使用LDC码的BI-STCM-ID系统中的星座映射分析

研究了BI-STCM-ID系统中的星座映射问题。证明了在使用LDC（Linear Dispersion Code）空时编码方案的BI-STCM-ID系统中,基于最大化编码增益的高维星座映射设计优化问题等价于基于最大化欧式距调和均值的一维星座映射设计优化问题。     

星型16QAM调制LDPC-BICM-ID系统的星座映射设计

针对比特交织编码调制—迭代检测系统中应用星型16进制正交幅度(16QAM)调制不存在Gray映射的问题,结合欧氏平方重量最大化原则和最小判决区域最大化原则,提出一种适合于此系统星座映射的新规则,设计新规则下的星型16QAM星座映射图,通过分析和计算机仿真,比较了各种星型16QAM星座映射性能。仿真结果表明,根据新规则设计的星型16QAM星座映射好于其他星座映射,在误码率达到10-6时,使用设计的映射方式使得系统性能有2.2 dB的增益。     

BI-STCM-ID系统的标识映射设计及性能分析

重点研究了对比特交织空时编码及迭代译码系统（BI-STCM-ID）性能影响较大的标识映射方法,提出了给定区间最佳映射（DRO）的设计方法以及自适应标识映射集的思想.通过对各种映射方法特征参数及渐进BER性能限的分析,得出了瑞利衰落信道环境下决定BICM-ID以及BI-STCM-ID系统性能的关键参数,进而给出了标识映射设计的参考准则.     

基于DSP Builder的星座映射与解映射器设计

提出了一种基于中国地面数字电视广播传输标准的星座映射与解映射器的设计方法.使用DSP Builder的各种模块搭建系统,并利用Signal Compiler生成HDL工程.在完成对所生成工程的优化后,进行相关软硬件仿真验证.实现了32QAM、16QAM星座映射器与解映射器的设计、仿真,并在利用DSP Builder进行DSP系统开发方面进行了深入的探索.     

基于可靠性的5G-低轨星座网络切片映射算法

随着国家天地一体化信息网络重大项目的 推进,5G-低轨星座网络切片的可靠映射成为业内的研究热点.在基于软件定义网络(software-defined network,SDN)和网络功能虚拟化(network function virtualization,NFV)的5G-低轨星座集成网络架构下,将5G-低轨星座网络切片的可靠映射问题建模为一个混合整数线性规划问题.在此基础上,研究了切片请求的资源编排,进而提出了基于广度优先搜索的可靠映射算法.该算法综合考虑切片请求的可靠性阈值及虚拟网络功能(virtual network function,VNF)的资源需求,在虚拟网络映射中根据节点的可靠重要度对节点进行排序.仿真结果表明,该算法在满足可靠性阈值约束的条件下,能够最大化收益开销比,提高虚拟网络映射成功率,在切片可靠性及接受率等方面优于对比算法.     

高斯信道下SCMA简易码本设计

针对稀疏码本多址（SCMA）码本设计复杂度高的问题,提出了一种基于高斯信道下的码本设计方案,先设计每个资源块上所有用户的总星座图,然后利用网格编码调制（TCM）中的子集分割法生成各用户对应的星座,结合低密度扩频序列生成星座矩阵,最后由星座矩阵对应生成用户码本。不仅保证了用户之间的星座点间最小欧氏距离最大化,同时保证了用户自身星座点间最小欧氏距离最大化。仿真结果表明,应用该文所设计的码本,系统的BER低,且在高过载率条件下,系统性能也表现良好。     

基于分解思想的导航星座多目标优化

导航星座多目标优化是寻找既能满足设计指标又能使代价最小的理想导航星座。首先,建立了一种由中轨道卫星(MEO)、地球静止轨道卫星(GEO)和倾斜同步轨道卫星(IGSO)组成的混合星座构型模型,并提出一种将星座性能和星座成本作为目标函数的多目标导航星座优化方法,其次,分析了优化目标函数加权几何精度因子WGDOP、可见星数目M和导航星座成本的选择及计算方法,并将基于分解思想的多目标优化算法(MOEA/D)引入导航星座优化中。最后,建立了导航星座多目标优化的数学模型,对基于北斗系统的混合星座进行优化,提出一种新的北斗系统的优化方案。     

