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Optimal machine learning for detecting lathe machining parameters

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第1-2期128卷 779-788页

作者： Rall, Keven Loker, David Nikhare, Chetan P. Penn State Univ Elect & Comp Engn Technol Dept Erie PA USA Penn State Univ Mech Engn Dept Erie PA 16801 USA

Remote manufacturing process monitoring is becoming more popular in industry for many reasons including increased production, reduced production costs, and better product quality. However, a downside to remote monitoring is that there is minimal human interaction with the machine to ensure optimal performance. Many types of sensors have been used to monitor the manufacturing process parameters of a machine, but these types of sensors are typically invasive to the machine, costly to install, and can interfere with the machining operation. A solution to this problem is to use a non-invasive sensor such as a microphone array to remotely detect manufacturing process parameters. Acoustic signals produced by a lathe are used to monitor spindle speed, depth of cut, and feed rate to ensure optimal performance. Based on these acoustic signals, machine learning algorithms are used to predict manufacturing process parameters. Two common learner algorithms were discussed and compared, such as k-nearest neighbor (kNN) and Gaussian support vector machine (Gaussian SVM). Each algorithm was classified as either "fine" or "medium." Various pre-processing algorithms were also discussed and compared, such as autocorrelation and/or cross-correlation in both time domain and frequency domain. This study found that autocorrelation in frequency domain yielded the best overall results for all three machining parameters, with learner algorithms of fine kNN or fine Gaussian SVM for depth of cut, fine Gaussian SVM for feed rate, and either fine kNN or fine Gaussian SVM for spindle speed. For prediction of machining parameters, an accuracy range of 90-100% was obtained.

关键词： machine monitoring machine learning algorithm Microphone Acoustic signal

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Simulation of sports injury prevention and rehabilitation monitoring based on fiber optic sensors and machine learning algorithms

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Optical and Quantum Electronics 2024年第4期56卷 616-616页

作者： Shiguang, Wang Department of Physical Education Yulin University Yulin 719000 China

Sports injury is a common problem in sports activities, which has a negative impact on the physical health and performance ability of athletes. Therefore, this study aims to explore a sports injury prevention and rehabilitation monitoring method based on fiber optic sensors and machine learning algorithms to improve the rehabilitation effect of athletes and reduce the risk of secondary injuries. The study used fiber-optic sensors to monitor athletes’ joint movement and muscle activity, and collected a large amount of data related to sports injuries. The data is then analyzed and modeled using machine learning algorithms to identify potential risk factors for sports injuries. The effectiveness and accuracy of the proposed method are verified by simulation experiments in the laboratory and in the field. Fiber-optic sensors are able to monitor athletes’ joint movements and muscle activity in real time and send alerts when they detect abnormal patterns. machine learning algorithms can accurately identify potential risk factors for sports injuries and provide guidance for the development of rehabilitation programs. © 2024, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： machine learning algorithm Optical fiber sensor Rehabilitation monitoring simulation Sports injury prevention

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How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms

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PACIFIC-BASIN FINANCE JOURNAL 2024年 84卷

作者： Lu, Yao Zhao, Zhihui Tian, Yuan Zhan, Minghua Guangdong Univ Foreign Studies Sch Finance Guangzhou Peoples R China Guangdong Univ Finance Sch Finance & Investment Guangzhou Peoples R China Zhejiang Sci Tech Univ Sch Econ & Management Hangzhou Peoples R China Inst Fortune Management Res IFMR Guangzhou Peoples R China Guangdong Univ Foreign Studies Univ Mega Ctr Sch Finance Guangzhou 510006 Peoples R China

This study examines quarterly data from China spanning 1993 to 2016, employing machine learning algorithms to investigate the predictive ability of money and credit on output amidst significant structural changes, including the 2007 Global Financial Crisis and shifts in financial structure. The findings highlight that credit is a more effective predictor of output than money, capable of forecasting both short and long-term output trends. However, the predictive strength of both money and credit has diminished post-2007, with the advent of financial development further diminishing their forecasting effectiveness. The analysis demonstrates that machine learning offers more nuanced, long-term predictive insights compared to traditional Vector Autoregression (VAR) methods. For developing economies like China, the results indicate a significant reliance on the bank credit channel for monetary policy transmission. The study emphasizes the importance of market-oriented reforms to minimize financial market arbitrage and advocates for a refined classification of non-monetary assets into bank loans and other bonds to enhance the accuracy of general equilibrium analyses.

