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Investigating the effect of residual stresses and distortion of laser welded joints for automobile chassis and optimizing weld parameters using random forest based grey wolf optimizer

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WELDING INTERNATIONAL 2023年第1期37卷 46-67页

作者： Surwase, Sanjay S. Bhosle, Santosh P. Maharashtra Inst Technol Dept Mech Engn Aurangabad India Maharashtra Inst Technol Aurangabad India

The present investigation analyses the selection of the right welding method and joint and advanced testing methods (NDT) for highly durable automotive frames. Moreover, the present investigation analysis suggests the best machine learning (ML) algorithm for selecting the best weld method and optimal solution. The experiment was performed based on the response surface methodology (RSM) based design of the experimental approach. As a result, laser beam welding (LBM) and cross joint are the significant weld methods for automotive frames. The proposed ML algorithm successfully optimized the LBM input parameters as laser power = 1277 W, welding speed (WS) = 32.2 mm/s, focal point: 1 mm and working angle = 0.14 Rad with an average error of approximately 0.033. Based on the results, the optimum output weld parameters are bead width = 4322.7 mu m, penetration depth (PD) = 3157.9 mu m, total strain = 0.0098 mm/mm and residual stress = 645.2340 MPa, respectively.

关键词： Laser beam welding random forest algorithm grey wolf optimization response surface methodology residual stresses

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Computation of the Thermal Residual Stresses in SiC/SiC Composites with Multi-Layered Interphases by Using ANN with the Structure of random forest

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HIGH TEMPERATURE MATERIALS AND PROCESSES 2018年第9-10期37卷 987-994页

作者： You, Tao Li, Tingfeng Zhai, Xiang Du, Chenglie Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China State Key Lab Intelligent Mfg Syst Technol Beijing 100039 Peoples R China Sci & Technol Space Phys Lab Beijing 100076 Peoples R China

In this paper, a hybrid strategy incorporating finite element computation, artificial neural network (ANN) method and random forest (RF) algorithm is proposed for computation of the thermal residual stresses (TRS) in SiC/SiC composites with multi-layered interphases. The finite element method is adopted to calculate the TRS of the unidirectional SiC/SiC composites. The ANN with the structure of random forest (RFANN) is employed to approximate the non-linear relationship between the multi-layered interphases thicknesses and the TRS of the SiC/SiC composites. The well-trained RFANN is finally used to compute the TRS of a unidirectional SiC/SiC composite with six layers of interphases.

关键词： ceramic matrix composites thermal residual stresses random forest algorithm artificial neural network finite element method

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Regional agricultural drought vulnerability prediction based on interpretable random forest

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第11期196卷 1123页

作者： Luo, Dang Qiao, Xinqing North China Univ Water Resources & Elect Power Sch Math & Stat Zhengzhou 450046 Peoples R China

Drought is one of the leading natural disasters in the world. It severely restricts the normal functioning of societies and economies. The purpose of this paper is to predict the drought vulnerability index in Henan Province so that timely measures can be taken to mitigate the impact of drought and to ensure the continued stability of people's lives and social development. First, the drought vulnerability index of Henan Province for 2010-2022 was calculated based on relevant literature and the regional situation of Henan Province. In addition, the main factors of sensibility and resilience are selected, the prediction indicator system is established, and the grey correlation analysis model is applied to select the top 10 main factors of drought vulnerability. Second, the random forest model was trained and simulated, and the drought vulnerability index of Henan Province was predicted for 2023-2025. Finally, the SHapley Additive exPlanations model was used to explain the specific effects of the influences of the random forest model. The interpretable random forest model not only accurately predicts the drought vulnerability index in Henan Province but also alleviates concerns about the "black box" problem of indirect interpretation of the random forest model. The results of the study show that the Average Percentage Error of the drought vulnerability index in Henan Province from 2010 to 2022 simulated by the random forest model is as low as 3.0457%, and the simulation accuracy is as high as 96.9643%. Drought vulnerability prediction for Henan Province was also made for the period 2023-2025, showing an increasing trend. Relevant sectors should enhance awareness and early warning of drought, improve resilience, and take effective measures to reduce the socio-economic impact of drought.

关键词： Drought vulnerability Grey correlation analysis random forest algorithm SHapley Additive exPlanations model

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Predicting the functional outcomes of anti-LGI1 encephalitis using a random forest model

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ACTA NEUROLOGICA SCANDINAVICA 2022年第2期146卷 137-143页

作者： Li, Gongfei Liu, Xiao Wang, Minghui Yu, Tingting Ren, Jiechuan Wang, Qun Capital Med Univ Beijing Tiantan Hosp Dept Neurol Beijing Peoples R China Capital Med Univ Beijing Tongren Hosp Beijing Tongren Eye Ctr Beijing Peoples R China China Natl Clin Res Ctr Neurol Dis Beijing Peoples R China Beijing Inst Brain Disorders Beijing Peoples R China

