We discuss and analyze the virtual element method on general polygonal meshes for the time-dependent Poisson-Nernst-Planck (PNP) equations, which are a nonlinear coupled system widely used in semiconductors and ion channels. After presenting the semi-discrete scheme, the optimal H-1 norm error estimates are presented for the time-dependent PNP equations, which are based on some error estimates of a virtual element energy projection. The Gummel iteration is used to decouple and linearize the PNP equations and the error analysis is also given for the iteration of fully discrete virtual element approximation. The numerical experiment on different polygonal meshes verifies the theoretical convergence results and shows the efficiency of the virtual element method.
In order to further improve the numerical accuracy of solving Volterra integro-differential,two kinds of Legendre spectral Galerkin numerical integration methods are investigated for the Volterra-type integro-differential equation with variable coefficients.Firstly,the Galerkin Legendre numerical integration is applied to deal with the integral term of the Volterra-type integro-differential equations.Secondly,the Legendre tau scheme is developed for the Volterra-type integral-differential equations with variable coeffi-cient,and the Chebyshev-Gauss-Lobatto collocation point is used to the calculation of the variable coefficient and integral term.Fi-nally,by decomposing the definition interval of the function,the multi-interval Legendre spectral Galerkin numerical integration method is also designed.Its scheme of the proposed method has symmetric structure for odd-order model.In addition,by introduc-ing the least squares function of the Volterra type integro-differential equation,the Legendre spectral Galerkin least-squares numeri-cal integration method of is constructed.The corresponding coefficient matrix of the algebraic equation is symmetric positive.Some numerical examples are given to test the high-order accuracy and the effectiveness of our methods.
In order to verify whether the given states satisfiy the bisimulation, a local algorithm of fuzzy bisimulation is proposed. The algorithm takes verification and traversal at the same. While verify the given states whether satisfiy the fuzzy bisimulation, the state space is dynamically increased, so that the algorithm only needs to traverse part of the state space to complete the verification. In some cases, the local algorithm of fuzzy bisimulation can verify whether the given states satisfiy the fuzzy bisimulation more quickly, especially when the two states do not satisfy the fuzzy bisimulation. The local algorithm and the existing global algorithm are implemented by Java and compared by experiments.The experiments, shows that this algorithm is more efficient than the existing global fuzzy bisimulation algorithm when the given states do not satisfiy the bisimulation.
The reconstruction problem of spatio-temporal signals can be cast as recovering differential smooth time-varying graph signals. For the optimization problem, the existing distributed algorithm based on gradient descent method shows slow convergence when the condition number of the Hessian matrix of the problem is large which leads to a large reconstruction error when the maximum iteration number is limited in an observation interval. Therefore, an online distributed reconstruction algorithm based on approximate Newton′s method is proposed in the paper, whose principle is to decompose the original optimization problem into a series of local problems on subgraphs through subgraph decomposition and find these solutions, and then obtain the approximate global optimal solution via fusion average of local solutions between each subgraph. According to the gap between the approximate solution and the actual one, it can be proved that the decompostion and fusion matrix obtained in this way is sparse and can be regarded as the approximate Hessian inverse. Hence, the algorithm replaces the approximate matrix into the classical Newton iterative formula which can be implemented in a distributed manner due to the structural sparsity of the approximate matrix. Simulation results show that the proposed algorithm has faster convergence rate and smaller reconstruction error, and requires less communication cost compared with the existing algorithm.
: Aiming at the situation of uneven graph frequency distribution, a design method of non-uniform graph filter banks is proposed. Firstly, according to the property of graph frequency distribution, the non-uniform analysis filter of well frequency selection characteristics and well sparse property in vertex domain is need to be designed. The method of it is that design the non-polynomial form filter with low order by approximating the polynomial form with high order. Secondly, given the non-uniform analysis filter and sub-band signal, the reconstruction problem could be formulated a least square problem. To avoid the high calculation cost of matrix inverse in this optimization when graph is with large scale, a precondition gradient descent method is proposed to solve this problem and which can be implemented in distributed manner. Numerical re-sults show that the non-uniform graph filter banks proposed in this paper can achieve perfect reconstruction and have well frequency selection characteristic and localized property in vertex domain. Compared with existing iteration methods, the proposed algorithm has the faster convergence rate and lower calculation cost.