BICM-ID中一种新的8PSK星座映射

在比特交织编码调制及迭代译码(BICM-ID)系统基础上,设计一种8PSK调制星座映射方法。该方法以映射符号之间的平均近邻汉明距离为设计准则,将8PSK调制的符号分为2组半径和相位不同的QPSK调制的映射符号,通过调整半径减小星座映射符号之间的平均近邻汉明距离。性能分析和仿真结果表明,与其他BICM-ID中的8PSK星座映射相比,在低信噪比下,该方法的误比特率更低。     

通过混合多目标优化算法优化本体映射结果

提出采用混合多目标优化算法以解决优化三类不同的相似度度量技术(基于语法学的相似度度量,基于语言学的相似度度量和基于分类学的相似度度量)的映射结果集成的问题.比起传统的基于遗传算法的方法,本文提出的方法能够同时实现三个目标,即最大化映射的查全率recall、查准率precision和f-度量f-measure值,获取的本体映射结果能够避免对于查全率或是查准率的偏好.实验结果表明本文提出的方法是有效的.     

广义空间调制系统的安全传输映射方案

为提高时分双工无线通信系统中广义空间调制(GSM)的安全性,提出一种新的GSM安全传输映射方案。将合法信道状态信息引入映射过程中,分别重选由空间比特和星座比特映射的激活天线组合索引和星座符号索引,以增强GSM系统的安全性。仿真结果表明,该方案中的被动窃听者无法恢复激活天线组合索引与星座符号索引所携带的信息,其保密速率初始值较基于空间调制的方案提高184.31%,增强了系统的安全性。     

Optimization formulations for the maximum nonlinear buckling load of composite structures

This paper focuses on criterion functions for gradient based optimization of the buckling load of laminated composite structures considering different types of buckling behaviour. A local criterion is developed, and is, together with a range of local and global criterion functions from literature, benchmarked on a number of numerical examples of laminated composite structures for the maximization of the buckling load considering fiber angle design variables. The optimization formulations are based on either linear or geometrically nonlinear analysis and formulated as mathematical programming problems solved using gradient based techniques. The developed local criterion is formulated such it captures nonlinear effects upon loading and proves useful for both analysis purposes and as a criterion for use in nonlinear buckling optimization.     

蒸馏与换热协同的约束多目标在线操作优化方法

针对蒸馏装置与换热网络间缺乏协同优化导致的分馏精度差和能耗高的问题,提出了一种基于代理模型的约束多目标在线协同操作优化方法.为了解决蒸馏装置与换热网络操作参数协同优化时存在的计算耗时和约束的问题,构建Kriging代理模型来近似目标函数和约束条件,提出了基于随机欠采样和Adaboost的分类代理模型（RUSBoost）来解决类别不平衡的收敛判定预测问题.提出了基于多阶段自适应约束处理的代理模型的模型管理方法,该方法采用基于参考向量激活状态的最大化改善期望准则和可行概率准则更新机制来平衡优化初始阶段种群的多样性和可行性,采用支配参考点的置信下限准则更新机制加快收敛速度.通过不断与机理模型交互来在线更新代理模型,实现在线操作优化.通过测试函数和仿真实例验证了本文方法的有效性.     

Multidisciplinary modeling and surrogate assisted optimization for satellite constellation systems

Satellite constellation system design is a challenging and complicated multidisciplinary design optimization (MDO) problem involving a number of computation-intensive multidisciplinary analysis models. In this paper, the MDO problem of a constellation system consisting of small observation satellites is investigated to simultaneously achieve the preliminary design of constellation configuration and the satellite subsystems. The constellation is established based on Walker-δ configuration considering the coverage performance. Coupled with the constellation configuration, several disciplines including payload, power, thermal control, and structure are taken into account for satellite subsystems design subject to various constraints (i.e., ground resolution, power usage, natural frequencies, etc.). Considering the mixed-integer and time-consuming behavior of satellite constellation system MDO problem, a novel sequential radial basis function (RBF) method using the support vector machine (SVM) for discrete-continuous mixed variables notated as SRBF-SVM-DC is proposed. In this method, a discrete-continuous variable sampling method is utilized to handle the discrete variables, i.e., the number of orbit planes and number of satellites, in the satellite constellation system MDO problem. RBF surrogates are constructed and gradually refined to represent the time-consuming simulations during optimization, which can efficiently lead the search to the optimum. Finally, the proposed SRBF-SVM-DC utilized to solve the satellite constellation system MDO problem is compared with a conventional integer coding based genetic algorithm (ICGA). The results show that SRBF-SVM-DC significantly decreases the system mass by about 28.63% subject to all the constraints, which greatly reduces the cost of the satellite constellation system. Moreover, the computational budget of SRBF-SVM-DC is saved by over 85% compared with ICGA, which demonstrates the effectiveness and practicality of the proposed surrogate assisted optimization approach for satellite constellation system design.     

