Table of Content

25 May 2023, Volume 51 Issue 5

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Contents



2023, 51(5):  0. 

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Computer Science & Technology

Security-Aware Scheduling Method for Time-Sensitive Networking



LU Yiqin, XIE Wenjing, WANG Haihan, et al

2023, 51(5):  1-12.  doi:10.12141/j.issn.1000-565X.220394

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The authenticity of information is the key security factor of system in time-sensitive networking (TSN). However, the direct introduction of traditional security authentication mechanism will lead to a significant reduction in schedulability of the system. The existing methods still have the problems of few application scenarios and high resource consumption. To address this problem, a security-aware scheduling method for TSN was proposed. Firstly, based on the traffic characteristics of TSN, a time-efficient one-time signature security mechanism was designed to provide efficient multicast source authentication for messages. Secondly, the corresponding security model was proposed to evaluate the mechanism and describe the impact of the security mechanism on tasks and traffic. Finally, the proposed security-aware scheduling method was modeled mathematically. On the basis of traditional scheduling constraints, some constraints related to security mechanisms were added. At the same time, the optimization objective was to minimize the end-to-end delay of applications, and constraint programming was used to solve the problem. Simulation results show that the introduction of the improved one-time signature mechanism can effectively protect the authenticity of key information in TSN, and has limited impact on scheduling. In multiple test cases of different sizes generated based on real industrial scenarios, the average end-to-end delay and bandwidth consumption of the generated applications only increased by 13.3% and 5.8% respectively. Compared with other similar methods, this method consumes less bandwidth, thus more suitable for TSN networks with strict bandwidth restrictions.

Contrastive Knowledge Distillation Method Based on Feature Space Embedding



YE Feng, CHEN Biao, LAI Yizong

2023, 51(5):  13-23.  doi:10.12141/j.issn.1000-565X.220684

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Because of its important role in model compression, knowledge distillation has attracted much attention in the field of deep learning. However, the classical knowledge distillation algorithm only uses the information of a single sample, and neglects the importance of the relationship between samples, leading to its poor performance. To improve the efficiency and performance of knowledge transfer in knowledge distillation algorithm, this paper proposed a feature-space-embedding based contrastive knowledge distillation (FSECD) algorithm. The algorithm adopts efficient batch construction strategy, which embeds the student feature into the teacher feature space so that each student feature builds N contrastive pairs with N teacher features. In each pair, the teacher feature is optimized and fixed, while student feature is to be optimized and tunable. In the training process, the distance for positive pairs is narrowed and the distance for negative pairs is expanded, so that student model can perceive and learn the inter-sample relations of teacher model and realize the transfer of knowledge from teacher model to student model. Extensive experiments with different teacher/student architecture settings on CIFAR-100 and ImageNet datasets show that, FSECD algorithm achieves significant performance improvement without additional network structures and data when compared with other cutting-edge distillation methods, which further proves the importance of the inter-sample relations in knowledge distillation.

Fast Modulus Algorithm for Internet of Things Key Exchange Based on Mersenne-like Numbers



QIN Jiancheng, ZHONG Yu, CHENG Zhe, et al

2023, 51(5):  24-35.  doi:10.12141/j.issn.1000-565X.220355

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In order to adapt to the limited computing performance and energy of numerous lightweight sensor nodes in the encrypted transmission of IoT (Internet of Things), this paper proposed a fast modulus algorithm (CZ-Mod algorithm) based on Mersenne-like numbers to slove the bottleneck problems of computing speed, power consumption and so on during the sensors run PKI (Public Key Infrastructure) encryption algorithms such as RSA (Rivest-Shamir-Adleman), DHM (Diffie-Hellman-Merkle), Elgamal, etc., and to simplify the corresponding hardware encrypting circuit logic design. CZ-Mod algorithm uses the mathematic characteristics of Mersenne numbers, and lowers the time complexity of its essential operation mod (modulo) into O(n). Firstly, a fast modulus algorithm mod1 using Mersenne-like numbers as modulus was presented, changing complex mod operation into simple binary shift/add operation; secondly, a fast modulus algorithm mod2 using any positive integers near Mersenne-like numbers as modulus was presented, expanding the modulus value range while simplifying mod operation; and then logic circuits of mod1 and mod2 operations were designed, simplifying mod operation hardware circuit. Finally, the above work was applied to the key exchange of IoT nodes, so as to lower the computing complexity and improve the speed of PKI encryption algorithms. The experiment test results indicate that the speed of DHM key exchange with CZ-Mod algorithm can reach 2.5 to 4 times of that of the conventional algorithm; CZ-Mod algorithm is concise and fits the hardware circuit design for the IoT sensors.

