Methodology for Evaluating and Monitoring of Waterworks Performance Efficiency—Part 2: Test of Filterability

The effectiveness of deep-bed filtration with respect to suspension formed during the preceding processes is evaluated by the test of filterability. The properties and concentration of the suspension being filtered are affected by the efficiency of the preceding aggregation and separation processes. The basic principles of the test of filterability are based on the mechanistic model of filtration. The equations in the mathematical model of the mechanistic conception of filtration are derived from the theory of filtration. The arrangement of the pilot filtration plant for the determination of filterability of flocculent suspension is presented in this paper. The test of filterability is carried out with a thin-layer filter element. The design of a filter element arrangement and its installation are also disclosed in this paper. The inter-dependence of the coefficient of filtration efficiency on the specific volume of intercepted suspensions, filter media grain sizes and different filtration velocities are graphically presented. In addition, the effect of the filter bed clogging resulting from the properties of different suspensions on the head loss generated, the length of filtration cycle and quality of filtrate produced are also shown in this paper.

Influence of coagulation mechanisms and floc formation on filterability

Minimizing particles in water is a key goal for improving drinking water quality and safety.The media filtration process,as the last step of the solid–liquid separation process,is largely influenced by the characteristics of flocs,which are formed and controlled within the coagulation process.In a laboratory-based study,the impacts of the physical characteristics of flocs formed using aluminum sulfate on the filtration treatment of two comparative water samples were investigated using a photometric dispersion analyzer and a filterability apparatus.In general,the optimum dosage for maximizing filterability was higher than that for minimizing turbidity under neutral p H conditions.For a monomeric aluminum-based coagulant,the charge neutralization mechanism produced better floc characteristics,including floc growth speed and size,than the sweep flocculation mechanism.In addition,the charge neutralization mechanism showed better performance compared to sweep flocculation in terms of DOC removal and floc filterability improvement for both waters,and showed superiority in turbidity removal only when the raw water had high turbidity.For the different mechanisms,the ways that floc characteristics impacted on floc filterability also differed.The low variation in floc size distribution obtained under the charge neutralization mechanism resulted in the flocs being amenable to removal by filtration processes.For the sweep flocculation mechanism,increasing the floc size improved the settling ability of flocs,resulting in higher filter efficiency.

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.

二十辊轧机辊系耦合振动特性及振动控制

精密极薄带因具备高精度、耐腐蚀、优异表面光洁度等性能,被广泛应用于微制造、微电子等高技术领域。二十辊轧机辊系的耦合振动对极薄带的生产有着重要的影响。考虑到轧制工艺和润滑状态等变化形成非线性轧制界面,建立了一种二十辊轧机辊系耦合动力学模型。应用多尺度法求解主共振和内共振的幅频特性方程,分析了不同参数对幅频特性曲线的影响。基于奇异性理论探究了不同参数与系统分岔的关系。同时,根据李雅普诺夫一次近似稳定性判据,结合系统动相平面相轨迹图,对系统稳定性进行判断分析。然后,基于Washout filter控制器原理设计了一种状态反馈控制器,通过对系统进行状态空间表达式变换,分析了控制增益对幅频特性的影响。利用时域图、相位图、频谱图以及Poincaré截面图进行仿真对比,验证了所设计控制器的可行性和有效性,为二十辊轧机振动及其控制的研究提供了一定的理论指导。

基于改进轻量化网络MobileViT的苹果叶片病虫害识别方法

针对苹果叶片病害识别准确率低以及现有模型难以适应真实复杂场景等问题,提出一种改进的轻量化网络——MobileViT_filter_FCN,以提高对苹果叶片病害的识别准确率,并使得模型可以适应户外的复杂光照及遮挡环境。首先收集5类常见苹果叶片病害(如落叶病、褐斑病等)的图像样本,并利用多种数据增强技术对样本数据进行预处理(如水平翻转、垂直翻转等),以增加样本数据的多样性并提高模型的泛化能力;接着利用傅里叶变换技术设计一个可学习的滤波器层Filter layer,替换原始MobileViT模型中的多头注意力结构,以降低图片中的噪声影响并提高模型性能;最后,在修改后的MobileViT模型基础上,利用深度卷积层和残差结构设计一种FCN结构,结合该结构增强模型对病害图像的特征学习能力,进一步提高模型性能。试验结果表明,改进后的MobileViT_filter模型对苹果叶片病害的平均识别准确率达到97.73%,较原模型提高0.95百分点;在该基础上加入FCN结构后,平均识别准确率达到98.03%,较原模型提高1.25百分点,同时参数量减少2.6 M。

