Anti-Noise Performance of the FT Continuous Zoom Analysis Method for Discrete Spectrum

As a discrete spectrum correction method, the Fourier transform （FT） continuous zoom analysis method is widely used in vibration signal analysis, but little effort had been made on this method＇s anti-noise performance. It is widely believed that the analysis accuracy of the method can be substantially improved by increasing the zoom multiple, however, with the zoom multiple increases, the frequency estimation accuracy may decline sometimes in practices. Aiming at the problems above, this paper analyzes the sources of frequency estimation error when a harmonic signal mixed with and without noise is processed using the FT continuous zoom analysis. According to the characteristics that the local maximum of the zoom spectrum may be wrongly selected when the signal is corrupted with noise, the number of wrongly selected spectrum lines is deduced under different signal-to-noise ratio and local zoom multiple, and then the maximum frequency estimation error is given accordingly. The validity of the presented analysis is confirmed by simulations results. The frequency estimation accuracy of this method will not improve any more under the influence of noise, and there is a best zoom multiple, when the zoom multiple is larger than the best zoom multiple; the maximum frequency estimation error will fluctuate back and forth. The best zoom multiple curves under different signal-to-noise ratios given provide a theoretical basis for the choice of the appropriate zoom multiples of the FT continuous zoom analysis method in engineering applications.

Removing Random-Valued Impulse Noises by a Two-Staged Nonlinear Filtering Method

Digital images are frequently contaminated by impulse noise(IN)during acquisition and transmission.The removal of this noise from images is essential for their further processing.In this paper,a two-staged nonlinear filtering algorithm is proposed for removing random-valued impulse noise(RVIN)from digital images.Noisy pixels are identified and corrected in two cascaded stages.The statistics of two subsets of nearest neighbors are employed as the criterion for detecting noisy pixels in the first stage,while directional differences are adopted as the detector criterion in the second stage.The respective adaptive median values are taken as the replacement values for noisy pixels in each stage.The performance of the proposed method was compared with that of several existing methods.The experimental results show that the performance of the suggested algorithm is superior to those of the compared methods in terms of noise removal,edge preservation,and processing time.

Compensation of image noise induced by difference in optical characteristics of array-LED chips of LED print head

There are various applied electro-optical devices, which utilize light emitting didoe(LED) chip array for applications to displays and opto-electronic sensors. In those devices, it is the one of the critical technical issues to minimize uncertain fluctuations including optical power and optical density. Due to variation in operating environment of a device, those are not corrected precisely by controlling parameters based on simple relation between parameters and resultant abovementioned outputs.Therefore, there is essential need to correct outputs in real-time based on correction function generated from the consideration on various operation condition. In this article, we introduce an output correction method through reporting real-time image noise reduction in the application to electro-photography with LED print head. In the technology of LED print head, as differences in optical characteristics between each LED cause vertical image noise, it should be corrected in order to obtain images that are comparable or better in quality compared to those produced by the conventional laser scanning method. Even though it seems that the method used to obtain uniform light power from each LED can solve this problem, it does not work well for high-resolution printing. Therefore, a scan method involving correction by a printed and scanned pattern is introduced through this work. The scan method is composed of correction patterns to minimize printing noise by its shape, the correction algorithm to calculate the optimized value and the printing algorithm to control gray levels in real-time precisely. We believe that the developed correction method upgrades the printing quality of the LPH printer better than commercial printers. The developed correction method can also be applied to various application areas that use an array-type light source such as display systems and lighting systems.

Characterizing noise correlation and enhancing coherence via qubit motion

The identification of spacial noise correlation is of critical importance in developing error-corrected quantum devices,but it has barely been studied so far.In this work,we utilize an effective method called qubit motion,to efficiently determine the noise correlations between any pair of qubits in a 7-qubit superconducting quantum system.The noise correlations between the same pairs of qubits are also investigated when the qubits are at distinct operating frequencies.What’s more,in this multi-qubit system with the presence of noise correlations,we demonstrate the enhancing effect of qubit motion on the coherence of logic qubits,and we propose a Motion-CPMG operation sequence to more efficiently protect the logic state from decoherence,which is experimentally demonstrated to extend the coherence time of logic qubits by nearly one order of magnitude.

