遥测参数数据载荷状态判别集成学习方法

针对载荷单机设备遥测参数维度高、数据量大、存在类别不平衡、无法直观判别单机设备运行情况等问题,考虑到航天任务对可解释性的要求,提出一种基于信息增益参数特征选择和集成学习方法的载荷单机状态快速识别方法。采用统计量性质和信息增益子集搜索方法对遥测数据进行特征筛选降维,通过集成学习模型算法实现载荷单机设备状态的自适应识别分类。所提方法将信息增益的参数分类信息量评价准则和集成学习拟合能力强、类别不平衡下准确率高和抗噪能力强等优点相结合,兼顾模型特征和结果的可解释性,提供了重点参数发现功能。采用科学卫星任务真实载荷遥测参数数据对该方法进行了验证,整体识别准确率高于90%,少数样本亦可准确识别,整体效果可达到在轨任务要求,证明了所提方法的有效性和实用性。     

【专题】无人机空战决策技术研究进展

随着无人机相关技术领域的飞速发展,无人机迅速成为世界各国军事领域的研究热点。无人机自主决策作为无人机领域的核心问题,指的是无人机基于空战态势,利用数学优化理论、人工智能等方法,独立自主地生成机动动作控制指令以完成设定目标的过程。本文首先介绍了世界各国该领域的研究进展,并基于空战决策的求解思路,将决策方法分为三类：基于对策理论、基于专家知识以及基于启发式学习算法的决策方法。其次,针对基于对策理论的空战决策方法,阐述了从微分对策到矩阵对策的发展及联系;针对基于专家知识的空战决策方法,介绍了该类方法的建模方法,改进方向;针对基于启发式学习算法的决策方法,论述了各典型方法的适用条件、改进途径等。最后,对无人机空战决策的研究难点进行分析,并展望了未来的研究方向与趋势。     

Two‐agent scheduling on a single sequential and compatible batching machine

We study two‐agent scheduling on a single sequential and compatible batching machine in which jobs in each batch are processed sequentially and compatibility means that jobs of distinct agents can be processed in a common batch. A fixed setup time is required before each batch is started. Each agent seeks to optimize some scheduling criterion that depends on the completion times of its own jobs only. We consider several scheduling problems arising from different combinations of some regular scheduling criteria, including the maximum cost (embracing lateness and makespan as its special cases), the total completion time, and the (weighted) number of tardy jobs. Our goal is to find an optimal schedule that minimizes the objective value of one agent, subject to an upper bound on the objective value of the other agent. For each problem under consideration, we provide either a polynomial‐time or a pseudo‐polynomial‐time algorithm to solve it. We also devise a fully polynomial‐time approximation scheme when both agents’ scheduling criteria are the weighted number of tardy jobs.     

一种改进的粒子群优化算法

针对基本粒子群算法存在着收敛速度慢、效率低、易陷入局部最优等缺陷,为了更好地平衡全局和局部搜索能力,在粒子群算法中引入收缩因子,使算法中粒子不仅向种群最优的粒子进行学习,而且向种群中比自己优秀的所有粒子学习,增加了粒子的多样性。实验结果证明,与基本蚁群算法相比,改进的粒子群算法提高了收敛速度和效率,能一定程度地避免局部最优解的产生。     

支持向量机规则提取

支持向量机是一种黑箱模型,其学习到的知识蕴含在决策函数中,不仅影响了用户对利用支持向量机技术构建智能系统的信心,还阻碍了支持向量机技术在数据挖掘领域的应用。由于对支持向量机规则提取进行研究有助于解决上述问题,因此该领域正成为机器学习和智能计算界的研究热点。分析了具有代表性的支持向量机规则提取算法,并提出该领域未来的研究重点。     

多窗口谱图分析的低截获概率雷达信号识别

在当前复杂的战场环境中,低截获概率雷达信号因其具有大时宽带宽积、强干扰性能、高分辨率和低截获性特点得到了广泛应用,传统的雷达侦察手段很难对其进行有效识别.在低截获概率雷达典型调制分析的基础之上,研究基于人工智能的雷达信号分类识别方法.从低截获概率雷达信号时频特征入手,提出基于多窗口时频谱图分析方法.该算法采用Hermi...     

Scheduling jobs and maintenance activities on parallel machines

Most machine scheduling models assume that the machines are available all of the time. However, in most realistic situations, machines need to be maintained and hence may become unavailable during certain periods. In this paper, we study the problem of processing a set of n jobs on m parallel machines where each machine must be maintained once during the planning horizon. Our objective is to schedule jobs and maintenance activities so that the total weighted completion time of jobs is minimized. Two cases are studied in this paper. In the first case, there are sufficient resources so that different machines can be maintained simultaneously if necessary. In the second case, only one machine can be maintained at any given time. In this paper, we first show that, even when all jobs have the same weight, both cases of the problem are NP-hard. We then propose branch and bound algorithms based on the column generation approach for solving both cases of the problem. Our algorithms are capable of optimally solving medium sized problems within a reasonable computational time. We note that the general problem where at most j machines, 1 ≤ j ≤ m, can be maintained simultaneously, can be solved similarly by the column generation approach proposed in this paper. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 145–165, 2000     

风速传感器在风幕机改进中的应用研究

从节能降耗角度探讨了对风幕机进行改进的必要性 ,通过计算分析确定了风速为系统改进所要考虑的主要环境因素 ,研究了利用热电式风速传感器和变频器实现系统智能化改进的可行性。     

基于强化学习的鱼群自组织行为模拟

自组织行为广泛存在于自然界中。为了通过学习的方式模拟鱼群自组织行为,构建了鱼群模拟环境模型、智能体模型和奖励机制,并提出了一种基于赫布迹和行动者-评价者框架的多智能体强化学习方法。该方法利用赫布迹加强游动策略的学习记忆能力,基于同构思想实现了多智能体的分布式学习。仿真结果表明,该方法能够适用于领航跟随、自主漫游、群体导航等场景中鱼群自组织行为学习,并且基于学习方法模拟的鱼群展现的行为特性与基于博德规则计算模拟的鱼群行为类似。     

Data-driven research in retail operations—A review

We review the operations research/management science literature on data-driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high-quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state-of-the-art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.