聚类混合型数据的密度峰值改进算法

聚类混合型数据，通常是依据样本属性类别的不同分别进行评价。但这种将样本属性划分到不同子空间中分别度量的方式，割裂了样本属性原有的统一性；导致对样本个体的相似性评价产生了非一致的度量偏差。针对这一问题，提出以二进制编码样本属性，再由海明差异对属性编码施行统一度量的新的聚类算法。新算法通过在统一的框架内对混合型数据实施相似性度量，避免了对样本属性的切割，在此基础上又根据不同属性的性质赋予其不同的权重，并以此评价样本个体之间的相似程度。实验结果表明，新算法能够有效地聚类混合型数据；与已有的其他聚类算法相比较，表现出更好的聚类准确率及稳定性。     

基于混合蚁群算法的异质车队低碳VRP研究

针对货运车辆在配送调度过程中产生大量碳排放的问题，建立模型将多种影响碳排放量的因素协同优化。模型中考虑了不同载重量的异质车队，两个节点之间有多条道路的柔性路径，以及车辆重量随卸货而减少的动态负载等因素，以碳排放量、行驶时间和行驶路程为优化目标，并加入了节点需求时间窗、根据速度变化划分路段、交接和卸货时间的约束。提出了一种混合蚁群算法，利用蚁群算法信息素强度更新方式保持群体记忆性，利用粒子群算法的快速收敛特性增加计算效率。通过随机数值算例的仿真优化与对比分析，验证了算法和模型的有效性。     

基于LBT-SA的LoRaWAN信道接入方法研究

标准LoRaWAN协议中定义的纯ALOHA信道接入方法在网络负载较大时会造成大量碰撞，使LoRa网络的可扩展性和可靠性受到极大挑战。为此提出了一种将先听后说与模拟退火相结合（Listen Before Talk combined with Simulated Annealing，LBT-SA）的LoRa信道接入方法。该方法通过在设备发送之前先监听信道，降低信道被同时接入的概率，并以成功传输所需退避时间为初始值，搜索更小的退避时间。仿真结果表明，LBT-SA和基于二元指数退避算法的先听后说（Listen Before Talk，LBT）方法在包成功传输率方面保持了高度的一致，并且LBT-SA在周期传输过程中逐渐减少了不必要的退避时间，从而减少了时延。在多达10 000个终端接入单个网关的场景中，该算法的平均退避时间至少降低了16.38%。     

基于虚拟现实的复杂场景加载优化算法

目前大规模场景模型生成的需求量日益增加, 现提出了一种基于区域的自动 LOD (Levels Of Details)构建算法. 该算法基于动态网格简化算法, 在游戏设计制作的过程中, 游戏开发人员会经常需要对美工部门人员提供的模型进行优化. 为了可以使模型的面数得到简化, 并且不改变模型的外观. 当今流行的 LOD技术非常擅于处理这种情况, 判断模型与摄像机的距离如果超过一定范围之后, 自动调取不同层次的模型, 当模型距离摄像机很远的情况下使用面数低的模型替换高模, 这样可以提高帧率并且降低摄像机前的三角面以及顶点数从而减少渲染压力. 一般模型的简化分为静态和动态模型的简化.大部分情况下, 程序员会让美工部门提供几套不同面数的模型或者通过模型简化工具对高模进行减面并存成多个Mesh, 并在程序运行的时候, 根据模型与摄像机的距离远近动态的替换Mesh. 这是一种静态的方法.这里将尝试使用一种动态的网格简化和LOD技术相结合的方法. 这种新型算法的大大的简化了操作流程, 美术人员只需要提供一个模型, 程序员可以使用这种方法生成量级不同的低模, 根据摄像机与模型的远近自动的调取不同精度的模型.     

Stochastic stabilization and destabilization of nonlinear and time‐varying hybrid systems by noise

The aim of this article is to design a suitable strength function g(t,x,r(t)) such that the Wiener noise g(t,x(t),r(t))dw(t) either stabilizes or destabilizes a given nonlinear and time‐varying hybrid system . To this end, the basic properties, including the existence and uniqueness of the local and global solutions and the nonzero property of solutions of the nonlinear and time‐varying hybrid stochastic systems, are first investigated as the theoretical basis of the article. Second, two theorems and the corresponding corollaries on the stability and instability of the hybrid stochastic systems are established. Third, the design method for the noise strength g(t,x,r(t)) is then proposed based on the established theorems. We also point out that the Markov jump r(t) may have a stabilizing (respectively, destabilizing) effect when we design the noise strength g(t,x,r(t)) so that the introduced noise g(t,x(t),r(t))dw(t) stabilizes (respectively, destabilizes) the corresponding hybrid system. Finally, we illustrate our method using two examples. Compared with the existing literature, our method is suitable for a wider class of nonlinear and time‐varying systems with weaker conditions than quasi‐linear systems.     

