Composite Dictionary Research Articles

Русская урбанонимия и ее лексикографическое представление: проблемы и перспективы

The aim of the research is to substantiate the concept of creating a composite dictionary of urbanonyms of the Russian Federation, accumulating information about the most frequent inner-city names, the time of their occurrence, the existing variants of onyms, to identify a range of problems facing this project. The absence of such a publication significantly hinders regional studies of urban toponymy. The scientific novelty lies in elaborating the concept and principles of compiling a dictionary of urbanonyms of the Russian Federation, the proposed model of the structure of a dictionary entry reflecting the main linguocultural features of the names of inner-city objects of the Russian Federation. The paper substantiates the need to develop a composite reference book on the urban toponymy of the country, indicates sources of information about the time of creation of names in various localities, provides the composition of the basic corpus, describes the principles of selection of urbanonyms. The author has developed the structure of a dictionary entry, has given examples of the dictionary entries ‘Sovetskaya Street/Avenue/Square’ and ‘Lenina Street/Avenue/Lane/Drive/Square’. The research findings have shown that the preparation of a composite dictionary of the Russian urbanonymy of the Russian Federation will make it possible to describe the main body of pre-revolutionary and modern urban toponyms and choronyms of the country, identify federal and regional trends in their development, highlight the main periods of occurrence, possible causes of creation and loss.

Sparse representation of acoustic emission signals and its application in pipeline leak location

The acoustic detection of pipeline leaks is strongly influenced by the associated interference and noise. In this paper, acoustic emission signals are analyzed using a sparse representation method and the main components associated with the leakage are extracted. Dictionary learning is performed using training samples composed of the leakage signals and noise signals. The measured signal is sparsely decomposed on the composite dictionary, allowing the main leakage components to be estimated. The effects of dictionary dimensionality, redundancy, sparsity constraints, and other parameters on the performance of the sparse representation algorithm are investigated. Finally, the leak location in the pipeline is determined through cross-correlation analysis of the reconstructed acoustic signals. Experimental results show that the proposed sparse representation method effectively improves the signal-to-noise ratio of the acoustic emission signals, and correspondingly improves the accuracy and reliability of pipeline leak location compared with traditional localization methods.

Enhanced Bayesian sparse representation of mechanical fault signals by structural feature-oriented matching composite dictionary construction

The traditional orthogonal matching pursuit algorithm exploits only the overall sparsity of signals without considering the effects caused by structural characteristics. To this end, this paper proposes an enhanced Bayesian sparse representation (EBSR) of mechanical fault signals by structural feature-oriented matching redundant dictionary construction. First, an EBSR model which can improve sparse reconstruction results is proposed. The proposed model improves the recovery accuracy and robustness of the sparse representation by using the structural information as a priori information. Subsequently, a composite dictionary is designed combining a Sin-Chirplet dictionary with an Impulse dictionary, and a multi-group and multi-strategy grey wolf optimizer algorithm is employed to enable the composite dictionary match the structural features of the fault signal and reduce its redundancy degree. Finally, the optimized matching composite dictionary is introduced into the EBSR algorithm, endowing it with an efficient atom selection strategy and reducing the complexity of the sparse representation. The simulation and experimental results demonstrated that the proposed method can effectively reduce the interference from background noise and impurity frequencies, verifying the effectiveness and applicability of the proposed method for the sparse representation of mechanical faults.

Online Estimation of Missing Data Using Sparse Optimization Techniques With Applications to Classical Control

This paper proposes and describes a method based on sparse optimization techniques for online estimation of missing data. The basic idea of the proposed method is to represent the noise-free signal of interest sparsely in a composite dictionary. The proposed algorithm offers three prime advantages: 1) handles nonlinear processes that are locally linearizable; 2) requires lower computational power than the existing algorithms; and 3) adapts to the changing process conditions by optimally selecting the most relevant feature for that window of time. Results show that the proposed algorithm outperforms the existing neural network-based method. An application of the proposed algorithm to classical (PID) control using measurements with missing data is presented. In this context, an added benefit of the proposed method is that it serves as a feature selection operator for the process, which can be useful in other applications. Numerical (simulation) studies are carried out to study the influence of user-defined parameter in the algorithm and two stochastic factors, namely, the random sampling scheme and noise , on the stability and performance of the closed-loop system. Results show that the closed-loop system with the proposed algorithm is stable and yields a satisfactory performance.

