Initial Dictionary Research Articles

Generalized Gini indices–enhanced sparsity decomposition for rotating machinery health assessment using a rotary encoder

ABSTRACT Rotary encoder signal has been attracting considerable attention for health estimation of the rotating elements, since its advantages in lower testing costs and sensitive to torsional stiffness loss caused by incipient faults. However, the practical encoder signal often keeps low signal-to-noise ratio due to its instantaneous angular acceleration conventional method, which brings new challenges for the commonly used feature extraction methods. Sparse decomposition is an efficient method for fault impact features extracting of rotating machinery. However, limited by the quality of initial dictionary and the learning efficiency of K-SVD algorithm, commonly used sparse decomposition is susceptible to the signal noise, especially when unsuitable initial atoms are applied. To overcome this problem, a simple but effective framework term Generalized Gini indices-enhanced sparsity decomposition is established in this paper for practical encoder signal analysis. Firstly, the de-trended and frequency domain weighting methods are used for instantaneous angular acceleration estimation. After that, a Generalized Gini indices guided tunable Q-factor wavelet filter is introduced for the atom selection. Then, the shift-invariant K-SVD method is introduced for dictionary updating and sparse coefficients calculation. Through the analysis of simulation and experiment, the transient characteristics of the gear can be extracted well which provides an alternative methodology for incipient fault detection of rotating machinery.

Ultrasonic Detection of Aliased Signal Separation Based on Adaptive Feature Dictionary and K–SVD Algorithm for Protective Coatings of Assembled Steel Structure

When using ultrasound to detect the thickness of protective coatings on assembled steel structures, the coatings are extremely thin, which can cause echo signals to overlap and impair the detection accuracy. Therefore, the study of the separation of the superimposed signals is essential for the precise measurement of the thickness of thinner coatings. A method for signal time domain feature extraction based on an adaptive feature dictionary and K–SVD is investigated. First, the wavelet transform, which is sensitive to singular signal values, is used to identify the extreme values of the signal and use them as the new signal to be processed. Then, the feature signal extracted by wavelet transform is transformed into Hankel matrix form, and the initial feature dictionary is constructed by period segmentation and random extraction. The optimized feature dictionary is subsequently obtained by enhancing the K–SVD algorithm. Finally, the time domain signal is reconstructed using the optimized feature dictionary. Simulations and experiments demonstrate that the method is more accurate in separating mixed signals and extracting signal time domain feature information than the conventional wavelet transform and Gabor dictionary-based MP algorithm, and that it is more advantageous in detecting the thickness of protective coatings.

Off-grid DOA estimation using improved root sparse Bayesian learning for non-uniform linear arrays

This paper concerns direction of arrival (DOA) estimation based on a sparse Bayesian learning (SBL) approach. We address two inherent problems of this class of DOA estimation methods: (i) a predefined dictionary can generate off-grid problems to a SBL DOA estimator; (ii) a parametric prior generally enforces the solution to be sparse, but the existence of noise can greatly affect the sparsity of the solution. Both of these issues may have a negative impact on the estimation accuracy. In this paper, we propose an improved root SBL (IRSBL) method for off-grid DOA estimation that adopts a coarse grid to generate an initial dictionary. To reduce the bias caused by dictionary mismatch, we integrate the polynomial rooting approach into the SBL method to refine the spatial angle grid. Then, we integrate a constant false alarm rate rule in the SBL framework to enforce sparsity and improve computational efficiency. Finally, we generalize the IRSBL method to the case of non-uniform linear arrays. Numerical analysis demonstrates that the proposed IRSBL method provides improved performance in terms of both estimation accuracy and computational complexity over the most relevant existing method.

