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.

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。

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.

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。

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.

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.

Correction of microwave pulse reflection by digital filters in superconducting quantum circuits

Reducing the control error is vital for high-fidelity digital and analog quantum operations.In superconducting circuits,one disagreeable error arises from the reflection of microwave signals due to impedance mismatch in the control chain.Here,we demonstrate a reflection cancelation method when considering that there are two reflection nodes on the control line.We propose to generate the pre-distortion pulse by passing the envelopes of the microwave signal through digital filters,which enables real-time reflection correction when integrated into the field-programmable gate array(FPGA).We achieve a reduction of single-qubit gate infidelity from 0.67%to 0.11%after eliminating microwave reflection.Real-time correction of microwave reflection paves the way for precise control and manipulation of the qubit state and would ultimately enhance the performance of algorithms and simulations executed on quantum processors.

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.

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.

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.

Bird species present in urban parks are more colorful than urban avoiders:A test in the Argentinian Pampas

Bird plumage color has been assessed as a possible trait driving the presence of bird species in urban areas.Although some species can see the ultraviolet(UV) spectrum,the mentioned studies did not take into account UV reflectance when characterizing bird plumage.This study aimed to use a recent database of the colorfulness in passerines that incorporated the UV spectrum to compare bird colorfulness and other traits between urban parks and rural areas in Central-East Argentina.Birds in urban parks were surveyed in 51 parks in 6 cities during breeding and non-breeding seasons.A list of Passeriformes species from parks was created,and a list of urban avoider species was created from the bibliography.Species traits were body mass,clutch size,migratory status,nesting site,diet and habitat breadth,and plumage colorfulness.A total of 85 species were detected in the regional pool,of which 30 species were detected in urban parks.Bird species present in urban parks were more colorful than bird species only present in rural areas.In addition,bird presence in urban parks was positively related to their regional frequency and diet breadth.Moreover,urban presence was related to nesting on trees and buildings,whereas species not present in urban parks nested on the ground.The results obtained showed that bird color is significantly associated with presence of bird species in urban parks.

3D robust anisotropic diffusion filtering algorithm for sparse view neutron computed tomography 3D image reconstruction

The most critical part of a neutron computed tomography(NCT) system is the image processing algorithm,which directly affects the quality and speed of the reconstructed images.Various types of noise in the system can degrade the quality of the reconstructed images.Therefore,to improve the quality of the reconstructed images of NCT systems,efficient image processing algorithms must be used.The anisotropic diffusion filtering(ADF) algorithm can not only effectively suppress the noise in the projection data,but also preserve the image edge structure information by reducing the diffusion at the image edges.Therefore,we propose the application of the ADF algorithm for NCT image reconstruction.To compare the performance of different algorithms in NCT systems,we reconstructed images using the ordered subset simultaneous algebraic reconstruction technique(OS-SART) algorithm with different regular terms as image processing algorithms.In the iterative reconstruction,we selected two image processing algorithms,the Total Variation and split Bregman solved total variation algorithms,for comparison with the performance of the ADF algorithm.Additionally,the filtered back-projection algorithm was used for comparison with an iterative algorithm.By reconstructing the projection data of the numerical and clock models,we compared and analyzed the effects of each algorithm applied in the NCT system.Based on the reconstruction results,OS-SART-ADF outperformed the other algorithms in terms of denoising,preserving the edge structure,and suppressing artifacts.For example,when the 3D Shepp–Logan was reconstructed at 25 views,the root mean square error of OS-SART-ADF was the smallest among the four iterative algorithms,at only 0.0292.The universal quality index,mean structural similarity,and correlation coefficient of the reconstructed image were the largest among all algorithms,with values of 0.9877,0.9878,and 0.9887,respectively.

A multi-scale second-order autoregressive recursive filter approach for the sea ice concentration analysis

To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.

CRBFT:A Byzantine Fault-Tolerant Consensus Protocol Based on Collaborative Filtering Recommendation for Blockchains

Blockchain has been widely used in finance,the Internet of Things(IoT),supply chains,and other scenarios as a revolutionary technology.Consensus protocol plays a vital role in blockchain,which helps all participants to maintain the storage state consistently.However,with the improvement of network environment complexity and system scale,blockchain development is limited by the performance,security,and scalability of the consensus protocol.To address this problem,this paper introduces the collaborative filtering mechanism commonly used in the recommendation system into the Practical Byzantine Fault Tolerance(PBFT)and proposes a Byzantine fault-tolerant(BFT)consensus protocol based on collaborative filtering recommendation(CRBFT).Specifically,an improved collaborative filtering recommendation method is designed to use the similarity between a node’s recommendation opinions and those of the recommender as a basis for determining whether to adopt the recommendation opinions.This can amplify the recommendation voice of good nodes,weaken the impact of cunningmalicious nodes on the trust value calculation,andmake the calculated resultsmore accurate.In addition,the nodes are given voting power according to their trust value,and a weight randomelection algorithm is designed and implemented to reduce the risk of attack.The experimental results show that CRBFT can effectively eliminate various malicious nodes and improve the performance of blockchain systems in complex network environments,and the feasibility of CRBFT is also proven by theoretical analysis.

Automatic velocity picking based on optimal key points tracking algorithm

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

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.