Table of Content

25 June 2021, Volume 49 Issue 6

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2021, 49(6):  0. 

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Mechanical Engineering

Influence of Filling Speed on Forming Accuracy and Printing Efficiency of Parts at Corner Contour Locations in Fused Deposition Modelling



CHEN Songmao CHEN Yulin

2021, 49(6):  1-8.  doi:10.12141/j.issn.1000-565X.200582

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It is difficult to simultaneously balance the forming accuracy and printing efficiency when filling parts at corner contour locations in fused deposition modelling due to the structural defects such as repeated and missing deposition zones and the influence of parameters such as filling speed and corner angle. In this paper, firstly, the influence of filling speed on the forming accuracy and the printing efficiency of parts at corner contour locations was studied with experimental and mathematical methods. The research results show that the forming accuracy is negatively related to the filling speed, but meanwhile relatively high forming accuracy and high printing efficiency could also be realized under a higher filling speed at corner contour locations in fused deposition modelling. The higher the filling speed, the weaker the effect on the printing efficiency,and the printing time tends to a constant value when the filling speed increases. Then, by setting the weight coefficients for forming accuracy and printing efficiency, a continuous function of the optimal filling speed and the preliminary design idea of adaptive control were proposed.

Numerical Simulation of Flow Pattern Structure in Boundary Layer on Flat Plate Surfaces



HE Shengtai LAN Wei HU Xingjun

2021, 49(6):  9-18.  doi:10.12141/j.issn.1000-565X.200454

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The γ-Reθt  transition model was used to numerically simulate the boundary layer on the surface of the plate. The development of the boundary layer in the normal and the flow direction, as well as the effect of factors such as wall roughness,incoming wind speed,turbulence intensity, and pressure gradient on the boundary layer were studied. The study show that, in the transition interval, skin-friction coefficient and the total pressure at a certain height from the wall undergo abrupt changes; in the flow direction, the boundary layer continues to thicken but its growth rate continues to decline; in the laminar boundary layer, the viscous stress is dominant, but in the turbulent boundary layer, the sub-layer is firstly dominated by viscous stress, then the viscous stress decreases rapidly and the Reynolds stress increases rapidly, finally Reynolds stress is dominant in the log-law region. During the transition process, the maximum value of the viscous stress near the wall continues to increase but its attenuation becomes faster. The maximum value of Reynolds stress continues to increase but the normal height corresponding to the maximum value continues to decrease. The increase in roughness makes the viscous sub-layer of the turbulent boundary layer disappears and the log-law layer moves down. The increase of turbulence intensity and wind speed advances the transition, shortens the transition interval, and reduces the growth rate of the turbulent boundary layer. The pressure gradient has a great influence on the growth rate of the turbulent boundary layer, and the adverse pressure gradient thickens the boundary layer quickly.

Research on Joint Motion Control of Soft Wearable Upper Limb Rehabilitation Robots



ZHAI Yuyi, MA Xinyuan, CHEN Dongdong, et al

2021, 49(6):  19-27.  doi:10.12141/j.issn.1000-565X.200498

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A soft wearable upper limb rehabilitation robot was designed based on the principles of light weight, flexibility, easy-wear and ergonomics to make up the shortcomings of traditional rehabilitation robot, such as heavy weight, difficult to wear and poor motion flexibility. The robot body was make of elastic cloth and the pneumatic artificial muscle（PAM）was used as the antagonistic driving joint of the actuator to realize the elbow flexion/extension and pronation/supination motion. Moreover, the dynamic model of the rehabilitation robot is established according to the mathematical model of the new PAM and Lagrange dynamic equation. Aiming at the control difficulties of the soft system driven by PAMs, such as easy chattering and delayed response, a PID controller based on RBF neural network was designed. Aiming at the problem that the initial parameters of PID controller depend on experience, the fuzzy PID controller was designed to determine the optimal initial parameters of PID controller. Finally, the performance of the RBF-PID controller was verified through simulation and experimental research, and was compared with the traditional PID controller. The results show that the RBF-PID controller has fast response speed and high control stability, and it can realize the stable control of the soft rehabilitation robot.

