Anisotropy of Mechanical Behavior in Commercially Pure Titanium Sheets

Commercially pure titanium( CP Ti) sheets show typical planar anisotropy due to inherently crystallographic texture and rolling process. To characterize the initial,planar,plastic anisotropy of CP Ti sheets in forming process,uniaxial tensile tests of cold-rolled TA1 sheets at 0°,45° and 90° to the rolling direction were performed at room temperature,corresponding stress-strain curves and Lankford coefficient( r value) were obtained. Based on Hill'48 and Barlat'89 yield criteria,the anisotropic behavior of TA1 sheets was investigated. In order to verify the accuracy of two models,the experimental values of yield stress and r were compared with predicted ones. It revealed that Barlat '89 criterion with M = 6 is the best agreement with experimental data,and the obtained model can be used in the simulation of forming process.     

Downlink Resource Allocation with Multi-Objective Optimization in OFDMA Systems

A downlink radio resource allocation algorithm is proposed for orthogonal frequency division multiple access( OFDMA) systems. The resource allocation problem about system throughput and user fairness is formulated based on the multi-objective optimization theory. Then the optimality conditions are derived,according to which a joint subcarrier and power allocation algorithm is proposed. The simulation results show that the proposed algorithm can dynamically achieve arbitrary levels of compromise between throughput and fairness by adjusting the weighting coefficient,outperforming some static algorithms. In comparison,the classic maximum rate algorithm( MRA),max-min algorithm and proportional fairness( PF) algorithm can only achieve tradeoff in a certain level and are all special cases of the proposed algorithm.     

Hybrid membrane computing and pigeon-inspired optimization algorithm for brushless direct current motor parameter design

In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current (BLDC) motor, which is based on the membrane computing (MC) and pigeon-inspired optimization (PIO) algorithm. The motor parameter design problem is converted to an optimization problem with five design parameters and six constraints. The PIO algorithm is introduced into the framework of MC for improving the global convergence performance. The hybrid algorithm can improve the population diversity with better searching efficiency. Comparative simulations are conducted, and comparative results are given to show the feasibility and effectiveness of our proposed hybrid algorithm for high nonlinear optimization problems.     

Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design

Pigeon-inspired optimization(PIO) is a new swarm intelligence optimization algorithm, which is inspired by the behavior of homing pigeons. A variant of pigeon-inspired optimization named multi-objective pigeon-inspired optimization(MPIO) is proposed in this paper. It is also adopted to solve the multi-objective optimization problems in designing the parameters of brushless direct current motors, which has two objective variables, five design variables, and five constraint variables. Furthermore, comparative experimental results with the modified non-dominated sorting genetic algorithm are given to show the feasibility, validity and superiority of our proposed MIPO algorithm.     

移动网络SFC部署与计算资源分配联合算法

在网络功能虚拟化的移动核心网中,提出了一种基于服务功能链（SFC）部署与计算资源分配联合算法.首先考虑SFC中虚拟网络功能（VNF）计算资源分配对处理时延的影响,建立SFC部署与计算资源分配联合优化问题,实现SFC的部署成本和端到端时延加权和的最小化.其次,为了求解所提优化问题,利用多智能体深度确定性策略梯度算法,从SFC各VNF的历史数据中学习策略指导即时的通用服务器节点选择和计算资源分配,提出了相应的SFC部署与计算资源分配联合算法.仿真结果表明,所提算法可以在保证SFC的服务质量需求的条件下实现部署成本和端到端时延的有效权衡.     

OFDMA解码转发中继系统的最优资源分配

针对正交频分多址接入-解码转发（OFDMA-DF）中继系统,提出了以最大化系统加权和速率为目标的子载波分配、功率分配、传输方式选择和中继选择联合优化问题. 基于凸优化理论,提出了一种最优资源分配算法,其复杂度仅与子载波数成线性关系. 理论分析和仿真结果表明,结合用户权重的调整,该算法既可实现资源分配的公平性,又可有效利用中继节点的能力,提高系统容量.     

Cross-Layer Adaptive Resource Allocation Algorithm with Diverse QoS Requirements for Single-Cell OFDMA Systems

The orthogonal frequency division multiple access( OFDMA) based communication system has been considered as the main trend of next-Generation communication system. But the existing resource allocation algorithm designed for such system is always with high complexity thus hard to be realized. To solve such problem with the constraints of spectrum efficiency and buffer state,a novel cross-layer resource allocation algorithm( RAA) is proposed in this paper. The goal of our RAA is to maximize the system throughput while satisfying several practical constraints,such as fairness among services,head of line( Ho L) delay and diverse quality of service( Qo S) requirements. Due to these constraints,finding the optimal solution becomes a NPhard problem. Therefore in this paper a novel method to solve such problem with acceptable complexity is proposed within following steps: firstly,based on the link state we formulate the ideal subchannel allocation strategy as a convex optimization problem,which can be efficiently solved by our proposed lagrange multiplier technique subchannel allocation( LMTSA) algorithm; secondly,according to the obtained channel allocation matrix,a power allocation algorithm based on the water-filling power allocation( WPA) idea is deployed to get the optimal power allocation matrix combining with adaptive modulation and coding( AMC); finally,through a greedy algorithm,the ultimate subchannel and power allocation matrix can be obtained based on iterative method. The simulation results illustrate that we can achieve the higher throughput and better Qo S performance than the widely-used maximum throughput( MT) algorithm and round robin( RR) algorithm.     

