Table of Content

25 June 2020, Volume 48 Issue 6

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Mechanical Engineering

Automatic Measurement Method of Wall Thickness of Spun Workpieces Based on Abaqus 



XIA Qinxiang, ZHANG Yilong, XIAO Gangfeng, et al

2020, 48(6):  1-7.  doi:10.12141/j.issn.1000-565X.190697

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When the numerical simulation of spinning forming is carried out by Abaqus finite element software，the wall thickness of spun workpieces can not be obtained quickly and accurately，and the process can not be visua-lized． In order to solve this problem，a method of automatic measurement and visualization of the wall thickness of spun workpieces based on Abaqus numerical simulation was proposed． Automatic measurement of wall thickness of spun workpieces was realized through coding in Python language． Using ＲSG dialog builder to customize menu in-terface，a toolkit for automatic measurement of wall thickness of spun workpieces based on Abaqus was developed．Automatic measurement of wall thickness of conical parts，cylindrical parts with inner ribs and other spinning parts was realized，and the results were displayed in the form of contour． By comparing the measurement results with the automatic wall thickness measurement tool and the three-dimensional software，it is found that the developed toolkit can accurately measure the wall thickness distribution of various kinds of spun workpieces．


Fast Prediction Model for the Dynamic Characteristics of the Hydraulic Mount Considering the Decoupler Membrane’s Stiffness Variation 



ZHOU Dawei, ZUO Shuguang, WU Xudong, et al

2020, 48(6):  8-15.  doi:10.12141/j.issn.1000-565X.190309

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To deal with the difficulties in the amplitude-sensitive characteristics prediction，a novel fast prediction model for the dynamic characteristics of the hydraulic mount considering decoupler membrane’s stiffness variation was proposed． Firstly，the working principles and characteristics of the hydraulic mount were clarified，and the nonlinear contact force between the decoupler membrane and its metallic cage was introduced to build a lumped pa-rameter model for the hydraulic mount． Secondly，a fluid-structure-interaction finite element analysis on the decou-pler membrane was carried out，and the relationship between the equivalent displacement and the nonlinear reaction force was described with a piecewise function． Moreover，the influences of the thickness，diameter of the decoupler membrane and the gap between the decoupler and its metallic cage on the stiffness variation characteristics were in-vestigated． The BP neural networks was employed to establish the prediction model for the stiffness of decoupler membrane． Finally，the dynamic characteristics of a typical hydraulic mount with a fixed decoupler was numerically simulated，and the simulation results were compared with the measured results． The comparison results show that
the proposed prediction model is more accurate，and it can largely reduce the time needed to calculate the dynamic characteristics of the hydraulic mount．


Study on Sliding Mode Control with Disturbance Observer Integrating Skyhook Model for Active Suspension Systems 



QIN Wu, KANG Yingzi, SHANGGUAN Wenbin, et al

2020, 48(6):  16-24,33.  doi:10.12141/j.issn.1000-565X.190364

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A sliding mode control with disturbance observer integrating skyhook model (RMDOSMC) was proposed to improve the performance of active suspension systems (ASSs)． The uniform boundedness and uniform ultimate boundedness of ASSs were proved． The sprung mass displacement and speed of skyhook model can provide refe-rence trajectory for that of the sprung mass in ASSs． The disturbance observer is designed for estimating uncertain-ties in ASSs，which are external disturbance force acting on the sprung mass and parameters of suspension stiffness and damping． Under the road excitation of step and random，experimental results were presented to validate the ef-fectiveness of the proposed approach． Furthermore，under the road excitation of step，the performance indexes，in-cluding acceleration of the sprung mass，the suspension deflection and the dynamical load of tire，were calculated by using the linear quadratic optimal control ( LQR)，the sliding model control with disturbance observer (DOSMC) and the proposed control． Compared with the linear quadratic optimal control and the sliding mode con-trol with disturbance observer，ASSs with the proposed control can obtain better performance indexes．


Analysis of Force and Contact Characteristics of Ｒotating Arm Bearings for RV Transmissions Mechanism



