检索结果-内蒙古大学图书馆

Automatic Correction of Parameters of Rice Phenology Prediction Model Based on random forest algorithm

Procedia Computer Science 2022年 208卷 435-441页

作者： Yingying Yang Xiaodong Li

With the rapid development of various Internet of Things and cloud computing, a large amount of data is rapidly accumulated in various fields, and the resulting data dimension is getting higher and higher. Evolutionary algorithm has been widely used in the field of crop model parameter calibration. Because the structure of crop model includes many sets of ordinary differential equations, which are nonlinear, discontinuous and multi peak, it is particularly important to choose an appropriate evolutionary algorithm according to the characteristics of crop model. At the same time, the parameters of evolutionary algorithm have a great impact on the performance of the algorithm. The improved differential evolution algorithm with adaptive control parameters shows good convergence speed and stability in the parameter calibration of rice phenology model. Variable selection is particularly important in high-dimensional data processing, and the importance rating of variables is the key problem. Aiming at the problem of low classification accuracy and generalization ability of the model in the single variable selection algorithm, this paper proposes a hybrid variable selection algorithm. The algorithm is divided into two stages: the filtering stage, which uses mutual information to quickly eliminate some irrelevant variables and reduce the dimension of the sample space; In the encapsulation stage, under the framework of replacement theory, the residual variables are selected by random forest, which shows that the algorithm has higher classification accuracy and generalization ability than the comparison algorithm.

关键词： random forest algorithm phenological phase of rice model parameter evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Pattern recognition based surface roughness prediction in turning hybrid metal matrix composite using random forest algorithm

引用

INDUSTRIAL LUBRICATION AND TRIBOLOGY 2013年第5期65卷 311-319页

作者： Radhika, N. Senapathi, S. Babudeva Subramaniam, R. Subramany, Rahul Vishnu, K. N. Amrita Sch Engn Coimbatore Tamil Nadu India PSG Coll Technol Coimbatore Tamil Nadu India

Purpose - The purpose of this paper is surface roughness prediction using pattern recognition for the aluminium hybrid metal matrix composite (HMMC). Design/methodology/approach - Hybrid composites were manufactured using liquid metallurgy technique. The cast HMMC was machined using an industrial CNC turning centre and the machining vibration signals were acquired using an accelerometer. The acquired signals were processed and used to build a machine learning model for predicting surface finish based on the tool signature. Findings - The authors established a technique for predicting and monitoring the surface quality during machining using a low cost accelerometer. It is capable of being integrated with the machine controller for online warning of deviations in surface roughness. The system is reconfigurable for any machining condition with a very short training period. The use of this model facilitates online surface roughness monitoring, avoiding the need for costly measuring equipment. Originality/value - The model developed is innovative and not reported widely to the best of the authors' knowledge. The use of accelerometer-based surface roughness prediction and control is an innovative approach for automation of machining process monitoring. These can be integrated into any existing machining centre as a standalone system or can be integrated into the CNC controller like Fanuc or Siemens.

关键词： Hybrid metal matrix composites Stir casting Surface roughness Decision trees random forest algorithm Metals Surface properties of materials

来源：评论

学校读者我要写书评

暂无评论

A Model Based on the random forest algorithm That Predicts the Total Oil-Water Two-Phase Flow Rate in Horizontal Shale Oil Wells

引用

PROCESSES 2023年第8期11卷 2346页

作者： Zhou, Huimin Liu, Junfeng Fei, Jiegao Shi, Shoubo Yangtze Univ Coll Geophys & Petr Resources Wuhan 430100 Peoples R China Yangtze Univ Minist Educ Key Lab Explorat Technol Oil & Gas Resources Wuhan 430100 Peoples R China PetroChina Chuanqing Drilling Co Changqing Downhole Technol Operat Co Xian 710000 Peoples R China Natl Engn Lab Explorat & Dev Low Permeabil Oil & G Xian 710000 Peoples R China