Automated recognition of cardiac arrhythmias using sparse decomposition over composite dictionary

Background and objective: Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. Due to an increase in the rate of global mortalities, biopathological signal processing and evaluation are widely used in the ambulatory situations for healthcare applications. For decades, the processing of pathological electrocardiogram (ECG) signals for arrhythmia detection has been thoroughly studied for diagnosis of various cardiovascular diseases. Apart from these studies, efficient diagnosis of ECG signals remains a challenge in the clinical cardiovascular domain due to its non-stationary nature. The classical signal processing methods are widely employed to analyze the ECG signals, but they exhibit certain limitations and hence, are insufficient to achieve higher accuracy.Methods: This study presents a novel technique for an efficient representation of electrocardiogram (ECG) signals using sparse decomposition using composite dictionary (CD). The dictionary consists of the stockwell, sine and cosine analytical functions. The technique decomposes an input ECG signal into stationary and non-stationary components or atoms. For each of these atoms, five features i.e., permutation entropy, energy, RR-interval, standard deviation and kurtosis are extracted to determine the feature sets representing the heartbeats that are classified into different categories using the multi-class least-square twin support vector machines. The artificial bee colony (ABC) technique is used to determine the optimal classifier parameters. The proposed method is evaluated under category and personalized schemes and its validation is performed on MIT-BIH data.Results: The experimental results reported a higher overall accuracy of 99.21% and 90.08% in category and personalized schemes respectively than the existing techniques reported in the literature. Further a sensitivity, positive predictivity and F-score of 99.21% each in the category based scheme and 90.08% each in the personalized schemes respectively.Conclusions: The proposed methodology can be utilized in computerized decision support systems to monitor different classes of cardiac arrhythmias with higher accuracy for early detection and treatment of cardiovascular diseases.

Sparse Group Representation Model for Motor Imagery EEG Classification

A potential limitation of a motor imagery (MI) based brain-computer interface (BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust classifier training. The calibration burden during data acquisition phase will most probably cause a subject to be reluctant to use a BCI system. To alleviate this issue, we propose a novel sparse group representation model (SGRM) for improving the efficiency of MI-based BCI by exploiting the intersubject information. Specifically, preceded by feature extraction using common spatial pattern, a composite dictionary matrix is constructed with training samples from both the target subject and other subjects. By explicitly exploiting within-group sparse and group-wise sparse constraints, the most compact representation of a test sample of the target subject is then estimated as a linear combination of columns in the dictionary matrix. Classification is implemented by calculating the class-specific representation residual based on the significant training samples corresponding to the nonzero representation coefficients. Accordingly, the proposed SGRM method effectively reduces the required training samples from the target subject due to auxiliary data available from other subjects. With two public EEG data sets, extensive experimental comparisons are carried out between SGRM and other state-of-the-art approaches. Superior classification performance of our method using 40 trials of the target subject for model calibration (Averaged accuracy = 78.2%, Kappa = 0.57 and Averaged accuracy = 77.7%, Kappa = 0.55 for the two data sets, respectively) indicates its promising potential for improving the practicality of MI-based BCI.

Supervised monaural speech enhancement using two-level complementary joint sparse representations

Recently, a new complementary joint sparse representations (CJSR) method was proposed for monaural speech enhancement, which utilizes the relationships among speech, noise and mixture. One of the joint sparse representations (JSRs) uses the mapping relationship between mixture and speech while the other uses the mapping relationship between mixture and noise. However, since they only use the joint information and overcomplete dictionaries, there may be some confusion components between the estimated speech and noise. In this paper, a novel model with fusion process is proposed, which further using an additional composite dictionary composed of clean speech dictionary and noise dictionary as prior knowledge. When the estimated speech and noise are obtained by JSRs, they are further sparsely represented on the composite dictionary, respectively. Because that the composite dictionary has the discriminative property, the source confusion problem of previous methods is coped well. In order to take advantage of the complementary knowledge of the two estimated signals, we propose a weighting parameter based on the residuals of sparse representation on composite dictionary. If one of the residuals is smaller, the corresponding weighting is greater, and this weighting parameter varies with noise type and speaker flexibly and effectively. Experimental results show that the proposed algorithm has superior performance compared with other tested approaches.

Discriminative Feature Representation to Improve Projection Data Inconsistency for Low Dose CT Imaging.