WU Suzhen HE Weidong ZHANG Yinghui

2020, 48(6):  25-33.  doi:10.12141/j.issn.1000-565X.190452

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A multi-body dynamic force calculation method was proposed to deal with the difficulty of accurate force calculation due to the complicated and time-varying characteristics of crank arm bearing force． This method was fur-ther employed to analyze the influence patterns of input speed and load condition on the force of the rotating arm bearing． The results indicate that the force of the arm bearing changes cyclically with the crank angle，and the rota-tion speed has no influence on the extreme value of the arm bearing force but has effects on its fatigue life． The force of the rotating arm bearing increases as the load increases． In addition，the comparision between the result of geometric analysis and that of the proposed algorithm suggests a good consistency between the two． Based on these results，the finite element method was further used to analyze the influence of different working loads on the contact stress of the rotating arm bearings． The results show that there is a single-sided force phenomenon in the arm bea-ring，and the contact stress of each needle is different from each other． These findings provide a theoretical basis for engineering application and optimization analysis of rotating arm bearings．


Study on Flexible Biomimetic Robotic Fish Modeling and Its Cruising Speed Based on CEL



ZHANG Kaisheng, ZOU Qingbiao, ZHAO Bo, et al

2020, 48(6):  34-41,57.  doi:10.12141/j.issn.1000-565X.190813

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A fluid-structure coupling model for autonomous swimming of the flexible biomimetic robotic fish was es-tablished based on the principle of coupled Eulerian Lagrangian (CEL) method，aiming to investigate the influence of the design parameters (fish stiffness，fish swing frequency and fish swing amplitude) of the flexible biomimetic robotic fish on its cruising speed，and to obtain the parameter combination of the bionic robot fish when it reaches the optimal cruise speed． The design parameters of different combinations were obtained by orthogonal test，and the cruise performance of flexible biomimetic robotic fish under different parameter combinations was simulated by nu-merical simulation． The influence of various design parameters on the cruise speed of flexible biomimetic robotic fish was calculated according to the simulation data，and accordingly the better parameter combination was ob-tained． The results show that the CEL method can consider the flexible deformation of flexible biomimetic robotic fish during swimming，so it can more truly reflect the swimming behavior of flexible biomimetic robotic fish in actual situation． In the sample range，the cruise speed of flexible biomimetic robotic fish model increases first and then
decreases with the increase of fish body stiffness and fish body swing amplitude，and it increases with fish body swing frequency． When fish body stiffness (elastic modulus) is 30 MPa，fish body swing amplitude is 0. 13 times longer of fish body and fish body swing frequency is 5. 16 Hz，the cruise speed of flexible biomimetic robotic fish reaches a high level．


Traffic & Transportation Engineering

Numerical Modeling of Two-Phase Turbulence Closure for Wave Transformation over a Coral Ｒeef



ZOU Xuefeng ZHU Liangsheng ZHANG Shanju

2020, 48(6):  41-49.  doi:10.12141/j.issn.1000-565X.190403

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Wave transmission over a coral reef is very complex，and the wave breaking over the reef flat has signifi-cant impacts on the reefs and the breakwaters． In order to better understand the characteristics of regular waves propagating over the coral reefs，a wave numerical model based on Ｒeynolds-Averaged Navier-Stokes equations was constructed． The turbulence model was designed with two-phase k-ω SST equations． The numerical simulation was carried out in open source library，OpenFOAM． The numerical model was validated with the experiments in the lite-rature． Wave heights，wave setups and surface elevations on the coral reef were analyzed and compared with the results of standard k-ω SST model． The results show that two-phase model can significantly weaken overproduction of turbulence energy near the free surface and provide better predictions of wave height and setup than standard model． Wave breaking in the surf zone causes large turbulence energy at the wave crest while little at the wave trough． Under the effects of turbulence energy，the wave height decreases and the wave setdown increases at the
breaking point，and the energy dissipation increases after the wave breaking． This numerical model can be used for calculating wave breaking in the design of harbor construction．


