一种具有强实时性、强鲁棒性的图像匹配算法

针对描述符BRIEF对图像旋转敏感的问题,提出一种改进的描述符RIBRIEF,该描述符具有识别能力强、提取速度快、占用空间小及抗干扰能力强等优点,并具有旋转不变性.经分析,图像匹配算法的实时性较大程度上由特征点数量、匹配点搜索次数及描述符相似度计算复杂度决定,因此提出通过描述符索引与描述符聚类相结合、基于FAST稳定特征点提取和逻辑运算计算相似度等方法提高算法的整体实时性.实验结果表明,与描述符BRIEF及SURF相比较,基于描述符RIBRIEF的图像匹配算法在鲁棒性及实时性方面均具有明显优势.     

FAST健康监测数据采集系统开发研究

对500m口径大射电望远镜(Five-hundred-meter Aperture Spherical Telescope,简称FAST)健康监测系统基础组成部分—FAST健康监测数据采集系统进行开发研究.分析了FAST传感器选型影响因素,结合传感器自身适用标准进行具体选型,完成了基于所选传感器的硬件系统设计,运用Labview平台开发了与FAST监测系统相适应的采集软件系统,形成一套完整的基于虚拟仪器技术的FAST健康监测数据采集系统.FAST30m模型试验是FAST建设过程中的关键步骤,通过在模型试验中的成功应用,表明该系统可为将来FAST健康监测系统的完善提供重要支撑条件.     

Global stability analysis scheme for a class of nonlinear time delay systems

We consider a class of nonlinear time delay systems created by generalizing the model for FAST TCP, an Internet congestion control algorithm. We achieve a time delay independent sufficient condition for the global asymptotic stability of the class of systems. The sufficient condition is verified by constructing two sequences that represent the lower and upper bound variations of the system trajectory in time, and showing that the two sequences converge to the equilibrium point of the system. The simulation results exemplify that the sufficient condition is valid for global asymptotic stability, and that the sufficient condition is a close approximation to the unknown necessary and sufficient condition for global asymptotic stability.     

K-S检验与mRMR相结合的基因选择算法

为了解决基因数据集的基因选择难题,提出一种基于K-S检验与最小冗余最大相关（Minimum Redundancy-Maximum Relevance,mRMR）原则的基因选择算法。该算法先采用K-S检验选择出具有一定区分能力的基因,然后对选择到的基因进行mRMR判断,保留与类别高度相关而其间相关性较小的基因构成最终被选基因子集。以SVM为分类器,以F1_measure、分类准确率和AUC为评价指标对本文算法选择的基因子集进行评估,并将本文算法与K-S检验、mRMR,以及经典的RELIEF和FAST算法进行比较。5个经典基因数据集上的平均实验结果揭示：本文算法的运行时间远低于mRMR算法,且其各项评价指标值优于其他比较算法。因此,本文提出的K-S检验与mRMR结合的基因选择算法能选择到非常有效的基因子集。     

基于扩展FAST的高速证券交易接口文件交换方法

针对现有方法冗余度高和文件分段造成带宽浪费、延迟大的问题,提出一种基于扩展FAST的高速证券交易接口文件交换方法-FastFile.将接口文件记录映射为消息进行编码并组成段传输,使接收方即便只收到部分分段,也可解析出所含记录,同时利用扩展FAST高效的编码有效降低数据冗余度.实现了扩展FAST及FastFile,实验比较了FastFile与常用接口文件格式.实验表明FastFile处理数据的粒度更小,可降低重传,数据格式比现有的接口文件格式小70%以上,验证了新方法的可行性与有效性.     

Global sensitivity analysis of outputs over rice-growth process in ORYZA model

Dynamic crop models usually have a complex structure and a large number of parameters. Those parameter values usually cannot be directly measured, and they vary with crop cultivars, environmental conditions and managements. Thus, parameter estimation and model calibration are always difficult issues for crop models. Therefore, the quantification of parameter sensitivity and the identification of influential parameters are very important and useful. In this work, late-season rice was simulated with meteorological data in Nanchang, China. Furthermore, we conducted a sensitivity analysis of 20 selected parameters in ORYZA_V3 using the Extended FAST method. We presented the sensitivity results for four model outputs (LAI, WAGT, WST and WSO) at four development stages and the results for yield. Meanwhile, we compared the differences among the sensitivity results for the model outputs simulated in cold, normal and hot years. The uncertainty of output variables derived from parameter variation and weather conditions were also quantified. We found that the development rates, RGRLMN and FLV0.5 had strong effects on all model outputs in all conditions, and parameters WGRMX and SPGF had relative high effects on yield in cold year. Only LAI was sensitive to ASLA. Those influential parameters had unequal effects on different outputs, and they had different effects at four development stages. With the interaction effects of parameter variation and different weather conditions, the uncertainty of model outputs varied significantly. However, the weather conditions had negligible effects on the identification of influential parameters, although they had slight effects on the ranks of the parameters' sensitivity for outputs in the panicle-formation phase and the grain-filling phase, including yield at maturity. The results suggested that the influential parameters should be recalibrated in priority and fine-tuned with higher accuracy during model calibration.     

Nonlinear PI control for variable pitch wind turbine

Wind turbine uses a pitch angle controller to reduce the power captured above the rated wind speed and release the mechanical stress of the drive train. This paper investigates a nonlinear PI (N-PI) based pitch angle controller, by designing an extended-order state and perturbation observer to estimate and compensate unknown time-varying nonlinearities and disturbances. The proposed N-PI does not require the accurate model and uses only one set of PI parameters to provide a global optimal performance under wind speed changes. Simulation verification is based on a simplified two-mass wind turbine model and a detailed aero-elastic wind turbine simulator (FAST), respectively. Simulation results show that the N-PI controller can provide better dynamic performances of power regulation, load stress reduction and actuator usage, comparing with the conventional PI and gain-scheduled PI controller, and better robustness against of model uncertainties than feedback linearization control.     

A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation time-scales

Assessing the time-varying sensitivity of environmental models has become a common approach to understand both the value of different data periods for estimating specific parameters, and as part of a diagnostic analysis of the model structure itself (i.e. whether dominant processes are emerging in the model at the right times and over the appropriate time periods). It is not straightforward to visualize these results though, given that the window size over which the time-varying sensitivity is best integrated generally varies for different parameters. In this short communication we present a new approach to visualizing such time-varying sensitivity across time scales of integration. As a case study, we estimate first order sensitivity indices with the FAST (Fourier Amplitude Sensitivity Test) method for a typical conceptual rainfall–runoff model. The resulting plots can guide data selection for model calibration, support diagnostic model evaluation and help to define the timing and length of spot gauging campaigns in places where long-term calibration data are not yet available.     

GIS技术支持下的滑坡涌浪灾害分析研究

引入水波动力学模型,结合GIS技术,开发形成滑坡涌浪灾害快速评价系统软件(FAST),该软件由前处理模块、涌浪计算模块和后处理模块组成,能够按滑坡失稳类型处理涌浪计算,能够更直观地展示水质点、剖面线、水面和水体的高程变化情况,具有易入手、易操作、高效率和可视化等特点。以三峡库区巫峡茅草坡滑坡为例,应用FAST预测该滑坡失稳后的涌浪效应,验证FAST的可行性和实用性。模拟结果表明茅草坡滑坡失稳会造成最大的涌浪高度为25 m,最大涌浪爬高为12.5 m,滑坡发生444.5 s后以2.5 m的最大爬坡浪到达巫山县城。该技术方法为快速合理确定水库崩滑体涌浪灾害预警范围及风险管理评价等工作提供一套新的分析手段和可视化平台。