Coordinated pricing and inventory control problems with capacity constraints and fixed ordering cost

This article addresses a single‐item, finite‐horizon, periodic‐review coordinated decision model on pricing and inventory control with capacity constraints and fixed ordering cost. Demands in different periods are random and independent of each other, and their distributions depend on the price in the current period. Each period's stochastic demand function is the additive demand model. Pricing and ordering decisions are made at the beginning of each period, and all shortages are backlogged. The objective is to find an optimal policy that maximizes the total expected discounted profit. We show that the profit‐to‐go function is strongly CK‐concave, and the optimal policy has an (s,S,P) ‐like structure. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

一个知识辅助的视频语义概念探测框架

视频语义概念探测是视频语义内容分析研究领域的热点和难点问题.语义概念探测方法的性能取决于其是否能够有效地建模和匹配视频语义内容特征.将视频内容抽象为感知概念和语义概念以及概念间的关系,提出了知识辅助的视频语义概念探测框架,利用本体建模概念间关系和上下文知识,从低层特征匹配和上下文匹配两个方面综合考虑语义概念的探测.通过线性融和策略,对匹配结果进行融合得到最终的探测结果.实验结果表明提出的方法探测性能良好.     

多径条件下子阵级低角测高算法

针对多径条件下的测高问题,提出了一种子阵级处理新算法.采用均匀子阵划分形成三个子阵;在子阵级解析求出协方差矩阵的正交矢量,进而构造仰角谱,谱峰的位置即目标仰角;还可以估计出复反射系数.仿真分析给出了该方法的性能与SNR、目标仰角以及复反射系数的关系,某米波雷达实测数据检验了该方法的有效性,仰角测量误差达到阵列天线波束宽度的1/84.研究表明:该算法本身不依赖未知的复反射系数,并且信号处理维数低,运算量较低,可以应用在舰载雷达和米波三坐标雷达中.     

短波自动链路建立性能分析与优化设计

文章在分析2G-ALE建链流程的基础上,建立了平均建链概率和平均建链时间性能指标数学模型,依据此模型分析了信道扫描速率以及引导呼叫的长度对平均建链时间的影响。通过仿真比较了两种信道驻留时间的优化方案,结果表明在差信道条件下适当降低信道扫描速率可以减少平均建链时间。指标模型和仿真分析对于ALE系统的设计优化具有一定的参考意义。     

基于改进粒子群优化RBF神经网络的算法

针对粒子群算法易陷入局部极小的缺陷,提出了一种改进的粒子群优化算法,并将改进后的算法应用到RBF神经网络核函数参数的选取中。依照文中提出的编码方式、迭代公式和适应度函数,在全局空间中搜索具有最优适应值的参数向量。实例仿真表明,基于改进粒子群算法优化的RBF神经网络不仅收敛速度快,且误差精度高。     

基于演化母板的EHW平台上电路的演化生成

提出一个基于演化母板的新型演化平台,介绍了它的系统结构及工作原理。进而,以一位全加器为例,详细阐述了在基于演化母板的EHW平台上,利用遗传算法演化生成一个具体电路的方法及实现效果。     

用于静电放电EMP实验的三模冗余技术

通过对TMR(Trip le Modu lar Redundancy)容错技术的分析和研究,介绍了一种基于FPGA芯片的TMR整体“硬化”技术,并分别对采用了TMR技术的电机容错控制系统和未采用TMR技术的一般电机控制系统进行了ESD EMP的辐照效应实验,得出TMR技术可有效增强控制系统抗ESD EMP能力的结论。     

精密坐标镗床CNC系统研究

介绍了作者研制的通用ＣＮＣ系统,该系统以ＡＳＴ３８６作为控制器,采用ＰＷＭ驱动直流伺服电机,并配以软、硬件接口;文中对该系统的主要组成部分作了分析,在此基础上应用传统的ＰＩＤ控制方法设计数字控制器;最后通过实验验证了该系统的输出响应和控制器的性能。     

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.     

Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.