: In order to solve the optimal test link problem when testing embedded instruments in reconfigurable scanning network, a method based on ACO algorithm is proposed. Firstly, the overall elements in the scanning network are abstracted into a node network structure that can be recognized by the computer. Secondly, aiming at the loop problem in the network, an "active" tabu table is proposed to release the node data in the tabu table when the designated-test-point is searched, so that the searched nodes can be searched again. Finally, in order to make the optimal test link better searched, the pheromone coefficient change factor is introduced to combine the pheromone update with the network scale to reduce the pheromone update range, so as to avoid falling into local optimization due to excessive enhancement of pheromone concentration in the later stage of search. Meanwhile, the adaptive pheromone volatilization coefficient is adopted to ensure the convergence rate of the algorithm and improve the global search ability. The simulation results show that the algorithm can effectively solve the optimal test link of the designated-test-point s in the reconfigurable scanning network. And compared with the basic ACO algorithm, it has higher search efficiency, practicability and applicability.
: Critically sampled graph filter banks with spectral domain sampling requires to perform eigendecomposition of the Laplacian matrix, which leads to high computational complexity. To solve this problem, an improved Jacobi algorithm is proposed to approximate the eigenmatrix of the framework to reduce the computational complexity. In this algorithm, the approximate solution of eigenmatrix is formulated into a constrained optimization problem, whose objective function is the approximation error of Laplacian matrix, and the constraint function is the sparse orthogonality of the approximate eigenmatrix. Theoretical and simulation experiments show that using the approximate feature matrix in the filter banks will not de-stroy its perfect reconstruction conditions. Compared with the existing critically sampled graph filter banks with spectral domain sampling, the improved algorithm reduces the computational complexity while maintaining good denoise performance
The hardware design of phased array three-dimension synthetic aperture radar（SAR） system based on wideband transmission signals is complicated, and the received signals are difficult to separate. By applying the frequency diverse array（FDA） to 3D-SAR, each array element only needs to transmit a single frequency signal to obtain wideband observation performance, which greatly reduces the hardware requirements of system. However, due to the space-frequency sparseness of FDA echo signals, the resolution is limited and the sidelobes of radar images are relatively high when using the back projection（BP） algorithm based on matched filtering. To solve this problem, this paper proposes a random frequency diverse array 3D-SAR imaging method based on compressed sensing（CS）. The array elements in the tangent-track and the observation positions in the along-track are selected randomly and sparsely to realize two-dimensional sparse sampling of echo data. In the imaging part, orthogonal matching pursuit（OMP） algorithm is used to reconstruct the scattering coefficient of targets. Simulation and experimental results show that CS method not only reduces the data processing amount of FDA-3D-SAR system during imaging, but also effectively suppresses the sidelobes of radar images, and the imaging quality is significantly improved. By using the compressed sensing algorithm, FDA-3D-SAR can accurately reconstruct the information of space targets when the echo is sparse, which verifies the rationality and effectiveness of the proposed method.
Aiming at the problem that the sorting clustering positioning algorithm has low matching accuracy, and there are abnormal fingerprint points in the fingerprint points used for position calculation, a Wi-Fi positioning algorithm with matching optimization and distance assistance is proposed. According to the user’s front and back position, distance and step length, a matching deviation detection model is designed to determine the user’s abnormal position and matching deviation; the adjacent elements in the sorted received signal strength vector are compared with the set threshold to determine the change position of the sorting feature vector of the point to be located, achieve the purpose of correction by exchange, and obtain the corrected and merged class matching result; according to the distance between the user’s position determined in the time period m before the positioning and the fingerprint point in the matching class, the abnormal fingerprint points used for position calculation are eliminated, so as to achieve more accurate indoor positioning. The simulation results show that the class matching accuracy and the average positioning accuracy of the proposed algorithm are improved respectively by 17% and 22%.
Aiming at the problem of blurred target position and high sidelobe when the back projection algorithm（BP algorithm） is imaging multi-targets, after analyzing the accumulation characteristics of the FDA target echo amplitude, a target imaging method of frequency diversity array radar based on clustering and coherent superposition is proposed. In the analysis and Simulation of BP algorithm imaging process, it is found that the target point has the characteristics of energy concentration and energy difference with the virtual image point. The K-means clustering algorithm can make full use of these characteristics of the target point to extract and classify the target points in the radar imaging area, and only compensate the time delay of the grid points of the specific cluster after classification, and then stack the echo amplitude, Thus, the energy value of the time delay compensation grid points in the imaging region is obtained, and finally the multi-target clear two-dimensional imaging is realized. The simulation results show that the proposed method can effectively solve the problems of fuzzy position and high sidelobe when BP algorithm imaging multi-target, and improve the accuracy of imaging results.
