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A novel adaptive generalized domain data fusion-driven kernel sparse representation classification method for intelligent bearing fault diagnosis

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 247卷

作者： Cui, Lingli Jiang, Zhichao Liu, Dongdong Wang, Huaqing Beijing Univ Technol Key Lab Adv Mfg Technol Beijing 100124 Peoples R China Beijing Univ Chem & Technol Sch Mech & Elect Engn Beijing 100029 Peoples R China

Dictionary learning has gradually attracted attention due to its powerful feature representation ability. However, the time -shift property of collected signals hinders the recognition of various bearing states. In addition, existing dictionary learning methods are mostly designed based on a single domain, while common data fusion methods used in data -driven cannot be directly extended to dictionary learning. In this paper, a novel adaptive generalized domain data fusion -driven kernel sparse representation classification method (AGDFDK-SRC) is proposed. First, to avoid the effect of the time -shift property on dictionary learning, a class -specific kernel sub -dictionary learning method is proposed, by which the non-linear signal data is mapped into high -dimensional feature space via a kernel trick. Second, the class -specific kernel sub -dictionaries are learned by kernel K -singular value decomposition in a data -driven manner. Then, an adaptive generalized domain data fusion strategy is developed for dictionary learning, which implements data fusion of multiple domain signals to enhance the feature mining ability and representation ability of the learned dictionary. Finally, a kernel sparse classification method is designed to achieve intelligent bearing fault diagnosis. Two bearing datasets are exploited to verify the recognition performance of AGDFDK-SRC, indicating that the AGDFDK-SRC obtains superior average classification accuracies of 98.23% and 99.50%, respectively.

关键词： sparse representation classification Adaptive generalized domain data fusion Kernel sub -dictionary learning Data -driven Bearing fault diagnosis

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A sparse representation classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing

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SENSORS 2023年第5期23卷 2480页

作者： Oikonomou, Vangelis P. Georgiadis, Kostas Kalaganis, Fotis Nikolopoulos, Spiros Kompatsiaris, Ioannis CERTH ITI Ctr Res & Technol Hellas Informat Technol Inst 6th km Charilaou Thermi Rd Thessaloniki 57001 Greece

In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).

关键词： sparse representation classification brain computer interfaces neuromarketing electroencephalography

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Dictionary Learning via a Mixed Noise Model for sparse representation classification of Rolling Bearings

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IEEE ACCESS 2020年 8卷 213416-213425页

作者： Zhang, Jialing Wu, Jimei Hu, Bingbing Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Univ Technol Fac Printing Packaging & Digital Media Engn Xian 710048 Peoples R China

Rotating machinery contains a great number of rolling bearings, which play an indispensable role. However, bearing vibration signals in complex environments are often mixed with various noises, which makes it difficult to extract fault characteristics from original signals. It is still challenging to identify the fault types of rolling bearings. To address this issue, a dictionary learning method based on a mixed noise model for the sparse representation classification of rolling bearings (DLMN-SRC) is proposed. Our framework constructs a dictionary learning method based on mixed noise, which has better robustness to complex noise pollution than a single noise model. Then, an alternating direction method of multipliers (ADMM) algorithm is used to solve the optimization problem of the proposed model. Eventually, the redundant errors between the detected and reconstructed signals in the dictionary learning model are calculated for sparse representation classification. The results of two examples prove that faults of the rolling bearing are successfully extracted and classified by DLMN-SRC. Compared with traditional diagnosis methods, the performance of this method has obvious superiority and good application prospects.

