Construct Feature Vectors Research Articles

Bearing fault diagnosis based on teager energy entropy and mean-shift fuzzy C-means

Feature extraction and fault recognition of vibration signals are two important parts of bearing fault diagnosis. In this article, a fault diagnosis method based on teager energy entropy of each wavelet subband and improved fuzzy C-means is proposed. First, bearing vibration signal is decomposed into wavelet packet and normalized teager energy entropy feature matrix is constructed as clustering index. Principal component analysis is applied to the high-dimensional teager energy entropy feature matrix, and the principal components are determined by cumulative contribution rate to construct feature vectors. Then, the mean-shift method is used to search for the high probability density region of principal components so as to determine the cluster number and cluster center. Finally, fuzzy C-means is used to update the clustering center and membership value, and confirm the optimal clustering center and the type of clustering. Through simulated and experimental analysis, the proposed method has two advantages. The feature vector constructed by this method has better specificity than wavelet energy entropy. The initial clustering center of fuzzy C-means is confirmed by the mean-shift method, which can improve the clustering performance of fuzzy C-means and solve the misclassification without preknowing the number of categories.

An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis

When the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. The traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to select suitable components that contain fault information. In order to address the problems above, in this paper, we proposed the improved empirical wavelet transform (IEWT) method. The simulation experiment proved that IEWT can solve the problem of a large number of EWT components and separate the impact component effectively which contains bearing fault information from noise. The IEWT method is combined with the support vector machine (SVM) to diagnosis the fault of the rolling bearings. The permutation entropy (PE) is used to construct feature vectors for its strong induction ability of dynamic changes of nonstationary and nonlinear signals. The crucial parameter penalty factor C and kernel parameter g of SVM are optimized by quantum genetic algorithm (QGA). Compared with traditional EWT and variational mode decomposition (VMD) methods, the effectiveness and advantages of this method are demonstrated in this study. The classification prediction ability of SVM is also better than that of K-nearest neighbor (KNN) and extreme learning machine (ELM).

Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters

Bearings are the most commonly used machine element in order to reduce rotational friction in machines and to compensate radial and axial loads. It is very important to determine the faults in the bearings in terms of the machine health. In order to accurately diagnose bearing-related faults with traditional machine learning methods, it is necessary to identify the features that characterize bearing fault most accurately. Therefore, a new feature extraction procedure has been proposed to determine the vibration signal velocities of different fault sizes and types in this study. The new approach has been employed to obtain features from the vibration signals for different scenarios. After different filtering based on 1D-LBP method, the F-1D-LBP method was used to construct feature vectors. The filters reduce the noise in the signals and provide different feature groups. In other words, it is aimed to generate filters in order to extract different patterns that can separate signals. For each filter applied, different patterns can be obtained for the same local point on signals. Thus, the signals can be represented by different feature vectors. Then, by using these feature groups with various machine learning methods, vibration velocities were separated from each other. As a result, it was observed that the obtained feature had promising results for classification of bearing vibrations.

A Framework for Detecting Phishing Websites using GA based Feature Selection and ARTMAP based Website Classification

Abstract Nowadays, Phishing attack has gained more attention among all the other attacks existing in online social media. The fraudulent E-mail sent form the fake website that looks like the legitimate website is the initial carter for launching the phishing attacks. This is a kind of social engineering attack in which, the user is targeted for stealing the personal information, viz., user name, password, and banking credentials for committing the financial crimes. The existing phishing website detection methods suffer from two issues in terms of feature selection scheme that does not consider the right set of features for detection and the classifier which is trained with the poor hyper parameters. In this paper, the phishing websites are detected by extracting the various features from the collection of phishing and legitimate websites obtained from PhishTank and starting point directory service. This constructed feature vector is further processed by the proposed feature selection module GenFea to obtain the reduced set of features. This reduced feature vector is further processed by the proposed phishing detection module PhiDec to predict the type of a website. The performance of the proposed approach is compared with the existing machine learning classifiers and neural network classifiers. From the experimental results, it is observed that the proposed approach outperformed the other existing classifiers for detecting the phishing websites.

