Application of the N + 2 Transversal Network Method to the Study of a Coupled Resonator Filter

This paper presents a new approach to synthesize admittance function polynomials and coupling matrices for coupled resonator filters. The N + 2 transversal network method is applied to study a coupled resonator filter. This method allowed us to determine the polynomials of the reflection and transmission coefficients. A study is made for a 4 poles filter with 2 transmission zeros between the N + 2 transversal network method and the one found in the literature. A MATLAB code was designed for the numerical simulation of these coefficients for the 6, 8, and 10 pole filter with 4 transmission zeros.

Algorithm for complex network diameter based on distance matrix

The network diameter is an important characteristic parameter of a complex network. Calculation for a large-scale complex network’s diameter has been an important subject in the study of complex networks. If the network diameter is calculated directly, the problem mainly exists in efficiency for searching and counting the shortest paths. If the network diameter is calculated indirectly by studying the statistical function about the relationship between the network diameter and parameters affecting the diameter, the problems not only exist in the efficiency of statistic, but also exist in the function which may be not applicable to all kinds of networks. An algorithm for the complex network diameter based on the k order distance matrix is proposed with a matrix multiplication approach, and a mathematical proof for the algorithm correctness is given as well. Furthermore, some relevant propositions and deductions for reducing the complexity of this algorithm are put forward. With a good theoretical basis and a simple calculation process, this algorithm can be used to calculate the diameter of a large-scale complex network with small-world effect more accurately and efficiently. Two cases about the advanced research projects agency(ARPA) network model and the Chinese airline network model are adopted to verify the effect of this algorithm.

Adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions

The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory and Barbalat＇s lemma, generalized matrix projective lag synchronization criteria are derived by using the adaptive control method. Furthermore, each network can be undirected or directed, connected or disconnected, and nodes in either network may have identical or different dynamics. The proposed strategy is applicable to almost all kinds of complex networks. In addition, numerical simulation results are presented to illustrate the effectiveness of this method, showing that the synchronization speed is sensitively influenced by the adaptive law strength, the network size, and the network topological structure.

A NEURAL NETWORK APPROACH TO GATE MATRIX LAYOUT

Gate matrix layout problem plays an important role in integrated circuit design, but its optimization is NP-hard. In this paper, typical gate layout problem is analysed and adapted to neural network representation, furthermore the simulated results are given.

Neural network-based matrix effect correction in EDXRF analysis

In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis,with detailed algorithm to classify the samples.The method can correct the matrix effect effectively through classifying the samples automatically,and influence of X-ray absorption and enhancement by major elements of the samples is reduced.Experiments for the complex matrix effect correction in EDXRF analysis of samples in Pangang showed improved accuracy of the elemental analysis result.

Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.

Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays

Fuzzy cellular neural networks （FCNNs） are special kinds of cellular neural networks （CNNs）. Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality （LMI） approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.

Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

The global stability problem of Takagi-Sugeno（T-S） fuzzy Hopfield neural networks（FHNNs） with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler＇s lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.

Estimating Functional Brain Network with Low-Rank Structure via Matrix Factorization for MCI/ASD Identification

Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been proposed currently, including the low-order Pearson’s correlation (PC) and sparse representation (SR), as well as the high-order functional connection (HoFC). However, most existing methods usually ignore the information of topological structures of FBN, such as low-rank structure which can reduce the noise and improve modularity to enhance the stability of networks. In this paper, we propose a novel method for improving the estimated FBNs utilizing matrix factorization (MF). More specifically, we firstly construct FBNs based on three traditional methods, including PC, SR, and HoFC. Then, we reduce the rank of these FBNs via MF model for estimating FBN with low-rank structure. Finally, to evaluate the effectiveness of the proposed method, experiments have been conducted to identify the subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) from norm controls (NCs) using the estimated FBNs. The results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate that the classification performances achieved by our proposed method are better than the selected baseline methods.

Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control

This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.