Radiometric normalization of overlapping LiDAR intensity data for reduction of striping noise

Airborne LiDAR data are usually collected with partially overlapping strips in order to serve a seamless and fine resolution mapping purpose.One of the factors limiting the use of intensity data is the presence of striping noise found in the overlapping region.Though recent researches have proposed physical and empirical approaches for intensity data correction,the effect of striping noise has not yet been resolved.This paper presents a radiometric normalization technique to normalize the intensity data from one data strip to another one with partial overlap.The normalization technique is built based on a second-order polynomial function fitted on the joint histogram plot,which is generated with a set of pairwise closest data points identified within the overlapping region.The proposed method was tested with two individual LiDAR datasets collected by Teledyne Optech’s Gemini(1064 nm)and Orion(1550 nm)sensors.The experimental results showed that radiometric correction and normalization can significantly reduce the striping noise found in the overlapping LiDAR intensity data and improve its capability in land cover classification.The coefficient of variation of five selected land cover features was reduced by 19–65%,where a 9–18%accuracy improvement was achieved in different classification scenarios.With the proven capability of the proposed method,both radiometric correction and normalization should be applied as a pre-processing step before performing any surface classification and object recognition.

从中国首台紫外-可见光高光谱卫星仪器反演得到的高空间分辨率臭氧廓线

Understanding the vertical distribution of ozone is crucial when assessing both its horizontal and vertical transport,as well as when analyzing the physical and chemical properties of the atmosphere.One of the most effective ways to obtain high spatial resolution ozone profiles is through satellite observations.The Environmental Trace Gases Monitoring Instrument(EMI)deployed on the Gaofen-5 satellite is the first Chinese ultraviolet-visible hyperspectral spectrometer.However,retrieving ozone profiles using backscattered radiance values measured by the EMI is challenging due to unavailable measurement errors and a low signal-to-noise ratio.The algorithm developed for the Tropospheric Monitoring Instrument did not allow us to retrieve 87%of the EMI pixels.Therefore,we developed an algorithm specific to the characteristics of the EMI.The fitting residuals are smaller than 0.3%in most regions.The retrieved ozone profiles were in good agreement with ozonesonde data,with maximum mean biases of 20%at five latitude bands.By applying EMI averaging kernels to the ozonesonde profiles,the integrated stratospheric column ozone and tropospheric column ozone also showed excellent agreement with ozonesonde data,The lower layers(0-7.5 km)of the EMI ozone profiles reflected the seasonal variation in surface ozone derived from the China National Environmental Monitoring Center(CNEMC).However,the upper layers(9.7-16.7 km)of the ozone profiles show different trends,with the ozone peak occurring at an altitude of 9.7-16.7 km in March,2019.A stratospheric intrusion event in central China from August 11 to 15,2019,is captured using the EMI ozone profiles,potential vorticity data,and relative humidity data.The increase in the CNEMC ozone co ncentration showed that downward transport enhanced surface ozone pollution.

基于Retinex先验引导的低光照图像快速增强方法

低光照图像增强旨在提高在低光照环境下所采集图像的视觉质量.然而,现有的低光照图像增强方法难以在计算效率与增强性能之间达到很好的平衡,为此,提出一种基于Retinex先验引导的低光照图像快速增强方法,将Retinex模型与Gamma校正相结合,快速输出具有对比度高、视觉效果好和低噪声的图像.为获取具有良好光照的图像以引导确定与输入图像尺寸大小一致的Gamma校正图,提出基于Retinex模型的先验图像生成方法.针对所提先验图像生成方法在极低光照区域中存在颜色失真的问题,提出一种基于融合的Gamma校正图估计方法,采用反正切变换恢复极低光照区域的颜色和对比度,以提升Gamma校正图在极低光照区域的增强性能.为抑制输出图像的噪声,考虑到完全平滑的Gamma校正图不会平滑细节纹理的特点,提出基于域变换递归滤波的Gamma校正图优化方法,降低输出图像噪声的同时保持颜色和对比度.实验结果表明,所提方法不仅在主客观图像质量评价上优于现有大多数主流算法,而且在计算效率上具有十分显著的优势.

