关键词： aerosol jet printing   direct writing   hybrid machine learning   line morphology   operating process window   quality optimization  

摘要： Aerosol jet printing (AJP) is a three-dimensional (3D) noncontact and direct printing technology for fabricating customized microelectronic devices on flexible substrates. Despite the capability of fine feature deposition, the complicated relationship between the main process parameters will affect the printing quality significantly in a design space. In this paper, a novel hybrid machine learning method is proposed to determine the optimal operating process window for the AJP process in various design spaces. The proposed method consists of classic machine learning methods, including experimental sampling, data clustering, classification, and knowledge transfer. In the proposed method, a two-dimensional design space is fully explored by a Latin hypercube sampling experimental design at a certain print speed. Then, the influence of the sheath gas flow rate (SHGFR) and the carrier gas flow rate (CGFR) on the printed line quality is analyzed by a K-means clustering approach, and an optimal operating process window is determined by a support vector machine. To efficiently identify more operating process windows at different print speeds, a transfer learning approach is applied to exploit relatedness between different operating process windows. Hence, at a new print speed, the number of line samples for identifying a new operating process window is greatly reduced. Finally, to balance the complex relationship among SHGFR, CGFR, and print speed, a 3D operating process window is determined by an incremental classification approach. Different from experiment-based approaches adopted in 3D printing technologies for quality optimization, the proposed method is developed based on the theory of knowledge discovery and data mining. Therefore, the knowledge in different design spaces can be fully explored and transferred for printed line quality optimization. Moreover, the data-driven-based characteristics can help the proposed method develop a guideline for quality optimization

关键词： augmentation   asymptotic efficiency   cross-validation   influence function   semiparametric theory   super learner  

摘要： Clinical trials are widely considered the gold standard for treatment evaluation, and they can be highly expensive in terms of time and money. The efficiency of clinical trials can be improved by incorporating information from baseline covariates that are related to clinical outcomes. This can be done by modifying an unadjusted treatment effect estimator with an augmentation term that involves a function of covariates. The optimal augmentation is well characterized in theory but must be estimated in practice. In this article, we investigate the use of machine learning methods to estimate the optimal augmentation. We consider and compare an indirect approach based on an estimated regression function and a direct approach that aims directly to minimize the asymptotic variance of the treatment effect estimator. Theoretical considerations and simulation results indicate that the direct approach is generally preferable over the indirect approach. The direct approach can be implemented using any existing prediction algorithm that can minimize a weighted sum of squared prediction errors. Many such prediction algorithms are available, and the super learning principle can be used to combine multiple algorithms into a super learner under the direct approach. The resulting direct super learner has a desirable oracle property, is easy to implement, and performs well in realistic settings. The proposed methodology is illustrated with real data from a stroke trial.

关键词： urbanization   urban extent   urban land use   remote sensing   machine learning   VIIRS   MODIS  

摘要： Urbanization poses significant challenges on sustainable development, disaster resilience, climate change mitigation, and environmental and resource management. Accurate urban extent datasets at large spatial scales are essential for researchers and policymakers to better understand urbanization dynamics and its socioeconomic drivers and impacts. While high-resolution urban extent data products - including the Global Human Settlements Layer (GHSL), the Global Man-Made Impervious Surface (GMIS), the Global Human Built-Up and Settlement Extent (HBASE), and the Global Urban Footprint (GUF) - have recently become available, intermediate-resolution urban extent data products including the 1 km SEDAC's Global Rural-Urban Mapping Project (GRUMP), MODIS 1km, and MODIS 500 m still have many users and have been demonstrated in a recent study to be more appropriate in urbanization process analysis (around 500 m resolution) than those at higher resolutions (30 m). The objective of this study is to improve large-scale urban extent mapping at an intermediate resolution (500 m) using machine learning methods through combining the complementary nighttime Visible Infrared Imaging Radiometer Suite (VIIRS) and daytime Moderate Resolution Imaging Spectroradiometer (MODIS) data, taking the conterminous United States (CONUS) as the study area. The effectiveness of commonly-used machine learning methods, including random forest (RF), gradient boosting machine (GBM), neural network (NN), and their ensemble (ESB), has been explored. Our results show that these machine learning methods can achieve similar high accuracies across all accuracy metrics (>95% overall accuracy, >98% producer's accuracy, and >92% user's accuracy) with Kappa coefficients greater than 0.90, which have not been achieved in the existing data products or by previous studies;the ESB is not able to produce significantly better accuracies than individual machine learning methods;the total misclassifications generated by GB

