Application of Dynamic Linear Detecting Method in Data Processing of the Portable Blood Sugar Analyzer

In this paper,a dynamic linear detecting method,that the non-linear coefficient NL% was led and the non-linearity of data were estimated continuously and dynamically and determined when NL% exceeded reference value (5%),was used for data processing and could solve the problem caused by the phenomenon of substrate depleting occurred following the redox reaction in portable blood sugar analyzer.By contrast to the conventional end-point method,the dynamic linear detecting method is based on multipoint data collecting.Experiments of measuring the calibration glucose solution with 8 various concentrations from 50 mg/dl to 400 mg/dl were carried out with the analyzer developed by our group.The linear regression curve,whose correlation for the data was 0.9995 and the residual was 2.8080,were obtained.The obtained correlation,residual, and the computation workload are all fit for the portable blood sugar analyzer.

Data processing and initial results of Chang'e-3 lunar penetrating radar

To improve our understanding of the formation and evolution of the Moon, one of the payloads onboard the Chang＇e-3 （CE-3） rover is Lunar Penetrating Radar （LPR）. This investigation is the first attempt to explore the lunar subsurface structure by using ground penetrating radar with high resolution. We have probed the subsur- face to a depth of several hundred meters using LPR. In-orbit testing, data processing and the preliminary results are presented. These observations have revealed the con- figuration of regolith where the thickness of regolith varies from about 4 m to 6 m. In addition, one layer of lunar rock, which is about 330 m deep and might have been accumulated during the depositional hiatus of mare basalts, was detected.

Data processing and initial results from the CE-3 Extreme Ultraviolet Camera

The Extreme Ultraviolet Camera （EUVC） onboard the Chang＇e-3 （CE-3） lander is used to observe the structure and dynamics of Earth＇s plasmasphere from the Moon. By detecting the resonance line emission of helium ions （He＋） at 30.4 nm, the EUVC images the entire plasmasphere with a time resolution of 10 min and a spatial resolution of about 0.1 Earth radius （RE） in a single frame. We first present details about the data processing from EUVC and the data acquisition in the commissioning phase, and then report some initial results, which reflect the basic features of the plas- masphere well. The photon count and emission intensity of EUVC are consistent with previous observations and models, which indicate that the EUVC works normally and can provide high quality data for future studies.

Scientific data products and the data pre-processing subsystem of the Chang'e-3 mission

The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1） investigate the morphological features and geological structures at the landing site; （2） integrated in-situ analysis of minerals and chemical compositions; （3） integrated exploration of the structure of the lunar interior; （4） exploration of the lunar-terrestrial space environment, lunar sur- face environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System （GRAS） is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem （DPS） is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper de- scribes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.

Data processing and error analysis for the CE-1 Lunar microwave radiometer

The microwave radiometer （MRM） onboard the Chang＇ E-1 （CE-I） lu- nar orbiter is a 4-frequency microwave radiometer, and it is mainly used to obtain the brightness temperature （TB） of the lunar surface, from which the thickness, temperature, dielectric constant and other related properties of the lunar regolith can be derived. The working mode of the CE-1 MRM, the ground calibration （including the official calibration coefficients）, as well as the acquisition and processing of the raw data are introduced. Our data analysis shows that TB increases with increasing frequency, decreases towards the lunar poles and is significantly affected by solar illumination. Our analysis also reveals that the main uncertainty in TB comes from ground calibration.

An accelerated direct demodulation method for image reconstruction using spherical data from the hard X-ray modulation telescope

The hard X-ray modulation telescope （HXMT） mission is mainly devoted to performing an all-sky survey at 1- 250 keV with both high sensitivity and high spatial resolution. The observed data reduction as well as the image reconstruction for HXMT can be achieved by using the direct demodulation method （DDM）. However the original DDM is too computationally expensive for multi-dimensional data with high resolution to be employed for HXMT data. We propose an accelerated direct demodulation method especially adapted for data from HXMT. Simulations are also presented to demonstrate this method.