Due to variables like wellbore deviation variation and flow rate, the local flow velocity in the output wellbore of horizontal shale oil wells varied significantly at various points in the wellbore cross-section, making it challenging to calculate the total single-layer production with accuracy. The oil-water two-phase flow rate calculation techniques for horizontal wells developed based on particular flow patterns and array spinners had excellent applicability in their respective niches but suffered from poor generalizability and demanding experience levels for logging interpreters. In this study, we employed five spinners in a triangular walled array instrument to create the multi-decision tree after figuring out how many leaf nodes there were and examining the defining characteristics of the observed values gathered under various experimental setups. The construction of the entire oil-water two-phase flow prediction model was made possible when the random forest regression approach was used with it. The total oil-water flow rate at each perforated layer was predicted using the model in sample wells, and the mean square error with the third party's interpretation conclusion was 1.42, indicating that the model had an excellent application effect. The approach, which offered a new interpretation method for calculating the oil-water two-phase flow rate of horizontal wells based on multi-location local flow rate, required less interpretation knowledge from the interpreter and had a stronger generalization capacity.

关键词： horizontal well oil-water two-phase multi-location local flow velocity flow rate prediction random forest algorithm

来源：评论

学校读者我要写书评

暂无评论

The TPRF: A Novel Soft Sensing Method of Alumina-Silica Ratio in Red Mud Based on TPE and random forest algorithm

引用

PROCESSES 2024年第4期12卷 663页

作者： Meng, Fanguang Shi, Zhiguo Song, Yongxing Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310013 Peoples R China Zhejiang JingLiFang Digital Technol Grp Co Ltd Hangzhou 310012 Peoples R China Shandong Jianzhu Univ Sch Thermal Engn Jinan 250101 Peoples R China Compressor Technol Lab Anhui Prov State Key Lab Compressor Technol Hefei 230031 Peoples R China

The online measurement of the aluminum-silicon ratio of red mud in the dissolution stage of the Bayer alumina production process is difficult to achieve. The offline assay method has a high cost and strong time delay. Soft sensors are an effective and economical method to solve such problems. In this paper, a hybrid model (TPRF model) based on a tree-structured Parzen estimator (TPE) optimized random forest (RF) algorithm is proposed to measure the Al-Si ratio of red mud. The probability distribution of the hyperparameters of the random forest model is estimated by combining the TPE optimization algorithm with the random forest algorithm. According to this probability distribution, the hyperparameters of the random forest algorithm are adjusted in the parameter search space to obtain the best combination of hyperparameters. We established a TPRF soft sensing model based on the optimal combination of hyperparameters. The results show that the best performance of the TPRF model is a mean absolute percentage error (MAPE) of 0.0015, a root-mean-square error (RMSE) of 0.00378, a mean absolute error (MAE) of 0.00162, and a goodness of fit (R2) of 0.9893. The goodness of fit improved by 93.2% compared to the linear model, 39.1% compared to the SVR model, about 21.2% compared to the GRU model, and 5.5% compared to the RF model. This level of performance is demonstrated to be better than traditional soft sensors.

关键词： TPE algorithm random forest algorithm soft sensor alumina-silica ratio

来源：评论

学校读者我要写书评

暂无评论

Automatic classification of multi-source and multi-granularity teaching resources based on random forest algorithm

引用

INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2023年第2-3期33卷 177-191页

作者： Li, Dahui Qu, Peng Jin, Tao Chen, Changchun Bai, Yunfei Qiqihar Univ Sch Comp & Control Engn Qiqihar 161006 Heilongjiang Peoples R China

In traditional teaching resource classification methods, the classification accuracy is low and the RDV value of classification convergence is high. Through fuzzy information mining and fusion clustering method, multi-source and multi-granularity teaching resource data is obtained. With the help of incremental orthogonal component analysis method, the dimension of multi-source and multi-granularity teaching resource data is reduced. First, the teaching resource data is brought into random forest. Then, the filtering error of teaching resource is determined according to the classification parameter nonlinear feature recognition results. Finally, the multi-source and multi-granularity teaching resource classification is completed. The experimental results show that the highest classification accuracy is about 98%, and the lowest RDV is about 0.015.

