力触觉增强的虚拟现实工厂系统

现有虚拟现实工厂只能依靠头戴式显示器和手柄进行单向的视、听觉虚拟操作，难以进行双向的沉浸式触觉交互。针对此问题，采用力反馈控制器设计了一种力触觉增强的虚拟现实工厂系统。以Unity3D为基础，采用3DMax对工厂内多类型工具和零部件进行三维视觉建模，并通过反射探针、光照探针等组件进行立体化视觉增强。在此基础上，为虚拟空间物体增加刚体、碰撞体、关节等组件，建立虚拟空间的触觉反馈模型，实现了力反馈控制器与虚拟空间物体的实时触觉交互。实验表明：系统可提供重力、摩擦力、恒力等多种力触觉感知，支持对20余种工具、零部件的触摸、抓取等触感操作，能感受模型、轮廓以及质量等物理属性，实现了虚拟工厂中“视触听”多维立体化的沉浸式操作。     

基于自适应遗传算法的超平面分类及遥感应用

智能分类算法是遥感影像分类研究的热点,遗传算法作为一种智能全局优化技术在遥感影像分类中具有良好应用前景.针对现有多光谱遥感影像分类方法的不足,提出了基于自适应遗传算法的超平面分类方法(hyper plane-adaptive genetic algorithm,HP-AGA)并应用于遥感影像分类,该方法利用神经网络中的神经元激活函数Sigmoid函数,对遗传算法中交叉率、变异率进行非线性自适应性调整,不再需要反复训练遗传参数,同时利用快速全局寻优特点,确定分类超平面的各个位置参数,从而获取最佳分类超平面集进行分类.多光谱遥感影像分类方法的应用实验表明,基于自适应遗传算法的超平面遥感分类方法能更快、更稳定地收敛到全局最优解,具有更好的效率及鲁棒性,并能取得优于简单遗传超平面分类算法及传统分类方法的分类精度.     

Global vegetation change analysis based on MODIS data in recent twelve years①

Vegetation cover change is critical for understanding impacts and responses of vegetation to climate change. A study found that vegetation in the regions between 45°N-70°N was increasing using normalized difference vegetation index( NDVI) from 1981 to 1991 ten years ago. The global vegetation growth has changed because of climate change in recent twelve years( 2001- 2012). After thorough analysis based on satellite data,it is found that it is evident that the global vegetation changed( NDVI) little,and it is increasing slightly in Northern hemisphere while it is decreasing slightly in Southern Hemisphere. For different latitudes,vegetation is increasing 0.17% every year from 60°N to 70 °N( R~2= 0.47,P 0.013),while the vegetation is decreasing 0.11% every year from 10°N to 10° S( R~2= 0.54,P 0.004). For different continents,the vegetation in South America is decreasing 0.16% every year( R~2= 0.78,P 0.0001) and it is increasing 0.05% every year in Asia( R~2= 0.28,P 0.072) and 0.25% every year in Oceania( R~2= 0.24,P 0.1). The analysis of global vegetation in different seasons indicates that spatial distribution of global temperature and water vapor will affect the spatial distribution of vegetation,in turn,the spatial distribution of vegetation will also regulate the global temperature and water vapor spatial distribution at large scale. The growth and distribution of vegetation are mainly caused by the orbit of the celestial bodies,and a big data model based on gravitational-magmatic change with the solar or the galactic system as its center is proposed to be built for analyzing how the earth's orbit position in the solar and galaxy system affects spatial-temporal variations of global vegetation and temperature at large scale. These findings promise a holistic understanding of the global climate change and potential underlying mechanisms.     