Smart Contract Vulnerability Detection Method Based on Capsule Network and Attention Mechanism



LU Lu, LAI Jinxiong

2023, 51(5):  36-44.  doi:10.12141/j.issn.1000-565X.220167

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In recent years, with the increasing number of smart contracts and the increasing economic losses caused by contract loopholes, the security of smart contracts has attracted more and more attention. The vulnerability detection method based on deep learning can solve the problems of low detection efficiency and insufficient accuracy of the early traditional smart contract vulnerability detection method. However, most of the existing deep learning-based vulnerability detection methods directly use smart contract source code, opcode sequence or bytecode sequence as the input of the deep learning model. This fact will weaken the effective information due to the introduction of too much invalid information. To solve this problem, this paper proposed a smart contract vulnerability detection method based on capsule network and attention mechanism. Considering the execution timing information of the program, the study extracted key operation code sequence of the smart contract as the source code feature. Then a hybrid network structure of capsule network and attention mechanism was used for training. The capsule network extracts the context information of the smart contract and the connection between the part and the whole; while the attention mechanism is used to assign different weights to different opcodes according to their importance. The experimental results show that the F₁ score and accuracy of the algorithm proposed in this paper in the smart contract data set are 94.48% and 97.15%, indicating that this algorithm is superior to other detection methods in performance.

Robust TruncatedL1-L2Total Variation Sparse Restoration Models



HAN Le, JIANG Yihua

2023, 51(5):  45-53,140.  doi:10.12141/j.issn.1000-565X.220485

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In addition to Gaussian noise, there is sparse noise with impulsive properties in the signal acquisition process. The common robust sparse signal recovery models can recover the original sparse signal under sparse noise environment. However, in many practical applications, the structural sparsity of the original signal, for example, gradient sparsity needs to be considered. In order to recover the sparse structure of the original high-dimensional signal from the coexistence of sparse noise and Gaussian noise, this paper proposed two nonconvex and nonsmooth optimization models based on truncated L₁-L₂ total variation (TV) and 3D truncated L₁-L₂ TV, respectively. These optimization models were solved by the proximal alternating linearized minimization algorithm with extrapolation, and the sub-problems involved were solved by the proximal convex difference algorithm with extrapolation. Under the assumption that the potential function has Kurdyka-Lojasiewicz (KL) property, the convergence analysis of these algorithms was given. The numerical experiments test grey images with Gaussian noise, color images with mixed noise, grey video with mixed noise and so on. The peak signal-to-noise ratio (PSNR) was used as the evaluation criterion for recovered quality. The experimental results show that the new models can correctly recover the original structured sparse signal, and have better PSNR values in the same noisy environment.

Cognitive Diagnosis Model Integrating Forgetting and Importance of Knowledge Points



LIU Yupeng, ZHANG Lei

2023, 51(5):  54-62.  doi:10.12141/j.issn.1000-565X.220279

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Intelligence education is the key research direction of artificial intelligence. The most important is to describe the students’ cognitive process by ultilizing the knowledge points in the test questions. Aiming at the problem that the cognitive diagnosis model is insufficient for mining students, test questions and their interactive information, this study proposed a cognitive diagnosis model integrating forgetting and the importance of knowledge points. According to the historical interaction between the test questions and knowledge points, the model introduces forgetting factors in combination with the difficulty information of knowledge points, thus alleviates the problem of insufficient information mining for students. Through the attention mechanism, the importance information of the test questions to the knowledge points was obtained to alleviate the problem of insufficient information mining of the test questions. Learning the interaction relation between students and test questions through Transformer alleviates the problem of insufficient interaction information between students and test questions. The results of experiments carried out on the classic dataset show that the accuracy A_cc, root mean square error (RMSE), and the area under curve (AUC) values of this method on the Math1, Math2, and Assistment datasets are 0.716, 0.445, 0.776, 0.725, 0.432, 0.807, 0.741, 0.427, 0.779, respectively. Compared with other existing models, the proposed method has better results. The proposed method illustrates the importance of knowledge importance and timeliness for cognitive modeling.

