Multivehicle coverage control for a nonstationary spatiotemporal field

This paper presents a multivehicle sampling algorithm to generate trajectories for nonuniform coverage of a nonstationary spatiotemporal field characterized by spatial and temporal decorrelation scales that vary in space and time, respectively. The sampling algorithm described in this paper uses a nonlinear coordinate transformation that renders the field locally stationary so that existing multivehicle control algorithms can be used to provide uniform coverage. When transformed back to the original coordinates, the sampling trajectories are concentrated in regions of short spatial and temporal decorrelation scales. For fields with coupled spatial statistics, i.e., the spatial decorrelation scales are functions of both spatial dimensions, the coordinate transformation is implemented numerically, whereas for decoupled spatial statistics, the transformation is expressed analytically. We show that the analytical transformation results in vehicle motion that preserves the vehicle sampling speed (which is a measure of vehicle speed scaled by the ratio of the spatial and temporal decorrelation scales), in the original domain; the sampling speed determines the minimum number of vehicles needed to cover a spatiotemporal domain. Theoretical results are illustrated by numerical simulations.     

低空复杂环境下基于采样空间约减的无人机在线航迹规划算法

针对低空复杂环境下障碍物密集且类型多样、带有多通道并存在不确定信息的无人机在线航迹规划问题,为了减少碰撞检测次数,提高航迹搜索速度,降低航迹代价,提出一种基于采样空间约减的无人机在线航迹规划算法. 算法通过引入代价模型,提出约减域逐步构造方法,引导规划树快速有效扩展,改善了基于动态域的快速拓展随机树（Dynamic domain rapidly-exploring random tree,DDRRT） 算法中存在的采样空间过度约减问题. 算法通过密度划分索引的方法逐步构建多棵Kd 树（K-dimensional tree）并采用多近邻节点搜索方法,加快了近邻树节点搜索速度. 仿真实验结果表明,与DDRRT方法相比,该方法在保证对采样空间约减合理性的同时,提高了航迹规划效率和通道内的寻路能力.     

恒温平台石墨炉原子吸收法直接进样测定含有悬浮物废水样品中的Pb、Cd、Cr、Ni、As

对含有悬浮物的废水样品加2%硝酸,用超声波粉碎,加入基体改进剂钯,分别用PE-AA800和上分4510恒温平台石墨炉原子吸收光谱仪,直接进样测定Pb、Cd、Cr、Ni、As,用标准加入法定值。测定结果同消解后的样品测定结果比较,结果一致,操作简便。加标回收率在88.7%~108%,RSD≤9.73%,检出限Pb、Cd、Cr、Ni、As分别为1.23、0.296、0.602、2.67、26.5 pg。     

农药残留分析样品前处理技术新进展

介绍了近年来农药残留样品前处理技术的新进展,并对此进行了评述和展望。     

基于CARS变量筛选的固态发酵pH值近红外检测

为了提高近红外光谱技术快速检测固态发酵过程中pH值的精度和稳定性,提出了采用竞争自适应重加权采样(competitive adaptive reweighted sampling,CARS)法筛选出与pH相关的波长变量建立PLS预测模型,对验证集样本进行预测的方法。并与2种常见的变量筛选法GA-PLS和蒙特卡罗无信息变量消除法(MC-UVE)相比较.实验结果表明:CARS方法能有效筛选有用波长26个变量建立PLS模型,其校正集交互验证均方根误差(RMSECV)以及交互验证相关系数(Rc)分别为0.0368和0.9950;验证集的预测均方根误差(RMSEP)以及预测相关系数(Rp)分别为0.0589和0.9895。     

不平衡数据的集成分类算法综述

集成学习是通过集成多个基分类器共同决策的机器学习技术,通过不同的样本集训练有差异的基分类器,得到的集成分类器可以有效地提高学习效果。在基分类器的训练过程中,可以通过代价敏感技术和数据采样实现不平衡数据的处理。由于集成学习在不平衡数据分类的优势,针对不平衡数据的集成分类算法得到广泛研究。详细分析了不平衡数据集成分类算法的研究现状,比较了现有算法的差异和各自存在的优点及问题,提出和分析了有待进一步研究的问题。     

基于FPGA的CCD低噪声测量系统设计

为了提高电荷耦合器件(CCD)一维尺度无接触测量系统的精度和集成度,设计了以现场可编程门阵列(FPGA)器件为核心的测量系统。对CCD输出信号进行低通滤波和相关双采样技术处理,降低了CCD信号噪声。模拟信号转换为12位数字信号后,传输至FPGA内嵌的FIFO中,提高了系统的集成度和稳定性。使用Verilog HDL语言对驱动时序发生器进行了硬件描述,并通过夫琅禾费单缝衍射实验来验证系统的可靠性和精度,实验表明:该系统稳定,精度达到0.82%。     

新一代水管式沉降仪控制模块的研制

新一代低功耗水管式沉降仪控制模块是基于16单片机MSP430F5436为处理器的低功耗装置.该文详细介绍了其硬件结构和工作原理,分别从电源系统、电磁阀控制电路、控制流程、数据采集等部分阐述了其特点,诠释了水管式沉降仪控制模块在人机交互、工作稳定性等多方面所具备的优势.新一代水管式沉降仪控制模块已在实际工程中得到了成功应用,其稳定性和精确性为大坝监控系统提供了准确的实时数据.     

Downscaling of the flood discharge in a probabilistic framework

Many modeled and observed data are in coarse resolution, which are required to be downscaled. This study develops a probabilistic method to downscale 3-hourly runoff to hourly resolution. Hourly data recorded at the Poldokhtar Stream gauge (Karkheh River basin, Iran) during flood events (2009–2019) are divided into two groups including calibration and validation. Statistical tests including Chi-Square and Kolmogorov–Smirnov test indicate that the Burr distribution is proper distribution functions for rising and falling limbs of the floods’ hydrograph in calibration (2009–2013). A conditional ascending/descending random sampling from the constructed distributions on rising/falling limb is applied to produce hourly runoff. The hourly-downscaled runoff is rescaled based on observation to adjust mean three-hourly data. To evaluate the efficiency of the developed method, statistical measures including root mean square error, Nash–Sutcliffe efficiency, Kolmogorov-Smirnov, and correlation are used to assess the performance of the downscaling method not only in calibration but also in validation (2014–2019). Results show that the hourly downscaled runoff is in close agreement with observations in both calibration and validation periods. In addition, cumulative distribution functions of the downscaled runoff closely follow the observed ones in rising and falling limb in two periods. Although the performance of many statistical downscaling methods decreases in extreme values, the developed model performs well at different quantiles (less and more frequent values). This developed method that can properly downscale other hydroclimatological variables at any time and location is useful to provide high-resolution inputs to drive other models. Furthermore, high-resolution data are required for valid and reliable analysis, risk assessment, and management plans.