关键词： Dictionaries Machine learning Feature extraction Fault diagnosis Rolling bearings Noise measurement Laplace equations Dictionary learning mixed noise sparse representation classification bearing fault diagnosis

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SEMI-SUPERVISED sparse representation classification FOR SLEEP EEG RECOGNITION WITH IMBALANCED SAMPLE SETS

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JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 2021年第5期21卷

作者： Wuzheng, Xiaolei Zuo, Shigang Yao, Li Zhao, Xiaojie Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

Sleep staging with supervised learning requires a large amount of labeled data that are time-consuming and expensive to collect. Semi-supervised learning is widely used to improve classification performance by combining a small amount of labeled data with a large amount of unlabeled data. The accuracy of pseudo-labels in semi-supervised learning may influence the performance of classifier. Based on semi-supervised sparse representation classification, this study proposed an improved sparse concentration index to estimate the confidence of pseudo-labels data for sleep EEG recognition considering both interclass differences and intraclass concentration. In view of class imbalance in sleep EEG data, the synthetic minority oversampling technique was also improved to remove mixed samples at the boundary between minority and majority classes. The results showed that the proposed method achieved better classification performance, in which the classification accuracy after class balancing was obviously higher than that before class balancing. The findings of this study will be beneficial for application in sleep monitoring devices and sleep-related diseases.

关键词： Semi-supervised sparse representation classification class imbalance synthetic minority oversampling technique

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Image Identification of Glass Defects Based on Non-negative Matrix Factorization and sparse representation classification

Image Identification of Glass Defects Based on Non-negative ...

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24th Chinese Control and Decision Conference (CCDC)

作者： Yang Bao Zhu Qibing Huang Min Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China

ISBN: (纸本)9781457720727

Identifying glass defects through machine visual has a vital importance for the efficient production of high quality glass. In this paper, a method based on the combination of Non-negative Matrix Factorization (NMF) and sparse representation classification (SRC) was proposed for the identification of glass defects. According to the properties of glass defect image, NMF algorithm is used to decompose a defect image into one base image and another weighted coefficient matrix, so the defect image is characterized by the coefficient matrix. Then, SRC algorithm is used to classify glass defects. Simulation results show that, with NMF and SRC, glass defects images could be effectively identified.

关键词： Identification of Glass Defects Feature Extraction Non-negative Matrix Factorization sparse representation classification

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Leakage aperture recognition based on ensemble local mean decomposition and sparse representation for classification of natural gas pipeline

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MEASUREMENT 2017年 108卷 91-100页

作者： Sun, Jiedi Peng, Zhitao Wen, Jiangtao Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Yanshan Univ Key Lab Measurement Technol & Instrumentat HeBei Qinhuangdao 066004 Peoples R China

Leakage in a natural gas pipeline is influenced by many factors, including aperture, distance from sensors, and pressure inside the pipeline. The feature extraction and recognition algorithm is complex;thus, a new leakage aperture recognition method is proposed that presents a feature extraction algorithm based on the Ensemble Local Mean Decomposition (ELMD)-K-L (Kullback-Leibler) model and sparse representation for classification. This method applied ELMD to perform adaptive decomposition of the leakage signals and obtain feature information of the leakage signals with different apertures. It then selected the product function components that contained major leakage information according to the K-L divergence from which we extracted a variety of time-frequency feature parameters to obtain the comprehensive and accurate eigenvector of the leakage signal. Realization of an accurate classification of leakage aperture using sparse representation classifiers was proposed to classify small samples of the complex signals. The classifiers obtained the sparsest solution of the test signal through the over complete dictionary and used this solution as the sparse reconstruction coefficients of the test signal to obtain the reconstructed signal of this test signal under different categories. Finally, it completed the classification by determining the residuals of the test and the reconstructed signals. The experimental results showed that the proposed algorithm can achieve higher accuracy than the traditional support vector machine and Back-Propagation classification algorithms. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Leakage aperture recognition Ensemble Local Mean Decomposition (ELMD) Kullback-Leibler (K-L) divergence sparse representation classification Over-complete dictionary

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Metasample-Based Robust sparse representation for Tumor classification

Metasample-Based Robust Sparse Representation for Tumor Clas...

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2013 International Conference on Biomedical Engineering(ICBE 2013)

作者： Bin Gan Chun-Hou Zheng Jin-Xing Liu College of Information technology and communication Qufu Normal University College of Electrical Engineering and Automation Anhui University

In this paper, based on sparse representation classification and robust thought, we propose a new classifier, named MRSRC (Metasample Based Robust sparse representation Classificatier), for DNA microarray data classification. Firstly, we extract Metasample from trainning sample. Secondly, a weighted matrix W is added to solve an l1-regularized least square problem. Finally, the testing sample is classified according to the sparsity coefficient vector of it. The experimental results on the DNA microarray data classification prove that the proposed algorithm is efficient.

