Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates

This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)guarantees.Contrary to frequency estimation of a single attribute,the multidimensional aspect demands particular attention to the privacy budget.Besides,when collecting user statistics longitudinally,privacy progressively degrades.Indeed,the“multiple”settings in combination(i.e.,many attributes and several collections throughout time)impose several challenges,for which this paper proposes the first solution for frequency estimates under LDP.To tackle these issues,we extend the analysis of three state-of-the-art LDP protocols(Generalized Randomized Response–GRR,Optimized Unary Encoding–OUE,and Symmetric Unary Encoding–SUE)for both longitudinal and multidimensional data collections.While the known literature uses OUE and SUE for two rounds of sanitization(a.k.a.memoization),i.e.,L-OUE and L-SUE,respectively,we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility(i.e.,L-OSUE).Also,for attributes with small domain sizes,we propose Longitudinal GRR(L-GRR),which provides higher utility than the other protocols based on unary encoding.Last,we also propose a new solution named Adaptive LDP for LOngitudinal and Multidimensional FREquency Estimates(ALLOMFREE),which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol,i.e.,either L-GRR or L-OSUE.As shown in the results,ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.

Genetic and Agronomic Parameter Estimates of Growth, Yield and Related Traits of Maize (Zea mays L.) under Different Rates of Nitrogen Fertilization

This study evaluated the genetic and agronomic parameter estimates of maize under different nitrogen rates. The trial was established at the Njala Agricultural Research Centre experimental site during 2021 and 2022 in a split block design with three maize varieties (IWCD2, 2009EVDT, and DMR-ESR-Yellow) and seven nitrogen (0, 30, 60, 90, 120, 150 and 180 kg∙N∙ha−1) rates. Findings showed that cob diameter and anthesis silking time (ASI) had intermediate heritability, ASI had high genetic advance, ASI and grain yield had high genotypic coefficient of variation (GCV), while traits with high phenotypic coefficient of variation (PCV) were plant height, ASI, grain yield, number of kernel per cob, number of kernel rows, ear length, and ear height. The PCV values were higher than GCV, indicating the influence of the environment in the studied traits. Nitrogen rates and variety significantly (p < 0.05) influenced grain yield production. Mean grain yields and economic parameter estimates increased with increasing nitrogen rates, with the 30 and 180 kg∙N∙ha−1 plots exhibiting the lowest and highest grain yields of 1238 kg∙ha−1 and 2098 kg∙ha−1, respectively. Variety and nitrogen effects on partial factor productivity (PFPN), agronomic efficiency (AEN), net returns (NR), value cost ratio (VCR) and marginal return (MR) indicated that these parameters were significantly affected (p < 0.05) by these factors. The highest PFPN (41.3 kg grain kg−1∙N) and AEN (29.4 kg grain kg−1∙N) were obtained in the 30 kg∙N∙ha−1 plots, while the highest VCR (2.8) and MR (SLL 1.8 SLL−1 spent on N) were obtained in the 180 kg∙N∙ha−1. The significant influence of variety and nitrogen on traits suggests that increasing yields and maximizing profits require use of appropriate nitrogen fertilization and improved farming practices that could be exploited for increased productivity of maize.

Method for rapid warning and activity concentration estimates in online waterγ-spectrometry systems

Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.

MAXIMAL FUNCTION CHARACTERIZATIONS OF HARDY SPACES ASSOCIATED WITH BOTH NON-NEGATIVE SELF-ADJOINT OPERATORS SATISFYING GAUSSIAN ESTIMATES AND BALL QUASI-BANACH FUNCTION SPACES

Assume that L is a non-negative self-adjoint operator on L^(2)(ℝ^(n))with its heat kernels satisfying the so-called Gaussian upper bound estimate and that X is a ball quasi-Banach function space onℝ^(n) satisfying some mild assumptions.Let HX,L(ℝ^(n))be the Hardy space associated with both X and L,which is defined by the Lusin area function related to the semigroup generated by L.In this article,the authors establish various maximal function characterizations of the Hardy space HX,L(ℝ^(n))and then apply these characterizations to obtain the solvability of the related Cauchy problem.These results have a wide range of generality and,in particular,the specific spaces X to which these results can be applied include the weighted space,the variable space,the mixed-norm space,the Orlicz space,the Orlicz-slice space,and the Morrey space.Moreover,the obtained maximal function characterizations of the mixed-norm Hardy space,the Orlicz-slice Hardy space,and the Morrey-Hardy space associated with L are completely new.

Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

SUFFICIENT AND NECESSARY CONDITIONS ON THE EXISTENCE AND ESTIMATES OF BOUNDARY BLOW-UP SOLUTIONS FOR SINGULAR p-LAPLACIAN EQUATIONS

Let?denote a smooth,bounded domain in R^(N)(N≥2).Suppose that g is a nondecreasing C^(1)positive function and assume that b(x)is continuous and nonnegative inΩ,and that it may be singular on■Ω.In this paper,we provide sufficient and necessary conditions on the existence of boundary blow-up solutions to the p-Laplacian problem△_(p)u=b(x)g(u)for x∈Ω,u(x)→+∞as dist(x,■Ω)→0.The estimates of such solutions are also investigated.Moreover,when b has strong singularity,the nonexistence of boundary blow-up(radial)solutions and infinitely many radial solutions are also considered.

An Example of a Bounded Potential q(x) on the Half-Line, for Estimates of A(α) Amplitude

We show an example of a bounded potential on the half-line obtained as the image of an Inverse Transformation Operator of the Bessel singular potential of the Reduced Radial Schr&#246;dinger Equation, and show us the Estimates of the A(α) amplitude.

Methodology for Obtaining Optimal Sleeve Friction and Friction Ratio Estimates from CPT Data

Cone penetration testing (CPT) is a cost effective and popular tool for geotechnical site characterization. CPT consists of pushing at a constant rate an electronic penetrometer into penetrable soils and recording cone bearing (qc), sleeve friction (fc) and dynamic pore pressure (u) with depth. The measured qc, fs and u values are utilized to estimate soil type and associated soil properties. A popular method to estimate soil type from CPT measurements is the Soil Behavior Type (SBT) chart. The SBT plots cone resistance vs friction ratio, Rf [where: Rf = (fs/qc)100%]. There are distortions in the CPT measurements which can result in erroneous SBT plots. Cone bearing measurements at a specific depth are blurred or averaged due to qc values being strongly influenced by soils within 10 to 30 cone diameters from the cone tip. The qcHMM algorithm was developed to address the qc blurring/averaging limitation. This paper describes the distortions which occur when obtaining sleeve friction measurements which can in association with qc blurring result in significant errors in the calculated Rf values. This paper outlines a novel and highly effective algorithm for obtaining accurate sleeve friction and friction ratio estimates. The fc optimal filter estimation technique is referred to as the OSFE-IFM algorithm. The mathematical details of the OSFE-IFM algorithm are outlined in this paper along with the results from a challenging test bed simulation. The test bed simulation demonstrates that the OSFE-IFM algorithm derives accurate estimates of sleeve friction from measured values. Optimal estimates of cone bearing and sleeve friction result in accurate Rf values and subsequent accurate estimates of soil behavior type.

锂离子电池健康状态估计及寿命预测研究进展综述

随着锂离子电池的应用越来越广泛,锂电池健康状态的精确估计和剩余寿命的实时预测对于锂电池系统的安全运行和降低运维成本具有重要意义。锂电池内部复杂的物理化学反应和外部复杂工作条件,使得实现精准的健康状态估计和寿命预测具有挑战性。该文综述近年来锂电池健康状态估计和剩余使用寿命预测方法的研究现状,分析基于物理/数学模型、数据驱动、模型法和数据驱动融合,以及多种数据驱动融合的锂电池健康状态估计方法的优缺点及适用条件,并对比分析不同数据驱动类型的锂电池寿命预测方法。指出锂电池健康状态估计及寿命预测尚存在的问题,并对未来研究方向进行展望,对完善锂电池健康状态估计和寿命预测算法理论体系、指导实际应用技术具有重要意义。

改进的Mertens第一定理估计及应用

在Mertens第一定理的基础上,利用定积分估计的主要思想和Stirling’s公式的估计,对Mertens第一定理中的估计不等式进行了进一步的精确化,得到了更好的估计。改进的估计结果比原来的Mertens第一定理中的估计更加精确。把改进的Mertens第一定理估计应用到一个具体的渐近公式中,提高了该渐近公式中Landau记号里对应常数的精度范围。

