一种WLAN下行链路MU-MIMO信道容量的分步优化方法

MU—MIMO（多用户多输入多输出）信道的信道容量优化问题是一个复杂的非凸问题,面对这一情况,文章提出了一种分步优化的解决方案,通过将这一问题分解为用户分组、空间流映射和功率分配三个子问题顺序加以解决,获得了该信道容量优化问题的次优解。仿真结果表明,这一解决方案可以有效逼近该优化问题的最优解,而运算复杂度相对较低,是一种适用于WLAN环境的解决方案。     

一种基于粒子群算法的螺旋迭代模型研究

实际工程优化过程中,对于多个目标的优化与求解最优值是值得研究的一个问题。文章基于粒子群算法研究多目标优化问题,实现二维多目标搜索,运用粒子群多目标求解模型迭代实现动态多目标搜索,最终得到非劣解在目标空间中的分布,构成了Pareto面,得到非劣解集,在实际问题中,提供最优解的备选,为工程实践优化和筛选最优解问题提供参考依据。     

基于均匀设计的多目标进化算法

现实生活中的很多决策问题都要考虑同时优化若干个目标,多目标优化算法就是要从所有可能的方案中找到最合理、最可靠的解决方案。如何在Pareto界面稀疏区域求得更多非劣解,则使所求出的解的分布更加均匀。如何求出距Pareto界面更近的非劣解以使所求出的解的质量更高。论文基于加权平均法和均匀设计方法设计了一种解决多目标优化问题的新算法。首先,为了找到在Pareto界面上尽可能多、且均匀分布的点,利用均匀设计方法设计了一个交叉算子,该算子让稀疏部分的相邻点进行均匀交叉,以使算法在稀疏部分能找到更多的非劣解,从而使其所求解分布更加均匀。其次,为了克服加权平均法不能找到Pareto界面非凸部分解的缺点,考虑到非劣解界面上相邻距离较远的一对点之间有可能是非劣解界面上非凸部分之一的情况,分别将此两点与距其最近的非劣解集外的点进行交叉,以期在该两点之间找到新的非劣解,这样可能在非劣解界面的非凸部分找到更多的解。最后对两个测试问题进行了数值试验,并和著名的NSGA-Ⅱ算法用算法性能评价的三种度量进行了比较,结果表明了本文算法是有效的。     

无源时差定位方程的半定松弛解法

针对基于到达时差量测的多站无源定位系统,提出了一种基于半定松弛的时差定位方程求解方法.该方法首先将关于目标位置估计的非凸二次优化问题转换成等价的非凸半定规划问题,然后通过秩1松弛得到一个凸优化问题,最后对松弛半定规划问题的最优解进行秩1近似,从而提取出最终的目标位置估计.计算机仿真结果表明这种松弛解法可以有效求解目标位置.     

遗传顺序IB算法

 本文提出一种遗传顺序IB算法,该算法以基本顺序IB算法的多次运行结果作为初始种群,并基于集成操作算子将初始种群组合为一个解;然后算法分别计算解中每个元素的不确定性统计量,对解中元素进行选择和变异,最后经过若干代变异后得到优化的解.在数据集上的实验结果表明,相对于顺序IB算法,遗传顺序IB算法具有运行效率高、解更优化的特点.     

基于加气站槽车调度的模型建立与算法研究

通过对CNG的发展形势介绍,深入到要研究的具体问题——槽车调度,槽车调度就是加气站母站向子站调度发送槽车。匈牙利算法是用到线性规划上解决最优化最优解的问题。考虑到槽车调度优化的问题的特殊性,因此在匈牙利算法的思想基础上,提出了最大差值法。此方法能更高效率地运算出最优解。     

无线供能移动边缘网络中计算时延最小化资源分配方法研究

针对无线供能移动边缘计算(MEC)网络,该文将计算时延定义为数据卸载与计算所消耗的时间,并提出一种节点计算时延之和最小化的多维资源分配方法。首先,在节点能量因果约束下,通过联合优化专用能量站工作时长、任务分割系数、节点计算频率和发射功率来建立一个计算时延之和最小化的多维资源分配问题。由于存在优化变量耦合与max-max函数,所建问题非凸且无法采用凸优化工具获取最优解。为此,通过引入一系列松弛变量和辅助变量来进行优化问题简化以及优化变量解耦,并在此基础上,通过深入分析简化问题的结构特性,提出一种基于二分法的迭代算法来求解原问题的最优解。最后,计算机仿真验证了所提迭代算法的正确性以及所提资源分配方法在计算时延方面的优越性。     

