No abstract

No abstract

No abstract

An accurate and efficient intelligent fault diagnosis of mobile robotic roller bearings can significantly enhance the reliability and safety of mechanical systems. To improve the efficiency of intelligent fault classification of mobile robotic roller bearings, this paper proposes a parallel machine learning algorithm using fine-grained-mode Spark on a Mesos big data cloud computing software framework. Through the segmentation of datasets and the support of a parallel framework, the parallel processing technology Spark is combined with a support vector machine (SVM), and a parallel single-SVM algorithm is realized using Scala language. In this approach, empirical mode decomposition (EMD) is applied to extract the energy of the acceleration vibration signal in different frequency bands as features. The parallel EMD-SVM approach is applied to detect faults in mobile robotic roller bearings from fault vibration signals. The experimental results show that it can accurately and effectively identify the faults, and it outperforms existing methods based on parallel deep belief network (DBN) and parallel radial basis function neural network under different training set sizes. Fault classification tests are conducted on outerrace and inner-race faults: in both cases, the proposed parallel EMD-SVM outperforms the serial EMD-SVM in terms of the classification accuracy and classification time under different test sizes. For a small number of nodes, the processing time of the proposed Spark model is less than that of Hadoop but close to that of Storm. For 17 slave nodes, the average precision, average recall, and average F1 score of Spark on Mesos in the fine-grained mode reach 97.3, 97.8, and 97.9%, respectively. The parallel EMD-SVM algorithm in the big data Spark cloud computing framework can improve the accuracy of intelligent fault classification, albeit by a small margin, with higher processing speed and learning convergence rate.

The cyber intrusion prevention model represents a new means of cyber protection with intelligent defense capability. It can not only detect intrusion behavior but also respond to such behavior in a timely manner. This study applies deep learning theory and semi-supervised clustering to cyber intrusion prevention technology. Deep learning based on deep structures represents the current development trend of neural networks. Semi-supervised learning uses a large amount of unlabeled cyber traffic data and a small amount of labeled cyber traffic data to achieve cyber intrusion prevention with a low recognition error rate. Discriminative deep belief network (DDBN)-based cyber defense technology has emerged as a research hotspot in the field of cyber intrusion prevention owing to its low error rate. This paper proposes a cyber intrusion prevention technology using DDBN for large-scale semi-supervised deep learning based on local and non-local regularization to overcome the problem of high classification error rates of the cyber intrusion prevention model. Through comparisons with the cyber intrusion prevention results of the Hopfield, support vector machine (SVM), generative adversarial network (GAN), and deep belief network-random forest (DBN-RFS) classifiers, the proposed DDBN model is shown to have the lowest error rate. Thus, the proposed approach can improve the performance of the cyber intrusion prevention system. The training and testing error rates of the exponent loss function with local and non-local regularization (exponent with LNR) are lower than those of the exponent, square, and hinge loss functions. The experimental results show that the running time decreases as the number of hidden layers increases, especially with 6144 and 4096 hidden layer nodes. INDEX TERMS Cyber security, discriminative deep belief networks, intrusion prevention, local and non-local regularization, semi-supervised deep learning.

This paper describes an effective on-line polymer characterization technique by using small-angle light-scattering (SALS) image processing software and wavelet analysis. The phenomenon of small-angle light scattering has been applied to give information about transparent structures on morphology. Real-time visualization of various scattered light image and light intensity matrices is performed by the optical image real-time processing software for SALS. The software can measure the signal intensity of light scattering images, draw the frequency-intensity curves and the amplitude-intensity curves to indicate the variation of the intensity of scattered light in different processing conditions, and estimate the parameters. The current study utilizes a one-dimensional wavelet to delete noise from the original SALS signal and estimate the variation trend of maximum intensity area of the scattered light. So, the system brought the qualitative analysis of the structural information of transparent film success.

A support vector machine (SVM)-based method, GPCRpred, has been developed for predicting families and subfamilies of GPCRs from the dipeptide composition of proteins. The method is further able to predict five major classes or families of GPCRs with an overall Matthew's correlation coefficient (MCC) and accuracy of 0.81 and 97.5% respectively [16].

A new pattern recognition method based on wavelet packet transform (WPT) and directed acyclic graph support vector machine (DAGSVM) is put forward for fault diagnosis of roller bearing. The fault pattern recognition model setup has two phases. The first phase is to extract the feature of faulty vibration signals from roller bearing by WPT via a db3 wavelet. The second phase is to use DAGSVM to recognize fault pattern of roller bearing. The testing results illustrates that WPT is more effective to diagnose fault types than the WT method. It is observed that among the strategy of multi-class SVM, DAGSVM acquires the highest accuracy, and therefore, this demonstrates the fact that suitable fault pattern recognition strategy can improve the overall performance of fault diagnosis. The present research illustrated that the features extracted by WPT represent the fault pattern of roller bearing, and the DAGSVM trained on these features achieved high recognition accuracies.