In Situ Atomic Reconstruction Engineering Modulating Graphene-Like MXene-Based Multifunctional Electromagnetic Devices Covering Multi-Spectrum

With the diversified development of big data,detection and precision guidance technologies,electromagnetic(EM)functional materials and devices serving multiple spectrums have become a hot topic.Exploring the multispectral response of materials is a challenging and meaningful scientific question.In this study,MXene/TiO_(2)hybrids with tunable conduction loss and polarization relaxation are fabricated by in situ atomic reconstruction engineering.More importantly,MXene/TiO_(2)hybrids exhibit adjustable spectral responses in the GHz,infrared and visible spectrums,and several EM devices are constructed based on this.An antenna array provides excellent EM energy harvesting in multiple microwave bands,with|S11|up to−63.2 dB,and can be tuned by the degree of bending.An ultra-wideband bandpass filter realizes a passband of about 5.4 GHz and effectively suppresses the transmission of EM signals in the stopband.An infrared stealth device has an emissivity of less than 0.2 in the infrared spectrum at wavelengths of 6-14μm.This work can provide new inspiration for the design and development of multifunctional,multi-spectrum EM devices.

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.

Research on the navigation method of large-scale differential tail-control improvised guided munitions based on rotational speed constraints

In the process of launching guided projectile under the conventional system, it is difficult to effectively obtain the precise navigation parameters of the projectile in the high dynamic environment. Aiming at this problem, this paper describes a new system of guided ammunition based on tail spin reduction. After analyzing the mechanism of the ammunition's tail spin reduction, a navigation method of large scale difference tail control simple guided ammunition based on speed constraint is proposed. In this method,the corresponding navigation constraints can be carried out by combining the rotation speed state of the ammunition itself, and the optimal solution of navigation parameters during the flight of the missile can be obtained by Extended Kalman Filter(EKF). Finally, the performance of the proposed method was verified by the simulation environment, and the hardware-in-the-loop simulation test and flight test were carried out to verify the performance of the method in the real environment. The experimental results show that the proposed method can achieve the optimal estimation of navigation parameters for simple guided ammunition with large-scale difference tail control. Under the conditions of simulation test and hardware-in-loop simulation test, the position and velocity errors calculated by the method in this paper converged. Under the condition of flight test, the spatial average error calculated by the method described in this paper is 6.17 m, and the spatial error of the final landing point is 3.50 m.Through this method, the accurate acquisition of navigation parameters in the process of projectile launching is effectively realized.

Analysis of gravity wave activity during stratospheric sudden warmings in the northern hemisphere

Due to the significant changes they bring to high latitude stratospheric temperature and wind,stratospheric sudden warmings(SSWs)can have an impact on the propagation and energy distribution of gravity waves(GWs).The variation characteristics of GWs during SSWs have always been an important issue.Using temperature data from January to March in 2014−2016,provided by the Constellation Observing System for Meteorology,Ionosphere and Climate(COSMIC)mission,we have analyzed global GW activity at 15−40 km in the Northern Hemisphere during SSW events.During the SSWs that we studied,the stratospheric temperature rose in one or two longitudinal regions in the Northern Hemisphere;the areas affected extended to the east of 90°W.During these SSWs,the potential energy density(E_(p)of GWs expanded and covered a larger range of longitude and altitude,exhibiting an eastward and downward extension.The E_(p)usually increased,while partially filtered by the eastward zonal winds.When zonal winds weakened or turned westward,E_(p)began to strengthen.After SSWs,the E_(p)usually decreased.These observations can serve as a reference for analyzing the interaction mechanism between SSWs and GWs in future work.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.

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.

