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Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms

Friction 2020年第1期8卷 107-114页

作者： Osman ALTAY Turan GURGENC Mustafa ULAS Cihan OZEL Software Engineering Firat UniversityElazig 23119Turkey Automotive Engineering Firat UniversityElazig 23119Turkey Mechanical Engineering Firat UniversityElazig 23119Turkey

In this study,experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with(wt.%)50FeCrC‐20FeW‐30FeB and 70FeCrC‐30FeB powder mixtures by plasma transfer arc welding were *** dataset comprised 99 different wear amount measurements obtained experimentally in the *** linear regression(LR),support vector machine(SVM),and Gaussian process regression(GPR)algorithms are used for predicting wear quantities.A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms.

关键词： surface coating plasma transfer arc(PTA)welding wear prediction machine learning algorithms

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An efficient gene bigdata analysis using machine learning algorithms

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第15-16期79卷 9847-9870页

作者： Wang, Ge Pu, Pengbo Shen, Tingyan Shandong Univ Sci & Technol Dept Informat Engn Tai An Shandong Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Sch Chinese PLA Beijing 100853 Peoples R China

Bioinformatics is one of the emerging and rapidly developing research areas that is predominantly used for genetic data analysis and processing. Bioinformatics is characterized by its huge and voluminous data that is growing in nature which in turn complicates data analysis. In most cases, Bioinformatics data analysis and processing involve big data analytics due to the complex nature of the data. Previous research works handled data analytics using traditional tools and conventional big data analytical methods. However, it can be proved that machine learning algorithms and approaches can be effectively deployed to perform parallel, distributed and incremental processing of complex big data analytics especially in the case of gene big data analytics to enhance the efficiency in processing this large chunk of Bioinformatics-based gene big data. This paper provides a machine learning algorithm-based Convolution Neural Network (ML-CNN) approach for the process of identifying potential target genes, predicting miRNAs, visualizing the unique miRNA patterns, and validating genomes. The proposed approach has experimented with MATLAB software using deep learning toolbox on the pre - miRNA dataset. Experimental results indicate that machine learning algorithms certainly increases the efficiency of Bioinformatics-based methods of processing gene data in terms of prediction accuracy and reduced processing time. The mean performance of ML-CNN is improved 7% high than the existing system.

关键词： Big data analytics Gene big data machine learning algorithms Bioinformatics Pattern recognition Prediction

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Diagnosis of rotating machine unbalance using machine learning algorithms on vibration orbital features

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JOURNAL OF VIBRATION AND CONTROL 2021年第3-4期27卷 468-476页

作者： Jablon, Leonardo S. Avila, Sergio L. Borba, Bruno Mourao, Gustavo L. Freitas, Fabrizio L. Penz, Cesar A. AQTECH Power Prognosis Inc Florianopolis SC Brazil Fed Inst Santa Catarina Dept Electrotech Engn Florianopolis SC Brazil

The diagnosis of failures in rotating machines has been subject to studies because of its benefits to maintenance improvement. Condition monitoring reduces maintenance costs, increases reliability and availability, and extends the useful life of critical rotating machinery in industry ambiance. machine learning techniques have been evolving rapidly, and its applications are bringing better performance to many fields. This study presents a new strategy to improve the diagnosis performance of rotating machines using machine learning strategies on vibration orbital features. The advantage of using orbits in comparison to other vibration measurement systems is the simplicity of the instrumentation involved as well as the information multiplicity contained in the orbit. On the other hand, rolling element bearings are prevalent in industrial machinery. This type of bearing has less orbital oscillation and is noisier than sliding contact bearings. Therefore, it is more difficult to extract useful information. Practical results on an industry motor workbench with rolling element bearings are presented, and the algorithm robustness is evaluated by calculating diagnosis accuracy using inputs with different signal-to-noise ratios. For this kind of noisy scenario where signal analysis is naturally tough, the algorithm classifies approximately 85% of the time correctly. In a completely harsh environment, where the signal-to-noise ratio can be smaller than -25 dB, the accuracy achieved is close to 60%. These statistics show that the strategy proposed can be robust for rotating machine unbalance condition diagnosis even in the worst scenarios, which is required for industrial applications.

关键词： Orbits diagnosis machine learning algorithms vibration industrial application

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Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 80卷 323-339页

