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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

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

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

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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

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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

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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

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

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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

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

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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

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random forests classifier for machine fault diagnosis

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2008年第9期22卷 1716-1725页

作者： Yang, Bo-Suk Di, Xiao Han, Tian Pukyong Natl Univ Sch Mech Engn Pusan 608739 South Korea Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China

This paper investigates the possibilities of applying the random forests algorithm (RF) in machine fault diagnosis, and proposes a hybrid method combined with genetic algorithm to improve the classification accuracy. The proposed method is based on RF, a novel ensemble classifier which builds a number of decision trees to improve the single tree classifier. Although there are several existing techniques for faults diagnosis, the application research on RF is meaningful and necessary because of its fast execution speed, the characteristics of tree classifier, and high performance in machine faults diagnosis. The proposed method is demonstrated by a case study on induction motor fault diagnosis. Experimental results indicate the validity and reliability of RF-based diagnosis method.

关键词： random forests algorithm genetic algorithm machine learning fault diagnosis rotating machinery

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random forests Based View Generation for Multiview TV

Random Forests Based View Generation for Multiview TV

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IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Pourazad, Mahsa T. Xu, Di Nasiopoulos, Panos TELUS Commun Inc Vancouver BC Canada

ISBN: (纸本)9780769549156;9781479902279

The appearance of multiview display systems in the consumer market is not far from reality. With technical knowledge in this field constantly improving, production of multiview content is the only other key factor that will determine the successful adoption of this technology. Multiview content can be generated from two or three views and their associated depth maps. Estimating a high quality depth map is challenging. Moreover transmission of depth map information requires extra bandwidth. In this study, we propose and effective algorithm, which utilizes a 3D visual attention model, multiple monocular depth cues and a fraction of depth information for estimating the whole depth map of the scene using the random forests (RF) machine learning algorithm. Having the estimated depth maps and stereo videos, other views may be synthesized. Performance evaluations have shown that the proposed method estimates high quality depth maps for stereo sequence from limited depth information. Implementation of our proposed technique in the future multiview pipeline eliminates the need for estimating and transmitting the whole depth map for all the views, producing high quality multiview content while reducing the required bandwidth.

关键词： multiview TV multiview content depth map estimation 3D visual attention model random forests algorithm

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Identification of immune correlates of protection in Shigella infection by application of machine learning

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JOURNAL OF BIOMEDICAL INFORMATICS 2017年 74卷 1-9页

作者： Arevalillo, Jorge M. Sztein, Marcelo B. Kotloff, Karen L. Levine, Myron M. Simon, Jakub K. Univ Nacl Educ Distancia Dept Stat & Operat Res Paseo Senda del Rey 9 E-28040 Madrid Spain Univ Maryland Sch Med Ctr Vaccine Dev Dept Pediat Baltimore MD 21201 USA Univ Maryland Sch Med Ctr Vaccine Dev Dept Med Baltimore MD 21201 USA Merck & Co Inc Kenilworth NJ USA

Background: Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical vaccine studies. Our goal is the development of a methodology to identify immunologic correlates of protection using the Shigella challenge as a model. Methods: The proposed methodology utilizes the random forests (RF) machine learning algorithm as well as Classification and Regression Trees (CART) to detect immune markers that predict protection, identify interactions between variables, and define optimal cutoffs. Logistic regression modeling is applied to estimate the probability of protection and the confidence interval (CI) for such a probability is computed by bootstrapping the logistic regression models. Results: The results demonstrate that the combination of Classification and Regression Trees and random forests complements the standard logistic regression and uncovers subtle immune interactions. Specific levels of immunoglobulin IgG antibody in blood on the day of challenge predicted protection in 75% (95% CI 67-86). Of those subjects that did not have blood IgG at or above a defined threshold, 100% were protected if they had IgA antibody secreting cells above a defined threshold. Comparison with the results obtained by applying only logistic regression modeling with standard Akaike Information Criterion for model selection shows the usefulness of the proposed method. Conclusion: Given the complexity of the immune system, the use of machine learning methods may enhance traditional statistical approaches. When applied together, they offer a novel way to quantify important immune correlates of protection that may help the development of vaccines. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Classification and Regression Trees random forests algorithm Logistic regression Correlate of protection Shigella

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A data-driven study of important climate factors on the achievement of LEED-EB credits

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BUILDING AND ENVIRONMENT 2015年 90卷 232-244页

作者： Cheng, Jack C. P. Ma, Lucky J. Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Hong Kong Peoples R China

Developed by the USGBC, LEED is currently the most widely adopted credit-based green building rating program worldwide. Buildings can be graded as Platinum, Gold, Silver, or Certified depending on the number of LEED credit points achieved. Selection of LEED credits is an important step in LEED certification planning and application. The relationships between LEED credit achievement and project factors like budget and schedule have already been studied. Apart from these, climate factors such as temperature and precipitation can also affect the selection of green building technologies and therefore the LEED credits achieved. However, study on the relationships between LEED credit achievement and the climate of the building location is lacking. This paper investigates the relationship between climate factors and LEED credits using data mining techniques. The LEED credits achieved and the local climate conditions of 912 LEED certified existing building projects were collected and analyzed. By setting the climate factors as variables and the credit achievements as the targets, 26 classification models were built using the random forests classification algorithm. The variable importance for each credit was then calculated based on the contribution to the classification performance. For the models with high AUC performance, high importance climate factors were identified and discussed. The results show that some climate factors, such as diurnal temperature range, have a notable correlation with the achievement of certain LEED credits. Implications and contributions of the results are also discussed. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Climate factors Data mining Green building LEED random forests algorithm

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

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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

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