关键词： Leaf area index   leaf/canopy chlorophyll content   radiative transfer model   look-up table   machine learning algorithms  

摘要： Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change ***,we compared a radiative transfer model(RTM)inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content(LCC),canopy chlorophyll content(CCC),and leaf area index(LAI),in a mixed temperate *** accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park,Germany,was evaluated using in situ measurements collected *** RTM inversion using merit function resulted in estimations of LCC(R^(2)=0.26,RMSE=3.9µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.33 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.73 m^(2)/m^(2)),comparable to the estimations based on the machine learning method Random forest regression of LCC(R^(2)=0.34,RMSE=4.06µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.34 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.75 m^(2)/m^(2)).Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit *** performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.

关键词： 机器学习   神经网络   支持向量机   随机森林   矿产勘查  

摘要： 机器学习是目前热门的研究课题,机器学习算法在地学研究中的应用越来越多。其中以神经网络、支持向量机、随机森林等为代表的算法已大量使用在矿产勘查中,这些方法的使用大大提高了矿产勘查数据的处理效率,为矿产勘查数据的处理提供了新的途径和思路。但这些方法的使用中也经常容易出现一些问题,必须熟悉每种方法的原理和特点,才能更好的应用。本文简要介绍了机器学习在矿产勘查中的应用,就几种常用的算法进行了深入介绍;提出了应用机器学习方法处理矿产勘查数据时应该注意避免的几个主要问题。

关键词： 机器学习   合成生物学   生物元件设计   生物网络设计  

摘要： 机器学习的目标是设计可以根据先验知识和观测数据不断改进其性能的算法.该算法可以帮助机器从大量的数据中提取知识,从而提升其在特定任务上的性能.作为数据驱动的方法,机器学习可以有效利用高通量实验技术产生的大批量生物数据,实现合成生物体的功能预测与智能化设计,改变合成生物学的研究范式.本文首先介绍机器学习在合成生物学领域广泛应用的几个模型及方法,如支持向量机、神经网络、生成式对抗网络、深度强化学习等.然后介绍机器学习方法在合成生物学领域的典型应用,如启动子预测、酶催化设计、代谢途径构建、基因线路设计等.本文综述面向合成生物学的机器学习方法及应用,并试图启发读者如何选择和设计机器学习方法用于合成生物学的研究.

关键词： Earth's outer radiation belt   relativistic electron flux   solar wind   prediction   machine learning   artificial neural networks   random forest   gradient boosting  

摘要： The ways are studied to improve the quality of prediction of the time series of hourly mean fluxes and daily total fluxes (fluences) of relativistic electrons in the outer radiation belt of the Earth 1 to 24 hours ahead and 1 to 4 days ahead, respectively. The prediction uses an approximation approach based on various machine learning methods, namely, artificial neural networks (ANNs), decision tree (random forest), and gradient boosting. A comparison of the skill scores of short-range forecasts with the lead time of 1 to 24 hours showed that the best results were demonstrated by ANNs. For medium-range forecasting, the accuracy of prediction of the fluences of relativistic electrons in the Earth's outer radiation belt three to four days ahead increases significantly when the predicted values of the solar wind velocity near the Earth obtained from the UV images of the Sun of the AIA (Atmospheric Imaging Assembly) instrument of the SDO (Solar Dynamics Observatory) are included to the list of the input parameters.

关键词： Rock   Electrical conductivity   Machine learning   XGBoost  

摘要： Machine learning (ML) methods are used for predicting the electrical conductivity of brine-saturated rocks. Three relevant ML methods including the extreme gradient boosting regression (XGB), the artificial neural networks (ANN) and the support vector machine regression (SVM) methods are considered and compared. The basic features such as the porosity, electrical conductivity of pore fluid aswell as the normalized cation-exchange capacity of the solid phase are considered for such prediction. A relevant laboratorymeasurement dataset available in literature is considered to train and test the ML models. All the three considered ML methods provide very good predictive capacity on the testing set. But the XGB method ismore robust and extremely fast to train comparing to the ANNmethod and the SVMmethods. A sensitivity analysiswas also realized to clarify the effect of themodel parameters including the number of estimators, the learning rate and the regularization parameters. A set of optimal parameters issue from such analysis is provided for the winning XGB model. Using those optimal parameters, the XGB method predicts an average R-squared score of 0.96 for different random splitting between the training and testing sets. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Machine learning method   Infrared thermography   Local temperature   Temperature field   Experimental validation  

摘要： Heat distribution in the submerged arc welding (SAW) process has a significant impact on the quality of welds. In this paper, a machine learning method is proposed to predict and analyze temperature in the SAW process. Thermal video data is obtained from an infrared camera at the bottom surface of the workpiece. Programs written in MATLAB are used to extract the temperature history and to generate transient isotherm using image processing techniques. The evolution of the transient isotherm throughout the heating and cooling cycle is analyzed quantitatively. The experimental datasets are suitably prepared for use in machine learning. Prediction of local temperature, temperature-time curve, and temperature field map is made through the proposed machine learning method. Various additional characteristics of the temperature field map are analyzed and predicted. The proposed method is experimentally validated and the predicted results agree closely with the experimental data.

