基于能效的多小区LTE系统资源分配算法

针对多小区LTE移动通信系统,考虑用户的最小速率要求,以最大化系统能效为目标,提出了一种迭代式的资源分配算法,通过不断迭代子信道分配和功率控制两个子过程来优化系统能效.针对子信道分配问题,提出了一种基于三种基本模式的子信道调整算法;针对功率控制问题,建立了多小区非合作博弈模型,理论证明了纳什均衡点的存在性,并设计了算法收敛于该纳什均衡点.仿真结果表明,与多小区最大化系统吞吐量算法相比,提出的算法获得了明显的能效增益,同时也达到了较好的系统吞吐量,尤其在强干扰环境下该算法的优势更加明显.     

鱼雷外形多学科设计优化中的博弈求解方法

为了鱼雷系统多学科设计优化的设计过程,解决处理耦合变量的技术难点,为减小阻力,改进鱼雷性能,减少设计工作量,提出了一种新的多学科设计优化方法.该基于Nash均衡思想,把设计变量作为决策空间,设计目标作为博弈方,各子学科在某一策略下的响应设为博弈论中博弈方的收益.与以往的多学科设计优化方法相比,方法具有计算步骤简单,易于编程实现,各子学科可以独立进行学科的自主优化,改善鱼雷阻力特性.通过对具体工程算例进行仿真.结果验证了算法的可行性和适用性,可优化鱼雷性能,为设计提供了参考依据.     

并行子空间优化在无人机总体设计中的应用

飞机设计是一个多学科的复杂的系统工程,各个学科通常相互影响、相互耦合.这使得飞机设计过程日趋复杂,设计周期越来越长,开发成本越来越高,而并行子空间优化(CSSO)是解决这些问题的一种有效方法.文中对基于神经网络响应面的并行子空间优化算法及其在无人机总体方案设计优化中的应用进行了研究.并行子空间优化算法将多学科耦合的无人机设计优化问题分解为不同的子空间问题,在不同的子空间中建立各自的神经网络响应面,通过响应面完成各子空间之间的数据交换与协调,以此来逼近设计空间最优解.应用结果表明,CSSO算法能有效地应用于无人机总体方案优化设计.     

飞剪机结构参数优化设计

飞剪机是连续式轧钢机组中重要而复杂的辅助设备，其结构参数设计的好坏直接影响剪切质量。飞剪机结构参数优化设计问题属非线性的多目标优化问题，以模拟退火算法为代表的计算智能方法在求解此类优化问题中体现了一定了优势，本文利用改进的模拟退火算法（Improved Simulated Annealing，ISA）对曲柄连杆式飞剪机结构参数进行优化设计，将整个优化过程分成若干个阶段，根据各个子目标函数优化的状态，采用不同的退火系数和归一化系数，综合考虑多个目标函数和约束条件，通过实例计算表明，该方法可以稳定、有效的获取到满意的优化设计结果。     

多速率分段线性系统预测控制的显式设计

针对输出采样周期是输入更新周期N倍的多速率分段线性(Piecewise linear, PWL)系统, 本文提出了保证稳定性的显式预测控制器设计方法. 首先, 基于动态规划原理将预测控制优化问题分解为多个单级优化问题; 然后, 根据分段线性系统各子模型以及目标函数的具体形式, 进一步将各单级优化问题分为若干个子问题, 再利用多参数二次规划(Multiparametric quadratic programming, MP-QP)方法求解;最后,通过比较各子问题的解从而得到系统的最优显式控制律. 在设计过程中, 将系统的最大正不变集作为优化问题的终端约束集, 从而保证了系统的稳定性. 仿真结果表明本文提出的显式预测控制方法能够有效降低多速率分段线性系统的在线计算时间, 在保证系统稳定性的同时, 满足其对输入更新速度的要求.     

玻璃排版优化中遗传算法的应用

研究了应用遗传算法求解非线性多目标组合优化问题———玻璃排版优化问题 ,详细讲解了如何设计求解该优化系统中三个典型组合优化子问题的遗传算法 ,并对三个子问题的求解关系进行了分析 ,总结出遗传算法的不同构造方法对系统优化结果的影响。     

业务逻辑层对象的优化设计

阐述了在面向对象设计过程中对业务逻辑层的对象优化的必要性,提出了根据业务逻辑层对象职责将其划分为三个子层的构想,并对各子层中的对象的优化方法和原则作了进一步讨论,最后结合实例给出了具体的优化过程。     

动态博弈框架下的分布式动态优化

为了实时在线求解复杂的大规模动态优化问题,本文基于动态博弈理论提出了一种分布式动态优化方案,滚动合作博弈优化(RCGO).首先基于滚动时域优化框架,该方案将原本复杂的大规模动态优化问题分解为若干简单的小规模局部优化子问题,使得计算复杂度降低从而保证优化求解的实时性.之后本文基于动态博弈提出了分解迭代法求解各局部动态优化子问题,并对RCGO优化方案下系统稳定性进行分析.最后本文选择一个化工过程网络作为仿真案例,基于RCGO方案得到了极大化经济效益下该网络的最优操作.优化结果表明在求解复杂大规模动态优化问题时, RCGO方案较传统的集中式优化方案在由系统经济效益、闭环控制性能及优化求解实时性等组成的综合指标上有较大优势.     