视觉SLAM方法综述

实时定位与建图(SLAM)技术搭载特定传感器,使移动机器人在无任何环境先验条件下,在运动过程中自主建立环境模型来计算自身位姿,大幅提高其自主导航能力,以及对不同应用环境的适应性。视觉SLAM方法以相机作为外部传感器,通过采集周围环境信息来创建地图并实时估计机器人自身位姿。为此,介绍了具有代表性的经典视觉SLAM方法及与深度学习相结合的视觉SLAM方法,分析了视觉SLAM方法中采用的不同特征检测方法、后端优化、闭环检测,以及动态环境下视觉SLAM方法的应用,总结了视觉SLAM方法的问题,并探讨了视觉SLAM方法在未来的热点研究方向和发展前景。

基于空间非一致模糊核标定的红外图像超分辨率重建方法

近年来,红外成像系统在工业、安防、遥感等领域获得了广泛的应用,但由于制造工艺及成本制约,红外系统的分辨率仍然较低。基于深度神经网络的单帧图像超分辨率重建技术是提高红外图像分辨率的有效方法,获得了广泛研究,并在仿真图像上取得了显著进展,但应用于实际场景图像时容易出现伪影或图像模糊等现象。造成这种性能差异的主要原因是目前方法大多假定造成图像退化的模糊核是空间一致的,然而实际红外光学系统不可避免地存在像差、热离焦等,由此造成的图像模糊的模糊核并非空间一致的。针对这一问题,提出了一种非盲模糊核估计方法,通过采集特定的靶标图像,并设计模糊核估计网络,求解空间非一致模糊核;设计基于图像分块的超分辨率重建方法,将图像块和对应区域的模糊核一起输入非盲超分辨率重建网络进行子块图像重建,再通过子块合并和重叠区域图像融合,得到最终的高分辨率图像。实验结果表明,光学系统自身引起了模糊核随空间位置缓慢变化,在实验室条件下标定模糊核并基于图像分块进行超分辨率重建的方法可显著提高红外图像超分辨率重建的效果。

可重构智能表面辅助通信系统时变级联信道估计

针对可重构智能表面(RIS)辅助通信系统时变级联信道的估计中需解决的级联信道稀疏表示、时变信道参数跟踪和信号重构等关键问题,提出了一种结合Khatri-Rao积的分层贝叶斯卡尔曼滤波(KR-HBKF)算法。该算法首先利用信道的稀疏特性,通过Khatri-Rao积和克罗内克积变换得到RIS级联信道的稀疏表示,将RIS级联信道估计问题转化为低维度的稀疏信号恢复问题。然后,根据RIS级联信道的状态演化模型,在HBKF算法的预测模型中引入了时间相关性参数,应用改进的HBKF解决时变信道参数跟踪和信号重构问题,完成时变级联信道的估计。KR-HBKF算法综合利用了信道的稀疏性和时间相关性,能以较小的导频开销获得更好的估计精度。仿真结果表明,与传统压缩感知算法相比,所提算法具有约5 dB的估计性能提升,且在不同的时变信道条件下具有更好的鲁棒性。

基于轻型自限制注意力的结构光相位及深度估计混合网络

相位提取与深度估计是结构光三维测量中的重点环节,目前传统方法在结构光相位提取与深度估计方面存在效率不高、结果不够鲁棒等问题。为了提高深度学习结构光的重建效果,本文提出了一种基于轻型自限制注意力(Light Self-Limited-Attention,LSLA)的结构光相位及深度估计混合网络,即构建一种CNN-Transformer的混合模块,并将构建的混合模块放入U型架构中,实现CNN与Transformer的优势互补。将所提出的网络在结构光相位估计和结构光深度估计两个任务上进行实验,并和其他网络进行对比。实验结果表明:相比其他网络,本文所提出的网络在相位估计和深度估计的细节处理上更加精细,在结构光相位估计实验中,精度最高提升31%;在结构光深度估计实验中,精度最高提升26%。该方法提高了深度神经网络在结构光相位估计及深度估计的准确性。

基于多掩膜技术的无监督深度与光流估计方法

针对自动驾驶领域现有方法在处理动态、遮挡等复杂实际场景时存在的估计不准确问题,提出了一种以多掩膜技术为基础的无监督深度与光流估计方法,通过无监督学习从单目视频序列中提取目标深度、相机运动位姿和光流信息。根据不同外点类型设计了多种特定掩膜,以有效抑制外点对光照一致性损失函数的干扰,并在位姿估计和光流估计任务中起到剔除外点的作用。引入预训练的光流估计网络,协助深度和位姿估计网络更好地利用三维场景的几何约束,从而增强联合训练性能。最后,借助训练得到的深度和位姿信息,以及计算得到的掩膜,对光流估计网络进行了优化训练。在KITTI数据集上的实验结果表明,该策略能够显著提升模型的性能,并优于其他同类型方法。

