关键词： Cooling energy   Energy prediction   Utility data   Machine learning method   XGBoost model   Parameter tuning   Building characteristics   Weather features  

摘要： The energy used for space cooling in residential buildings has a significant influence on household energy performance. This study aims to develop a user-friendly, infrastructure-free, and accurate prediction model based on large-scale utility datasets from anonymized volunteer homes located in three different climate zones in the US, along with the corresponding weather data and building information. Notably, several new weather- and building characteristics-related parameters were designed in the modeling procedure and tested to be useful for enhancing the model?s prediction performance. A few regression techniques were examined and compared through hyperparameter optimization and k-fold cross-validation. Subsequently, a workflow was also described for how to implement the developed model. The research results showed that the eXtreme Gradient Boosting (XGBoost) model offered optimal performance, and the feature importance analysis also identified as well as ranked the key predictors to enhance the interpretability of this model. An R2 value of around 97% was obtained with that model on the whole dataset, while an R2 value of 92% was achieved with various subsets of the dataset through the cross-validation approach. The RMSE and RAE for this model were 0.294 and 0.153, respectively. The resultant model for predicting cooling energy consumption will facilitate homeowners better understanding their buildings? performance levels with minimum input information and without additional hardware installations, ultimately aiding their decision making related to energy-saving strategies.

关键词： 泉水潜在出露   证据权重   随机森林模型   分类回归树模型   北京市  

摘要： 泉水出露受到多种因素影响,在传统地质勘查手段之外,各种模型方法及影响因子预测手段,也被越来越多地应用于泉水的研究中。本文尝试利用机器学习的方法进行泉水出露位置的预测研究。根据北京市野外调查,确定了1378个测试样本点,选取了高程、坡度、坡向、地形湿度指数、径流强度指数、距河流距离、距断裂距离、岩性、归一化植被指数及土地利用类型作为影响因子,对比了2种机器学习方法(随机森林模型、分类回归树模型)和地统计方法(证据权重模型)的预测效果。研究发现:随机森林模型的预测效果最好(Area Under Curve,AUC=0.86),分类回归树和证据权重模型效果相当(AUC分别为0.81、0.80);随机森林模型同时揭示,岩性、距断裂距离和距河流距离这3个影响因子对泉潜在出露的影响最大。本研究表明,在强烈人类活动影响下机器学习方法仍然具有较好的泉水出露预测能力,有望为泉水保护、恢复提供新的技术方法。

关键词： 机器学习   粒子重构   层析粒子图像测速   卷积神经网络   重构质量  

摘要： 通过三维粒子重构获取粒子场的分布情况是层析粒子图像测速的关键步骤,有限二维投影下的三维粒子重构是一个欠定的反问题,其精确解往往很难得到。一般情况下,可以通过优化方法得到近似解。为了获取质量更高的粒子场并用于层析粒子图像测速,提出了一种基于卷积神经网络(Convolutional Neural Networks,CNN)的粒子重构方法。所提出的技术可以从基于传统的代数重构技术(Algebraic Reconstruction Technique,ART)的方法所得到的粗略粒子分布中进一步提高粒子重构质量。与现有的基于ART的算法相比,新技术在重构质量方面有了显著的改进,可以有效剔除虚假粒子并更准确地还原粒子形状,并且在粒子浓度较稠密的情况下计算速度至少快了一个数量级。

关键词： hydrological simulation   long short-term memory   extreme gradient boosting   support vector regression   SWAT_Glacier model   Tianshan Mountains  

摘要： The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water *** this study,long shortterm memory(LSTM),a state-of-the-art artificial neural network algorithm,is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central *** other classic machine learning methods,namely extreme gradient boosting(XGBoost)and support vector regression(SVR),along with a distributed hydrological model(Soil and Water Assessment Tool(SWAT)and an extended SWAT model(SWAT_Glacier)are also employed for *** paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological *** two typical basins in this study are the main tributaries(the Kumaric and Toxkan rivers)of the Aksu River in the south Tianshan Mountains,which are dominated by snow and glacier meltwater and *** comparative analysis indicates that simulations from the LSTM shows the best agreement with the *** performance metrics Nash-Sutcliffe efficiency coefficient(NS)and correlation coefficient(R^(2))of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin,and NS and R^(2) are also higher than 0.70 in the Toxkan River *** to classic machine learning algorithms,LSTM shows significant advantages over most evaluating *** also has high NS value in the training period,but is prone to overfitting the *** with the widely used hydrological models,LSTM has advantages in predicting accuracy,despite having fewer data ***,LSTM only requires meteorological data rather than physical characteristics of underlying *** an extension of SWAT,the SWAT_Glacier model shows good adaptability in discharge simulation,outperforming the original SWAT model,but at

关键词： 二程语录   二程   程颐   程颢   BERT   机器学习   文本分类  

摘要： 以数字人文的新视角审视二程语录归属这一传统问题,运用机器学习的方法,将二程语录归属判断的问题转化为有监督的文本分类问题,构造BERT预训练语言模型加sigmoid激活函数的深度学习模型.以二程材料当中已知归属的文字作为模型的训练语料,对不明归属的二程语录之归属进行预测,准确率最高可达88%,证明了深度学习在小规模古汉语文本研究上的潜力.实验利用该训练后的模型,尝试对二程语录中不明归属的语录进行判断,发表了部分判断结果:《程氏遗书》中有30%、《程氏外书》中有20%是程颢语录,尤其值得关注的是对一些长久以来归属不明的著作文字比如《粹言》《经说》的作者进行了初步判断.

