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Thermal post-buckling measurement of the advanced nanocomposites reinforced concrete systems via both mathematical modeling and machine learning algorithm

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ADVANCES IN NANO RESEARCH 2024年第6期16卷 623-638页

作者： Zhou, Minggui Yan, Gongxing Hu, Danping Mahmoud, Haitham A. Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou 646000 Sichuan Peoples R China Luzhou Key Lab Intelligent Construct & Low Carbon Luzhou 646000 Peoples R China King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

关键词： advanced nanocomposites concrete eccentric systems machine learning algorithm TDQM thermal post buckling

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Non-linearities mitigation with fuzzy neural networks using a machine learning algorithm in a CO-OFDM system

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IET OPTOELECTRONICS 2020年第1期14卷 44-51页

作者： Kaur, Gurpreet Kaur, Gurmeet Indian Inst Informat Technol ECE Una Himachal Prades India Punjabi Univ ECE Patiala Punjab India

This study presents a fuzzy neural network based non-linear equaliser to diminish the non-linearities in a coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. The numerical results show that the proposed technique based CO-OFDM system outperforms the CO-OFDM system without non-linear equaliser by 5 and 7.5% EVM performance, and 2.36 and 4.87 dB Q-factor performance after 1000 km transmission and -3 dBm input launch power at a bit rate of 40 and 80 Gbps, respectively. Moreover, it has been generally accepted in statistics that the rank-based Wilcoxon methodology provide more robust results in a contradiction of outliers. Therefore, the aim of this study is to analyse fuzzy neural network based non-linear equaliser and compare the results with that of Wilcoxon approach fuzzy neural network based non-linear equaliser.

关键词： OFDM modulation fuzzy neural nets optical fibre communication Q-factor equalisers CO-OFDM system machine learning algorithm fuzzy neural networks coherent optical orthogonal frequency division multiplexing system nonlinear equaliser Q-factor performance input launch power rank-based Wilcoxon methodology Wilcoxon approach fuzzy neural network based non-linear equaliser

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Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system

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IET POWER ELECTRONICS 2019年第1期12卷 112-119页

作者： Xia, Kun Guo, Haotian He, Sheng Yu, Wei Xu, Jingjun Dong, Hui Univ Shanghai Sci & Technol Elect Engn Dept 516 JunGong Rd Shanghai 200093 Peoples R China Hella Shanghai Elect Co Ltd R&D Ctr Shanghai 201201 Peoples R China

Direct current (DC) serial arc faults usually occur in the damaged insulation lines or line connections, which will cause serious accidents such as fires and explosions. With the rapid increase of electric vehicles, DC serial arc faults are more and more dangerous to battery system. Therefore, a binary classification model based on machine learning algorithm was proposed to detect DC serial arc faults effectively in this study. It was optimised according to the characteristic signals of the arc to be satisfied with different loads for higher detection accuracy and robustness. In the simulative experiments for the power system electric vehicle, while the loads changing to the motor, the resistor or the inverter, it will all reach a highly successful detection rate, respectively.

关键词： DC motors power engineering computing arcs (electric) learning (artificial intelligence) fault diagnosis pattern classification electric vehicles battery powered vehicles binary classification model machine learning algorithm DC serial arc detection electric vehicle battery system direct current serial arc faults damaged insulation lines line connections electric vehicles DC serial arc faults higher detection accuracy robustness power system electric vehicle

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Application of functionally graded graphene nanoplatelets to improve transient vibrations of the car's roof under mechanical loading: Introducing machine learning algorithm for transient problems

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MATERIALS TODAY COMMUNICATIONS 2024年 39卷

作者： Chen, Yeye Bi, Shaoping Zhang, Enming Fouly, Ahmed Awwad, Emad Mahrous Quzhou Coll Technol Quzhou 324000 Peoples R China King Saud Univ Coll Engn Dept Mech Engn POB 800 Riyadh 11421 Saudi Arabia King Saud Univ Coll Engn Dept Elect Engn POB 800 Riyadh 11421 Saudi Arabia

