Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Tài liệu tham khảo
Otles, 2019, Industry 4.0: The smart factory of the future in beverage industry, 439
Yang, 2018, The internet of things in manufacturing: Key issues and potential applications, IEEE Syst. Man Cybern. Mag., 4, 6, 10.1109/MSMC.2017.2702391
Sakhnini, 2020, AI and security of critical infrastructure, 7
Lu, 2017, Industry 4.0: A survey on technologies, applications and open research issues, J. Ind. Inform. Integr., 6, 1
Li, 2017, Industrial internet: A survey on the enabling technologies, applications, and challenges, IEEE Commun. Surv. Tutor., 19, 1504, 10.1109/COMST.2017.2691349
Kampker, 2018, Enabling data analytics in large scale manufacturing, Procedia Manuf., 24, 120, 10.1016/j.promfg.2018.06.017
Lade, 2017, Manufacturing analytics and industrial internet of things, IEEE Intell. Syst., 32, 74, 10.1109/MIS.2017.49
Copeland, 2007, Artificial intelligence, 429
Masood, 2019
Fethi, 2010, Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey, European J. Oper. Res., 204, 189, 10.1016/j.ejor.2009.08.003
Palle, 2019, Artificial intelligence using DBS-QOS in banking organizations, J. Sci. Res. Eng. Trends, 5
Jakšič, 2019, Relationship banking and information technology: The role of artificial intelligence and fintech, Risk Manage., 21, 1, 10.1057/s41283-018-0039-y
Chen, 2019
Liu, 2019, Research on personal credit scoring model based on artificial intelligence, 466
Perez, 2018, 178
Ransbotham, 2018, Artificial intelligence in business gets real, MIT Sloan Manage. Rev. Boston Consult. Group
Huerta, 2018, Machine learning and artificial intelligence in consumer banking, J. Digital Bank., 3, 22
Dao, 2019, Prediction of compressive strength of geopolymer concrete using entirely steel slag aggregates: Novel hybrid artificial intelligence approaches, Appl. Sci., 9, 1113, 10.3390/app9061113
Jiang, 2017, Artificial intelligence in healthcare: past, present and future, Stroke Vasc. Neurol., 2, 230, 10.1136/svn-2017-000101
Zang, 2015, Advances of flexible pressure sensors toward artificial intelligence and health care applications, Mater. Horizons, 2, 140, 10.1039/C4MH00147H
Koh, 2011, Data mining applications in healthcare, J. Healthcare Inform. Manage., 19, 65
Hamet, 2017, Artificial intelligence in medicine, Metabolism, 69, S36, 10.1016/j.metabol.2017.01.011
He, 2019, The practical implementation of artificial intelligence technologies in medicine, Nat. Med., 25, 30, 10.1038/s41591-018-0307-0
Topol, 2019, High-performance medicine: the convergence of human and artificial intelligence, Nat. Med., 25, 44, 10.1038/s41591-018-0300-7
Reddy, 2019, Artificial intelligence-enabled healthcare delivery, J. R. Soc. Med., 112, 22, 10.1177/0141076818815510
Begli, 2019, A layered intrusion detection system for critical infrastructure using machine learning, 120
Tjahjono, 2017, What does industry 4.0 mean to supply chain?, Procedia Manuf., 13, 1175, 10.1016/j.promfg.2017.09.191
Lima-Junior, 2017, Quantitative models for supply chain performance evaluation: a literature review, Comput. Ind. Eng., 113, 333, 10.1016/j.cie.2017.09.022
Zhang, 2016, Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels, Expert Syst. Appl., 65, 87, 10.1016/j.eswa.2016.08.037
Cardoso, 2019
Baryannis, 2019, Decision support systems and artificial intelligence in supply chain risk management, 53
Baryannis, 2019, Supply chain risk management and artificial intelligence: state of the art and future research directions, Int. J. Prod. Res., 57, 2179, 10.1080/00207543.2018.1530476
Calatayud, 2019, The self-thinking supply chain, Supply Chain Manage. Int. J., 10.1108/SCM-03-2018-0136
Hellingrath, 2019, Applications of artificial intelligence in supply chain management and logistics: Focusing onto recognition for supply chain execution, 283
Chawla, 2019, Demand forecasting using artificial neural networks—A case study of american retail corporation, 79
Barclay, 2018
Salamanis, 2015, Managing spatial graph dependencies in large volumes of traffic data for travel-time prediction, IEEE Trans. Intell. Transp. Syst., 17, 1678, 10.1109/TITS.2015.2488593
Dahlman, 2019, Artificial intelligence in future evolution of mobile communication
R.S. Bapi, K.S. Rao, M.V. Prasad, First International Conference on Artificial Intelligence and Cognitive Computing.
