Attention mechanism based multi-scale feature extraction of bearing fault diagnosis

Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating machinery.In practical applications,bearings often work at various rotational speeds as well as load conditions.Yet,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further explored.Therefore,a multi-scale deep belief network(DBN)method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals,containing four primary steps:preprocessing of multi-scale data,feature extraction,feature fusion,and fault classification.The key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention mecha-nism.The benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this method.Furthermore,the aforementioned method is compared with four classical fault diagnosis methods reported in the literature,and the comparison results show that our pro-posed method has higher diagnostic accuracy and better robustness.

Clothing Parsing Based on Multi-Scale Fusion and Improved Self-Attention Mechanism

Due to the lack of long-range association and spatial location information,fine details and accurate boundaries of complex clothing images cannot always be obtained by using the existing deep learning-based methods.This paper presents a convolutional structure with multi-scale fusion to optimize the step of clothing feature extraction and a self-attention module to capture long-range association information.The structure enables the self-attention mechanism to directly participate in the process of information exchange through the down-scaling projection operation of the multi-scale framework.In addition,the improved self-attention module introduces the extraction of 2-dimensional relative position information to make up for its lack of ability to extract spatial position features from clothing images.The experimental results based on the colorful fashion parsing dataset(CFPD)show that the proposed network structure achieves 53.68%mean intersection over union(mIoU)and has better performance on the clothing parsing task.

Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.

Mechanism of Aperture Blinding in Screening Wet and Fine Coal

The agglomeration of moist fine coal and the mechanism of aperture blinding in screening were analyzed. The theoretical analysis and a pilot test on that the elastic screen mesh can overcome the aperture blinding problem were presented.

Pay-based Screening Mechanism: Personnel Selection in the View of Economics Theory

Based on economic theories, the paper studies the personnel selection at the asymmetric job market using signaling and screening model. The authors hold the opinion that an organization can screen the candidates' signaling based on the self-selection principle by providing an appropriate compensation choice. A pay-based screening mechanism is proposed to help the organization drive away the nonqualified applicants and retain the excellent applicants.

Surrogate-based modeling and dimension reduction techniques for multi-scale mechanics problems

Successful modeling and/or design of engineering systems often requires one to address the impact of multiple ＂design variables＂ on the prescribed outcome.There are often multiple,competing objectives based on which we assess the outcome of optimization.Since accurate,high fidelity models are typically time consuming and computationally expensive,comprehensive evaluations can be conducted only if an efficient framework is available.Furthermore,informed decisions of the model/hardware＇s overall performance rely on an adequate understanding of the global,not local,sensitivity of the individual design variables on the objectives.The surrogate-based approach,which involves approximating the objectives as continuous functions of design variables from limited data,offers a rational framework to reduce the number of important input variables,i.e.,the dimension of a design or modeling space.In this paper,we review the fundamental issues that arise in surrogate-based analysis and optimization,highlighting concepts,methods,techniques,as well as modeling implications for mechanics problems.To aid the discussions of the issues involved,we summarize recent efforts in investigating cryogenic cavitating flows,active flow control based on dielectric barrier discharge concepts,and lithium（Li）-ion batteries.It is also stressed that many multi-scale mechanics problems can naturally benefit from the surrogate approach for ＂scale bridging.＂

Attention-relation network for mobile phone screen defect classification via a few samples

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

Processing, characterization, room temperature mechanical properties and fracture behavior of hot extruded multi-scale B_4C reinforced 5083 aluminum alloy based composites

Microstructural characteristics and mechanical behavior of hot extruded Al5083/B4C nanocomposites were studied.Al5083and Al5083/B4C powders were milled for50h under argon atmosphere in attrition mill with rotational speed of400r/min.For increasing the elongation,milled powders were mixed with30%and50%unmilled aluminum powder(mass fraction)with meanparticle size of>100μm and<100μm and then consolidated by hot pressing and hot extrusion with9:1extrusion ratio.Hot extrudedsamples were studied by optical microscopy,scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),transmission electron microscopy(TEM),tensile and hardness tests.The results showed that mechanical milling process andpresence of B4C particles increase the yield strength of Al5083alloy from130to566MPa but strongly decrease elongation(from11.3%to0.49%).Adding<100μm unmilled particles enhanced the ductility and reduced tensile strength and hardness,but usingthe>100μm unmilled particles reduced the tensile strength and ductility at the same time.By increasing the content of unmilledparticles failure mechanism changed from brittle to ductile.

