检索结果-内蒙古大学图书馆

Estimation of the dynamic modulus of asphalt concretes using random forests algorithm

INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING 2022年第2期23卷 250-260页

作者： Daneshvar, Dana Behnood, Ali Vienna Univ Technol Fac Civil Engn TU Wien Vienna Austria Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA

Dynamic modulus () of asphalt can be estimated using predictive models to avoid the time-taking and costly laboratory-based measurements. Several predictive models such as the Witczak model have been widely used by many researchers for the prediction of the . Previously developed models have been widely reported to either overpredict or underpredict the values of . In this study, to overcome the issues related to the previously developed models, a random forests algorithm was used to develop predictive models of the using a comprehensive dataset. The performance of the developed models was compared with that of the Witczak model using an independent dataset. The results show that random forests algorithm can be successfully used to develop a model for the estimation of the with better performance than of the Witczak model. The R-2 values of the developed model in this study and the Witczak model were obtained as 0.9462 and 0.7371, respectively. Through a logarithmic transformation, the R-2 value increased from 0.9462 to 0.9634. A sensitivity analysis was also performed to find the most significant factors that affect the . The variables defined for test temperature and loading frequency were found to have the most effective impact on the prediction of .

关键词： Dynamic modulus asphalt mixture random forests algorithm Witczak model sensitivity machine learning predictive model

来源：评论

学校读者我要写书评

暂无评论

Effective Improvement of the Accuracy of Snow Cover Discrimination Using a random forests algorithm Considering Multiple Factors: A Case Study of the Three-Rivers Headwater Region, Tibet Plateau

引用

REMOTE SENSING 2023年第19期15卷 4644-4644页

作者： He, Rui Qin, Yan Zhao, Qiudong Chang, Yaping Jin, Zizhen Chinese Acad Sci Northwest Inst Ecoenvironm & Resources State Key Lab Cryospher Sci Lanzhou 730000 Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Key Lab Ecohydrol Inland River Basin Lanzhou 730000 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Xinjiang Univ Coll Geog & Remote Sensing Sci Urumqi 830017 Peoples R China Xinzhou Normal Univ Dept Geog Xinzhou 034000 Peoples R China

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover' extent, but accurate identification of shallow snow cover on the Tibetan Plateau has remained challenging. Focusing on the Three-Rivers Headwater Region (THR), this study addressed this issue by developing a snow cover discrimination model (SCDM) using a random forests (RF) algorithm. Using daily observed snow depth (SD) data from 15 stations in the THR during the period 2001-2013, a comprehensive analysis was conducted, considering various factors influencing regional snow cover distribution, such as land surface reflectance, land surface temperature (LST), Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Forest Snow Index (NDFSI). The key results were as follows: (1) Optimal model performance was achieved with the parameters Ntree, Mtry, and ratio set to 1000, 2, and 19, respectively. The SCDM outperformed other snow cover products in both pixel-scale and local spatial-scale discrimination. (2) Spectral information of snow cover proved to be the most influential auxiliary variable in discrimination, and the combined inclusion of NDVI and LST improved model performance. (3) The SCDM achieved accuracy of 99.04% for thick snow cover (SD > 4 cm) and 98.54% for shallow snow cover (SD <= 4 cm), significantly (p < 0.01) surpassing the traditional dynamic threshold method. This study can offer valuable reference for monitoring snow cover dynamics in regions with limited data availability.

关键词： snow cover discrimination model random forests algorithm shallow snow cover Three-Rivers Headwater Region

来源：评论

学校读者我要写书评

暂无评论

Prediction of Dissolved Gas in Power Transformer Oil Based on random forests algorithm 5

Prediction of Dissolved Gas in Power Transformer Oil Based o...

引用

5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)

作者： Deng, Ke Xiong, Weihong Zhu, Liming Zhang, Hongzhi Li, Zhengtian State Grid Hubei Elect Power Co Overhaul Branch Wuhan 430050 Peoples R China Ctr China Grid Co Ltd Wuhan 430077 Peoples R China HUST Wuhan 430074 Peoples R China

ISBN: (纸本)9781467371063

In order to adopt reasonable measures to anticipate and avoid possible internal failures in power transformers, accurate prediction of power transformer oil dissolved gas trends is useful. It is necessary to realize the condition based maintenance of power transformers, and the prediction of dissolved gas in the oil is solid foundation. The gas concentration is affected by many factors, so prediction model built by reasonable selection of the larger correlation factors will help to improve prediction accuracy. random forests algorithm is used to predict the gas trends and the prediction performance evaluation is realized by appropriate evaluation indexes. By comparison with support vector machine prediction, the advantage of random forests method for power transformer oil dissolved gas prediction is proved.

