基于遥感的黄土丘陵沟壑区人口密度分布模拟——以甘肃省榆中县为例

基于“面向对象”的人口分配原则,通过扩展的城市人口-面积异速生长模型,以SPOT5遥感影像为基本数据源,提取居民地信息;以榆中县为例,建立居民地分类体系,采取城乡人口分割算法,以人口统计数据为总量控制建立了人口分布的反演模型,模拟了榆中县域人口的空间分布。研究方法可为黄土丘陵沟壑区中尺度人口分布模拟的理论与应用研究提供借鉴。     

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.     

UCLINUX下自主移动机器人运动系统设计

设计了在嵌入式操作系统UCLINUX下的自主移动机器人运动系统。该系统主控制器采用了ARM7微处理器,操作系统使用UCLINUX,利用脉冲宽度调制(PWM)技术对直流电机转速进行控制。详细介绍了UCLINUX下电机控制器的设备驱动程序设计和移动机器人基本动作的应用程序设计。该系统已经应用于一个移动机器人原型中,能够成功地实现各种动作。     

粮食烘干试验台的开发与研究

谷物在不同的温度、湿度条件下具有不同的干燥特性.探索和掌握谷物在不同大气条件下的干燥规律对谷物烘干机的设计和参数选择具有指导意义.本文研究开发的粮食烘干过程的温度在线监测和烘干自动控制系统,可在室内模拟谷物的干燥过程,进而为烘干机的设计与开发提供参考和依据.     

全回转Z型推进装置在汽车渡船上的应用

对转桨全回转Z型推进装置是一种新型的船舶推进装置,它汇集对转桨和全回转Z型推进装置的双重优点,而且有推进效率高、空泡性能好、减振降噪、节能等优点,是各种自航船舶理想的推进装置,本文着重介绍它的结构特点、安装及其试验有关问题.     

节育器系统的可靠性分析

对宫内节育器系统的寿命进行了分析、建模,并进行了参数估计,得到其寿命的Ｗｅｉｂｕｌ分布的解析表达式,最后对某地区的实际情况进行了分析,结果令人满意.     

CWNs中机会协作传输策略及其差错性能分析

在主用户和感知用户共存的认知无线网(cognitive wireless networks, CWNs)中,针对如何在保证主用户有效传输的同时满足感知用户无线业务通信要求的问题,提出了一种基于频谱感知的机会协作传输策略。在瑞利衰落信道下对所提方案进行理论分析,给出了协作传输与非协作传输策略下平均误比特率的解析表达式,理论分析和仿真结果表明,相对于传统非协作传输而言,机会协作传输策略的差错性能得到明显改善。     

接地选线装置在煤矿矿区电网的应用

对选线装置在屯川煤矿矿区电网应用中的几个问题进行了研究和分析,有针对性地提出了解决的措施.实施情况表明,通过在减少资金投入的基础上,实现对单相接地故障高正确率的选线判断,可以提高电网安全运行水平.     

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.     

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.