Sentiment classification model for bullet screen based on self-attention mechanism

With the development of short video industry,video and bullet screen have become important ways to spread public opinions.Public attitudes can be timely obtained through emotional analysis on bullet screen,which can also reduce difficulties in management of online public opinions.A convolutional neural network model based on multi-head attention is proposed to solve the problem of how to effectively model relations among words and identify key words in emotion classification tasks with short text contents and lack of complete context information.Firstly,encode word positions so that order information of input sequences can be used by the model.Secondly,use a multi-head attention mechanism to obtain semantic expressions in different subspaces,effectively capture internal relevance and enhance dependent relationships among words,as well as highlight emotional weights of key emotional words.Then a dilated convolution is used to increase the receptive field and extract more features.On this basis,the above multi-attention mechanism is combined with a convolutional neural network to model and analyze the seven emotional categories of bullet screens.Testing from perspectives of model and dataset,experimental results can validate effectiveness of our approach.Finally,emotions of bullet screens are visualized to provide data supports for hot event controls and other fields.

Multi-scale modeling for prediction of mechanical performance in brazed GH99 thin-walled structure

Superalloy thin-walled structures are achieved mainly by brazing,but the deformation process of brazed joints is non-uniform,making it a challenging research task.This paper records a thorough investigation of the effect of brazing parameters on the microstructure of joints and its mechanical properties,which mainly inquires into the deformation and fracture mechanisms in the shearing process of GH99/BNi-5a/GH99 joints.The macroscopic-microscopic deformation mechanism of the brazing interface during shearing was studied by Crystal Plasticity(CP)and Molecular Dynamics(MD)on the basis of the optimal brazing parameters.The experimental results show that the brazing interface is mainly formed by(Ni,Cr,Co)(s,s)and possesses a shear strength of approximately 546 MPa.The shearing fracture of the brazed joint occurs along the brazing seam,displaying the characteristics of intergranular fracture.MD simulations show that dislocations disassociate and transform into fine twinning with increased strain.CP simulated the shear deformation process of the brazed joint.The multiscale simulation results are consistent with the experimental results.The mechanical properties of thin-walled materials for brazing are predicted using MD and CP methods.

Mechanical properties of multi-scale germanium specimens from space solar cells under electron irradiation

During long-term service in space,Gallium Arsenide(GaAs)solar cells are directly exposed to electron irradiation which usually causes a dramatic decrease in their performance.In the multilayer structure of solar cells,the germanium(Ge)layer occupies the majority of the thickness as the substrate.Due to the intrinsic brittleness of semiconductor material,there exist various defects during the preparation and assembly of solar cells,the influences of which tend to be intensified by the irradiation effect.In this work,first,Ge specimens for mechanical tests were prepared at scales from microscopic to macroscopic.Then,after different doses of electron irradiation,the mechanical properties of the Ge specimens were investigated.The experimental results demonstrate that electron irradiation has an obvious effect on the mechanical property variation of Ge in diverse scales.The four-point bending test indicates that the elastic modulus,fracture strength,and maximum displacement of the Ge specimens all increase,and reach the maximum value at the irradiation dose of 1×10^(15)e/cm^(2).The micrometer scale cantilever and nanoindentation tests present similar trends for Ge specimens after irradiation.Atomic Force Microscope(AFM)also observed the change in surface roughness.Finally,a fitting model was established to characterize the relation between modulus change and electron irradiation dose.