关键词： Structural break Money Credit machine learning algorithm

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Combining Sample Plot Stratification and machine learning algorithms to Improve Forest Aboveground Carbon Density Estimation in Northeast China Using Airborne LiDAR Data

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REMOTE SENSING 2022年第6期14卷 1477-1477页

作者： Chen, Mingjie Qiu, Xincai Zeng, Weisheng Peng, Daoli Beijing Forestry Univ Coll Forestry State Forestry & Grassland Adm Key Lab Forest Res Beijing 100083 Peoples R China Hainan Univ Coll Forestry Intelligent Forestry Key Lab Haikou City Haikou 570228 Hainan Peoples R China Natl Forestry & Grassland Adm Acad Inventory & Planning Beijing 100714 Peoples R China

Timely, accurate estimates of forest aboveground carbon density (AGC) are essential for understanding the global carbon cycle and providing crucial reference information for climate-change-related policies. To date, airborne LiDAR has been considered as the most precise remote-sensing-based technology for forest AGC estimation, but it suffers great challenges from various uncertainty sources. Stratified estimation has the potential to reduce the uncertainty and improve the forest AGC estimation. However, the impact of stratification and how to effectively combine stratification and modeling algorithms have not been fully investigated in forest AGC estimation. In this study, we performed a comparative analysis of different stratification approaches (non-stratification, forest type stratification (FTS) and dominant species stratification (DSS)) and different modeling algorithms (stepwise regression, random forest (RF), Cubist, extreme gradient boosting (XGBoost) and categorical boosting (CatBoost)) to identify the optimal stratification approach and modeling algorithm for forest AGC estimation, using airborne LiDAR data. The analysis of variance (ANOVA) was used to quantify and determine the factors that had a significant effect on the estimation accuracy. The results revealed the superiority of stratified estimation models over the unstratified ones, with higher estimation accuracy achieved by the DSS models. Moreover, this improvement was more significant in coniferous species than broadleaf species. The ML algorithms outperformed stepwise regression and the CatBoost models based on DSS provided the highest estimation accuracy (R-2 = 0.8232, RMSE = 5.2421, RRMSE = 20.5680, MAE = 4.0169 and Bias = 0.4493). The ANOVA of the prediction error indicated that the stratification method was a more important factor than the regression algorithm in forest AGC estimation. This study demonstrated the positive effect of stratification and how the combination of DSS and the CatBo

关键词： aboveground carbon density LiDAR stratified estimation machine learning algorithm Northeast China

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Comparison of machine learning algorithms over Prediction of Titanic Database

Comparison of Machine Learning Algorithms over Prediction of...

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作者： Chunzhu Ma School of Computer Science University of CaliforniaIrvine

With the popularize of artificial intelligent,algorithms such as machine learning algorithm takes an important part in many *** terms of data prediction,it is important to know which machine learning algorithm fits the specific dataset the *** between machine learning algorithms helps us figure out the advantage and disadvantage of them and thus save time,and money during the working *** this paper,this paper will be comparing K-nearest neighbours' algorithm,Random Forest algorithm,and Support vector machine algorithm mostly focusing on their performance over the survival prediction of Titanic *** could be seen that algorithm with more adjustable hyper parameters could produce more accurate ***,finding the suitable hyper parameters is time *** the future,this paper may try to explore ensembled algorithms for better result.