关键词： Power transformer dissolved gas condition based maintenance prediction random forests algorithm

来源：评论

学校读者我要写书评

暂无评论

Boosting SISSO performance on small sample datasets by using random forests prescreening for complex feature selection

引用

Frontiers of physics 2025年第1期20卷 117-123页

作者： Xiaolin Jiang Guanqi Liu Jiaying Xie Zhenpeng Hu School of Physics Nankai UniversityTianjin 300071China

In materials science,data-driven methods accelerate material discovery and optimization while reducing costs and improving success *** regression is a key to extracting material descriptors from large datasets,in particular the Sure Independence Screening and Sparsifying Operator(SISSO)*** SISSO needs to store the entire expression space to impose heavy memory demands,it limits the performance in complex *** address this issue,we propose a RF-SISSO algorithm by combining random forests(RF)with *** this algorithm,the random forests algorithm is used for prescreening,capturing non-linear relationships and improving feature selection,which may enhance the quality of the input data and boost the accuracy and efficiency on regression and classification *** a testing on the SISSO’s verification problem for 299 materials,RF-SISSO demonstrates its robust performance and high ***-SISSO can maintain the testing accuracy above 0.9 across all four training sample sizes and significantly enhancing regression efficiency,especially in training subsets with smaller sample *** the training subset with 45 samples,the efficiency of RF-SISSO was 265 times higher than that of original *** collecting large datasets would be both costly and time-consuming in the practical experiments,it is thus believed that RF-SISSO may benefit scientific researches by offering a high predicting accuracy with limited data efficiently.

关键词： random forests algorithm SISSO symbolic regression algorithm machine learning small datasets prescreening complex feature selection

来源：评论

学校读者我要写书评

暂无评论

Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features

引用

IET POWER ELECTRONICS 2020年第16期13卷 3571-3579页

作者： Kou, Lei Liu, Chuang Cai, Guo-wei Zhou, Jia-ning Yuan, Quan-de Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China Changchun Inst Technol Sch Comp Technol & Engn Changchun 130012 Peoples R China

A three-phase pulse-width modulation (PWM) rectifier can usually maintain operation when open-circuit faults occur in insulated-gate bipolar transistors (IGBTs), which will lead the system to be unstable and unsafe. Aiming at this problem, based on random forests with transient synthetic features, a data-driven online fault diagnosis method is proposed to locate the open-circuit faults of IGBTs timely and effectively in this study. Firstly, by analysing the open-circuit fault features of IGBTs in the three-phase PWM rectifier, it is found that the occurrence of the fault features is related to the fault location and time, and the fault features do not always appear immediately with the occurrence of the fault. Secondly, different data-driven fault diagnosis methods are compared and evaluated, the performance of random forests algorithm is better than that of support vector machine or artificial neural networks. Meanwhile, the accuracy of fault diagnosis classifier trained by transient synthetic features is higher than that trained by original features. Also, the random forests fault diagnosis classifier trained by multiplicative features is the best with fault diagnosis accuracy can reach 98.32%. Finally, the online fault diagnosis experiments are carried out and the results demonstrate the effectiveness of the proposed method, which can accurately locate the open-circuit faults in IGBTs while ensuring system safety.

关键词： fault location fault diagnosis PWM rectifiers neural nets insulated gate bipolar transistors support vector machines data-driven design three-phase PWM rectifier random forests technique transient synthetic features three-phase pulse-width modulation rectifier open-circuit faults fault diagnosis method open-circuit fault features fault location different data-driven fault diagnosis methods random forests algorithm random forests fault diagnosis classifier multiplicative features fault diagnosis accuracy online fault diagnosis experiments

来源：评论

学校读者我要写书评

暂无评论

Maize and sorghum field segregation using multi-temporal Sentinel-2 data in central Mexico

引用

JOURNAL OF APPLIED REMOTE SENSING 2021年第2期15卷

作者： Soler-Perez-Salazar, Maria J. Ortega-Garcia, Nicolas Vaca-Mier, Mabel Cram-Hyedric, Silke Univ Autonoma Metropolitana Energy Dept Azcapotzalco Mexico Inst Tecnol Super Salvatierra Guanajuato Mexico Univ Nacl Autonoma Mexico Inst Geog Coyoacan Mexico