A Study of the Method for the Recognition of Anomalies in Geochemical Hydrocarbon Exploration

The greatest difficulties in recognizing geochemical hydrocarbon anomalies are: (1) how to objectively and accurately separate anomalies from background; (2) how to distinguish hydrocarbon pool related apical anomalies from lateral anomalies controlled by faults; and (3) how to eliminate interferences. These uncertainties are serious obstacles for the wide acceptance and use of geochemical techniques in hydrocarbon exploration. In this paper, the features of hydrocarbon anomalies were analyzed based on the micro migration mechanisms. In most cases, there are two anomalous populations or point groups, which are produced by two distinct mechanisms: (1) a population that directly reflects oil and gas fields, and (2) one that is related to structures such as faults. Statistical studies show that background anomalous populations and the boundaries between them can be described by the population means, prior probabilities, which are the proportions of population sizes, and covariance matrices, when background and anomalous populations have normal distributions. When this normality condition is met, a series of formulas can be derived. The method is designed on the basis of these allows: (1) univariate anomaly recognition, (2) elimination of interferences, (3) multivariate anomaly recognition, and (4) multivariate anomaly combination which depicts a more representative picture of morphology of the anomalous target than individual anomalies. The univariate and multivariate anomaly recognition can not only separate anomalies from background objectively, but also simultaneously distinguish the two types of anomalies objectively. This method was applied to the hydrocarbon data in Yangshuiwu region, Hebei Province. The interferences from regional variation of background were eliminated, and the interpretation uncertainty was reduced greatly as the anomalous populations were separated. The method was also used in Daxing region within the confines of Beijing City, and Aershan and Jiergalangtu regions in Inner Mongolia.

Methods to increase the depth and precision of transient Rayleigh wave exploration

In order to increase the exploration depth of Rayleigh wave, new idea that dif-ferent from the former principles in data acquisition was applied. Suitable data acquisition parameter was given out on the basis of large amount of experiments. By reducing the group interval, the low frequency signal are enhanced instead of been attenuated. Fur-thermore, to solve the problem that the precision of Rayleigh wave exploration method count much to the signal-to-noise ratio, some preprocessing methods were put forward. By using zero shift rectifying, digital F-K filtering and cutting, noises can be effectively eliminated.

A method and results of color calibration for the Chang'e-3 terrain camera and panoramic camera

The terrain camera （TCAM） and panoramic camera （PCAM） are two of the major scientific payloads installed on the lander and rover of the Chang＇e 3 mission re- spectively. They both use a Bayer color filter array covering CMOS sensor to capture color images of the Moon＇s surface. RGB values of the original images are related to these two kinds of cameras. There is an obvious color difference compared with human visual perception. This paper follows standards published by the International Commission on Illumination to establish a color correction model, designs the ground calibration experiment and obtains the color correction coefficient. The image qual- ity has been significantly improved and there is no obvious color difference in the corrected images. Ground experimental results show that： （1） Compared with uncor- rected images, the average color difference of TCAM is 4.30, which has been reduced by 62.1%. （2） The average color differences of the left and right cameras in PCAM are 4.14 and 4.16, which have been reduced by 68.3% and 67.6% respectively.

Research on Comparison and Evaluation Studies of Several Smoothing Denoising Method Based on γ-ray Spectrum Detector

The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI （TI） gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment, some smoothing filtering methods are proposed to solve the problem. These methods include adopting method of arithmetic moving average, center of gravity, least squares of polynomial, slide converter of discrete funcion convolution etc. The process of spectrum data is realized, and the results are assessed in H/FWHM（ Peak High/Full Width at Half Maximum） and peak area based on the Matlab programming. The results indicate that different methods smoothed spectrum have respective superiority in different ergoregion, but the Gaussian function theory in discrete function convolution slide method is used to filter the complex y-spectrum on Embedded system nlatform, and the statistical fluctuation of y-snectrum filtered wall.