相控阵雷达搜索和跟踪资源博弈分配策略

为了解决搜索和跟踪（SAT）资源分配（RA）实时性的问题,提出博弈论视角下的资源分配策略. 建立搜索与跟踪的系统模型,将SATRA建模为非合作博弈问题,将模型中搜索子空域和跟踪多目标间的资源分配问题看作合作博弈关系,利用Shapley值完成相应资源的分配,给出纳什均衡求解的双目标优化模型;为了快速找到符合决策者偏好的分配解,将双目标优化模型通过动态加权的理想点法转化为单目标优化问题,提出并行混合遗传粒子群优化（PHGAPSO）算法求解上述优化问题最优分配方案,仿真验证了博弈分配策略的有效性和先进性以及混合算法性能的优越性. 在相同的条件下,与帕累托双目标优化方法进行对比. 实验结果表明,博弈论的方法具有更高的灵活性,平均搜索信噪比提高了1.02%,平均跟踪目标误差降低了1.55%.     

MEC系统中面向网络切片的3C联合资源分配算法

移动边缘计算（MEC）相关研究已经成为未来移动网络的热点研究之一.在基于网络切片的雾无线接入网络中,提出了一种面向网络切片的MEC系统通信计算缓存（3C）联合的资源分配算法.给出了面向网络切片的多MEC协作资源分配模型,其中考虑了无线接入带宽与回程链路带宽的限制以及MEC系统计算与缓存资源分配对网络切片服务时延的影响;基于服务时延定义了用户获得资源的效用值,构造了系统效用值最大化问题优化不同网络切片下的用户接入、计算与缓存资源分配,并采用连续凸近似交替迭代方法获得近似最优解.对所提算法性能进行了仿真验证,仿真结果表明,所提算法实现了系统总效用值优化,提高了面向网络切片的MEC系统资源利用效率.     

认知反向散射网络通信容量公平的资源优化

为保证认知反向散射通信网络中物联网节点通信容量的公平性,提出一种基于最大最小准则的资源分配方案. 在考虑物联网节点动态电路能耗模型与非线性能量收集模型的基础上,通过联合优化发射功率、反射及时间分配系数构建一个最大最小物联网节点通信容量的非凸多维资源分配问题. 首先,采用反证法、连续凸近似及辅助变量等方法将原问题转换为凸优化问题;然后提出一种迭代算法来求解转换问题. 仿真结果表明,所提的资源分配方案在提升物联网节点通信容量的同时保障了节点通信的公平性.     

Efficient and fair resource allocation in downlink OFDMA systems

A resource allocation problem considering both efficiency and fairness in orthogonal frequency division multiple access (OFDMA) systems is studied. According to the optimality conditions, a downlink resource allocation algorithm consisting of subcarrier assignment and power allocation is proposed. By adjusting the tradeoff coefficient, the proposed algorithm can achieve different levels of compromise between efficiency and fairness. The well-known classic resource allocation policies such as sum-rate maximization algorithm, proportional fairness algorithm and max-min algorithm are all special cases of the proposed algorithm. Simulation results show that the compromise between efficiency and fairness can be continuously adjusted according to system requirements.     

双功能雷达通信系统的多用户功率分配算法

为解决传统多用户双功能雷达通信系统因载波分配导致通信性能较差的问题，提高双功能雷达通信系统的资源利用率，提出了一种通信用户载波共享模型下的多用户功率分配方案，并通过构造合理的资源分配问题使得系统具有更优的通信性能。首先，建立双功能雷达通信系统的通信用户载波共享信号模型。其次，为保证双功能雷达通信系统性能，构造了以系统通信和速率最大并满足雷达信噪比下界、总功率和用户功率约束的优化问题，通过理论分析严格证明了优化问题的可行性。最后，为求解凸优化问题，提出一种多用户功率分配算法，通过引入辅助变量对目标函数进行二次转换，将其分解为两个优化子问题，继而通过交替迭代的方式进行求解。研究结果表明，相较于传统通信用户载波分配模型，优化后的通信用户载波共享模型通信和速率提高了约40%,从而验证了所提方案具有更优的通信性能，有效提高了系统资源利用率。本研究成果为提高多用户双功能雷达通信系统的通信性能提供了一种新思路和手段。     

基于非合作博弈的中继网络分布式资源分配

提出一种可应用于正交频分复用多址(OFDMA)中继网络的分布式资源分配算法. 基于将模型描述为基站与中继的非合作功率分配博弈(RNCPAG), 设计出2种效用函数, 并以最大化效用函数为准则, 证明在总功率受限的约束下, 该算法存在并收敛于唯一的纳什均衡点. 研究表明, 同传统的平均功率分配算法相比, 分布式博弈算法以牺牲少量的迭代步数为代价, 获得更高的系统容量和资源效率.     

