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Benchmark of machine learning algorithms on capturing future distribution network anomalies

IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第8期13卷 1441-1455页

作者： Mohammadpourfard, Mostafa Weng, Yang Tajdinian, Mohsen Sahand Univ Technol Dept Elect & Comp Engn Tabriz Iran Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

The conventional distribution network is undergoing structural changes and becoming an active grid due to the advent of smart grid technologies encompassing distributed energy resources (DERs), aggregated demand response and electric vehicles (EVs). This establishes a need for state estimation-based tools and real-time monitoring of the distribution grid to correctly apply active controls. Although such new tools may be vulnerable to cyber-attacks, cyber-security of distribution grid has not received enough attention. As smart distribution grid intensively relies on communication infrastructures, the authors assume in this study that an attacker can compromise the communication and successfully conduct attacks against crucial functions of the distribution management system, making the distribution system prone to instability boundaries for collapses. They formulate the attack detection problem in the distribution grid as a statistical learning problem and demonstrate a comprehensive benchmark of statistical learning methods on various IEEE distribution test systems. The proposed learning algorithms are tested using various attack scenarios which include distinct features of modern distribution grid such as integration of DERs and EVs. Furthermore, the interaction between transmission and distribution systems and its effect on the attack detection problem are investigated. Simulation results show attack detection is more challenging in the distribution grid.

关键词： active control Smart grids Network security machine learning algorithms BENCHMARKS Power grids dispensing system

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Measurement of Users' Well-Being Through Domotic Sensors and machine learning algorithms

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IEEE SENSORS JOURNAL 2020年第14期20卷 8029-8038页

作者： Casaccia, Sara Romeo, Luca Calvaresi, Andrea Morresi, Nicole Monteriu, Andrea Frontoni, Emanuele Scalise, Lorenzo Revel, Gian Marco Univ Politecn Marche Dept Ind Engn & Math Sci I-60121 Ancona Italy Univ Politecn Marche Dept Informat Engn I-60121 Ancona Italy

This paper proposes a specific domotic sensor network to measure the well-being of elderly people in private home environments through machine learning (ML) algorithms trained with daily surveys. The tests have been conducted in 5 apartments lived by 8 older people where the non-obtrusive sensor network is installed. Two ML algorithms are compared, Random Forest (RF) and Regression Tree (RT), such that to verify whether the users' well-being is encoded in behavioural patterns obtained from the domotic data. These data are used to measure users' well-being and compared with three reference indices obtained through a daily survey: a physical (Phy), a mental (Mind) and a general health index (Avg). The extracted indices from the daily survey are used to train ML algorithms in the estimation of user's well-being for users that live alone (single-resident) or with others (multi-resident). Single-house and multi-house procedures are tested, both to extract a user-specific behaviour, and assess whether the model is able to generalise across different users and environments. Results show that the RF algorithm provides better performance than the RT algorithm in predicting the level of well-being with a Mean Absolute Error in the multi-house procedure of 32%, 13% and 17% for the Avg, Mind and Phy indices, respectively.

关键词： Monitoring machine learning algorithms Temperature sensors Temperature measurement Prediction algorithms Intelligent sensors Aging sensors smart home machine learning measurement uncertainty

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Modelling human health vulnerability using different machine learning algorithms in stone quarrying and crushing areas of Dwarka river Basin, Eastern India

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ADVANCES IN SPACE RESEARCH 2020年第6期66卷 1351-1371页

作者： Mandal, Indrajit Pal, Swades Univ Gour Banga Dept Geog Malda India

Stone dust induced risk and vulnerability in the developing nations is very thought-provoking and therefore is necessary to address scientifically to manage it. Identifying vulnerable areas using the robust method is preliminary and essential steps. The present study has intended to delineate Human health vulnerability models (HHVMs) using machine learning algorithms and justify whether ensemble prediction can provide improved results in stone quarry and crushing dominated Middle catchment of Dwarka river basin. Support Vector machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Reduced Error Pruning (REP) Tree, Gaussian Process algorithms and ensemble prediction have been applied for HHV modelling. Total 6.9 to 7.37% in cluster 1, 9.11 to 9.6% in cluster 2, 19.63 to 26.79% and 5.34 to 12.85% areas in cluster 3 and 4 is found into very high human health vulnerable class as per the five applied models. REPTree model is found consistent for predicting vulnerability in case of all the clusters followed by GP model. As per the result of RMSE, the RF model is appeared as the most consistent model followed by REPTree. Some of the ensemble prediction models are potential for yielding improved result than individual algorithm. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Dust emission Human health vulnerability Risk and exposure factors machine learning algorithms Ensemble prediction