某在役重载铁路32 m预应力混凝土T梁后装声屏障适应性分析

随着我国货运铁路重载扩能运输的发展,大轴重、长编组引起的噪声污染日趋严重,后装声屏障已成为减小噪声污染的主要措施之一,但通过扩能改造提升的在役重载铁路桥梁原设计时并未考虑后装声屏障对桥梁结构的影响。为研究在重载铁路桥梁上后装声屏障的适应性,以在役铁路桥梁主梁型32 m简支T梁(专桥-2059A)为研究对象,采用数值模拟与结合现场实测桥梁现状分析的方法,基于不同荷载组合作用,开展T梁加装声屏障后主梁、声屏障与桥梁连接的位置及道砟槽板适应性分析,并对T梁翼缘板及声屏障结构的潜在薄弱位置进行破坏分析。研究结果表明:T梁加装声屏障后,主梁纵向强度受梁体L/8~L/4处抗剪控制,其中梁体L/8处抗剪安全系数为1.70,小于规范要求值1.80;声屏障与桥梁连接位置安全储备不足,其中,在最不利荷载组合作用下,挡砟墙钢筋应力超过容许值1.25倍、裂缝宽度超过容许值0.54 mm;道砟槽板在最不利荷载组合作用下,其钢筋应力安全储备剩余23.2%、裂缝宽度比规范容许值小0.07 mm。综合考虑3种分析,该类型T梁不宜后装声屏障;在最不利情况下,T梁及声屏障结构破坏顺序依次为预埋化学锚栓处、挡砟墙处、声屏障立柱与水平杆交接处、道砟槽板变高度处、翼缘板根部处。

开关噪声对电流控制精度影响及抑制策略

在高精度应用场合,对伺服系统的控制精度提出了极高的要求。伺服系统的控制精度很大程度上取决于反馈测量的精度,位置、速度反馈精度可通过提高编码器的绝对精度进行提升,且对外界干扰并不敏感,而电流采样环节对开关噪声较为敏感,噪声会造成输出转矩波动,降低伺服系统控制精度。从伺服系统高精度电流采样原理出发,研究开关噪声对电流控制精度的影响,提出一种具有连续输出的SINC3滤波器结构和一种提高稳态电流控制精度的方法。通过实验结果可知,该方法对开关噪声造成的电流波动有明显的抑制效果,具有很强的工程指导意义。

面向超大面阵CMOS图像传感器的全局斜坡一致性校正方法

针对大面阵CMOS图像传感器(CIS)中存在的斜坡信号不一致性问题,该文提出一种用于CMOS图像传感器的斜坡一致性校正方法。该误差校正方法基于误差存储和电平移位思想,在列级读出电路中引入用于存储各列斜坡不一致性误差的存储电容,根据存储的斜坡不一致性误差对各列的斜坡信号进行电平移位,确保斜坡信号的一致性。该文基于55 nm 1P4M CMOS工艺对提出的斜坡一致性校正方法完成了详细电路设计和全面仿真验证。在斜坡信号电压范围为1.4 V,斜坡信号斜率为71.908 V/ms,像素面阵规模为8 192(H)×8 192(V),单个像素尺寸为10μm的设计条件下,该文提出的校正方法将斜坡不一致性误差从7.89 mV降低至36.8μV。斜坡信号的微分非线性(DNL)为+0.001 3/–0.004 LSB,积分非线性(INL)为+0.045/–0.02 LSB,列级固定模式噪声(CFPN)从1.9%降低到0.01%。该文提出的斜坡一致性校正方法在保证斜坡信号高线性度,不显著增加芯片面积和不引入额外功耗的基础上,斜坡不一致性误差降低了99.53%,为高精度CMOS图像传感器的设计提供了一定的理论支撑。

强干扰与通道误差条件下弱信号DOA估计的实验研究

在实际测向系统中,当弱信号和强干扰空间临近时,空间谱测向系统仅能对强干扰进行波达方向(Direction of Arrival,DOA)估计,弱信号DOA估计性能下降甚至失效。针对这一问题,研究了空间谱扩展噪声子空间算法结合通道幅相误差校正,在强干扰抑制条件下对弱信号进行DOA估计的方法。该方法对采样信号的噪声协方差进行去加权处理,并对空间谱扩展噪声子空间算法的空间谱导向矢量进行修正。基于通用软件无线电外设(Universal SoftwareRadioPeripheral,USRP)和印刷偶极子线形天线阵构建实验平台,实验结果证明空间谱扩展噪声子空间算法结合改进的通道幅相误差校正方法,能对临近干扰源进行空间谱抑制的同时,实现对弱信号的DOA估计。

