关键词： co-registration   individual tree detection   tree matching   point set registration   machine-learning   forest inventory  

摘要： Determining the exact position of a forest inventory plot—and hence the position of the sampled trees—is often hampered by a poor Global Navigation Satellite System (GNSS) signal quality beneath the forest canopy. Inaccurate geo-references hamper the performance of models that aim to retrieve useful information from spatially high remote sensing data (e.g., species classification or timber volume estimation). This restriction is even more severe on the level of individual trees. The objective of this study was to develop a post-processing strategy to improve the positional accuracy of GNSS-measured sample-plot centers and to develop a method to automatically match trees within a terrestrial sample plot to aerial detected trees. We propose a new method which uses a random forest classifier to estimate the matching probability of each terrestrial-reference and aerial detected tree pair, which gives the opportunity to assess the reliability of the results. We investigated 133 sample plots of the Third German National Forest Inventory (BWI, 2011-2012) within the German federal state of Rhineland-Palatinate. For training and objective validation, synthetic forest stands have been modeled using the Waldplaner 2.0 software. Our method has achieved an overall accuracy of 82.7% for co-registration and 89.1% for tree matching. With our method, 60% of the investigated plots could be successfully relocated. The probabilities provided by the algorithm are an objective indicator of the reliability of a specific result which could be incorporated into quantitative models to increase the performance of forest attribute estimations.

关键词： Diffusion   Data-mining   Machine learning   DFT   Neural network  

摘要： We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional theory (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural network (ANN). We separately optimize key physical descriptors favored by each method to model diffusion barriers. We also assess the ability of each method to extrapolate when faced with new hosts with limited known data. GKRR and ANN were found to perform the best, showing 0.15 eV cross-validation errors and predicting impurity diffusion in new hosts to within 0.2 eV when given only 5 data points from the host. We demonstrate the success of a combined DFT + data mining approach towards solving materials science challenges and predict the diffusion barrier of all available impurities across all FCC hosts. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multilevel ROI features   support vector machine (SVM)   magnetic resonance imaging (MRI)   Parkinson's disease  

摘要： Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper-based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. Tl-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. (C) 2017 Elsevier B.V. All rights reserved.

关键词： P2P信贷   信用评价   模型选择   机器学习方法  

摘要： 本文通过对信用评价方法中的统计分析方法及机器学习方法进行比较,根据评价指标体系数据的适用性要求,最终选择决策树及其组合方法对P2P信贷个人信用进行评价。通过对模型的准确性及稳定性的综合分析,对本文数据拟合最好的分类方法是随机森林分类方法。

关键词： stationary battery systems   charging/discharging profile   capacity loss   machine-learning approaches  

摘要： The number of Stationary Battery Systems (SBS) connected to various power distribution networks across the world has increased drastically. The increase in the integration of renewable energy sources is one of the major contributors to the increase in the number of SBS. SBS are also used in other applications such as peak load management, load-shifting, voltage regulation and power quality improvement. Accurately modeling the charging/discharging characteristics of such SBS at various instances (charging/discharging profile) is vital for many applications. Capacity loss due to the aging of the batteries is an important factor to be considered for estimating the charging/discharging profile of SBS more accurately. Empirical modeling is a common approach used in the literature for estimating capacity loss, which is further used for estimating the charging/discharging profiles of SBS. However, in the case of SBS used for renewable integration and other grid related applications, machine-learning (ML) based models provide extreme flexibility and require minimal resources for implementation. The models can even leverage existing smart meter data to estimate the charging/discharging profile of SBS. In this paper, an analysis on the performance of different ML approaches that can be applied for lithium iron phosphate battery systems and vanadium redox flow battery systems used as SBS is presented for the scenarios where the aging of individual cells is non-uniform.

关键词： 3D-QSAR   CoMFA   Support vector regression   Molecular modeling   Alzheimer's disease  

