Effective detection of benzoyl peroxide in flour based on parameter selection of Raman hyperspectral system

Penetration depth and spatial resolution of Raman hyperspectral imaging system were studied for effective detection of benzoyl peroxide in flour. The determinations of parameters were achieved by using the single-band background-correct image of a benzoyl peroxide Raman characteristic band and a simple threshold method. The selected parameters were used to detect mixture samples with different concentrations. Percentage of detected benzoyl peroxide pixels was positively correlated to its concentration. The result shows that parameters selected in this study are effective for the detection of benzoyl peroxide additive in flour and can be used for quantitative analysis in the future.     

Observation of defect clustering in Gd-doped calcium fluoride

Abstract

Solid solutions Ca_1-xGd_xF_2+x for 3 × 10^?7≤ x ≤10^?1 have been studied by electron paramagnetic resonance (EPR) and ionic thermal currents (ITC). The EPR experiments show the presence of two single-ion sites a cubic and a tetragonal Gd³⁺ center which co-exist with comparable abundances for intermediate impurity concentrations. The cubic center predominates at very low and high concentrations. Seven different relaxation processes have been identified from the ITC spectra and the variation of their intensity vs. x was measured. The absolute concentrations of the cubic and nn Gd³⁺ dipoles were calculated. The scavenging of interstitial fluorines by the neutral clusters explains both the abundance of cubic sites at high concentration and the variety of orientable clusters detected by ITC.     

非正态变异下的非线性轮廓异常点识别方法研究

在质量控制领域,非线性轮廓异常点识别问题是重点研究问题之一。本文综合运用了小波分析、数据深度、聚类分析等数据分析处理技术,提出了一种新的非正态变异的异常点识别方法。文章通过仿真分析技术,将新方法χ²与控制图方法进行性能对比,结果证实新方法能够以更高的准确率和稳定性识别异常点,表现出更好的异常点识别性能。最后将新方法应用于木板垂直密度轮廓实例对新方法进行验证,分析结果表明本方法能够有效识别出异常轮廓数据。     

Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms

Any decision-making process that relies on a probabilistic forecast of future events necessarily requires a calibrated forecast. This article proposes new methods for empirically assessing forecast calibration in a multivariate setting where the probabilistic forecast is given by an ensemble of equally probable forecast scenarios. Multivariate properties are mapped to a single dimension through a prerank function and the calibration is subsequently assessed visually through a histogram of the ranks of the observation’s preranks. Average ranking assigns a prerank based on the average univariate rank while band depth ranking employs the concept of functional band depth where the centrality of the observation within the forecast ensemble is assessed. Several simulation examples and a case study of temperature forecast trajectories at Berlin Tegel Airport in Germany demonstrate that both multivariate ranking methods can successfully detect various sources of miscalibration and scale efficiently to high-dimensional settings. Supplemental material in form of computer code is available online.     

基于路径选择的深海水下运动目标被动深度估计

深海水下运动目标的深度估计是目标分辨与识别中的重要一环。基于深海运动目标在深海中的直达波与海面反射波的到达时延与位置之间的关系，寻找时延图上运动目标的候选路径，利用候选路径模拟目标的运动路径，提出了一种可用单水听器实现的水下运动目标深度估计方法。通过用候选路径的估计时延与实际接收时延的比较对路径进行筛选，从而减少了需要的位置先验信息。2018年在南海进行的深海实验数据处理的结果表明该估计方法可行，对于实验中距离15km以内的拖曳声源，深度相对估计误差可以达到15%以内。     

Aerial Video Trackers Review

Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.     

On cohomological dimension and depth under linkage

Some relations between cohomological dimensions and depths of linked ideals are investigated and discussed by various examples.     

Experimental shift allowance in the deconvolution of SIMS depth profiles

We study the deconvolution of the secondary ion mass spectrometry (SIMS) depth profiles of silicon and gallium arsenide structures with doped thin layers. Special attention is paid to allowance for the instrumental shift of experimental SIMS depth profiles. This effect is taken into account by using Hofmann's mixing‐roughness‐information depth model to determine the depth resolution function. The ill‐posed inverse problem is solved in the Fourier space using the Tikhonov regularization method. The proposed deconvolution algorithm has been tested on various simulated and real structures. It is shown that the algorithm can improve the SIMS depth profiling relevancy and depth resolution. The implemented shift allowance method avoids significant systematic errors of determination of the near‐surface delta‐doped layer position. Copyright © 2013 John Wiley & Sons, Ltd.     

An analytical depth resolution function for the MRI model

The mixing roughness information depth model is frequently used for the quantification of sputter depth profiles. In general, the solution of the convolution integral for any kind of in‐depth distributions is achieved by numerical methods. For a thin delta layer, an analytical depth resolution function is presented, which enables a simple and user‐friendly quantification of measured delta layer profiles in AES, XPS and SIMS. Copyright © 2013 John Wiley & Sons, Ltd.