关键词： process equipment   detection of abnormalities   predictive analytics   committee of classifiers   logistic regression  

摘要： -The article discusses a method for early detection and prediction of abnormality in operation of power-unit process equipment taking as an example the PTN 1100-350-17-4 turbine driven feedwater pump of a 300 MW power unit. The importance of the problem of predicting possible process equipment malfunctions at an early state of their occurrence is determined, and the specific features of solving it in the power industry are explained. The range of process equipment defects that can be efficiently detected using the predictive analytics methods is outlined. The fundamental assertion stating that the scope of analog and discrete measurements available in the process control system's set of computerized automation tools is sufficient for applying the predictive analytics methods is emphasized. Modern predictive analytics methods are briefly reviewed, and the specific features of model training algorithms are mentioned. Separate attention is paid to the problems of preparing initial data for training the model. The mathematical problem of modeling an abnormality indicator taking the values from 0 (normal operation) to 1 (abnormal operation) is formulated. In turn, this problem is formulated as the binary classification problem of attribute vectors characterizing the equipment state at the given moment of time. An original approach is suggested, which combines the multivariate state estimation technique (MSET), in which the degree of abnormality in a technical state is determined from the extent to which the Hotelling criterion exceeds a threshold level (which is automatically calculated in the algorithm), and machine learning methods, the use of which makes it possible to overcome a number of difficulties inherent in the MSET. For solving the problem of determining the composition of the most informative attributes from the values of which early development of an emergency can be detected, it is proposed to use an ensemble of regression models. A method for selecting the

关键词： Fault detection and location   Modular multilevel converter   Mixed kernel support tensor machine   Machine learning  

摘要： In this paper, the fault diagnosis and location (FDL) problem of the open circuit fault for modular multilevel converter (MMC) is investigated. A mixed kernel support tensor machine (MKSTM) is provided, and it's employed to improve the support tensor machine which is an important algorithm of machine learning. By extracting the characteristic data of ac current and internal circulation current in either normal operation or open-circuit fault, then training and classifying the obtained samples with MKSTM, FDL of MMC can be realized with the supplied algorithm. Finally, experimental results show that the classification accuracy of MKSTM algorithm is improved observably than single kernel function STM such as linear, Radial Basis function (RBF), sigmoid and polynomial types. Synchronously, the open-circuit fault can be effectively diagnosed and located with the proposed method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： MACHINE learning   METHYLCYTOSINE   EPIGENETICS   DNA repair   ELECTRON-ion collisions  

摘要： Motivation: N-4-methylcytosine (4mC), an important epigenetic modification formed by the action of specific methyltransferases, plays an essential role in DNA repair, expression and replication. The accurate identification of 4mC sites aids in-depth research to biological functions and mechanisms. Because, experimental identification of 4mC sites is time-consuming and costly, especially given the rapid accumulation of gene sequences. Supplementation with efficient computational methods is urgently needed. Results: In this study, we developed a new tool, 4mCPred, for predicting 4mC sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Escherichia coli, Geoalkalibacter subterraneus and Geobacter pickeringii. 4mCPred consists of two independent models, 4mCPred_I and 4mCPred_II, for each species. The predictive results of independent and cross-species tests demonstrated that the performance of 4mCPred_I is a useful tool. To identify position-specific trinucleotide propensity (PSTNP) and electron-ion interaction potential features, we used the F-score method to construct predictive models and to compare their PSTNP features. Compared with other existing predictors, 4mCPred achieved much higher accuracies in rigorous jackknife and independent tests. We also analyzed the importance of different features in detail.

关键词： 平滑肌肉瘤   表达谱   机器学习   分子分型   预测标签  

摘要： [目的]应用机器学习方法,利用TCGA与GEO数据,构建平滑肌肉瘤分子分型预测标签。[方法]收集TCGA和GSE45510数据,建立机器学习模型,利用pamr包预测标签,将预测亚型与实际值对比以评估预测效果。[结果]经测试,阈值取6.639时,分类准确率为87.8%,相关基因159个。[结论]利用机器学习方法构建平滑肌肉瘤分子分型预测标签,可以用于预测平滑肌肉瘤分子亚型,提高诊断精度,指导未来平滑肌肉瘤亚型特异性靶向治疗,推动精准治疗发展。

