基于高通量监测数据的PMF源解析数据输入量研究

为探究数据输入量的变化对源解析结果的影响,以上海某工业区的大气重金属高通量监测数据为例,按不同数据量将监测数据分别输入至正定矩阵因子分解(Positive Matrix Factorization, PMF)模型中,通过考察模型中Q理论值(Qtheo)与Q计算值(Qtrue)的接近程度、源分类以及源贡献与研究区污染源分布特点的吻合情况,分析数据输入量对源解析结果的影响。结果显示:该区域大气重金属污染受工业生产主导(64.44%),其次是扬尘(19.60%)和交通运输(15.96%)。通过对数据量的考察,发现输入量为60~120时能够得出研究区域的污染源数量与贡献率,但考虑到测试成本、获取数据的时间,认为输入量为60~80时,也能得出合理的源解析结果。短期高通量的分钟级数据集,有益于PMF模型输出高精密度、高时效性的源解析结果,是解决应急污染监控的最佳手段。

煤矿聚集区土壤重金属污染风险及PMF-HHR模型溯源

为弄清采煤作用下矿区土壤重金属污染来源和生态健康风险,以内蒙古伊金霍洛旗典型煤矿为研究区,于2021~2023年采集矿区周围表层土壤样品共180份,测试了8种重金属元素的含量,利用正定矩阵因子分解模型与健康风险(PMF-HHR)评价相耦合,定量评估不同来源重金属对人体健康的风险.研究区Cd和Hg的污染较为严重,露天矿Cd的含量是内蒙古背景值的18倍,井工矿Hg含量是内蒙古背景值的4.8倍.露天矿Cd、Cr和Ni的含量高于井工矿.污染严重的区域主要分布在煤矿开采区和公路附近.矿区周围土壤属于中度生态危害,林地、草地和农田的风险等级较高.PMF源解析得到5个污染源:工业排放与大气沉降(18.0%)、交通运输(12.6%)、农业活动与燃煤(14.8%)、矿山开采(17.0%)、自然母质源(37.6%).成人和儿童均存在一定致癌风险,且儿童更高;癌症风险的主要来源为工业排放和交通运输,且主要方式为经手口摄入.PMF-HHR模型可有效定量评价不同来源的健康风险.

基于PMF模型的矿区土壤重金属来源解析

通过在某矿区采集农田土壤和菠菜样品,分析其中重金属含量,基于相关性分析和PMF模型对该矿区农田土壤重金属来源进行解析。结果表明,研究区土壤中Cd、As、Zn、Cr和Cu元素明显富集,分别是当地土壤背景值的5.7倍、4.4倍、2.4倍、1.5倍和1.3倍;相关性分析结果显示,研究区内Cu、Zn、As、Cd元素存在一定的相关性,可能具有同一污染源;PMF模型结果说明,研究区土壤中重金属主要来源分别为工业污染源、自然母质源、交通污染源和农业污染源,其对当地土壤重金属污染贡献率分别为39.8%、22.8%、21.6%和15.8%。

基于PMF-FFT的北斗B2a信号捕获算法研究

为了对北斗三号B2a信号进行快速且准确地捕获,提出了一种基于PMF-FFT的捕获算法。首先,通过建立数学模型,分析了PMF-FFT算法的特性;然后,对PMF分段长度、FFT运算点数、相关积分时间、非相干积分次数和判决门限等参数的选取进行了详细地分析;最后,利用Matlab工具对PMF-FFT捕获算法性能进行了仿真与分析,验证了算法的有效性,为算法的实现解决了首要问题。

PMF+双门限Tong检测的北斗B1I信号捕获算法

针对传统Tong检测算法检测时间长、漏警概率大的问题,提出将PMF+双门限Tong检测算法应用于北斗B1I信号的捕获中。首先,将射频前端模块处理得到的中频信号下变频至基带,对基带信号进行降速等预处理;然后,利用部分匹配滤波(PMF)算法计算相关值,通过PMF算法将数据信号分成几个均等片段,对每一段信号都分别计算相关值,会出现三组具有同相位的相关结果;最后,利用双门限Tong检测算法对PMF算法计算得到的相关结果检测,达到捕获B1I信号的目的。仿真结果表明,利用该算法捕获北斗B1I信号,有效提高了捕获时的检测概率并缩短了检测时间,提高了捕获B1I信号的速度。