基于多种群进化算法的多目标并行博弈设计

针对多目标决策问题,提出一种基于多种群进化算法的多目标并行博弈设计方法,利用Fortran语言编制了相应计算程序.将多目标设计问题描述为博弈问题,在博弈分析中,根据各博弈方在博弈次序和过程上具有同时性和独立性,采用并行计算技术.在以MPI为平台的集群环境中实现数值算例、补偿滑轮组变幅机构、拱型结构的多目标并行博弈设计.计算分析结果显示:该方法的计算精度高、收敛速度快、可以防止早熟、有较好的平衡负载能力,可有效求解多目标问题.     

教学点播(EOD)的SOCP子空间模型与实现

总结了教学点播的概念,分析了目前教学点播研究中存在的问题。在对教学活动原型深入分析的基础上,提出了教学点播的SOCP子空间模型,概括了SOCP子空间模型的设计目标,详细介绍了SOCP子空间模型实现的总体架构和各个子空间的实现技术。最后叙述了在目前教学点播实现的基础上进一步的工作设想。     

A Predator-prey Particle Swarm Optimization Approach to Multiple UCAV Air Combat Modeled by Dynamic Game Theory

Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, each side seeks the best scheme with the purpose of maximizing its own objective function. In this paper, a game theoretic approach based on predatorprey particle swarm optimization (PP-PSO) is presented, and the dynamic task assignment problem for multiple unmanned combat aerial vehicles (UCAVs) in military operation is decomposed and modeled as a two-player game at each decision stage. The optimal assignment scheme of each stage is regarded as a mixed Nash equilibrium, which can be solved by using the PP-PSO. The effectiveness of our proposed methodology is verified by a typical example of an air military operation that involves two opposing forces: the attacking force Red and the defense force Blue.      

MEC中资源分配与卸载决策联合优化策略

针对移动边缘计算(MEC)中用户任务处理时延与能耗过高的问题,提出了“云-边-端”三层MEC计算卸载结构下的资源分配与卸载决策联合优化策略。首先,考虑系统时延与能耗,将优化问题规划为系统总增益(任务处理时延与能耗相对减少的加权和)最大化问题;其次,为用户任务设置优先级,并根据任务数据量初始化卸载决策方案;然后,采用均衡传输性能的信道分配算法为卸载任务分配信道资源,对于卸载至同一边缘服务器上的任务以最大化资源收益为目标进行资源竞争,实现计算资源最优配置;最后,基于博弈论证明优化问题为关于卸载决策的势函数,即存在纳什均衡,并利用迭代增益值比较法得到了纳什均衡下的卸载决策方案。仿真结果表明,所提联合优化策略在满足用户处理时延要求的情况下最大化系统总增益,有效地提高了计算卸载的性能。     

基于博弈论和启发式算法的超密集网络边缘计算卸载

超密集网络与边缘计算相结合时,高密度的基站分布可能会对同一用户重复覆盖,该用户选择不同基站进行卸载将会对系统性能产生不同影响,由此引出卸载对象选取问题。同时边缘计算可以将部分任务卸载到边缘服务器进行处理,选择合适的卸载比例能够显著降低所需的时延和能耗,由此引出卸载比例选取问题。提出一种超密集网络环境中基于博弈论和启发式算法的边缘计算卸载策略。针对卸载对象选取问题,根据边缘服务器到用户之间的距离和工作负载定义偏好度指标,各用户根据偏好度进行博弈后选择卸载对象,并对用户进行分组,将原问题分解为若干个并行的子问题。针对卸载比例选取问题,基于萤火虫群优化算法对各用户的卸载比例进行优化,得到适当的卸载比例。与全本地处理（ALP）策略、全卸载策略（AOS）和基于粒子群优化（PSO）算法的卸载策略进行对比,实验结果表明,ALP和AOS策略在总能耗和平均时延上具有一定的局限性,相比基于PSO的卸载策略,所提策略的时延降低22%,能耗降低20%,可以有效减少系统损失。     

Multi-task clustering via domain adaptation

Clustering is a fundamental topic in pattern recognition and machine learning research. Traditional clustering methods deal with a single clustering task on a single data set. However, in many real applications, multiple similar clustering tasks are involved simultaneously, e.g., clustering clients of different shopping websites, in which data of different subjects are collected for each task. These tasks are cross-domains but closely related. It is proved that we can improve the individual performance of each clustering task by appropriately utilizing the underling relation. In this paper, we will propose a new approach, which performs multiple related clustering tasks simultaneously through domain adaptation. A shared subspace will be learned through domain adaptation, where the gap of distributions among tasks is reduced, and the shared knowledge will be transferred through all tasks by exploiting the strengthened relation in the learned subspace. Then the object is set as the best clustering in both the original and learned spaces. An alternating optimization method is introduced and its convergence is theoretically guaranteed. Experiments on both synthetic and real data sets demonstrate the effectiveness of the proposed approach.     