浅谈机电一体化

随着社会的不断进步,机电一体化技术不仅影响到整个工程的质量、工期、投资和预期效果,还对整体设备的安全运行、节能效果及工程投入使用后的使用功能等有影响。在以计算机网络为基础的网络化、智能化时代,机电一体化的作用越来越显得重要,涉及领域越来越广,技术更新更快,也将更加复杂。     

Temporal and spatial distribution of tropospheric CO<Subscript>2</Subscript> over China based on satellite observations

Mid-troposphere CO₂ data retrieved by the AIRS (atmospheric infrared sounder) were validated with five ground-based stations and aircraft measurements in the Northern Hemisphere. AIRS CO₂ products show good agreement with ground and aircraft observations. The data had a monthly average accuracy better than 3 ppmv. In this study, the spatial and temporal distribution of mid-troposphere CO₂ from January 2003 to December 2008 was analyzed based on this satellite product. The average concentration of atmospheric CO₂ was higher in the Northern Hemisphere than in the Southern Hemisphere. The yearly average results show a gradual increase from 2003 to 2008. In China, the annual growth rate was about 2 ppmv/a, similar to the United States, Europe, Australia and India, but was slightly lower than Canada and Russia. Mid-troposphere CO₂ concentrations were higher over northern China than over southern areas, due to differences in natural conditions and industrial layout. There were four centers of high CO₂ concentration between 35° and 45°N over China, with low concentrations over Yunnan Province. There was a significant seasonal CO₂ variation with peak concentration in spring and the lowest concentration in autumn.     

智能视频分析的现状与未来发展趋势

概述了国内外智能视频分析技术的现状以及智能视频分析的核心,分析了智能视频分析的不足,探讨了智能视频分析的未来发展趋势。     

Dempster-Shafer （D-S） algorithm with credit scale in spectrum sensing

In cognitive radio, the detection probability of primary user affects the signal receiving performance for both primary and secondary users significantly. In this paper, a new Dempster-Shafer (D-S) algorithm with credit scale for decision fusion in spectrum sensing is proposed for the purpose to improve the performance of detection in cognitive radio. The validity of this method is established by simulation in the environment of multiple cognitive users who know their signal to noise ratios (SNR) and a central node. The channels between the cognitive users and the central node are considered to be additive white Gaussian noise (AWGN). Compared with traditional data fusion rules, the proposed D-S algorithm with credit scale provides a better detection performance.     

基于被分块的无线传感器网络故障恢复方法

针对网络间不能正常通信的问题,提出了一种解决该类故障的恢复方法。此方法将整个网络监测区域划分成若干个同心圆,被孤立的区域将自己区域的转置节点代表移动到最近的同心圆上,从而实现了重建连通性的目的。     

中小型智能变频电动执行器设计

为了解决国内智能电动执行器存在的精度差、网络通讯能力弱、功能简单等问题,采用以TMS320F2812 DSP芯片为核心,开发了中小功率的智能电动执行器.该智能电动执行器具有网络通信功能、高精度定位执行功能、组态设定与控制功能和自检显示功能,该型执行器结构紧凑,智能程度较高,执行部分采用了先进的磁束向量变频控制方法.     

碎石桩加固土质边坡的机理及稳定性评价

云南省华电镇雄电厂边坡的工程地质条件复杂,软弱夹层较多,地基土体基本处于饱和状态,抗剪强度较低.由于以上的工程实际,设计采取碾压堆石压坡的工程措施,局部辅以振冲碎石桩置换,以期对坡体的受力、变形和稳定性等进行改善.利用FLAC3D软件对镇雄边坡的碎石桩加固过程及碾压堆石压坡填土过程进行了三维数值仿真分析,并分析了碎石桩加固土质边坡的机理.考虑复杂地基与碎石桩、挡土墙之间的相互作用,以及碎石桩加固后的排水情况,采用分级加载的方式模拟边坡的填土过程,通过坡体的应力、位移对比,定量分析了碎石桩加固措施的效果.基于强度折减法,采用结点位移法评价边坡的稳定安全状况.计算结果表明,采用碎石桩加固并进行排水后,提高了土体材料的力学性质,减小了孔隙水压力,边坡的受力得到了较大的改善,变形得到了控制,边坡的稳定性较好.