SNR improvement for Φ-OTDR with sparse representation denoising method

A Rayleigh backscattering (RBS) signal denoising method based on sparse representation is proposed in this paper. The method can effectively reduce the noise of the RBS signal and improve the signal-to-noise ratio (SNR) of vibration location results for the phase-sensitive optical time-domain reflectometry (Φ-OTDR) system. In the experiments, the initial dictionary is constructed by discrete cosine transform, the dictionary matrix and sparse matrix are updated by the k-singular value decomposition method, and then RBS signal reconstruction is executed. In this process of reconstruction, the effective signals can be sparsely represented by the linear combination of dictionary atoms, while the noise signals are discarded as the residual between the original RBS signals and the reconstructed target signals, which makes the noise separated and improves the SNR of the system. To validate the feasibility and effectiveness of the proposed approach for the SNR enhancement, we have carried out experiments on the actual signals obtained from the Φ-OTDR system. The experimental result indicates that the average increment of SNR is 3.44 dB at 10 km, 20 km, 30 km, and 40 km of the optical fiber after using the sparse representation denoising method. Moreover, the vibration detection range is enhanced to 80 km and the SNR reaches 3.27 dB. This method provides a novel scheme to improve the SNR for the Φ-OTDR system.

Coherent Noise Attenuation by Kurtosis-Guided Adaptive Dictionary Learning Based on Variational Sparse Representation

In seismic data denoising, coherent noise is usually challenging to remove because it has similar features with signal. Therefore, attenuating coherent noise is of great importance for seismic data. In order to obtain a good denoising result, it is necessary to fully consider the features of coherent noise and choose the appropriate denoising methods. Here, we propose a kurtosis-guided adaptive dictionary learning (KGADL) algorithm based on variational sparse representation model. First, the variational sparse representation is to construct initial dictionary that depends on the seismic data, so that it can accurately contain the features of the seismic data, so as to improve the accuracy of sparse representation. Then, we use the K-singular value decomposition (K-SVD) algorithm to update the dictionary and introduce kurtosis to measure each atom after updating. Atoms with high kurtosis values usually exhibit strong irregularities and are considered noise, while atoms with low kurtosis values have more feature of valid waves and are considered signal. The atoms with low kurtosis values characterizing valid signal are retained to get the special dictionary that adapts to the complexity of the input seismic data, and this dictionary is used to suppress the coherent noise in seismic data. Finally, the denoising performance of the proposed method is illustrated by examples of both synthetic and field seismic data.

Sentiment lexicon for cross-domain adaptation with multi-domain dataset in Indian languages enhanced with BERT classification model

Many websites are attempting to offer a platform for users or customers to leave their reviews and comments about the products or services in their native languages. The cross-domain adaptation (CDA) analyses sentiment across domains. The sentiment lexicon falls short resulting in issues like feature mismatch, sparsity, polarity mismatch and polysemy. In this research, an augmented sentiment dictionary is developed in our native regional language (Tamil) that intends to construct the contextual links between terms in multi-domain datasets to reduce problems like polarity mismatch, feature mismatch, and polysemy. Data from the source domain and target domain both labeled and unlabeled are used in the proposed dictionary. To be more specific, the initial dictionary uses normalised pointwise mutual information (nPMI) to derive contextual weight, whereas the final dictionary uses the value of terms across all reviews to compute the accurate rank score. Here, a deep learning model called BERT is used for sentiment classification. For cross-domain adaptation, a modified multi-layer fuzzy-based convolutional neural network (M-FCNN) is deployed. This work aims to build a single dictionary using large number of vocabularies for classifying the reviews in Tamil for several target domains. This extendible dictionary enhances the accuracy of CDA greatly when compared to existing baseline techniques and easily handles a large number of terms in different domains.

