Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

Rolling bearing plays an important role in rotating machinery and its working condition directly affects the equipment efficiency. While dozens of methods have been proposed for real-time bearing fault diagnosis and monitoring, the fault classification accuracy of existing algorithms is still not satisfactory. This work presents a novel algorithm fusion model based on principal component analysis and Dempster-Shafer evidence theory for rolling bearing fault diagnosis. It combines the advantages of the learning vector quantization (LVQ) neural network model and the decision tree model. Experiments under three different spinning bearing speeds and two different crack sizes show that our fusion model has better performance and higher accuracy than either of the base classification models for rolling bearing fault diagnosis, which is achieved via synergic prediction from both types of models

Explosion gravitation field algorithm with dust sampling for unconstrained optimization

This research was funded by the National Natural Science Foundation of China (Nos. 61572227, 61772227, 61702214), the Development Project of Jilin Province of China (Nos 20170101006JC, 20180414012GH, 20170203002GX, 20190201293JC), Zhuhai Premier-Discipline Enhancement Scheme, China (Grant 2015YXXK02) and Guangdong Premier Key-Discipline Enhancement Scheme, China (Grant 2016GDYSZDXK036). This work was also supported by Jilin Provincial Key Laboratory of Big Date Intelligent Computing, China (No. 20180622002JC).Peer reviewedPostprin

EGFAFS:A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data

An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition

Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms

Control of astrocyte progenitor specification, migration and maturation by Nkx6.1 homeodomain transcription factor.

Although astrocytes are the most abundant cell type in the central nervous system (CNS), little is known about their molecular specification and differentiation. It has previously been reported that transcription factor Nkx6.1 is expressed in neuroepithelial cells that give rise to astrocyte precursors in the ventral spinal cord. In the present study, we systematically investigated the function of Nkx6.1 in astrocyte development using both conventional and conditional Nkx6.1 mutant mice. At early postnatal stages, Nkx6.1 was expressed in a subpopulation of astrocytes in the ventral spinal cord. In the conventional Nkx6.1KO spinal cord, the initial specification of astrocyte progenitors was affected by the mutation, and subsequent migration and differentiation were disrupted in newborn mice. In addition, the development of VA2 subtype astrocytes was also inhibited in the white matter. Further studies with Nkx6.1 conditional mutants revealed significantly delayed differentiation and disorganized arrangement of fibrous astrocytes in the ventral white matter. Together, our studies indicate that Nkx6.1 plays a vital role in astrocyte specification and differentiation in the ventral spinal cord

C-Silicon-based metasurfaces for aperture-robust spectrometer/imaging with angle integration

Compared with conventional grating-based spectrometers, reconstructive spectrometers based on spectrally engineered filtering have the advantage of miniaturization because of the less demand for dispersive optics and free propagation space. However, available reconstructive spectrometers fail to balance the performance on operational bandwidth, spectral diversity and angular stability. In this work, we proposed a compact silicon metasurfaces based spectrometer/camera. After angle integration, the spectral response of the system is robust to angle/aperture within a wide working bandwidth from 400nm to 800nm. It is experimentally demonstrated that the proposed method could maintain the spectral consistency from F/1.8 to F/4 (The corresponding angle of incident light ranges from 7{\deg} to 16{\deg}) and the incident hyperspectral signal could be accurately reconstructed with a fidelity exceeding 99%. Additionally, a spectral imaging system with 400x400 pixels is also established in this work. The accurate reconstructed hyperspectral image indicates that the proposed aperture-robust spectrometer has the potential to be extended as a high-resolution broadband hyperspectral camera

5-{2-(4-Chloro­phen­yl)-1-[2-(4-chloro­phen­yl)-1-(3,4,5-trimeth­oxy­phen­yl)eth­oxy]eth­yl}-1,2,3-trimeth­oxy­benzene

The title compound, C34H36Cl2O7, is a by-product from the reaction of 4-chloro­benzyl­zinc chloride with 3,4,5-trimeth­oxy­benzaldehyde. In each of the two 1,2-diphenyl­ethyl moieties, the two benzene rings are arranged in a trans conformation and make Car—C—C—Car torsion angles of 163.64 (19) and 174.43 (18)°. The crystal structure is stabilized by van der Waals inter­actions only