An algebraic approach to on-line signal denoising and derivatives estimation

In this paper, a new algebraic approach to the on-line signal derivatives estimation is proposed. The proposed approach is based on the conversion of the truncated Taylor series expansion to the set of linearly independent equations regarding the signal derivatives. The nonhomogeneous parts of the obtained set of equations are convolution integrals, which can be transformed to the stable linear state-space filter realization. The proposed algebraic estimator provides stable convergence without the need for periodic re-initialization, as in the case of the conventional algebraic estimators. In contrast to the Taylor series-based tracking differentiators, the proposed estimator also provides an estimation of the arbitrary number of the higher-order signal derivatives. In addition, the tuning of the estimator parameters does not depends on the filter dimension. The efficiency of the proposed estimator is illustrated by the simulation examples and experimental results related to the monitoring of the surgical drilling process.     

Fast algorithm for image denoising with different boundary conditions

In recent works several authors have considered the L₁ fidelity term, the L₂ fidelity term and the combined L₁ and L₂ fidelity term for denoising models, and they used the fast Fourier transform (FFT) algorithm which can only use periodic boundary conditions (BCs). In this paper, we combine the augmented Lagrangian method (ALM) and the symmetric Red–Black Gauss–Seidel (SRBGS) method to propose three algorithms that are suitable for different BCs. Experimental results show that the proposed algorithms are effective and the model with the combined L₁ and L₂ fidelity term demonstrates more advantages in efficiency and accuracy than other models with the L₁ fidelity term or the L₂ fidelity term.     

An adaptive high-voltage direct current detection algorithm using cognitive wavelet transform

This paper proposes an algorithm that uses wavelet level adaptive decision-making for detecting high-voltage direct current (HVDC) discharge in wavelet transform cognitively. The identification and detection of HVDC discharge is an essential area of investigation, which contributes to ensuring pipeline safety and the optimal operation of an electrical power system. The proposed algorithm overcomes the wavelet packet transform’s disadvantage of needing to determine the level in advance. The decomposition level of wavelet packet transform is controlled by calculating relative wavelet energy change to decide its wavelet level. Our proposal extracts richer features of HVDC discharge by comparing other feature extraction algorithms. To select the best-suited mother wavelet function, we also design a selection method based on quantitative and qualitative approaches. An additional objective of this study is to detect the phenomenon of HVDC discharge using CP time-series data to assess the corrosion of energy pipelines. Moreover, a third primary discovery is that a wavelet-based application framework is designed to detect the HVDC discharge and further protect the energy pipeline. These discoveries can be valuably applied to the protection of power systems. They also provide brighter perspectives on future opportunities to expand on studies-to-date on the detection and classification of time-series data.     

An algorithm for matrix symmetrization

In this paper we characterize a symmetrizability property using the theory of output sets. Employing the basic properties of symmetric matrices and an efficient algorithm for systematic generation of output sets, an algorithm for testing the symmetrizability of a matrix is presented and illustrated.     

一种高阶平滑表面并行提取方法

针对高阶平滑表面算法计算复杂和数据量大的问题,提出一种加快高阶平滑表面算法速度的并行方法.首先对高阶平滑表面算法进行并行化,然后采用优化技术提高算法性能,同时采用矩阵压缩改善内存空间性能.实验表明,在双核处理器上平均加速比达到1.87.     

An algorithm for Eigenvalue assignment in multi-input systems

This paper describes a computational method for solving the problem of eigenvalue assignment in a multi-input linear system. The given system is first reduced to an upper block Hessenberg form by means of orthogonal state coordinate transformations. It is then shown how a sequence of state feedback matrices and orthogonal state coordinate transformations can be applied to obtain a block triangular structure for the resulting state matrix, where the matrices on the diagonal are square matrices in upper Hessenberg form and of dimensions equal to the controllability indices of the multi-input system. Furthermore, the structure of the corresponding input matrix is such that the problem of eigenvalue assignment in the multi-input system can be reduced to several single-input eigenvalue assignment problems where the dimensions of the single-input systems are equal to the controllability indices of the multi-input system.     

