Quantum correlations for bipartite continuous-variable systems

Two quantum correlations Q and \(Q_\mathcal P\) for \((m+n)\)-mode continuous-variable systems are introduced in terms of average distance between the reduced states under the local Gaussian positive operator-valued measurements, and analytical formulas of these quantum correlations for bipartite Gaussian states are provided. It is shown that the product states do not contain these quantum correlations, and conversely, all \((m+n)\)-mode Gaussian states with zero quantum correlations are product states. Generally, \(Q\ge Q_{\mathcal P}\), but for the symmetric two-mode squeezed thermal states, these quantum correlations are the same and a computable formula is given. In addition, Q is compared with Gaussian geometric discord for symmetric squeezed thermal states.     

基于两体量子正交纠缠态集的局域删除

虽然在全局操作（Global）下,由于量子操作线性性的限制,非正交态不可精确克隆和精确删除,但是正交态却可以精确克隆和精确删除。不过在局域操作（LOCC）下,即使是正交的量子态也不一定可以精确克隆和精确删除。考虑在局域操作下,两体最大正交纠缠态和量子信道的局域删除问题,得到了它们可以实现局域删除的充要条件。     

Nonlinear finite-time bipartite consensus protocol for multi-agent systems associated with signed graphs

In this paper, finite-time multi-agent consensus problems are considered under networks associated with signed graphs whose edge weights can be not only positive but also negative. A nonlinear consensus protocol is proposed to guarantee the states of all agents to converge in a finite time. If the signed graph is structurally balanced, then the final consensus states of all agents are the same in modulus but not in sign. Otherwise, if the signed graph is structurally unbalanced, then the states of all agents converge to zero. Moreover, the final consensus states of agents can be provided uniformly regarding a signed-average quantity that depends on both the initial states of agents and the topology structure of the whole multi-agent network. Numerical simulations illustrate that the protocol is effective in achieving the finite-time consensus of agents under signed graphs and can particularly solve the finite-time average consensus problem of agents when their associated graph has all positive edge weights.     

基于几何均值分解的V-BLAST非线性预编码方法

针对垂直分层空时方案传统检测存在误层传输效应及复杂度高的问题,提出了一种MIMO-OFDM下行链路系统中基于几何均值分解的V-BLAST非线性模代数预编码方法。该方法首先采用几何均值分解获得各子信道具有相同等效噪声增益的预编码矩阵,再在发射端正交频分复用(OFDM)子载波信道间进行非线性模代数预编码,可以有效地消除分层空时码的误层传输效应,在接收端采用最小均方误差准则。仿真实验表明,该方法比传统方法有效改善了系统的误码性能,一定程度上降低了下行链路接收机的复杂度。     

State estimation for discrete-time markov jump linear systems based on orthogonal projective theorem

In this paper, state estimation problem for discrete-time Markov jump linear systems is considered. Based on orthogonal projective theorem, a novel suboptimal algorithm for state estimate of discrete-time Markov jump linear systems in the sense of minimum mean square error estimate is proposed. The proposed suboptimal algorithm is recursive and finite-dimensionally computable. Computer simulations are carried out to evaluate the performance of the proposed suboptimal algorithm.     

MISO 系统基于正交匹配追踪算法的参数与时滞联合估计

在有限采样情况下, 研究具有时滞的多输入单输出受控自回归系统的参数辨识和时滞估计问题. 当采样次数少于未知变量数时, 描述系统的方程组是欠定的, 对其目标函数求解是NP-hard 问题, 传统方法无法有效辨识出系统参数. 受压缩感知理论的启发, 基于参数向量所具有的稀疏特性, 提出一种新的阈值正交匹配追踪算法辨识系统的参数和时滞. 仿真实验表明, 所提出的算法能在少量采样时有效地辨识系统参数、估计未知时滞, 同时验证了算法的有效性.