改进YOLOv5对病理图像下噪声标签的检测与自动纠正应用

病理图像细胞检测是医学诊断的基础部分,正确、精准地检测靶向细胞及其数量对疾病诊疗至关重要。传统医学采用手工镜检的估计方式检测病理图像,依赖病理医生的工作经验,存在主观性、检测精度较低的问题。为此,提出改进YOLOv5的噪声标签检测与自动纠正网络检测病理图像中的靶向细胞,通过Conf、IOU函数使网络具有区分真值标签和噪声标签的能力,从而实现噪声标签的自动纠正,以辅助医生对鼻窦炎疾病类型进行临床诊断。结果表明,改进网络在鼻窦炎病理图像数据集上的平均精度、召回率分别提升至88.9%和95.6%,可满足检测病理图像的精度和纠正噪声标签的需求。

海洋低信噪比小道距地震处理关键技术

海洋高分辨小道距地震探测方法具有高精度、作业方式灵活、适应区域广等优势,在浅表天然气水合物高精度识别、近海底地层层序确定方面都有广泛应用。基于现阶段研发设备的固有问题,原始地震数据的信噪比低、成像品质差。针对小道距高分辨率电缆固有的背景噪声严重、沉放深度和主频高引起的虚反射严重、电缆姿态不稳导致信号反射时间-距离不符合双曲线变化规律等问题,采用“叠前多域噪声压制技术”“基于F-K鬼波压制技术”“基于相干函数控制的电缆等浮校正技术”等关键手段,逐步质控,处理后的地震剖面信噪比高、波组特征清楚、地层反射连续性好,能满足浅层地质构造特征识别及地层层序划分的需求。

基于多分类器差异的噪声矫正域适应学习

无监督域适应学习旨在利用相关标签丰富的源域知识帮助缺少标签信息的目标域学习,目前常见的域适应方法通常假设源域数据是正确标记的。然而,实际存在的噪声环境会使得源域样本的标签和特征被破坏。为解决源域带噪这一问题,提出基于多分类器差异的噪声矫正域适应学习(NCDA)。首先,利用模型中多分类器间的输出差异,提出一个更精细的分类标准,将源域数据分为特征噪声样本、标签噪声样本、干净样本;其次,针对噪声类型提出不同的矫正方法,并将矫正后的样本重新投入模型中训练;最后,采用随机分类器的思想优化模型。在Office-31、Office-Home及Bing-Caltech数据集上与现有算法进行比较,分类准确率比次优方法高0.2%~1.6%,实验结果表明了NCDA的有效性与鲁棒性。

液压式沉降仪监测系统误差分析与修正研究

通过对影响液压式沉降仪监测精度的物理因素进行数据分析,得出了不同的影响因素对测量精度的影响程度。其中气压误差、温度误差以及噪声误差是造成测量误差的主要因素。因此,针对每一种误差,提出了误差修正措施,即:通过气管串联组成独立的封闭等压系统,并始终保持各沉降仪液面上方气压一致,从而消除了气压误差;通过扩大恒液位箱横截面与液体管道横截面半径比、采用温度标定系数的方法,使得温度误差得以忽略不计;通过给压力传感器、信号转换装置和传输线设置屏蔽层,消除了噪声误差。这些措施的实施,提高了液压式沉降仪监测系统对环境的适应能力,使之满足了工程监测的精度需求,而且有利于自动化沉降监测技术的推广,对同类型沉降仪的数据分析整理和研制生产具有借鉴意义。

基于时间卷积网络残差校正的短期风电功率预测

为提高短期风电功率预测的准确性,提出一种基于时间卷积网络残差校正模型的短期风电功率预测方法。首先,采取自适应噪声完备集合经验模态分解算法分离出风电功率的局部特征信息,以网格搜索与交叉验证算法优化的支持向量回归模型对各分量进行预测。然后,构建时间卷积网络残差预测模型,并使用灰色关联度分析方法选择输入特征,对支持向量回归预测结果进行校正。最后,基于提出的模型对某风电场实际运行功率进行预测并与其他方法的预测精度进行比较,结果表明,该文所提方法提高了短期风电功率预测的精度。