Makridakis, 2018, Forecasting the impact of artificial intelligence, part 3 of 4: The potential effects of AI on businesses, manufacturing, and commerce, Foresight Int. J. Appl. Forecasting, 18
Küfner, 2018, Lean data in manufacturing systems: Using artificial intelligence for decentralized data reduction and information extraction, Procedia CIRP, 72, 219, 10.1016/j.procir.2018.03.125
Crandall, 2019, Artificial intelligence and manufacturing, 10
Vafeiadis, 2016, Robust malfunction diagnosis in process industry time series, 111
Lee, 2018, Industrial artificial intelligence for industry 4.0-based manufacturing systems, Manuf. Lett., 18, 20, 10.1016/j.mfglet.2018.09.002
Copeland, 2015
Miller, 2010
Berral-García, 2016, A quick view on current techniques and machine learning algorithms for big data analytics, 1
Pedregosa, 2011, Scikit-learn: Machine learning in python, J. Mach. Learn. Res., 12, 2825
Paszke, 2019, Pytorch: An imperative style, high-performance deep learning library, 8024
Chollet, 2015
2016
Abadi, 2015
Wuest, 2016, Machine learning in manufacturing: advantages, challenges, and applications, Prod. Manuf. Res., 4, 23
Wijesinghe, 2018, Machine learning for pre-auction sample selection, 1
Nielsen, 2009
Jensen, 1996
Al-Aidaroos, 2010, Naive Bayes variants in classification learning, 276
Ardhapure, 2016, Comparative study of classification algorithm for text based categorization, IJRET: Int. J. Res. Eng. Technol., 5, 217, 10.15623/ijret.2016.0502037
Anguita, 2010, Model selection for support vector machines: Advantages and disadvantages of the machine learning theory, 1
Auria, 2008
Bredensteiner, 1999, Multicategory classification by support vector machines, 53
Wang, 2005, Theory and applications
Suykens, 1999, Least squares support vector machine classifiers, Neural process. Lett., 9, 293, 10.1023/A:1018628609742
Schölkopf, 2002
Liu, 2018, Monitoring machine tool based on external physical characteristics of the machine tool using machine learning algorithm, 5
An, 2006, Study on support vector machine in calculating steel quenching degree, 7780
Lieber, 2013, Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning, Procedia Cirp, 7, 193, 10.1016/j.procir.2013.05.033
Loyer, 2016, Comparison of machine learning methods applied to the estimation of manufacturing cost of jet engine components, Int. J. Prod. Econ., 178, 109, 10.1016/j.ijpe.2016.05.006
Aha, 1991, Instance-based learning algorithms, Mach. Learn., 6, 37, 10.1007/BF00153759
Brighton, 2002, Advances in instance selection for instance-based learning algorithms, Data Mining Knowl. Discov,, 6, 153, 10.1023/A:1014043630878
Zhang, 1992, Selecting typical instances in instance-based learning, 470
Romeo, 2018, An innovative design support system for industry 4.0 based on machine learning approaches, 1
Keller, 1985, A fuzzy k-nearest neighbor algorithm, IEEE Trans. Syst. Man Cybern., 580, 10.1109/TSMC.1985.6313426
Fukunaga, 1975, A branch and bound algorithm for computing k-nearest neighbors, IEEE Trans. Comput., 100, 750, 10.1109/T-C.1975.224297
Triguero, 2019, Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data, Wiley Interdiscip. Rev. Data Mining Knowl. Discov., 9, 10.1002/widm.1289