关键词： suburban land cover classification   LiDAR   aerial imagery   machine learning   nDSM   surface roughness   surface intensity  

摘要： It is valuable to study the land use/land cover (LULC) classification for suburbs. The fusion of Light Detection and Ranging (LiDAR) data and aerial imagery is often regarded as an effective method for the LULC classification;however, more in-depth analysis would be required to explore effective information for enhancing the suburban LULC classification. In this study, first, both aerial imageries and point clouds were simultaneously collected. Then, LiDAR-derived models, i.e., normalized digital surface model (nDSM) and surface intensity model (IM), were generated from the elevation and intensity of point clouds. Further, considering the surface characteristics of ground objects in suburb, we proposed a new LiDAR-derived model, namely surface roughness model (RM), to reveal the degree of surface fluctuations. Additionally, various combinations of aerial imageries and the LiDAR-derived data were used to analyze the effects of multi-variable fusion under different scenarios and optimize the multi-variable integration for suburban LULC classification. The mean decrease impurity method was used to identify the importance of variables;three machine learning classifiers, i.e., random forest (RF), k-nearest neighbor (KNN) and artificial neural network (ANN) were adopted in various scenarios. The results were as follows. The fusion of aerial imagery and all the LiDAR-derived models, i.e., nDSM, RM and IM, with RF classifier performs best in the suburban LULC classification (overall accuracy = 84.75%, kappa coefficient = 0.80). Variable importance analysis shows that nDSM has the highest variable importance proportion (VIP) value, followed by RM, IM, and spectral information, indicating the feasibility of this proposed LiDAR-derived model-RM. This research presents effective methods relating to the application of aerial imagery and LiDAR-derived model for the complex suburban surface scenarios.

关键词： 机器学习   web   shell   PHP  

摘要： 本文简述了web shell分类和基于PHP的web shell的变形及检测方法,回顾了用机器学习方法检测基于PHP的web shell的现状,最后从样本准备、特征提取和实现方法等方面给出了提高机器学习算法检测效果的改进方向。

关键词： 机器学习方法   非条件Logistic回归法   支持向量机法   评价模型   Ⅰ类切口手术患者   抗菌药物   处方点评   合理用药  

摘要： 目的:建立Ⅰ类切口手术患者使用抗菌药物合理性的评价模型,为临床药师进行处方点评提供依据。方法:以某院2017年1月1日-2017年12月31日住院的432例Ⅰ类切口手术患者作为研究对象,提取患者年龄、院内感染、使用抗菌药物的种类数等多项诊疗信息;结合临床药师对患者预防及治疗使用抗菌药物合理性的点评结果,以抗菌药物类别(预防或治疗使用)为因变量、患者诊疗信息为自变量,分别利用机器学习方法中的非条件Logistic回归法和支持向量机法,将药师点评结果转化为机器学习模型可识别的客观指标,建立Ⅰ类切口手术患者使用抗菌药物合理性的分类判别模型,并以灵敏度、特异度、约登指数等为指标,另采用61例Ⅰ类切口手术患者样本对建立的模型进行验证;收集使用模型前(人工点评,2017年1-12月)、后(2018年1-10月)Ⅰ类切口手术患者使用抗菌药物处方合理性点评情况,对模型效果进行应用评价。结果:以非条件Logistic回归法建立的模型的灵敏度为65.63%、特异度为75.00%、约登指数为40.63%;以支持向量机法建立的模型主要参数gamma为0.01、cost为10,其灵敏度为92.19%、特异度为87.50%、约登指数为79.69%,支持向量机法建立的模型优于非条件Logistic回归法;对采用支持向量机法建立的模型进行验证,结果灵敏度为100%、特异度为88.57%、约登指数为88.57%;与使用模型前比较,使用模型后处方点评比例由69.44%升高到100%,抗菌药物预防使用率由23.84%下降到16.43%,品种选用合理率由37.86%升高到54.39%,使用疗程由5.01 d缩短到3.26 d。结论:应用机器学习方法中的支持向量机法建立Ⅰ类切口手术患者使用抗菌药物的评价模型,可实现处方点评全覆盖,提高Ⅰ类切口手术患者使用抗菌药物的合理水平,同时为药师的处方点评工作提供了新思路。

关键词： natural gas price   natural gas price forecasting   prediction model   machine learning methods  

摘要： Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.