Performance of Vehicle Electromagnetic Coupling Flywheel Energy Storage Device



LI hong CHU Jiangwei YUAN Shankun

2020, 48(6):  50-57.  doi:10.12141/j.issn.1000-565X.190719

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A new type of flywheel energy storage structure-electromagnetic coupling flywheel energy storage system (EC-FESS) was proposed by considering the advantages of zero friction，none impact and adjustable speed of elec-tromagnetic slip clutch (ESC)． Power transmission route in vehicle deceleration state (braking or sliding) with EC-FESS was analyzed． Double closed-loop controller was designed to achieve the rapid response of magnetic pole shaft． Ｒecovery efficiency and its influencing factors of EC-FESS were quantitatively analyzed based on Simulink software． Through simulated test in platform，the influence of ESC stepless speed regulation on the energy recovery efficiency was verified． The results show that the stable value of energy recovery efficiency of EC-FESS is 33. 6% under different initial braking speeds，and EC-FESS and controller in Simulink are reasonable． In addition，energy recovery efficiency is the highest when ESC is adjusted．


Wind Barrier’s Effect on Aerodynamic Load and Driving Safety of High-Speed Trains at Tunnel-Bridge-Tunnel Section



SHI Chenghua, WANG Ang, DENG E, et al

2020, 48(6):  58-68,76.  doi:10.12141/j.issn.1000-565X.190807

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The tunnel-bridge-tunnel section of high-speed railway is often accompanied by strong cross-wind． When the train runs through the section，it is often impacted by the cross wind，which seriously endanger the driving safe-ty of the train． A three-dimensional CFD numerical model of train-tunnel-bridge-wind barrier and a dynamic cou-pling analysis model of wind-train-rail-bridge were established based on the computational fluid dynamics ＲNG tur-bulence model and the theory of porous media． The variation characteristics of aerodynamic load and driving safety index of high-speed trains in the process of passing through the tunnel-bridge-tunnel section were studied． The re-sults show that the abrupt amplitude of aerodynamic load of each compartment was significantly reduced by more than 50%，after the wind barrier was set at the tunnel-bridge-tunnel section． The transverse force and overturning moment were most significantly affected by the wind barrier，with a decrease of more than 88%． The wind barrier can significantly decrease the driving safety index of the train，and the fluctuation amplitude of the safety index of the windward and leeward wheelsets (except the first and third wheelsets of the head car) was the same． The safety
index of the head car plays a role in controlling the safety of the whole train，especially the front wheels of the bogie of the head car (the first and third wheelsets)． The process of running from the tunnel onto the bridge is less safe than that of running from the bridge into the tunnel．


Condition Assessment Model for Cable-Stayed Bridges Based on Evidence Reasoning Framework



LIU Xiaoling, WANG Bing, HUANG Qiao, et al

2020, 48(6):  69-76.  doi:10.12141/j.issn.1000-565X.190691

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A bridge condition assessment method based on hierarchical evidence reasoning framework was proposed to help understand the overall technical condition of cable-stayed bridges． Firstly，the index system of cable-stayed bridge was divided into three levels． The first level is the bridge overall condition，the second level is primary，se-condary，tertiary and traffic safety components，and the bottom level is the actual detection index． Then，the under-lying index information was transformed into evidence，and the reliability of index evidence considered the impor-tance of bridge elements and the reliability of collected data． Afterwards，the Dempster-Shafer combination rule was fused to convey the accidental and cognitive uncertainty in the whole model，and the primary，secondary，tertiary and traffic safety component indicators as well as the overall state indicators of the bridge were obtained． Finally，the rationality of the proposed evidential reasoning framework was validated by a single-tower cable-stayed bridge．The results show that: the combination based on Dempster-Shafer rule loses less data in the normalization process thus more applicable; the index reliability coefficient corrects the subjective judgment error in bridge evaluation，making the evaluation result more conservative and objective; the established bridge evaluation method can provide
more accurate and detailed status of bridge sub-components，and can also be used for bridge group status ranking．


Neural Network Model for Ｒoad Aggregate Size Calculation Based on Multiple Features