Electronics, Communication & Automation Technology

Low Complexity Blood Flow Velocity Estimation Algorithm via Sparse Pulse Sampling



MA Biyun, WU Gang, LIU Jiaojiao, et al

2023, 51(5):  63-69.  doi:10.12141/j.issn.1000-565X.220380

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Dual-mode ultrasound is widely used in medical clinical diagnosis. The B-mode pulse is used for imaging and Doppler pulse is used for blood flow velocity estimation. The data collection time is shared between the two modes. To improve the update frequency of B-mode image, it is necessary to reduce the number of Doppler pulses, that is, to estimate the blood flow velocity by sparse Doppler emissions. However, the existing algorithms for sparse pulse sampling, such as iterative adaptive algorithm, sparse Bayesian algorithm and subspace method based on array virtual expansion, are huge in expense and can not meet the requirements of real-time imaging. What’s more, they will lead to obvious artifacts in the case of large sparsity. Therefore, this paper proposed a low complexity blood flow velocity estimation algorithm via sparse pulse sampling. Based on the fact that ultrasonic Doppler echo signal is generated by the scattering of red blood cells, so echoes are strong coherence signals with time-variation sources number, this paper firstly explained the cause of artifacts from the perspective of subspace, and verified that the sparse emission pulse arrangement with uniform pulse can effectively suppress artifacts. Then the covariance matrix was constructed with uniform pulse echo, and the eigenvalues were obtained after spatial smoothing. The frequency distribution characteristics of blood flow at different segments were derived by the number of larger eigenvalues and the ratio of each other. Finally, based on the frequency distribution characteristics, the B-MUSIC algorithm or TBVAM algorithm was adaptively used for blood flow velocity estimation to reduce the complexity of the algorithm. The experimental results with Matlab simulation and human body measurement data show that the algorithm can obtain continuous, clear blood flow velocity estimation results with well artifact suppression while reducing the computational complexity significantly.

Multi-View Lip Motion and Voice Consistency Judgment Based on Lip Reconstruction and Three-Dimensional Coupled CNN



ZHU Zhengyu, LUO Chao, HE Qianhua, et al

2023, 51(5):  70-77.  doi:10.12141/j.issn.1000-565X.220435

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The traditional consistency judgment methods of lip motion and voice mainly focus on processing the frontal lip motion video，without considering the impact of angle changes on the result during the video acquisition process. In addition, they are prone to ignoring the spatio-temporal characteristics of the lip movement process．Aiming at these problems, this paper focused on the influence of lip angle changes on consistency judgment，combined the advantages of three dimensional convolutional neural networks for non-linear representation and spatio-temporal dimensional feature extraction, and proposed a multi-view lip motion and voice consistency judgment method based on frontal lip reconstruction and three dimensional（3D） coupled convolutional neural network．Firstly，the self-mapping loss was introduced into the generator to improve the effect of frontal reconstruction, and then the lip reconstruction method based on self-mapping supervised cycle-consistent generative adversarial network (SMS-CycleGAN) was used for angle classification and frontal reconstruction of multi-view lip image．Secondly，two heterogeneous three dimensional convolution neural networks were designed to describe the audio and video signals respectively, and then the 3D convolution features containing long-term spatio-temporal correlation information were extracted．Finally, the contrastive loss function was introduced as the correlation discrimination measure of audio and video signal matching, and the output of the audio-video network was coupled into the same representation space for consistency judgment. The experimental results show that the method proposed in this paper can reconstruct frontal lip images of higher quality， and it is better than a variety of comparison methods on the performance of consistency judgment．

Cross-Domain Pressure Footprint Images Retrieval Based on Mutual Information Disentangled Representations



