptive patch dictionary and using a sparse rank-one matrix to approximate the undersampled data. Then, the block coordinate descent (BCD) method is used to optimize the variables. The BCD optimization process involves valid closed-form solutions. Extensive numerical experiments show that the proposed method has a better performance than previous methods in image reconstruction based on compressed sensing or deep learning.