兰州地铁站灰尘重金属的PMF模型源解析与累积特征研究

通过在兰州地铁站站台布设11个调查样点,监测分析地表灰尘元素As、Cr、Cu、Mn、Ni和Pb累积特征及来源。结果表明:兰州地铁站站外和站内地表灰尘中As、Cr、Cu、Pb的平均值均高于甘肃省和兰州市土壤背景值;站内地表灰尘以Cr、Cu、Ni和Pb为主要污染物,站外污染以Cu和Pb为主,且站内地表灰尘重金属综合污染整体高于站外;源解析结果显示,站内地表灰尘重金属主要来源于交通源和人为污染源的混合源、工业源、交通源和大气沉降源的混合源,贡献率分别为30.0%、33.6%、36.4%,站外主要来源为交通源和大气沉降源的混合源、燃煤源和交通源的混合源、交通源,贡献率分别为38.7%、29.6%、31.7%。

同位素结合PMF模型解析珠三角PM_(2.5)来源研究

二次气溶胶是大气细颗粒物(PM_(2.5))中最多的组分,客观识别其来源是PM_(2.5)源解析的难点,也是精确控制PM_(2.5)污染来源的关键。本研究在广东省鹤山超级大气监测站采集了76个PM_(2.5)样品,分析了其碳质组分(元素碳和有机碳)、水溶性离子(SO_(4)^(2-)、NH4^(+)、K^(+)、Ca^(2+)、Cl^(-)、Na^(+)、Mg^(2+)、NO_(2)^(-)和NO_(3)^(-))和金属元素(Fe、Cr、Cu、Mn、Ni、Pb、V、Zn、Cd、As)共20种化学组分,测定了7个PM_(2.5)代表样品的总碳(TC)放射性碳(^(14)C)。基于PM_(2.5)化学组分的PMF模型结果显示,生物质燃烧、海盐、土壤扬尘、轮船排放、工业过程、燃煤源和二次气溶胶分别贡献PM_(2.5)的18.98%、4.32%、13.85%、7.61%、14.08%、12.72%和30.03%。基于已发表的δ^(15)N(NO_(3)^(-))、δ^(18)O(NO_(3)^(-))结果,利用贝叶斯模型定量解析了NO_(3)^(-)的来源,并应用线型回归将PMF模型解析的二次气溶胶源再分配给一次颗粒源,结果显示,燃煤源(36.50%)、生物燃料源(29.35%)、土壤扬尘+海盐源(21.00%)和移动源(13.15%)是珠三角PM_(2.5)的主要来源。二次分配后PMF模型结果中TC的非化石源贡献与基于^(14)C结果的相对误差小于9%,说明PMF模型能很好地捕获PM_(2.5)主要贡献源,并且较准确地源解析珠三角的PM_(2.5),研究结果可为区域污染防治政策措施的制定提供科学依据。

基于PMF模型的吉木萨尔县土壤重金属空间分布特征与来源解析

土壤重金属污染对区域土壤环境和人类健康构成严重威胁,因此土壤重金属污染评价和预警在土壤资源保护和区域可持续发展中至关重要.共采集了48个土壤样本,并测定了其中六种重金属含量,运用潜在生态风险指数法、地累积指数法和克里金插值分别对该区域土壤中Zn、Cu、Cr、Pb、Hg和As六种重金属元素的污染状况和空间分布特征进行了分析,并利用PMF模型对土壤重金属来源进行解析.1)研究区土壤Cu、Pb和As含量均超出中国新疆土壤背景值和全国土壤背景值,Zn和Cr的含量未超过这两种标准.2)污染评价结果显示:土壤未受到Zn、Cu和Cr的污染,Hg处于轻-中污染,Pb和As处于中等和中-强污染.结合潜在生态风险指数分析可知,Zn、Cu、Cr和Pb为低风险状态,Hg和As为中风险状态.3)从重金属元素空间分布来看,Zn元素的空间分布呈中心区域低、四周高的格局;Cu和Hg元素的分布规律存在一定的相似性,均为从西北向东南递减的变化趋势;Cr元素的空间分布呈现从西向东递减的趋势;Pb元素在研究区的分布较均匀.4)PMF模型结果显示:煤炭开采、煤化工和一系列煤电等人类活动容易引起土壤中Hg和As的积累;Cr和Pb主要来自汽车排放;Zn和Cu主要与土壤母质等自然因素密切相关;煤矿开采和工业活动对土壤重金属的贡献最高.吉木萨尔县土壤受到Hg、Pb和As不同程度污染,且该区域污染来源主要为工业生产和汽车尾气.