摘要： A comparative molecular field analysis has been developed to study the three-dimensional quantitative structure-activity relationship of a series of triterpene-based gamma-secretase modulators. We have performed the genetic algorithm on a large set of comparative molecular field analysis fields to select the most responsible fields contributing to inhibitory activities of these compounds against Alzheimer's disease. The genetic algorithm-selected comparative molecular field analysis fields were introduced into the partial least squares and principal component analysis to reduce the dimensionality of the input features. The extracted partial least squares components were used as inputs to build partial least squares regression (genetic algorithm-partial least squares regression), and the extracted principal components were used as inputs for principal component regression (genetic algorithm-principal component regression) and support vector regression (genetic algorithm-principal component analysis-support vector regression). The classic three-dimensional quantitative structure-activity relationship comparative molecular field analysis analysis (partial least squares regression) is also carried out for the sake of comparison. The results show that among the constructed models, in terms of root mean squares and leave-one-out cross-validated R (2)(q (2)), the combination of principal component analysis and support vector machine can effectively improve the prediction performance (RMSEtrain = 0.231, RMSEtest = 0.360, and q (2) = 0.638) compared with PLSR (RMSEtrain = 0.415, RMSEtest = 0.680, and q (2) = 0.311). The performances of the genetic algorithm-principal component regression and genetic algorithm-partial least squares regression were also comparable but less powerful than genetic algorithm-principal component analysis-support vector regression. Finally, based on the information derived from the comparative molecular field analysis contour map, some key features

关键词： Colon   Bowels   ANN   RNN   ALGA   WDS   Cloud serve  

摘要： Computer-aided diagnosis plays a vital role in many medical analysis especially with CT Images. The diagnosis of colorectal cancer detection becomes more challenging since the perfect detection of polyps (An opacified fluid lie on the colon walls) is difficult even for advanced segmentation methods. Many methods have been already developed, which lacks in segmentation of structure. In-order to achieve an accurate segmentation, a machine learning based algorithm such as regression neural network enhanced with the augmented Lagrangian genetic algorithm (RNN-ALGA). Since the segmentation was processed for multispectral image, the optimization strategy was included in-order to process all the 2D slices with reduced time span. The segmentation was done in-order to process the differentiation of colons, partial volume effect and bowels. By using this algorithm (RNN-ALGA) it is possible to achieve 97 % accuracy with some minor error. The comparison results shows that this algorithms best suitable abdominal slices of CT image is due to its enhanced nonlinearity nature. This methodology was developed especially for the hospital applications. RNN-ALGA works virtually in the common cloud server with an effective wireless distributive system accessed by many clients placed at the remote regions (medical centers, hospitals etc.).

关键词： Machine learning   Forecasting   Quality   Apples   Skin color  

摘要： In this study, we focused on the possibility of forecasting the development of skin color in apples on the basis of weather forecast by using a machine learning methods. We used supervised learning and generated models via the use of six decision trees. The purpose of the research was to build models that would allow for in-practice-acceptable accuracy in the prediction of the development of fruit skin color (especial a colour parameter a*), for three apple varieties. For cv. 'Gala, Brookfield', the most accurate models were generated by using decision tree J48 (89.13% accuracy). For late ripening cv. 'Fuji, Kiku 8' and cv. 'Braeburn, Maririred', the most accurate model was obtained by using decision tree LMT (91.73 and 96.65% accuracy). The data confirm that the applicability of predictive models strongly depends on the accuracy of weather forecasts. In regard to the seven-day weather forecast, which was used for expert models, the accuracy of the models was, on average, reduced by 10.73%.

关键词： 通用图像距离   特征提取   特征向量   图像分类  

摘要： 针对图像分类识别问题,提出了一种用于图像特征提取的新方法。首先定义了基于图像字符串的复杂度和以及通用图像距离(UID),然后依次提出了测量通用图像距离的UID距离测量算法,在维持特征类别之间的固有差异条件下对图像原型进行选择的原型选择算法,利用原型选择算法创建图像的特征向量表示从而生成待分类图像的特征向量的特征向量生成算法,最后基于前述算法提出了对图像的感兴趣区域进行分离的图像分类学习算法。将所提出的方法应用于卫星图像数据的几个监督和非监督学习实验,结果表明文中所提方法效果理想。

关键词： Solar radiation forecasting   Machine learning   Artificial neural networks   Support vector machines   Regression  

摘要： Forecasting the output power of solar systems is required for the good operation of the power grid or for the optimal management of the energy fluxes occurring into the solar system. Before forecasting the solar systems output, it is essential to focus the prediction on the solar irradiance. The global solar radiation forecasting can be performed by several methods;the two big categories are the cloud imagery combined with physical models, and the machine learning models. In this context, the objective of this paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches. Although, a lot of papers describes methodologies like neural networks or support vector regression, it will be shown that other methods (regression tree, random forest, gradient boosting and many others) begin to be used in this context of prediction. The performance ranking of such methods is complicated due to the diversity of the data set, time step, forecasting horizon, set up and performance indicators. Overall, the error of prediction is quite equivalent. To improve the prediction performance some authors proposed the use of hybrid models or to use an ensemble forecast approach. (C) 2017 Elsevier Ltd. All rights reserved.