Improving reconstruction-based coding methods for image classification: a visual dictionary refining method

The reconstruction-based coding methods utilize a dictionary to approximate an input feature as a sparse representation in image classification tasks by solving a least-squares problem with constraints. The input feature can be a handcrafted feature calculated over a small patch or a deep feature extracted by a pre-trained convolutional neural network model. As known, the contribution degrees of features extracted within different regions are distinct to the same classification task. Given this, we argue that for features with a large contribution degree, if their reconstruction errors induced by a reconstruction-based coding method can be decreased, more discriminative information will be retained to improve classification accuracy. Considering that different coding methods have the same lower bound of reconstruction error, we propose a visual dictionary refining method that adjusts only the lower bounds of the errors of features according to their contribution degrees. Concretely, given an initial dictionary used by a reconstruction-based coding method, the indexes (coding indexes) of the words from the dictionary for encoding each feature are obtained. Then, the initial dictionary is updated according to the contribution degrees and coding indexes of features. The highlight of our method is that it can be applied to many reconstruction-based coding methods without damaging their original constraints. Experimental results on 15-Scenes, Caltech-101, and UIUC-Sports show that the classification accuracies of four representative coding methods are improved by 0.17% to 1.5% after applying our method. These results exhibit the effectiveness and universality of our method.

A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory

This paper presents a novel method for condition monitoring using the RMS residual of vibration signal reconstruction based on trained dictionaries through sparse representation theory. Measured signals were firstly decomposed into intrinsic mode functions (IMFs) for training the initial dictionary. In this step, an adaptive variational mode decomposition (VMD) was proposed for providing information with higher accuracy, and the decompositions were used as discriminative atoms for sparse representation. Then, the overcomplete dictionary for sparse coding was learned from IMFs to reserve the highlight feature of the signals. As the dictionaries were trained, newly measured signals could be directly reconstructed without any signal decompositions or dictionary learning. This meant errors likely introduced by signal process techniques, such as VMD, EMD, etc., could be excluded from the condition monitoring. Moreover, the efficiency of the fault diagnosis was greatly improved, as the reconstruction was fast, which showed a great potential in online diagnosis. The RMS of the residuals between the reconstructed and measured signals was extracted as a feature of condition. A case study on operating condition identification of a diesel engine was carried out experimentally based on vibration accelerations, which validated the availability of the proposed feature extraction and condition-monitoring approach. The presented results showed that the proposed method resulted in a great improvement in the fault feature extraction and condition monitoring, and is a promising approach for future research.

Overview of Naming Entities Based on Natural Language Processing

Named entity recognition is an important research direction of natural language processing. This paper first reviews the development process and main stages of named entity recognition, then expounds the research contents and methods of named entity recognition, and puts forward the key and difficult points of named entity recognition, The development process of named entity recognition is from the initial rule-based and dictionary based method to the later statistical learning method, and then to the mixed learning method and some popular learning methods. Finally, the development of named entity recognition is prospected.

SAR Image Change Detection Based on Joint Dictionary Learning With Iterative Adaptive Threshold Optimization

Synthetic aperture radar (SAR) image change detection is still a challenge due to inherent speckle noise and scarce datasets. This article proposes a joint-related dictionary learning algorithm based on the k-singular value decomposition (K-SVD) algorithm called JR-KSVD and an iterative adaptive threshold optimization (IATO) algorithm for unsupervised change detection. The JR-KSVD algorithm adds dictionary correlation learning to the K-SVD algorithm to generate a uniform initial dictionary for dual-temporal SAR images, thereby reducing the instability of sparse representations due to atomic correlations and enhancing the extraction of image edges and details. The IATO approach employs thresholds obtained by the “difference-log ratio” fusion image for indefinite residual energy minimization iterations to gradually shrink the threshold variation range and finally generate the change images, which have a high degree of adaptivity and strong real-time performance. Finally, experiments on six real datasets demonstrate that the proposed algorithm exhibits superior detection performance and robustness against some state-of-the-art algorithms.