关键词： 空间负荷预测   数据挖掘   地块   Softmax概率分类器   负荷曲线  

摘要： 提出了一种数据驱动空间负荷预测方法。将网格化体系下的功能地块作为空间负荷预测的基本单元,并且通过多维指标体系进行属性描述。基于大量调研数据,通过数据挖掘方法对不同类型地块的空间负荷密度分布规律和负荷曲线典型形态进行提取。建立Softmax多元概率分类模型对未知地块的负荷水平类型进行匹配。自下而上对相邻地块负荷预测结果进行时域叠加,得到更大区域的预测信息,包括其负荷量和预测负荷曲线。算例仿真结果表明提出的空间负荷预测方法在预测精度上有一定提升。

关键词： Metagenomics   microbiota   embedded feature selection   operational taxonomic units (OTUs)   classification  

摘要： "Metagenomics" is the study of genomic sequences obtained directly from environmental microbial communities with the aim to linking their structures with functional roles. The field has been aided in the unprecedented advancement through high-throughput omics data sequencing. The outcome of sequencing are biologically rich data sets. Metagenomic data consisting of microbial species which outnumber microbial samples, lead to the 'curse of dimensionality" in datasets. Hence, the focus in metagenomics studies has moved towards developing efficient computational models using Machine Learning (ML), reducing the computational cost. In this paper, we comprehensively assessed various ML approaches to classifying high-dimensional human microbiota effectively into their functional phenotypes. We propose the application of embedded feature selection methods, namely, Extreme Gradient Boosting and Penalized Logistic Regression to determine important microbial species. The resultant feature set enhanced the performance of one of the most popular state-of-the-art methods, Random Forest (RF) over metagenomic studies. Experimental results indicate that the proposed method achieved best results in terms of accuracy, area under the Receiver Operating Characteristic curve (ROC-AUC), and major improvement in processing time. It outperformed other feature selection methods of filters or wrappers over RF and classifiers such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), and k- Nearest Neighbors (k-NN).

关键词： Bioassay   Biological properties   Feature relevance   Machine learning   Pollution   Regression   Soil   Trace element   Waste  

摘要： Purpose The full understanding of the effect of mineral waste-based fertilizer in soil is still unrelieved, because of the extreme complex chemical composition and plethora of their action pathways. The purposes of this paper is to quantify the input of PG into the soil ecosystem process, considering the direct effects of PG as a whole on soil environment using of a plethora of chemical, toxicological, and biological tests. Materials and methods Greenhouse experiment includes different PG doses (0, 1%, 3%, 7.5%, 15%, 25%, and 40%) and two-time collection points after treatments-7 and 28 days. For each treatment and each time collection point, we measure (i) soil pH, bioavailable (H(2)0 and NH4COOH-extractable) element content (S, P, K, Na, Mg, Ca, Fe, Zn, Sr, Ba, F);(ii) soil enzyme activities-dehydrogenase, urease, acid phosphatase, FDA;(iii) soil CO2 respiration activity with and without glucose addition;(iv) Eisenia fetida, Sinapis alba, and Avena sativa responses. Finally, we combine the ordinary chemical, toxicology, and biological measuring of soil properties with state-of-the-art mathematical analysis, namely (i) support vector machines (used for prediction), (ii) mutual information test (variable importance tasks), (iii) t-SNE and LLE algorithms (used for unsupervised classification). Results and discussion The results show similarity between the 0%, 1%, and 3% PG treatments in all collection times based on the toxicological and biological properties. Beyond 7.5% PG, some biological test was significantly inhibited in response to trace element stress. Among all tested parameters, soil urease activities, soil respiration activities after glucose addition, S. alba root lengths, and E. fetida survival rates show sensitivity to PG addition. Furthermore, the machine learning algorithms revealed that only several elements (mobile and water-soluble forms of Ca, Ba, Sr, S, and Na;water-soluble F) could be responsible to elevated soil toxicity for those indicators. S