PEI Lili, SUN Zhaoyun, HU Yuanjiao, et al

2020, 48(6):  77-86.  doi:10.12141/j.issn.1000-565X.190870

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In the process of road construction and maintenance，the efficient and accurate measurement of aggregate gradation in asphalt mixture is an important factor to ensure the stability of mixture skeleton structure and construc-tion quality． Considering the methods based on a single geometric model can not meet the requirements of particle size calculation accuracy in construction practice，a neural network model for road aggregate size calculation based on multiple features was proposed． Firstly，geometric features were extracted from the collected aggregate particle images，and the extracted feature data were cleaned and normalized to establish the sample data set． Secondly，the characteristic factors with strong correlation with aggregate particle size were extracted by correlation analysis． Fi-nally，multi-layer perceptron (MLP) neural network was constructed to train the data set，and the important cha-racteristics weight representing aggregate particle size was obtained by the sensitivity analysis． Thus the particle size of coarse aggregate can be accurately calculated． The results show that the aggregate particle size calculation meth-od proposed in this paper has a higher fitting accuracy (R² =0. 91) than the results measured by the traditional geo-metric models such as secondary moment and equivalent ellipse． It not only improves the accuracy obviously，but also realizes fast virtual screening and significantly improves the subsequent screening efficiency．


Computer Science & Technology

Adjustment Strategy for Automobile Hood Matching in Virtual Environment



JIN Xia, HU Juncong, WANG Wei, et al

2020, 48(6):  87-96.  doi:10.12141/j.issn.1000-565X.190384

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Based on the virtual environment of finite element analysis，a virtual model for the automobile hood matching was constructed to study the matching process in automobile manufacturing and assembly． The influence of gravity and the deformation of elastic adjusting elements on the matching process was clarified through the com-parative analysis of different mechanical factors and the sensitivity analysis of the parameters of the adjusting ele-ments． And the sensitivity of the adjusting elements’parameters to the position deviation of each reference point was obtained． Finally，a fast adjustment strategy for the automobile hood matching considering the deformation of gravity and elastic adjustment elements was proposed by combining the analysis results and the multi-objective opti-mization method． The strategy was successfully applied to the virtual matching of automobile hood，and the simula-tion and experimental results show that this strategy can effectively reduce the cost of adjustment assembly and im-prove the adjustment efficiency．


Joint Deep Recommendation Model Based on Double-Layer Attention Mechanism



LIU Huiting, JI Qiang, LIU Huimin, et al

2020, 48(6):  97-105.  doi:10.12141/j.issn.1000-565X.190830

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Many e-commerce websites keep a large amount of customer’s reviews． The reviews was exploited by most recommendation systems by only considering their importance in the word-level rather than in the comment-level． The effectiveness of the recommendation model will be reduced by exclusively considering important words in the reviews and ignoring really useful reviews． Based on this，a joint deep recommendation model based on double-layer attention mechanism (DLALSTM) was proposed． First，DLALSTM uses bidirectional long short-term memory network (BiLSTM) to jointly model the customer and reviews from both word and customer levels，and aggregates the reviews representation and the customer/item representation by a double-layer attention mechanism． Then，the latent representation of customer and item learned from the reviews was incorporated into the customer preference and item feature obtained from the rating matrix to make rating prediction． DLALSTM was compared with the com-monly used recommendation methods through experimental evaluation on different domain datasets of Yelp and Ama-zon． It finds that the performance of DLALSTM exceeds the state of art recommended methods． Meanwhile，the pro-posed model can alleviate the sparsity problem to some extent and has good interpretability．


FtH-Net Method for Predicting Height Based on Footprint Image



WANG Nian, FAN Xuchen, ZHANG Yuming, et al

2020, 48(6):  106-113,133.  doi:10.12141/j.issn.1000-565X.190860

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A regression prediction algorithm based on deep learning was proposed to solve the problem of predicting height through footprint information in criminal investigation． Firstly，the original data was preprocessed to obtain the data set suitable for the deep learning regression model． Secondly，a new network architecture foot to height-net (FtH-Net) containing edge extraction and regression was proposed according to the characteristics of the footprint data． Finally，a prediction model with good performance was achieved by the data set based on the first two steps and regression network training． The experimental results show that，compared with the traditional one，the new method can greatly improve the accuracy of prediction while ensuring the generalization ability of the model，and the accuracy of prediction for people within 2cm height can reach 67%．