关键词： 数据分析   机器学习   支持向量机   中文语料   特征提取   文本分类  

摘要： 为提高游客在互联网旅游服务平台上评论数据分析的准确度,提出一种基于机器学习分类方法的数据智能分析技术。利用成熟的自动抓取等软件技术,从某旅游网络平台上抓取了游客的初始评论数据,同时设置了相应的训练集与测试集,完成了评论数据的文本清理、词性标注与人工分类等预处理操作。在此基础上,通过引入支持向量机等机器学习的分类方法,实现待处理评论数据的智能分类与分析,从而进一步改进游客情感数据挖掘的准确度,优化旅游目的地的顾客体验。数据测试仿真结果表明,与语义分析方法相比,基于机器学习方法的智能分析技术具有更高的数据分析准确度。

关键词： ANFIS   EEG   epilepsy diagnosis   NSCT   soft computing  

摘要： The electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. This study describes an automated classification of EEG signal for the detection of Epilepsy disease using soft computing methods. The proposed method is comprised of three modules: (a) transformation, (b) feature computation, and (c) feature classifications. In the first module, the nonsubsampled contourlet transform is applied on the EEG signal which decomposes the signal into approximate and directional subbands. The decomposition is done using nonsubsampled pyramid filter bank and nonsubsampled directional filter bank respectively. Secondly, the statistical features are extracted from the decomposed directional subbands using wavelet packet decomposition method. Finally, these features are classified by adaptive neuro-Fuzzy inference system classification method, which classifies the EEG signal into either focal or nonfocal signal. The proposed method is tested on a set of EEG signals for validation. The average classification rate of the proposed EEG signal classification system is 99.4%. The proposed EEG signal classification methodology achieves a sensitivity of 99.7%, a specificity of 99.7%, and an accuracy of 99.4%. The results confirmed that the proposed method has a potential in the classification of EEG signals and thereby could further improve the diagnosis of epilepsy.

关键词： PM2   5 exposure   Respiratory diseases   Emergency room visits   Machine learning  

摘要： The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak of PM2.5 is of great importance in terms of public health, medical resource allocation, and policy decision support. Recently, the machine learning methods bring promising solutions for ERV prediction in view of their powerful ability of short-term forecasting, while their performances still exist unknown. Therefore, we aim to check the feasibility of machine learning methods for ERV prediction of respiratory diseases. Three different machine learning models, including autoregressive integrated moving average (ARIMA), multilayer perceptron (MLP), and long short-term memory (LSTM), are introduced to predict daily ERV in urban areas of Beijing, and their performances are evaluated in terms of the mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-2). The results show that the performance of ARIMA is the worst, with a maximum R-2 of 0.70 and minimum MAE, RMSE, and MAPE of 99, 124, and 26.56, respectively, while MLP and LSTM perform better, with a maximum R-2 of 0.80 (0.78) and corresponding MAE, RMSE, and MAPE of 49 (33), 62 (42), and 14.14 (9.86). In addition, it demonstrates that MLP cannot detect the time lag effect properly, while LSTM does well in the description and prediction of exposure-response relationship between PM2.5 pollution and infecting respiratory disease.

关键词： ANN model   cold spray   critical velocity   feature selection  

摘要： Cold spraying has a potential application prospect in the field of repairing and additive manufacturing. The critical velocity of the cold spray is a key factor that determines the adhesion of particles during the cold spraying process, and it only depends on the particle parameters under the same working conditions. In the present study, the relationship between particle parameters and critical velocity is investigated using a feature selection method to obtain the influence weight of different particle parameters. Based on the results of feature selection, linear and nonlinear artificial neural networks are established to predict the critical velocity, respectively. The results of the feature selection show that the mechanical parameters of the material have a higher influence weight on the critical velocity than thermal parameters. In the prediction model, the ANN (artificial neural network) method shows a good prediction, and the nonlinear ANN model achieves better generalization ability than the linear ANN model and empirical formula with 95.24% prediction accuracy on the original data set and 96.45% prediction accuracy on the new data set.

关键词： Cognitive training   Cognitive remediation   Machine learning   Meta-analysis   Personalized treatment  

摘要： Accumulating evidence suggests that cognitive training may enhance well-being. Yet, mixed findings imply that individual differences and training characteristics may interact to moderate training efficacy. To investigate this possibility, the current paper describes a protocol for a data-driven individual-level meta-analysis study aimed at developing personalized cognitive training. To facilitate comprehensive analysis, this protocol proposes criteria for data search, selection and pre-processing along with the rationale for each decision. Twenty-two cognitive training datasets comprising 1544 participants were collected. The datasets incorporated diverse training methods, all aimed at improving well-being. These training regimes differed in training characteristics such as targeted domain (e.g., working memory, attentional bias, interpretation bias, inhibitory control) and training duration, while participants differed in diagnostic status, age and sex. The planned analyses incorporate machine learning algorithms designed to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.