ZHANG Yan, XU Changkang, CAO Liqing, et al

2023, 51(5):  78-85.  doi:10.12141/j.issn.1000-565X.220572

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As one of human biometric features, footprint is of great significance in the field of biometric identification. However, the pressure footprint images of different shoe types for the same person have significant differences in the footprint contour features, leading to large intra-class differences. For cross-domain retrieval of pressure footprint images, this paper proposed a cross-domain pressure footprint images retrieval method based on mutual information disentangled representations. Firstly, a multi-domain pressure footprint dataset containing 200 people’s footprint images was constructed and the characteristics of cross-domain pressure footprint images were analyzed from qualitative and quantitative perspectives. Secondly, two independent encoders were used to construct an image disentanglement module, which disentangles the pressure footprint images into a domain-specific representation and a domain-shared representation, and ensures that the domain-specific representation contains more domain-related information through domain classification. Then, the distance between the domain-specific representation and the domain-shared representation was enlarged by minimizing mutual information loss. At the same time, in order to avoid the loss of information in the disentangled process, the original pressure footprint image was reconstructed based on the domain-specific representation and the domain-shared representation. Finally, the deep convolution features of the domain-shared representation were further extracted by feature extraction module and the cross-domain pressure footprint images retrieval was realized through the metric module which calculates the correlation degree between different features. The results of comparison and ablation experiments show that the disentanglement module of this method is effective and performs well on multi-domain pressure footprint dataset. The retrieval accuracy of the first query result reached 79.83%, and the average accuracy reached 65.48%.

Research on Dynamic Characteristics of Force Sensor Based on VFF-RLS



YAO Bin, ZHANG Zihao, DAI Yu, et al

2023, 51(5):  86-94.  doi:10.12141/j.issn.1000-565X.220374

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In scientific experiments and industrial production, the dynamic characteristics of the force sensor will directly affect the accuracy, so it is of great significance to research the dynamic characteristics of the force sensor. Aiming at the practical problem that the dynamic characteristics of strain gauge force sensor used in surgical robots are difficult to meet the accuracy requirements, this paper studied the application of least square parameter identification method in the vibration structure of force sensor. Because recursive least squares (RLS) is difficult to ensure the rapidity and anti-interference of the second order vibration system model identification, therefore, this paper proposed a recursive least squares parameter identification method based on variable forgetting factor. Firstly, the parameters of the forgetting factor function were determined by establishing the random vibration system model, simulating and analyzing the input/output characteristics of the system. The simulation results show that the proposed method in the paper can significantly reduce the parameter identification error and convergence prediction error compared with RLS while maintaining a faster convergence speed, and has better time variability compared with the least squares. Furthermore, the dynamic parameters of the force sensor used in minimally invasive surgical robot were identified based on the step test calibration method to obtain the structural dynamic characteristics (i.e. natural frequency and damping ratio) of the sensor system. The experimental results show that the proposed method in the paper has good convergence and stability, and can effectively improve the identification accuracy.

Parallel Pipeline Hardware Design of Intra Rate-Distortion Optimization Prediction Mode in HEVC



LIN Zhijian, DING Yongqiang, YANG Xiuzhi, et al

2023, 51(5):  95-103.  doi:10.12141/j.issn.1000-565X.220612

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In recent years, the resolution and frame rate of video have been continuously improved to meet people’s increasing demand for video data. However, the compression encoding speed of real-time video sequence is often restricted by frame rate and resolution. The higher the frame rate and resolution are, the longer the encoding time will be. In order to achieve real-time compression encode for video sequences with higher resolution and frame rate, this paper designed a new parallel pipeline hardware architecture of intra rate-distortion optimization prediction mode, which supports intra prediction coding of up to 64×64 coding tree unit. Firstly, a parallel scheme with 9-way prediction mode was designed. Secondly, a pipeline hardware architecture was implemented based on a 4×4 block as the basic processing unit in a Z-shaped scanning order, and the prediction data of 32×32 prediction units were reused to replace the prediction data of 64×64 prediction units so as to reduce the amount of calculation. Lastly, a new Hadamard transform circuit was proposed based on this pipelined architecture for efficient pipelined processing. The experimental results show that: on the Altera Arria 10 series field programmable gate array, the 9-way mode parallel architecture only occupies 75 kb look up table and 55 kb register resources, the main frequency can reach 207 MHz, and it only takes 4 096 clocks cycles to complete a 64×64 coding tree unit prediction and can support real-time encoding of 1 080 P resolution 99 f/s full I-frame at most. Compared with the existing design scheme, the scheme designed in this paper can realize higher frame rate 1 080 P real time video encoding with smaller circuit area.