Mechanical response of a tunnel subjected to strike-slip faulting processes,based on a multi-scale modeling method

The stick-slip action of strike-slip faults poses a significant threat to the safety and stability of underground structures.In this study,the north-east area of the Longmenshan fault,Sichuan,provides the geological background;the rheological characteristics of the crustal lithosphere and the nonlinear interactions between plates are described by Burger’s viscoelastic constitutive model and the friction constitutive model,respectively.A large-scale global numerical model for plate squeezing analysis is established,and the seemingly periodic stick-slip action of faults at different crust depths is simulated.For a second model at a smaller scale,a local finite element model(sub-model),the time history of displacement at a ground level location on the Longmenshan fault plane in a stick-slip action is considered as the displacement loading.The integration of these models,creating a multi-scale modeling method,is used to evaluate the crack propagation and mechanical response of a tunnel subjected to strike-slip faulting.The determinations of the recurrence interval of stick-slip action and the cracking characteristics of the tunnel are in substantial agreement with the previous field investigation and experimental results,validating the multi-scale modeling method.It can be concluded that,regardless of stratum stiffness,initial cracks first occur at the inverted arch of the tunnel in the footwall,on the squeezed side under strike-slip faulting.The smaller the stratum stiffness is,the smaller the included angle between the crack expansion and longitudinal direction of the tunnel,and the more extensive the crack expansion range.For the tunnel in a high stiffness stratum,both shear and bending failures occur on the lining under strike-slip faulting,while for that in the low stiffness stratum,only bending failure occurs on the lining.

Low-salinity-based enhanced oil recovery literature review and associated screening criteria

A thorough literature review is conducted that pertains to low-salinity-based enhanced oil recovery(EOR).This is meant to be a comprehensive review of all the refereed published papers,conference papers,master’s theses and other reports in this area.The review is specifically focused on establishing various relations/characteristics or"screening criteria"such as:(1)classification/grouping of clays that have shown or are amenable to low-salinity benefits;(2)clay types vs.range of residual oil saturations;(3)API gravity and down hole oil viscosity range that is amenable for low salinity;(4)salinity range for EOR benefits;(5)pore sizes,porosity,absolute permeability and wettability range for low-salinity EOR;(6)continuous low-salinity injection vs.slug-wise injection;(7)grouping of possible low-salinity mechanisms;(8)contradictions or similarities between laboratory experiments and field evidence;and(9)compositional variations in tested low-salinity waters.A proposed screening criterion for low-salinity waterflooding is introduced.It can be concluded that either one or more of these mechanisms,or a combination thereof,may be the case-specific mechanism,i.e.,depending on the particular oil–brine–rock(OBR)system rather than something that is"universal"or universally applicable.Therefore,every OBR system that is unique or specific ought to be individually investigated to determine the benefits(if any)of low-salinity water injection;however,the proposed screening criteria may help in narrowing down some of the dominant responsible mechanisms.Although this review primarily focuses on sandstones,given the prominence of carbonates containing^60%of the world’s oil reserves,a summary of possible mechanisms and screening criteria,pertaining to low-salinity waterflooding,for carbonates is also included.Finally,the enhancement of polymer flooding by using low-salinity water as a makeup water to further decrease the residual oil saturation is also discussed.

Numerical failure analysis of a continuous reinforced concrete bridge under strong earthquakes using multi-scale models

Previous failure analyses of bridges typically focus on substructure failure or superstructure failure separately. However, in an actual bridge, the seismic induced substructure failure and superstructure failure may influence each other. Moreover, previous studies typically use simplified models to analyze the bridge failure; however, there are inherent defects in the calculation accuracy compared with using a detailed three-dimensional （3D） finite element （FE） model. Conversely, a detailed 3D FE model requires more computational costs, and a proper erosion criterion of the 3D elements is necessary. In this paper, a multi-scale FE model, including a corresponding erosion criterion, is proposed and validated that can significantly reduce computational costs with high precision by modelling a pseudo-dynamic test of an reinforced concrete （RC） pier. Numerical simulations of the seismic failures of a continuous RC bridge based on the multi-scale FE modeling method using LS-DYNA are performed. The nonlinear properties of the bridge, various connection strengths and bidirectional excitations are considered. The numerical results demonstrate that the failure of the connections will induce large pounding responses of the girders. The nonlinear deformation of the piers will aggravate the pounding damages. Furthermore, bidirectional earthquakes will induce eccentric poundingsto the girders and different failure modes to the adjacent piers.