Imandoust, 2013, Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background, Int. J. Eng. Res. Appl., 3, 605
Mijwel, 2018
Tu, 1996, Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, J. Clin. Epidemiol., 49, 1225, 10.1016/S0895-4356(96)00002-9
Friedl, 1997, Decision tree classification of land cover from remotely sensed data, Remote Sens. Environ., 61, 399, 10.1016/S0034-4257(97)00049-7
Olanow, 2001, An algorithm (decision tree) for the management of parkinson’s disease (2001):: Treatment guidelines, Neurology, 56, S1, 10.1212/WNL.56.suppl_5.S1
Wu, 2017, A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests, J. Manuf. Sci. Eng., 139, 10.1115/1.4036350
Kolokas, 2018, Forecasting faults of industrial equipment using machine learning classifiers, 1
Schaal, 2000, Real-time robot learning with locally weighted statistical learning, 288
Scime, 2019, Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process, Addit. Manuf., 25, 151
Myers, 1990
Preacher, 2006, Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis, J. Educ. Behav. Stat., 31, 437, 10.3102/10769986031004437
Andrews, 1974, A robust method for multiple linear regression, Technometrics, 16, 523, 10.1080/00401706.1974.10489233
Wang, 2007, A comparison of neural network, evidential reasoning and multiple regression analysis in modelling bridge risks, Expert Syst. Appl., 32, 336, 10.1016/j.eswa.2005.11.029
Ayer, 2010, Comparison of logistic regression and artificial neural network models in breast cancer risk estimation, Radiographics, 30, 13, 10.1148/rg.301095057
Safavian, 1991, A survey of decision tree classifier methodology, IEEE Trans. Syst. Man Cybern., 21, 660, 10.1109/21.97458
Westreich, 2010, Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression, J. Clin. Epidemiol., 63, 826, 10.1016/j.jclinepi.2009.11.020
Swain, 1977, The decision tree classifier: Design and potential, IEEE Trans. Geosci. Electron., 15, 142, 10.1109/TGE.1977.6498972
Podgorelec, 2002, Decision trees: an overview and their use in medicine, J. Med. Syst., 26, 445, 10.1023/A:1016409317640
Bottou, 2010, Large-scale machine learning with stochastic gradient descent, 177
Ruder, 2016
Mason, 2000, Boosting algorithms as gradient descent, 512
Hastie, 1990
Buja, 1989, Linear smoothers and additive models, Ann. Statist., 453
Guisan, 2002, Generalized linear and generalized additive models in studies of species distributions: setting the scene, Ecol. Modell., 157, 89, 10.1016/S0304-3800(02)00204-1
Atkeson, 1997, Locally weighted learning for control, 75
Vijayakumar, 2000, Locally weighted projection regression: An o (n) algorithm for incremental real time learning in high dimensional space, 288
Kim, 2017, Bag-of-concepts: Comprehending document representation through clustering words in distributed representation, Neurocomputing, 266, 336, 10.1016/j.neucom.2017.05.046
Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Statnikov, 2008, A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification, BMC Bioinform., 9, 319, 10.1186/1471-2105-9-319
Buskirk, 2018, Surveying the forests and sampling the trees: an overview of classification and regression trees and random forests with applications in survey research, Survey Practice, 11, 1
