关键词： Comprehensive two-dimensional gas chromatography   GCxGC   Machine learning   Classification   Data mining  

摘要： Machine learning (ML) has been used previously to recognize particular patterns of constituent compounds. Here, ML is used with comprehensive chemical fingerprints that capture the distribution of all constituent compounds to flexibly perform various pattern recognition tasks. Such pattern recognition requires a sequence of chemical analysis, data analysis, and pattern analysis. Chemical analysis with comprehensive multidimensional chromatography is a maturing approach for highly effective separations of complex samples and so provides a solid foundation for undertaking comprehensive chemical fingerprinting. Data analysis with smart templates employs marker peaks and chemical logic for chromatographic alignment and peak-regions to delineate chromatographic windows in which analytes are quantified and matched consistently across chromatograms to create chemical profiles that serve as complete fingerprints. Pattern analysis uses ML techniques with the resulting fingerprints to recognize sample characteristics, e.g., for classification. Our experiments evaluated the effectiveness of seventeen different ML techniques for various classification problems with chemical fingerprints from a rich data set from 126 wine samples of different varieties, geographic regions, vintages, and wineries. Results of these experiments showed an accuracy range from 58% to 88% for different ML methods on the most difficult classification problems and 96% to 100% for different ML methods on the least difficult classification problems. Averaged over 14 classification problems, accuracy for the different methods ranged from 80% to 90%, with some relatively simple ML techniques among the top-performing methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Aluminium electrolysis   Electrolyte   Liquidus temperature   Prediction   Support vector machine   Machine learning method  

摘要： Liquidus temperature is an important physicochemical property parameter of electrolyte system. It is significant to construct a practical model to predict the liquidus temperature of electrolyte with universal adaptability, since the current formulas is unsatisfactory with many limitations. In this work, different kinds of machine learning algorithms were used to explore the correlations of the liquidus temperature with the components of electrolyte. It was found that the performance of SVM(Support vector machine) model and GBR(Gradient boosting regression) model were better than those of PIS (Partial least squares regression), MLR(Multiple linear regression), ANN(Artificial neural network) and RFR(Random forest regression) for predicting liquidus temperatures with the component of electrolyte. Furthermore, the optimal models were applied to 20 independent electrolyte samples to predict the liquidus temperatures. The predicted results based on SVM model is also much better than that of the linear formula reported in the literature and GBR model. The SVM model outlined here can provide the accurate prediction method of the liquidus temperature for industrial complex electrolyte, it is crucial for balance and stability of the electrolytic cell in the process of electrolytic aluminum production.

关键词： focuses on   robotics   Machine Learning   Learning methods  

关键词： GNSS-IR   NMRI   vegetation water content   artificial neural network   random forest   drought  

摘要： Vegetation water content (VWC) is recognized as an important parameter in vegetation growth studies, natural disasters such as forest fires, and drought prediction. Recently, the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an important technique for monitoring vegetation information. The normalized microwave reflection index (NMRI) was developed to reflect the change of VWC based on this fact. However, NMRI uses local site-based data, and the sparse distribution hinders the application of NMRI. In this study, we obtained a 500 m spatially continuous NMRI product by integrating GNSS-IR site data with other VWC-related products using the point-surface fusion technique. The auxiliary data in the fusion process include the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and precipitation. Meanwhile, the fusion performance of three machine learning methods, i.e., the back-propagation neural network (BPNN), generalized regression neural network (GRNN), and random forest (RF) are compared and analyzed. The machine learning methods achieve satisfactory results, with cross-validation R values of 0.71-0.83 and RMSEs of 0.025-0.037. The results show a clear improvement over the traditional multiple linear regression method, which achieves R (RMSE) values of only about 0.4 (0.045). It indicates that the machine learning methods can better learn the complex nonlinear relationship between NMRI and the input VWC-related index. Among the machine learning methods, the RF model obtained the best results. Long time-series NMRI images with a 500 m spatial resolution in the western part of the continental U.S. were then obtained. The results show that the spatial distribution of the NMRI product is consistent with a drought situation from 2012 to 2014 in the U.S., which verifies the feasibility of analyzing and predicting drought times and distribution ranges by using the 500 m fusion product.

