关键词： Bayesian methods   Site characterization   Spatial variability   Sparse data  

摘要： Identification of subsurface stratification and characterization of spatially varying soil properties profiles in multiple soil layers are indispensable in geotechnical site investigation. Subsurface soils are often stratified first through visual inspection of soil samples obtained from boreholes or soil behavior type index from cone penetration tests. Then, the soil property profile within each layer is characterized via interpolation of the data points measured within the corresponding layer. Although this stratification first procedure is commonly used in engineering practice, it is difficult to apply when measured data points within each layer are sparse and limited (e.g., only a few data points within each layer), a scenario often encountered in geotechnical site characterization. When the number of measurements within each layer is small, it is difficult to properly interpolate spatially varying soil property profiles in each layer. To address this difficulty and increase the number of data points for enabling proper interpolation, an interpolation first procedure is proposed which utilizes measurements from all layers as input for interpolation, followed by soil stratification using the interpolation results. The proposed procedure includes two key elements: (1) a Bayesian supervised learning method for interpolation of non-stationary data from multiple layers, and (2) an unsupervised machine learning method (e.g., clustering) for soil stratification. An index is also proposed to determine when the proposed method is beneficial and performs better than the stratification first procedure in engineering geology practice. Both numerical and real-life data are used to illustrate the proposed method.

关键词： acid solubility   artificial neural network (ANN) TensorFlow   Extreme Gradient Boosting (XGBoost)   mid-infrared spectra   multivariate data analysis (MVDA)  

摘要： Interests in the use of chemometric and data science methods for laboratory techniques have grown rapidly over the last 10 years, for the reason that they are cheaper and faster than traditional analytical methods of material testing. This study uses 888 rock samples collected from the exploration and production (E&P) sector of the oil industry. Based on the Fourier-transform infrared (FT-IR) spectra of these rock samples their solubility predictions have been developed and investigated with nine methods including both linear and non-linear ones. Two of these methods such as Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) are available in a commercial software package and the other seven methods, Extreme Gradient Boosting (XGBoost), Ridge Regression (RR), k-nearest neighbours (k-NN), Decision Tree (DT), Multilayer Perceptron (MLP), Support Vector Regression (SVR), Artificial Neural Network (ANN) with TensorFlow (TF), were coded by the authors based either on commercial applications or open source libraries. The investigation starts with spectral data pre-processing carried out by standard normal variate (SNV), baseline correction and feature selection methods creating the feature set for all machine learning (ML) applications. The accuracy of predictions has been evaluated with mean squared error as a performance metric for each investigated method. The comparisons of predicted values to real data of test samples have shown that mineral solubility in acids can be well predicted in the range of the uncertainties of real laboratory measurements, therefore it can be used to improve the response time of these investigations and reduce the risk in industrial applications. In those cases, where the unknown samples have got some out of the range features, the limitations in the accuracy of predictions have become clear. We have also identified the limitations in the methodology and planned steps to further improve the prediction capabilities. Th

关键词： SAR   Mediterranean forests   forest features   forest   non-forest areas   land classification   machine learning  

摘要： In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo-SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in-situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L-, C- and X-bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non-forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient (sigma degrees) was computed for each sensor-polarization pair and labeled on a pixel basis according to the reference map. Seeral classification methods based on the machine learning framework were applied and alidated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers' performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L- and X-bands. In the former case, the best oerall aerage accuracy (83.1%) is achieed by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors.

关键词： HIV-1 protease inhibitors (HIV-1 PIs)   Quantitative structure-activity relationship (QSAR)   Support vector machine (SVM)   Random forest (RF)   Deep neural networks (DNN)   Applicability domain  