Electric-magnetic-force characteristics of rare earth barium copper oxide superconductor high-field coils based on screening effect and strain sensitivity

Rare earth barium copper oxide(REBCO)is the most researched and commercialized second-generation high-temperature superconducting material.Due to the anisotropic structure,strong deformation sensitivity,and central field errors caused by screening current effects,it is still a challenge for commercialization applications.In this study,the transversely isotropic constitutive relationship is selected as the mechanical model based on the structural characteristics of REBCO tapes,and suitable microelements are selected to equate the elastic constants using their average stress-strain relationships.Then,a two-dimensional axisymmetric model for coils wound by single-layer tapes is constructed to analyze the dependence of the electric-magnetic-force distribution in the tape on the strain.Finally,the anisotropic approximation of the homogenized bulk method is used to equate large-turn high-field coils,and the electric-magnetic-force distribution characteristics of the coils with/without screening effects and mechanical strain conditions are investigated,respectively.The results reveal that the mechanical strain has a weakening effect on the electromagnetic field distribution of superconducting tapes,but causes a significant enhancement in the force field distribution.In the presence of 0.5% mechanical strain,the maximum weakening of the peak value of the current density and the critical current density inside the high-field coil can reach about 8% and 13%,respectively,with a nearly 5 times increase in the peak stress.The screening current makes the current field distribution inside the coil improve by about 10 times.The screening current induced magnetic field can reach up to 0.8 T,making the relative error of the high-field coil center up to 7.8%.

Multi-Scale Superhydrophobic Anti-Icing Coating for Wind Turbine Blades

As a surface functional material,super-hydrophobic coating has great application potential in wind turbine blade anti-icing,self-cleaning and drag reduction.In this study,ZnO and SiO2 multi-scale superhydrophobic coatings with mechanical flexibility were prepared by embedding modified ZnO and SiO2 nanoparticles in PDMS.The prepared coating has a higher static water contact angle(CA is 153°)and a lower rolling angle(SA is 3.3°),showing excellent super-hydrophobicity.Because of its excellent superhydrophobic ability and micro-nano structure,the coating has good anti-icing ability.Under the conditions of−10
C and 60%relative humidity,the coating can delay the freezing time by 1511S,which is 10.7 times slower than the normal freezing time.More importantly,due to the mechanical properties provided by SiO2 and the synergistic effect of micro-nano particles,the coating has excellent mechanical durability.After 10 wear tests,the contact angle of the coating is still as high as 141°and the rolling angle is 6.8°.This research provides a theoretical reference for the preparation of a mechanically stable coating with a simple preparation process,as well as a basic research on the anti-icing behavior of the coating.

MULTI-SCALE AND MULTI-PHASE NANOCOMPOSITE CERAMIC TOOLS AND CUTTING PERFORMANCE

An advanced ceramic cutting tool material Al2O3/TiC/TiN （LTN） is developed by incorporation and dispersion of micro-scale TiC particle and nano-scale TiN particle in alumina matrix. With the optimal dispersing and fabricating technology, this multi-scale and multi-phase nanocomposite ceramic tool material can get both higher flexural strength and fracture toughness than that of A1203/TiC （LZ） ceramic tool material without nano-scale TiN particle, especially the fracture toughness can reach to 7.8 MPa . m^0.5. The nano-scale TiN can lead to the grain fining effect and promote the sintering process to get a higher density. The coexisting transgranular and intergranular fracture mode induced by micro-scale TiC and nano-scale TiN, and the homogeneous and densified microstructure can result in a remarkable strengthening and toughening effect. The cutting performance and wear mechanisms of the advanced multi-scale and multi-phase nanocomposite ceramic cutting tool are researched.

Unified Treatment of Screening Coulomb and Anharmonic Oscillator Potentials in Arbitrary Dimensions

A mapping is obtained relating radial screened Coulomb systems with low screening parameters to radial anharmonic oscillators in N-dimensional space. Using the formalism of supersymmetric quantum mechanics, it is shown that exact solutions of these potentials exist when the parameters satisfy certain constraints.