Phase equilibria of the Sn-Ag-Cu-Ni quaternary system at the sn-rich corner

Knowledge of phase equilibria of the Sn-Ag-Cu-Ni quaternary system at the Sn-rich corner is important for the understanding of the interfacial reactions at the Sn-Ag-Cu/Ni contacts, which are frequently encountered in recent microelectronic products. Various Sn-Ag-Cu-Ni alloys were prepared and equilibrated at 250°C. The alloys were then quenched and analyzed. The phases were determined by metallography, compositional analysis, and x-ray diffraction (XRD) analysis. No quaternary phases were found. The isoplethal sections at 60at.%Sn, 70at.%Sn, 80at.%Sn, and 90at.%Sn at 250°C are determined. The phase equilibrium relationship was proposed based on the quaternary experimental results and the 250°C isothermal sections of the four constituent ternary systems, Sn-Ag-Cu, Sn-Ag-Ni, Sn-Cu-Ni, and Cu-Ag-Ni. Because there are no ternary phases in all these three systems, all the compounds are in fact binary compounds with various solubilities of the other two elements.     

基于重复博弈的Ad hoc网络合作转发模型

针对噪音环境下的Ad hoc网络合作问题,运用不完美信息重复博弈模型分析节点之间的交互过程,使用贝尔曼方程构造满足序贯均衡的合作激励机制。对于该机制,节点间无需交换观察信息,节省了节点能量和网络负担。与已有的序贯均衡策略相比,该机制避免使用对观测误差敏感的触发策略,提高了不完美信息环境下网络的合作率和节点的平均收益。仿真结果表明,使用贝尔曼方程构造的序贯均衡策略既提高了网络的合作率,又有很好的适应性。     

基于平衡流形展开模型的涡扇发动机传感器容错控制

基于某型涡扇发动机平衡流形展开模型设计了一种传感器容错控制方案。首先,利用非线性系统平衡流形建模理论建立某型涡扇发动机性能仿真模型。然后,利用该模型在线估计传感器输出,并根据解析冗余方法,重构故障传感器的输出。最后,针对传感器常见典型故障进行了故障容错控制过程仿真。仿真结果表明,所研究的方法能及时、有效的检测到故障传感器,并实现容错控制,表明该方案的可行性。     

基于Stackelberg博弈的动态频谱接入控制

为了解决多主用户和多次级用户共存网络的频谱资源分配问题,提出了一种基于斯塔科尔伯格（Stackelberg）博弈的动态频谱接入控制算法。该算法通过三阶段Stackelberg博弈模拟主用户频谱竞价,博弈过程中次级用户以最大化传输速率为目的接入主用户频谱,同时设计了一种迭代过程来求解纳什均衡。实验计算与结果分析证明了纳什均衡唯一存在性的充要条件,并说明了迭代过程的收敛性以及主用户最佳效用的影响因素。     

Defense decision-making method based on incomplete information stochastic game and Q-learning

Most of the existing stochastic games are based on the assumption of complete information,which are not consistent with the fact of network attack and defense.Aiming at this problem,the uncertainty of the attacker’s revenue was transformed to the uncertainty of the attacker type,and then a stochastic game model with incomplete information was constructed.The probability of network state transition is difficult to determine,which makes it impossible to determine the parameter needed to solve the equilibrium.Aiming at this problem,the Q-learning was introduced into stochastic game,which allowed defender to get the relevant parameter by learning in network attack and defense and to solve Bayesian Nash equilibrium.Based on the above,a defense decision algorithm that could learn online was designed.The simulation experiment proves the effectiveness of the proposed method.     