无线供能反向散射通信中能耗最小化资源分配方法

针对无线供能反向散射通信网络,提出了一种满足传感设备通信需求及能量因果的专用能量站能耗最小化资源分配方法。在考虑非线性能量收集模型及不完美串行干扰消除基础上,通过联合优化专用能量站发射功率、传感设备反向散射通信时间、反向散射系数及其能量收集时间,构建了一个专用能量站能耗最小化的非凸多维资源分配问题。首先,构建辅助变量对反向散射系数与时间进行解耦,再利用目标函数是关于专用能量站发射功率的单调递减函数这一特性来设计一种基于二分法的迭代算法来获取原问题的最优解。仿真验证了所提算法的快速收敛性,同时,与同类算法相比,所提方法可为专用能量站节约更多的能量。     

非支配解竞争模型下的电力系统无功优化

传统的电力系统多目标粒子群优化算法利用权重系数将问题转化为单目标求解,从而忽视了各目标函数间的竞争关系。针对这一问题,设计出一套非支配解竞争模型并运用到电力系统多目标无功优化中,同时以降低有功网损和减少电压偏差为目标,使二者在充分竞争的情况下得出Pareto最优解。在IEEE-14节点系统上进行仿真实验,仿真结果给出了有功网损和电压偏差之间的竞争关系,该算法一次运行可以得出多组非支配解,电力决策者可根据实际问题的需要选择最终满意的Pareto最优解,具有很好的灵活性与多样性。仿真结果表明,该方法是一种能够有效求解电力系统多目标无功优化问题的新思路。     

基于K最短路径算法的轨迹关联应用研究

多目标跟踪可以通过轨迹关联来实现,这种方法具有很强的鲁棒性。然而,当处理多目标跟踪问题时,连接检测目标可能的轨迹簇是一个很难解决的优化问题。使用抽样或贪婪的动态规划方法不能保证得到全局最优解。利用此问题的特殊结构,可以把它重新规划为一个受约束的网络流模型的优化问题,并利用K最短结点不相交路径算法高效地解决,同时得到全局最优解。     

An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems

Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles’ efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.     

基于TLBO优化算法的电动汽车充电站选址

随着电动汽车使用规模的不断增加,建设电动汽车充电站已成为当务之急。文中将每个区域中心作为电动汽车充电负荷中心,以分区内电动汽车充电桩数量作为充电站选址的权重系数,建立了充电站位置和定容的优化模型。采用TLBO优化算法计算出电动汽车充电站的数量、建设地点、每个充电站的覆盖范围以及充电站的充电桩数量。仿真结果表明,TLBO优化算法在电动汽车充电站的选址和定容方面,计算准确、收敛速度快,并且具有良好的全局优化性能。     

Innovative iteration algorithm for a vehicle simulation program

Resulting from Ph.D. research, a vehicle simulation program is proposed and continuously developed, which allows simulation of the behavior of electric, hybrid, fuel cell, and internal combustion vehicles while driving any reference cycle. The goal of the simulation program is to study power flows in the drivetrains of vehicles and the corresponding component losses, as well as to compare different drivetrain topologies. This comparison can be realized for energy consumption and emissions, as well as for performance (acceleration, range, maximum slope, etc.). The core of this program, consisting of a unique iteration algorithm, will be highlighted in this paper. This algorithm not only allows the calculation of the limits of vehicle acceleration in the function of drivetrain component characteristics, but at the same time is able to develop and evaluate the different power-management strategies of hybrid vehicles, combining combustion engines and electric motors. Furthermore, the comprehensive iteration algorithm is demonstrated to be very efficient in handling any type of working limit for all components in different types of drivetrains, which results in an accurate and modular vehicle simulation program with high data flexibility.     

一种基于IEEE 802.16e的新型切换算法研究

提出一种基于IEEE 802.16e无线网络的新型切换算法。算法充分考虑了IEEE 802.16e的测距、认证、注册等主要使用行为,通过引入业务流(Service Flow)认知,使基于业务流的移动终端处的切换时间达到最小。仿真分析表明,利用标准提出的多种不同能力的交互,算法能够有效降低切换时间。     

基于建模仿真的战车分类算法研究

战车类型的识别分类是现代情报获取的重要研究内容。为了获得数据并研究战车分类算法,常进行外场真实实验,但其时间长、耗资巨大。本文在虚拟战场仿真平台上建立坦克、装甲车、运兵车三种战车模型。利用仿真环境中的战车噪声、磁场、振动特征信号作为样本数据,进行战车的分类算法研究。同时基于一对一多分类支持向量机,设计了一种战车分类算法,并给出了分类器交叉验证参数调整策略。实验表明,相比于AdaBoost算法,文章提出的战车分类算法的分类准确率较高。     

