Morphology Similarity Distance for Bearing Fault Diagnosis Based on Multi-Scale Permutation Entropy

Bearings are crucial components in rotating machines,which have direct effects on industrial productivity and safety.To fast and accurately identify the operating condition of bearings,a novel method based on multi⁃scale permutation entropy(MPE)and morphology similarity distance(MSD)is proposed in this paper.Firstly,the MPE values of the original signals were calculated to characterize the complexity in different scales and they constructed feature vectors after normalization.Then,the MSD was employed to measure the distance among test samples from different fault types and the reference samples,and achieved classification with the minimum MSD.Finally,the proposed method was verified with two experiments concerning artificially seeded damage bearings and run⁃to⁃failure bearings,respectively.Different categories were considered for the two experiments and high classification accuracies were obtained.The experimental results indicate that the proposed method is effective and feasible in bearing fault diagnosis.

Multi⁃Scale Dilated Convolutional Neural Network for Hyperspectral Image Classification

Aiming at the problem of image information loss,dilated convolution is introduced and a novel multi⁃scale dilated convolutional neural network(MDCNN)is proposed.Dilated convolution can polymerize image multi⁃scale information without reducing the resolution.The first layer of the network used spectral convolutional step to reduce dimensionality.Then the multi⁃scale aggregation extracted multi⁃scale features through applying dilated convolution and shortcut connection.The extracted features which represent properties of data were fed through Softmax to predict the samples.MDCNN achieved the overall accuracy of 99.58% and 99.92% on two public datasets,Indian Pines and Pavia University.Compared with four other existing models,the results illustrate that MDCNN can extract better discriminative features and achieve higher classification performance.

Lumbar spine localisation method based on feature fusion

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is proposed.First,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft tissues.In the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent spine.The 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning accuracy.On an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is achieved.In addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s respectively.Therefore,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

Path Planning for Lunar Surface Robots Based on Improved Ant Colony Algorithm

In the real-world situation,the lunar missions’scale and terrain are different according to various operational regions or worksheets,which requests a more flexible and efficient algorithm to generate task paths.A multi-scale ant colony planning method for the lunar robot is designed to meet the requirements of large scale and complex terrain in lunar space.In the algorithm,the actual lunar surface image is meshed into a gird map,the path planning algorithm is modeled on it,and then the actual path is projected to the original lunar surface and mission.The classical ant colony planning algorithm is rewritten utilizing a multi-scale method to address the diverse task problem.Moreover,the path smoothness is also considered to reduce the magnitude of the steering angle.Finally,several typical conditions to verify the efficiency and feasibility of the proposed algorithm are presented.

Bending behavior of carbon nanotube-reinforced composites

The multi scale modeling method was utilized to study the bending characteristics of a carbon nanotube （CNT） and CNT reinforced composites. Through combining molecular dynamics and continuum mechanics, the tensional and flexural modulus of a CNT were calculated by a finite element model constructed by reticulate beams with solid cylinder shape and energy equal to C-C bonds. Then, another beam element with hollow cylinder shape and equivalent stiffness was utilized in place of a CNT in a matrix, thus, a multi scale representative volume element （RVE） model of CNT reinforced composite was established. Using this RVE model, the bending behavior of CNT based composites was analyzed. The influence of diameter D, length L, aspect ratio L/D, volume fraction, chiral of CNTs and shape of RVE as well as the arrangement of CNTs in matrix on the rein forcement effect of flexural modulus of resultant nanocomposites were further discussed. The ob tained data provide useful information for the design of CNT reinforced composites.

A new multi-scale analytic algorithm for edge extraction of strawberry leaf images in natural light

In this study,a new algorithm was proposed for edge extraction of greenhouse strawberry leaf in natural light based on the 4-level daubechies 5(‘db5’)wavelet decomposition.This algorithm adopts different segmentation methods for the reconstructed images at different scales to erase the external background and the internal leaf vein interference.There were two advantages of this method.One was that it can provide the abstraction from different spaces to express a same image.The other one was that some image features are hard to be acquired in some scale spaces,while the features are easy to be obtained in other scale spaces.In this image process methods,the Otsu threshold segmentation was to obtain the binary image areas,and the Canny segmentation is to obtain the accurate gradient edges,then the morphological methods and the logical calculus methods were to avoid the fragments inside the leaf area and the adhesions outside the leaf area.Since the strawberry leaf images were different respectively,and the greenhouse optical radiation and reflection may cause local non-uniform illumination of leaf image,the pseudo canny edges of leaf image ere divided into three categories in this research.The first category was the external pseudo canny edges area of the first layer reconstructed leaf image,the second category was the internal pseudo canny edges area in highlight of the third layer reconstructed leaf image,the third category was the internal pseudo canny edges area of significantly different grayscale of the third layer reconstructed leaf image.The different processing methods were constructed for the three kinds of different texture features based on the multi scale reconstructed images,then the complete and the accurate leaf edges without interference were obtained.Finally,the multi scale method was simplified and a remarkably effective segmentation algorithm was deduced for the greenhouse strawberry leaf in natural light.

Estimation on structural responses using multi scale measurements

Different measurands from the different types of sensors can obtain different information regarding the structural behavior in a real structural health monitoring system.To enrich information and estimate the structural responses based on much more known information,the estimation on structural responses using multi scale measurements from multi-type sensors is proposed in this paper.Pattern identification is constructed with the pattern library given by strain measurements and deformation measurements.Considering the uncertainty of the measurements as well as to enhance the robustness of the proposed algorithm,more than one best pattern is selected to synthesize the finally estimated stress responses.To validate the capacity of the proposed acquisition method using multi scale measurements,finite element model analysis is conducted to estimate the structural stress response in Shenzhen Bay Stadium as an example.The performance of the pattern identifications,constructed by two kinds of pattern libraries captured by sole strain measurement,and multi scale measurements which are constructed by both kinds of strain measurements and deformation measurements,respectively,are compared in this paper to observe measurements constructed from strain measurements and deformation measurements outperformed others.Errors analysis for a series of parametric studies in which noise at different levels has also included in the measurements are further carried out,and robustness of the proposed information acquisition scheme under noisy measurement is demonstrated.

Retrieving Aerial Scene Images with Learned Deep Image-SketchFeatures

This paper investigates the problem of retrieving aerial scene images by using semantic sketches, since the state-of-the-art retrieval systems turn out to be invalid when there is no exemplar query aerial image available. However, due to the complex surface structures and huge variations of resolutions of aerial images, it is very challenging to retrieve aerial images with sketches and few studies have been devoted to this task. In this article, for the first time to our knowledge, we propose a framework to bridge the gap between sketches and aerial images. First, an aerial sketch-image database is collected, and the images and sketches it contains are augmented to various levels of details. We then train a multi-scale deep model by the new dataset. The fully-connected layers of the network in each scale are finally connected and used as cross-domain features, and the Euclidean distance is used to measure the cross-domain similarity between aerial images and sketches. Experiments on several commonly used aerial image datasets demonstrate the superiority of the proposed method compared with the traditional approaches.