Human Motion Gesture Recognition Algorithm in Video Based on Convolutional Neural Features of Training Images

The main work of human motion gesture recognition is to recognize and analyze the behavior of human objects in the video. Although the current research in the field of human motion gesture recognition has achieved certain results, the human motion gesture recognition in real life scenes has great effects due to factors such as camera movement, target scale transformation, dynamic background, viewing angle, and illumination. This article first proposes a new method of constructing human motion posture features to describe human behavior. This method is based on deep convolutional neural network features and topic models. Experiments have verified that compared with the traditional feature map extracted from the convolutional neural network fully connected layer, the feature map extracted from the convolutional neural network convolutional layer is not only lower in dimension but also has higher discrimination. Secondly, based on the feature map of the convolutional neural network, the training map downsampling strategy is used to overcome the interference caused by the object's scale change and shape change. Finally, based on the basketball gesture recognition method, the behavior performance of the legs and arms in 9 basketball actions of walking, running, jumping, standing dribbling, walking dribbling, running dribbling, shooting, passing and receiving is analyzed. As well as the corresponding signal waveform characteristics, a two-stage data division method for basketball is proposed. The unit action data is extracted for analysis to realize feature extraction. In order to select the most suitable classifier for basketball gesture recognition, the constructed feature vector uses four Different classifiers are trained to construct different classifiers to realize the division of actions.

Fault Diagnosis for Rolling Bearings Based on Composite Multiscale Fine-Sorted Dispersion Entropy and SVM With Hybrid Mutation SCA-HHO Algorithm Optimization

The health condition of rolling bearing possesses a significant impact on the safety and efficiency of rotating machinery. Accordingly, to diagnose the faults in rolling bearings effectively and accurately, a novel hybrid approach coupling variational mode decomposition (VMD), composite multiscale fine-sorted dispersion entropy (CMFSDE) and support vector machine (SVM) optimized by mutation sine cosine algorithm and Harris hawks optimization (MSCAHHO) is proposed in the paper. Firstly, VMD is employed to decompose raw vibration signals with various fault types into different sets of intrinsic mode functions (IMFs) to weaken the non-stationarity of signals, before which the parameter $K$ of VMD is decided through central frequency observation method. Subsequently, CMFSDE is put forward in this paper to analyze the complexity of fault signals by fully considering the relationship between neighboring elements based on composite multiscale technique, with which the representative features of different fault samples are extracted to construct feature vectors. Later, an enhanced hybrid optimization approach called MSCAHHO is proposed by integrating sine cosine algorithm (SCA) and a periodic mutation strategy to improve Harris hawks optimization (HHO). Then, MSCAHHO is employed to optimize the parameters of SVM, after which the optimal SVM model is utilized for fault classification. Finally, the performance of the proposed methodology is evaluated with four validity indices through comparative experiments. The experimental results reveal that the proposed VMD-CMFSDE-MSCAHHO-SVM method achieves favorable diagnosis results comparing with other relevant methods.

Pose detection of parallel robot based on improved Hough‐K‐means and SURF algorithms

It is difficult to detect end pose of auto electrocoating conveying parallel robot based on binocular vision because of less features, noise and blurred edges in image. So a pose detection method is presented. Firstly, according to distance and similarity between image points, the bilateral filtering is adopted to reduce noise. Secondly, for extracting straight lines on blurred edges and feature points in lines accurately, a Hough-K-means algorithm using K-means clustering analysis in parameter space after Hough transform is proposed. Thirdly, the discrete Gaussian–Hermite moment is used as feature descriptor to solve the low accuracy problem of principal directions of feature points described by speeded-up robust features (SURF) descriptor. The extracted feature points are selected according to the similarity metric between constructed feature vectors. Finally, the vision model is developed to calculate the three-dimensional pose parameters of parallel robot based on obtained point pairs. The experiment results show that, compared with the SURF algorithm, the average matching time decreases by 21.58%, the average deviations of detected poses in x, z and β decrease by 1.149 mm, 0.646 mm and 1.164° respectively by using the proposed method. The speed and accuracy of pose detection of parallel robot can be improved.