关键词： 钢材   机器学习方法   技术开发   计算机学   电子显微镜   材料学   兰州大学   材料结构  

摘要： 德国萨尔兰州大学的计算机学和材料学专家通过机器学习技术开发了一种分析钢材类别的软件,其比其他分析软件更客观准确。在开发过程中,材料学专家需帮助计算机人员了解材料结构与性能间的关系,同样,计算机人员也帮助材料学家学习使用机器学习方法,以便得到更为精确的结果。这款软件主要关注钢材的微观结构,要求钢产品生产过程对金属内部结构的影响。在材料开发和质量控制阶段,传统使用光学电子显微镜进行样品检测,而使用新技术后,材料专家们发现用户使用时的错误率更低,它对客户的专业水平要求不高。

关键词： Distributed energy systems   Supervised learning   Transfer-learning   Multi-objective optimization  

摘要： This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique (by using artificial neural network) in order to bypass computationally intensive Actual Engineering Model (AEM). Eight different neural network architectures are considered in the process of developing the surrogate model. Subsequently, a hybrid optimization algorithm (HOA) is developed combining Surrogate and AEM in order to speed up the optimization process while maintaining the accuracy. Pareto optimization is conducted considering Net Present Value and Grid Integration level as the objective functions. Transfer learning is used to adapt the surrogate model (trained using supervised learning technique) for different scenarios where solar energy potential, wind speed and energy demand are notably different. Results reveal that the surrogate model can reach to Pareto solutions with a higher accuracy when grid interactions are above 10% (with reasonable differences in the decision space variables). HOA can reach to Pareto solutions (similar to the solutions obtained using AEM) around 17 times faster than AEM. The Surrogate Models developed using Transfer Learning (SMTL) shows a similar capability. SMTL combined with the optimization algorithm can predict Pareto fronts efficiently even when there are significant changes in the initial conditions. Therefore, STML can be used along with the HOA, which reduces the computational time required for energy system optimization by 84%. Such a significant reduction in computational time enables the approach to be used for energy system optimization at regional or national scale.

关键词： 信号处理方法   智能化处理   大数据   征稿通知   机器学习方法   信息技术   社会生活   学习能力  

摘要： 随着信息技术的高速发展,在现代社会生活中每天都有着各式各样的大量数据产生。大数据时代已然来临,而如何合理地存储、处理以及利用这些数据就成为了受到广泛关注的焦点性问题。一方面,大数据更加依赖具有学习能力、推理能力和优化能力的智能信号处理方法;另一方面,大数据为智能信号处理方法的发展提供了条件。大数据时代的智能信号处理是以信号模型为驱动,以大样本为基础,通过机器学习方法提取信号特征,进而实现对信号的智能化处理和利用。本次征文的主要目的是给大数据时代下的智能信号处理提供新的视角和思路。

关键词： 心电信号   分类   ARMA模型   机器学习   心律失常  

摘要： 近年来,心血管疾病已成为威胁人们生命的主要疾病,而心血管疾病患者早期发病时往往出现心律失常症状,因此早期检测心律失常对提前预防心血管疾病,及早介入治疗具有至关重要的意义。基于此,本研究利用小波分析方法对不同类型的心电序列进行去噪声处理,再利用ARMA模型以及时间序列的方法对分解后的信号序列进行特征提取,将ARMA模型的系数作为心电信号的特征指标;结合决策树分类、支持向量机分类和随机森林分类机器学习方法对提取后的心电特征指标进行分类研究,并根据实验结果对三种算法的性能进行比较分析。

关键词： 气象观测   社会化   数据获取   应用   技术   爬虫   观测数据   机器学习方法  

摘要： 如何获取及利用网络中蕴含的大量社会观测数据成为新媒体时代气象观测发展的重点。深圳市气象局利用爬虫技术获取数据,并通过机器学习方法对数据进行筛选过滤,建立了一套高扩展性、高效性和低成本的气象社会观测数据的采集系统,快速获取、筛选、分析和提取有价值的、多样化的气象相关的社会观测数据,并对数据加以分析应用,为预报员进行公众服务、大城市气象灾害风险预警提供支撑。

关键词： 气动特性建模   机器学习   分类与回归树   浅层学习   深度学习  

摘要： 气动数据建模是飞行性能仿真评估的基础。气动特性建模主要有机理建模方法和“黑箱”建模方法。本文对“黑箱”建模的三类机器学习方法——分类与回归树方法、浅层学习方法和深度学习方法,进行了算法说明与分析应用。将分类与回归树方法、浅层学习方法中的Kriging建模方法、RBF神经网络方法及SVM支持向量机方法分别应用于火箭气动特性建模、三角翼大迎角非定常气动特性建模、气动热试验数据融合,对这几类建模方法的优势和不足进行了比较分析。同时,将流动条件参数组成向量,再映射为图像,与翼型图像构成“合成图像”,建立了基于翼型几何图像、来流马赫数、迎角的翼型气动特性深度神经网络模型,得到了比较好的预测效果,拓展了气动特性深度学习建模方法的使用范围。