Stepped-up development of accelerator mass spectrometry method for the detection of ^(60)Fe with the HI-13 tandem accelerator

The Moon provides a unique environment for investigating nearby astrophysical events such as supernovae.Lunar samples retain valuable information from these events,via detectable long-lived“fingerprint”radionuclides such as^(60)Fe.In this work,we stepped up the development of an accelerator mass spectrometry(AMS)method for detecting^(60)Fe using the HI-13tandem accelerator at the China Institute of Atomic Energy(CIAE).Since interferences could not be sufficiently removed solely with the existing magnetic systems of the tandem accelerator and the following Q3D magnetic spectrograph,a Wien filter with a maximum voltage of±60 kV and a maximum magnetic field of 0.3 T was installed after the accelerator magnetic systems to lower the detection background for the low abundance nuclide^(60)Fe.A 1μm thick Si_(3)N_(4) foil was installed in front of the Q3D as an energy degrader.For particle detection,a multi-anode gas ionization chamber was mounted at the center of the focal plane of the spectrograph.Finally,an^(60)Fe sample with an abundance of 1.125×10^(-10)was used to test the new AMS system.These results indicate that^(60)Fe can be clearly distinguished from the isobar^(60)Ni.The sensitivity was assessed to be better than 4.3×10^(-14)based on blank sample measurements lasting 5.8 h,and the sensitivity could,in principle,be expected to be approximately 2.5×10^(-15)when the data were accumulated for 100 h,which is feasible for future lunar sample measurements because the main contaminants were sufficiently separated.

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。

A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.

A design scheme for interference suppression based on CWBIM

An interference suppression design scheme based on conjugate weighted butterfly interleaving mapping(CWBIM)is proposed for inter-carrier interference(ICI)and inter-subband interference(IBI)in the received signals of universal filtered multi-carrier(UFMC)systems.It applies an interleaving mapping operation to subtract the interference coefficients of adjacent terms in ICI and IBI twice,thereby achieving suppression effects similar to the self-cancellation(SC)algorithm while maintaining the original data transmission efficiency.Meanwhile,conjugate and complex weighting operations can effectively suppress the impact of phase rotation errors in high-speed mobile channel environments,thereby further improving the bit error rate(BFR)performance of the system,Moreover,butterfly operation can effectively control the computational complexity of the interleaving mapping process.Theoretical analysis and simulation results show that,compared with the PSC-UFMC algorithm,the CWBIM-UFMC scheme proposed in this paper can effectively suppress ICI and IBI in the received signal without compromising data transmission efficiency and reducing computational complexity,thereby achieving good BER performance of the system.

基于ASIT-UKF算法的锂电池荷电状态估计

针对无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计锂电池荷电状态(state of charge,SOC)时精度低、稳定性差、产生的sigma点过多导致计算难度大等不足,提出一种基于自适应球形不敏变换方式的无迹卡尔曼滤波(unscented Kalman filter based on adaptive spherical insensitive transformation,ASIT-UKF)算法。该算法通过使用球形不敏变换方式选择权系数以及初始化一元向量对sigma点的产生进行选取。与UKF算法相比,ASIT-UKF算法产生的sigma点减少近50%,使得算法的计算复杂度大大降低。同时,将产生的所有sigma点进行单位球形面上的归一化处理,提高了数值的稳定性。考虑到实际运行中锂电池系统噪声干扰带来的不确定性,加入Sage-Husa自适应滤波器对不确定性噪声的干扰进行实时更新和修正,以达到提高在线锂电池SOC估计精度的目的。最后,将均方根误差和最大绝对误差计算公式引入到性能估计指标中。实验结果表明,ASIT-UKF算法在准确度、鲁棒性和收敛性方面具有优越的性能。

基于Mean-Shift算法的目标跟踪研究

目标跟踪技术是计算机视觉的关键底层技术。采用一种基于Mean-Shift算法的目标跟踪方法实现运动物体的目标跟踪。将Mean-Shift与Particle Filter算法实现目标跟踪效果进行对比。实验结果显示,Mean-Shift比Particle Filter算法实现目标运动的轨迹更加清晰准确。

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.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.