认知无线电网络中的参数优化问题

针对在多协作用户宽带信道情况下,认知无线电网络中当信道增益固定时,检测时间、功率以及带宽分配等参数的优化问题,提出了各参数的联合优化算法.以信道容量为目标函数,给出了最优的资源分配算法.结果表明,该算法能够实现频谱接入并且极大地提高了次用户系统的总信道容量,且资源分配与主用户占用的概率、峰值发射功率和干扰约束等因素有关.在多认知用户网络中,参与合作的用户越多,接收信号的信噪比越高,次用户系统的总信道容量越大.     

性能感知的核心网控制面资源分配算法

针对网络功能虚拟化（NFV）环境下核心网控制面资源分配问题,提出性能感知的资源分配算法. 基于排队网络理论建立核心网控制面性能评估模型,推导出信令流程平均响应时间的近似表达式. 为了确定核心网控制面虚拟网络功能（VNF）实例的最优配置数量,综合考虑处理性能和VNF实例部署成本,建立核心网控制面资源分配多目标优化模型,并提出改进的多目标遗传算法. 仿真结果表明,该性能评估模型误差在10%以内,优于Jackson排队网络模型;与NSGA-II和HaD-MOEA相比,所提算法获得的近似Pareto前沿收敛性和多样性更好,更逼近真实Pareto前沿.     

比例公平保证的MIMO-OFDM系统能效资源分配

针对下行多输入多输出-正交频分复用( MIMO-OFDM)系统,提出了满足在总功率限制和用户比例公平性约束下,使系统能量效率最大化的资源分配算法。为了降低计算复杂度,提出了两步优化的次优子载波和功率分配算法。第1步由子载波共享和平坦衰落假设得到估计的能效发射功率,并采用等功率分配方法完成子载波分配;第2步在子载波分配基础上完成能效最优的功率分配。仿真结果表明,所提算法以较低的复杂度逼近最优算法的性能,并且在保证用户比例公平性的同时带来明显的能效提升。     

OFDMA波束赋形中继系统资源分配

针对采用波束赋形技术的正交频分多址接入放大转发中继系统,提出了一种以最大化比例公平因子为目标的资源分配算法. 该算法基于凸优化理论,对功率分配、子载波配对、中继选择和用户选择进行了联合优化. 理论分析和仿真验证结果表明,所提的资源分配算法能以较低的复杂度实现系统资源分配的公平性,并且可以有效利用波束赋形技术,提高系统容量.     

一种采用并行免疫优化的频谱分配算法

基于图论的认知无线网络频谱分配是一个NP难问题,智能优化是求解此问题的有效方法．由于实时性是认知无线网络频谱分配不同于其他无线网络频谱分配的显著特点,故提出了一种基于主从式并行模型的并行免疫优化频谱分配算法,即在多个节点上同时并行计算种群中抗体的亲和度．给出了算法的主要思想、关键技术及基本实现步骤．仿真实验结果表明,该算法可以获得更高的网络收益,缩短频谱分配时间．与传统的经典串行算法相比,可以获得较理想的加速比和效率．     

Biological object recognition approach using space variant resolution and pigeon-inspired optimization for UAV

Recognizing the target from a rotated and scaled image is an important and difficult task for computer vision. Visual system of humans has a unique space variant resolution mechanism (SVR) and log-polar transformations (LPT) is a mapping method that is invariant to rotation and scale. Motivated by biological vision, we propose a novel global LPT based template-matching algorithm (GLPT-TM) which is invariant to rotational and scale changes; and with pigeon-inspired optimization (PIO) used to optimize search strategy, a hybrid model of SVR and pigeon-inspired optimization (SVRPIO) is proposed to accomplish object recognition for unmanned aerial vehicles (UAV) with rotational and scale changes of the target. To demonstrate the efficiency, effectiveness and reliability of the proposed method, a series of experiments are carried out. By rotating and scaling the sample image randomly and recognizing the target with the method, the experimental results demonstrate that our proposed method is not only efficient due to the optimization, but effective and accurate in recognizing the target for UAV.     

多小区下行NOMA系统中最大公平的功率分配方案

对于多小区多簇且每个簇包含任意用户的下行非正交多址接入（NOMA）系统,提出了一种最大公平的功率分配方案.首先构建了最大公平的功率分配优化问题;推导了单簇内以最大公平为准则的分配功率时,该簇内每个用户的速率与每个簇的总功率之间的关系,并基于该结论,将优化问题中用户间的功率分配转化为簇间功率分配;最后给出了一种基于迭代的簇间功率分配算法,为每个簇平均分配功率并计算单簇内最大公平准则下用户的速率.通过多次调整最大速率所在小区簇的功率和最小速率所在小区簇的功率,得到该系统中最大公平的功率分配.仿真结果显示,所提方案的公平性优于相同场景中已有的功率分配方案.