作者： Liu, Yang Bi, Jian-Wu Fan, Zhi-Ping Northeastern Univ Sch Business Adm Dept Management Sci & Engn Shenyang 110167 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Multi-class sentiment classification has extensive application backgrounds, whereas studies on this issue are still relatively scarce. In this paper, a framework for multi-class sentiment classification is proposed, which includes two parts: 1) selecting important features of texts using the feature selection algorithm, and 2) training multi-class sentiment classifier using the machine learning algorithm. Then, experiments are conducted for comparing the performances of four popular feature selection algorithms (document frequency, CHI statistics, information gain and gain ratio) and five popular machine learning algorithms (decision tree, naive Bayes, support vector machine, radial basis function neural network and K-nearest neighbor) in multi-class sentiment classification. The experiments are conducted on three public datasets which include twelve data subsets, and 10-fold cross validation is used to obtain the classification accuracy concerning each combination of feature selection algorithm, machine learning algorithm, feature set size and data subset. Based on the obtained 3600 classification accuracies (4 feature selection algorithms x 5 machine learning algorithms x 15 feature set sizes x 12 data subsets), the average classification accuracy of each algorithm is calculated, and the Wilcoxon test is used to verify the existence of significant difference between different algorithms in multi-class sentiment classification. The results show that, in terms of classification accuracy, gain ratio performs best among the four feature selection algorithms and support vector machine performs best among the five machine learning algorithms. In terms of execution time, the similar comparisons are also conducted. The obtained results would be valuable for further improving the existing multi-class sentiment classifiers and developing new multi-class sentiment classifiers. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multi-class sentiment classification Experimental comparison Feature selection algorithms machine learning algorithms

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Identification and Application of machine learning algorithms for Transformer Dissolved Gas Analysis

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2021年第5期28卷 1828-1835页

作者： Rao, U. Mohan Fofana, I Rajesh, K. N. V. P. S. Picher, P. Univ Quebec Chicoutimi Dept Appl Sci Res Chair Aging Power Network Infrastruct ViAHT Chicoutimi PQ Canada Gayatri Vidya Parishad Coll Engn A Dept Elect & Commun Visakhapatnam Andhra Pradesh India Inst Rech Hydroquebec Varennes PQ Canada

Power transformers represent one of the most abundant and expensive components in the electric power industry. Dissolved gas analysis (DGA) of transformer is the most widely accepted diagnostic tool across the globe to understand insulation incipient failures. Nevertheless, DGA fault gas interpretation is a remarkable challenge for transformer owners and utility engineers. Several computational techniques have been adopted for DGA fault classification along with offline methods. However, limited data availability, high ambiguity in DGA interpretation, suitability, and model accuracy are critical challenges in the DGA fault classification using computational techniques. In this work, highly diverse and large DGA data samples of in-service transformer fleets from five different utilities have been used to develop an efficient fault classification methodology. A total of 4580 DGA samples and IEC TC 10 database are used for training and testing, respectively, for various machine learning algorithms. Discussions on performance indicators and evaluation of several algorithms to verify the most suitable class algorithms are also the focus of this work. Furthermore, a best-performing model is identified based on various performance indicators. The hyperparameters of the best model are further tuned to achieve a most precise fault classification. It is inferred that non-parametric methods and non-linear SVM are best suitable for transformer DGA fault classification. Importantly, the rankings in the present study suggest that transformer DGA fault prediction is better with ensemble learning methods.

关键词： Support vector machines learning systems Industries Training machine learning algorithms Dissolved gas analysis Tools transformers insulation liquids DGA fault diagnosis

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Application of machine learning algorithms in quality assurance of fermentation process of black tea- based on electrical properties

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JOURNAL OF FOOD ENGINEERING 2019年 263卷 165-172页

作者： Zhu, Hongkai Liu, Fei Ye, Yang Chen, Lin Li, Jingyuan Gu, Anhui Zhang, Jianqiang Dong, Chunwang China Acad Agr Sci Tea Res Inst Hangzhou 310008 Zhejiang Peoples R China Minist Agr Key Lab Tea Biol & Resource Utilizat Hangzhou 310008 Zhejiang Peoples R China Univ Copenhagen Fac Sci Dept Food Sci Rolighedsvej 26 DK-1958 Frederiksberg Denmark Sichuan Acad Agr Sci Tea Res Inst Chengdu 610066 Sichuan Peoples R China

Fermentation process directly determines the product quality of black tea. This work aimed to develop a rapid method for detecting the degree of fermentation of black tea based on electrical properties of tea leaves. An LCR meter employed to identify 11 electrical parameters of tea leaves during the fermentation process, and the content of catechins and tea pigments in tea leaves were measured by using HPLC and UV-Vis spectrometer, respectively. Principal component analysis and hierarchical clustering analysis applied to divide samples into different groups in the degree of fermentation. Correlation analysis used to characterize the responding strength of electrical parameters on the variation of catechins and pigments. Finally, multilayer perceptron, random forest, and support vector machine algorithm used to build discrimination models of fermentation degree, and the average accuracy rate on the testing set reached to 88.90%, 100%, and 76.92%, respectively.