Aiming at the problem of medium parameter estimation around buried objects, a method of medium parameter estimation based on image entropy is proposed. Firstly, the point spread function（PSF） of frequency diversity array ground penetrating radar（FDA-GPR） in medium is derived, and the relationship between PSF and back projection imaging algorithm is analyzed. Then, the imaging results of FDA-GPR and UWB-GPR are compared and analyzed. For the same region containing the target to be imaged, the medium parameters have a great influence on the propagation velocity of electromagnetic wave. When different medium parameters are used to image the region, the imaging results are also different. The image entropy of the imaging results under different parameters is calculated. The smaller the image entropy of the imaging results, the better the focusing degree of the imaging, and the closer the corresponding medium parameters are to their true values. The experimental results show that: when the conductivity of the medium is not zero, FDA-GPR has better performance in target location and imaging than UWB-GPR, and when the target is a slender target, the proposed parameter estimation method can effectively estimate the parameters of the medium around the target.
UAV swarms are widely used in radar signal interception due to their advantages of wide sensing range and rapid information sharing. Aiming at the problem that the signal samples intercepted by UAV cluster are difficult to be fused and analyzed directly, and the recognition accuracy of multi-function radar（MFR） working mode is low under the condition of few training samples and unbalanced working mode samples, an MFR working mode recognition method based on smooth graph signal generated by self-organizing map（SOM） clustering is proposed. Firstly, the intercepted signal samples are clustered by using distributed SOM algorithm to extract the similarity between samples; Then, according to the clustering results, the signal sample set is characterized by smooth graph signal, and the correlation of signal samples under the same working mode is established; Finally, the graph attention network is used to fuse and classify the graph node data of the above graph signals to complete the MFR working pattern recognition. The experimental results show that, when the imbalance of working mode samples is about 10∶1 and the number of training samples in each class is 25, the recognition accuracy and F1 measure of this method are improved by 22.8% and 22.34% respectively compared with the existing methods, and can be applied to the case of noise interference.
The traditional network is increasingly difficult to face the complex network structure, so a new network architecture is born, namely software defined network（SDN）. The service flow of SDN data center mainly includes long stream and short stream. The long stream has the characteristics of long duration, insensitive delay and high bandwidth demand; while short flow has short duration, high delay sensitivity and low bandwidth requirement. Short flows account for less than 20% of the total traffic, but the number of traffic bars is about 80% or more of the total traffic; long flows account for more than 80% of the total traffic, but the number of traffic bars is less than 20% of the total traffic. It is found that the long flow is often ahead of the short flow in the outgoing port queue, causing the short flow to wait for a long time, which is very likely to cause network congestion. The design proposes a queuing mechanism and a route optimization guarantee mechanism based on the characteristics of the two service flows, setting the short flows as high-priority queues, which are prioritized and scheduled by the SDN controller; setting the long flows as low-priority queues, while using a route guarantee algorithm for compensation. The routing assurance algorithm first removes the links that do not meet the bandwidth requirements of the long flows, and then calculates the shortest delay path. In order to improve the efficiency of the algorithm of this design′ FPGA and 10G ethernet are used to simulate the service flow in SDN, and simulate on FPGA to verify the advantages of this design for the optimization of network delay, bandwidth and FPGA parallel computing.
Aiming at the structure of the lightweight authentication encryption algorithm ASCON, a differential power analysis) method is proposed. It combines the implementation characteristics of the algorithm S-box, uses the Hamming weight model as the power consumption discrimination function, groups the traces, and recovers the master key for encryption. Furthermore, for the "ghost peaks" what appear in DPA attacks, a traces preprocessing method is given. First, the traces are grouped according to plaintext and averaged, and then DPA attacks are launched on the preprocessed traces. The 44 bit master key of ASCON cipher can be recovered by attacking its s a permutation, where 1 500 traces are collected. In addition, the time required to directly attack the original traces is 21 849.888 9 ms, and the time required to attack the preprocessed traces is 198.911 3 ms. After preprocessing the traces, the time taken to attack the preprocessed traces is about 1/109 of that of directly attacking the original traces.
