关键词： Sulfur solubility   Natural gas   Sour gases   Multilayer perceptron (MLP)   Cascaded forward neural network (CFNN)   Gene expression programming (GEP)  

摘要： Accurate determination of sulfur solubility in pure hydrogen sulfide (H2S) and sour gas mixtures has a leading role and a fundamental importance in handling and addressing sulfur deposition issues. In this study, rigorous paradigms based on two artificial neural network (ANN) types, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN), optimized by Levenberg-Marquardt (LM) algorithm were proposed as machine learning (ML) modeling tools to predict the solubility of sulfur in sour gas mixtures and pure H2S. Besides, explicit and simple-to-use correlations were established using gene expression programming (GEP). The paradigms derived from the methods aforementioned were developed using widespread experimental database. The obtained results indicated that the outcomes gained from the proposed MLP, CFNN and GEP-based correlations are in a high coherence and agreement with the experimental data. In addition, it was found that among the all suggested schemes, CFNN models are the most accurate paradigms for estimating the solubility of sulfur in sour gas mixtures and pure H2S with root mean square error (RMSE) of 0.0232 and 3.8101, respectively. Furthermore, a comparison between the performance of CFNN and the prior alternatives demonstrated that the CFNN models predict the solubility of sulfur in sour gas mixtures and pure H2S more accurately. Moreover, based on the trend analysis, it was concluded that the predictions of CFNN follow the real tendency of sulfur solubility in pure H2S and sour gas mixtures with respect to the input parameters. Besides, the sensitivity analysis dictated that pressure and temperature have the most significant impact on sulfur solubility calculation in pure H2S and sour gas mixtures. The results reported in this investigation revealed that implication of the considered soft computing approaches in the estimation of sulfur solubility in sour gas mixtures and pure H2S can lead to the generation of more reliable predicti

关键词： Coronary artery disease   fluid-structure interaction   machine learning   plaque vulnerability prediction   intravascular ultrasound  

摘要： Plaque vulnerability prediction is of great importance in cardiovascular research. In vivo follow-up intravascular ultrasound (IVUS) coronary plaque data were acquired from nine patients to construct fluid-structure interaction models to obtain plaque biomechanical conditions. Morphological plaque vulnerability index (MPVI) was defined to measure plaque vulnerability. The generalized linear mixed regression model (GLMM), support vector machine (SVM) and random forest (RF) were introduced to predict MPVI change (Delta MPVI = MPVIfollow-up-MPVIbaseline) using ten risk factors at baseline. The combination of mean wall thickness, lumen area, plaque area, critical plaque wall stress, and MPVI was the best predictor using RF with the highest prediction accuracy 91.47%, compared to 90.78% from SVM, and 85.56% from GLMM. Machine learning method (RF) improved the prediction accuracy by 5.91% over that from GLMM. MPVI was the best single risk factor using both GLMM (82.09%) and RF (78.53%) while plaque area was the best using SVM (81.29%).

摘要： The ability to accurately predict leukemic relapse post-HSCT would improve outcomes by allowing pre-emptive therapeutic strategies. Recent studies have identified post-transplant T- and CD34 cell chimerism as predictors of relapse in patients, who had undergone HSCT for hematologic malignancies (Preuner et al, 2016; Lee et al, 2015). However, these studies assess relapse risk looking at only a single threshold of chimerism using standard regression analysis, which permits only limited consideration of other patient variables. As the result, the findings of these analysis are frequently not applicable to patients generally. Machine learning methods offer the possibility to capture nonlinear relationships and simultaneous interactions between multiple variables, thus better recapitulate the dynamics and nuances of the relapse process in different patients. We use machine learning methods, specifically random forest classification (RF), to build a predictive model of post-transplant relapse and to analyze the data from a cohort of 46 pediatric patients, who received HSCT for acute lymphoblastic leukemia (ALL) and had serial lineage-specific chimerism testing post-transplant. Our model achieved 58 % sensitivity and 98% specificity at predicting relapses in cross validation compared to a baseline model (24% sensitivity, 76% specificity). Consistent with previous reports, our model implicates both peripheral blood (PB) donor CD34 and CD3 chimerism as important variables for relapse. More importantly, the RF showed how different variables interacted with each other, providing additional insights into how to best interpret post-transplant chimerism results. To our knowledge, this is the first study featuring RF machine learning methods in the clinical setting of relapse after HSCT. We use a dataset of patients with ALL undergoing HSCT at Lucile Packard Children's Hospital from 2012 to 2018. Variables collected are summarized in Table 1. The analytical sensitivity of STR-bas