Content-based image retrieval using local ternary wavelet gradient pattern

With the invention of low cost image capturing devices, image acquisition is no longer a difficult task. The immense popularity of such devices has led to the production of large number of images. For accessing these images easily, efficient indexing and organization of images is required. The field of Content-Based Image Retrieval (CBIR) attempts to achieve this goal. This paper proposes a new multiresolution descriptor- Local Ternary Wavelet Gradient Pattern (LTWGP), for CBIR which combines shape feature and texture feature and utilizes this combination at multiple scales of image to construct feature vector for retrieval. Discrete Wavelet Transform (DWT) coefficients of grayscale image are computed followed by computation of Local Ternary Pattern (LTP) codes of resulting DWT coefficients. Finally, Histogram of Oriented Gradients (HOG) of resulting LTP codes is computed to construct feature vector. The advantage of this technique is that it computes texture through LTP which extracts complex structural arrangement of pixels more efficiently than other texture features such as Local Binary Pattern (LBP), and shape feature through HOG which measures shape of an object as a local feature without performing any segmentation operation. The combination of LTP and HOG is exploited at multiple resolutions of image through DWT to extract varying level of details so that the features left undetected at one level get detected at another level. The combination of LTP, HOG, and DWT constructs efficient feature descriptor which exploits multiple features at more than one resolution of image. The proposed feature descriptor efficiently extracts local directional information obtained through the combination of LTP and HOG at multiple levels of resolution decomposed through DWT. Performance of the proposed method is measured in terms of precision and recall and tested on four benchmark datasets, namely, Corel-1 K, Corel-5 K, Corel-10 K, and GHIM-10 K. The experimental results demonstrate that the proposed method outperforms other state-of-the-art CBIR techniques in terms of precision and recall.

Target Recognition in SAR Image by Joint Classification of Target Region and Shadow

Background: A synthetic aperture radar (SAR) automatic target recognition (ATR) method is proposed in this paper via the joint classification of the target region and shadow. Methods: The elliptical Fourier descriptors (EFDs) are used to describe the target region and shadow extracted from the original SAR image. In addition, the relative positions between the target region and shadow are represented by a constructed feature vector. The three feature vectors complement each other to provide more comprehensive descriptions of the target’s physical properties, e.g., sizes and shape. In the classification stage, the three feature vectors are jointly classified based on the joint sparse representation (JSR). JSR is a multi-task learning algorithm, which can not only represent each component properly but also exploit the inner correlations of different components. Finally, the target type is determined to the class with the minimum reconstruction error. Results: Experiments have been conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The proposed method achieves a high recognition accuracy of 96.86% for 10-class recognition problem under the standard operating condition (SOC). Moreover, robustness of the proposed method is also superior over the reference methods under the extended operating conditions (EOCs) like configuration variance, depression angle variance, and noise corruption. Conclusion: Therefore, the effectiveness and robustness of the proposed method can be quantitatively demonstrated by the experimental results.

Atom table convolutional neural networks for an accurate prediction of compounds properties

Machine learning techniques are widely used in materials science. However, most of the machine learning models require a lot of prior knowledge to manually construct feature vectors. Here, we develop an atom table convolutional neural networks that only requires the component information to directly learn the experimental properties from the features constructed by itself. For band gap and formation energy prediction, the accuracy of our model exceeds the standard DFT calculations. Besides, through data-enhanced technology, our model not only accurately predicts superconducting transition temperatures, but also distinguishes superconductors and non-superconductors. Utilizing the trained model, we have screened 20 compounds that are potential superconductors with high superconducting transition temperature from the existing database. In addition, from the learned features, we extract the properties of the elements and reproduce the chemical trends. This framework is valuable for high throughput screening and helpful to understand the underlying physics.

IMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou's 5-steps Rule.

Background: Methylation is one of the most important post-translational modifications in the human body which usually arises on lysine among the most intensely modified residues. It performs a dynamic role in numerous biological procedures, such as regulation of gene expression, regulation of protein function and RNA processing. Therefore, to identify lysine methylation sites is an important challenge as some experimental procedures are time-consuming.Objective: Herein, we propose a computational predictor named iMethylK_pseAAC to identify lysine methylation sites.Methods: Firstly, we constructed feature vectors based on PseAAC using position and composition rel-ative features and statistical moments. A neural network is trained based on the extracted features. The performance of the proposed method is then validated using cross-validation and jackknife testing.Results: The objective evaluation of the predictor showed accuracy of 96.7% for self-consistency, 91.61% for 10-fold cross-validation and 93.42% for jackknife testing.Conclusion: It is concluded that iMethylK_pseAAC outperforms the counterparts to identify lysine methylation sites such as iMethyl_pseACC, BPB_pPMS and PMeS.

Bearings fault diagnosis based on adaptive local iterative filtering–multiscale permutation entropy and multinomial logistic model with group-lasso

Bearing fault diagnosis attracts great attention because the bearing condition has direct effects on productivity and safety in industry. To accurately identify the operating condition of bearings, a novel bearing fault diagnosis method based on adaptive local iterative filtering–multiscale permutation entropy and multinomial logistic model with group-lasso is first put forward in this article. In the proposed method, adaptive local iterative filtering was applied to decompose the nonlinear and non-stationary vibration signals into intrinsic mode functions. The multiscale permutation entropy values of the first several intrinsic mode functions were calculated to characterize the complexity of intrinsic mode functions in different scales, and they constructed feature vectors after normalization. Multinomial logistic model with group-lasso could perform multiple classifications with an embedded approach for feature selection, which is distinct from the traditional methods with two steps of dimensionality reduction and classification. Finally, the proposed method was verified with experiment data from Case Western Reserve University considering four conditions: different fault types, different damages, multiple types, and different loads. The results indicate that the proposed method is effective in identifying different categories of rolling bearings.

Cascaded Static and Dynamic Local Feature Extractions for Face Sketch to Photo Matching

The automatic identification of a corresponding photo from a face sketch can assist in criminal investigations. The face sketch is rendered based on the descriptions elicited by the eyewitness. This may cause the face sketch to have some degrees of shape exaggeration that make some parts of the face geometrically misaligned. In this paper, we attempt to address the effect of these influences by a cascaded static and dynamic local feature extraction method so that the constructed feature vectors are built based on the correct patches. In the proposed method, the feature vectors from the local static extraction on a sketch and photo are matched using the nearest neighbors. Then, some n most similar photos are shortlisted based on the nearest neighbors. These photos are eventually re-matched using feature vectors from the local dynamic extraction method. The feature vectors are matched using the L 1 -distance measure. The experimental results for The Chinese University of Hong Kong (CUHK) Face Sketch Database (CUFS) and CUHK Face Sketch FERET Database (CUFSF) datasets indicate that the proposed method outperforms the state-of-the-art methods.

Automatic Diagnosis of Rolling Element Bearing Under Different Conditions Based on RVMD and Envelope Order Capture

Fault characteristic frequency is the main basis for rolling element bearing diagnostics but finding a suitable frequency band for demodulation and searching for the fault characteristic frequencies consume a lot of time and manpower in practice. A data-driven method based on recursive variational mode decomposition (RVMD), and an envelope order capture is proposed to realize the automatic fault diagnosis of bearing under different operating conditions. The process starts with a new proposed RVMD of the vibration signal, where the mode with maximum kurtosis of the unbiased autocorrelation of the envelope is selected to get envelope order spectrum. Thereafter, an order capture algorithm is designed to automatically search for the fault characteristic orders in theory, which are used for constructing feature vectors for diagnosis. The proposed method is tested on two test-beds which both contain the same type of bearing (SKF6205) but operate in different conditions, and gets good performance in bearing diagnosis. In addition, the fault diagnosis of test-bed two using training samples that are from test-bed one is investigated. This method reveals well generalization capability in the fault diagnosis of the same type of rolling element bearing under different operating conditions.