一种基于Filter Faster R-CNN的数字PCR液滴检测技术

目的研究液滴数字聚合酶链式反应(ddPCR)液滴检测技术,去除图像中灰尘、气泡、芯片表面的划痕以及微小凹陷等因素产生的异常点对结果的影响,实现高通量、稳定和准确的ddPCR液滴的自动检测。方法提出Filter Faster R-CNN ddPCR液滴检测模型。使用Faster R-CNN生成液滴预测框,之后使用异常点过滤模块(Filter)去除阳性液滴预测框中的异常点。以诺如病毒片段的质粒为模板进行ddPCR实验,建立一个ddPCR数据集,用于模型的训练(2462例,约占78.56%)和测试(672例,约占21.44%)。对异常点过滤模块的3个过滤支路在验证集上进行消融实验,通过与其他ddPCR液滴检测模型进行比较的对比实验以及进行ddPCR的绝对定量实验。结果在少尘和多尘的环境中,Filter Faster R-CNN阳性液滴准确率为98.23%和88.35%,综合指标F1分数分别达到了99.15%和99.14%,高于其他相比较的模型。独立样本T检验的结果证明,相比未添加过滤模块的网络,添加过滤模块后能够显著提示模型在多尘环境中的阳性准确率。在ddPCR绝对定量实验中,将商业化流式检测设备的结果作为标准浓度,绘制了回归线。结果显示,回归线斜率为1.0005,截距为-0.025,决定系数达到了0.9997,二者结果高度一致。结论本文提出了一种基于Filter Faster R-CNN的ddPCR液滴检测技术,为在多种环境条件下的ddPCR实验提供了鲁棒的液滴检测方法。

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Estimating the subsolar magnetopause position from soft X-ray images using a low-pass image filter

The Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)missions will image the Earth’s dayside magneto pause and cusps in soft X-rays after their respective launches in the near future,to specify glo bal magnetic reconnection modes for varying solar wind conditions.To suppo rt the success of these scientific missions,it is critical to develop techniques that extract the magnetopause locations from the observed soft X-ray images.In this research,we introduce a new geometric equation that calculates the subsolar magnetopause position(RS)from a satellite position,the look direction of the instrument,and the angle at which the X-ray emission is maximized.Two assumptions are used in this method:(1)The look direction where soft X-ray emissions are maximized lies tangent to the magnetopause,and(2)the magnetopause surface near the subsolar point is almost spherical and thus RSis nea rly equal to the radius of the magneto pause curvature.We create synthetic soft X-ray images by using the Open Geospace General Circulation Model(OpenGGCM)global magnetohydrodynamic model,the galactic background,the instrument point spread function,and Poisson noise.We then apply the fast Fourier transform and Gaussian low-pass filte rs to the synthetic images to re move noise and obtain accurate look angles for the soft X-ray pea ks.From the filte red images,we calculate RS and its accuracy for different LEXI locations,look directions,and solar wind densities by using the OpenGGCM subsolar magnetopause location as ground truth.Our method estimates RS with an accuracy of<0.3 RE when the solar wind density exceeds>10 cm-3.The accuracy improves for greater solar wind densities and during southward interplanetary magnetic fields.The method ca ptures the magnetopause motion during southwa rd interplaneta ry magnetic field turnings.Consequently,the technique will enable quantitative analysis of the magnetopause motion and help reveal the dayside reconnection modes for dynamic solar wind conditions.This technique will suppo rt the LEXI and SMILE missions in achieving their scientific o bjectives.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

