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Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

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Chinese Journal of Aeronautics 2015年第2期28卷 488-498页

作者： Deng Sen Jing Bo Sheng Sheng Huang Yifeng Zhou Hongliang Aeronautics and Astronautics Engineering College Air Force Engineering University Unit 94371 of People's Liberation Army

Impulse components in vibration signals are important fault features of complex machines. sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding （FSC） and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.

关键词： Dictionary learning Fault detection Impulse feature extraction Information fusion sparse coding

Speech enhancement with a GSC-like structure employing sparse coding

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第12期15卷 1154-1163页

作者： Li-chun YANG Yun-tao QIAN College of Computer Science and Technology Zhejiang University Intelligent Control Research Institute Zhejiang Wanli University

Speech communication is often influenced by various types of interfering signals. To improve the quality of the desired signal, a generalized sidelobe canceller(GSC), which uses a reference signal to estimate the interfering signal, is attracting attention of researchers. However, the interference suppression of GSC is limited since a little residual desired signal leaks into the reference signal. To overcome this problem, we use sparse coding to suppress the residual desired signal while preserving the reference signal. sparse coding with the learned dictionary is usually used to reconstruct the desired signal. As the training samples of a desired signal for dictionary learning are not observable in the real environment, the reconstructed desired signal may contain a lot of residual interfering signal. In contrast,the training samples of the interfering signal during the absence of the desired signal for interferer dictionary learning can be achieved through voice activity detection(VAD). Since the reference signal of an interfering signal is coherent to the interferer dictionary, it can be well restructured by sparse coding, while the residual desired signal will be removed. The performance of GSC will be improved since the estimate of the interfering signal with the proposed reference signal is more accurate than ever. Simulation and experiments on a real acoustic environment show that our proposed method is effective in suppressing interfering signals.

关键词： Generalized sidelobe canceller Speech enhancement Voice activity detection Dictionary learning sparse coding

Forward stagewise regression with multilevel memristor for sparse coding

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Journal of Semiconductors 2023年第10期44卷 105-113页

作者： Chenxu Wu Yibai Xue Han Bao Ling Yang Jiancong Li Jing Tian Shengguang Ren Yi Li Xiangshui Miao School of Integrated Circuits Huazhong University of Science and TechnologyWuhan 430074China Hubei Yangtze Memory Laboratories Wuhan 430205China

sparse coding is a prevalent method for image inpainting and feature extraction,which can repair corrupted images or improve data processing efficiency,and has numerous applications in computer vision and signal ***,sev-eral memristor-based in-memory computing systems have been proposed to enhance the efficiency of sparse coding ***,the variations and low precision of the devices will deteriorate the dictionary,causing inevitable degradation in the accuracy and reliability of the *** this work,a digital-analog hybrid memristive sparse coding system is pro-posed utilizing a multilevel Pt/Al_(2)O_(3)/AlO_(x)/W memristor,which employs the forward stagewise regression algorithm:The approxi-mate cosine distance calculation is conducted in the analog part to speed up the computation,followed by high-precision coeffi-cient updates performed in the digital *** determine that four states of the aforementioned memristor are sufficient for the processing of natural ***,through dynamic adjustment of the mapping ratio,the precision require-ment for the digit-to-analog converters can be reduced to 4 *** to the previous system,our system achieves higher image reconstruction quality of the 38 dB peak-signal-to-noise ***,in the context of image inpainting,images containing 50%missing pixels can be restored with a reconstruction error of 0.0424 root-mean-squared error.

关键词： forward stagewise regression in-memory computing memristor sparse coding

Weak Correlation Dictionary Construction Method for sparse coding

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Journal of Shanghai Jiaotong university(Science) 2017年第1期22卷 77-81页

作者：龙海霞卓力屈盼玲张菁 Signal&Information Processing Laboratory Beijing University of TechnologyBeijing 100124China

For sparse coding, the weaker the correlation of dictionary atoms is, the better the representation capacity of dictionary will be. A weak correlation dictionary construction method for sparse coding has been proposed in this paper. Firstly, a new dictionary atom initialization is proposed in which data samples with weak correlation are selected as the initial dictionary atoms in order to effectively reduce the correlation among ***, in the process of dictionary learning, the correlation between atoms has been measured by correlation coefficient, and strong correlation atoms have been eliminated and replaced by weak correlation atoms in order to improve the representation capacity of the dictionary. An image classification scheme has been achieved by applying the weak correlation dictionary construction method proposed in this paper. Experimental results show that, the proposed method averagely improves image classification accuracy by more than 2%, compared to sparse coding spatial pyramid matching(Sc SPM) and other existing methods for image classification on the datasets of Caltech-101, Scene-15, etc.

关键词： image classification sparse coding correlation coefficient dictionary initialized TP 391.1 A

Surface Defects Classification Using Transfer Learning and Deep sparse coding

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Surface Defects Classification Using Transfer Learning and D...

Single Image Super-Resolution via the Implementation of the Hardware-Friendly sparse coding

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作者： Qizi Huangpeng Xiaojun Duan Wenwei Huang College of Arts and Science National University of Defense Technology College of Basic Military Education National University of Defense Technology

Defect classification is an important part of automated surface defect detection ***,the types of surface defects of industrial products are complex and diverse,and the correct defect type classification can help to subsequently extract the characteristic of ***,defect classification can help to count the number of defect modes automatically and provide data support for the precise maintenance of product *** to the large number of surface defects and the small difference between defect types,traditional classification methods are difficult to classify defect ***,in order to improve the accuracy of defect image classification,this paper proposes a defect image classification method based on transfer learning and sparse ***,a deep CNN feature extraction algorithm for defect images is proposed in combination with transfer ***,the deep CNN features of the defect image are dimension-reduced and sparsely optimized using the sparse coding techniques,and the sparse CNN features are ***,the sparse CNN features are classified to realize the defect type determination using the linear *** accuracy of the proposed method is verified by using a steel surface defect image benchmark database,and the effectiveness of the proposed method is proved.