关键词： Gaussian Process Regression   Support Vector Regression   Decision Tree   Tunneling  

摘要： This research intends to use machine learning approaches to predict tunnel geology and its construction time and costs. For this purpose, the Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree (DT) have been utilized. An estimation of the geological conditions of the Garan road tunnel and its construction time and cost has been conducted. In addition, after constructing about 200 m from the inlet and outlet sides of the tunnel, using the field-observed data of these sectors in the tools, all the previously forecasted results were updated for unconstructed parts. Fivefold cross-validation has been applied to assess the performance of each model. The obtained models are used to predict construction time and cost in real scenarios, and the accuracy of each model was investigated through different statistical evaluation criteria. Finally, it turns out that all the models provide relatively high performance and reduce the uncertainties of tunnel geology. However, the GPR provides more accurate results compared to the SVR and DT tools. Thus, we recommend the GPR for the prediction of geology and construction time and costs in future levels of a tunnel.

摘要： Drought is one of the most environmentally impactful hydrologic processes with devastating economic consequences for many rural communities in arid and semi-arid countries all over the world. In this research, we have employed satellite data and a stochastic approach for forecasting the changes in lake surface areas and demonstrated for the application of the new technique for the case study of the Lake Gregory in Australia. High-resolution Landsat satellite images are used on a monthly time scale from Landsat 5, 7, and 8, on days that are not cloudy. The software ENVI 5.3, using normalized difference vegetation index (NDVI), and modify normalized difference water index (MNDWI) indices were employed to obtain the lake surface maps, and satellite images have been split into water and non-water using a decision tree. The ArcGIS 10.3 software was used to calculate the area of the Lake monthly. The overall trend data shows that from 2004 to 2019, the LS is steadily declining, reaching its lowest area in *** TRMM satellite monthly precipitation (P) and temperature (T) measurement were obtained to investigate the correlation between these changes and regional precipitation. We developed a novel generalized group method of data handling (GGMDH) to forecast lake surface (LS) fluctuations, in which the LS time-series database is extracted from the satellite imagery. For downscaling, precipitation and three different scenarios are defined based on climate change projections to forecast the LS in the 2020-2060 period. The comparison of the GGMDH with stochastic models integrated with preprocessing scenarios indicates the GGMDH in long-term LS forecasting outperforms the stochastic model. The result showed GGMDH is the best model among other ones to modeling lake surface by R-2 (%) = 94.16, RMSE = 8.77 for the forecasting stage. The forecasted surface of the Lake Gregory fluctuated from 226 to 0.008 km(2) in the future.

关键词： Machine learning  

摘要： Machine learning methods are considered a promising approach for improving operations and processes in manufacturing. However, the application of machine learning often requires the expertise of a data scientist combined with thorough knowledge of the manufacturing processes. Small and medium-sized companies that specialize in certain high value-added, variant rich production processes often lack an in-house data scientist and therefore miss out on generating a deeper data driven insight from their production data streams. This paper proposes a three-step machine learning methodology to empower process experts with limited knowledge in machine learning: 1) data exploration through clustering, 2) representation of the production systems behaviour through specially structured neural networks and 3) querying this representation through evolutionary algorithms to achieve decision support through online optimization or scenario simulation. The chosen algorithms focus on parameter-light, well-established, general use algorithms in order to lower knowledge requirements for their application.

关键词： eddy current sensors   multifrequency eddy currents   machine learning   partial least squares regression   support vector machines   convolutional neural networks   deep learning   data driven approach  

摘要： Eddy current (EC) measurements, widely used for diagnostics of conductive materials, are highly dependent on physical properties and geometry of a sample as well as on a design of an EC-sensor. For a sensor of a given design, the conductivity and thickness of a sample as well as the gap between the sample and the sensor (lift-off) are the most influencing parameters. Estimation of these parameters, based on signals acquired from the sensor, is quite complicated in case when all three parameters are unknown and may vary. In this paper, we propose a machine learning based approach for solving this problem. The approach makes it possible to avoid time and resource-consuming computations and does not require experimental data for training of the prediction models. The approach was tested using independent sets of measurements from both simulated and real experimental data.