Influence of Angle Element on Crashworthiness of Thin-Walled Tubes



LIU Wangyu, TIAN Pengfei, JIN Lin, et al

2021, 49(6):  28-33,39.  doi:10.12141/j.issn.1000-565X.190898

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Angle elements play a very important role in energy absorption characteristics of tubes. However, the existing researches only study the influence of specific angle elements on crashworthiness, which is not universal. In this paper, concave corners were introduced into the cross-section design of thin walled tubes, a new concave tube was put forward to investigate the influence of angle element between 90°and 180°on crashworthiness. Crushing responses of concave tubes were studied by using both experiments and numerical simulations, through which the finite element models were well validated. Further, a range of concave tubes were parametrically modeled with angle elements. The result showed that the angle elements observably affected deformation mode, when angle element changed from 100°to 170°, Fm decreased 45.64%. Finally, theoretical expression of the mean crushing force of concave tubes was derived and the mean crushing force decreased with the increase of angle element, which was coincident with the numerical result.

Rheology Design for Flow Channel with Variant Radius of Manifold in T-type Die



MA Xiangjun

2021, 49(6):  34-39.  doi:10.12141/j.issn.1000-565X.200547

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Base on the analysis of melt flow characteristics in flow channel in T-type extrusion die, a new structure of flow channel with radius-varying manifold and two different thickness choke zones was proposed in this paper. The radius-varying manifold is beneficial to shorten the melt residence time, while two different thickness choke zones is beneficial to reduce the extrusion pressure. Under the condition of uniform melt exit flow rate across the width direction of the flow channel, the differential equation of demarcation curve between two choke zones was deduced based on rheology and solved by numerical method and fitted in design. Case design was conducted with the method proposed in this paper and compared with flow channel designed with constant radius manifold and choke zone thickness. The results show that the differential equation of demarcation curve is reliable and can be used to guide the design of flow channel in T-type extrusion die. When the flow channel with radius-varying manifold and two different thickness choke zones meets the requirement of the uniformity of melt exit flow rate across the width direction of the flow channel, it can not only shorten the melt residence time , but also decrease the extrusion pressure significantly.

Computer Science & Technology

Building Energy Consumption Prediction Based on Word Embedding and Convolutional Neural Network 



JI Tianyao WANG Tingshao

2021, 49(6):  40-48.  doi:10.12141/j.issn.1000-565X.200079

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Building energy consumption prediction needs both time series features and categorical features, but traditional models can only deal with one of the features. Aiming at this problem, a new neural network integrating one-dimensional convolutional kernel and word embedding was proposed in this paper. The one-dimensional convolutional kernel can extract the continuous time series features, and the word embedding model can embed the discrete categorical features, based on which a building energy consumption prediction model is established that can simultaneously process both time series features and categorical features. By comparing with the gradient boosting decision regression tree and the long short time memory network, it is proved that the proposed model has good performance in efficiency and accuracy. In terms of hyperparameter adjustment, the automatic hyperparameter optimization algorithm based on Bayesian optimization was adopted, and the algorithm can find the optimal hyperparameter in the search space. Compared with manual optimization, the automatic hyperparameter optimization algorithm can improve the perfor-mance of the model in a relatively short time. Finally, simulation studies were conducted and the results demonstrate that the proposed model is better in performance than the ensemble learning model and the long short-time memory network.

Social Relationship-Based Task Distribution Mechanism of Crowdsensing



ZHANG Wendong. SHI Gang, TIAN Shengwei, et al

2021, 49(6):  49-55.  doi:10.12141/j.issn.1000-565X.200395

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In order to establish a permanent and stable task distribution link in the perceptual service process, firstly, an intimacy quantification method based on social attributes(IQSA) was proposed; secondly, a community detection algorighm based on information entropy similarity（CDIES）was proposed by combining the information entropy theory with social relationship; finally, IQSA algorithm was compared with two popular models by experiments, and the accuracy and validity of CDIES algorithm was assessed according to the result of community devision, modularity and time cost. The experimental results show that, compared with the content-based friend recommendation and relationship-based two typical recommendation algorithms, the IQSA algorithm has best comprehensive performance in accuracy, recall, and f1-score. And the modularity and time cost of the result of community devision of CDIES algorithm outperforms that of GN algorithm and FN algorithm. 

Fast Point Feature Histogram Descriptor Algorithm Combined With Point Cloud Texture Information



MO Haijun CHEN Jie WANG Shundong

2021, 49(6):  56-65,76.  doi:10.12141/j.issn.1000-565X.200696

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A fast point feature histogram descriptor algorithm combined with point cloud texture information is proposed to improve the feature extraction efficiency and matching accuracy in the point cloud matching and recognition process. Firstly, a shape feature histogram was constructed based on the fast point feature histogram descriptor and a texture feature histogram was constructed by using CIELab color space and multiple point-to-texture attribute metrics. Then the two feature histograms were connected to obtain a fast point feature histogram descriptor combined with point cloud texture information. The feature histogram descriptor was verified by using public point cloud data sets and real spot cloud data. and the feature matching test and point density change test were carried out for this feature descriptor and multiple existing descriptors. The test results show that the comprehensive performance of the descriptor is the best when the CIE00 color difference is used as the point-to-texture attribute metrics. The algorithm has a good feature description performance and high feature extraction efficiency and matching efficiency and it has strong robustness when the point cloud density changes.