关键词： machine learning algorithm prediction Artificial intelligence

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A Methodology to Analyze and Estimate the Software Development Process Using machine learning Techniques

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2023年第6期33卷 815-835页

作者： Lalitha, R. Sreelekha, P. Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai Tamil Nadu India RMK Coll Engn & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

Analyzing the software development process and estimating the effort required for its completion is an essential task. In the case of Agile methodology, the values of the parameters used for estimation vary frequently as the scope of the project changes with changes in the requirements of the clients. Hence, the estimation done at the initial phase will not be appropriate until the completion of the project. Therefore, to overcome this issue, a methodology is proposed to estimate the duration of a project by applying machine learning techniques. The use-case point method is used for estimating the duration. Information about the number of use cases and values for environmental and technical factors is stored in a repository. Few values may be uncertain, and to estimate the effort for a new project with few unknown or uncertain values, the machine learning algorithm Gaussian Process Regression (GPR) is used. The repository information is taken as the training dataset, and the new project data is taken as the test dataset. The estimated value shows the accurate duration for the new project. The result is validated with a popular dataset.

关键词： Agile methodology software development process effort estimation machine learning algorithm software engineering use cases use case point method Gaussian process regression project duration and estimation

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machine learning approach for the prediction of macrosomia

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 132-141页

作者： Xiaochen Gu Ping Huang Xiaohua Xu Zhicheng Zheng Kaiju Luo Yujie Xu Yizhen Jia Yongjin Zhou Eye Hospital Wenzhou Medical UniversityWenzhouZheijang 325027China School of Biomedical Engineering Medical SchoolShenzhen UniversityShenzenGuangdong 518058China Marshall Laboratory of Biomedical Engineering ShenzenGuangdong 518058China Division of Ultrasound Department of Medical Imagingthe University of Hong Kong-Shenzhen HospitalShenzenGuangdong 518058China Ultrasound Department the First Affiliated Hospital of Shenzhen UniversitySecond People’s HospitalShenzenGuangdongChina 518058 Core Laboratory the University of Hong Kong-Shenzhen HospitalShenzenGuangdong 518058China

Fetal macrosomia is associated with maternal and newborn complications due to incorrect fetal weight estimation or inappropriate choice of delivery *** early screening and evaluation of macrosomia in the third trimester can improve delivery outcomes and reduce ***,traditional clinical and ultrasound examinations face difficulties in obtaining accurate fetal measurements during the third trimester of *** study aims to develop a comprehensive predictive model for detecting macrosomia using machine learning(ML)*** accuracy of macrosomia prediction using logistic regression,k-nearest neighbors,support vector machine,random forest(RF),XGBoost,and LightGBM algorithms was *** approach was trained and validated using data from 3244 pregnant women at a hospital in southern *** information gain method was employed to identify deterministic features associated with the occurrence of *** performance of six ML algorithms based on the recall and area under the curve evaluation metrics were *** develop an efficient prediction model,two sets of experiments based on ultrasound examination records within 1-7 days and 8-14 days prior to delivery were *** ensemble model,comprising the RF,XGBoost,and LightGBM algorithms,showed encouraging *** each experimental group,the proposed ensemble model outperformed other ML approaches and the tra-ditional Hadlock *** experimental results indicate that,with the most risk-relevant features,the ML algo-rithms presented in this study can predict macrosomia and assist obstetricians in selecting more appropriate delivery models.

关键词： Macrosomia Fetal weight prediction machine learning algorithm Feature selection Ensemble learning

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Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions

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CORROSION SCIENCE 2022年 206卷

作者： Bahrebar, Sajjad Homayoun, Sajad Ambat, Rajan Tech Univ Denmark Dept Civil & Mech Engn Sect Mat & Surface Engn Ctr Elect Corros DK-2800 Lyngby Denmark Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark

A printed circuit board (PCB) surface can fail by corrosion due to various environmental factors. This paper focuses on machine learning (ML) techniques to build predictive models to forecast PCB surface failure due to electrochemical migration (ECM) and leakage current (LC) levels under corrosive conditions containing the combination of six critical factors. The modeling methodology in this paper used common supervised ML algorithms by accomplishing significant evaluation metrics to show the performance of each algorithm. The conclusion of this study presents that ML algorithms can create predictive models to forecast PCB failures and estimate LC values effectively and quickly.

