关键词： 疾病负担   生存质量   发病风险   机器学习   深度学习   大数据   慢性病   防控工作  

摘要： 近年来,我国慢性病发病率呈现逐年上升趋势,由此造成沉重的疾病负担[1];同时,慢性病并发症所带来的致死和致残现象极为严峻,已成为影响我国居民健康和生存质量的重大威胁[2]。因而,对慢性病发病风险进行预测,以便及早采取积极的干预措施,对于疾病的防控工作显得尤为重要。机器学习作为一类经典的预测模型,具有良好的预测性能[3],在慢性病研究中已经得到了较为广泛的应用[4-6]。另外,随着大数据的积累以及云计算的普及,基于健康大数据开展深度学习正成为慢性病研究领域的热点,并有望为智能医疗和健康决策提供强有力的支撑。

关键词： 海洋天然气水合物   地层稳定性   数值模拟   机器学习   混合整数   优化模型   开采方案制订   水平井  

摘要： 由天然气水合物(以下简称水合物)开采引起的含水合物沉积层力学变形问题不能被忽视,因其直接威胁到海域水合物的安全开采。为了找到甲烷累计产气量最优值与地层稳定性的关系,基于机器学习方法形成模拟-优化耦合技术,构建起水合物降压开采传热-流动-力学数值模拟模型、可以替代数值模拟模型的机器学习模型和以甲烷累计产气量最优为目标的混合整数非线性规划优化模型;在此基础上,选取南海北部神狐海域厚层Ⅱ类水合物藏W11站位为研究对象,获得了海底面沉降量约束下的水合物储层甲烷累计产气量及相对应的最优开采方案参数。研究结果表明:①模拟-优化耦合技术的关键是机器学习方法的运用,基于径向基函数人工神经网络方法而建立的替代模型计算精度较高,可以替代模拟模型来确定输入输出变量的关系,从而摆脱既定方案的限制,找到全局最优解;②模拟-优化耦合技术可以解决受含水合物沉积层力学响应特征影响的水合物开采方案优选问题,根据试采工程安全要求改变海底面沉降量最大允许值,可以计算得到相应的甲烷累计产气量,以及降压幅度、开采时间、井位布置、水平井段长度等最优开采方案参数;③随着最大允许沉降量增大,甲烷累计产气量增大,二者满足正相关关系;④海底面沉降量随着开采时间增长而增大,也随降压幅度增大而增大;⑤水合物开采引起海底面沉降主要发生在开采初期,为了获得较高甲烷累计产气量及较小海底面沉降量最大允许值,在开采初期必须减小降压幅度。结论认为,所形成的模拟-优化耦合技术适用性强,可以为水合物安全、高效规模化开采方案的制订提供支撑。

关键词： 机器学习   方法   SAR   图像分类   系统建构  

摘要： 本文在对图像分类与识别过程中运用机器学习方法,进行不同机器算法下的SAR-ATR系统构建,运用Adaboost算法、PCA算法、NMF算法等进行研究,对SAR图像进行分类对比实验,研究可见Adaboost算法检测识别效果有限,可能陷入局部最优的困境之中。NMF算法、PCA算法在对SAR图像分析处理过程中不需要进行图像的预处理,两者联合在图图像特征的提取层面具有良好的代表性。

关键词： 钙钛矿   结构稳定性   SISSO   新型容许因子  

摘要： 由于钙钛矿型材料具有广泛的应用前景,因此对其结构及物理、化学性质的研究一直是材料研究领域的热点之一。其中,利用容许因子(Tolerance factor)来预测钙钛矿型材料的结构稳定性可以帮助研究者发现更多的新型功能材料,而传统的基于离子半径定义的容许因子tIR存在一定的局限性。本文基于SISSO(Sure independence screening and sparsifying operator)方法和键价模型提出一种新型的容许因子τBV,其可以有效地避免由离子半径带来的局限性。本工作使用机器学习中的决策树算法建立容许因子验证模型,实验结果表明,新型容许因子τBV可以很好地预测ABO3型化合物是否具有钙钛矿结构,并大大提高了预测精度。

关键词： GNSS-R   海面风速   反演   机器学习  

摘要： GNSS-R是基于GNSS卫星反射信号的一种新技术.GNSS-R技术可以运用到海面风场反演中,传统的GNSS-R技术反演海面风场主要有波形匹配和经验函数两种方法,风速反演精度约为2m·s^-1.波形匹配方法耗时多,计算量大;经验函数方法通常只使用少量物理观测量,会造成信息浪费,损失一定的反演精度.为了提高海面风速的反演精度,引入机器学习领域常用的树模型算法决策树、随机森林、GBDT等对海面风速进行预测.利用GNSS-R与ECMWF数据构成训练集和验证集,训练集用于模型学习,验证集用于检验模型的反演效果.实验结果显示,决策树和随机森林预测误差约为0.6 m·s^-1,GBDT等算法的预测误差约为2 m·s^-1,满足风速反演要求.与GNSS-R传统反演方法相比,机器学习树模型算法效果更好,在验证集上表现稳定且误差较小.因此,可以将机器学习树模型算法运用到海面风速反演中.