关键词： random forest algorithm multi-source multi-granularity teaching resources automatic automatic classification

来源：评论

学校读者我要写书评

暂无评论

Personal and social predictors of use and non-use of fitness/diet app: Application of random forest algorithm

引用

TELEMATICS AND INFORMATICS 2020年 55卷 101301-101301页

作者： Cho, Jaehee Kim, Sehwan Sogang Univ Sch Commun 811 Bldg MatthewBaekbeom Ro 35 Seoul South Korea Dankook Univ Sch Med Dept Biomed Engn Yongin South Korea

This study investigated the various groups of factors that predict individuals' use and non-use of fitness and diet apps on smartphones. Unlike previous research on fitness and diet apps which have mainly studied individuals' intentions to use the apps, this study focused on the prediction accuracy of various factors that lead people to use fitness and diet apps through analysis of data collected from users as well as non-users of these apps. To examine prediction accuracy, this study applied the random forest algorithm. According to the findings, prediction accuracy higher than that of 70 percent was observed for nine factors: age, annual income, education, perceived obesity, dieting efforts, number of smartphone apps currently used, daily time spent with smartphone apps, perceived benefits from exercise, and social influence. A major contribution of this study is its detection of those factors predicting actual behavioral decisions regarding use of fitness and diet apps, as opposed to future intentions.

关键词： Fitness and diet apps mHealth Technology adoption Decision tree algorithm random forest algorithm

来源：评论

学校读者我要写书评

暂无评论

Analysis on the spatial dynamic characteristics of land use in the urban agglomeration in central Yunnan based on random forest algorithm

引用

INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT 2024年第1-2期27卷 92-109页

作者： Niu, Lede Lin, Jingzhi Zhou, Lifang Zhou, Yan Yunnan Normal Univ Fac Geog Kunming 650500 Peoples R China

In order to improve the accuracy of land use spatial analysis results, this paper takes the urban agglomeration in central Yunnan as an example, and proposes a land use spatial dynamic characteristics analysis method based on random forest algorithm. Using GIS technology to collect and process land remote sensing image data, we extract sparse description features of remote sensing images through the dictionary learning method, build a random forest classification model, classify land use space, and analyse dynamic features. The detailed analysis of land use change in the study area from 2005 to 2020 shows that the cultivated land area in this area has increased by 3,661 km(2), the dry land area has increased by 3,704 km(2), and the grassland area has decreased by 2,727 km(2), with the highest annual change rate of 0.62%.

关键词： remote sensing image land use spatial dynamic feature random forest algorithm sparse description feature

来源：评论

学校读者我要写书评

暂无评论

Research on Diagnosis Method of Common Knee Diseases Based on Subjective Symptoms and random forest algorithm

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 156-168页

作者： Wang, Guangjun Hu, Mengxia Lv, Linlin Zhang, Hanyuan Sun, Yining Su, Benyue Ma, Zuchang Chinese Acad Sci Hefei Inst Phys Sci Inst Intelligent Machines Anhui Prov Key Lab Med Phys & Technol Hefei 230031 Peoples R China Univ Sci & Technol China Grad Sch Sci Isl Branch Hefei 230026 Peoples R China Anqing Normal Univ Univ Key Lab Intelligent Percept & Comp Anhui Pro Anqing 246013 Peoples R China Anhui Med Univ Affiliated Hosp 1 Dept Sports Med & Arthroscop Surg Hefei 230022 Peoples R China