关键词： DNA Microarray Data sparse representation classification MRSRC Robust

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Adaptive feature transformation for classification with sparse representation

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OPTIK 2015年第23期126卷 4452-4459页

作者： Sun, Yaxin Wen, Guihua Jiaxing Univ Coll Math Phys & Informat Engn Jiaxing 314001 Peoples R China S China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

sparse representation based classification (SRC) has been shown to be an effective method for face recognition. Furthermore, the input features of more and more classifiers are extracted by dimensional reduction methods. However, we find that the reconstruction ability of a basis for a testing sample is related with cosine distance between this basis and this testing sample, but most dimensional reduction methods are based on Euclidean distance. Obviously, a gap is existing between dimensional reduction methods and SRC. In this paper, we propose an adaptive feature transformation based on self-tuning point to point distances (SPPDAFT) to transform features to a new feature space. The cosine distances among samples in new feature space that is obtainted by SPPDAFT can increase with the Euclidean distances among samples in original space. As a result, the reconstruction ability of a basis to a testing sample in new feature space would be indirectly related with the Euclidean distance between this basis and this sample in original space, and then the gap between dimensional reduction methods and SRC can be reduced. The experimental results on benchmark databases show the effectiveness of SPPDAFT. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： sparse representation classification Dimensional reduction Feature transformation Face recognition

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sparse representation Frameworks for Acoustic Scene classification 25th

Sparse Representation Frameworks for Acoustic Scene Classifi...

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25th International Conference on Speech and Computer (SPECOM)

作者： Tyagi, Akansha Rajan, Padmanabhan Indian Inst Technol Sch Comp & Elect Engn Mandi Himachal Prades India

ISBN: (纸本)9783031483080;9783031483097

This work addresses the task of acoustic scene classification (ASC) by using sparse representation frameworks, motivated by the inherent sparseness of audio data. We explore three different sparse representation classification (SRC) frameworks, generating sparse acoustic scene representations. The first two frameworks focus on producing linear and non-linear features respectively. On the other hand, the third framework presents a novel approach-a two-branch deep sparse auto-encoder (DSAE) representation framework that generates non-linear and discriminative features. In the proposed framework, the first branch induces sparsity, while the second focuses on enforcing discrimination within the learned sparse acoustic scene representations. These representations are later used to classify the acoustic scene data into different acoustic scene classes. We also compare the performance of the three sparse frameworks by evaluating them on three ASC datasets. Our results indicate that acoustic scene representations based on DSAE outperform the sparse representations obtained from the other two frameworks. This results in an average performance gain of approximately 8% across all the ASC datasets.

关键词： Acoustic Scene classification sparse representation classification sparse Auto-Encoder sparse representation

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Motor Imagery classification via Clustered-Group sparse representation 19

Motor Imagery Classification via Clustered-Group Sparse Repr...

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19th Annual IEEE International Conference on Bioinformatics and Bioengineering (BIBE)

作者： Oikonomou, Vangelis P. Nikolopoulos, Spiros Kompatsiaris, Ioannis CERTH Inst Informat Technol Thessaloniki Greece

ISBN: (纸本)9781728146171

A significant limitation on the wide use of a Motor Imagery (MI) Brain-Computer Interfaces is the acquisition of electroenchephalogram (EEG) data over a significant amount of EEG trials to achieve accurate classification. In this work, we propose a new sparse representation classification scheme to overcome the above limitation by training the proposed model using only a limited amount of EEG trials. Our algorithm extends current sparse representation classification schemes by exploiting the group sparsity of EEG features. We have evaluated the proposed algorithm on two MI EEG datasets, showing state-of-the-art performance against well known classification methods of MI BCI literature.

关键词： Motor Imagery sparse representation classification Group Sparsity Collaborative representation classification

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