Optimal design of plastic cylindrical shells of von Mises material

Optimal design of plastic circular cylindrical shells of von Mises material is studied. The optimization problem is stated as the maximization problem of the load carrying capacity for given weight of the shell. Shells with constant and piecewise-constant thickness are considered. The maximization problem is performed under the requirement that the material volume of the stepped shell is equal to the case of the reference shell of constant thickness. The material of the shell is assumed to be an ideal rigid plastic obeying von Mises yield criterion. The considered nonlinear problems are solved by using the CASes method.     

A new optimality criteria method for shape optimization of natural frequency problems

A new shape optimization method for natural frequency problems is presented. The approach is based on an optimality criterion for general continuum solids, which is derived in this paper for the maximization of the first natural frequency with a volume constraint. An efficient redesign rule for frequency problems is developed to achieve the required shape modifications. The optimality criterion is extended to volume minimization problems with multiple frequency constraints. The nonparametric geometry representation creates a complete design space for the optimization problem, which includes all possible solutions for the finite element discretization. The combination with the optimality criteria approach results in a robust and fast convergence, which is independent of the number of design variables. Sensitivity information of objective function and constraints are not required, which allows to solve the structural analysis task using fast and reliable industry standard finite element solvers like ABAQUS, ANSYS, I-DEAS, MARC, NASTRAN, or PERMAS. The new approach is currently being implemented in the optimization system TOSCA.     

多数据流隐马尔可夫模型的流权值优化方法*

提出了一种流权值的优化方法.这种优化方法是基于似然比最大化准则和N-best算法.实验表明,这种新的方法即使在少量优化数据的条件下,也可以得到合适的流权值.而且,在不同的信噪比条件下,利用这种方法优化的多数据流隐马尔可夫模型,都可以有效、合理地融合音频和视频语音,提高语音识别系统的识别率.     

Exponential quality function for community detection in complex networks

One of the most popular topics in analyzing complex networks is the detection of its community structure. In this paper, we introduce a new criterion for community detection, called the E‐quality function. The quality of an individual community is defined as a difference between its benefit and its cost, where both are exponential functions of the number of internal edges and the number of external edges, respectively. The obtained optimization problem, maximization of the E‐quality function over all possible partitions of a network, is solved by the variable neighborhood search (VNS)‐based heuristic. Comparison of the new criterion and modularity is performed on the usual test instances from the literature. Experimental results obtained both on artificial and real networks show that the proposed E‐quality function allows detection of the communities existing in the network.     

General solutions for information-based and Bayesian approaches to model selection in linear regression and their equivalence

In the paper we consider the problem of model selection for linear regression within Bayesian and information-based frameworks. For both cases we generalize known approaches (evidence-based and Akaike information criterion) and derive criterion functions in terms of (in general case non-factorial) weight priors which are assumed to be Gaussian. Optimization of these criterion functions leads to two semidefinite optimization problems which can be solved analytically. We present a method that finds best priors in both approaches and show their equivalence. Surprisingly it appears that optimal prior has rank one covariance matrix. We derive explicit condition of degenerative decision rule, i.e., regression with all weights equal to zero. We conclude with experiments that show that the proposed approach significantly reduces the time needed for model selection in comparison with alternatives based on cross-validation and iterative evidence maximization while keeping generalization ability     

Evolutionary structural optimization for stress minimization problems by discrete thickness design

Stress minimization is a major aspect of structural optimization in a wide range of engineering designs. This paper presents a new evolutionary criterion for the problems of variable thickness design whilst minimizing the maximum stress in a structure. On the basis of finite element analysis, a stress sensitivity number is derived to estimate the stress change in an element due to varying the thickness of other elements. Following the evolutionary optimization procedure, an optimal design with a minimum maximum stress is achieved by gradually removing material from those elements, which have the lowest stress sensitivity number or adding material onto those elements, which have the highest stress sensitivity number. The numerical examples presented in this paper demonstrate the capacity of the proposed method for solving stress minimization problems. The results based on the stress criterion are compared with traditional ones based on a stiffness criterion, and an optimization scheme based on the combination of both the stress minimization and the stiffness maximization criteria is presented.