摘要： HIV-1 protease inhibitors (PIs) make a vital contribution on highly active antiretroviral therapy (HAART) of human immunodeficiency virus (HIV). In this study, 14 quantitative structure-activity relationship (QSAR) models on 1238 PIs were built by four machine learning methods, including multiple linear regression (MLR), support vector machine (SVM), random forest (RF) and deep neural networks (DNlN). For the best model Model2G constructed by DNN algorithm, the coefficient of determination (R-2) of 0.88 and 0.79, the root mean squared error (RMSE) of 0.39 and 0.51 were obtained on training set and test set, respectively. For model Model2G, the applicability domain threshold (ADT) of 1.765 was obtained for training set, a compound that has a similarity distance (d) less than the ADT is considered to be inside the applicability domain, could be predicted accurately, and thus 65.37% compounds in test set performed reliable. In addition, the 1238 PIs were manually divided into eight subsets containing different scaffolds. It was found that hydroxylamine derivatives and sevenmember cyclic urea derivatives showed highly inhibitory activity comparing with other subsets. We also built QSAR models with SVM, RF and DNN methods on two subsets of 299 hydroxylamine derivatives inhibitors (Dataset2) and 377 seven-member cyclic urea derivatives inhibitors (Dataset3). For the best model Model3A on Dataset2, R-2 of 0.71 and RMSE of 0.53 were obtained for test set. For the best model Model4B on Dataset3, R-2 of 0.82 and RMSE of 0.51 were obtained for test set. At last, we analyzed the descriptors which make significant contributions on the bioactivity of inhibitors among these two subsets. It was found that highly active inhibitors of seven-member cyclic urea derivatives usually contained several aromatic nitrogen heterocyclic ring substituents such as the inidazole and the pyrazole. The oxazolidinone group and sulfanilamide mainly appeared in highly active inhibitors of hydroxylamin

关键词： 动物遗传与育种   主题识别   学科热点   机器学习   趋势分析   集成预测  

摘要： 采用Relim算法对科技文献数据进行热点主题词挖掘,利用时间序列集成实现未来一段时间的热点主题词预测。从Web of Science数据库中采集2000—2017年动物遗传与育种领域相关的71990研究论文作为研究对象,运用频繁项集算法Relim,对该领域的热点研究主题进行识别研究,并利用时间序列集成方法对热点研究主题在未来一段时间内的演变趋势进行预测。结果表明,该方法能较好地对某一个领域的热点主题词进行预测,且集成后的预测模型对时间序列预测效果较好,可帮助科研人员和政策制定者了解特定学科领域的主题热点状况。

关键词： 解离速率常数   配体结合动力学   随机森林模型   蛋白-配体相互作用   基于结构的药物设计  

摘要： 越来越多的研究表明:药物分子与靶标分子的结合动力学性质与其在体内的药效有很强的相关性。因此,以改善结合动力学性质为导向的分子设计为药物研发提供了新的思路。本工作的研究目标在于得出预测药物分子解离速率常数(koff)的通用型定量结构-动力学关系(QSKR)模型。我们从文献中收集了406个配体分子的解离速率常数实验值,采用分子模拟方法构建了所有配体与靶蛋白复合物的三维结构模型。然后基于蛋白-配体原子对描述符,采用随机森林算法来构建预测配体分子解离速率常数的QSKR模型。通过探索不同条件(如距离区间,划分区间宽度和特征选择标准)下产生的描述符集合对模型预测精度的影响,确定当采用距离阈值为15?、划分区间宽度为3?、特征选择方差水平为2时得到的QSKR模型为最优,在两个独立测试集上获得良好的预测精度(相关系数为0.62)。本工作对预测药物分子解离速率常数这一关键科学问题进行了有益的探索,可为后续研究提供思路。

关键词： Community noise   Noise prediction   Gaussian process   Machine learning  

摘要： A framework for community noise modelling is proposed, where noise sources are treated as their equivalent noise sources defined in a multidimensional space. The noise levels at measurement locations under different settings of noise sources in the space are predicted using a noise propagation simulator and the method of Design of Computer Experiments (DoEs). A surrogate model is built using a Gaussian process for machine learning where the predictions are used as training data. Sound power levels of the equivalent noise sources are obtained by means of measured noise levels and the surrogate model. As a case study, six LA(eq-ihr) models from 2:30 pm to 8:30 pm of an outdoor food court and shopping area are reported to demonstrate the effectiveness and flexibility of the framework in noise prediction, noise source modelling and its verification. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： 机器学习法   无人机   室内导航  

摘要： 在无人机环境感知、控制系统与路径规划方面,移动节点的准确定位发挥着十分重要的积极作用。本文首先介绍了机器学习的发展历程,之后介绍了机器学习方法在无人机定位中的应用。

关键词： 农业大数据   数据挖掘   分类   综述  

摘要： 农业生产经营产生大量数据,这些数据中隐藏了有价值的知识。数据挖掘技术可以有效地从海量的农业数据中探索出各种因素之间的联系,从而发现其中隐藏的规律。本文对数据挖掘中常用的几个分类预测算法的内容及其特点进行综述,通过论述各算法思想和优缺点,使相关研究者对算法及改进有所了解,以便在实践中选择相应的分类算法。

关键词： debris flow   susceptibility mapping   machine learning   remote sensing   geographical information system  

摘要： Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards.