Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings by Jianbin Xiong, Qinghua Zhang, Qiong Liang, Hongbin Zhu, Haiying Li

“…Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection. …”
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Exploring spatial machine learning techniques for improving land surface temperature prediction by Arunab K.S., Mathew A.

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Research on Visualization of Cylindrical Gear Hobbing Cutting Force and the Influence of Cutting Parameter on Hobbing Cutting Force by Xiaoqing Tian, Xinrong Zheng, Ruofeng Chen, Jiang Han, Lian Xia

“…Hobbing is one of the most important gear machining process，forming process of which is complicated and involves a large number of cutting parameters. …”
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New Simplified Method for Parameter Estimation of Three-Dimensional Channel Model in Third Generation Partnership Project by Hanene Zormati, Jalel Chebil, Jamel Belhadj Taher

“…This model is complex and not easy to use due to the employment of a large number of parameters. For these reasons, a new simplified method for parameter estimation of the three-dimensional model of third generation partnership project based on estimation of signal parameters via rotational invariant techniques algorithm is proposed. …”
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Experimental research on multi-structural parameter optimization of rhombic tooth endmill based on DOE in CFRP milling by Xiaochen Zuo, Junxue Ren, Tiejun Song, Tao Zeng, Mengliu Zhang, Hexuan Liu

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Improved Modulated Model Predictive Control: Reducing Total Harmonic Distortion, Computational Burden, and Parameter Sensitivity by Seyed Rasul Eftekhari, Ali Mosallanejad, Hamidreza Pairo, Jose Rodriguez

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Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel by Dennis Ochengo, Li Liang, Zhao Wei, He Ning

“…In this experimental study, the machinability of hardened steel under dry machining on a CNC lathe is undertaken to optimize the cutting parameters for minimum surface roughness and energy consumption with the cutting speed (320, 450, and 575), tool type (coated and uncoated), and feed rate (0.1, 0.18, and 0.26) as the input parameters. …”
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An Analytical Cost Function Design and Implementation for Predictive Control of Induction Machine Drives by Wei Wei, Liming Yan, Shun Tian, Xisheng Xu, Keke Sun

“…In finite control set-model predictive torque control (FCS-MPTC) of induction machine (IM), the optimal design of weighting factors for the cost function has always been a research difficulty in community of scholars. …”
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Research on Load Adaptive Control for High-Frequency Motion of Voice Coil Motor With Online Parameter Optimization by Feng Huang, Chenxi Wang, Qipeng Li

“…The fast tool servo system is a crucial intelligent equipment for workpiece machining and forming in the precision manufacturing field. …”
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Transformer-Based Optimization for Text-to-Gloss in Low-Resource Neural Machine Translation by Younes Ouargani, Noussaim El Khattabi

“…To optimize our transformer-based model and obtain the optimal hyper-parameter set, we propose a consecutive hyper-parameter exploration technique. …”
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Investigation of Droplet Spreading and Rebound Dynamics on Superhydrophobic Surfaces Using Machine Learning by Samo Jereb, Jure Berce, Robert Lovšin, Matevž Zupančič, Matic Može, Iztok Golobič

“…In this work, we employed a collection of 1498 water–glycerin droplet impact experiments on monolayer-functionalized laser-structured aluminum samples to train, validate and optimize a machine learning regression model. To elucidate the role of each influential parameter, we analyzed the model-predicted individual parameter contributions on key descriptors of the phenomenon, such as contact time, maximum spreading coefficient and rebound efficiency. …”
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Anomaly detection using unsupervised machine learning algorithms: A simulation study by Edmund Fosu Agyemang

“…This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope). …”
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Method on intrusion detection for industrial internet based on light gradient boosting machine by Xiangdong HU, Lingling TANG

“…Intrusion detection is a critical security protection technology in the industrial internet, and it plays a vital role in ensuring the security of the system.In order to meet the requirements of high accuracy and high real-time intrusion detection in industrial internet, an industrial internet intrusion detection method based on light gradient boosting machine optimization was proposed.To address the problem of low detection accuracy caused by difficult-to-classify samples in industrial internet business data, the original loss function of the light gradient boosting machine as a focal loss function was improved.This function can dynamically adjust the loss value and weight of different types of data samples during the training process, reducing the weight of easy-to-classify samples to improve detection accuracy for difficult-to-classify samples.Then a fruit fly optimization algorithm was used to select the optimal parameter combination of the model for the problem that the light gradient boosting machine has many parameters and has great influence on the detection accuracy, detection time and fitting degree of the model.Finally, the optimal parameter combination of the model was obtained and verified on the gas pipeline dataset provided by Mississippi State University, then the effectiveness of the proposed mode was further verified on the water dataset.The experimental results show that the proposed method achieves higher detection accuracy and lower detection time than the comparison model.The detection accuracy of the proposed method on the gas pipeline dataset is at least 3.14% higher than that of the comparison model.The detection time is 0.35s and 19.53s lower than that of the random forest and support vector machine in the comparison model, and 0.06s and 0.02s higher than that of the decision tree and extreme gradient boosting machine, respectively.The proposed method also achieved good detection results on the water dataset.Therefore, the proposed method can effectively identify attack data samples in industrial internet business data and improve the practicality and efficiency of intrusion detection in the industrial internet.…”
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Characterization of Residual Stresses Induced into Bearing Rings by Means of Turning in Soft State Using Different Turning Parameters by Jawad Zaghal, Valeria Mertinger, Adam Filep, Gyula Varga, Marton Benke

“…The residual stresses which are induced by machining processes play important role in determining the service life of machined components, depending on their magnitude, sign, and direction. …”
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A machine learning model for automated contact tracing during disease outbreaks by Zeyad Aklah, Amean Al-Safi, Marwa H. Abdali, Khalid Al-jabery

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Dataset Construction Using Item Response Theory for Educational Machine Learning Competitions by Takeaki Sakabe, Yuko Sakurai, Emiko Tsutsumi, Satoshi Oyama

“…In recent years, the number of individuals studying machine learning has grown substantially, leading to the emergence of numerous educational competitions focused on building expertise in machine learning. …”
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Research and Application of CNC Machining Method Based on CAD/CAM/Robot Integration by Xiangsong Yan

“…Through the tool processing calculation of the representative curved surface, this paper studies the basic theory, process planning, and tool parameter selection of the blade of the representative curved surface in the five-axis CNC machining and generates the blade CNC machining tool path through the simulation software. …”
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Machine Learning-Assisted Prediction of Ambient-Processed Perovskite Solar Cells’ Performances by Dowon Pyun, Seungtae Lee, Solhee Lee, Seok-Hyun Jeong, Jae-Keun Hwang, Kyunghwan Kim, Youngmin Kim, Jiyeon Nam, Sujin Cho, Ji-Seong Hwang, Wonkyu Lee, Sangwon Lee, Hae-Seok Lee, Donghwan Kim, Yoonmook Kang

“…Therefore, the dew point is introduced as a parameter which accounts for both temperature and humidity. …”
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A new benchmark for machine learning applied to powder X-ray diffraction by Sergio Rincón, Gabriel González, Mario A. Macías, Pablo Arbeláez

“…Abstract Although crystal parameter prediction from powder X-ray diffraction has recently attracted the interest of the machine learning community, most existing datasets for this task are private and lack structural diversity. …”
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Mechanical characteristics analysis and multi-objective optimization design of industrial washing machine by Kunquan Song, Yunjing Jiao, Jilong Su, Jiaqi Lian

“…Abstract The inner cylinder as the main force structure of the industrial washing machine, the parameters of its components are too large to cause the waste of raw materials and too small to affect the working performance of the washing machine. …”
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