无线传感器网络节点调度算法及研究进展

随着无线网络技术的发展,无线传感器网络近年来引起了广泛的关注。该网络由大量具有有限感知能力和传输能力的节点组成。受节点自身能量的制约,如何能源有效的利用节点使得尽可能的延长网络工作时间成为了无线传感器网络设计的重中之重。针对无线传感器网络设计能源有效的节点调度算法可以完成这一目标。本文从技术角度给出了研究节点调度算法的意义,对现有的节点调度算法进行了分类、分析和比较。最后给出未来需要解决的若干个问题,相信会推动该领域的工作人员展开进一步的探索和研究。     

基于循环神经网络的卫星姿态执行器故障诊断

针对卫星姿态控制系统执行器机构故障问题，提出了一种基于循环神经网络的故障诊断方法。对卫星姿态控制系统建模，进行故障分析并采集星敏感器和角速度陀螺的连续时刻故障数据。设计六种异构的循环神经网络，对故障数据进行故障诊断和分类, 分别从网络深度、反馈单元、激活函数和训练算法对比网络效果。带有门循环单元的 (gate recurrent unit，GRU)深层循环神经网络训练效果更好，其故障诊断准确率达到了95.7%。结果表明对于时序的卫星数据，门循环单元和带有一定深度的循环神经网络故障诊断效果更优。     

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

基于Dynaform的阶梯形件液压拉深成形仿真技术研究

用Dynaform有限元仿真软件对阶梯形件液压拉深成形过程进行了模拟，分析了压边力和液体压力对阶梯形件成形质量的影响。研究结果表明，合适的压边力和液体压力能防止阶梯形件拉深缺陷的发生。     

Nonlinear dynamics of a flapping rotary wing: Modeling and optimal wing kinematic analysis

The analysis of the passive rotation feature of a micro Flapping Rotary Wing(FRW)applicable for Micro Air Vehicle(MAV) design is presented in this paper. The dynamics of the wing and its influence on aerodynamic performance of FRW is studied at low Reynolds number(~10~3).The FRW is modeled as a simplified system of three rigid bodies: a rotary base with two flapping wings. The multibody dynamic theory is employed to derive the motion equations for FRW. A quasi-steady aerodynamic model is utilized for the calculation of the aerodynamic forces and moments. The dynamic motion process and the effects of the kinematics of wings on the dynamic rotational equilibrium of FWR and the aerodynamic performances are studied. The results show that the passive rotation motion of the wings is a continuous dynamic process which converges into an equilibrium rotary velocity due to the interaction between aerodynamic thrust, drag force and wing inertia. This causes a unique dynamic time-lag phenomena of lift generation for FRW, unlike the normal flapping wing flight vehicle driven by its own motor to actively rotate its wings. The analysis also shows that in order to acquire a high positive lift generation with high power efficiency and small dynamic time-lag, a relative high mid-up stroke angle within 7–15° and low mid-down stroke angle within -40° to -35° are necessary. The results provide a quantified guidance for design option of FRW together with the optimal kinematics of motion according to flight performance requirement.     

职业院校教师队伍建设的现状、问题及对策分析

通过采取面上调研与点上调研相结合的方式对湖南省职业院校教师队伍建设进行了专题调研,摸清了职业院校教师队伍建设的基本情况,发现了职业院校教师队伍建设中存在的问题,并提出了对策建议。     

基于深度学习的单排孔气膜冷却性能预测

气膜冷却是增强涡轮叶片的高温耐受力,间接提高涡轮进口温度的有效手段之一。目前气膜冷却孔布局的主流设计方法是先通过计算流体力学（CFD）筛选和优化初始方案,再进行模型实验。这种方法设计周期长,时间成本高。传统上用于快速评估冷却效率的经验公式法存在函数形式复杂,拟合精度有限,参数适用范围较窄等问题。因此基于深度学习原理,设计了一种基于多层感知器模型（MLP）的深度神经网络,建立了绝热气膜冷却效率的预测模型。使用CFD数据训练网络,结果表明：深度学习模型在训练集和验证集上具有大于0.95的拟合度,在测试集上具有大于0.99的拟合度,可以较好地识别数据集中的抽象特征,具有较高的精度和较好的泛化能力。此外,在满足精度要求的前提下,一个完成训练的深度学习模型能够有效减少预测耗时,提高预测效率,在快速评估冷却布局性能方面具有较好的应用前景。     

管制-飞行状态相依网络演化过程

动态、准确的管制系统运行态势预测是航空运输系统各相关单位开展协同决策的关键基础。基于航空器间的冲突情况、管制员对航空器的管控情况以及管制移交情况构建管制-飞行状态相依网络,探究、分析其演化规律,采用相关性分析和主成分分析证明了所选5项指标的合理性。设置自由飞行和固定航线飞行两种仿真场景,通过计算平均节点度、平均点强等拓扑指标的最大李雅普诺夫指数证明各时间序列均具有混沌特性,选择长短期记忆（LSTM）人工神经网络方法对各时间序列的演化规律进行预测,并与其他预测算法进行对比。仿真结果表明LSTM算法能对管制系统的演化过程进行有效的预测,且预测精度高于贝叶斯算法和支持向量机算法;在自由飞行条件下,5项指标的预测误差绝大部分在20%以内,固定航线飞行的预测效果优于自由飞行。     

多滑翔飞行器时间协同轨迹快速规划

从高超声速飞行器集群"探测-打击-评估"一体化任务需求出发,针对多滑翔飞行器时间协同再入轨迹规划问题进行研究,提出集群再入的协同形式及轨迹规划方案,基于改进序列凸化算法解决了再入总飞行时间的精确控制问题,从而实现滑翔段时间协同。首先,给出了滑翔飞行器集群的协同策略,将求解模型转化为协同时间的确定、协同时间约束下的轨迹规划子问题。将模型中的时间项误差等加入罚函数,提高了协同轨迹求解可行性。引入飞行路径角预设剖面作为软约束,并通过罚函数与信赖域自适应调整,以避免轨迹求解时的振荡问题,提高了序列凸化算法的收敛性。以CAV-H飞行器模型为例验证了算法的有效性,仿真结果表明,所提算法对初值的敏感性低,求解得到的再入总时间可调范围与伪谱法一致,轨迹规划结果的平滑性及计算时间均优于伪谱法。     

基于B/S架构的高职院校新生报到管理系统的设计与实现

依据高职院校新生报到管理的工作要求,讨论以B/S架构为基础架构,使用Microsoft Visual studio2010作为前端开发工具,以SQL Server 2008作为后台数据库的新生报到管理系统的设计与实现。