关键词： classification   depression   healthcare   machine learning   prediction   random-forest  

摘要： The health sector collects a very large amount of data, hence the diagnostic process processes a very large and varied amount of data type which makes the process of analyzing these data very complicated, specifically the healthcare sector, mental health is very composed and varied by various data criteria. However, the forecast of health in modern life becomes very important. To this end, the proposed work aims to analyze patient data based on their represented symptoms, in order to help clinicians and mental health practitioners classify and refine the type of depression disorder "characterized" in patients intelligently, in order to make a relevant decision. In this context, the proposed system called CP-DDC is based on machine learning algorithms supervised more precisely by the random-forest algorithm. The dataset used in the case study contains 150 instances and 11 attributes, which define the different patient criteria, obtained from the Mohammed VI University Hospital Center of Marrakech "CHU." The results of the experiment show that the proposed system offers the highest performance.

关键词： Machine learning   Absolute information   Relative information   Ordinal classification  

摘要： The performance of an ordinal classifier is highly affected by the amount of absolute information (labelled data) available for training. In order to make up for a lack of sufficient absolute information, an effective way out is to consider additional types of information. In this work, we focus on ordinal classification problems that are provided with additional relative information. We augment several classical machine learning methods by considering both absolute and relative information as constraints in the corresponding optimization problems. We compare these augmented methods on popular benchmark datasets. The experimental results show the effectivenesses of these methods for combining absolute and relative information. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Channel head extraction   Topographic attribute   Unsupervised machine learning   Clustering analysis  

摘要： Channel head extraction is fundamental to understand the catchment hydrological processes, catchment origin, runoff generation and landscape evolution. A spatially constant threshold value such as upslope area, slope-area, and curvature have been widely used for channel head extraction because of their simplicity and efficiency. However, it is very difficult to determine the threshold values within and between catchments with different topography, soil types, and land cover. In this study, a fuzzy unsupervised machine learning method (e.g., the fuzzy c means clustering) is introduced to determine the channel head locations to avoid the need for a threshold parameter. The topographic attributes (e.g., upslope area, slope, curvature, and elevation) and slope-area combined attributes (e.g., AS(2) and S/ln(A)) are used as input variables. The sensitivity of surface terrain resolution is also analyzed in terms of the proposed method's ability to predict channel heads. Two catchments with field mapped channel heads, namely Indian Creek and Mid Bailey Run in Ohio, are selected for investigation and comparison. The proposed method performs well in terms of locating the channel head. The accuracy of identified channel heads from the proposed method is comparable to published state-of-the-art channel extraction methods. Meanwhile, the proposed method has a low computational burden. Our findings also reveal the comprehensive impact of topographic attributes in locating channel heads. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Neutron detection   Event reconstruction   Monte-Carlo simulations   Machine learning  

摘要： NeuLAND, the New Large Area Neutron Detector, is a key component to investigate the origin of matter in the universe with experimental nuclear physics. It is a core component of the Reactions with Relativistic Radioactive Beams setup at the Facility for Antiproton and Ion Research, Germany. Neutrons emitted from these reactions create a wide range of patterns in NeuLAND. From these patterns, the number of neutrons (multiplicity) and their first interaction points must be reconstructed to determine the neutrons' four momenta. In this paper, we detail the challenges involved in this reconstruction and present a range of possible solutions. Scikit-Learn classification models and simple Keras-based neural networks were trained on a wide range of input-scaler combinations and compared to classical models. While the improvement in multiplicity reconstruction is limited due to the overlap between features, the machine learning methods achieve a significantly better first interaction point selection, which directly improves the resolution of physical quantities.

关键词： 轨道不平顺   机器学习   Prophet模型   神经网络   时间序列预测  

摘要： 针对日常运营中火车轨道在列车荷载冲击作用下导致的轨道不平顺问题,使用机器学习方法Prophet算法和基于卷积神经网络的时间卷积网络,对轨道质量指数(TQI)数据进行建模分析和预测。对北京–上海某区段的TQI数据进行分析,并与传统模型ARIMA和三次指数平滑模型进行比较,发现其精确度更高,拟合效果更好。说明了所使用方法处理轨道不平顺数据的有效性。

关键词： 高斯过程回归   智能数值模拟框架   流致振动   圆柱  

摘要： 圆柱在横流中的流致振动响应是一个复杂的非线性过程,受到多个参数的影响。传统研究方法采用在多维度参数空间内对所有参数组合进行顺序试验的方法,研究其振动响应和受力特征,无论对于物理模型实验还是数值模拟,完成一个多参数系统的特性识别需要的工况数较多。该文采用基于高斯过程回归的机器学习方法,将人工智能与数值模拟方法相结合,建立了智能数值模拟框架。通过对已有结果进行高斯过程回归,将不确定性最大的点作为下一次数值模拟的工况,迭代直到达到收敛准则为止。通过对单参数和双参数单、双圆柱流致振动的振动响应进行预测,发现:该方法通过少量试验,即可合理预测,达到大量顺序试验的效果,显著提高了圆柱流致振动的研究效率。

关键词： Remaining useful life   Lithium-ion battery   Neural network   Long short-term memory   Early prediction  

摘要： Early prediction of battery remaining useful life (RUL) is critical to ensure a steady energy supply and the effective usage of energy. To reduce the amount of degradation data required for accurate RUL prediction, this study proposed a deep-learning-based prediction algorithm, namely ADLSTM-MC, which combines the features of adaptive dropout long short-term memory (ADLSTM) and Monte Carlo (MC) simulation. The adaptive dropout method was optimized by the Bayesian optimization. MC simulation is combined to describe the uncertainties of prediction results. To evaluate the performance of the proposed method, we developed two data sets including LiFePO4/graphite and LiNi0.8Co0.15Al0.05O2/graphite batteries with different charge/discharge rates and discharge cut-off voltages. The ADLSTM-MC method is shown to precisely achieve early prediction with only 25% of the entire degradation data, while existing models typically require 40%-70% of the degradation data. The R2 of the proposed capacity prediction model is up to 0.957-0.982, and the prediction errors including root mean square error (RMSE) and mean absolute error (MAE) are less than 0.033 and 0.027, respectively. The results show that the proposed method outperforms the mainstream regression algorithms and several recent published hybrid methods in terms of data requirement and prediction accuracy.

关键词： SRTM   ASTER   神经网络模型   地形因子   精度  

摘要： 为了克服SRTM和ASTER各自缺陷,充分结合二者优势得到更高质量的DEM,提出了一种基于神经网络模型的加权融合方法。首先,统一两种DEM坐标系和高程基准;其次,借助后向传播神经网络分别建立SRTM与ASTER高程误差和地形因子的非线性关系模型;然后,利用此模型估计各DEM的误差分布;最后,根据该误差计算SRTM和ASTER融合权重,并实现SRTM和ASTER加权融合。以董志塬为研究区进行分析。结果表明:融合后DEM精度有明显提高,相比于原始SRTM和ASTER,平均绝对误差分别降低了1.29 m和3.66 m,中误差分别降低了0.40 m和3.16 m,标准差分别降低了0.79 m和2.07 m;各地形因子对DEM高程精度的影响在融合之后均得到降低。以美国爱达荷州北部某区域为验证区,实验结果与研究区相似。