Ao, 2019, The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling, J. Pet. Sci. Eng., 174, 776, 10.1016/j.petrol.2018.11.067
Hartigan, 1979, Algorithm AS 136: A k-means clustering algorithm, J. R. Statist. Soc. Ser. C, 28, 100
Krishna, 1999, Genetic K-means algorithm, IEEE Trans. Syst. Man Cybern. B, 29, 433, 10.1109/3477.764879
Kanungo, 2002, An efficient k-means clustering algorithm: Analysis and implementation, IEEE Trans. Pattern Anal. Mach. Intell., 24, 881, 10.1109/TPAMI.2002.1017616
Pena, 1999, An empirical comparison of four initialization methods for the k-means algorithm, Pattern Recognit. Lett., 20, 1027, 10.1016/S0167-8655(99)00069-0
Santini, 2016
Litvinenko, 2019, Clusterization by the K-means method when k is unknown, 01013
Yuan, 2019, Research on K-value selection method of K-means clustering algorithm, J. Multidiscip. Sci. J., 2, 226
Qu, 2019, A survey on the development of self-organizing maps for unsupervised intrusion detection, Mob. Netw. Appl., 1
Dragomir, 2014, Matlab application of kohonen self-organizing map to classify consumers’ load profiles., 474
Mahadevan, 1998, Optimizing production manufacturing using reinforcement learning., 377
Das, 1998, Solving semi-Markov decision problems using average reward reinforcement learning, Manage. Sci., 45
Hesse, 2018, A reinforcement learning strategy for the swing-up of the double pendulum on a cart, Procedia Manuf., 24, 15, 10.1016/j.promfg.2018.06.004
Dominici, 2002, On the use of generalized additive models in time-series studies of air pollution and health, Am. J. Epidemiol., 156, 193, 10.1093/aje/kwf062
Ning, 2019, Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming, Comput. Chem. Eng., 125, 434, 10.1016/j.compchemeng.2019.03.034
Wang, 2018, Deep learning for smart manufacturing: Methods and applications, J. Manuf. Syst., 48, 144, 10.1016/j.jmsy.2018.01.003
Helbing, 2018, Deep learning for fault detection in wind turbines, Renew. Sustain. Energy Rev., 98, 189, 10.1016/j.rser.2018.09.012
Zhao, 2019, Deep learning and its applications to machine health monitoring, Mech. Syst. Signal Process., 115, 213, 10.1016/j.ymssp.2018.05.050
Mescheder, 2017
Zhu, 2017, Modulation classification for cognitive radios using stacked denoising autoencoders, Int. J. Satell. Commun. Netw., 35, 517, 10.1002/sat.1202
Jia, 2016, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech. Syst. Signal Process., 72, 303, 10.1016/j.ymssp.2015.10.025
Guo, 2014, Structural health monitoring by using a sparse coding-based deep learning algorithm with wireless sensor networks, Pers. Ubiquitous Comput., 18, 1977, 10.1007/s00779-014-0800-5
Sun, 2016, A sparse auto-encoder-based deep neural network approach for induction motor faults classification, Measurement, 89, 171, 10.1016/j.measurement.2016.04.007
Wang, 2016, Transformer fault diagnosis using continuous sparse autoencoder, SpringerPlus, 5, 1
Kamilaris, 2018, A review of the use of convolutional neural networks in agriculture, J. Agric. Sci., 156, 312, 10.1017/S0021859618000436
Mikołajczyk, 2018, Data augmentation for improving deep learning in image classification problem, 117
Kotsiopoulos, 2020, Deep multi-sensorial data analysis for production monitoring in hard metal industry, Int. J. Adv. Manuf. Technol.