Doubly curved structures under external force due to their applicable structure can be used in the roofs of various structures. Good knowledge about the transient dynamics of this kind of structure under external shock loading is very important in the engineering field. So, in the current work, steady and transient vibrations of composite doubly curved structures under time-dependent external force for the first time are presented. The current structure is reinforced by graphene nanoplatelets with high efficiency to improve the mechanical properties of the system in the thickness direction due to external shock loading in this direction. The peak pulse pressure, the Laplace transform, and some mathematical work to get time-dependent vibration responses of the composite doubly curved structures under external force. With the aid of different pre-processing of machine learning algorithms (Z-Score, Rrobust, Min-Max, and Quantile transformation), the results are predicted. The optimization algorithm, neural network structure, learning rate, mean of square error, and other training data are obtained for the current machine learning algorithm. The outputs of the machine learning method show that instead of simulating the complex structures in various situations with the aid of mathematical simulation, the researchers can use machine learning algorithms to correctly simulate this kind of system in different situations and material properties to obtain optimum conditions for doubly curved structure structures. The findings in the results section suggest that the time-dependent displacement and stress fields of the system are significantly influenced by the geometrical and physical characteristics of the current composite curved structure.

关键词： Improving transient response Elasticity theory Multi-layer graphene sheets machine learning algorithm Mechanical loading

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A framework for prediction of extrusion responses using machine learning algorithm

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2024年 1-14页

作者： Manohar, Grandhi Francy, K. Anupama Rao, Ch. Srinivasa Andhra Univ Coll Engn Visakhapatnam 530003 Andhra Pradesh India Vishnu Inst Technol Bhimavaram 534202 Andhra Pradesh India

The objective of the present study is to develop a Customized Automated machine learning (CAML) framework to support machine learning models in manufacturing processes. The proposed CAML framework features a user-friendly interface that enables users to perform key tasks and predict outcomes based on user-defined process parameters. In the present study, machine learning-based prediction of output responses is employed to estimate the process parameters during the cold extrusion process. The input responses are Die angle, Ram speed and Coefficient of Friction while the output responses are Extrusion Force, Damage Factor, Displacement of work piece and Extrusion Time. The framework utilized various machine learning algorithms, including Linear Regression, Ridge, Lasso, Elastic Net, Polynomial Regression, Gaussian Process Regressor, XGB Regressor, LGBM Regressor, Random Forest, Gradient Boosting Regressor, AdaBoost Regressor, Bagging Regressor, Extra-Trees Regressor, KNN Regressor, Nu SVR, Support Vector Regression (SVR), Kernel Ridge, RANSAC Regressor, Huber Regressor, LarsCV, Orthogonal Matching Pursuit, and Bayesian Ridge. The evaluation was performed by analyzing the aggregate R-2 score and aggregate Root Mean Absolute Error (RMSE). Additionally, for optimization, a machine learning-based algorithm, Multimodal Optimization NSGA-2 (Non-dominated Sorting Genetic algorithm 2), is employed to predict extrusion process parameters and enhance the efficiency of the actual extrusion operation. This approach bridges the gap between simulation results and real-world production systems. For Extrusion Force, Displacement of work piece and Extrusion Time. accuracies are 99.38%, 99.80%, and 99.25% respectively, with error percentages below 1% for GBR. However, the Damage Factor, with smaller values 0.017, 0.014), showed higher error (8.24%), where XGBR proved more consistent. The training of all machine learning models (MLMs) took 17.89645 s based on R2 and 18.9131 s based on R

关键词： CAML framework machine learning algorithm Non-dominated Sorting Genetic algorithm 2 Cold extrusion process

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The application of improving machine learning algorithm and voice technology in the teaching evaluation of ideological and political education

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第4期22卷 1277-1285页

作者： Sun, Qimeng Jiangxi Univ Tradit Chinese Med Sci & Technol Coll Nanchang 330004 Jiangxi Peoples R China

After entering the new millennium, the computing capacity of information terminal has shown a rapid development. This progress has caused cross generational changes in various fields, especially in the field of communication technology, which directly spawns a new field. Compared with the development speed of information terminal, the development of communication technology is always in the position of "catch-up", and the main work performer is the traditional data form. This backward leads to the primitive evaluation of Ideological and political education. This paper mainly studies the application of improved machine learning algorithm and voice technology in the teaching evaluation of Ideological and political education. The weighted naive Bayesian algorithm is applied to the teaching evaluation of Ideological and political education creatively. By inference of hypothesis model, the intervention curve of various conditions on the evaluation results is verified. The influence of class attribute probability on condition assignment is obtained, and it is used as a calculation tool for our evaluation of Ideological and political education teaching. The experimental results show that the improved weighted naive Bayesian algorithm can better integrate the speech technology and improve the evaluation accuracy.

