基于TSMD-SVM-HMM的机内测试智能降虚警方法

机内测试虚警问题是影响系统完好性和使用保障费用的重要因素。针对环境因素导致的虚警问题,设计了时间环境应力测量装置,应用支持向量机的小样本学习优点,建立虚警与环境因素的关联关系,应用隐马尔可夫模型的连续动态信号处理能力,描述系统长期工作历程中虚警的发生规律,提出了基于时间环境应力测量装置-支持向量机-隐马尔可夫模型的机内测试智能降虚警方法。最后,在某型直升机航向姿态系统上进行了应用与验证,试验结果表明:该方法有效识别出了机内测试的虚警。     

一种支持向量机增量学习淘汰算法

针对大规模数据集的分类问题,支持向量机的训练成为一个难题。增量学习是解决这一难题的思路之一。分析了新增样本加入训练集后支持向量集的变化情况,提出了一种基于密度法的支持向量机增量学习淘汰算法,淘汰了对最终分类无用的样本,在保证测试精度的同时减少了训练时间。实验仿真证明这种算法是有效的。     

基于支持向量机的彩色图像恢复新方法

引起图像退化的因素众多,因而难以用一个统一的方法来进行恢复处理。鉴于图像的像素和各颜色分量通道间本质上存在某种相关性,以及以支持向量机为核心的统计学习理论具有较好地解决小样本、非线性、高维数问题的能力,提出了一种新的空域图像恢复方法,并通过对来自于待处理图像本身的训练样本的学习,构造自适应的回归插值函数;然后基于该函数对图像作有选择的修改,从而达到图像恢复的目的。实验表明,该方法是有效的,并且具有较好的泛化性能。     

利用支持向量机的矢量跟踪通道故障检测算法

为了克服卫星导航矢量跟踪接收机中故障通道对其他正常通道的影响,提高矢量跟踪接收机的鲁棒性,提出一种利用支持向量机的通道故障检测算法,利用矢量跟踪接收机的导航滤波器的新息序列作为支持向量机的输入。支持向量机的输出为通道的状态,根据支持向量机的输出通道的状态来判断该通道是否纳入导航滤波器,用来跟踪滤波器状态值,这样能够有效地避免故障通道对导航结果的污染。仿真实验结果表明:该方法能准确地检测出有故障的通道,提高矢量跟踪环路的鲁棒性。     

SVM在空中目标威胁值评估中的应用

构建了基于回归型支持向量机的空中目标威胁值评估模型,确定了基于粗糙集理论的空中目标威胁因素,并建立了数据离散化、归一化的标准。将常用的4种核函数应用于对空中目标威胁值的估计,并通过基于网格法和遗传算法的搜索方法找到模型的最优参数。最后通过Matlab对算例进行了仿真验证。     

Single batch machine scheduling with deliveries

We consider the problem of scheduling a set of n jobs on a single batch machine, where several jobs can be processed simultaneously. Each job j has a processing time p_j and a size s_j. All jobs are available for processing at time 0. The batch machine has a capacity D. Several jobs can be batched together and processed simultaneously, provided that the total size of the jobs in the batch does not exceed D. The processing time of a batch is the largest processing time among all jobs in the batch. There is a single vehicle available for delivery of the finished products to the customer, and the vehicle has capacity K. We assume that K = rD, where and r is an integer. The travel time of the vehicle is T; that is, T is the time from the manufacturer to the customer. Our goal is to find a schedule of the jobs and a delivery plan so that the service span is minimized, where the service span is the time that the last job is delivered to the customer. We show that if the jobs have identical sizes, then we can find a schedule and delivery plan in time such that the service span is minimum. If the jobs have identical processing times, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most 11/9 times the optimal service span. When the jobs have arbitrary processing times and arbitrary sizes, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most twice the optimal service span. We also derive upper bounds of the absolute worst‐case ratios in both cases. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 470–482, 2015     

Single machine just‐in‐time scheduling problems with two competing agents

In scheduling problems with two competing agents, each one of the agents has his own set of jobs to be processed and his own objective function, and both share a common processor. In the single‐machine problem studied in this article, the goal is to find a joint schedule that minimizes the total deviation of the job completion times of the first agent from a common due‐date, subject to an upper bound on the maximum deviation of job completion times of the second agent. The problem is shown to be NP‐hard even for a nonrestrictive due‐date, and a pseudopolynomial dynamic program is introduced and tested numerically. For the case of a restrictive due‐date (a sufficiently small due‐date that may restrict the number of early jobs), a faster pseudopolynomial dynamic program is presented. We also study the multiagent case, which is proved to be strongly NP‐hard. A simple heuristic for this case is introduced, which is tested numerically against a lower bound, obtained by extending the dynamic programming algorithm. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 61: 1–16, 2014     

Stochastic scheduling subject to machine breakdowns: The preemptive‐repeat model with discounted reward and other criteria

We consider the problem of scheduling a set of jobs on a single machine subject to random breakdowns. We focus on the preemptive‐repeat model, which addresses the situation where, if a machine breaks down during the processing of a job, the work done on the job prior to the breakdown is lost and the job will have to be started from the beginning again when the machine resumes its work. We allow that (i) the uptimes and downtimes of the machine follow general probability distributions, (ii) the breakdown process of the machine depends upon the job being processed, (iii) the processing times of the jobs are random variables following arbitrary distributions, and (iv) after a breakdown, the processing time of a job may either remain a same but unknown amount, or be resampled according to its probability distribution. We first derive the optimal policy for a class of problems under the criterion to maximize the expected discounted reward earned from completing all jobs. The result is then applied to further obtain the optimal policies for other due date‐related criteria. We also discuss a method to compute the moments and probability distributions of job completion times by using their Laplace transforms, which can convert a general stochastic scheduling problem to its deterministic equivalent. The weighted squared flowtime problem and the maintenance checkup and repair problem are analyzed as applications. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

基于AdaBoost-SVM算法的某火炮炮闩技术状态评估

针对现役装备技术状态评估多依赖于手工拆卸的现状,提出一种基于AdaBoost-SVM模式识别算法的在线技术状态评估方法。利用人工后坐在线检测设备对炮闩装置技术状态参数进行检测,在对检测数据进行相关性分析特征提取的基础上,引入支持向量机模式识别方法,建立炮闩装置技术状态评估模型。通过将评估模型与Ada-Boost算法相结合,每次迭代都根据测试精度对分类错误的样本点和各分量分类器的权重重新赋值,在下一次迭代中形成新的分量分类器以优化分类结果,最终将各分量分类器依其权重综合完成评估。实例分析结果验证了评估模型的正确性和有效性。     

支持向量机的OTHR多频特征目标分类识别法

针对低信噪比下多频法在天波超视距雷达(Over-The-Horizon Radar,OTHR)目标分类识别中分类精度不高的缺点,充分利用雷达量测的一些先验信息,将基于支持向量机(Support Vector Machine,SVM)的多分类器引入多频法中,提出了一种基于SVM的OTHR多频特征目标分类识别方法。仿真结果表明,利用较少的3个频率点,在信噪比较低的条件下可获得较好的分类识别效果,说明了该方法在OTHR目标分类识别中的有效性和可行性。