关键词： Surface Defects Classification Transfer Learning sparse coding

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Single Image Super-Resolution via the Implementation of the ...

LOW-DIMENSIONAL STRUCTURES:SP ARSE coding FOR NEURONAL ACTIVITY

第37届中国控制会议

作者： Zhekang Dong Chun Sing Lai Zhao Xu Donglian Qi College of Electrical Engineering Zhejiang University Department of Electrical Engineering The Hong Kong Polytechnic University Department of Engineering Science University of Oxford

sparse representation is one of the effective approaches to perform the feature extraction on the high-dimensional signals and has been widely used in the field of image processing. In this paper, a novel single image super-resolution（SR） reconstruction algorithm based on sparse coding is presented. Specifically, due to the fact that sparse coding has the potential to greatly facilitate the biological-neural system and to deal with a large amount of complex data, while consuming a finite amount of energy, a memristor crossbar array synthesized by a threshold-type memristor model is constructed for the implementation of a hardware-friendly sparse coding method, i.e., the Locally Competitive Algorithm（LCA）. Meanwhile, the relevant online dictionary learning method is provided, along with the description of weight programming strategy. Furthermore, the specific description of the image SR via the LCA sparse coding is provided. For the verification purpose, a series of experiments are carried out to illustrate the validity and effectiveness of the entire scheme.

关键词： Image super-resolution sparse coding memristor crossbar array dictionary learning

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Journal of Innovative Optical Health Sciences 2013年第1期6卷 44-52页

作者： YUNHUA XU WENWEN BAI XIN TIAN School of Biomedical Engineering Tianjin Medical University Tianjin 300070P.R.China The First Affiliated Hospital of Soochow University Suzhou 215006 P.R.China

Neuronal ensemble activity codes working *** this work,we developed a neuronal ensemble sparse coding method,which can effectively reduce the dimension of the neuronal activity and express neural *** spike trains were recorded in rat prefrontal cortex during a work memory task in *** discretesignals,spikes were transferred into cont inuous signals by estinating *** the normalized continuous signals were decomposed via non-negative sparse met *** non-negative components were extracted to reconstruct a low-dimensional ensemble,while none of the feature components were *** results showed that,for well-trained rats,neuronal ensemble activities in the prefrontal cortex changed dynamically during *** memory *** the neuronal ensemble is more explicit via using non-negative sparse *** results indicate that the neuronal ensemblesparse coding method can effectively reduce the dimnension of neuronal activity and it is a useful tool to express neural coding.

关键词： Low dimensional structures sparse coding neuronal ensemble activity working memory rat

Semisupervised sparse Multilinear Discriminant Analysis

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Journal of Computer Science & Technology 2014年第6期29卷 1058-1071页

作者：黄锴张丽清 MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Department of Computer Science and EngineeringShanghai Jiao Tong University IEEE

Various problems are encountered when adopting ordinary vector space algorithms for high-order tensor data input. Namely, one must overcome the Small Sample Size （SSS） and overfitting problems. In addition, the structural information of the original tensor signal is lost during the vectorization process. Therefore, comparable methods using a direct tensor input are more appropriate. In the case of electrocardiograms （ECGs）, another problem must be overcome; the manual diagnosis of ECG data is expensive and time consuming, rendering it difficult to acquire data with diagnosis labels. However, when effective features for classification in the original data are very sparse, we propose a semisupervised sparse multilinear discriminant analysis （SSSMDA） method. This method uses the distribution of both the labeled and the unlabeled data together with labels discovered through a label propagation Mgorithm. In practice, we use 12-lead ECGs collected from a remote diagnosis system and apply a short-time-fourier transformation （STFT） to obtain third-order tensors. The experimental results highlight the sparsity of the ECG data and the ability of our method to extract sparse and effective features that can be used for classification.

关键词： ECG analysis semisupervised learning sparse coding dimension reduction tensor learning approach

Context-aware local abnormality detection in crowded scene

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Science China(Information Sciences) 2015年第5期58卷 134-144页

作者： ZHU XiaoBin JIN Xin ZHANG XiaoYu LI ChangSheng HE FuGang WANG Lei Beijing Technology and Business University Beijing Electronic Science and Technology Institute Information Technology Research Base of Civil Aviation Administration of China Civil Aviation University of China

In this paper, we propose a novel algorithm by jointly modeling motion and context information targeting at detecting abnormal events in crowded scenes. In our algorithm, context pattern information,extracted through volume local binary patterns computation on three orthogonal planes(LBP-TOP) between local target areas with surrounding areas, is explicitly taken into consideration for localizing abnormality. To capture motion information, a novel feature descriptor named Multi-scale Histogram of Frequency Coefficient is explored by taking Fourier Transform on the extracted dense trajectories. For detection of abnormality, sparse reconstruction cost from a learned event dictionary is adopted to classify local normal and abnormal *** conducted on three benchmark datasets show superior performance to many related state-of-the-art methods.

关键词： local abnormality detection crowded scene context local binary pattern sparse coding