关键词： Teaching evaluation machine learning algorithm data mining weighted naive Bayesian

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Performance analysis and prediction of hybrid battery thermal management system integrating PCM with air cooling based on machine learning algorithm

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APPLIED THERMAL ENGINEERING 2024年 257卷

作者： Zhang, Ning Zhang, Zhiyuan Li, Jintao Cao, Xing Qingdao Univ Sci & Technol Coll Electromech Engn Qingdao 266061 Peoples R China

Targeted at addressing the battery temperature concerns of rapid discharging and extreme ambient temperature, a hybrid battery thermal management system (BTMS) integrating phase change material (PCM) with air cooling is introduced, in which the Cantor fractal fin and segmented PCM block are employed to enhance thermal management capacity. Through the numerical simulation, the effects of fin parameters, filling strategy of segmented PCM block and airflow direction are analyzed. Moreover, the machine learning algorithm is adopted to predict the thermal control of hybrid BTMS. The results demonstrate that compared to the rectangular fin, the Cantor fractal fin reduces the maximum temperature (T-max) of batteries by 0.53 K, and continually increasing the fractal number of fin improves the cooling performance, but the improvement extent is restricted. The strategy that the middle section is filled with RT35 and the bilateral section is filled with RT35HC provides better temperature management capacity, and so does the strategy that the PCM filling proportion of middle section maintains at 0.5. Applying the segmented PCM block, the T-max and maximum temperature difference (Delta T-max) of batteries are kept at 308.43 K and 1.64 K at 4C discharge rate respectively. Varying airflow direction hardly affects the T-max, however the Delta T-max is obviously decreased when the reverse flow is utilized. The Delta T-max with reverse flow is reduced by 17 % and 21.2 % separately in contrast to those with concurrent flow and staggered flow. The proposed hybrid BTMS owns superior cooling and insulating capacities under rapid discharge rate, high ambient temperature and low ambient temperature. Based on the machine learning algorithm, a back propagation neural network is established and can accurately predict T-max and Delta T-max of batteries.

关键词： Battery thermal management Phase change material Air cooling Cantor fractal fin machine learning algorithm

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Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm

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PRECISION AGRICULTURE 2022年第5期23卷 1688-1706页

作者： Xie, Ziwen Chen, Song Gao, Guizhen Li, Hao Wu, Xiaoming Meng, Lei Ma, Yuntao China Agr Univ Coll Land Sci & Technol Beijing 100193 Peoples R China Jiangsu Acad Agr Sci Inst Ind Crops Nanjing 210014 Peoples R China Chinese Acad Agr Sci Key Lab Biol & Genet Improvement Oil Crops Minist Agr Oil Crops Res Inst Wuhan 430062 Peoples R China Western Michigan Univ Dept Geog Environm & Tourism Kalamazoo MI 49008 USA

Rapeseed (Brassica napus L.) is an important oil-bearing cash crop. Effective identification of the rapeseed flowering date is important for yield estimation and disease control. Traditional field measurements of rapeseed flowering are time-consuming, labour-intensive and strongly subjective. In this study, red, green and blue (RGB) images of rapeseed flowering derived from unmanned aerial vehicles (UAVs) were acquired with a total of seventeen available orthomosaic images, covering the whole flowering period for 299 rapeseed varieties. Five different machine learning methods were employed to identify and to extract the flowering areas in each plot. The results suggested that the accuracy of flowering area extraction by the decision tree-based segmentation model (DTSM) was higher than that of naive Bayes, K-nearest neighbours (KNN), random forest (RF) and support vector machine (SVM) in all varieties and flowering dates, with R-2 = 0.97 and root mean square error (RMSE) = 0.051 pixels/pixels. Data on the proportion of flowering area and its dynamics showed differences in the time and duration of each flowering date among varieties. All varieties were classified into four clusters based on k-means clustering analysis. There were significant differences in eight phenotypic parameters among the four clusters, especially in the time of maximum flowering ratio and the time entering the early and medium flowering dates. The results from this study could provide a basis for rapeseed breeding based on flowering dynamics.