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Comparison of machine learning algorithms for optimization and improvement of process quality in conventional metallic materials

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第1-4期60卷 237-249页

作者： Ferreiro, Susana Sierra, Basilio Diag & Predict Technol Unit Tekniker Eibar Gipuzkoa Spain Univ Basque Country UPV EHU San Sebastian Spain

This paper presents a particular problem dealing with the apparition of burr during the drilling process in the aeronautic industry. This burr cannot exceed a height limit of 127 mu m as set out by the aeronautical guidelines and must be eliminated before riveting. If this is not performed, it can cause structural damage which would constitute a danger due to the lack of safety. Moreover, the industry needs to find an automated and optimised process in which the drilling and deburring can be carried out in real time, eliminating those other unnecessary tasks, in order to obtain high-quality pieces. The work presents the applicability of data mining and machine learning techniques so as to obtain a real time burr detection model. This model could be implanted in the computer numerical control of the machine allowing the whole process to be automated and optimised. These techniques can be applied to other types of processes.

关键词： machine learning algorithms Data mining Drilling process Burr detection Quality control Process optimization

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Applying machine learning algorithms for the Classification of Sleep Disorders

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IEEE ACCESS 2024年 12卷 36110-36121页

作者： Alshammari, Talal Sarheed Univ Hail Coll Comp Sci & Engn Dept Informat & Comp Sci Hail 81481 Saudi Arabia

Sleep disorder classification is crucial in improving human quality of life. Sleep disorders and apnoea can have a significant influence on human health. Sleep-stage classification by experts in the field is an arduous task and is prone to human error. The development of accurate machine learning algorithms (MLAs) for sleep disorder classification requires analysing, monitoring and diagnosing sleep disorders. This paper compares deep learning algorithms and conventional MLAs to classify sleep disorders. This study proposes an optimised method for the Classification of Sleep Disorders and uses the Sleep Health and Lifestyle Dataset publicly available online to evaluate the proposed model. The optimisations were conducted using a genetic algorithm to tune the parameters of different machine learning algorithms. An evaluation and comparison of the proposed algorithm against state-of-the-art machine learning algorithms to classify sleep disorders. The dataset includes 400 rows and 13 columns with various features representing sleep and daily activities. The k-nearest neighbours, support vector machine, decision tree, random forest and artificial neural network (ANN) deep learning algorithms were assessed. The experimental results reveal significant performance differences between the evaluated algorithms. The proposed algorithms obtained a classification accuracy of 83.19%, 92.04%, 88.50%, 91.15% and 92.92%, respectively. The ANN achieved the highest classification accuracy of 92.92%, and its precision, recall and F1-score values on the testing data were 92.01%, 93.80% and 91.93%, respectively. The ANN algorithm that achieved high accuracy than other tested algorithms.

关键词： Sleep apnea Classification algorithms machine learning algorithms Feature extraction Deep learning Brain modeling Radio frequency Artificial neural networks Support vector machines Random forests Optimization methods deep learning classification sleep disorder genetic algorithm

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A Comparison of Prediction Models with machine learning algorithms for Traction Characteristics in Linear Traction Induction Motors

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2022年第3期17卷 470-478页

作者： Zeng, Dihui Ge, Qiongxuan Degano, Michele Chinese Acad Sci Inst EE Key Lab Power Elect & Elect Drive Beijing 100190 Peoples R China Univ Nottingham Dept EEE Nottingham NG7 2RD England Univ Nottingham Dept EEE Ningbo 315000 Peoples R China

