水声信道线性均衡的研究

本文在对LMS和稀疏权自适应均衡算法进行分析的基础上，将其引入到水声数据传输中，利用LMS算法对时不变信道和慢变信道进行了仿真实验；利用稀疏权自适应均衡算法对深海信道进行了仿真实验，并获得了仿真结果。     

Vapour pressures and excess functions of N, N, N′, N′-tetramethylalkanediamine + cyclohexane. A group contribution study of the N---N proximity effect

The vapour pressuresof liquid cyclohexane + N, N, N′, N′-tetramethylalkanediamine, (CH₃)₂ N(CH₂)_uN(CH₃)₂ (u = 1,2) + cyclohexane mixtures were measured by a static method between 303.15 and 343.15 K at 10 K intervals. The excess molar enthalpies at 303.15 K were also measured.

The molar excess Gibbs energies, calculated from the vapour-liquid equilibrium data, and the molar excess enthalpies compare satisfactorily with group contribution (DISQUAC) predictions.

The proximity effect of N atoms produces a regular decrease of the interactional parameters.     

A multi-objective-ACO-based data association method for bearings-only multi-target tracking

The study is concerned with data association of bearings-only multi-target tracking using two stationary observers in a 2-D scenario. In view of each target moving with a constant speed, two objective functions, i.e., distance and slope differences, are proposed and a multi-objective-ant-colony-optimization-based algorithm is then introduced to execute data association by minimizing the two objective functions. Numerical simulations are conducted to evaluate the effectiveness of the proposed algorithm in comparison with the data association results of the joint maximum likelihood (ML) method under different noise levels and track figurations.     

Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application

Benchmarking is a widely cited method to identify and adopt best-practices as a means to improve performance. Data envelopment analysis (DEA) has been demonstrated to be a powerful benchmarking methodology for situations where multiple inputs and outputs need to be assessed to identify best-practices and improve productivity in organizations. Most DEA benchmarking studies have excluded quality, even in service-sector applications such as health care where quality is a key element of performance. This limits the practical value of DEA in organizations where maintaining and improving service quality is critical to achieving performance objectives. In this paper, alternative methods incorporating quality in DEA benchmarking are demonstrated and evaluated. It is shown that simply treating the quality measures as DEA outputs does not help in discriminating the performance. Thus, the current study presents a new, more sensitive, quality-adjusted DEA (Q-DEA), which effectively deals with quality measures in benchmarking. We report the results of applying Q-DEA to a U.S. bank's 200-branch network that required a method for benchmarking to help manage operating costs and service quality. Q-DEA findings helped the bank achieve cost savings and improved operations while preserving service quality, a dimension critical to its mission. New insights about ways to improve branch operations based on the best-practice (high-quality low-cost) benchmarks identified with Q-DEA are also described in the paper. This demonstrates the practical need and potential benefits of Q-DEA and its efficacy in one application, and also suggests the need for further research on measuring and incorporating quality into DEA benchmarking. The review process of this paper was handled by the Edit-in-Chief Peter Hammer.     

The Cauchy Problem for a Class of Semilinear Wave Equations with Piecewise Constants Data

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小波系数压缩用于柴油十六烷值近红外光谱分析建模

近红外光谱分析建模中存在多变量高维数据处理问题,导致计算量大,不利于过程控制中应用.为此提出利用小波变换压缩近红外光谱数据的算法与准则,并结合柴油十六烷值定量分析研究压缩数据的建模效果.研究表明,经小波方法处理后,变量维数压缩30倍左右,光谱主要信息基本保留,而模型的预测精度和常规预处理方法分析相比有明显提高.光谱数据压缩的同时包含了噪声滤除和基线校正,简化数据处理步骤,有利于NIRS实际应用时提高建模效率.     

Data-driven preventive maintenance for a heterogeneous machine portfolio

We describe a data-driven approach to optimize periodic maintenance policies for a heterogeneous portfolio with different machine profiles. When insufficient data are available per profile to assess failure intensities and costs accurately, we pool the data of all machine profiles and evaluate the effect of (observable) machine characteristics by calibrating appropriate statistical models. This reduces maintenance costs compared to a stratified approach that splits the data into subsets per profile and a uniform approach that treats all profiles the same.     

Missing value imputation strategies for metabolomics data

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros.     

Data fusion methodologies for food and beverage authentication and quality assessment – A review

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.