关键词： Additive   manufacturing   3D   printing   Neural   network   Machine   learning   Algorithm  

摘要： Additive manufacturing(AM),also known as three-dimensional printing,is gaining increasing attention from academia and industry due to the unique advantages it has in comparison with traditional subtractive ***,AM processing parameters are difficult to tune,since they can exert a huge impact on the printed microstructure and on the performance of the subsequent *** is a difficult task to build a process-structure-property-performance(PSPP)relationship for AM using traditional numerical and analytical ***,the machine learning(ML)method has been demonstrated to be a valid way to perform complex pattern recognition and regression analysis without an explicit need to construct and solve the underlying physical *** ML algorithms,the neural network(NN)is the most widely used model due to the large dataset that is currently available,strong computational power,and sophisticated algorithm *** paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain,including model design,in situ monitoring,and quality *** challenges in applying NNs to AM and potential solutions for these problems are then ***,future trends are proposed in order to provide an overall discussion of this interdisciplinary area.

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： 小推力   燃料最优   快速估计   机器学习  

摘要： 深空探测任务设计初段往往需要求解复杂的全局优化问题。小推力轨迹的设计与优化问题精确求解较为复杂,求解速度较慢。由于计算能力与时间要求,不可能在全局优化的过程中对每一个方案都进行精确的小推力数值求解,所以在全局优化阶段需要对小推力转移进行快速准确地估计。采用机器学习的方法,对燃料最优小推力转移的燃料消耗进行了估计,其结果明显优于目前最为常用的Lambert估计方法。根据轨道描述方法的不同以及是否带有Lambert估计特征,采用不同的特征组合进行机器学习,分析结果发现带有Lambert估计特征的春分点轨道根数的特征组合为较好的机器学习特征组合。可为未来深空探测任务轨道设计提供参考。

关键词： 智能机器人   机器学习   传感器   智能决策  

摘要： 人工智能技术的发展程度反映了中国高新科技领域的实力,同时也是未来工业革命的关键技术。智能机器人又是人工智能领域发展的重要一环,该方向全面揭示了当前人工智能领域的发展程度,并详细展示人工智能和智能机器人之间的紧密关系。本文将简略说明智能机器人的发展情况,以及智能机器人的构成部分,阐述基于机器学习方法的智能机器人的使用原理,最后将对智能机器人的应用方向和未来发展等几个部分展开分析。

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.