基于PMF模型和稳定同位素的地下水污染源解析

识别地下水污染来源、理清污染责任是地下水环境保护工作的重要基础。该研究以珠三角地区某化工园区及其相邻的垃圾填埋场为调查对象,分析了研究区地下水污染分布特征,利用正定矩阵因子分析(PMF)模型和稳定同位素技术,开展了地下水污染溯源解析。结果显示:研究区地下水中pH、NH_(4)^(+)、Fe、Mn均存在不同程度超标,且具有空间异质性,超标最严重点位靠近化工园区与垃圾填埋场交界处,NH_(4)^(+)、Fe、Mn浓度分别为68.20、28.10、15.80 mg/L,超标倍数分别为44.5倍、13.1倍、9.5倍;PMF模型初步揭示了研究区地下水水质影响因子,分别为水文地球化学演化(43.1%)、人为污染作用(27.9%)、原生地质作用(15.7%)。氨氮的氮稳定同位素分析结果进一步明确了人为污染来源,垃圾渗滤液δ^(15)N-NH_(4)^(+)值位于地下水特征值区间且与地下水氨氮超标点位δ15N-NH_(4)^(+)值接近,表明垃圾填埋场是地下水氨氮污染的主要贡献源。通过结合PMF模型与稳定同位素技术,有效识别了地下水污染来源及其贡献率,明确了不同污染源之间的污染责任,为地下水污染防治和环境管理提供科学支撑。

基于空间插值与PMF模型的国道沿线土壤重金属源解析:以107国道岳阳段为例

为了评估国道交通对两侧土壤的影响,查明其重金属污染特征和来源,选取107国道岳阳县段作为研究区域。在该区域两侧各1 km范围内采集了246个表层土壤样品,并测定了Pb、As、Cd、Cr、Cu、Ni、Zn、Hg这8种重金属元素的含量。通过对比分析国道两侧土壤中重金属污染情况和元素含量差异,结合克里金空间插值法和PMF模型,进一步揭示这些重金属的潜在来源。结果表明:(1)107国道岳阳县段两侧土壤属强酸性土壤,重金属之间的差异反映了土地利用类型和人类活动对土壤重金属分布的显著影响。(2)单因子污染指数均值显示Cr、Ni、Zn、Cd无污染,Cu、As、Hg、Pb为轻度污染,内梅罗综合污染指数表明研究区土壤整体轻度污染。(3)空间插值分析显示,As、Cr、Cu、Ni在空间分布上高度重叠;Pb和Zn在空间上分布在农田耕地区域;Cd元素的空间分布异常区域主要为新开镇;Hg元素的空间分布特征显示林地为低值区域,说明了自然地质背景与土地利用类型对土壤重金属的复杂相互作用。(4)PMF源解析识别了4种污染源,分别为交通源、农业源、自然源、大气源,其中交通源和农业源合计占比80.3%,是影响107国道岳阳县段两侧土壤中重金属元素的主要因素,说明107国道两侧土壤受交通运输和人类活动影响较明显。综上所述,空间插值分析和PMF模型的结果相互之间验证效果较好,这一综合方法对重金属潜在来源的成分识别较为理想。

Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security.Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

Recursive Filtering for Stochastic Systems With Filter-and-Forward Successive Relays

In this paper,the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay(FFSR)networks.An FFSR is located between the sensor and the remote filter to forward the measurement.In the successive relay,two cooperative relay nodes are adopted to forward the signals alternatively,thereby existing switching characteristics and inter-relay interferences(IRI).Since the filter-and-forward scheme is employed,the signal received by the relay is retransmitted after it passes through a linear filter.The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays.First,a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR.Then,novel filter structures with switching parameters are constructed for both FFSR and stochastic systems.With the help of the inductive method,filtering error covariances are presented in the form of coupled difference equations.Next,the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances.Moreover,the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance.Finally,the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.

Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.Moreover,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.

Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems:Tackling Multiplicative Noises and Missing Measurements

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission scheme.The multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable.Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense.To this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented system.Subsequently,we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters.With the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain parameters.Finally,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.