This paper compares the machine learning algorithm-based prediction methods for the traction characteristics in linear traction induction motors operating at common working conditions, i.e. different slip and running velocity, with a symmetric or asymmetric secondary. These models provide a method for obtaining the dynamic characteristics in the motor that considers nonlinear effects. First, some analytical results for the prototype machine under different working conditions is calculated. Second, classification and feature extraction of traction characteristics results including thrust, transversal and vertical forces is made according to the different slip, running speed and lateral secondary displacement, and the results set is divided into training sets and test sets. Third, the prediction model established by different machine learning algorithms are analyzed and compared in principle. These algorithms in this paper mainly contain: artificial neural networks (ANNs), linear regression (LR), symbolic regression using GP, k-Nearest Neighbour (kNN), random forests (RFRs). The machine learning algorithm-based prediction methods are trained with the training set, and then the verified with the test set. Finally, this paper discusses the most optimal model for predicting traction characteristics. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： machine learning algorithms traction characteristics linear induction motors prediction model

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Comprehensive and Empirical Evaluation of machine learning algorithms for Small Molecule LC Retention Time Prediction

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ANALYTICAL CHEMISTRY 2019年第5期91卷 3694-3703页

作者： Bouwmeester, Robbin Martens, Lennart Degroeve, Sven VIB UGent Ctr Med Biotechnol Ghent Belgium Univ Ghent Dept Biomol Med Ghent Belgium

Liquid chromatography is a core component of almost all mass spectrometric analyses of (bio)molecules. Because of the high-throughput nature of mass spectrometric analyses, the interpretation of these chromatographic data increasingly relies on informatics solutions that attempt to predict an analyte's retention time. The key components of such predictive algorithms are the features these are supplies with, and the actual machine learning algorithm used to fit the model parameters. Therefore, we have evaluated the performance of seven machine learning algorithms on 36 distinct metabolomics data sets, using two distinct feature sets. Interestingly, the results show that no single learning algorithm performs optimally for all data sets, with different types of algorithms achieving top performance for different types of analytes or different protocols. Our results thus show that an evaluation of machine learning algorithms for retention time prediction is needed to find a suitable algorithm for specific analytes or protocols. Importantly, however, our results also show that blending different types of models together decreases the error on outliers, indicating that the combination of several approaches holds substantial promise for the development of more generic, high-performing algorithms.

关键词： small molecule Core Assembly Retention Time machine learning algorithms

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Developing robust arsenic awareness prediction models using machine learning algorithms

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2018年 211卷 125-137页

作者： Singh, Sushant K. Taylor, Robert W. Rahman, Mohammad Mahmudur Pradhan, Biswajeet Montclair State Univ Dept Earth & Environm Studies 1 Normal Ave Montclair NJ 07043 USA Univ Newcastle UON Fac Sci GCER Callaghan NSW Australia Univ Technol Sydney Sch Syst Management & Leadership Fac Engn & IT Bldg 11Level 0681 Broadway Ultimo NSW 2007 Australia Sejong Univ Dept Energy & Mineral Resources Engn 209 Neungdong Ro Seoul 05006 South Korea Virtusa Corp Irvington NJ 07111 USA

Arsenic awareness plays a vital role in ensuring the sustainability of arsenic mitigation technologies. Thus far, however, few studies have dealt with the sustainability of such technologies and its associated socioeconomic dimensions. As a result, arsenic awareness prediction has not yet been fully conceptualized. Accordingly, this study evaluated arsenic awareness among arsenic-affected communities in rural India, using a structured questionnaire to record socioeconomic, demographic, and other sociobehavioral factors with an eye to assessing their association with and influence on arsenic awareness. First a logistic regression model was applied and its results compared with those produced by six state-of-the-art machine-learning algorithms (Support Vector machine [SVM], Kernel-SVM, Decision Tree [DT], k-Nearest Neighbor [k-NN], Nave Bayes [NB], and Random Forests [RF]) as measured by their accuracy at predicting arsenic awareness. Most (63%) of the surveyed population was found to be arsenic-aware. Significant arsenic awareness predictors were divided into three types: (1) socioeconomic factors: caste, education level, and occupation;(2) water and sanitation behavior factors: number of family members involved in water collection, distance traveled and time spent for water collection, places for defecation, and materials used for handwashing after defecation;and (3) social capital and trust factors: presence of anganwadi and people's trust in other community members, NGOs, and private agencies. Moreover, individuals' having higher social network positively contributed to arsenic awareness in the communities. Results indicated that both the SVM and the RF algorithms outperformed at overall prediction of arsenic awareness a nonlinear classification problem. Lower-caste, less educated, and unemployed members of the population were found to be the most vulnerable, requiring immediate arsenic mitigation. To this end, local social institutions and NGOs could play a cruci