Crop segregation in optical remote sensing cannot always be easily achieved among similar species since their spectral response can be similar. We aimed to distinguish fields of two C4 gramineae with similar plant structure and crop calendar, maize (Zea mays) and sorghum (Sorghum bicolor), in an agricultural area of the state of Guanajuato, in central Mexico. One hundred and forty-three fields of these two crops were identified and their average spectral signatures were derived at different phenological stages, using digital information from 11 Sentinel 2A/B images, corresponding to the 2019 spring-summer cycle. We also calculated the temporal profile of six spectral indices for each crop, to evaluate seasonal variations and conducted a cropland classification, using the random forests algorithm and different combinations of dates and indices. Results show that segregation of maize and sorghum fields can be achieved due to small differences in the near-infrared region of their spectral signatures and disparities in the rate of decay during the senescence stage. Classification results also confirmed the separability of these two crops, obtaining a very high overall accuracy and kappa values (98.43% and 97.07%, correspondingly) based on the reflectance of nine images and only three spectral indices: the normalized difference vegetation index, the normalized difference water index, and the structure insensitive pigment index. This investigation demonstrates that the combined use of time series of high-resolution images and spectral indices can contribute to distinguish fields of similar crop species. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： crop classification maize sorghum Sentinel-2 random forests algorithm spectral indices

来源：评论

学校读者我要写书评

暂无评论

A refined zenith tropospheric delay model for Mainland China based on the global pressure and temperature 3 (GPT3) model and random forest

引用

GPS SOLUTIONS 2023年第4期27卷 172页

作者： Li, Junyu Zhang, Qinglan Liu, Lilong Yao, Yibin Huang, Liangke Chen, Fade Zhou, Lv Zhang, Bao Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541004 Peoples R China Guangxi Key Lab Spatial Informat & Geomat Guilin 541004 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China

Zenith Tropospheric Delay (ZTD) plays a vital role in Global Navigation Satellite System (GNSS) navigation, positioning, and meteorology. The generally accepted empirical models that are now in use can only reflect the periodic changes in ZTD. Nevertheless, capturing its subtle nonlinear changes, like rapid ZTD variations, is challenging as its accuracy needs further improvement. To overcome these drawbacks, the relationship between the residuals of GPT3 ZTD minus GNSS ZTD and the spatiotemporal information was fitted by using random forests (RF). Consequently, a refined model of GPT3 ZTD was established in Mainland China (named RGPT3), and the performance of the proposed model was compared to two accepted empirical models and another model based on a popular algorithm of machine learning (backpropagation neural network algorithm). Based on the results of the study, the RMSE of RGPT3 is 1.83 cm, which has an improvement of 28.0, 16.8, and 34.4% over the three compared models. The RGPT3 performs better in capturing the instantaneous ZTD changes than the empirical models. The result of RGPT3 ZTD constraint GNSS precise point positioning (PPP) is also superior to that of GPT3 ZTD, with the U direction convergence time reduction of 12.3% and accuracy improvement of 7.9%. The new model can offer higher-precision ZTD predictions in the study area.

关键词： GNSS Zenith tropospheric delay Empirical model random forests algorithm Prediction

来源：评论

学校读者我要写书评

暂无评论

Nonlinear overall annoyance level modeling and interior sound quality prediction for pure electric vehicle with extreme gradient boosting algorithm

引用

APPLIED ACOUSTICS 2022年 195卷

作者： Wang, Yanqin Zhang, Shuai Meng, Dejian Zhang, Lijun Minist Transport Res Inst Highway Beijing 100088 Peoples R China Natl Environm Protect Engn & Technol Ctr Rd Traff Beijing 100088 Peoples R China Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