A Discriminative Target Equation-Based Face Recognition Method for Teaching Attendance

In this paper, we use discriminative objective equations to conduct an in-depth study and analysis of face recognition methods in teaching attendance and use the model in actual teaching attendance. It focuses on the design and implementation of the attendance module, which uses wireless network technology to record students’ access to classrooms in real time, and relies on face recognition technology to identify students’ sign-in images to achieve attendance records of students’ independent attendance sign-in. Real-time detection of student attendance is achieved by combining face detection and face recognition technology through regular camera photography and automatic attendance check-in by the server. Based on the recognition results of the attendance check-in image, an attendance mechanism is proposed, and the attendance score of the student for the current course can be calculated using the attendance mechanism, which realizes the automatic management of student attendance. For the face recognition process, the system uses the Ad boost algorithm based on Hear features to achieve face detection, preprocesses the face samples with gray normalization, rotation correction, and size correction, and uses the method based on LBP features to achieve face recognition. Firstly, a combination of histogram equalization and wavelet denoising is chosen to preprocess the training sample images to obtain the face image light invariance description, and then, the initial dictionary is constructed using the dimensionality reduction performance of the PCA method; next, the initial dictionary is updated, and a new dictionary with representation and discrimination capabilities is obtained using the LC-KSVD algorithm that makes improvements in the dictionary update stage. The sparse coefficients of the feature matrix of the test sample image under the new dictionary are calculated, and the class correlation reconstruction is performed on the feature matrix of the test sample image, and the corresponding reconstruction error is solved; finally, the discriminative classification of the test sample image is achieved according to the solved class correlation reconstruction error. The relevant experiments on the face database prove that the algorithm can improve the recognition accuracy to a certain extent and better solve the influence of changing lighting conditions on the face recognition accuracy.

Sparse representation-based classification with two-dimensional dictionary optimization for motor imagery EEG pattern recognition

BackgroundSparse representation-based classification (SRC) has more advantages in motor imagery EEG pattern recognition, and the quality of dictionary construction directly determines the performance of SRC. In this paper, we proposed a two-dimensional dictionary optimization (TDDO) method to directly improve the performance of SRC. New MethodFirstly, an initial dictionary was constructed with multi-band features extracted by filter band common spatial pattern (FBCSP). Then Lasso regression is used to select significant features in each atom synchronously in the horizontal direction, and the KNN-based method is used to clean up noise atoms in the vertical direction. Finally, an SRC method by training samples linearly representing test samples was implemented in classification. ResultsThe results show the necessity and rationality of TDDO-SRC method. The highest average classification accuracy of 86.5% and 92.4% is obtained on two public datasets. Comparison with Existing Method(s)The proposed method has more superior classification accuracy compared to traditional methods and existing winners’ methods. ConclusionsThe quality of dictionary construction has a great impact on the robustness of SRC. And compared with the original SRC, the classification accuracy of the optimized TDDO-SRC is greatly improved.

Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification. We made an optimization model for geometry-aware discrimination dictionary learning in which the dictionary learning (GADDL) is generalized from Euclidian space to Riemannian manifolds, and dictionary atoms are composed of manifold data. An efficient optimization algorithm based on an alternating direction multiplier method was developed to solve the model. Experiments were implemented on three public datasets: Flevoland-1989, San Francisco and Flevoland-1991. The experimental results show that the proposed method learned a discriminative dictionary with accuracies better those of comparative methods. The convergence of the model and the robustness of the initial dictionary were also verified through experiments.

The Noise Attenuation and Stochastic Clutter Removal of Ground Penetrating Radar Based on the K-SVD Dictionary Learning

The ground penetrating radar (GPR) data in the complex detection environment is non-stationary, non-Gaussian, and non-uniform, so the traditional noise attenuation methods are difficult to meet the requirements of denoising. Therefore, we introduced the K-singular value decomposition (K-SVD) dictionary learning into the denoising of GPR signals. It uses the orthogonal matching pursuit (OMP) algorithm to sparse decompose different radar data and trains the overcomplete dictionary with sample characteristics. K-SVD makes full use of the prior information and can extract features according to the sample data adaptively, which means it has strong sparse representation competence. Because radar signals can be sparsely represented in the dictionary, whereas the random noise does not have a sparse representation, the K-SVD dictionary can be used to distinguish effective signals from noise in the GPR data. We used the discrete cosine transform (DCT) and K-SVD dictionaries to process the Gaussian noise and stochastic clutter in the GPR profile. The results show that both K-SVD and DCT can effectively suppress the Gaussian noise. But for the clutter generated by the random medium, the DCT dictionary also causes damage to the effective signals while removing the noise; whereas the K-SVD dictionary learning algorithm uses the DCT dictionary as the initial dictionary and carries out adaptive learning on the noisy data, considering the information in the block and the global observation to complete the denoising, with good denoising effect and high fidelity. We finally verified the effectiveness and practicability of the K-SVD method for measured data.