关键词： 土地利用/土地覆被分类   Landsat   OLI影像   机器学习   人工神经网络   决策树   支持向量机   随机森林   湟水流域  

摘要： 针对高海拔复杂地形区地貌类型复杂、多样,沟壑纵横、地形破碎等特点,研究快速、有效的土地利用/土地覆被分类方法对土地利用信息获取及更新是非常重要的。以位于黄土高原向青藏高原过渡带的湟水流域为研究区域,基于Landsat 8 OLI影像数据、DEM数据,并结合各种专题特征,在对研究区进行地理分区的基础上,采用人工神经网络、决策树、支持向量机和随机森林4种机器学习方法进行土地利用信息提取并进行精度评价,探索适合于复杂地形区最优的分类方法。研究结果表明:随机森林和决策树的分类精度明显高于支持向量机和人工神经网络。其中随机森林方法的分类精度最高,总体分类精度达85.65%,Kappa系数达0.84。在上述分类基础上,选择随机森林分类方法对Landsat 8全色与多光谱影像融合数据进行进一步的分类研究,总体分类精度达到86.49%,Kappa系数达0.85。这表明随机森林分类方法在保证分类精度的同时又能获得较高的分类效率,对于复杂地形区土地利用信息提取是非常有效的,数据融合在一定程度上提高了分类精度。

关键词： crop identification   pre-constrained classification method   multi-year mapping   crop phenology   support vector machine   random forest  

摘要： The accurate mapping of crops can provide effective information for regional agricultural management, which is helpful to improve crop production efficiency. Recently, remote sensing data offers a comprehensive approach to achieve crop identification on a regional scale. However, the classification methods for multi-year mapping needs further study in regions with a complex planting structure, due to the mixed pixels at a spatial distribution and the high error in different years at a temporal scale. The objective of this study is to map the multi-year spatial distribution of three main crops (maize, sunflower, and wheat) in the Hetao irrigation district of China for the period 2012-2016 based on a pre-constrained classification method. The pre-constrained method integrates a parameterized phenology-based vegetation indexes classifier and two non-parametric machine learning algorithms-support vector machine (SVM) and random forest (RF). Results indicated that the performance of the pre-constrained classification method was excellent in the multi-year mapping of major crops in the study area, with absolute relative errors mainly less than 14% in the whole irrigation district and less than 20% in the five counties. The corresponding overall accuracy was 87.9%, and the Kappa coefficient was 0.80. Mapping results showed that maize is mainly distributed in Hangjinhouqi, southern Linhe, northern Wuyuan, and eastern Wulateqianqi, while wheat is relatively less and scatteredly distributed in Hangjinhouqi and Wuyuan. Moreover, the sunflower planting area increased significantly and expanded spatially from Wuyuan and western Wulateqianqi to northern Hangjinhouqi and Linhe from 2012 to 2016. In addition, the phenology-based vegetation indexes classifier was found to be effective in improving the classification accuracy based on the contribution analysis.

关键词： multilayer soil moisture mapping   RF method   remote sensing   ground monitoring  

摘要： Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m(3)/m(3)) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m(3)/m(3)). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

关键词： 机器学习方法   滑动   断层   预测   科学家   宾夕法尼亚州立大学   洛斯阿拉莫斯   地震信号  

摘要： 2018 年12 月,Nature Geoscience 发表了两篇涉及机器学习与地震研究的论文。这两篇论文的主要研究者来自美国洛斯阿拉莫斯国家实验室(Los Alamos National Laboratory),同时还有来自宾夕法尼亚州立大学(Pennsylvania State University)的合作者。研究人员利用机器学习方法检测地震信号,准确地预测了卡斯卡迪亚(Cascadia)断层的缓慢滑动?这种破裂在其他俯冲带发生大地震之前曾观测到。

关键词： 神经网络   机器学习   人工智能  

摘要： 随着科学技术的不断发展,以及智能化新的需要,人工智能技术逐渐走进人们的视野。而随着神经网络技术的不断发展,机器学习也有新的理论出现。本文通过分析神经网络技术,研究生物神经网络在机器学习方法中的发展现状及趋势,讨论神经网络机器学习的技术与应用。