Ma, 2019, Ensemble deep learning-based fault diagnosis of rotor bearing systems, Comput. Ind., 105, 143, 10.1016/j.compind.2018.12.012
Dimitriou, 2020, Fault diagnosis in microelectronics attachment via deep learning analysis of 3-d laser scans, IEEE Trans. Ind Electron., 67, 5748, 10.1109/TIE.2019.2931220
Sutherland, 2018, Locating photovoltaic installations with deep learning, Joule, 2, 2512, 10.1016/j.joule.2018.12.004
Lee, 2016, Localization of slab identification numbers using deep learning, 1174
Janssens, 2016, Convolutional neural network based fault detection for rotating machinery, J. Sound Vib., 377, 331, 10.1016/j.jsv.2016.05.027
Lu, 2017, Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification, Adv. Eng. Inform., 32, 139, 10.1016/j.aei.2017.02.005
Guo, 2016, Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis, Measurement, 93, 490, 10.1016/j.measurement.2016.07.054
Weimer, 2016, Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection, CIRP Annals, 65, 417, 10.1016/j.cirp.2016.04.072
Ren, 2017, A generic deep-learning-based approach for automated surface inspection, IEEE Trans. Cybern., 48, 929, 10.1109/TCYB.2017.2668395
Sutskever, 2009, The recurrent temporal restricted boltzmann machine, 1601
Shao, 2015, Rolling bearing fault diagnosis using an optimization deep belief network, Meas. Sci. Technol., 26, 10.1088/0957-0233/26/11/115002
Tamilselvan, 2013, Failure diagnosis using deep belief learning based health state classification, Reliab. Eng. Syst. Saf., 115, 124, 10.1016/j.ress.2013.02.022
Bengio, 1994, Learning long-term dependencies with gradient descent is difficult, IEEE Trans. Neural Netw., 5, 157, 10.1109/72.279181
Mikolov, 2011, Extensions of recurrent neural network language model, 5528
Tamilselvan, 2013, Failure diagnosis using deep belief learning based health state classification, Reliab. Eng. Syst. Saf., 115, 124, 10.1016/j.ress.2013.02.022
Zhao, 2017, Machine health monitoring using local feature-based gated recurrent unit networks, IEEE Trans. Ind. Electron., 65, 1539, 10.1109/TIE.2017.2733438
Pearlmutter, 1995, Gradient calculations for dynamic recurrent neural networks: A survey, IEEE Trans. Neural Netw., 6, 1212, 10.1109/72.410363
Chan, 2015
Pal, 1992
Gardner, 1998, Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627, 10.1016/S1352-2310(97)00447-0
Ruck, 1990, The multilayer perceptron as an approximation to a Bayes optimal discriminant function, IEEE Trans. Neural Netw., 1, 296, 10.1109/72.80266
Wood, 2017, Automated industry classification with deep learning, 122
Francis, 2019, Deep learning for distortion prediction in laser-based additive manufacturing using big data, Manuf. Lett., 20, 10, 10.1016/j.mfglet.2019.02.001
J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779–788.
Haifeng, 2020, Natural scene text detection based on YOLO V2 network model
Zhihuan, 2018, Rapid target detection in high resolution remote sensing images using YOLO model, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 42, 3
Cheng, 2020, A survey: Comparison between convolutional neural network and YOLO in image identification
Zhang, 2018, Automatic recognition of oil industry facilities based on deep learning, 2519
Li, 2018, Restricted Boltzmann machine-based approaches for link prediction in dynamic networks, IEEE Access, 6, 29940, 10.1109/ACCESS.2018.2840054
Rafiei, 2017, Supervised deep restricted Boltzmann machine for estimation of concrete., ACI Mater. J., 114
Y.T. Quek, W.L. Woo, T. Logenthiran, A naïve Bayes Classification Approach for Short-Term Forecast of Photovoltaic System, in: Proceedings of the Sustainable Energy and Environmental Sciences, Singapore, 2017, pp. 6–7.