Global Fabric Defect Detection Based on Unsupervised Characterization

Fabric texture intelligent analysis comprises the following characteristics: objective detection results, high detection efficiency, and accuracy. It is significantly vital to replace manual inspection for smart green manufacturing in the textile industry, such as quality control and rating, and online testing. For detecting the global image, an unsupervised method is proposed to characterize the woven fabric texture image, which is the combination of principal component analysis (PCA) and dictionary learning. First of all, the PCA approach is used to reduce the dimension of fabric samples, the obtained eigenvector is used as the initial dictionary, and then the dictionary learning method is operated on the defect-free region to get the standard templates. Secondly, the standard templates are optimized by choosing the appropriate dictionary size to construct a fabric texture representation model that can effectively characterize the defect-free texture region, while ineffectively representing the defective sector. That is to say, through the mechanism of identifying normal texture from imperfect texture, a learned dictionary with robustness and discrimination is obtained to adapt the fabric texture. Thirdly, after matching the detected image with the standard templates, the average filter is used to remove the noise and suppress the background texture, while retaining and enhancing the defect region. In the final part, the image segmentation is operated to identify the defect. The experimental results show that the proposed algorithm can adequately inspect fabrics with defects such as holes, oil stains, skipping, other defective types, and non-defective materials, while the detection results are good and the algorithm can be operated flexibly.

An enhanced sentiment dictionary for domain adaptation with multi-domain dataset in Tamil language (ESD-DA)

Mostly sentiment analysis employs dictionary approaches for recognizing the polarity of terms in a review. However, in sentiment analysis between different domains called domain adaptation (DA), the sentiment lexicon disappoints that leads to the feature mismatch problem. Now, many e-commerce sites try to process reviews in their native languages. In this paper, we propose an enhanced dictionary in our native language (Tamil) that aims at building contextual relationships among the terms of multi-domain datasets that tries to minimize the feature mismatch problem. The proposed dictionary employs both labeled and unlabeled data from the source domain and unlabeled data from the target domain. More precisely, the initial dictionary explores pointwise mutual information for calculating contextual weight then the final dictionary estimates the rank score based on the importance of terms among all the reviews. This work intends to classify reviews of multiple target domains in Tamil by using the unified dictionary with a large number of vocabularies. This extendible dictionary significantly improves the accuracy of DA with the other baseline methods and handles many words in multiple domains with ease.

A Dictionary Learning Algorithm Based on Dictionary Reconstruction and Its Application in Face Recognition

In recent years, dictionary learning has received more and more attention in the study of face recognition. However, most dictionary learning algorithms directly use the original training samples to learn the dictionary, ignoring noise existing in the training samples. For example, there are differences between different images of the same subject due to changes in illumination, expression, etc. To address the above problems, this paper proposes the dictionary relearning algorithm (DRLA) based on locality constraint and label embedding, which can effectively reduce the influence of noise on the dictionary learning algorithm. In our proposed dictionary learning algorithm, first, the initial dictionary and coding coefficient matrix are directly obtained from the training samples, and then the original training samples are reconstructed by the product of the initial dictionary and coding coefficient matrix. Finally, the dictionary learning algorithm is reapplied to obtain a new dictionary and coding coefficient matrix, and the newly obtained dictionary and coding coefficient matrix are used for subsequent image classification. The dictionary reconstruction method can partially eliminate noise in the original training samples. Therefore, the proposed algorithm can obtain more robust classification results. The experimental results demonstrate that the proposed algorithm performs better in recognition accuracy than some state-of-the-art algorithms.