Objectives To establish a model in order to predict the functional outcomes of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis and identify significant predictive factors using a random forest algorithm. Methods Seventy-nine patients with confirmed LGI1 antibodies were retrospectively reviewed between January 2015 and July 2020. Clinical information was obtained from medical records and functional outcomes were followed up in interviews with patients or their relatives. Neurological functional outcome was assessed using a modified Rankin Scale (mRS), the cutoff of which was 2. The prognostic model was established using the random forest algorithm, which was subsequently compared with logistic regression analysis, Naive Bayes and Support vector machine (SVM) metrics based on the area under the curve (AUC) and the accuracy. Results A total of 79 patients were included in the final analysis. After a median follow-up of 24 months (range, 8-60 months), 20 patients (25%) experienced poor functional outcomes. A random forest model consisting of 16 variables used to predict the poor functional outcomes of anti-LGI1 encephalitis was successfully constructed with an accuracy of 83% and an F1 score of 60%. In addition, the random forest algorithm demonstrated a more precise predictive performance for poor functional outcomes in patients with anti-LGI1 encephalitis compared with three other models (AUC, 0.90 vs 0.80 vs 0.70 vs 0.64). Conclusions The random forest model can predict poor functional outcomes of patients with anti-LGI1 encephalitis. This model was more accurate and reliable than the logistic regression, Naive Bayes, and SVM algorithm.

关键词： anti-leucine-rich glioma-inactivated 1 (LGI1) antibody functional outcomes limbic encephalitis random forest algorithm

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Prediction of open stope hangingwall stability using random forests

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NATURAL HAZARDS 2018年第2期92卷 1179-1197页

作者： Qi, Chongchong Fourie, Andy Du, Xuhao Tang, Xiaolin Univ Western Australia Sch Civil Environm & Min Engn Perth WA 6009 Australia Univ Western Australia Sch Mech & Chem Engn Perth WA 6009 Australia Univ Western Australia Planning & Transport Res Ctr Perth WA 6009 Australia

The prediction of open stope hangingwall (HW) stability is a crucial task for underground mines. In this paper, a relatively novel technique, the random forest (RF) algorithm, is introduced for the prediction of HW stability. The objective of this study is to verify the feasibility of the RF algorithm on HW stability prediction and investigate the relative importance of influencing variables. The training and verification of RF models were conducted on a dataset from the literature and a total of 115 HW cases were analysed. Thirteen influencing variables were selected as the inputs with the HW stability being selected as the output. The dataset was randomly divided into the training set and the testing set. Fivefold cross-validation was used as the validation method, and the grid search method was used for the hyper-parameters tuning. Performance measures were chosen to be the confusion matrix, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). The results show that the RF algorithm had great potential for the prediction of HW stability. AUC values achieved by the optimum RF model on the training set and the testing set were 0.884 and 0.873, respectively, indicating that the optimum RF model was excellent at predicting HW stability. The stope design method was found to be the most sensitive variable among all variables evaluated, with an importance score of 0.168 out of 1. The RQD and HW height also had a strong influence on the stability of an open stope HW.

关键词： Hangingwall stability random forest algorithm Fivefold cross-validation Hyper-parameters tuning Performance measures Variables importance

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Impact of an improved random forest-based financial management model on the effectiveness of corporate sustainability decisions

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SYSTEMS AND SOFT COMPUTING 2024年 6卷

作者： Zhang, Jianhui Xian Univ Sch Econ & Management Xian 710065 Peoples R China

With the development of the economy, more and more electronic manufacturing enterprises are emerging like mushrooms after rain. These enterprises, while developing, also face financial risks caused by various reasons. In order to provide early warning for financial risks of enterprises, improve the accuracy of identifying financial risks, avoid financial crises, and provide assistance for sustainable development decisions, this paper proposes a financial management model based on modified random forest. In order to improve the generalization ability of financial management models, pruning methods were adopted in the study to avoid overfitting. Synthetic minority oversampling technique is used to optimize the financial management model and reduce the calculation deviation of the model through its sampling ability. At the same time, the prediction index system is proposed to improve the analysis ability of the financial management model. The results show that the accuracy and recall rate of the improved algorithm based on random forest proposed in this study in identifying corporate financial distress are 98.03 % and 100 % respectively. The importance value of operating income and cash flow in enterprise indicators is 0.391, which is the most relevant indicator for enterprise financial forecasting. The results show that after the improvement of synthetic minority oversampling technique, the stochastic forest model can effectively improve the recognition and early warning ability of enterprises' financial distress, and is conducive to maintaining good operating efficiency and sustainable operation of enterprises. Electronic manufacturing enterprises need to strengthen their attention to cash flow, improve their cash flow, and enhance their profitability. The financial management model designed by the research institute can provide technical and information support for financial early warning and sustainable development of electronic manufacturing enterprises.