关键词： Computational models   Biomechanics   Artificial intelligence   Musculoskeletal  

摘要： Many biomedical, orthopaedic, and industrial applications are emerging that will benefit from personalized neuromusculoskeletal models. Applications include refined diagnostics, prediction of treatment trajectories for neuromusculoskeletal diseases, in silico design, development, and testing of medical implants, and human-machine interfaces to support assistive technologies. This review proposes how physics-based simulation, combined with machine learning approaches from big data, can be used to develop high-fidelity personalized representations of the human neuromusculoskeletal system. The core neuromusculoskeletal model features requiring personalization are identified, and big data/machine learning approaches for implementation are presented together with recommendations for further research.

关键词： AdaBoost   EC   Lasso regression   NO2   Random forest regression  

摘要： Narrow city streets surrounded by tall buildings are favorable to inducing a general effect of a "canyon" in which pollutants strongly accumulate in a relatively small area because of weak or inexistent ventilation. In this study, levels of nitrogen-oxide (NO2), elemental carbon (EC) and organic carbon (OC) mass concentrations in PM10 particles were determined to compare between seasons and different years. Daily samples were collected at one such street canyon location in the center of Zagreb in 2011, 2012 and 2013. By applying machine learning methods we showed seasonal and yearly variations of mass concentrations for carbon species in PM10 and NO2, as well as their covariations and relationships. Furthermore, we compared the predictive capabilities of five regressors (Lasso, Random Forest, AdaBoost, Support Vector Machine and Partials Least squares) with Lasso regression being the overall best performing algorithm. By showing the feature importance for each model, we revealed true predictors per target. These measurements and application of machine learning of pollutants were done for the first time at a street canyon site in the city of Zagreb, Croatia. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Landslide   Natural hazard   Machine learning   Random forest   Susceptibility  

摘要： Landslides are one of the catastrophic natural hazards that occur in mountainous areas, leading to loss of life, damage to properties, and economic disruption. Landslide susceptibility models prepared in a Geographic Information System (GIS) integrated environment can be key for formulating disaster prevention measures and mitigating future risk. The accuracy and precision of susceptibility models is evolving rapidly from opinion-driven models and statistical learning toward increased use of machine learning techniques. Critical reviews on opinion-driven models and statistical learning in landslide susceptibility mapping have been published, but an overview of current machine learning models for landslide susceptibility studies, including background information on their operation, implementation, and performance is currently lacking. Here, we present an overview of the most popular machine learning techniques available for landslide susceptibility studies. We find that only a handful of researchers use machine learning techniques in landslide susceptibility mapping studies. Therefore, we present the architecture of various Machine Learning (ML) algorithms in plain language, so as to be understandable to a broad range of geoscientists. Furthermore, a comprehensive study comparing the performance of various ML algorithms is absent from the current literature, making an assessment of comparative performance and predictive capabilities difficult. We therefore undertake an extensive analysis and comparison between different ML techniques using a case study from Algeria. We summarize and discuss the algorithm's accuracies, advantages and limitations using a range of evaluation criteria. We note that tree-based ensemble algorithms achieve excellent results compared to other machine learning algorithms and that the Random Forest algorithm offers robust performance for accurate landslide susceptibility mapping with only a small number of adjustments required before training

关键词： drone   anthropogenic debris   OBIA   random forest   support vector machine   k-nearest neighbor  

摘要： Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments.

关键词： epoxidation   site of metabolism   machine learning   classification model  

摘要： Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxidation experimentally. In silico models based on big data and machine learning methods are hence valuable approaches to predict whether a compound would undergo epoxidation. In this study, we collected 884 epoxidation data manually from various sources, and finally got 829 unique sites of epoxidation. Three types of molecular fingerprints with different lengths (1024, 2048 or 4096 bits) were used to describe the reaction sites. Six machine learning methods were used to build the classification models. The training set and test set were randomly divided into 8 : 2, and 54 models were constructed and evaluated. Four best models were selected for feature selection. The features were then chosen and verified by external validation set. The resulted optimal model had the accuracy and AUC (area under the curve) values at 0.873 and 0.944 for the test set, 0.838 and 0.987 for the external validation set, respectively. The models built in this study could accurately predict whether a compound will undergo epoxidation and which part is most susceptible to epoxidation, which is of great significance for drug design.