关键词： machine learning algorithm Classification Regression Predictive analytics PCB failure Leakage current

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Real-time error correction for flood forecasting based on machine learning ensemble method and its uncertainty assessment

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2023年第4期37卷 1557-1577页

作者： Xu, Chengjing Zhong, Ping-an Zhu, Feilin Yang, Luhua Wang, Sen Wang, Yiwen Hohai Univ Coll Hydrol & Water Resources Xikang Rd Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utiliz Xikang Rd Nanjing 210098 Peoples R China

Real-time error correction is an effective measure to improve forecast accuracy. This paper develops a real-time error correction model based on machine learning (ML) ensemble method, including the establishment of base learners, heterogeneity test, and Stacking combination. Then the Copula-based Bayesian processor of forecast (BPF) is adopted for probabilistic forecasts, which quantitatively describes the uncertainty of the correction results. Finally, the model performance is comprehensively evaluated from accuracy and stability, with deterministic metrics used to evaluate the correction effect and uncertain metrics for assessing probabilistic forecasts. The proposed model is applied to the multireservoir system in the Huai River Basin, and the results reveal the following: (1) Unlike the single ML algorithm with the performance of oscillations, the Stacking ensemble method can aggregate the advantages of multiple learners, showing robust correction effect and high adaptability across all data samples. (2) The forecasting error is significantly reduced by the Stacking method, with the average Nash-Sutcliffe efficiency coefficient (NSE) value increasing above 0.9, which is 4.93% higher than that of the autoregressive (AR) method. The greater superiority is also shown in the remaining evaluation metric values. Moreover, as the lead time increases, the performance of the stacking method tends to have a slower decline trend than the AR method. (3) The changes in the structure of the Stacking method have a relatively small influence on the forecast uncertainty, with all the Containing ratio (CR) values over 80% for different samples. The flexible combination of ML algorithms in the ensemble method will not add additional uncertainty factors and ensure the stability of the correction performance. The framework for real-time error correction and its uncertainty assessment has overall optimal correction performance with less observed data and lower computational cost requ

关键词： Flood forecasting Real-time error correction machine learning algorithm Stacking ensemble method Uncertainty assessment

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machine learning combined with Geodetector quantifies the synergistic effect of environmental factors on soil heavy metal pollution

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第60期30卷 126195-126213页

作者： Lei, Kaige Li, Yan Zhang, Yanbin Wang, Shiyi Yu, Er Li, Feng Xiao, Fen Shi, Zhou Xia, Fang Zhejiang Univ Inst Land Sci & Property Sch Publ Affairs Hangzhou 310058 Peoples R China Zhejiang Land Consolidat & Rehabil Ctr Hangzhou 310007 Peoples R China Hangzhou Dianzi Univ Coll Mat & Environm Engn Hangzhou 310018 Peoples R China Zhejiang Univ Coll Environm & Resource Sci Key Lab Environm Remediat & Ecol Hlth Minist Educ Hangzhou 310058 Peoples R China Zhejiang A&F Univ Coll Econ & Management Hangzhou 311300 Peoples R China

The critical prerequisite for the prevention and control of soil heavy metal (HM) pollution is the identification of factors that influence soil HM accumulation. The dominant factors have been individually identified and apportioned in existing studies. However, the accumulation of soil HMs results from a combination of multiple factors, and the influence of a single factor is less than the interaction of multiple parameters on soil HM pollution. In this study, we employed Geodetector to delve into the interaction effect of the influencing factors on the variations of soil HMs. We performed partial dependence plot to depict how these factors interact with each other to affect the HM content. We found that both individually and interactively, pH and agricultural activities significantly impact soil HM content. Except for Hg and Cu, the pairs with the most significant interaction effects all involve pH. For Pb, As and Zn, interaction with pH has the most significant driving force compared to the other factors. For Cu, Hg, and Ni, all environmental factor interactions increased their explanatory power, while for Cr, the single most significant driver decreased its driving power when interacting with other factors. Additionally, the study area exhibited a widespread prevalence of changes in HM concentration being governed by the synergistic effect of two factors. For the response of HMs to the interaction of pH and fertilizer, soil HM concentration was sensitive to pH, while fertilizer had less effect. These results provide a dependable method of investigating the interaction of environmental factors on soil HM content and put forth efficacious and potent tactical measures for soil HM pollution prevention and control based on the interaction type.

关键词： Heavy metals Pollution source Interaction effect machine learning algorithm Geodetector

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