关键词： random forest algorithm Financial management Sustainable decision-making Synthetic minority oversampling technique algorithm

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RFCRYS: Sequence-based protein crystallization propensity prediction by means of random forest

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JOURNAL OF THEORETICAL BIOLOGY 2012年 306卷 115-119页

作者： Jahandideh, Samad Mahdavi, Abbas Univ Alabama Birmingham Dept Biostat Sect Stat Genet Birmingham AL 35294 USA Vali E Asr Univ Rafsanjan Dept Stat Fac Math Sci Rafsanjan Iran

Production of high-quality diffracting crystals is a critical step in determining the 3D structure of a protein by X-ray crystallography. Only 2%-10% of crystallization projects result in high-resolution protein structures. Previously, several computational methods for prediction of protein crystallizability were developed. In this work, we introduce RFCRYS, a random forest: based method to predict crystallizability of proteins. RFCRYS utilizes mono-, di-, and tri-peptides amino acid compositions, frequencies of amino acids in different physicochemical groups, isoelectric point, molecular weight, and length of protein sequences, from the primary sequences to predict crystallizabillity by using two different databases. RFCRYS was compared with previous methods and the results obtained show that our proposed method using this set of features outperforms existing predictors with higher accuracy. MCC, and Specificity. Especially, our method is characterized by high Specificity of 0.95, which means RFCRYS rarely mispredicts a protein chain to be crystallizable which consequently would be useful for saving time and resources. In conclusion RFCRYS provides accurate crystallizability prediction for a protein chain that can be applied to support crystallization projects getting higher success rate towards obtaining diffraction-quality crystals. Published by Elsevier Ltd.

关键词： random forest algorithm X-ray crystallography Protein structure

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The random forest based Detection of Shadowsock's Traffic 9

The Random Forest based Detection of Shadowsock's Traffic

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9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Deng, Ziye Liu, Zihan Chen, Zhouguo Guo, Yubin Ctr Cyber Secur Beijing Peoples R China Sci & Technol Commun Secur Lab Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538630228

With the development of anonymous communication technology, it has led to the fact that the network monitoring is becoming more and more difficult. If the anonymous traffic can be effectively identified, the abuse of such technology can be prevented. Since the study of machine learning is rapidly developing these years, this paper applies the random forest algorithm - a semi-supervised learning method - into the traffic detection of Shadowsocks. We can get over 85% detection accuracy rate in our experiments after applying random forest algorithm by collecting train set, gathering features, training models and predicting results. With the scale of train set and test set increase, the detection accuracy rate gradually increases until it becomes constant. We will make several adjustments on train set, test set and feature set to reduce the false alarm rate and false rate when detecting.

关键词： detection shadowsocks random forest algorithm machine learning

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Wearable Non-invasive Blood Glucose Estimation via Empirical Mode Decomposition Based Hierarchical Multiresolution Analysis and random forest 23

Wearable Non-invasive Blood Glucose Estimation via Empirical...

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23rd IEEE International Conference on Digital Signal Processing (DSP)

作者： Li, Zheng Ye, Qiuliang Guo, Yitong Tian, Zikang Ling, Bingo Wing-Kuen Lam, Ringo Wai-Kit Guangdong Univ Technol Sch Informat Engn Guangzhou Guangdong Peoples R China Add Care Ltd Tsuen Wan Room A14th FloorRichwealth Ind Bldg77-87 Wang Hong Kong Peoples R China

ISBN: (纸本)9781538668122;9781538668115

Wearable non-invasive blood glucose estimation plays an important role in the biomedical signal processing community. The common blood glucose estimation method is via the direct random forest algorithm. However, since the useful information of the signal is usually corrupted due to the low SNR, the distorted features inputted fur the training algorithm result to a poor estimation performance. This paper proposes to employ an empirical mode decomposition (EMD) based hierarchical multiresolution analysis for performing the pre-processing and the random forest for performing the wearable non-invasive blood glucose estimation. More precisely, two levels of decompositions are employed in the EMD based hierarchical multiresolution analysis and only the first two intrinsic mode functions (IMF) in the second level of decomposition are discarded. Next, the features exacted from the processed near infrared (NIR) signal are trained via the random forest regression algorithm. The computer numerical simulation results show that the proposed method outperforms the classical method without the EMD pre-processing and with conventional EMD based pre-processing in terms of the average estimation accuracy and the distribution error shown on the Clarke error gird.

关键词： Wearable non-invasive blood glucose estimation empirical mode decomposition hierarchical multiresolution analysis random forest algorithm

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Improved random forest classification approach based on hybrid clustering selection

Improved random forest classification approach based on hybr...

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Chinese Automation Congress (CAC)

作者： Yuan, Dong Huang, Jian Yang, Xu Cui, Jiarui Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728176871

The random forest algorithm is an ensemble learning method, with the decision tree as its base classifier. In the ensemble model, it is not always true that the more the base classifiers, the better the classification effect, since if there are more base classifiers with poor performance in the model, they may have negative impacts on the final classification result. In order to modify the random forest classification method under the premise of ensuring the diversity of the random forest model, based on the random forest algorithm of cluster integration selection and personal indoor thermal preference model, this paper proposes a random forest method of clustering ensemble selection with Dunn index. Considering the shortcomings of the irrevocable merging strategy of hierarchical clustering algorithm, a random forest method of hybrid clustering ensemble selection based on hierarchical clustering and k-medoids partition clustering is developed. The effectiveness of the proposed methods is verified by classifying personal indoor thermal preferences.

关键词： random forest algorithm clustering ensemble selection personal indoor thermal preference

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