关键词： 功能材料   材料基因组   机器学习   特征工程   深度学习  

摘要： 近年来,在功能材料领域通过手工方式从数百上千的候选结构中挖掘合理的功能材料结构.该方法过度依赖于经验知识,无法满足材料科学高效精准的结构预测.为了克服该问题,初步探究面向功能材料属性预测的机器学习方法,通过深入结合统计机器学习方法和预测功能材料结构,进而挖掘合理的影响材料结构属性的因素.具体而言,通过不同带隙建立材料数据,利用传统机器学习和深度学习算法搜索出可能的材料结构属性,进而利用DFT计算确定最优材料结构.从钙钛矿带隙数据和具有判别和表达能力的属性之间确定一组封闭的结构-性质关系映射,通过利用机器学习技术所学到的关系映射从精度和效率两个方面优化提升功能材料设计模式.

关键词： 机器学习   早产儿   低出生体重儿   逻辑回归   支持向量机   随机森林  

摘要： 应用机器学习方法构建早产儿和低出生体重儿的预测模型,包括逻辑回归、支持向量机和随机森林算法,运用交叉验证法得到不同算法的最优模型,综合准确率、F1值和AUC值评估3种模型的预测性能,结果表明基于随机森林算法的模型预测效果最好。

关键词： Vehicle simulation   Energy consumption estimation   Artificial neural networks   Machine learning   Numerosity reduction   Random sampling   Autonomie   CAFE  

摘要： Vehicle energy consumption and powertrain operations for future vehicle powertrain technologies are predicted through full-vehicle simulations using Autonomie, a simulation tool developed at Argonne National Laboratory. Over 1 million vehicles, with different powertrain types (conventional and numerous electrified powertrains) and component technologies (advanced engines, batteries, electrical machines, light weighting) are simulated for outputs including vehicle fuel economy and cost. Despite the ability of Autonomic to predict fuel economy given various combination of vehicle inputs, the full-vehicle simulation is very time-consuming. It takes approximately 84 h to simulate for 33,060 vehicles even using distributed computing on a cluster of 128 worker nodes. Moreover, Autonomic is unable to populate a continuous output space through simulation since it only yields outputs with discrete values instead of an input-output relation. This paper proposes a novel large-scale learning and prediction process (LSLPP) via machine learning approaches to answer the need for a continuous space of vehicle fuel economy and a more efficient simulation process. By learning in a supervised fashion to discover structures in data, the machine learning approaches seek to train a model that can explain the data with which we can perform prediction over unseen data. As such, we are able to accomplish analytic continuation on the current discrete space of simulation results. In addition, we present a random sampling -based numerosity reduction algorithm incorporated in LSLPP that reduces simulation runs and improves efficiency of data procurement. We analytically derive a theoretical bound in order to guarantee the effectiveness of our algorithm. We also compare random sampling to stratified sampling and suggest sampling strategies. A user-friendly tool is developed to support the process. Our experimental results confirm that the proposed LSLPP is able to greatly accelerate prediction a

关键词： Renewable energy   Wind farms   Condition monitoring   Machine learning   Prognostic maintenance  

摘要： This paper reviews the recent literature on machine learning (ML) models that have been used for condition monitoring in wind turbines (e.g. blade fault detection or generator temperature monitoring). We classify these models by typical ML steps, including data sources, feature selection and extraction, model selection (classification, regression), validation and decision-making. Our findings show that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression. Neural networks, support vector machines and decision trees are most commonly used. We conclude with a discussion of the main areas for future work in this domain. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licensesiby/4.0/).

关键词： Cyberattacks detection   cybersecurity   application layer   anomaly detection  

摘要： Currently, we can observe that vulnerable web pages are a common element in many high-profile cyberattacks. On the other hand, web applications (as well as web services) have become an inherent element of many supply chains. In order to protect the web services against some of the cyberattacks, one can adapt web application firewalls (WAF) solutions. However, quite often a challenge for typical WAF software is the fact that HTTP can be used as transport by other protocols or as a mix of other various encoding techniques. Therefore, in this paper, we propose a flexible schema for automated application layer request structure identification. Our experiments show that this technique allows us to further improve the effectiveness of known detection methods used in this research domain. In this paper, we show the possible practical application of the proposed cybersecurity solution in the public domain, namely public administration web-based systems and healthcare sectors. From the scientific point of view, we presented the results of various classifiers and pattern extraction techniques. We have also addressed the possible deployment of the proposed solution in Big Data-enabled environments.