Scheduling Automated Traffic on a Network of Roads

The authors consider the problem of scheduling automated traffic in a city. Such automatic scheduling can improve efficiency of the system by decreasing delays, increasing capacity, and easing congestion. The algorithms described in this paper can be thought of as which belongs to a layer in the corresponding control–communication–computational system, which is responsible for generating timed trajectories for individual vehicles [S. Graham, G. Baliga, and P. Kumar, “Issues in the convergence of control with communication and computing: Proliferation, architecture, design, services, and middleware,” in Proceedings of CDC ’04, Nassau, Bahamas, December 2004]. Each vehicle has a specified route from its origin to its destination, and the task of the scheduler is to provide timed trajectories for all vehicles, which follow the respective vehicles' routes and further ensure that no collisions or deadlock will result. The authors' approach reduces the problem to a discrete-time graph-scheduling problem, by defining an appropriate graph to model the road network. Their main result is a sufficient condition on the graph of the road network and on the initial distribution of vehicles, under which there exists a scheduling algorithm that is guaranteed to clear the system in finite time. The nature of this result allows the design of provably correct scheduling algorithms that require only a small portion of future routes of all vehicles to be known, and are consequently able to work in real time. The authors also address the optimization of performance with respect to delay, by focusing on the “one-step move” problem. Finding an optimal solution for the one-step problem would provide a greedy solution of the original network-wide scheduling problem, but is itself NP-hard. They present a polynomial time heuristic algorithm and evaluate its performance through simulations.     

Providing air-time usage fairness in IEEE 802.11 networks with the deficit transmission time (DTT) scheduler

Wireless systems based on the IEEE 802.11 standard are known to suffer a performance degradation when just a single station in the network experiences bad channel conditions toward the Access Point (AP). This phenomenon, known as the “performance anomaly”, is mainly due to the max-min throughput fairness of the CSMA/CA algorithm of the 802.11 MAC. The simple FIFO scheduling policy usually implemented in the AP also contributes to this problem. In order to overcome the performance anomaly, we propose the Deficit Transmission Time (DTT) scheduler. The aim of DTT is guaranteeing each station a fair medium usage in terms of transmission time. This feature, directly related to the proportional fairness concept, allows to ideally achieve exact isolation among the traffic flows addressed to different stations. DTT achieves this goal taking advantage of measurements of actual frame transmission times. Experiments carried out using a prototype implementation of DTT are compared with analogous tests performed with a classic FIFO queue of a commercial AP and a recently proposed traffic shaping scheme aimed at solving the same 802.11 performance anomaly.     

基于北斗和边缘计算的车联网导航技术研究

随着车辆保有量的不断增长和车联网应用的普及,车辆终端会产生大量需要实时处理的数据消息。在车辆高速移动场景下,传统的车联网导航系统由于车辆差分定位数据存在传输时延,导致车辆定位结果存在一定的偏差,无法及时获得高精度定位结果。基于此,文中提出了一种基于北斗定位和边缘计算的车联网导航技术方案,采用改进的遗传算法进行终端定位请求的资源分配,有效降低整个边缘网络的服务时延,并利用基于边缘节点的优化无损卡尔曼滤波算法来提高车联网节点的定位精度。实验表明,文中所提出的方法能够为大规模车联网终端提供实时精准、低延迟和高精度的定位服务,具有较高的实际应用价值。     

A state-constrained vehicle-follower approach to the station-egress problem

Vehicle following represents one approach to the longitudinal control of vehicles in an automated guideway transit (AGT) system. Requirements for the use of such control for the merging of a vehicle from an off-line station into a mainline vehicle stream are discussed. Recently, a technique has been developed that incorporates state-variable constraints into the control law of a vehicle-follower controller. A successful application of this technique to the station-egress problem has been formulated which safely merges the egressing vehicle between two vehicles initially operating at minimum headway on the mainline. It is demonstrated that for short headway operations of approximately three seconds or less, the finite length of station ramps introduces to the developed control laws an additional constraint, which must be implemented to assure safe operations. Finally, design trade-offs are shown to exist between the egress ramp lengths and the relative disturbances to mainline traffic as a result of station-egress maneuvers.     

Online Energy Management for Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) are equipped with multiple power sources for improving the efficiency and performance of their power supply system. An energy management (EM) strategy is needed to optimize the internal power flows and satisfy the driver's power demand. To achieve maximum fuel profits from EM, many solution methods have been presented. Optimal solution methods are typically not feasible in an online application due to their computational demand and their need to have a priori knowledge about future vehicle power demand. In this paper, an online EM strategy is presented with the ability to mimic the optimal solution but without using a priori road information. Rather than solving a mathematical optimization problem, the methodology concentrates on a physical explanation about when to produce, consume, and store electric power. This immediately reveals the vehicle characteristics that are important for EM. It is shown that this concept applies to many existing HEVs as well as possible future vehicle configurations. Since the method only focuses on typical vehicle characteristics, the underlying algorithm requires minor computational effort and can be executed in real time. Clear directions for online implementation are given in this paper. A parallel HEV with a 5-kW integrated starter/generator (ISG) is selected to demonstrate the performance of the EM strategy. Simulation results indicate that the proposed EM strategy exhibits similar behavior as an optimal solution obtained from dynamic programming. Profits in fuel economy primarily arise from engine stop/start and energy obtained during regenerative braking. This latter energy is preferably used for pure electric propulsion where the internal combustion engine is switched off.