Community detection in multiplex networks via consensus matrix

In complex network of real world, there are many types of relationships between individuals, and the more effective research ways for this kind of network is to abstract these relationship as a multiplex network. More and more researchers are attracted to be engaged in multiplex network research. A novel framework of community detection of multiplex network based on consensus matrix was presented. Firstly, this framework merges the structure of multiplex network and the information of link between each node into monoplex network. Then, the community structure information of each layer network was obtained through consensus matrix, and the traditional community division algorithm was utilized to carry out community detection of combine networks. The experimental results show that the proposed algorithm can get better performance of community partition in the real network datasets.

On Synchronization of Pinning-Controlled Networks with Reducible and Asymmetric Coupling Matrix

This paper investigates the synchronization of directed networks whose coupling matrices are reducible and asymmetrical by pinning-controlled schemes. A strong sufficient condition is obtained to guarantee that the synchronization of the kind of networks can be achieved. For the weakly connected network, a method is presented in detail to solve two challenging fundamental problems arising in pinning control of complex networks: (1) How many nodes should be pinned? (2) How large should the coupling strength be used in a fixed complex network to realize synchronization? Then, we show the answer to the question that why all the diagonal block matrices of Perron-Frobenius normal matrices should be pinned? Besides, we find out the relation between the Perron-Frobenius normal form of coupling matrix and the differences of two synchronization conditions for strongly connected networks and weakly connected ones with linear coupling configuration. Moreover, we propose adaptive feedback algorithms to make the coupling strength as small as possible. Finally, numerical simulations are given to verify our theoretical analysis.

基于对抗自编码填补网络的MIMO雷达故障阵元缺失数据重构

多输入多输出(Multiple In Multiple Out,MIMO)雷达的阵元故障会导致其协方差矩阵出现整行整列数据缺失,从而降低其角度估计性能。为此,提出一种对抗自编码填补网络(Adversarial Autoencoder Imputation Network,AAEIN)来重构故障阵元的缺失数据。该网络由负责重构缺失数据的自动编码(Autoencoder,AE)网络和负责分辨数据来源的鉴别器组成。在二者的对抗训练中,AE网络的重构能力和鉴别器的分辨能力不断得到提升,直至两者收敛。为避免网络训练过程中参数量大和计算复杂度高的问题,文中结合MIMO雷达协方差矩阵的Hermitian特性,使用协方差矩阵上三角部分构建数据集用于网络训练。仿真结果表明,文中方法可以有效地重构故障阵元的缺失数据且具有较高的重构精度。

Fabrication and abrasive wear properties of metal matrix composites reinforced with three-dimensional network structure

Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.

Compressive Creep Behavior of TiC/AZ91D Magnesium-matrix Composites with Interpenetrating Networks

The 42.1 vol. pct TiC/AZ91D magnesium-matrix composites with interpenetrating networks were fabricated by in-situ reactive infiltration process. The compressive creep behavior of as-synthesized composites was investigated at temperature ranging from 673 to 723 K under loads of 95-108 MPa. For a comparative purpose,the creep behavior of the monolithic matrix alloy AZ91D was also conducted under loads of 15-55 MPa at 548-598 K. The creep mechanisms were theoretically analyzed based on the power-law relation. The results showed that the creep rates of both TiC/AZ91D composites and AZ91D alloy increase with increasing the temperature and load. The TiC/AZ91D composites possess superior creep resistance as compared with the AZ91D alloy. At deformation temperature below 573 K, the stress exponent n of AZ91D alloy approaches theoretical value of 5, which suggests that the creep process is controlled by dislocation climb. At 598 K, the stress exponentof AZ91D is close to 3, in which viscous non-basal slip deformation plays a key role in the process of creep deformation. However, the case differs from that of AZ91D alloy when the stress exponent n of TiC/AZ91D composites exceeds 9, which shows that there exists threshold stress in the creep process of the composites, similar to other types of composites. The average activation energies for the creep of the AZ91D alloy and TiC/AZ91D composites were calculated to be 144 and 152 k J/mol, respectively. The existence of threshold stress in the creep process of the composites leads to an increase in activation energy for creep.

Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check Matrix

A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However,it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property(St RIP) via regular low density parity check(RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that St RIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.

增材制造TiB/Ti复合材料网状组织形成与力学性能

采用快速凝固技术将TiB直接植入基体钛合金,形成一种新型超细网状结构钛基复合材料(titanium matrix composites,TMCs)粉体,并采用激光增材制造技术,制备出一种等轴网状和柱状网状组织交替分布的新型钛基复合材料,系统分析和讨论增材制造TMCs超常凝固网状组织形成机制与力学特性。研究发现:增材制造TiB/Ti复合材料网状组织(约9μm)主要由原位自生纳米TiB晶须组成,呈现B27和Bf两种晶体结构;B元素的直接引入,易于在凝固界面形成成分过冷,不仅促使交替形成等轴网状组织和柱状网状结构,也同步细化基体晶粒尺寸,实现基体合金片层α相的等轴化。经原位力学观察分析发现,增材制造形成的原位自生纳米TiB网状组织结构,不仅能够抑制裂纹偏转并钝化裂纹,还将大量滑移迹线聚集于网络结构内部,并在晶界诱发高密度位错,限制材料的塑性变形,大幅度提高了复合材料的强度,增材制造TiB/Ti复合材料抗拉强度提高42%,伸长率保持在约10%。

随机矩阵理论在高速路关键路径辨识中的应用

高速公路网络是我国各地区相互连接的重要纽带,高速公路网络关键路径辨识对确保高速网络的可靠运行具有重要意义。传统的关键路径分析方法基于拓扑结构,未考虑交通网络的运输量特性;而现有的基于运输量数据的分析方法只考虑部分路径的运输量特性,难以反映交通网络的实际运行情况。利用路径运输量数据,搭建运输量随机矩阵模型,针对高速公路网络异常后的运输量变化特性,定义关键路径评估指数,实现异常影响程度的量化评估,在此基础上提出一种基于数据驱动的高速公路网络关键路径辨识方法。最后,采用辽宁省高速公路网络进行分析,验证了所提方法的合理性和有效性,并将该方法应用于城市路网案例中,进一步证明该方法具有普适性。

基于智能优化算法的边坡稳定性预测方法研究

针对边坡稳定性预测中数据分析片面、模型预测精度低的问题,基于302个边坡案例,选取6个变量特征,利用麻雀搜索算法(SSA)更新BP神经网络的敏感因子,建立SSA-BP边坡稳定性预测模型。采用混淆矩阵、受试者工作特征(ROC)曲线及曲线下面积A_(UC)值作为衡量指标,通过五折交叉验证法提高模型的泛化能力并与RF、BP、SVM、PSO-BP、GA-BP和LSTM 6种机器学习算法进行预测效果对比。结果表明,SSA-BP模型的A_(UC)值、准确率和F_1分数均最高,分别为91.90%、85.81%和85.87%,相较于优化前的BP网络A_(UC)值提高了23%。经典算例证明SSA-BP预测模型与ABAQUS计算的安全系数相近,并可给出可靠的预测结果,为岩土工程中边坡稳定性预测提供了一种新方法。

基于编码矩阵估计的极化码参数盲识别算法

针对当前极化码参数识别算法缺少对码字起点的识别以及识别信息位算法计算复杂的问题,提出一种基于编码矩阵估计的极化码参数盲识别算法。所提算法首先将截获的码字矩阵、相应码长下的克罗内克矩阵以及逆向重排矩阵相乘得到编码矩阵估计,然后通过编码矩阵的分布特征识别出码长和码字起点,最后使用训练好的卷积神经网络对极化码信息位以及冻结位进行识别。实验结果表明,所提方法不仅完成了码字起点的识别,而且在未知码字起点的情况下完成了对码长的识别,且码长的识别准确率优于现有算法,误比特率在0.19时,参数为(32,12)的极化码码长识别率仍然可以达到90%以上。