摘要： Introduction: Sickle Cell Disease (SCD) is one of the most common genetic disease worldwide. The Acute Chest Syndrome (ACS) is a leading cause of death for SCD patients. The PRESEV1 study was set to produce a predictive score to assess the risk of an ACS development (Bartolucci et al ., 2016). PRESEV2 was an international, multicenter prospective confirmatory study to validate the PRESEV score. This study aims at improving these predictions with the addition of a machine learning (ML) method. Patients and methods: Included patients follow PRESEV1 and PRESEV2 studies 'rules. The dataset thus contains 97 patients who developed an ACS episode (18.3%) against 434 patients who did not (81.7%). To compute the PRESEV score, we firstly used the method developed previously with the following variables as input: leukocytes, reticulocytes, hemoglobin levels and cervical spine pain. This method is based on a decision tree with fixed rules and is referred to as the decision tree method throughout this abstract. Secondly we used a ML method using a combined sampling method named SMOTEENN to balance the data and a C-Support Vector Classification (SVC) with fixed parameters to predict the score. This method produces a probability, with a threshold of 0.2, under which the patient is predicted to declare an ACS. We considered the dataset composed of PRESEV1 dataset and 80 percent of PRESEV2 with a randomly choice. The test dataset is thus composed of the remaining 20 percent of PRESEV2. This technique of random choice allowed us to use a 50-cross-validation and compute with Python an average score and a standard deviation (std). In order to allow comparison of the developed score with or without the addition of the ML method, rates were calculated by adding the weight of ACS representation in the dataset. Results: Among all parameters analyzed, the SVC method considered the following variables for calculation of the score: leukocytes, LDH, urea, reticulocytes and hemoglobin levels. A

关键词： 链路状态   网络链路   网络可靠性   机器学习方法   编队控制   无人机编队   量化计算   预警信息  

摘要： 无人机编队网络具有高动态特性,链路状态变化频繁,影响网络可靠性。本文提出以投递率表征链路状态,通过量化计算,采用机器学习方法拟合链路时变规律,预测未来某时段链路状态,为编队控制体系提供预警信息。本文算法经QualNet仿真实现,预测效果较好。

关键词： 石油化工   有毒有害气体监测   权值模型   消防指挥  

摘要： 针对近年来石油化工厂区、危险化学品存放港口火灾事故多发,消防员在灭火救援过程中多有人员伤亡的现状,提出一种专用于消防救援现场的有毒有害气体监测系统,阐述了系统的构成、工作原理,建立了现场有毒有害气体监测区域危险性和消防员现场作业危险性权值模型。应用机器学习方法对数据模型进行训练,在灭火救援现场为指挥员调度指挥提供科学的辅助决策支撑,有利于保障现场参战消防员的生命安全。

关键词： 傅立叶级数   机器学习   预测   小行星  

摘要： 利用傅立叶级数对散点小行星光变数据进行曲线拟合,以获得小行星表示参数,选取傅立叶级数为6的拟合方式对单体或双体小行星光变曲线进行分类,并用机器学习算法中的SVM和决策树建立预测模型。检验结果表明,SVM模型对单体和双体小行星的预测正确率达到95%,相较于决策树正确率提高了10%,为从小行星实际观测数据直接推测双体小行星潜在相关应用提供了参考。