关键词： Arsenic Awareness Socioeconomic Demographics Sociobehavioral machine learning algorithms SVM RF GIS India

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A Comparison of machine learning algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery

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REMOTE SENSING 2016年第6期8卷 514-514页

作者： Li, Xianju Chen, Weitao Cheng, Xinwen Wang, Lizhe China Univ Geosci Fac Informat Engn Wuhan 430074 Peoples R China China Univ Geosci Fac Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands

Land cover mapping (LCM) in complex surface-mined and agricultural landscapes could contribute greatly to regulating mine exploitation and protecting mine geo-environments. However, there are some special and spectrally similar land covers in these landscapes which increase the difficulty in LCM when employing high spatial resolution images. There is currently no research on these mixed complex landscapes. The present study focused on LCM in such a mixed complex landscape located in Wuhan City, China. A procedure combining ZiYuan-3 (ZY-3) stereo satellite imagery, the feature selection (FS) method, and machine learning algorithms (MLAs) (random forest, RF;support vector machine, SVM;artificial neural network, ANN) was proposed and first examined for both LCM of surface-mined and agricultural landscapes (MSMAL) and classification of surface-mined land (CSML), respectively. The mean and standard deviation filters of spectral bands and topographic features derived from ZY-3 stereo images were newly introduced. Comparisons of three MLAs, including their sensitivities to FS and whether FS resulted in significant influences, were conducted for the first time in the present study. The following conclusions are drawn. Textures were of little use, and the novel features contributed to improve classification accuracy. Regarding the influence of FS: FS substantially reduced feature set (by 68% for MSMAL and 87% for CSML), and often improved classification accuracies (with an average value of 4.48% for MSMAL using three MLAs, and 11.39% for CSML using RF and SVM);FS showed statistically significant improvements except for ANN-based MSMAL;SVM was most sensitive to FS, followed by ANN and RF. Regarding comparisons of MLAs: for MSMAL based on feature subset, RF achieved the greatest overall accuracy of 77.57%, followed by SVM and ANN;for CSML, SVM had the highest accuracies (87.34%), followed by RF and ANN;based on the feature subsets, significant differences were observed for MSM

关键词： remote sensing land cover ZiYuan-3 stereo satellite machine learning algorithms complex landscape surface mining agricultural landscape feature selection

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Medical Data Clustering and Classification Using TLBO and machine learning algorithms

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Computers, Materials & Continua 2022年第3期70卷 4523-4543页

作者： Ashutosh Kumar Dubey Umesh Gupta Sonal Jain Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Institute of Engineering and Technology JK Lakshmipat UniversityJaipurIndia

This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of clustering and *** the first phase,the clustering(k-means and FCM)algorithms were employed independently and the clustering accuracy was evaluated using different *** the second phase,the non-clustered data obtained from the first phase were preprocessed with *** was performed using k-means(TLBO-KM)and FCM(TLBO-FCM)(TLBO-KM/FCM)*** objective function was determined by considering both minimization and maximization ***-clustered data obtained from the first phase were further utilized and fed as input for threshold *** benchmark datasets were considered from theUniversity of California,Irvine(UCI)machine learning Repository for comparative study and *** are breast cancer Wisconsin(BCW),Pima Indians Diabetes,Heart-Statlog,Hepatitis,and Cleveland Heart Disease *** combined average accuracy obtained collectively is approximately 99.4%in case of TLBO-KM and 98.6%in case of *** approach is also capable of finding the dominating *** findings indicate that TLBO-KM/FCM,considering different computational measures,perform well on the non-clustered data where k-means and FCM,if employed independently,fail to provide significant *** different feature sets,the TLBO-KM/FCM and SVM(GS)clearly outperformed all other classifiers in terms of sensitivity,specificity and ***/FCM attained the highest average sensitivity(98.7%),highest average specificity(98.4%)and highest average accuracy(99.4%)for 10-fold cross validation with different test data.

关键词： K-means FCM TLBO TLBO-KM TLBO-FCM TLBO-KM/FCM machine learning algorithms

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