关键词： Food security   Poverty   Dietary diversity   Program targeting   Machine learning  

摘要： Identifying food insecure households in an accurate and cost-effective way is important for targeted food policy interventions. Since predictive accuracy depends partly on which indicators are used to identify food insecure households, it is important to assess the performance of indicators that are relatively easy and inexpensive to collect yet can proxy for the "gold standard" food security indicator, calorie intake. We study the effectiveness of different variable combinations and methods in predicting calorie-based food security among poor households and communities in rural Bangladesh. We use basic household information as a benchmark set for predicting calorie-based food security. We then assess the gain in predictive power obtained by adding subjective food security indicators (e.g., self-reported days without sufficient food), the dietary diversity score (DDS), and the combination of both sets to our model of calorie-based food security. We apply machine learning as well as traditional econometric methods in estimation. We find that the overall predictive accuracy rises from 63% to 69% when we add the subjective and DDS sets to the benchmark set. Our study demonstrates that while alternative indicators and methods are not always accurate in predicting calorie intake, DDS related indicators do improve accuracy compared to a simple benchmark set.

关键词： Retina   Blood vessel   Image processing   Vessel extraction   Classification   Clustering  

摘要： In medicine, retinal vessel analysis of fundus images is a prominent task for the screening and diagnosis of various ophthalmological and cardiovascular diseases. In this research, a method is proposed for extracting the retinal blood vessels employing a set of effective image features and combination of supervised and unsupervised machine learning techniques. Further to the common features used in extracting blood vessels, three strong features having a significant influence on the accuracy of the vessel extraction are utilized. The selected combination of the different types of individually efficient features results in a rich local information with better discrimination for vessel and non-vessel pixels. The proposed method first extracts the thick and clear vessels in an unsupervised manner, and then, it extracts the thin vessels in a supervised way. The goal of the combination of the supervised and unsupervised methods is to deal with the problem of intra-class high variance of image features calculated from various vessel pixels. The proposed method is evaluated on three publicly available databases DRIVE, STARE and CHASE_DB1. The obtained results (DRIVE: Acc = 0.9531, AUC = 0.9752;STARE: Acc = 0.9691, AUC = 0.9853;CHASE_DB1: Acc = 0.9623, AUC = 0.9789) demonstrate the better performance of the proposed method compared to the state-of-the-art methods.

关键词： Statistics  

摘要： The aim of this work is to investigate the possibility of using machine learning (ML) methods in order to generate novel, safety-relevant knowledge from existing flight data. Airlines routinely generate vast amounts of flight data from routine monitoring, but the concept of extracting safety knowledge from this data is still based on detecting exceedances of expert-defined thresholds. This system is conceptually unable to detect novel occurrences for which no such filters exist. ML techniques are able to close this gap. This paper first reviews the literature to select an appropriate ML method. A form of unsupervised learning called "Local Outlier Probability" is selected. Next, an appropriate feature space is developed and implemented in the flight data monitoring system of a supporting airline to generate the dataset. This dataset is cleaned and the outlier calculation performed. The results are statistically analysed. Furthermore, the top outliers are reviewed by the airline's review pilots in the same way as the traditional exceedance events. Last, the severities and safety relevance of both types of events are compared. This work successfully shows that the chosen approach is able to reduce the number of undetected safety-relevant occurrences by finding novel occurrence types which were undetected by a contemporary and mature flight data monitoring system. This research builds on recent literature by developing a novel method which can be scaled to work in an airline production environment with large datasets, as demonstrated by the efficient analysis of 1.2 million flights.

关键词： energy efficiency   GPGPU   machine learning   mixed precision   MPDATA   random forest  

摘要： In this work, we propose a method that allows us to reduce energy consumption of an application executed on supercomputing centers. The proposed method is based on a mixed precision arithmetic where the precision of data is calibrated at runtime. For this reason, we develop a modified version of the random forest algorithm. The effectiveness of the proposed approach is validated with a real-life scientific application called MPDATA, which is part of the numerical model used in weather forecasting. The energy efficiency of the proposed method is examined using two GPU-based clusters. The first of them is the Piz Daint supercomputer, currently ranked 3rd at the TOP500 list (November 2017). It is equipped with NVIDIA Tesla P100 GPU accelerators based on the Pascal architecture. The second is the MICLAB cluster containing NVIDIA Tesla K80 based on the Kepler architecture. The achieved results show that the proposed machine learning method allows us to provide the accuracy of computation comparable with that achieved double precision and reduce the energy consumption up to 36% compared to the double precision version of MPDATA.

关键词： Machine learning   anomaly detection   deep learning   extreme learning machine   smart water grids   water quality  

摘要： Traditional machine learning (ML) techniques such as support vector machine, logistic regression, and artificial neural network have been applied most frequently in water quality anomaly detection tasks. This paper presents a review of progress and advances made in detecting anomalies in water quality data using ML techniques. The review encompasses both traditional ML and deep learning (DL) approaches. Our findings indicate that: 1) Generally, DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive rates. However, it is difficult to make a fair comparison between studies because of different datasets, models and parameters employed. 2) We notice that despite advances made and the advantages of the extreme learning machine (ELM), its application is sparsely exploited in this domain. This study also proposes a hybrid DL-ELM framework as a possible solution that could be investigated further and used to detect anomalies in water quality data.