红外菲涅尔透镜的注射超声辅助成型优化

为了提升聚合物红外菲涅尔透镜的光学性能,以其表面微沟槽的成型质量为目标,提出了一种高效的注射超声辅助成型方法,并对工艺参数进行了综合质量优化。首先分析了超声振动对聚合物的加热和加压效应,设计了一套一模两腔的对比试验模具;接着以红外菲涅尔透镜的调制传递函数MTF和齿形平均高度h为优化质量目标,设计了四步骤的多目标优化流程,通过试验设计、基于BP神经网络的质量目标与注射工艺参数关系建模、基于NSGA-Ⅱ的多目标优化和试验验证进行工艺参数的综合优化。实验结果表明:该多目标优化流程具有很高的精度,MTF和h的平均预测误差MPE分别为4.16%和3.32%;注射超声辅助成型的菲涅尔透镜微沟槽具有更高的复制质量,其齿沟槽平均高度h增加了15.6%,且h值的波动量随着h值的增大而增大,MTF值受齿高h均匀性的影响大于齿高h对其的影响。     

工程建设标准客观优先度评价研究

在工程建设标准的制定和修订过程中，如何科学地确定编制顺序一直是困扰标准管理部门的难题，采用传统方式“按需制标”，具有盲目性、主观性，容易造成标准在制定和修订过程中的互相重复、滞后与矛盾。为了保证标准制定和修订工作的协调性，指导标准体系中各标准的制修订顺序，引入复杂网络拓扑结构，将同一标准类型下的标准构成功能团，根据相互之间的关系形成复杂网络，采用剥落算法对其网络节点重要性排序，进而确定标准制修订的优先度。通过对建筑给水排水工程规范标准修订的实证分析，总结工程建设标准的网络拓扑优先度评价的算法模型，实现制定和修订顺序的自动计算，从而更加科学地指导标准的制定和修订工作     

Supply chain network design under the risk of uncertain disruptions

Facility disruptions in the supply chain often lead to catastrophic consequences, although they occur rarely. The low frequency and non-repeatability of disruptive events also make it impossible to estimate the disruption probability accurately. Therefore, we construct an uncertain programming model to design the three-echelon supply chain network with the disruption risk, in which disruptions are considered as uncertain events. Under the constraint of satisfying customer demands, the model optimises the selection of retailers with uncertain disruptions and the assignment of customers and retailers, in order to minimise the expected total cost of network design. In addition, we simplify the proposed model by analysing its properties and further linearise the simplified model. A Lagrangian relaxation algorithm for the linearised model and a genetic algorithm for the simplified model are developed to solve medium-scale problems and large-scale problems, respectively. Finally, we illustrate the effectiveness of proposed models and algorithms through several numerical examples.     

The thermodynamic characteristics of a Stirling/pulse tube hybrid cryocooler

A Stirling/pulse tube hybrid cryocooler (SPC), comprised of a Stirling cryocooler as the first stage and a pulse tube cryocooler as the second stage, features the ability of shifting cooling capacity between stages by adjusting the movement of the displacer in the first stage. Such an ability allows an SPC to accommodate itself to time-varying heat loads at different temperatures, which makes it a competitive candidate in space applications. However, due to the gas coupling, there exists a significant mutual effect between stages which endows an SPC with special thermodynamic characteristics and has a significant effect on the SPC’s capability of shifting cooling capacity between stages. With the phasor analysis and the thermodynamic analysis, this paper establishes an idealized model of an SPC. The model is then used to study the effect of the second stage on the first stage and reveal the condition that an SPC is able to shift cooling capacity between stages. Also, the model is compared with a Sage numerical model and the two models are consistent on the overall trend. Though it is unable to reflect reality precisely, the idealized model can interpret the mechanism and highlight some of the essential nature of an SPC, which will eventually benefit the appropriate design of an SPC.     

基于自适应阈值活动语音检测和最小均方误差对数谱幅度估计的低信噪比降噪算法

针对低信噪比(SNR)环境下传统方法对声信号降噪的局限性，提出了一种联合自适应阈值活动语音检测（VAD）算法和最小均方误差对数谱幅度估计（MMSE-LSA）的实时降噪算法。首先，在VAD算法中通过基于能量概率最大值的概率统计来对背景噪声进行估计，对得到的背景噪声进行实时更新并保存；然后，将实时更新的背景噪声作为MMSE-LSA的参考噪声，并对噪声幅度谱进行自适应更新，最后进行降噪处理。通过在真实场景中对四类声信号进行实验，结果表明，该算法在保证对低SNR声信号的实时处理的情况下，相较于传统MMSE-LSA算法，降噪信号的SNR能够提高10~15 dB，且不存在信号过减的情况，可应用于实际工程。