关键词： Black tea Fermentation Electrical properties Quality components Random forest machine learning algorithms

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In-depth exploration of machine learning algorithms for predicting sidewall displacement in underground caverns

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GEOMECHANICS AND ENGINEERING 2024年第4期37卷 307-321页

作者： Samadi, Hanan Alanazi, Abed Muhodir, Sabih Hashim Alsubai, Shtwai Alqahtani, Abdullah Marzougui, Mehrez Univ Halabja Civil Engn Dept IRO Halabja 46018 Iraq Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj Dept Comp Sci POB 151 Al Kharj 11942 Saudi Arabia Cihan Univ Erbil Dept Architectural Engn Erbil Kurdistan Iraq King Khalid Univ Coll Comp Sci Abha 61413 Saudi Arabia

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

关键词： importance ranking machine learning algorithms sidewall displacement underground caverns

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Performance Comparison of an LSTM-based Deep learning Model versus Conventional machine learning algorithms for Streamflow Forecasting

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WATER RESOURCES MANAGEMENT 2021年第12期35卷 4167-4187页

作者： Rahimzad, Maryam Moghaddam Nia, Alireza Zolfonoon, Hosam Soltani, Jaber Danandeh Mehr, Ali Kwon, Hyun-Han Univ Isfahan Fac Civil Engn & Transportat Dept Geomat Engn Esfahan Iran Univ Tehran Fac Nat Resources Dept Reclamat Arid & Mountainous Reg Karaj Iran Univ Coimbra DEEC INESC Coimbra Coimbra Portugal Univ Tehran Dept Water Engn Aburaihan Campus Pakdasht Iran Antalya Bilim Univ Dept Civil Engn Antalya Turkey Sejong Univ Dept Civil & Environm Engn Seoul South Korea

Streamflow forecasting plays a key role in improvement of water resource allocation, management and planning, flood warning and forecasting, and mitigation of flood damages. There are a considerable number of forecasting models and techniques that have been employed in streamflow forecasting and gained importance in hydrological studies in recent decades. In this study, the main objective was to compare the accuracy of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting. For this purpose, three scenarios were defined based on historical precipitation and streamflow series for 26 years of the Kentucky River basin located in eastern Kentucky, US. Statistical criteria including the coefficient of correlation (R), Nash-Sutcliff coefficient of efficiency (E), Nash-Sutcliff for High flow (E-H), Nash-Sutcliff for Low flow (E-L), normalized root mean square error (NRMSE), relative error in estimating maximum flow (REmax), threshold statistics (TS), and average absolute relative error (AARE) were employed to compare the performances of these methods. The results show that the LSTM network outperforms the other models in forecasting daily streamflow with the lowest values of NRMSE and the highest values of E-H, E-L, and R under all scenarios. These findings indicated that the LSTM is a robust data-driven technique to characterize the time series behaviors in hydrological modeling applications.

关键词： Streamflow forecasting Data-driven modeling LSTM Deep learning machine learning algorithms

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Integrating machine learning algorithms With Quantum Annealing Solvers for Online Fraud Detection

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IEEE ACCESS 2022年 10卷 75908-75917页

作者： Wang, Haibo Wang, Wendy Liu, Yi Alidaee, Bahram Texas A&M Int Univ Div Int Business & Technol Studies Laredo TX 78041 USA Univ North Alabama Dept Comp Sci & Informat Syst Florence AL 35632 USA Univ Massachusetts Dept Comp & Informat Sci Dartmouth MA 02747 USA Univ Mississippi Sch Business Adm Dept Mkt Oxford MS 38677 USA

machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems detect duplicitous activities after they have already occurred, not at or near real time. Since spurious transactions are far fewer than the normal ones, the highly imbalanced data makes fraud detection very challenging and calls for ways to address it beyond the traditional machine learning approach. This study has proposed a detection framework, and implemented it using quantum machine learning (QML) approach by applying Support Vector machine (SVM) enhanced with quantum annealing solvers. To evaluate its detection performance, we have further implemented twelve machine learning methods, and compared the performance of QML application with these machine learning implementations on two datasets: Israel credit card transactions (non-time series) which is moderately imbalanced, and a bank loan dataset (time series) that is highly imbalanced. The result shows that, the quantum enhanced SVM has categorically outperformed the rest in both speed and accuracy with the bank loan dataset. However, its detection accuracy is similar to others with Israel credit card transactions data. Furthermore, for both datasets, feature selection has been shown to significantly improve the detection speed, although the improvement on accuracy is marginal. These findings have demonstrated the potential of QML applications on time series based, highly imbalanced data, and the merit of traditional machine learning approaches in non-time series data. This study provides insight on selecting appropriate approach with different types of datasets while taking into consideration the tradeoffs of speed, accuracy, and cost.

关键词： Support vector machine classification Credit cards machine learning algorithms Kernel machine learning Business Time series analysis Fraud detection machine learning quantum computing

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An Energy-Efficient Programmable Mixed Signal Accelerator for machine learning algorithms

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IEEE MICRO 2019年第5期39卷 64-72页

作者： Kang, Mingu Srivastava, Prakalp Adve, Vikram Kim, Nam Sung Shanbhag, Naresh R. IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Univ Illinois Urbana IL USA Univ Illinois Comp Sci Urbana IL USA

We propose PROMISE, the first end-to-end design of a PROgrammable Mixed-Signal accElerator from Instruction Set Architecture to high-level language compiler for acceleration of diverse machine learning algorithms by e... 详细信息

关键词： Computer architecture Hardware Instruction sets Kernel machine learning algorithms Neural networks Task analysis

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