Screening Method for Feature Matching Based on Dynamic Window Motion Statistics



XIANG Hengyong, ZHOU Li, BA Xiaohui, et al

2020, 48(6):  114-122.  doi:10.12141/j.issn.1000-565X.190769

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During the image local feature matching process，error matches will be eliminated effectively by conside-ring the motion statistics of features． However，the current grid-based method of motion statistics works poorly with zoom and rotation． To solve this problem，a screening method for feature matching based on dynamic window mo-tion statistics was proposed． Firstly，the algorithm builds a fast approximate nearest neighbor index structure based on the location of image feature points． Then it sets up the dynamic window and computes motion statistics． Fina-lly，it eliminates error matches with the score of motion statistics． The experimental results show that，compared with other methods，the proposed method has a significant advantage over the algorithm based on grid in predicating precision and recall rate when the scale and angle change greatly． And in more general scenarios，the overall matc-hing effect of this algorithm is better than other real-time matching methods． Meanwhile，this algorithm has good time performance and can be applied to real-time tasks．


MSER and CNN-Based Method for Character Detection in Ancient Yi Books



CHEN Shanxiong, HAN Xu, LIN Xiaoyu, et al

2020, 48(6):  123-133.  doi:10.12141/j.issn.1000-565X.190812

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The detection of Yi character is the basis for the recognition of ancient Yi character． The detection preci-sion directly affects the accuracy of recognition． Due to the fact that the ancient Yi books have complex layouts，non-normative typesetting，and mixed text and graphics，a character detection method for ancient Yi books based on maximally stable extremal regions (MSEＲ) and convolutional neural network (CNN) was proposed． Firstly，the scanned images of ancient Yi books with non-local mean filtering were preprocessed． Secondly，the binary image result was obtained by an improved method of local adaptive binarization． Then，non-text areas were removed by a-dopting the method based on heuristic rules． Finally，a combining method of MSEＲ and CNN was used to detect single character． The experimental results show that the proposed approach can effectively separate the text and non-text areas，and achieves high accuracy and recall rate in single character detection experiments． And it effec-tively solves the problem of character detection in character recognition of ancient books．


Group Decision Making Method Based on Evidential Reasoning Rule Under Conflicting Interval Belief Structures



ZHANG Xingxian WANG Yingming CHEN Shengqun

2020, 48(6):  134-142.  doi:10.12141/j.issn.1000-565X.190789

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A group decision making method based on evidential reasoning rule under conflicting interval belief structures was proposed to solve the problem of interval belief structures combination，considering the conflict expert opinions in the group decision-making environment． Firstly，group decision-making problems were formed based on the evaluation information of decision makers (or experts) on alternatives． Expert evaluation information (points of view) was taken as evidence，and the validity of evaluation information was checked and normalized． Secondly，the expert weight vector was determined by the support of the evidence，and all the evaluation information was fused with the evidential reasoning rule． Finally，the minimum and maximum regret value method was used to select the optimal scheme． The case analysis shows that the proposed method has consistent evidence fusion results in the process of evidence combination，and the combination results are reasonable and convergent． In addition，the spe-cificity of the evidence can also be maintained．


Clustering Method of Power Load Profiles Based on KPCA and Improved K-means



LIANG Jingzhang, HUANG Xingshu, WU Lijuan, et al

2020, 48(6):  143-150.  doi:10.12141/j.issn.1000-565X.200009

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A clustering method of power load profiles based on kernel principal component analysis (KPCA) and improved K-means algorithm was proposed to improve clustering accuracy of power load profiles． Firstly，a K-means algorithm based on density idea，namely，density K-means (DK-means) was proposed by combining the density clustering method with partitioning clustering algorithm K-means． And the clustering effect was comparatively ana-lyzed on the experimental set of power load profiles． Then the dimensionality reduction accuracy and speed of vari-ous dimensionality reduction algorithms were compared on the experimental set． Finally，the dimensionality reduc-tion and clustering ability of the KPCA + DK-means combination algorithm was analyzed． The results show that，firstly，Davies-Bouldin Index (DBI) is more suitable for the evaluation index of power load profiles clustering; se-condly，taking DBI as the evaluation index，the clustering accuracy of DK-means is higher than that of K-means，BIRCH，DBSCAN and EnsClust algorithms; thirdly，compared with LLE，MDS and ISOMAP，the dimensionality reduction speed of KPCA is faster; finally，the KPCA + DK-means combination algorithm has good dimensionality reduction and clustering ability，and its clustering accuracy and clustering efficiency are better than those of DK-means． In short，the KPCA + DK-means combination algorithm can achieve efficient dimensionality reduction and accurate clustering of power load profiles，thus plays a key technical role in accurately extracting information of electricity consumption behavior．