Knee diseases are common diseases in the elderly, and timely and effective diagnosis of knee diseases is essential for disease treatment and rehabilitation training. In this study, we construct a diagnostic model of common knee diseases based on subjective symptoms and random forest algorithm to realize patients' self-initial diagnosis. In this paper, we first constructed a questionnaire of subjective symptoms of knee, and set up a questionnaire system to guide users to fill out the questionnaire correctly. Then clinical data collection is carried out to obtain clinical questionnaire data. Finally, the diagnostic analysis of three common diseases of knee joint is carried out by random forest machine learning method. Through leave-one-out cross validation, the accuracy of meniscus injury, anterior cruciate ligament injury and knee osteoarthritis diseases are 0.79, 0.84, 0.81 respectively;the sensitivity is 0.79, 0.84, 0.88 respectively;and the specificity is 0.80, 0.84, 0.79 respectively. The results show that the method can achieve a good effect of self- diagnosis, and can provide a knee joint disease screening a convenient and effective approach.

关键词： Knee diseases subjective symptoms random forest algorithm self-diagnosis

来源：评论

学校读者我要写书评

暂无评论

OPTIMIZATION OF INTERNAL CONTROL FOR BUDGET OPERATIONS IN PUBLIC INSTITUTIONS BASED ON random forest algorithm

引用

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第3期25卷 1863-1871页

作者： Jin, Yang Henan Polytech Inst Sch Econ Nanyang 473000 Henan Peoples R China

In order to promote the construction of internal control in public institutions and improve work efficiency, the author proposes an optimization of internal control in public institution budget business based on random forest algorithm. We have constructed a big data audit framework for internal control of A Maritime Bureau based on the financial cloud platform and sorted out its audit process. By using the random forest algorithm to identify suspicious points in the internal control audit of administrative institutions at the data level, an example analysis is conducted using some data from A Maritime Bureau's assets, budget, revenue and expenditure, infrastructure, and contract business. The results indicate that the design of the internal control big data audit plan for administrative institutions will promote the innovation of audit information technology and application in A Maritime Bureau, provide theoretical guidance for the internal control big data audit carried out by administrative institutions, and effectively solve the problems of high workload and low work efficiency when A Maritime Bureau conducts internal control big data audits, thereby improving audit efficiency.

关键词： random forest algorithm Public utility units Budget business internal controls

来源：评论

学校读者我要写书评

暂无评论

Downscaling of Satellite Remote Sensing Soil Moisture Products Over the Tibetan Plateau Based on the random forest algorithm: Preliminary Results

引用

EARTH AND SPACE SCIENCE 2020年第6期7卷

作者： Chen, Qingqing Miao, Fang Wang, Hao Xu, Zi-Xin Tang, Zhiya Yang, Ling Qi, Shengxiu Chengdu Univ Technol Coll Geophys Chengdu Peoples R China Chengdu Univ Informat Technol Coll Meteorol Observat Chengdu Peoples R China China Meteorol Adm Key Lab Atmospher Sounding Chengdu Peoples R China Sichuan Prov Agr Meteorol Ctr Chengdu Peoples R China

Soil moisture (SM) is an important index of soil drought, and it directly controls the energy balance and water cycle of the land surface. As an indicator and amplifier of global warming, the Tibetan Plateau (TP) is becoming warmer and wetter. Because of its particular geographical environment, large-scale measurements of SM on the TP can only be achieved by satellite remote sensing. The resolution of current SM product of the Soil Moisture Active Passive (SMAP) satellite is 36 lcm, which is insufficient for many practical applications. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Digital Elevation Model (DEM) are applied to increase the resolution of SM down to 1 km using the random forest (RF) algorithm. The preliminary results of the proposed algorithm are evaluated by station observations and other reanalysis products. The downscaled results are more consistent with the in situ observations, the Land Data Assimilation System (CLDAS) from China Meteorological Administration (CMA), and the Global Land Data Assimilation System (GLDAS) from National Aeronautics and Space Administration (NASA) than the original SMAP product. The downscaling algorithm is most effective for grasslands. It is demonstrated that high-resolution SM products can be generated by fusing various features using machine-learning algorithms.

关键词： soil moisture downscaling SMAP MODIS DEM random forest algorithm

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：