Blind Parameter Identification of MAR Model and Mutation Hybrid GWO‐SCA Optimized SVM for Fault Diagnosis of Rotating Machinery

As a crucial and widely used component in industrial fields with great complexity, the health condition of rotating machinery is directly related to production efficiency and safety. Consequently, recognizing and diagnosing rotating machine faults remain to be one of the main concerns in preventing failures of mechanical systems, which can enhance the reliability and efficiency of mechanical systems. In this paper, a novel approach based on blind parameter identification of MAR model and mutation hybrid GWO‐SCA optimization is proposed to diagnose faults for rotating machinery. Signals collected from different types of faults were firstly split into sets of intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the decomposing mode number K of which was preset with central frequency observation method. Then the multivariate autoregressive (MAR) model of all IMFs was established, whose order was determined by Schwartz Bayes Criterion (SBC), and all parameters of the model were identified blindly through QR decomposition, where key features were subsequently extracted via principal component analysis (PCA) to construct feature vectors of different fault types. Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO‐SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). The optimal SVM model was later employed to classify different fault samples. The engineering application and contrastive analysis indicate the availability and superiority of the proposed method.

Revealing Privacy Vulnerabilities of Anonymous Trajectories

The proliferation of various mobile devices equipped with GPS positioning modules makes the collection of trajectories more easier than ever before, and more and more trajectory datasets have been available for business applications or academic researches. Normally, published trajectories are often anonymized by replacing real identities of mobile objects with pseudonyms (e.g., random identifiers); however, privacy leaks can hardly be prevented. In this paper, we introduce a novel paradigm of de-anonymization attack re-identifying trajectories of victims from anonymous trajectory datasets. Different from existing attacks, no background knowledge or side channel information about the target dataset is required. Instead, we claim that, for each moving object, there exist some mobility patterns that reflect the preference or usual behavior of the object, and will not change dramatically over a period of time. As long as those relatively stable patterns can be extracted from trajectories and be utilized as quasi-identifiers, trajectories can be linked to anonymous historical ones. To implement such kind of de-anonymization attacks, an adversary only needs to collect a few trajectory segments of a victim, the durations of which do not necessarily overlap with that of trajectories in the target dataset (in simple terms, those trajectory segments are not necessary sub-trajectories included in the target dataset). Since the movements of victims in public areas could be observed openly, an adversary can obtain traces or locations about the victims either by direct monitoring them (e.g., tracking) or from third parties (e.g., social-networks). Then, the adversary extracts useful patterns from both the historical trajectories in the accessible dataset and newly obtained trajectory segments of victims, the historical trajectory with most similar patterns to that of a victim is considered as belonging to the victim. In order to demonstrate the feasibility of such attacks, we conduct extensive trace-driven simulations. We extract road segment preferences and stop of interests from trajectories of vehicles, and construct feature vectors (mobility patterns) of vehicles according to them, used for trajectory comparisons. Simulation results show that the adversary could re-identify anonymous trajectories effectively.

Automatic extraction and identification of narrow butt joint based on ANFIS before GMAW

To improve the automation level of the welding robots, the automatic extraction and identification of the weld seam is the pre-requisite of the intelligent welding robots. During the real industrial environment, the welding environment often has the characteristics of weak texture, poor contrast, reflections from metallic surfaces, and imperfections on the work piece. These characteristics will seriously affect the accurate extraction of weld seam. To realize the automatic path planning of welding robots, a new method of automatic extraction and identification of narrow butt joint under weak contrast is realized to serve the 3D path teaching of welding robots. To achieve accurate extraction of narrow butt joint under weak contrast, this paper designs a new seam extraction operator to achieve accurate and fast extraction of weld seam with various shapes and sizes. And the shape feature of weld seam is used to construct feature vector. Finally, to get optimal classification performance, the adaptive network-based fuzzy inference system (ANFIS) is adopted in this paper to finish identification of narrow butt joints. The experimental results show that the proposed algorithm could quickly and accurately realize the extraction and identification of the narrow butt joints under weak contrast.