     

Sensitivity analysis for biometric systems: A methodology based on orthogonal experiment designs

The purpose of this paper is to introduce an effective and structured methodology for carrying out a biometric system sensitivity analysis. The goal of sensitivity analysis is to provide the researcher/developer with insight and understanding of the key factors—algorithmic, subject-based, procedural, image quality, environmental, among others—that affect the matching performance of the biometric system under study. This proposed methodology consists of two steps: (1) the design and execution of orthogonal fractional factorial experiment designs which allow the scientist to efficiently investigate the effect of a large number of factors—and interactions—simultaneously, and (2) the use of a select set of statistical data analysis graphical procedures which are fine-tuned to unambiguously highlight important factors, important interactions, and locally-optimal settings. We illustrate this methodology by application to a study of VASIR (Video-based Automated System for Iris Recognition)—NIST iris-based biometric system. In particular, we investigated k = 8 algorithmic factors from the VASIR system by constructing a (2^6?1 × 3¹  × 4¹) orthogonal fractional factorial design, generating the corresponding performance data, and applying an appropriate set of analysis graphics to determine the relative importance of the eight factors, the relative importance of the 28 two-term interactions, and the local best settings of the eight algorithms. The results showed that VASIR’s performance was primarily driven by six factors out of the eight, along with four two-term interactions. A virtue of our two-step methodology is that it is systematic and general, and hence may be applied with equal rigor and effectiveness to other biometric systems, such as fingerprints, face, voice, and DNA.     

Quantum network dialogue protocol based on continuous-variable GHZ states

Quantum dialogue network, as a considerable topic, promotes high efficiency and instantaneousness in quantum communication through simultaneously deducing the secret information over the quantum channel. A new quantum network dialogue protocol is proposed based on continuous-variable GHZ states. In the protocol, the quantum dialogue could be conducted simultaneously among multiple legitimate communication parties. The security of the proposed protocol is ensured by the correlation of continuous-variable GHZ entangled states and the decoy states inserted into the GHZ states in the randomly selected time slots. In addition, the proposed quantum network dialogue protocol with continuous-variable quantum states improves the communication efficiency with the perfect utilization of quantum bits greatly.     

Entanglement detection and distillation for arbitrary bipartite systems

We present an inequality for detecting entanglement and distillability of arbitrary dimensional bipartite systems. This inequality provides a sufficient condition of entanglement for bipartite mixed states, and a necessary and sufficient condition of entanglement for bipartite pure states. Moreover, the inequality also gives a necessary and sufficient condition for distillability.     

Violation of Bell inequalities for arbitrary-dimensional bipartite systems

In this paper, we consider the violation of Bell inequalities for quantum system \(\mathbb {C}^K\otimes \mathbb {C}^K\) (integer \(K\ge 2\)) with group theoretical method. For general M possible measurements, and each measurement with K outcomes, the Bell inequalities based on the choice of two orbits are derived. When the observables are much enough, the quantum bounds are only dependent on M and approximate to the classical bounds. Moreover, the corresponding nonlocal games with two different scenarios are analyzed.     

局部加权组合状态空间系统正交梯度辨识

提出了局部加权组合状态空问模型参数的正交梯度辨识方法.局部状态空间模型用全参数化形式描述,同时选用正则化径向基函数作为分状态的加权因子.通过优化系统输出误差得到了系统矩阵、径向基函数中心与带宽的参数估计.仿真结果表明,所提出的方法用于非线性动态系统的建模是有效的.     

基于结构自适应滤波方法的非线性系统辨识

针对非线性系统辨识中定结构参数辨识局限性高和辨识率低的问题,将结构自适应引入辨识的优化,提出一种基于子系统的结构自适应滤波（SSAF）方法。该方法的模型由若干子系统级联而成,每一个子系统均为线性-非线性混合结构。子系统的线性部分是一个一阶或二阶可选的无限脉冲响应滤波器（IIR）,非线性部分则是一个静态的非线性函数。初始化中,子系统的参数随机产生,生成的若干子系统按照设定的连接规则进行随机连接,而不含反馈的连接机制确保了非线性系统的有效性。采用一种自适应多精英引导的复合差分进化（AMECoDEs）算法用于自适应模型循环优化,直至找到最优的结构和参数,即全局最优。实验结果表明,SSAF方法在非线性测试函数以及真实数据集上的表现优异,辨识率高且收敛性好,与聚焦时滞递归神经网络（FTLRNN）相比,它所用参数的个数仅为FTLRNN的1/10,且适应值精度提高了7%,验证了所提方法的有效性。     

