The Impact of Linear Filter Preprocessing in the Interpretation of Permutation Entropy

Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter’s intrinsic PE and separated its contribution from the signal’s ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able to theoretically decouple the signal-specific contribution from that induced by the linear filter. Therefore, by providing a proper framework of PE linear filter characterization, we improved the PE interpretation by identifying possible artifact information introduced by the preprocessing steps.     

X射线光电子能谱仪的数据采集及预处理技巧研究

X射线光电子能谱(XPS)广泛应用于材料表面分析表征,因XPS分析方法的主观性相对较强,谱图测试及分析水平会对数据分析质量产生很大影响。为获得更好的数据质量,文章根据国际标准和实际测试经验总结出一种通用于不同类型材料表面分析表征的XPS数据采集和预处理分析思路,以方便测试人员能够更加系统的进行数据采集和分析,为材料研发人员提供更加全面的元素组成信息和谱图解析注意事项,从而更有效地助力材料表面分析研究,该方法对科研工作者分析准确度的提高具有重要的理论意义和实际应用价值。     

Aerosol time-of-flight mass spectrometry data analysis: a benchmark of clustering algorithms

Airborne particulate matter is an important component of atmospheric pollution, affecting human health, climate, and visibility. Modern instruments allow single particles to be analyzed one-by-one in real time, and offer the promise of determining the sources of individual particles based on their mass spectral signatures. The large number of particles to be apportioned makes clustering a necessary step. The goal of this study is to compare using mass spectral data the accuracy and speed of several clustering algorithms: ART-2a, several variants of hierarchical clustering, and K-means. Repeated simulations with various algorithms and different levels of data preprocessing suggest that hierarchical clustering methods using derivatives of Ward's algorithm discriminate sources with fewer errors than ART-2a, which itself discriminates much better than point-wise hierarchical clustering methods. In most cases, K-means algorithms do almost as well as the best hierarchical clustering. These efficient algorithms (clustering derived from Ward's algorithm, ART-2a and K-means) are most accurate when the relative peak areas have been pre-scaled by taking the square root. Analysis times vary within a factor of 30, and when accuracy above 95% is required, run times scale up as the square of the number of particles. Algorithms derived from Ward's remain the most accurate under a wide range of conditions and conversely, for an equal accuracy, can deliver a shorter list of clusters, allowing faster and maybe on-the-fly classification.     

Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms

The need to minimize the potential impact of air pollutants on humans has made the accurate prediction of concentrations of air pollutants a crucial subject in environmental research. Support vector regression (SVR) models have been successfully employed to solve time series problems in many fields. The use of SVR models for forecasting concentrations of air pollutants has not been widely investigated. Data preprocessing procedures and the parameter selection of SVR models can radically influence forecasting performance. This study proposes a support vector regression with logarithm preprocessing procedure and immune algorithms (SVRLIA) model which takes advantage of the structural risk minimization of SVR models, the data smoothing of preprocessing procedures, and the optimization of immune algorithms, in order to more accurately forecast concentrations of air pollutants. Three pollutants, namely particulate matter (PM₁₀), nitrogen oxide, (NO_x), and nitrogen dioxide (NO₂), are collected and examined to determine the feasibility of the developed SVRLIA model. Experimental results reveal that the SVRLIA model can accurately forecast concentrations of air pollutants.     

三平动绳牵引并联机器人轨迹预处理算法

研究了一种新型飞行摄像平台,能够实现摄像机在大范围的三维空间内可以沿任意轨迹快速运动的摄像平台;摄像平台的运动由四台伺服电机带动四根缆绳并联拖动;绳牵引并联机器人是一种将电机的运动和力以绳为介质并行地转换为末端执行器运动和力的装置,具有结构简单、工作空间大、惯性小等优点;运动过程中满足轨迹光滑、不能出现停顿、振动,为了获得连续、平稳、无噪声的轨迹,文章采用轨迹预处理的方法,当出现轨迹不光滑,且影响了拍摄质量的情况下,采用三次样条圆弧插补的方法对其进行光滑处理,仿真结果表明该圆弧插补预处理算法能够很好地解决吊舱在高速运行而引起加速度过大的问题,进而达到提高运行效率的目的。     

Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration

Preprocessing of raw near-infrared (NIR) spectral data is indispensable in multivariate calibration when the measured spectra are subject to significant noises, baselines and other undesirable factors. However, due to the lack of sufficient prior information and an incomplete knowledge of the raw data, NIR spectra preprocessing in multivariate calibration is still trial and error. How to select a proper method depends largely on both the nature of the data and the expertise and experience of the practitioners. This might limit the applications of multivariate calibration in many fields, where researchers are not very familiar with the characteristics of many preprocessing methods unique in chemometrics and have difficulties to select the most suitable methods. Another problem is many preprocessing methods, when used alone, might degrade the data in certain aspects or lose some useful information while improving certain qualities of the data. In order to tackle these problems, this paper proposes a new concept of data preprocessing, ensemble preprocessing method, where partial least squares (PLSs) models built on differently preprocessed data are combined by Monte Carlo cross validation (MCCV) stacked regression. Little or no prior information of the data and expertise are required. Moreover, fusion of complementary information obtained by different preprocessing methods often leads to a more stable and accurate calibration model. The investigation of two real data sets has demonstrated the advantages of the proposed method.     

铁水含硅量预测系统中数据预处理技术的研究

通过使用自适应预报模型与时差方法相结合的方法对铁水含硅量进行预测,模型所需的工艺参数的原始数据经采集后需使用OPC技术标准进行传输以存入数据库系统中。硅预测程序通过对数据库查询得到所需的操作数据,由于生产环境对数据的干扰,需对其作均值滤波和插值运算的一次处理,然后进行求平均值、梯度和标准差统计学运算的二次处理,之后运用模糊理论将各个工艺参数归一化以提取参数特征值,作为铁水含硅量预测模型的输入参数,在预测过程中使用多元线性回归不断修正模型中的权重系数,以提高数学模型预测的准确率。     

骨关节炎软骨的近红外光谱学研究及分期诊断

骨关节炎是一种威胁中老年人群公共健康和生活质量的重大医学疾病。骨关节炎的早期病变主要表现在细胞外基质成分含量的变化,患者自身很难发现,现有的临床方法和实验方法也不能较准确地识别骨关节炎的早期病变。近年来,傅里叶变换近红外（FTNIR）光谱技术因为其分析速度快、成本低、易于穿透组织获得样本的光谱信息等特点已被用于手术导航、无损检测和疾病诊断等各个领域。基于以上优势,采用FTNIR技术对不同深度分区（表层区、过渡区、深层区）的健康和骨关节炎的关节软骨进行NIR光谱采集和预处理,结合主成分分析（PCA）和Fisher判别（FDA）分别研究不同的预处理方式对判别结果的影响、不同深度下基质成分含量的变化以及骨关节炎分期识别。比较其他2种（基线校正、二阶导数3次多项式25点Savitzky-Golay平滑）预处理方式,同分区中一阶导数2次多项式21点Savitzky-Golay平滑预处理的判别结果最优,其中表层区的识别率高达95%（初始案例）和90%（交互验证案例）;表层区的判别结果优于过渡区,更优于深层区,恰可证明骨关节炎的早期病变主要发生在表层区。在骨关节炎分期识别中,经数据优化后模型的初始案例识别率为100%,交互验证识别率为93.3%,预测集的识别率为87.5%。结果表明：NIR光谱的一阶导数预处理结合PCA-FDA方法能有效地鉴别关节软骨病变与否并进行骨关节炎的分期诊断,对骨关节炎监测和早期诊断研究具有重要意义,并有潜力应用于骨关节炎的原位分期和早期临床诊断。     

基于非线性预处理的SAR图像浮点数据压缩

提出了一种基于非线性预处理和DCT的SAR图像浮点数据压缩方法,通过对预处理前后DCT系数的熵和恢复图像信噪比的分析,确定符合SAR数据特征的预处理方法,再应用区域编码对其进行定值压缩。实验表明该方法可取得非常明显的效果。     

A regularized nonnegative canonical polyadic decomposition algorithm with preprocessing for 3D fluorescence spectroscopy

We consider blind source separation in chemical analysis focussing on the 3D fluorescence spectroscopy framework. We present an alternative method to process the Fluorescence Excitation‐Emission Matrices (FEEM): first, a preprocessing is applied to eliminate the Raman and Rayleigh scattering peaks that clutter the FEEM. To improve its robustness versus possible improper settings, we suggest to associate the classical Zepp's method with a morphological image filtering technique. Then, in the second stage, the Canonical Polyadic (CP or Candecomp/Parafac) decomposition of a nonnegative three‐way array has to be computed. In the fluorescence spectroscopy context, the constituent vectors of the loading matrices should be nonnegative (since standing for spectra and concentrations). Thus, we suggest a new nonnegative third order CP decomposition algorithm (NNCP) based on a nonlinear conjugate gradient optimization algorithm with regularization terms and periodic restarts. Computer simulations performed on real experimental data are provided to enlighten the effectiveness and robustness of the whole processing chain and to validate the approach. Copyright © 2015 John Wiley & Sons, Ltd.