Analysing Event-Related Sentiments on Social Media with Neural Networks

<span lang="EN-US">Sentiment analysis is performed to determine the polarity of opinion on a subject. It has been applied to text corpora such as movie reviews, financial documents to glean information about overall-sentiment anc produce actionable data. Recent events have demonstrated that polling can be sometimes unreliable. People can be difficult to access through conventional polling methods and less than frank in polls. In the era of social media, voters are likely to more freely express their opinion on social media forums about divisive events especially in media where anonymity exists. Analyzing the prevailing opinion on these forums can indicate if there are any deficiencies in polling and can be a valuable addition to conventional polling. We analyzed text corpora from Reddit forums discussing the recent referendum in Britain to exit from the EU (known as Brexit). Brexit was an important world event and was very divisive in the run-up and post vote. We analyzed sentiment in two ways: Initially we tried to gauge positive, negative, and neutral sentiments. In the second analysis, we further split these sentiments into six different polarities based on the directionality of the positive and negative sentiments (for or against Brexit). Our technique utlilized paragraph vectors (Doc2Vec) to construct feature vectors for sentiment analysis with a Multilayer Perceptron classifier. We found that the second analysis yielded overall better results; although, our classifier didn’t perform as well in classifying positive sentiments. We demonstrate that it is possible glean valuable information from complicated and diverse corpora such as multi-paragraph comments from reddit with sentiment analysis.</span>

Ensemble Prediction of Synergistic Drug Combinations Incorporating Biological, Chemical, Pharmacological, and Network Knowledge

Combinatorial therapy may reduce drug side effects and improve drug efficacy, making combination therapy a promising strategy to treat complex diseases. However, in the existing computational methods, the natural properties and network knowledge of drugs have not been adequately and simultaneously considered, making it difficult to identify effective drug combinations. Computational methods that incorporate multiple sources of information (biological, chemical, pharmacological, and network knowledge) offer more opportunities to screen synergistic drug combinations. Therefore, we developed a novel Ensemble Prediction framework of Synergistic Drug Combinations (EPSDC) to accurately and efficiently predict drug combinations by integrating information from multiple-sources. EPSDC constructs feature vector of drug pair by concatenating different types of drug similarities, and then uses these groups in a feature-based base predictor. Next, transductive learning is applied on heterogeneous drug-target networks to achieve a network-based score for the drug pair. Finally, two types of ensemble rules are introduced to combine the feature-based score and the network-based score, and then potential drug combinations are prioritized. To demonstrate the effect of the ensemble rule, comprehensive experiments were conducted to compare single models and ensemble models. The experimental results indicated that our method outperformed the state-of-the-art method in five-fold cross validation and de novo prediction tests on the two benchmark datasets. We further analyzed the effect of maximum length of the meta-path and the impacts of different types of features. Moreover, the practical usefulness of our method was confirmed in the predicted novel drug combinations. The source code of EPSDC is available at https://github.com/KDDing/EPSDC.

Social community detection and message propagation scheme based on personal willingness in social network

Personal willingness is one of the most important factors influencing the construction of social community and the message propagation in social network. Personal willingness is used to describe the subjective initiative of node (user) to communicate information with outside world. The personal willingness is greater, the corresponding user is more willing to make communication with outside world, then the user is more likely to join the corresponding community. So, personal willingness may reduce the probability of generating large-scale communities so as to improve the accuracy and reliability of community detection and increase the stability of community structure. This paper proposes a social community detection and message propagation scheme based on personal willingness in social network. In the proposed scheme, the social community detection algorithm extracts node attributes and then uses modularity degree, interest degree and personal willingness to sophisticatedly detect social communities; also, the message propagation method is based on the exponential model, which constructs feature vector by edge feature and node feature, willingness vector by personal willingness and community willingness, and related basic relationship by propagation probability and propagation delay. Based on the Weibo, YouTube and Digg data, the experiments show that our proposed scheme can ensure the stability and reliability of social community detection and add the initiative and effectiveness of message propagation among users.