Transfer Dictionary Learning Method for Cross-Domain Multimode Process Monitoring and Fault Isolation

Data-driven methods have shown its great latent capacity in the field of industrial process monitoring. However, the existing methods usually achieve good results under the assumption that the offline learning data and the online monitoring data are drawn from the same distribution. Unfortunately, in the industrial system, the assumption is often violated due to the harsh operating environment. Especially, with the increasing complexity and scale of industrial production, the supervisory control and data acquisition (SCADA) data of the industrial production process often collected from different machines, seasons, or operating modes. In addition, due to the cost of manual data labeling and real-time requirement of process monitoring, the offline learning data, which was used to build the model, often have abundant source-domain data and insufficient target-domain data. Consequently, these methods have bad performance on the online monitoring data collected from the target domain. In order to make full use of the knowledge from the abundant source-domain data, a transfer dictionary learning method is proposed to address the cross-domain problem in this article. The proposed method can learn an initial dictionary from the abundant source-domain data, and then, the final dictionary is updated by incorporating the feature of insufficient target-domain data in a smooth subspace interpolation way. The effectiveness of the proposed method is evaluated through a numerical simulation case, a continuous stirred tank heater (CSTH) case, and a wind turbine system case, from which we can see the proposed method has a better performance compared with some state-of-the-art methods.

Denoising Method for Underwater Acoustic Signals Based on Sparse Decomposition

In order to improve the problem of relatively low signal-to-noise in the extraction of underwater acoustic signals under the background of strong interference, a sparse decomposition-based underwater acoustic signal denoising method is proposed. The main work is as follows: First, the signal is decomposed into a complete dictionary that can reflect the characteristics of the signal environment through the singular value decomposition method. Then, a cyclic shift is used to construct a signal matrix and an initial dictionary. A newly generated super-complete dictionary is obtained through training and updating. The atoms in the dictionary matrix are correlated and orthogonalized by an adaptive orthogonal matching pursuit method. Finally, the linear combination of the atoms that can best reflect the characteristic information of the underwater acoustic signal is used to reconstruct the underwater acoustic signal to achieve the purpose of denoising. The noise filtering underwater noise signal is simulated and compared with the traditional filtering method. The simulation results show that this method improves the signal-to-noise ratio of the signal after sparse decomposition and reconstruction of the original underwater acoustic signal, and has a certain denoising ability under strong noise and various types of reverberation interference.

CASE SYSTEM OF THE RUSSIAN LANGUAGE: DIFFICULTIES OF MASTERING AND EXPERIENCE OF OVERCOMING THEM

The system of the Russian language is very difficult for foreign students because all cases are polysemantic, the amount of values is not the same; in addition to the main system endings, there are a large number of exceptions; there is an internal interference associated with homonymous endings of different cases and with homographs. The choice of a particular way of presenting grammatical material, or approach, is due to some reasons, the main of which are: a) the intensity of the course (how much time can be dedicated to studying the system); b) intellectual level of the group; C) national traditions of education (for example, students from China are used to memorizing and returning, repeating many times). For different approaches the different order of studying the cases are shown. In the concentric approach, after the nominative, which is always studied first since it is the initial (dictionary) form of the word, the genitive in the meaning of absence should naturally be studied. This approach is logical when getting acquainted with the second before studying the verb system. The construction “У меня есть ‘I have’ + noun in the nominative case requires a logical continuation - the construction У меня нет ‘I don’t have’ + noun in the genitive case (genitive in the meaning of absence – нет кого? нет чего?). Further, the following order is optimal: prepositional (in the meaning of the place – где?) → accusative (in the sense of a direct object – что? кого?) → dative (in the meaning of the addressee – кому?) → instrumental (in the sense of complicity – с кем?). With repeated, complete and detailed studying cases, as well as with a linear approach, the order is another: nominative → prepositional → genitive → accusative → dative → instrumental. The article explains the rationale for these approaches. From all the richness and diversity of the meanings of Russian cases, the most important ones are chosen at the initial stage, which ensures the survival in the new language environment, social adaptation of foreign students. A detailed description of these critical values is also given in the article.