针对纹理图像压缩的改进SPIHT算法

针对纹理图像压缩提出了一种改进的SPIHT算法. 算法对一阶小波变换产生的LL、HL、LH、HH 4个子带系数分别进行小波再分解,之后对上述4个子带利用SPIHT算法进行联合编码;子带之间的量化比特分配嵌入在编码过程之中,因此可实现4个子带编码比特的自动精确分配. 编码过程充分利用了小波系数的高频分量信息,可有效保持图像中的纹理细节. 最后通过对纹理图像的压缩实验,证实了改进算法的有效性.     

DistanceRank: An intelligent ranking algorithm for web pages

A fast and efficient page ranking mechanism for web crawling and retrieval remains as a challenging issue. Recently, several link based ranking algorithms like PageRank, HITS and OPIC have been proposed. In this paper, we propose a novel recursive method based on reinforcement learning which considers distance between pages as punishment, called “DistanceRank” to compute ranks of web pages. The distance is defined as the number of “average clicks” between two pages. The objective is to minimize punishment or distance so that a page with less distance to have a higher rank. Experimental results indicate that DistanceRank outperforms other ranking algorithms in page ranking and crawling scheduling. Furthermore, the complexity of DistanceRank is low. We have used University of California at Berkeley’s web for our experiments.     

一种CMMB系统信道估计改进算法的研究

针对CMMB系统中接收信号因受传输环境的大、小尺度衰落、多普勒衰落影响,通过最小二乘信道估计和低密度奇偶校验码LDPC（Low Density Parity Check Code）译码结合的算法在接收端对信号进行接收处理,实验仿真表明,该算法可以达到与经典算法的性能相接近效果的同时大量减少了系统的复杂度。     

An event-triggering algorithm for decentralized stochastic optimization over networks

In this paper, we study the problem of decentralized optimization to minimize a finite sum of local convex cost functions over an undirected network. Compared with the existing works, we focus on improving the communication efficiency of the stochastic gradient tracking method and propose an effective event-triggering decentralized stochastic gradient tracking algorithm, namely, ET-DSGT. ET-DSGT utilizes the event-triggering mechanism in which each agent only broadcasts its estimators at the event time to effectively avoid real-time communication, thus improving communication efficiency. In addition, we present a theoretical analysis to show that ET-DSGT with a decaying step-size can converge to the exact global minimum. Moreover, we show that for each agent, the time interval between two successive triggering times is greater than the iteration interval under certain conditions. Finally, we provide several simulations to demonstrate the effectiveness of ET-DSGT.     

一种适用于双天线InSAR系统的成像定位算法

针对机载双天线InSAR系统实用化测图问题,提出一种将目标定位至数据获取几何的成像算法.该算法可以高效结合自配准处理.相对于将目标定位至零多普勒几何的传统算法,该算法优势在于减少了后续干涉处理过程三维定位的计算量,且成像算法效率与传统算法一致.该算法尤其适用于实用化机载双天线InSAR系统.     

一种改进的组合策略评审专家推荐算法

从项目管理的角度出发,综合考虑用户选择偏好、项目与专家的知识匹配度、项目间的相似性以及专家的历史表现等因素,融合基于内容特征、潜在主题特征、协同过滤和用户选择偏好等评审专家推荐算法,构建一种改进的组合策略的评审专家推荐算法,并通过设计实验,利用航天项目进行实验研究,结果分析表明推荐算法是可行且实用的。     

An algorithm for the calculation of exact term discrimination values

Term discrimination values have been suggested as an effective means for the selection and weighting of index terms in automatic document retrieval systems. This paper reports an algorithm for the calculation of term discrimination values that is sufficiently fast in operation to permit the use of exact values, rather than the approximate values studied in previous work. Evidence is presented to show that the relationship between term discrimination and term frequency is crucially dependent upon the type of inter-document similarity measure that is used for the calculation of the discrimination values.     