关键词： PM2.5 prediction   Environmental exposure   Random forest   Ensemble methods   London case study  

摘要： The adverse health effects of air pollutants, especially those of PM2.5, are well documented. However, a lack of adequate monitoring and weaknesses in modelling approaches do not allow a good assessment of health effects in many areas of the World. Advances in computational methods and the availability of new data sets, e.g. satellite remote observations, have enlarged the possibilities of modelling for application in large scale health effects studies. However, PM2.5 monitoring is very recent in most of the World and more limited compared to other pollutants, and understanding how to use PM(10 )monitors to estimate PM2.5 exposure is therefore important. Since interest in these methods is relatively recent, there is a need for testing their performance against ambient measurements, but long term PM2.5 datasets are less readily available than PM10 in many regions. In the present study we report the methodology and results of using regression modelling and a machine learning method (Random Forest-RF), as well as a combination of the two, to enhance a PM2.5 measurement data base in London using PM10 and NOx measurements as well as other predictors and compare the relative performance of each method. We found that the combination of predictions by the regression model and the RF performs best and we obtain a cross-validation R-2 of 99.29% and 98.22% for the 5-year periods 2004-2008 and 2009-2013, respectively, and a Mean Square Error near 1. Our enhanced data base for PM2.5 is available for use by other researchers.

关键词： Turbulence  

摘要： Deep artificial neural networks (ANNs) are used for modeling sub-grid scale mixing quantities such as the filtered density function (FDF) of the mixture fraction and the conditional scalar dissipation rate. A deep ANN with four hidden layers is trained with carrier-phase direct numerical simulations (CP-DNS) of turbulent spray combustion. A priori validation corroborates that ANN predictions of the mixture fraction FDF and the conditional scalar dissipation rate are in very good agreement with CP-DNS data. ANN modeled solutions show much better performance with a mean error of around 1%, which is one order of magnitude smaller than that of standard modeling approaches such as the beta -FDF and its modified version. The predicted conditional scalar dissipation rate agrees very well with CP-DNS data over the entire mixture fraction space, whereas conventional models derived for pure gas phase combustion fail to describe N|xi = eta in regions with higher mixture fraction and low probability. In the second part of this paper, uncertainties associated with ANN predictions are analyzed. It is shown that a suitable selection of training sets can reduce the size of the necessary test database by similar to 50% without compromising the accuracy. Feature importance analysis is used to analyze the importance of different combustion model parameters. While the droplet evaporating rate, the droplet number density, and the mixture fraction remain the dominant features, the influence of turbulence related parameters only becomes important if turbulence levels are sufficiently high.

关键词： Artificial neural network   Fuzzy regression function   Outlier   Robustness   Support vector machine  

摘要： The presence of outliers in the dependent and/or independent features distorts predictions with machine learning techniques and may lead to erroneous conclusions. It is important to implement methods that are robust against the outliers to make reliable predictions and to know the accuracy of the existing methods when data is contaminated with outliers. The focus of this study is to propose a robust fuzzy regression functions (FRFN) approach against the outliers and evaluate the performance of the proposed and several mainstream machine learning approaches in the presence of outliers for the regression problem. The proposed FRFN approach is based on fuzzy k-means clustering with a noise cluster. We compare the accuracy of Artificial Neural Networks (ANN), Support Vector Machines (SVM) and the proposed FRFN approaches with different training algorithms kernel functions via simulated and real benchmark datasets. In total, accuracies of 36 ANN, SVM, and FRNF implementations with training algorithms and kernel and loss functions have been evaluated and compared to each other with samples containing outliers via a Monte Carlo simulation setting. It is observed in both Monte Carlo simulations and applications with benchmark dataset that FRFN with ANN trained with Bayes regularization algorithm and FRFN with SVM with Gaussian kernel outperforms the classical implementations of ANN and SVMs under the existence of outliers. The proposed noise cluster implementation considerably increases the robustness of fuzzy regression functions against outliers. (C) 2020 Elsevier B.V. All rights reserved.