不同光照条件下遥感图像边缘检测方法研究

开展遥感图像边缘检测时,若不能考虑不同光照条件对遥感图像的影响,会影响图像边缘检测时的检测精度,为提升图像检测效果,提出不同光照条件下遥感图像边缘检测方法研究。该方法首先分析不同光照条件下遥感图像影响因素,并对图像实施畸变校正、去噪等处理;使用图像的融合技术对图像实施模糊化处理,实现图像的增强降低光照条件对图像检测的影响;最后通过Zernike边缘检测方法识别图像边缘点,完成图像的边缘检测。实验结果表明,本方法在开展遥感图像边缘检测时,平均梯度为0.133,信息熵为4.213,检测时间为1.352 s。

基于拟合优化的多传感器测距误差残差补偿修正算法设计

在复杂环境下,多传感器协同测距过程,受信号强弱、传感器性能、光照的影响,测距过程存在一定误差。为解决上述问题,提出一种多传感器测距误差补偿算法。该算法首先利用小波变换将多传感器信号分解成低频和高频信号,并对高频信号采取阈值量化处理。将处理后的高频信号与低频信号重构,完成信号降噪。其次采用坐标投影法构建误差模型,并在模型中采用带有拟合优化的残差补偿法修正误差。最后将灰度重心算法与电荷耦合原理结合消除光斑,完成多传感器测距误差补偿。实验结果显示:这种算法的残差平均值在0.37附近波动、标准差平均值为0.42、信号采集误差平均值小于1 Hz,能够实现多传感器测距误差的准确补偿。

利用GNSS反射信号监测海面高度变化——基于法国BRST站2019~2021年数据

GNSS反射测量(GNSS-R)技术凭借其数据来源广泛、低成本、高时空分辨率等优势,在地表与海洋环境监测等方面展现巨大潜力,已成为海面高度(SSH)反演的重要技术途径。现有研究大多聚焦于3~6个月内的短期GPS潮位反演,难以反映海面高度的季节性变化及年际特征,且在动态海面改正时仅考虑了垂向速度的影响,忽视了海面波动的垂向加速度,导致低潮位与高潮位的反演精度较差。基于此,以法国某一岸基跟踪站——BRST站为例,利用其连续3年的BDS/GPS/GLONASS/Galileo四系统反射信号,通过Lomb-Scargle谱分析和二阶动态潮位改正模型,采取稳健回归策略反演海面高度,并将最终结果与验潮站观测值进行对比,分析潮位变化趋势。结果表明:GNSS-R技术反演结果与验潮站观测值具有较好的一致性,反演精度有逐年提升的趋势,均方根误差(RMSE)为7.57 cm,相关系数为0.935;海面高度的季节性变化特征明显,秋、冬季平均海面高度偏高,夏季平均海面高度偏低,且海面高度的季节性变化与温度的季节性变化存在着相反的趋势;M2、S2、K1、O1、N2、K2、P1、Q1、M4等9个分潮的振幅差为0.06~6.76 cm,其平均绝对误差(MAE)为1.60 cm,迟角差为0.03°~6.96°,其平均绝对误差为2.45°,在频域上进一步验证了GNSS-R技术监测海面高度变化的可靠性。

基于ICEEMDAN-SE-MSGJO-LSTM-EC的短期风电功率预测

为了提高风电功率短期预测精度,本文提出了一种基于ICEEMDAN-SE-MSGJO-LSTM-EC模型的短期风电功率预测模型。首先,通过ICEEMDAN对原始风功率信号进行分解并通过样本熵计算熵值相近的分量相加重构。其次,建立MSGJO-LSTM预测模型,通过改进金豺优化算法(MSGJO)优化LSTM网络参数,对各模态分量进行预测。最后,通过对各模态分量预测结果进行误差修正(EC)并将所有模态预测结果相加得到最终预测结果。以新疆某风电场为例,采用本文所提预测模型进行仿真分析,试验结果表明本文基于ICEEMDAN-SE-MSGJO-LSTM-EC的预测模型预测精度更高。