浅议基于云计算的高校信息平台在学生工作中的应用前景

目前,高校的信息化建设初见成效,但高校学生工作的信息化发展仍有待提高。随着云计算的兴起,其特有的平台化、共享化、低成本化、安全化的特点使云计算成为解决目前高校信息化建设难题的突破口。本文首先阐述了当前高校学生管理中的各种矛盾;其次,介绍了云计算的特点和优势;最后,对云计算在高校学生管理应用前景进行了展望。通过本论文的研究,从云计算的视角对不断加快高校信息化建设提出了新的思路。     

深刻理解办学指导思想,创造性开展工作

本文从力学指导思想的认识与落实教学改革，师资队伍建设３个方面结合本院实际进行浅议。     

电磁超介质研究进展

电磁超介质是当前电磁学和物理研究领域中的前沿与热点，文章将左手材料、复合左／右手传输线、光子晶体统一在电磁超介质的体系下进行介绍，并论述了电磁超介质的发展现状。     

Predictive Modeling and Parameter Optimization of Cutting Forces During Orbital Drilling

To optimize cutting control parameters and provide scientific evidence for controlling cutting forces,cutting force modeling and cutting control parameter optimization are researched with one tool adopted to orbital drill holes in aluminum alloy 6061.Firstly,four cutting control parameters(tool rotation speed,tool revolution speed,axial feeding pitch and tool revolution radius)and affecting cutting forces are identified after orbital drilling kinematics analysis.Secondly,hybrid level orthogonal experiment method is utilized in modeling experiment.By nonlinear regression analysis,two quadratic prediction models for axial and radial forces are established,where the above four control parameters are used as input variables.Then,model accuracy and cutting control parameters are analyzed.Upon axial and radial forces models,two optimal combinations of cutting control parameters are obtained for processing a13mm hole,corresponding to the minimum axial force and the radial force respectively.Finally,each optimal combination is applied in verification experiment.The verification experiment results of cutting force are in good agreement with prediction model,which confirms accracy of the research method in practical production.     

复合材料层合板下陷区结构设计

复合材料层合板下陷是飞机结构中应用广泛的典型结构细节。以下陷长深比、相同方向下陷区间距、两个不同方向下陷区间距为下陷区设计变量,建立参数化有限元模型;通过下陷应力应变分布、初始失效和最终承载能力分析,确定各设计变量对下陷区结构力学性能的影响,并进行参数化设计。分析结果表明:下陷区结构最先发生损伤的部位为下陷区转折处;合理的下陷参数设计变量取值为长深比B/A≥20、主受力方向上两个下陷区间距A/H≥12、不同方向上两个下陷区间距C/A_(min)≥1.5。     

面向林火持续侦察的多无人机分布式控制方法

为解决目前面向林火持续侦察多无人机（UAV）协同控制实用性与自主性不足的问题，基于蔓延速度诱导元胞自动机（SVICA）林火蔓延算法、无人机与传感器建模，构建了较为真实的三维多无人机火场侦察仿真环境与侦察效能指标，提出了一种面向林火持续侦察的多无人机双层分布式控制架构，在行动层基于强化学习训练的人工神经网络（ANN），实现了有风条件下无人机自主火场环绕与地形跟随功能，在策略层设计通过时域均匀分布算法进行各无人机空速的离散自主调节，最终达到多无人机林火持续侦察时域分布的均匀性与即时性目的。通过一系列数值仿真实验，验证了所提出的无人机分布控制策略在无人机损失和补充突发情况下的自适应性。基于无人机数量与侦察效能指标关系的实验与研究，定义了无人机出动阈值并验证了无人机长时间出动回收策略。最终实验结果表明，针对林火持续侦察任务，所提出的多无人机分布式控制方法具备一定的有效性与实用性。     

空域拥挤风险管理时间决策模型与方法

针对目前日益严重的空域拥挤现象,缺乏有效的空域拥挤风险管理机制,造成拥挤难以应对等问题,建立了空域拥挤风险预测模型、空域拥挤风险解决模型和风险损失成本模型。采用风险预测模型和解决模型预测可能产生空域拥挤的时段、提出不同时段的实施流量管理策略,基于风险损失成本模型给出了不同时段调整拥挤的运行成本,通过比较运行成本和空域拥挤风险管理决策方法确定出解决空域拥挤的最佳时间点。采用实际运行数据进行验证,结果表明:所建立的空域拥挤预测模型、空域拥挤风险解决模型、风险损失成本模型和空域拥挤风险管理时间决策方法能有效地预测未来空域发生拥挤的时段,迅速找到运行成本最小的调整拥挤的时间点,为空中交通流量动态管理提供了新途径。     

A novel signal processing approach for LEO space debris based on a fence-type space surveillance radar system

The increase in space debris can seriously threaten regular activities in the Low Earth Orbit (LEO) environment. Therefore, it is necessary to develop robust, efficient and reliable techniques to understand the potential motions of the LEO debris. In this paper, we propose a novel signal processing approach to detect and estimate the motions of LEO space debris that is based on a fence-type space surveillance radar system. Because of the sparse distribution of the orbiting debris through the fence in our observations, we formulate the signal detection and the motion parameter estimation as a sparse signal reconstruction problem with respect to an over-complete dictionary. Moreover, we propose a new scheme to reduce the size of the original over-complete dictionary without the loss of the important information. This new scheme is based on a careful analysis of the relations between the acceleration and the directions of arrival for the corresponding LEO space debris. Our simulation results show that the proposed approach can achieve extremely good performance in terms of the accuracy for detection and estimation. Furthermore, our simulation results demonstrate the robustness of the approach in scenarios with a low Signal-to-Noise Ratio (SNR) and the super-resolution properties. We hope our signal processing approach can stimulate further work on monitoring LEO space debris.     

Co-registration of Chang’E-1 stereo images and laser altimeter data with crossover adjustment and image sensor model refinement

Topographic information is of fundamental importance for various scientific investigations in lunar and planetary exploration. To provide high-precision, seamless mapping capability, it is critical to co-register image and altimetry data, the two major data sources for topographic modeling. This paper presents a method for co-registration of Chang’E-1 (CE-1) stereo images and laser altimeter (LAM) data with crossover adjustment and refinement of the image sensor model. The crossover adjustment is tested in a larger area (0–60°N, 50–0°W); the image refinement and co-registration with LAM data are tested in an area (46.2–50.0°N, 31.8–28.8°W) within the larger area. Experimental results demonstrate that this co-registration reduces the mean differences of inconsistency from more than 200 m to just 3.21 m in the Z direction of object space. In image space, the mean errors of homologous points both in the column and row directions are reduced to below 0.1 pixel. This indicates that the proposed crossover adjustment of LAM data and refinement of the CE-1 stereo image model can effectively improve co-registration of the two data sets.