A hierarchical framework of dynamic relay selection for mobile users and profit maximization for service providers in wireless relay networks

Although extensive research has been carried out on the issue of how to optimally select relays in wireless relay networks, relay selection for mobile users is still a challenging problem because of the requirement that the dynamic selection should adapt to user mobility. Moreover, because the selected relays consume their energy on relaying data for the users, it is required that the users have to pay for this relay service. The price of selecting relays will affect the users' decisions. Assuming that different relays can belong to different service providers, we consider the situation that the service providers can strategically set the prices of their relays to maximize their profits. In this paper, we jointly study the dynamic relay selection for mobile users and profit maximization for service providers. Also, we design a Stackelberg‐game hierarchical framework to obtain the solution. At the lower level, we investigate the relay selection problem for the mobile users under given prices of selecting the relays. It is formulated as a Markov decision process problem with the objective to minimize the mobile user's long‐term average cost (which consists of the payment to the relay service and the cost due to packet loss), and solved by applying the linear programming technique. At the upper level, we study the game of setting relay prices for the service providers, with the knowledge that the mobile users will make relay selections based on their given prices. Nash equilibrium is obtained as the solution. Our results can help to provide a guidance for service providers to compete for providing relay services. Copyright © 2012 John Wiley & Sons, Ltd.     

无线传感器网络自适应区域路由算法

2009年8月,随着‘感知中国’的提出,国内众多学者对无线传感器网络开展了日益深入的研究,而节省网络能耗则是研究的首要问题。主要针对延长传感器网络生存时间、增强网络的可靠性的目标,提出一种自适应区域路由算法（ARRA）,它包括对WSN进行区域规划、区域自治以及区域负载均衡三个方面。分析和仿真同时表明,ARRA实现了传感器网络的能量均衡,降低了网络能耗,并能提升某些热点区域的生存周期和可靠性。     

博弈条件下雷达波形设计策略研究

为提高电子战中弹载雷达检测性能,该文提出基于纳什均衡的雷达波形设计方法。首先建立电子战条件下雷达与干扰信号博弈模型,基于最大化信干噪比(SINR)准则,分别设计了雷达和干扰的波形策略;然后通过数学推导论证了博弈纳什均衡解的存在性,设计了一种重复剔除严格劣势的多次迭代注水方法来实现纳什均衡;通过二步注水法推导了非均衡的maxmin优化方案;最后通过仿真实验测试不同策略下雷达检测性能。仿真结果证明,基于纳什均衡的雷达信号设计有助于提升博弈条件下雷达检测性能,对比未博弈时,雷达检测概率最高可提升12.02%,较maxmin策略最高可提升3.82%,证明所设计的纳什均衡策略更接近帕累托最优。     

认知雷达抗干扰中的博弈论分析综述

雷达对抗的核心研究内容主要是干扰策略与抗干扰策略之间的对抗博弈,其作为电子战研究领域的热点一直备受学者们关注.该文综述了学者们利用合作与非合作博弈方法来分析雷达在进行目标探测和干扰抑制时所使用的策略,主要通过不同体制的雷达利用认知技术感知和学习外界复杂的电磁环境,合理地分配发射功率、控制编码序列、设计波形、研究检测和跟踪方法以及分配雷达通信资源等.这样雷达既节约发射所消耗的功率,又可以自适应地搜索和跟踪目标而不被敌方所发现,从而使雷达在复杂多变的现代战场环境中达到自身最优的性能.最后,对认知雷达抗干扰中的博弈论分析研究进行总结和展望,并指出了一些博弈论在认知雷达抗干扰策略应用中所面临的潜在问题和挑战.     

Automatic foreground extraction from imperfect backgrounds using multi-agent consensus equilibrium

Extracting accurate foreground objects from a scene is an essential step for many video applications. Traditional background subtraction algorithms can generate coarse estimates, but generating high quality masks requires professional softwares with significant human interventions, e.g., providing trimaps or labeling key frames. We propose an automatic foreground extraction method in applications where a static but imperfect background is available. Examples include filming and surveillance where the background can be captured before the objects enter the scene or after they leave the scene. Our proposed method is very robust and produces significantly better estimates than state-of-the-art background subtraction, video segmentation and alpha matting methods. The key innovation of our method is a novel information fusion technique. The fusion framework allows us to integrate the individual strengths of alpha matting, background subtraction and image denoising to produce an overall better estimate. Such integration is particularly important when handling complex scenes with imperfect background. We show how the framework is developed, and how the individual components are built. Extensive experiments and ablation studies are conducted to evaluate the proposed method.