Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.     

基于小波分析的谐波在线监测系统

针对传统谐波在线监测系统存的结果不准确，无法描述谐波动态变化特点，设计了基于小波分析的谐波在线监测系统。首先设计了谐波在线监测系统的硬件模块，然后谐波在线监测系统的软件模块，依据小波变换分析谐波信号特征，得到的谐波变化率，从而谐波在线监测系统的监测结果，最后采用具体实验进行了谐波在线监测系统性能的仿真实验，由实验结果可知，该系统的谐波监测精度高，可以有效检测各种谐波分量，保证电力系统稳定运行。     

物联网本体存储数据可撤销加密仿真研究

针对物联网密钥不可随机拆分与撤销,本体存储数据容易遭到外界入侵,导致用户隐私信息泄露,提出物联网本体存储数据可撤销加密方法。根据数据可撤销加密的理论确定双线性映射函数,通过加密撤销的困难假设构建了物联网本体存储数据可撤销模型。对存储数据进行可撤销运算,采用了周期更新思想,将用户密钥属性、用户身份以及时间进行划分,并根据访问树的节点数据对用户存储数据进行加密计算。通过主要密钥随机拆分撤销运算,根据结果对存储数据可撤销加密进行安全认证。仿真结果表明,采用可撤销加密方法能够实现物联网密钥的随机拆分,缩短用户密钥更新周期,数据安全性更高。     

A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

Explicit and implicit measures of emotions: Data-science might help to account for data complexity and heterogeneity

Measuring emotions is a real challenge for fundamental and applied research, especially in ecological contexts. de Wijk and Noldus propose combining two types of measures - explicit to characterize a specific food, and implicit -physiological- to capture the whole experience of a meal in real-life situations. This raises several challenges including development of new and miniaturized sensors and devices but also developing new ways of data analysis. We suggest a path to follow for future studies regarding data analysis: to include Data Science in the game. This field of research may enable developing predictive but also explicative models that link subjective experience of emotions and physiological responses in real-life contexts. We suggest that food scientists should go out of their comfort zone by collaborating with computer scientists and then be trained with the new tools of Data Science, which will undoubtedly enable them 1/ to better manage complex and heterogeneous data sets, 2/ to extract knowledge that will be essential to this field of research.     

A survey of outlier detection in high dimensional data streams

The rapid evolution of technology has led to the generation of high dimensional data streams in a wide range of fields, such as genomics, signal processing, and finance. The combination of the streaming scenario and high dimensionality is particularly challenging especially for the outlier detection task. This is due to the special characteristics of the data stream such as the concept drift, the limited time and space requirements, in addition to the impact of the well-known curse of dimensionality in high dimensional space. To the best of our knowledge, few studies have addressed these challenges simultaneously, and therefore detecting anomalies in this context requires a great deal of attention. The main objective of this work is to study the main approaches existing in the literature, to identify a set of comparison criteria, such as the computational cost and the interpretation of outliers, which will help us to reveal the different challenges and additional research directions associated with this problem. At the end of this study, we will draw up a summary report which summarizes the main limits identified and we will detail the different directions of research related to this issue in order to promote research for this community.     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.     

喀拉通克铜镍矿选矿车间管理信息子系统的设计与实现

介绍了选矿车间管理信息子系统的用户界面设计和通用模块的主要代码设计，讨论了基于C／S模式，综合运用Power Builder7．0和Oracle8．0开发管理信息系统(MIS)的设计思路与开发步骤。该系统经现场调试和使用表明，运行良好，效益显著，为同类系统的开发提供了借鉴经验。     

基于小波包频带能量特征提取的低速重载滑动轴承故障诊断研究

滑动轴承的代表性故障是轴承与轴颈的接触摩擦故障,而高频振动信号的出现是产生接触摩擦的重要征兆,基于这一特性借助小波包将信号细分到各个频段,分别求出各频段内信号的能量,观察高频能量的变化就可以判断出是否发生了故障以及故障的程度。以某矿风机出现2种不同程度故障时的实验数据为例,详细地说明了该方法的可行性,取得了准确的结果。     

基于相对小波包能量特征向量的故障诊断

在小波及小波包分析的基础上引进相对小波包能量特征向量的概念,提出了一种建立旋转机械故障库的新方法,并结合模式判别的知识,引进欧几里德距离公式作为判据,使故障诊断公式化、定量化。实验结果表明这种方法能够很快地诊断出转子台故障类型,为旋转机械的故障诊断提供了新方向。