我国水-能源-粮食耦合关系及影响因素

为缓解我国水、能源和粮食资源紧张问题，促进资源可持续利用，构建水-能源-粮食系统，利用耦合协调度模型对我国的30个省（自治区、直辖市）进行测算,并利用空间杜宾模型分析主要影响因素。结果表明：2003—2017年，我国能源、粮食评价[JP]指数高于水资源评价指数，系统综合评价指数逐年递增；大部分省份耦合协调度处于初级协调水平且呈现逐年上升的态势，个别省份耦合协调度濒临失调；耦合协调度空间自相关性较强，虽有明显波动，但是呈现逐年加强的态势；影响耦合协调度的主要因素有从业人口数、固定资产投资额、人均生产总值、人口总数、[JP]文盲人口占比、工业污染排放、城镇化。     

New sampling strategies to reduce the effect of autocorrelation on the synthetic T2 chart to monitor bivariate process

In this paper, synthetic T² chart is developed to monitor bivariate process with correlated variables and autocorrelated observations. The proposed chart is a combination of the Hotelling's T² chart and the conforming run length chart. The operation and design of the chart are described when observations are autocorrelated and cross correlated. The first‐order vector autoregressive process VAR (1) is used to model the bivariate data from an autocorrelated process of interest. Using an average run length as performance measure criterion in the VAR (1) model, it is observed that autocorrelation seriously impact the performance of the synthetic T² chart. To reduce the effect of autocorrelation on the performance of the synthetic T² chart, the skip and mixed sampling strategies are implemented to form rational subgroups in the construction of synthetic T² chart. The average run length performance of the synthetic T² chart implementing these strategies is compared with that of the standard strategy of formation of rational subgroups. It is observed that implementing skip and mixed sampling strategies within rational subgroup improves the performance of the synthetic T² chart.     

Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation

Traditional regression techniques such as ordinary least squares (OLS) can hide important local variations in the model parameters, and are not able to deal with spatial autocorrelations existing in the variables. A recently developed technique, geographically weighted regression (GWR), is used to examine the relationships between land use and water quality in eastern Massachusetts, USA. GWR models make great improvements of model performance over OLS models, which is proved by F-test and comparisons of model R² and corrected Akaike Information Criterion (AIC_c) from both GWR and OLS. GWR models also improve the reliabilities of the relationships by reducing spatial autocorrelations. The application of GWR models finds that the relationships between land use and water quality are not constant over space but show great spatial non-stationarity. GWR models are able to reveal the information previously ignored by OLS models on the local causes of water pollution, and so improve the model ability to explain local situation of water quality. The results of this study suggest that GWR technique has the potential to serve as a useful tool for environmental research and management at watershed, regional, national and even global scales.     

Sub-micrometer particulate air pollution and cardiovascular mortality in Beijing, China

Background

While the link between particulate matter and cardiovascular mortality is well established, it is not fully investigated and understood which properties of the aerosol might be responsible for the health effects, especially in polluted mega-city areas.

Objectives

Our goal was to explore the association between daily cardiovascular mortality and different particle metrics in the sub-micrometer range in Beijing, China.

Methods

We obtained daily counts of cause-specific cardiovascular deaths in the Beijing urban area for the period March 2004 to August 2005. Concurrently, continuous measurements of particle number size distributions were performed. Particle number concentrations (NC) between 0.003 μm and 0.8 μm were converted to particle mass and surface area concentrations assuming spherical particles. Semi-parametric Poisson regression models adjusting for trend, seasonality, day of the week, and meteorology were used to estimate immediate, delayed and cumulative particle effects. Additionally, effect modification by air mass origin was investigated.

Results

We observed associations between daily cardiovascular mortality and particle NC for a 2-days delay. Moreover, nearly all particle metrics showed 2-days delayed associations with ischemic heart disease mortality. The strongest association was found for particle NC in the size range 0.03-0.1 μm (7.1% increase in daily mortality with a 95%-confidence interval of 2.9%-11.5%, per an increase of 6250 particles/cm³). Results for surface and mass concentrations with a lag of two days indicated effect modification by air mass origin, whereas effects of particle NC were not modified.