Aiming at the problem of the large number of array elements and low utilization rate of the three-dimensional synthetic aperture radar（3D-FDA-SAR） imaging method of frequency diversity arrays, a 3D-FDA-SAR imaging method based on multiple input and multiple output arrays is proposed. The frequency diversity array of 3D-FDA-SAR tangent track direction is changed to multiple-input multiple-output frequency diversity array. The multiple input multiple output frequency diversity array moves with the moving platform to form a synthetic aperture along the track direction, combined with the tangent track direction The real array is combined with a virtual two-dimensional frequency diversity array plane to obtain the downward-looking three-dimensional imaging capability of the target. Firstly, a multi-input multi-output 3D-FDA-SAR imaging model and signal model are established, using multi-input multi-output technology, the waveform quadrature signal single-frequency narrowband signal is sent at the transmitting end, and all the transmitting arrays are received through the full-frequency receiving mode at the receiving end. The echo signal reflected by the target is separated by a quadrature matched filter to obtain the echo data of different receiving and sending channels, and then the echo data is imaged by the backward projection algorithm, and finally the three-dimensional imaging result of the target is obtained. Experimental simulation results show that the 3D-FDA-SAR imaging method based on multiple-input multiple-output arrays uses a small number of array elements, improves the utilization of the array elements, and obtains the three-dimensional imaging capability of downward-looking targets.
With the advent of digital journal publishing, online journal editing management system is used to collect, submit, distribute, edit, review, expert review, finalize, query the results of manuscript processing, keyword and reference review, layout settings, distribution and typesetting, three-level issuance, typesetting and proofreading, sample production, data statistics, cost management, year-end index and other functions. Realize the process control of journal editing and review, and improve the reliability and transparency of journal article editing and review. This paper introduces the process of system implementation, including requirement analysis, system design, database design, system implementation and system testing. The system has been put into use and the system is in normal use.
Aiming at the influence of the noise and other factors in the process of classical correlation power analysis, based on the linear correlation between Hamming weight and power traces, a correlation power analysis method for AES cryptographic chip is proposed. According to the uneven distribution of the median Hamming weight of the S-box output of the cryptographic algorithm, a set of plaintexts with strong correlation with the power traces is obtained by filtering the correct keys and the wrong keys by using the discrimination ratio. In the stage of key recovery, the leakage points of the first two S-boxes are found by observing this set of plaintext inputs, and the leakage intervals of the remaining 14 S-boxes are found one by one by using the separate guessing method, so that the key information of the remaining bytes can be captured without traversing all power traces. The experimental analysis of AT89S52 chip shows that the proposed method can correctly recover the one-byte key of AES with 90% success rate by using only 9 plaintexts and corresponding power traces, and the computational complexity is only 4.1% of the classical correlation power analysis, which significantly improves the efficiency of the correlation power analysis.
为了提高热界面材料的导热性能,采用电泳沉积法制备还原氧化石墨烯/铜复合材料,对比分析还原氧化石墨烯/铜、多层石墨烯和纳米银对环氧树脂热导率的增强效果.采用扫描电镜对还原氧化石墨烯/铜复合材料的微观形貌进行表征;使用热常数分析仪、数字式粘度计和接触热阻测试仪分别对环氧树脂基复合热界面材料的热导率、粘度和界面热阻进行调控测试.结果表明,实验成功制备了还原氧化石墨烯/铜,且金属铜颗粒均匀分布在石墨烯片层间;还原氧化石墨烯/铜、多层石墨烯、纳米银对环氧树脂的导热系数均有提高;还原氧化石墨烯/铜复合材料质量分数为30%时,环氧树脂基复合热界面材料的导热系数提高了4.5倍;在0.9 MPa压力下,界面接触热阻为37.06 mm2·K·W-1,与不添加界面材料时相比降低了35.9%(未添加热界面材料时界面接触热阻为57.84 mm2·K·W-1).高导热材料能提高环氧树脂基热界面材料的热导率,可显著改善接触面的传热性能.
Modern neural networks may produce high confidence prediction results for inputs from outside the training distribution, posing a potential threat to machine learning models. Detecting inputs from out-of-distributions is a central issue in the safe deployment of models in the real world. Detection methods based on energy models directly use the feature vectors extracted by the model to calculate the energy score of a sample, and reliance on features that are not significant may affect the performance of the detection. To alleviate this problem, a loss function based on sparse regularization is proposed to fine-tune a classification model that has been pre-trained to increase the sparsity of in-distribution sample features while maintaining the classification power of the model during the learning process. This results in a lower energy score for in-distribution samples and a larger difference in scores between in-distribution and out-of-distribution samples, thus improving detection performance. Furthermore, the method does not introduce an external auxiliary dataset, avoiding the effect of correlation between samples. Experimental results on datasets CIFAR-10 and CIFAR-100 show that the method reduced the average FPR 95 of detecting the six abnormal datasets by 15.02% and 15.41% respectively.