有序标签噪声的鲁棒估计与过滤方法

较大规模的标注数据集中难免会存在标签噪声,这在一定程度上限制了模型的泛化性能。有序回归数据集的标签是离散值,但不同标签之间又有一定次序关系。虽然有序回归的标签兼有分类和回归标签的特征,但面向分类和回归任务的标签噪声过滤算法对有序标签噪声并不完全适用。针对此问题,提出了标签含噪时回归模型的Akaike泛化误差估计,在此基础上设计了面向有序回归任务的标签噪声过滤框架。此外,提出了一种鲁棒的有序标签噪声估计方法,其采用基于中位数的融合策略以降低异常估计分量的干扰。最后,该方法与所提框架结合形成了噪声鲁棒融合过滤(Robust Fusion Filtering,RFF)算法。在标准数据集和真实年龄估计数据集上均验证了算法的有效性。实验结果表明,在有序回归任务中,RFF算法性能优于其他分类和回归过滤算法,能够适应不同类型的噪声数据,并有效提升数据质量和模型泛化性能。

基于匝道合流数据的自动驾驶汽车安全性测试评价方法

针对自动驾驶汽车测试场景不明确、评价模型主观性强等问题,研究了高速匝道汇入场景下的典型测试场景提取方法和自动驾驶汽车匝道汇入安全性客观评价方法。深入分析了匝道汇入功能场景下的逻辑场景要素,对自然驾驶数据中的自车速度、车间距离、前车车速等逻辑场景要素进行聚类,得到两类典型的匝道汇入测试场景用于自动驾驶汽车的仿真测试。构建多层次自动驾驶汽车评价体系,引入基于自然驾驶数据的核密度估计模型来获取指标最优阈值,建立以最优阈值为参考序列、以层次分析法(AHP)和客观赋权法(CRITIC)为权重输入的灰色关联理评价模型,对自动驾驶汽车在汇入过程中的安全性进行客观评价。评价结果表明:基于核密度估计的灰色关联理论模型评价结果与主观模糊综合分析模型的评价结果相似率达98.01%,验证了客观模型的有效性。

信息物理多重攻击下配电网状态估计关键技术评述

配电网数字化转型将进一步促进信息系统与物理系统的深度耦合,由于配电网信息安全防御资源有限,难以将信息侧安全风险隔离于物理系统之外,这也使得配电网状态估计正面临全新的挑战。首先,文中简要介绍了配电网信息物理系统体系结构,并构建了面向信息物理系统的配电网状态估计技术框架;其次,较为全面地梳理了信息物理融合背景下配电网状态估计技术的国内外研究现状,包括考虑网络攻击的配电网伪量测建模与分析、配电网虚假数据注入攻击分析与防御以及配电网信息物理系统安全风险分析与可靠性评估等方向;最后,对该领域未来进一步发展所面临的关键问题进行了探讨和分析。

数控磨床电控系统可靠性建模与评估研究

电控系统作为数控磨床关键子系统之一,尚未有学者对其开展可靠性建模与评估研究。文章对北京第二机床厂提供的MKS型数控磨床电控系统故障数据进行预处理,并绘制概率密度散点图,发现图像趋势符合威布尔分布或指数分布,再利用最小二乘法进行参数估计,经模型检验后引入灰关联度进行模型优选,确定电控系统故障数据所属分布模型为指数分布。最后计算了电控系统平均故障间隔时间MTBF、平均维修时间MTTR和固有可用度A_(inh)。评估结果已反馈给厂家并获得认可,对提升数控磨床整机的可靠性水平有重要意义。

“双碳”目标下火电厂CO_(2)计量技术研究现状与展望

中国电力行业CO_(2)排放量是CO_(2)排放的主要来源,其中火电厂CO_(2)排放量在电力行业中占比最大。在“双碳”目标下,CO_(2)计量技术可以实现对火电厂中CO_(2)排放量的直观判断,为火电厂CO_(2)减排提供重要支撑,促进火电厂参与碳交易,带动区域经济发展。结合国内外政策,讨论了目前通用CO_(2)计量方法的实施进展,总结归纳了以碳核算为主、碳监测为辅的火电厂CO_(2)计量方法存在的问题,并对火电厂CO_(2)计量技术应用的重难点进行了分析。最后,对火电厂CO_(2)计量技术的发展及应用进行了展望。