Design and Implementation of Hardware Structure for Online Learning of Spiking Neural Networks Based on FPGA Parallel Acceleration



LIU Yijun, CAO Yu, YE Wujian, et al

2023, 51(5):  104-113.  doi:10.12141/j.issn.1000-565X.220623

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Currently, the hardware design of spiking neural networks based on digital circuits has a low synaptic parallel nature in terms of learning function, leading to a large overall hardware delay, which limits the speed of online learning of spiking neural network models to some extent. To address the above problems, this paper proposed an efficient spiking neural network online learning hardware architecture based on FPGA parallel acceleration, which accelerates the training and inference process of the model through the dual parallel design of neurons and synapses. Firstly, a synaptic structure with parallel spike delivery function and parallel spike time-dependent plasticity learning function was designed; then, the learning layers of input encoding layer and winner-take-all structure were built, and the implementation of lateral inhibition of the winner-take-all network was optimized, forming an impulsive neural network model with a scale of 784~400. The experiments show, the hardware has a training speed of 1.61 ms/image and an energy consumption of about 3.18 mJ/image for the SNN model and an inference speed of 1.19 ms/image and an energy consumption of about 2.37 mJ/image on the MNIST dataset, with an accuracy rate of 87.51%. Based on the hardware framework designed in this paper, the synaptic parallel structure can improve the training speed by more than 38%, and reduce the hardware energy consumption by about 24.1%, which can help to promote the development of edge intelligent computing devices and technologies.

Mechanical Engineering

Numerical Simulation on Melt Flow Characteristics of UHMWPE in Eccentric Rotor Extruder



MA Xiangjun, WANG Zhen, YAO Zhiqiang, et al

2023, 51(5):  114-121.  doi:10.12141/j.issn.1000-565X.220528

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The processing difficulties of UHMWPE caused by the wall slip can be solved effectively by the eccentric rotor extruder, which make the melt conveyed in positive displacement. To reveal the melt flow characteristics of UHMWPE in the conveying section of the eccentric rotor extruder, the model parameters of wall slip were determined by the combination of experiment and numerical simulation, and the flow field of UHMWPE melt was numerically simulated by ANSYS Polyflow. The results show that the UHMWPE melt in the cavity formed by the meshing of the rotor and the stator can be conveyed in positive displacement, and the change in the cross-sectional size of the cavity leads to the elongation flow field in the melt. When the meshing gap exists, the positive pressure and negative pressure formed around every inlet and outlet respectively, will lead to the counterflow and leakage of the melt in the cavity and make the average flow rate at the outlet of the cavity lower than the theoretical flow rate. When UHMWPE is processed by the eccentric rotor extruder, the instantaneous flow rate and average flow rate of the melt at the outlet of the cavity are proportional to the rotor rotation speed, while the pulsation rate of the flow rate remains unchanged. Reducing the meshing gap between the rotor and the stator increases the average flow rate of the melt at the outlet of the cavity and decreases the pulsation rate of the flow rate, which enhances the positive displacement conveying of the melt in the eccentric rotor extruder.

Analysis of Temperature Uniformity of Pulp Molding Hot Pressing Heating Plate Based on Fluent Simulation



WU Shangsheng, HU Jinrong, ZHOU Yunqi

2023, 51(5):  122-129.  doi:10.12141/j.issn.1000-565X.220363

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In-mold hot-pressing is a common drying method in the production of pulp moulded tableware. The wet paper mold embryo obtained after moulding is heated under the condition of extrusion and vacuum. Heating plate is the heat source of hot-pressing machine, and the temperature uniformity of its working surface affects the drying quality of products. Aiming at the temperature non-uniformity of the heating plate in the process of pulp molding hot pressing, this paper proposed an optimization method which combined simulation and orthogonal experiment. Firstly, the working process of heating plate was analyzed and the heat transfer model of heating plate was established. Then, based on Fluent, the temperature field was simulated numerically. According to the results of temperature field distribution, the high-temperature area and low-temperature area in the oil circuit structure were staggered as far as possible, and four new labyrinth oil circuit structures were designed. Finally, an orthogonal test with 4 factors and 4 levels was designed based on the structure of oil circuit, the plane height of oil circuit, the thickness of heating plate and the section diameter of oil circuit, and the range analysis and variance analysis were carried out. The results show that during the actual drying process, the maximum temperature and minimum temperature of the working surface are 224.47 ℃ and 209.92 ℃, respectively, and the temperature range is as high as 14.55 ℃ and the temperature standard deviation is 3.01 ℃. The thickness of heating plate and the diameter of oil passage have a significant influence on the temperature difference, and the structure of oil passage has a significant influence on the temperature standard deviation. Based on the above analysis, the structure of heating plate was improved. Compared with the original design, the temperature range of the working surface of heating plate has been reduced to 7.27 ℃ and the temperature standard deviation has been reduced to 1.09 ℃, which ensures the uniformity of temperature of heating plate and improves the quality of pulp molded products.