2D Footprint Classification Based on Multiple-Module Relation Network



ZHANG Yan, WU Luotian, WANG Nian, et al

2021, 49(6):  66-76.  doi:10.12141/j.issn.1000-565X.200400

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Due to the limited samples of footprint data and its high similarity between types and large gap within a type, there is no effective method to express footprint data and classify footprint. In order to solve the problem of bimodal footprint classification, the multiple-module relation network (MulRN) based on few-shot learning was proposed in this paper. Multiple modules were applied in the algorithm to improve the ability of extraction and mea-surement of characters. Inception module and MRFB module which possess a multi-branch structure were used to improve the character extraction ability. Spatial Attention Module (SAM) and Channel Attention Module (CAM) were adopted to extract the character of footprint with high discrimination for accurate classification. Also, experiments were carried out on few-shot data sets such as miniImageNet, Omniglot and bimodal 2D footprint data sets. Experimental results show that the proposed method is effective for few-shot data sets and bimodal 2D footprint data sets. It is worth mentioning that the accuracy of 5-way 5-shot experiment on bimodal data sets of right foot is up to 95.41%.

Stereo Matching Network Based on Multi-Stage Fusion and Recurrent Aggregation



ZHANG Ruifeng, REN Guoming, LI Qiang, et al

2021, 49(6):  77-87,99.  doi:10.12141/j.issn.1000-565X.200430

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Aiming at the poor matching effect of ill-conditioned regions and excessive model parameters in the stereo matching network based on deep learning, an end-to-end stereo matching network based on multi-level feature fusion and recurrent cost aggregation（MFRANet）was proposed. Firstly, in order to take into account both the low-level detail information and high-level semantic information of the image, a multi-stage feature fusion module, which uses a phased and step-by-step feature fusion strategy to effectively fuse multi-level and multi-scale features, was proposed. Secondly, a recurrent mechanism was proposed in the cost aggregation stage to optimize the aggregation of the matching cost volume in a recurrent manner, and it can improve the aggregation effect while avoid introducing too many parameters. Finally, the disparity calculation module based on the Soft Argmin algorithm was used to calculate the image disparity. And through the two public datasets of KITTI 2012/2015 and SceneFlow, the network was trained and tested, and a comparative study with other end-to-end stereo matching networks was caaried out. Experimental results show that, for the two public datasets of SceneFlow and KITTI 2015, MFRANet has more accurate matching results than other end-to-end stereo matching networks; for the SceneFlow dataset, the end-point error is reduced to 0.92 pixels; for the KITTI 2015 dataset, the mismatching rate is reduced to 2.21%.

Electronics, Communication & Automation Technology

Two-Stage Multi-Hypothesis Network for Compressed Video Sensing Reconstruction Algorithms Based on Deep Learning



YANG Chunling LING Xi

2021, 49(6):  88-99.  doi:10.12141/j.issn.1000-565X.200623

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Traditional Compressed Video Sensing (CVS) reconstruction algorithm is highly time-consuming. Newly developed CVS neural networks can successfully deal with the speed problem, but it fails to make full use of the spatiotemporal correlation of video and leads to a poor performance. To solve this problem, a novel two-stage multi-hypothesis neural network (2sMHNet) was proposed. Firstly, the Temporal Deformable Alignment Network（TDAN）was used to realize pixel based multi-hypothesis prediction. While avoiding block effects, it improves the matching accuracy of the hypothesis set and obtains accurate multi-hypothesis weights by adaptively parameters learning. Then, the residual reconstruction module was constructed to reconstruct the prediction residual with measurements to further improve the reconstruction quality. Finally, in order to make full use of the inter-frame correlation, a two-stage serial reconstruction mode was proposed. In the first stage, as the reconstructed key frames have rich details, they are selected as the reference frame to improve the non-key frames quality. In the second stage, the more relevant adjacent frames are used for motion compensation, which is more conducive to fast and complex sequences. Experimental results demonstrate that the proposed 2sMHNet outperforms the existing good CVS reconstruction algorithms.