应用生成对抗网络的地震数据重建和去噪一体化方法

在实际采集过程中,受地形条件和人为因素的影响,地震数据不仅在空间上会出现采样不足或不规则的情况,而且会混入噪声,不利于后续地震数据的处理和解释。通常将重建与去噪分为两个阶段处理,这样往往会引入额外的误差。为此,文中提出了一种基于条件韦氏生成对抗网络(cWGAN)的地震数据重建去噪一体化方法,该方法研究的重点是在缺失道和噪声的混合干扰下,准确提取地震数据的有效特征。首先,以U-Net模型为基本网络结构来构建生成器模型,分级提取地震数据同相轴特征;在判别器模型中引入条件约束,引导生成器优化梯度方向。其次,建立重建和去噪误差描述模型,该模型设计了一体化损失函数,可以兼顾重建与去噪两方面的处理任务。最后,经过合成数据和实际数据测试,证明文中所提的网络模型恢复的地震数据信噪比更高且具有较强鲁棒性。

基于ADASYN和WGAN的混合不平衡数据处理方法

为了解决不平衡数据集中少数类样本分类精度较低的问题,提出了一种处理不平衡数据集的ADASYN-WGAN方法。首先,采用ADASYN(adaptive synthetic sampling)算法生成少数类样本,用这些生成样本代替WGAN(wasserstein generative adversarial networks)中的随机噪声;其次,利用WGAN算法生成符合原始数据集分布规律的少数类样本,构建平衡数据集;然后,在6个公开数据集上,采用随机森林分类器对所提方法和4种过采样算法得出的处理结果分别与原始数据集进行对比;最后,通过F1-Score,G-mean和AUC等分类评估指标的表现验证所提方法的有效性。结果表明:在对比实验中,经过ADASYN-WGAN方法得到的平衡数据集在随机森林分类器的十折交叉验证中,4个公开数据集中的各项分类评估指标值均达到最优,虽然另2个公开数据集中的AUC值略低,但其F1-Score和G-mean取得了最高值。所提出的ADASYN-WGAN方法可生成高质量的数据样本,并可为解决不平衡数据集中少数类样本的预测偏差问题提供参考。

大地电磁测深数据处理方法技术进展

大地电磁测深法以其探测深度大、不受高阻层屏蔽、设备轻便、成本低等优势,被广泛用于地球动力学、成矿系统等研究以及深部资源和(清洁)能源探测、灾害调查等领域。然而,该方法在观测天然电磁场信号方面信号较弱,容易受到噪声干扰,一般需长时间观测和多次叠加方式来获取电性信息,且需同时开展相应的数据处理和分析,以获得更为可靠的地下电性结构。其中,数据处理一般包括时频转换、去噪、阻抗张量和倾子矢量估计,数据分析主要包括电性主轴和介质维度分析。基于前人关于大地电磁测深探测方法的研究成果,针对探测体系中处理、分析的关键环节,总结目前普遍使用的方法,并基于数据处理中的去噪、数据分析中的阻抗和相位张量,介绍探测体系中重要环节的前沿研究方向和成果,分析现有方法各自的特点和优点及其在不同环境和背景下大地电磁场观测数据的适用性,为大地电磁测深的有效、高效应用提供理论信息和基础保障。

环境工程专业学位硕士试验设计与数据处理课程教学优化——基于国内外三所高校案例

文章比较了清华大学试验设计与分析线上课程和昆士兰大学试验设计线下课程在教学内容和教学方法方面的差异,并分析了各自的特点;在此基础上,介绍了中国矿业大学环境工程专业学位硕士试验设计与数据处理课程结合人才培养目标,进行教学内容和教学方法优化,并实施线上线下混合式教学模式的实践。中国矿业大学的试验设计与数据处理课程教学达到了使学生“懂理论、会应用、能实践”的目标。

证据方法视阈下大数据证据诉讼应用及正当程序保障

证据方法的作用在于通过从客观上揭示证据内含的信息内容,联接证据与刑事证明。大数据证据表现出独特的建构性与质素多样性特征,使得现有证据的证据方法无所适从,需要依托融合性证据方法为其诉讼应用提供可操作性指引。而在刑事诉讼中运用融合性证据方法,可能对现有诉讼构造构成挑战。对此,可以从证据方法的工具与程序二维属性出发,建构正当程序保障:在工具属性维度,发挥法官庭外调查的能动、兜底性作用;在程序属性维度,构造“对等”性权利保障,弱化微观权力。