Multi-objective design method based on evolution game and its application for suspension

Through research and bionics of biology survival mode, game players with competition, cooperation and self-adaptation capacity are introduced in the multi-objective design. The dynamic behavior and bounded rationality in game processes for players are considered according to Chinese saying “In success, commit oneself to the welfare of the society; in distress, maintain one’s own integrity”. An evolution rule, Poor-Competition-Rich-Cooperation (short for PCRC), is proposed. Then, the corresponding payoff functions of competition and cooperation behavior are established and a multi-objective design method based on evolution game is proposed. The calculation steps are as follows: 1) Taking the design objectives as different game players, and calculating factors of the design variables to objective and fuzzy clustering. The design variables are divided into multiple strategy subsets owned by each game player. 2) According to the evolution rule, each player determines its behavior and payoff function in this game round. 3) In their own strategy subsets, each game player takes their payoff as mono-objective for optimization. It gives the best strategy upon other players. And so the best strategies of all players conform the group strategy in this round. The final equilibrium solution is obtained through multi-round game based on convergence criterion. The validity and reliability of this method are shown by the results of an example of a tri-objective optimization design of passive suspension parameters.     

机器人班组的自组织递阶控制

研究了机器人班组在执行各种任务时的协调控制.由于机器人班组是由多个能力有限的 机器人组成的,被操作的对象可以是一个刚体、柔性体或机械系统,而且需要跟踪的运动也可 以是各种各样的,所以整个系统是相当复杂的.这样的机械系统,按其力学性质可以将要实现 的控制任务加以分解,从而实现递阶控制,各层的控制只完成被分解出来的特定的较简单的任 务,而各机器人之间的协调由自组织算法自动完成.     

Classification by nearness in complementary subspaces

This study introduces a classifier founded on k-nearest neighbours in the complementary subspaces (NCS). The global space, spanned by all training samples, can be decomposed into the direct sum of two subspaces in terms of one class: the projection vectors of this class into one subspace are nonzero, and that into another subspace are zero. A query sample is projected into the two subspaces for each class, respectively. In each subspace, the distance from the projection vector to the mean of its k-nearest neighbours can be calculated, and the final classification rules are designed in terms of the two distances calculated in the two complementary subspaces, respectively. Allowing for the geometric meaning of Gram determinant and kernel trick, the classifier is naturally implemented in the kernel space. The experimental results on 1 synthetic, 13 IDA binary class, and five UCI multi-class data sets show that NCS compares favourably to the comparing classifiers, which is founded on the k-nearest neighbours or the nearest subspace, on almost all the data sets. The classifier can straightforwardly solve multi-classification problems, and the performance is promising.     

一种基于八叉树与流水线技术的快速碰撞检测算法

针对存在大量运动物体的虚拟环境,提出一种基于空间八叉树剖分与流水线技术的并行碰撞检测算法.通过八叉树剖分,把虚拟空间剖分成一系列的子空间,然后只对同一空间中的结点进行碰撞检测.对空间内的每个物体构建包围盒树,同一空间中的任意两棵包围盒树遍历构成任务树,把任务树中的任务分配给不同的进程进行碰撞检测,并采用流水线与多线程技...     

Distributed object transportation on a desired path based on Constrain and Move strategy

In this paper, a distributed strategy to move objects on different arbitrary paths in a 2D plane is proposed and analyzed. This algorithm which is based on Constrain and Move strategy [M.N. Ahmadabadi, E. Nakano, A Constrain and Move approach to distributed object manipulation, IEEE Trans. Robotics Automation 17 (2) (2001) 157], organizes the robots in two groups. The object manipulation task also is decomposed to two different tasks. The task given to one group is control of linear velocity and that assigned to the other group is control of angular velocity of the object. The independence of these tasks makes the design of the distributed architecture of the team possible. To calculate each robot's desired velocity, a simple method using Constrain and Move strategy and robot's local sensors is developed. To prevent small errors in the robot sensory system from affecting the system performance, limited compliance is assumed in robot arms. The basic behaviors of the robots are presented. Moreover, simulation results are given to verify the proposed strategy.