On Integrated Adaptive GPR-RVM Multi-Output Model Based on Co-Training Algorithm



LI Dong, HUANG Daoping, XU Chong, et al

2021, 49(6):  100-108.  doi:10.12141/j.issn.1000-565X.200669

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In the wastewater treatment process, due to the complexity of the industry process, incomplete monitoring equipment and hostile working environment, it is difficult to achieve accurate and timely measurement of important effluent indices. To solve the problem, an ensemble adaptive soft sensor model based on semi-supervision learning was proposed. Firstly, Gaussian process regression (GPR) and Relevance vector machine (RVM) were used to establish a heterogeneous soft-sensor model. Then, the structure and parameters of the models  were optimized by using the moving window and the Kalman filter gain, respectively. Finally, the prediction performance and self-adaptability of the model were verified by experiments on a wastewater plant. The results demonstrate that the proposed method improves the prediction accuracy and self-adaptability of the soft sensor model.

Edge-Preserving Image Smoothing Algorithm Based on Reweighted l1 Norm



SONG Yu SUN Wenyun

2021, 49(6):  109-121.  doi:10.12141/j.issn.1000-565X.200231

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Edge-preserving image smoothing is a key preprocessing step of many computer vision and graphics algorithms. Edge-preserving image smoothing algorithm based on l1 norm outperforms many existing image smoothing algorithms. However, there are still many remaining textures in the smoothed results. In order to improve the smoothing effect of the algorithm, a new image smoothing algorithm based on reweighted l1 norm was proposed. The proposed algorithm combines the original l1 norm-based image smoothing algorithm and reweighed l1 norm minimization. The solution was made sparser through the reweighting method. The performance of the algorithm was evaluated through experiments. Experimental results show that, compared to the original l1 norm-based image smoothing algorithm and several other existing image smoothing algorithms, the proposed algorithm can smooth images very effectively and there is little texture remained in the smoothed results. Using reweighted l1 norm can improve the smoothing effect of the original l1 norm-based image smoothing algorithm.

Mobile Virtual Reality-Orinted Partial Cached Contents Sharing Methods



LI Song, YU Yi, SUN Yanjing, et al

2021, 49(6):  122-130,140.  doi:10.12141/j.issn.1000-565X.200429

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Facing the high data volume and low latency requirements of mobile Virtual Reality(VR) services, this paper established a partial cache content sharing model for mobile VR and simulated the social attribute-based content sharing cost minimization problem, in order to effectively reduce the data traffic of the core network and reduce the requesting cost of devices. The problem was decomposed into two sub-problems, namely, power optimization with mode selection and device association. A cost-based power optimization and mode selection algorithm and a matching algorithm based on social relationship and requesting cost were respectively proposed. Finally, the performance of proposed algorithms was evaluated through simulations and was compared with other schemes. The simu-lation results show that the content request mode selection and power optimization scheme based on partial cache can effectively reduce the requesting cost of content requesters.

Design of Ultra-Wideband Active Magnetic Field Probe for Near-Field Measurement



CHEN Zhijian, WANG Yuchen, HUANG Pengcheng, et al

2021, 49(6):  131-140.  doi:10.12141/j.issn.1000-565X.200312

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The current active probe, whose detection frequency is mainly concentrated in the low frequency band, cant meet the detection requirements of the high frequency band. Therefore, a small, high-bandwidth, non-contact active magnetic field probe was proposed. The active magnetic field probe is made of a multilayer printed circuit board (PCB). An active amplifier module and its supporting power management chip were added to the passive probe to improve the transmission gain of the ultra-wideband type probe. The probe was tested and analyzed from four aspects: frequency response, spatial resolution, calibration factor, and differential electric field suppression capability. The results show that the designed probe has a transmission gain of -20dB, a spatial resolution of 900μm, and is of good differential electric field suppression. The probe can be used for ultra-wideband PCB board and more complex integrated circuit measurements.

On Multi-Parameter Influence of TDLAS Detection System Based on LabVIEW



YE Weilin, TU Zihan, XIAO Xupeng, et al

2021, 49(6):  141-148.  doi:10.12141/j.issn.1000-565X.190940

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In the infrared gas detection system based on tunable diode laser absorption spectroscopy (TDLAS), the second harmonic signal is used to calculate the gas percentage by volume. However, this signal is easily influenced by many parameters of the hardware. Based on Lambert Beers law and HITRAN database, this paper used LabVIEW to study the influence of parameters on the amplitude, symmetry and peak width of the second harmonic, and carried out the harmonic optimization simulation experiment. The results show that, for the general TDLAS detection system, the appropriate scanning amplitude and filter order should be chosen firstly, so that the harmonic line would be complete and undistorted. Furthermore, the amplitude and peak width of the harmonic should be increased as much as possible to get the optimal modulation depth. Finally, the suitable modulation and scan frequency should be determined choose according to the system requirements and the hardware conditions.