基于三维激光扫描的H型钢尺寸误差与接长精度技术研究

H型钢具有良好的强度、灵活多样的连接方式、结构重量轻等多种优势,在海洋工程、陆地结构工程大量使用,对其制造精度有较高要求。大部分大型H型钢通过钢板焊接而成,由于焊接变形的影响,其长度方向、腹板和翼缘板都存在不同程度的变形,目前采用的是人工钢丝拉线测量方法,通过卡尺逐点测量,效率低、精度差。本文利用三维激光扫描对大型H型钢进行扫描,形成不同坐标系下的点云数据,将多坐标系的点云数据进行空间转换和拼接等处理,形成归一化的H型钢三维空间点云模型;利用平面误差分析算法、结构特征轴线拟合、二维投影直线拟合等方法对H型钢的点云数据进行特征分析,形成H型钢尺寸精度评判模型,进而对H型钢的平面度和对接精度进行分析,形成相关的拟合方程,该方法具有效率高,精度高等特点,对使用H型钢的相关工程有重要的意义。

基于数据挖掘的大学生就业指导资源挖掘方法

采用目前方法对大学生就业指导资源进行数据挖掘时,由于去噪性能差导致方法存在数据冗余、挖掘效率低和精准度较差的问题,因此提出了基于数据挖掘的大学生就业指导资源挖掘方法。利用H-BIRCH算法对就业指导数据进行聚类处理,结合EMD分解方法和小波去噪方法对不同类别的就业信息数据进行去噪,对去噪后的资源数据进行白化处理,采用图模型提取就业指导资源数据特征,完成就业指导资源挖掘。实验结果表明,该方法可以有效简化数据结构,数据冗余纠错率、数据挖掘效率和数据挖掘准确度较高。

3D点云数据处理方法研究进展

点云是理解三维场景的重要形式之一,3D点云在海洋平台逆向建模、海底地形测绘、深水浮式结构系泊系统损伤测量及海底管线可视化等方面都有着重要应用。基于此,文中梳理了点云数据处理方法,将其分为传统处理算法和基于深度学习方法两大类;传统处理算法从滤波、对象识别与分类和配准3方面进行了介绍总结;基于深度学习方法从点云、体素化和多视图3方面进行了介绍总结。对各种算法的优缺点进行了归纳对比,并展望了3D点云处理技术未来的发展趋势与方向。

雅安地区监测井水位预测效能对比研究

为提高监测井水位映震效果的判别准确度并提升预测效能,本文运用不同方法对雅安地区两条断裂附近的6口监测井水位的映震效果开展了对比判别研究。首先,使用差分法对监测井水位数据进行处理和分析。然后,将水位数据与区域地震活动性的两种参数(能级和最大震级)相结合,分析区域地震活动性与水位波动的关系。接着,在时域和频域两个层面对该组合序列进行分析处理。最后,采用Molchan图表法检验5种方法的预测效能,并选出最佳处理方法及断裂附近预测效能最优的监测井。结果表明:水位与地震活动性参数结合处理相比单独使用水位差分法具有优势,前者能够准确排除地震活动性对水位异常的干扰。6口监测井的最优预测方法各不相同,最优预测天数均在60 d内。通过比较监测井水位对地震的预测效能检验结果,确定了两条断裂区域预测效能最优的监测井。虽然水位与地震活动性两种参数结合处理并不适用于所有监测井,但该方法能够体现水位受地震活动性影响的程度。

基于组合赋权法和模糊综合评价的电力设备状态数据质量评估

随着电力网络的扩大以及工业信息化的迅速发展,在电力领域中采集和待处理的数据量呈现爆发式增长,数据的丢失、冗余、异常、冲突等问题也日益突出,影响数据的质量。数据质量评估作为保证数据质量的关键一环,发挥着重要作用。针对电力设备的状态数据提出一种数据质量评估方法,选择完整性、准确性、唯一性、一致性、及时性5个评估指标构建数据质量评估体系,设置了定量计算的评估规则;组合运用模糊层次分析法和熵权法来确定各评估维度的权重,以提高数据质量评估的科学性;再运用模糊综合评价法,依据隶属函数来确定数据质量的所属等级。最后运用上述方法,对某地的油色谱数据质量进行评估,该方法计算得到数据质量的评分为77.15,处于“中等”等级。评估结果与实际应用场景相符,表明该文提出的方法适用于电力设备状态数据质量评估。