A single psychoacoustic metric cannot reflect pure electric vehicle (PEV) interior overall sound quality (SQ) accurately. There exists a complex nonlinear mapping relationship between psychoacoustic indexes and SQ prediction. In this work, a nonlinear overall annoyance level modeling method using the extreme gradient boosting (XGBoost) algorithm is proposed. Firstly, comprehensive noise samples inside the cabins of PEV and conventional fuel vehicle (FV) at high speed are collected. Subjective and objective SQ evaluations are conducted for FV and PEV interior noise, respectively. Their differences are analyzed by comparison. The correlation among various SQ indexes is studied, revealing a significant multicollinearity disruption. Then, the relative importance ranking is determined by the random forests algorithm, which provides a basis for feature index selection. Finally, the XGBoost-based nonlinear overall annoyance level model for SQ evaluation is established. Using the XGBoost-based model, traditional multiple linear regression (MLR) model, and linear least absolute shrinkage and selection operator (LASSO) model, PEV SQ is predicted. The results show that the XGBoost-based model exhibits high prediction accuracy, improving 75.6% relative to the MLR model and 74.9% relative to the LASSO model. Meanwhile, it has excellent stability and goodness-of-fitting. Thus, it provides an alternative method for PEV SQ evaluation and prediction. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Pure electric vehicle Sound quality Nonlinear overall annoyance model XGBoost algorithm Multiple linear regression random forests algorithm

来源：评论

学校读者我要写书评

暂无评论

Prediction method for galloping features of transmission lines based on FEM and machine learning

引用

COLD REGIONS SCIENCE AND TECHNOLOGY 2020年第0期173卷 103031-000页

作者： Mou, Zheyue Yan, Bo Lin, Xiang Huang, Guizao Lv, Xin Chongqing Univ Coll Aerosp Engn Chongqing 400044 Peoples R China Chongqing Univ Coll Civil Engn Chongqing 400044 Peoples R China

Parameter study on galloping of iced transmission lines with different structural, ice and wind parameters is carried out by means of the finite element method (FEM), and a dataset is then created based on the numerical results. The random forests algorithm is used to set up a classification model to predict the galloping mode and vertical vibration mode. Using the span length, initial conductor tension, ice thickness, initial wind attack angle, wind velocity and the galloping mode as well as the vertical vibration mode obtained by the classification model as the input variables, a back-propagation (BP) neural network model is created to predict the galloping features including the frequencies, vibration amplitudes and the maximum conductor tension. The dataset created based on the parameter study is divided into two sub-datasets for training and testing of the two machine learning models respectively. The effects of the input variables on the output variables are quantified with a feature importance analysis for the classification model and a global sensitivity analysis for the feature prediction model respectively. The two models are finally integrated into one to predict galloping features efficiently and quickly. Making use of the proposed method, an early warning system for galloping of transmission lines may be able to be set up in the future.

关键词： Galloping Transmission line Feature prediction random forests algorithm BP neural network

来源：评论

学校读者我要写书评

暂无评论

A social learning approach to carbon capture and storage demonstration project management: An empirical analysis

引用

APPLIED ENERGY 2021年 299卷 117336-117336页

作者： Kang, Jia-Ning Wei, Yi-Ming Liu, Lan-cui Yu, Bi-Ying Liao, Hua Beijing Inst Technol Ctr Energy & Environm Policy Res Beijing 100081 Peoples R China Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China Beijing Key Lab Energy Econ & Environm Management Beijing 100081 Peoples R China Beijing Normal Univ Business Sch Beijing 100875 Peoples R China

Carbon capture and storage (CCS) is an essential technology option for limiting global warming to well below 2 degrees Celsius. Demonstration is an important engineering measure towards large-scale CCS promotion. Implementing a demonstration project is a complicated process of system engineering management that involves a large number of strategic decisions. It is critical to investigate the potential knowledge-generating and decision-making processes embedded in the CCS demonstration, as well as to learn from past successes and failures. Learning-oriented approaches used to be primarily concerned with technical costs, which sparked a lengthy debate regarding the economic viability of CCS projects, but little attention was devoted to the social aspects of project implementation. Based on historical evidence on CCS demonstrations globally, this study developed a data-driven social learning model with high accuracy in estimating the chances of project success, combined with 19 socio-technical characteristics. Our results imply that project performance (79.8%) is more relevant than policy incentives (12.8%) and media hype (7.4%) in explaining previous CCS triumphs. Among the seven policy -level characteristics, tax credits and regulatory legislation have grown in importance over the last 20 years, while the significance of grant subsidies has gradually waned. Furthermore, we found that modest hype assisted in the completion of planned projects, notably carbon dioxide storage projects. The sociocultural narrative of CCS does not yet have a set ending. This highlights the significance of telling compelling CCS stories in steering the future trajectory of technology and the low-carbon energy revolution.

关键词： Carbon Capture and Storage Projects Decision Simulation Feasibility Evaluation random forests algorithm Social learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：