Simulation and Experimental Research on Dual-Disk Magnetic Preparation Based on Magneto-Elastic Abrasive



ZHAO Xuefeng, YOU Ke, YUAN Yin, et al

2023, 51(5):  130-140.  doi:10.12141/j.issn.1000-565X.220404

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Magneto-elastic abrasive is magnetic and has low elastic modulus as well as excellent grinding performance. It can improve the quality and efficiency of process. Firstly, the magnetic edge preparation mechanism was analyzed based on the theory of magnetic field and magneto-elastic abrasive characteristics. Secondly, secondary development for discrete element software EDEM was carried out based on magnetic force of magneto-elastic abrasive in magnetic field, and a simulation model of dual-disk magnetic edge preparation process was established. The effects of particle size, magnetic susceptibility and disk spacing on the number of edge collisions and abrasive rotation velocity were studied. Finally, Matlab software was used to reconstruct the edge contour and an improved shape factor characterization method based on preparation area was proposed. The influence of particle size, magnetic susceptibility and disk spacing on edge preparation value was studied by orthogonal experiment, and the feasibility of proposed improved shape factor characterization was verified. The results show that, the number of edge collisions and the rotation speed of abrasive increase with the increase of particle size, the increase of magnetic susceptibility and the decrease of disk spacing. In addition, the degree of influence of preparation parameters on the preparation amount of the cutting edge in descending order is particle size, disk spacing and magnetic susceptibility, and the optimal preparation parameter groups are particle size 40 mesh, magnetic susceptibility 0.1, and disk spacing 15 mm. The maximum relative error of preparation area between simulation and experiment is 16.33% and the minimum relative error is 0.42%. Simulation can better predict preparation morphology of the cutting edge, and the improved edge shape factor can better characterize the preparation morphology of the cutting edge.

Influence of Coupling Factors on Characteristics of Mixed Lubrication Sliding Bearings



GUO Hong, QIN Lichuang, SHI Minghui, et al

2023, 51(5):  141-150.  doi:10.12141/j.issn.1000-565X.220289

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Sliding bearings in mixed lubrication state are prone to wear due to deformation or misalignment under low-speed conditions. In order to analyze the influence of journal misalignment and wear on the mixed lubrication characteristics of sliding bearings, this study established a coupled model of average flow equation, G-T contact equation and Archard wear equation considering journal misalignment and elastic deformation. The bearing characteristic parameters and time-varying wear parameters under mixed lubrication were calculated by finite difference method and over-relaxation iteration method. The lubrication performance of bearings before and after journal misalignment or wear was compared, and the effect of roughness and boundary friction coefficient on various performance parameters was analyzed. A friction and wear test rig was built to test the lubrication characteristics of bearings in misaligned state, which verified the correctness of the theoretical model. The theoretical analysis and experimental results show that, when heavy load and large eccentricity occur, the bearing will change to mixed lubrication state. The greater the journal tilt, the more likely the bearing to have mixed lubrication. When the bearing is misaligned, the peak pressure and the shape of contact area will change, resulting in a difference in wear and the distribution of wear depth is tilted either axially or circumferential. The wear reduces the fluid hydrodynamic effect and decreases the film thickness ratio, leading to a decrease of about 20% in the hydrodynamic pressure peak, a decrease of about 90% in the contact pressure peak, and a maximum decrease of about 19.71% in bearing capacity. By comparing the bearing morphologies before and after wear, it finds that the journal tilts leads to that the wear concentrates on the end where the clearance reduces. This research provides theoretical basis for sliding bearing design in practical engineering.