In low dose computed tomography (LDCT) imaging, the data inconsistency of measured noisy projections can significantly deteriorate reconstruction images. To deal with this problem, we propose here a new sinogram restoration approach, the sinogram- discriminative feature representation (S-DFR) method. Different from other sinogram restoration methods, the proposed method works through a 3-D representation-based feature decomposition of the projected attenuation component and the noise component using a well-designed composite dictionary containing atoms with discriminative features. This method can be easily implemented with good robustness in parameter setting. Its comparison to other competing methods through experiments on simulated and real data demonstrated that the S-DFR method offers a sound alternative in LDCT.

A Novel Sparse Classifier for Automatic Modulation Classification using Cyclostationary Features

Automatic modulation classification plays a key role in cognitive radio for recognizing the modulation scheme. In this paper, we propose a new classifier based on sparse signal decomposition using an overcomplete composite dictionary (constructed using cyclostationary coefficients) for the classification of modulation format of primary user or to identify noise. The basic principle of the classifier is to classify the received signal modulation format based on reconstructed sparse coefficients after solving $$l_1$$ norm minimization using the overcomplete dictionary. Then, relative energies of reconstructed sparse coefficients are compared for recognition of modulation format of the received signal. It is a promising candidate for the cognitive radio due to its robust classification ability. The performance of the proposed classifier is compared with other well known classifiers available in literature. Results show the superiority of the proposed classifier over other classifiers.

Sensor-based vibration signal feature extraction using an improved composite dictionary matching pursuit algorithm.

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective.

Applied Research on Segmented-Hilbert-Huang Transform Algorithm in Fault Feature Extraction of Main Reducer

For the multi-time scale characteristics of vibration signal, a composite multi-frequency dictionary combining the low-frequency Fourier dictionary and the high-frequency impulse time-frequency dictionary is constituted, to decompose multi-component vibration signal into the combination of several one-component signals. The use of empirical model decomposition (EDM) in high-frequency impulse Component signal including feature information is to realize segmented Hilbert-Huang transform of signal and to acquire the time-frequency representation of every one-component signal, which is the process of fault information extraction of vibration signal. The application of the method in main reducer fault diagnosis verifies the engineering practicability and validity of the new algorithm.

Speech Enhancement Using Generative Dictionary Learning

The enhancement of speech degraded by real-world interferers is a highly relevant and difficult task. Its importance arises from the multitude of practical applications, whereas the difficulty is due to the fact that interferers are often nonstationary and potentially similar to speech. The goal of monaural speech enhancement is to separate a single mixture into its underlying clean speech and interferer components. This under-determined problem is solved by incorporating prior knowledge in the form of learned speech and interferer dictionaries. The clean speech is recovered from the degraded speech by sparse coding of the mixture in a composite dictionary consisting of the concatenation of a speech and an interferer dictionary. Enhancement performance is measured using objective measures and is limited by two effects. A too sparse coding of the mixture causes the speech component to be explained with too few speech dictionary atoms, which induces an approximation error we denote source distortion. However, a too dense coding of the mixture results in source confusion, where parts of the speech component are explained by interferer dictionary atoms and vice-versa. Our method enables the control of the source distortion and source confusion trade-off, and therefore achieves superior performance compared to powerful approaches like geometric spectral subtraction and codebook-based filtering, for a number of challenging interferer classes such as speech babble and wind noise.

Research on the Composite Dictionary Multi-atoms Matching Algorithm Based on the Signal Character

Research on the Composite Dictionary Multi-atoms Matching Algorithm Based on the Signal Character

Application of Composite Dictionary Multi-Atom Matching in Gear Fault Diagnosis

The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results.

Building a Controlled Health Vocabulary in Japanese

This study is aimed at developing a controlled clinical vocabulary for use in electronic patient record (EPR) systems. In this paper, we propose a model for building the vocabulary. The model is composed of a Canonical Term Dictionary, an Atom Dictionary, a Composite Atom Dictionary, and an Index. Parsing and composing functions are included in this model. Canonical terms were extracted from reference terminologies. Atoms were extracted from the Canonical Term Dictionary and reduced to a set from which the Composite Atom Dictionary can be built. The index was built to link these two dictionaries. For testing the model, we compiled a sample vocabulary and applied the model to a SNOMED translation system (English to Japanese) and a term similarity estimation system. The sample vocabulary consisted of 15,600 atomic terms and 4,450 composite terms. 33,441 SNOMED terms were translated by the SNOMED translation system. The system gave adequate Japanese candidates in 56.3% of cases. The similarity estimation system found an average of 5.4 candidates when the equality ratio was over 50%. The trial applications produced good results. The model seems promising for building a standard clinical vocabulary system. This system can be applied in certain other Asian countries, such as China and Korea.