一种基于OPT的MPS编制方法

借助OPT的DBR法，对单一瓶颈环节条件下的主生产计划（MPS）进行了探讨。阐述了研究OPT的MPS问题的基本思路，建立了在单一瓶颈条件下，最小传输批量的基本算法及编制MPS的有关算法。     

An efficient adaptive degree-based heuristic algorithm for influence maximization in hypergraphs

Influence maximization (IM) has shown wide applicability in immense fields over the past decades. Previous researches on IM mainly focused on the dyadic relationship but lacked the consideration of higher-order relationship between entities, which has been constantly revealed in many real systems. An adaptive degree-based heuristic algorithm, i.e., Hyper Adaptive Degree Pruning (HADP) which aims to iteratively select nodes with low influence overlap as seeds, is proposed in this work to tackle the IM problem in hypergraphs. Furthermore, we extend algorithms from ordinary networks as baselines. Results on 8 empirical hypergraphs show that HADP surpasses the baselines in terms of both effectiveness and efficiency with a maximally 46.02% improvement. Moreover, we test the effectiveness of our algorithm on synthetic hypergraphs generated by different degree heterogeneity. It shows that the improvement of our algorithm effectiveness increases from 2.66% to 14.67% with the increase of degree heterogeneity, which indicates that HADP shows high performance especially in hypergraphs with high heterogeneity, which is ubiquitous in real-world systems.     

An iterative algorithm for generalized periodic multiple coupled Sylvester matrix equations

The paper is indicated to constructing a modified conjugate gradient iterative (MCG) algorithm to solve the generalized periodic multiple coupled Sylvester matrix equations. It can be proved that the proposed approach can find the solution within finite iteration steps in the absence of round-off errors. Furthermore, we provide a method for choosing the initial matrices to obtain the least Frobenius norm solution of the system. Some numerical examples are illustrated to show the performance of the proposed approach and its superiority over the existing method CG.     

An accelerated exact distributed first-order algorithm for optimization over directed networks

Distributed optimization over networked agents has emerged as an advanced paradigm to address large-scale control, optimization, and signal-processing problems. In the last few years, the distributed first-order gradient methods have witnessed significant progress and enrichment due to the simplicity of using only the first derivatives of local functions. An exact first-order algorithm is developed in this work for distributed optimization over general directed networks with only row-stochastic weighted matrices. It employs the rescaling gradient method to address unbalanced information diffusion among agents, where the weights on the received information can be arbitrarily assigned. Moreover, uncoordinated step-sizes are employed to magnify the autonomy of agents, and an error compensation term and a heavy-ball momentum are incorporated to accelerate convergency. A linear convergence rate is rigorously proven for strongly-convex objective functions with Lipschitz continuous gradients. Explicit upper bounds of step-size and momentum parameter are provided. Finally, simulations illustrate the performance of the proposed algorithm.     

An improved algorithm for practical byzantine fault tolerance to large-scale consortium chain

Consortium chain can better combine blockchain technology with market business, so it is adopted by all walks of life and develops at a large scale. Practical Byzantine Fault Tolerance (PBFT) is more suitable for consortium chain, which are partially decentralized, resistant to Byzantine nodes and strong consistency. However, the limited network scale that PBFT can support is not conducive to the large-scale development of consortium chain. Based on the analysis of the working principle and consensus mechanism of PBFT, this study proposed an algorithm to improve PBFT: feature grouping and credit optimization Byzantine Fault Tolerance (FCBFT). In this algorithm, a feature grouping model is proposed to optimize the node structure of large-scale consortium chain, which divides large-scale network nodes into different institutions to form independent consensus groups by feature grouping. On this basis, a reputation score reward mechanism is proposed to improve the consensus efficiency of large-scale consortium chains. It introduces a reputation score calculation formula to select high-reputation primary nodes. At the same time, a replacement cycle is established to replace high-reputation nodes with low-reputation nodes, so as to optimize the consensus efficiency of the consortium chain. The experimental results show that FCBFT has shorter delay and higher throughput (TPS) than PBFT, which becomes more obvious as the number of nodes increases. After the number of nodes exceeds the threshold, the TPS of PBFT drops rapidly, while FCBFT can maintain high efficiency and stability. FCBFT's block generation speed is higher than PBFT, and its relative growth rate has steadily increased.     

An augmented Lagrangian algorithm for total bounded variation regularization based image deblurring

The augmented Lagrangian strategy has recently emerged as an important methodology for image processing problems. In this paper, based on this strategy, we propose a new projected gradient algorithm for image deblurring with total bounded variation regularization. The convergence property of our algorithm is discussed. Numerical experiments show that the proposed algorithm can yield better visual quality than the Rudin–Osher–Fatemi (ROF) method and the split Bregman iteration method.