关键词： Brassica napus L. Flowering date machine learning algorithm Clustering analysis Variety classification

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Efficient infrared image processing and machine learning algorithm for breast cancer screening

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2023年第6期11卷 2226-2238页

作者： Bandyopadhyay, Asok Mondal, Himanka Sekhar Dam, Bivas Patranabis, Dipak Chandra Ctr Dev Adv Comp ICT & Serv Grp Kolkata India Jadavpur Univ Dept Instrumentat & Elect Kolkata India Ctr Dev Adv Comp ICT & Serv Plot E2-1Block GPSect 5Saltlake Elect Complex Kolkata 700091 W Bengal India

Breast cancer is a significant global health concern, and early detection is crucial for improving survival rates. The article reviews various studies that investigate different methods for detecting breast cancer using non-invasive imaging techniques with a focus on thermal imaging, including feature extraction, image segmentation, and machine learning. It concludes that developing efficient and accurate breast cancer screening software requires an integrated approach to data collection, image processing, and machine learning algorithms. The article presents a novel technique for developing breast cancer screening software using Rotational Thermographic Imaging, dynamic temperature-based data collection, Colour-based Infrared Image Processing, and a machine learning algorithm to provide a complete breast imaging in sitting position to reduce the chances of missing abnormalities. Image processing and machine learning techniques are utilised to extract a comprehensive relevant feature set from the captured images and used to train a machine learning model. The system was tested on an increasing patient population in a clinical setting deployed at a hospital. The algorithm's performance was evaluated using several metrics, including sensitivity (82.14%), specificity (98.33%), and accuracy (93.27%). The results demonstrate that the proposed algorithm achieved high accuracy and sensitivity, making it a promising tool for breast cancer screening.

关键词： Infrared (IR) image processing breast cancer screening machine learning algorithm abnormality detection IR image analysis

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Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm

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AMERICAN JOURNAL OF SURGERY 2018年第4期216卷 764-777页

作者： Bartz-Kurycki, Marisa A. Green, Charles Anderson, Kathryn T. Alder, Adam C. Bucher, Brian T. Cina, Robert A. Jamshidi, Ramin Russell, Robert T. Williams, Regan E. Tsao, KuoJen Univ Texas Hlth Sci Ctr Houston McGovern Med Sch 6431 Fannin St Houston TX 77030 USA Childrens Med Ctr 1935 Med Dist Dr Dallas TX 75235 USA Univ Utah Sch Med 30 N 1900 E Salt Lake City UT 84132 USA Med Univ South Carolina 180 Calhoun St Charleston SC 29401 USA Phoenix Childrens Hosp 1919 E Thomas Rd Phoenix AZ 85016 USA Univ Alabama Birmingham Childrens Hosp Alabama 1600 7th Ave S Birmingham AL 35233 USA Univ Tennessee Hlth Sci Ctr 910 Madison Ave Memphis TN 38163 USA

Background: machine-learning can elucidate complex relationships/provide insight to important variables for large datasets. This study aimed to develop an accurate model to predict neonatal surgical site infections (SSI) using different statistical methods. Methods: The 2012-2015 National Surgical Quality Improvement Program-Pediatric for neonates was utilized for development and validations models. The primary outcome was any SSI. Models included different algorithms: full multiple logistic regression (LR), a priori clinical LR, random forest classification (RFC), and a hybrid model (combination of clinical knowledge and significant variables from RF) to maximize predictive power. Results: 16,842 patients (median age 18 days, IQR 3-58) were included. 542 SSIs (4%) were identified. Agreement was observed for multiple covariates among significant variables between models. Area under the curve for each model was similar (full model 0.65, clinical model 0.67, RF 0.68, hybrid LR 0.67);however, the hybrid model utilized the fewest variables (18). Conclusions: The hybrid model had similar predictability as other models with fewer and more clinically relevant variables. machine-learning algorithms can identify important novel characteristics, which enhance clinical prediction models. (c) 2018 Elsevier Inc. All rights reserved.

关键词： Prediction model Surgical wound infection Infants machine learning algorithm Risk factors

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