基于图像局部纹理方向概率统计模型的非线性滤波算法

为了更有效地利用图像的局部特征恢复被噪声感染的图像,基于图像局部纹理方向概率统计模型,提出一种针对混合噪声的非线性滤波算法。算法利用Radon变换对图像进行主纹理方向分析,得到图像的局部纹理方向概率密度分布,然后基于概率统计模型,借助中心像素的若干邻近像素对中心像素进行估计,得到中心像素点的灰度值。此算法充分利用了图像的局部特征,既具有良好的去噪能力,又兼顾了对图像细节的保持特性。在处理同时感染脉冲噪声和高斯噪声的混合噪声图像时,算法效果明显优于其他滤波算法。     

基于二分图的网络能控性指数研究

网络系统的规模不断扩展,趋向于庞大复杂化。文章针对系统内部信息传递所带来的控制滞后等问题,在网络系统能控的研究基础上引入能控性指数的概念,用以描述网络系统能控的性能指标;基于二分图提出算法获得网络系统能控性指数,并提供每个控制量相应的控制链,为后续划分大规模网络系统的节点群等研究工作提供科学依据。     

一种改进的基于二部图网络结构的推荐算法

基于网络结构的推荐算法得到了研究者越来越多的关注,以往的基于二部图网络结构的推荐算法只是判断用户是否选择过项目,不区分用户对项目评分的高低。这些算法倾向于推荐流行商品,没有考虑项目度和权值的影响。针对这些问题,在区分高低分的情况下提出了改进的基于加权网络结构的推荐算法。算法在计算用户间的相似性系数时,引入项目度与项目的权值之和的比值θ,以提高推荐多样性。实验结果表明,改进后的算法能够提高推荐准确性和多样性,并且降低了推荐项目的流行性。     

基于二部图的概念聚类研究

传统概念聚类算法中簇的更新和存储不仅依赖于对象数目和属性数目,而且依赖于属性值的数目,这种局限性使其不适用于大型数据集。提出一种新的基于二部图的概念聚类算法（BGBCC）,该算法通过获得二部图的近似极大ε二元组集,有效地进行数据与属性的关联聚类。实验表明,该算法能得到较好的聚类结果,且能在较短的时间内进行大型数据集的概念聚类。     

Hybrid learning for interval type-2 fuzzy logic systems based on orthogonal least-squares and back-propagation methods

This paper presents a novel learning methodology based on a hybrid algorithm for interval type-2 fuzzy logic systems. Since only the back-propagation method has been proposed in the literature for the tuning of both the antecedent and the consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses a recursive orthogonal least-squares method for tuning the consequent parameters and the back-propagation method for tuning the antecedent parameters. Systems were tested for three types of inputs: (a) interval singleton, (b) interval type-1 non-singleton, and (c) interval type-2 non-singleton. Experiments were carried out on the application of hybrid interval type-2 fuzzy logic systems for prediction of the scale breaker entry temperature in a real hot strip mill for three different types of coil. The results proved the feasibility of the systems developed here for scale breaker entry temperature prediction. Comparison with type-1 fuzzy logic systems shows that hybrid learning interval type-2 fuzzy logic systems provide improved performance under the conditions tested.     

考虑量化的多智能体系统数据驱动双向一致性控制

针对未知动力学模型非线性离散时间多智能体系统,在信息传递过程中的数据量化问题,以及智能体之间的合作与竞争关系,提出了一种数据驱动控制算法,实现了多智能体系统的双向一致性跟踪控制.首先,利用紧凑形动态线性化(CFDL)方法,将未知动力学模型的非线性智能体转化为含有时变参数的数据模型,并通过设计性能指标函数获得时变参数的估...     

Dynamic event-triggered bipartite consensus for uncertain high-order nonlinear multi-agent systems

In this paper, the bipartite consensus problem is studied for a class of uncertain high-order nonlinear multi-agent systems. A signed digraph is presented to describe the collaborative and competitive interactions among agents. For each agent with lower triangular structure, a time-varying gain compensator is first designed by relative output information of neighboring agents. Subsequently, a distributed controller with dynamic event-triggered mechanism is proposed to drive the bipartite consensus error to zero. It is worth noting that an internal dynamic variable is introduced in triggering function, which plays an essential role in excluding the Zeno behavior and reducing energy consumption. Furthermore, the dynamic event-triggered control protocol is developed for upper triangular multi-agent systems to realize the bipartite consensus without Zeno behavior. Finally, simulation examples are provided to illustrate the effectiveness of the presented results.