Conclusions

Results show an elevated risk of cardiovascular mortality in Beijing from short-term exposure to particulate air pollution in the sub-micrometer range. Results also indicate that locally produced smaller particles and regionally transported particles may exhibit different effects in Beijing.     

基于虚拟仪器的频率测量研究

阐述了一种基于虚拟仪器的正弦信号频率测量设计方案.结合相关原理和求导原理,提出了一种新的频率测量算法,该设计方案已成功应用于某型导弹的自动测试系统.实践证明,这项技术能提高测量精度,同时也能降低费用.     

基于EMD-小波包的随钻测量信号去噪方法

为了降低随钻测量过程中噪声对信号的影响,针对经验模态分解(EMD)去噪方法粗糙和小波包去噪方法缺乏自适应的问题,提出了一种基于EMD-小波包的随钻测量信号去噪方法。利用EMD分解自适应的特点将随钻测量信号分解成几个IMF分量;根据信号自相关函数的特性找出主要含噪的IMF分量,再利用小波包阈值去噪方法将含噪IMF分量中的噪声去除;将去噪后的IMF分量和剩余的分量重构,得到去噪后的随钻测量信号。为验证方法的有效性,进行了随钻测量实验并用该方法对采集的信号进行处理。结果表明该方法能够很好地去除随钻测量信号中噪声的干扰,提高信号的信噪比。     

Six Sigma experience as a stochastic process

This study empirically investigates the states of Six Sigma from a stochastic point of view. By the means of an advanced survey, 97 respondents are asked to rate the effect of Six Sigma on different performance categories, the cost of implementing Six Sigma, the level of enthusiasm and expectations from Six Sigma over 20 years. The autocorrelation and cross-correlation functions of these processes are analyzed to investigate the stages of Six Sigma. Consequently, new concepts namely steady state of Six Sigma and Six Sigma experience functions are introduced which shed light on the life cycle of Six Sigma within the companies.     

Parameter estimation of MC-CDMA signals based on modified cyclic autocorrelation

An algorithm, based on frequency domain accumulation for the cyclic autocorrelation of MC-CDMA signals, is proposed to estimate the signal parameters: useful data duration, symbol duration, and chip duration in multipath fading channel. Firstly the cycle autocorrelation of received MC-CDMA signals is computed, then frequency domain accumulation is used to reduce the influence of noise. At last, through interval measurement between the peak pulses in different slices, parameters mentioned above can be estimated. Meanwhile, for the estimation of symbol duration, a novel accumulative average method is developed: by averaging amplitudes of the spectral lines in each column parallel to the time-delay axis in the three-dimensional diagram, the symbol duration can be obtained. The cyclic autocorrelation expression of MC-CDMA signal has been derived, and simulation results show that the proposed algorithm is effective and works well under the condition of low signal-to-noise ratio (SNR).     

Nonlinear adaptive noise-induced algorithm and its application in penetration signal

A nonlinear adaptive noise induced algorithm with nonlinear weights was proposed to extract rigid body deceleration during penetration events; it has 3rd-order nonlinear weight, which ensures deceleration curve is smooth everywhere (not only continuous) and avoids sharp points (crucial for targets detection). In addition, an autocorrelation algorithm was improved by applying moving window method to be compared with the proposed nonlinear adaptive algorithm. By calculating penetration depth and Power Spectrum Density (PSD) of 4 deceleration time series, we show that the nonlinear adaptive algorithm more effectively reduces noise in deceleration for striking velocities between 538 and 800 m/s compared with Adaptive Paйta Criterion, moving window autocorrelation and wavelet algorithms. It is further shown that the proposed adaptive algorithm is of the same order as the other 3 methods in terms of computational complexity.     

基于压缩感知的分步合作频谱感知方法

在研究自相关矩阵检测理论的基础上,提出一种采用压缩感知的分步合作频谱感知方法.通过压缩感知将海量采样数据量减小,提取能够有效代表原信号的采样点;通过随机选择参与运算的认知用户及其数量进行合作检测,并反馈有益信息,减少了传统合作检测所有认知用户参与检测带来的运算复杂度.仿真表明该方法提高了频谱检测率,缩短了频谱检测时长,...