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Novelty detection: a review—part 1: statistical approaches
2003
Signal Processing
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In this paper we provide stateof-the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. ...
In addition, the amount and quality of training data becomes very important in the robust determination of training data distribution parameters. ...
Conclusion In this paper we have presented a survey of novelty detection using statistical approaches. ...
doi:10.1016/j.sigpro.2003.07.018
fatcat:7lbpn2wrrnfprim3bd7ah45i5u
Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis
2019
Renewable Energy
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A supervised classification was performed through 21 Machine Learning with twenty classifiers such as Decision tree, Discriminant Analysis, 22 Support Vector Machines, K-Nearest Neighbours, and Ensemble ...
11 The mass of ice on wind turbines blades is one of the main problems that energy 12 companies have in cold climates. ...
Xiao et al. presented a reconfigurable tolerant control of uncertain 7 mechanical systems with actuator faults [38] . ...
doi:10.1016/j.renene.2018.08.050
fatcat:hryjmyob2rcmdaf2wqt76zyery
A Data Mining Method Using Deep Learning for Anomaly Detection in Cloud Computing Environment
2020
Mathematical Problems in Engineering
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Aiming at problems such as slow training speed, poor prediction effect, and unstable detection results of traditional anomaly detection algorithms, a data mining method for anomaly detection based on the ...
density in the data set. ...
Acknowledgments is study was financially supported by the National Social Science Foundation of China. e project name is "Online Estimation and Whole Process Management of Short-Circuit Current Level in ...
doi:10.1155/2020/6343705
doaj:0730c9ab0d5c4a9e872a33adf2010c3a
fatcat:grkazjsygnclvekkthm2sxn3bq
A review of novelty detection
2014
Signal Processing
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[208] explore a clustering algorithm based on a shared nearest-neighbour approach. ...
Non-parametric CUSUM algorithms sequentially accumulate data values that are higher than the mean value observed under normal conditions. ...
doi:10.1016/j.sigpro.2013.12.026
fatcat:ha6kc4bzhbajxbo2mdyh5cw5hu
Learning with Imbalanced Data in Smart Manufacturing: A Comparative Analysis
2020
IEEE Access
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Imbalanced data limits the success of ML in predicting faults, thus presents a significant hindrance in the progress of smart manufacturing. ...
The main pillar in smart manufacturing looks at harnessing IoT data and leveraging machine learning (ML) to automate the prediction of faults, thus cutting maintenance time and cost and improving the product ...
Moreover, Mani and Zhang [20] have used a K-Nearest Neighbours (KNN) classifier as an undersampling preprocessing step. ...
doi:10.1109/access.2020.3047838
fatcat:6dhmvj2ugvewdkrztj4zo5t7v4
Guest Editorial: Multimedia for Predictive Analytics
2017
Multimedia tools and applications
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We would like to express our gratitude to all the authors who have submitted their work for publication in this special issue. ...
Borko Furht, Editor-in-Chief, Multimedia Tools and Applications (MTAP), for his support, encouragement and guidance throughout the process. ...
Fourthly, genes involved in pain and no pain conditions were taken and classified using machine learning algorithms. ...
doi:10.1007/s11042-017-5107-x
fatcat:mgp6pimnbzcypneluv43dujqcm
Recent Advances in Anomaly Detection Methods Applied to Aviation
2019
Aerospace (Basel)
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We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance. ...
As far as we know, some of the presented methods have not yet found an application in the aviation domain. ...
One of the basic distance-based techniques is the k-Nearest Neighbours (kNN) method in which an anomaly score is computed for each data instance defined as the distance to its k-Nearest Neighbours. ...
doi:10.3390/aerospace6110117
fatcat:kprkb643xrhcnmjy2c2lbzoa7m
A Survey of Outlier Detection Techniques in IoT: Review and Classification
2022
Journal of Sensor and Actuator Networks
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Second, comparison and discussion of the most recent outlier detection techniques are presented and classified into seven main categories, which are: statistical-based, clustering-based, nearest neighbour-based ...
In this paper, we propose a comprehensive literature review of recent outlier detection techniques used in the IoTs context. ...
In [72] , authors proposed an algorithm for Fault Detection and Isolation (FDI) in WSN using the Fuzzy Knowledge-based Control (FKBC). ...
doi:10.3390/jsan11010004
fatcat:5b2h43grsrewxdtag3ezgtjjle
A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest
2020
Sci
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In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates ...
The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using ...
(LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), regular decision tree (CART), Naïve Bayesian (NB) and Support Vector Machines (SVM). ...
doi:10.3390/sci2040075
fatcat:kec7wdou5zelli7xvgat3tsuua
Sensor data quality: a systematic review
2020
Journal of Big Data
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Acknowledgements The article processing charge was funded by the German Research Foundation (DFG) and the University of Freiburg in the funding programme Open Access Publishing. ...
For independent variables, k-Nearest Neighbour is used for fault detection and identification and a Grey Predictive Model GM(1,1) is used for fault correction. ...
Then, the algorithm searches the tree to find the nearest neighbours and impute missing values based on the values obtained from its neighbours (hot deck imputation). ...
doi:10.1186/s40537-020-0285-1
fatcat:cbl346kh35cqvn6nh7njzvrq5e
A Data-Driven Fault Detection Framework Using Mahalanobis Distance Based Dynamic Time Warping
2020
IEEE Access
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In this framework, the multivariate time series (MTS) pieces which are extracted from measurement signals in a time interval are used as the training and testing samples, and a K -nearest neighbour rule ...
Fault detection module is one of the most important components in modern industrial systems. ...
Jiangyuan Mei in Midea Corporate Research Center. The computational resources in the HPC Center of Zhejiang University (Zhoushan Campus) are also acknowledged. ...
doi:10.1109/access.2020.3001379
fatcat:dl66lrid4zeb5cifhtjsfsjyzy
A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest
2020
Sci
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In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates ...
The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using ...
(LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), regular decision tree (CART), Naïve Bayesian (NB) and Support Vector Machines (SVM). ...
doi:10.3390/sci2040061
fatcat:kpwatw25fjdtbcjeo7r6gfqegi
Non-Markovian Quantum Process Tomography
[article]
2021
arXiv
pre-print
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noise threshold in a variety of different noise conditions. ...
The characterisation is the pathway to diagnostics and informed control of correlated noise. ...
in a |+ state, and finally two nearest neighbours and one next-to-nearest neighbour in a |+ state. ...
arXiv:2106.11722v1
fatcat:mhldthi2sffn3ibpgle4wavf74
IoT-enabled Flood Severity Prediction via Ensemble Machine Learning Models
2020
IEEE Access
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There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures ...
This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. ...
K-NEAREST NEIGHBOUR CLASSIFIER K-Nearest Neighbour Classifier (KNN) is a lazy supervised learning algorithm that has been applied in various fields, such as statistical analysis, data mining, and pattern ...
doi:10.1109/access.2020.2986090
fatcat:qinirs64ljcy5ehoqbf2esfzky
Quantum computers: Definition and implementations
2011
Physical Review A. Atomic, Molecular, and Optical Physics
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2009 pre-print arXiv:0906.4344v1 fatcat:6eiy75lbuzf7djnsaqmert2lqmOther Versions
The DiVincenzo criteria for implementing a quantum computer have been seminal in focussing both experimental and theoretical research in quantum information processing. ...
The criteria are met when the device is scalable and operates fault-tolerantly. We discuss various existing quantum computing paradigms, and how they fit within this framework. ...
This work was supported in part by the QIP IRC. ...
doi:10.1103/physreva.83.012303
fatcat:5tytfdgngzbi7m55qf7hbknuqq
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