Luo, 2018, Three-layer Bayesian network for classification of complex power quality disturbances, IEEE Trans. Ind. Inform., 14, 3997, 10.1109/TII.2017.2785321
Babar, 2020, Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid, Sustainable Cities Soc., 62, 10.1016/j.scs.2020.102370
Vinagre, 2016, Electrical energy consumption forecast using support vector machines
Yong, 2015, An effective power quality classifier using wavelet transform and support vector machines, Expert Syst. Appl., 42, 6075, 10.1016/j.eswa.2015.04.002
Schuetz, 2017, Poster abstract: state of operation recognition for heat pumps from smart grid monitoring data, Comput. Sci. Res. Dev., 33, 259
Behera, 2018, Smartpeak
Efstathopoulos, 2019, Operational data based intrusion detection system for smart grid
Weng, 2017, Robust data-driven state estimation for smart grid, IEEE Trans. Smart Grid, 8, 1956, 10.1109/TSG.2015.2512925
Valgaev, 2016, Low-voltage power demand forecasting using K-nearest neighbors approach
Macedo, 2015, Demand side management using artificial neural networks in a smart grid environment, Renew. Sustain. Energy Rev., 41, 128, 10.1016/j.rser.2014.08.035
Förderer, 2018, Towards the modeling of flexibility using artificial neural networks in energy management and smart grids
Kim, 2018, Short-term electric load prediction using multiple linear regression method
Le, 2018, A data imputation model in phasor measurement units based on bagged averaging of multiple linear regression, IEEE Access, 6, 39324, 10.1109/ACCESS.2018.2856768
Eissa, 2019, A frequency control technique based on decision tree concept by managing thermostatically controllable loads at smart grids, Int. J. Electr. Power Energy Syst., 108, 40, 10.1016/j.ijepes.2018.12.037
Terzi, 2018, Smart grid security evaluation with a big data use case, 1
Achlerkar, 2016, Variational mode decomposition and decision tree based detection and classification of power quality disturbances in grid-connected distributed generation system, IEEE Trans. Smart Grid, 9, 3122, 10.1109/TSG.2016.2626469
Bessa, 2015, Probabilistic solar power forecasting in smart grids using distributed information, Int. J. Electr. Power Energy Syst., 72, 16, 10.1016/j.ijepes.2015.02.006
Punmiya, 2019, Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing, IEEE Trans. Smart Grid, 10, 2326, 10.1109/TSG.2019.2892595
Razavi, 2019, A practical feature-engineering framework for electricity theft detection in smart grids, Appl. Energy, 238, 481, 10.1016/j.apenergy.2019.01.076
Pompey, 2015, Massive-scale simulation of electrical load in smart grids using generalized additive models, 193
Taieb, 2015, Probabilistic time series forecasting with boosted additive models: an application to smart meter data
Thouvenot, 2015, Electricity forecasting using multi-stage estimators of nonlinear additive models, IEEE Trans. Power Syst., 31, 3665, 10.1109/TPWRS.2015.2504921
Zhang, 2015, Instantaneous electromechanical dynamics monitoring in smart transmission grid, IEEE Trans. Ind. Inf., 12, 844, 10.1109/TII.2015.2492861
Lahouar, 2015, Day-ahead load forecast using random forest and expert input selection, Energy Convers. Manage., 103, 1040, 10.1016/j.enconman.2015.07.041
Lin, 2020, A voted based random forests algorithm for smart grid distribution network faults prediction, Enterp. Inform. Syst., 14, 496, 10.1080/17517575.2019.1600724
Singh, 2018, Towards hybrid energy consumption prediction in smart grids with machine learning, 44
Ahmed, 2019, Unsupervised machine learning-based detection of covert data integrity assault in smart grid networks utilizing isolation forest, IEEE Trans. Inf. Forensics Secur., 14, 2765, 10.1109/TIFS.2019.2902822
Al-Wakeel, 2017, K-means based load estimation of domestic smart meter measurements, Appl. Energy, 194, 333, 10.1016/j.apenergy.2016.06.046
Yu, 2016, A sparse coding approach to household electricity demand forecasting in smart grids, IEEE Trans. Smart Grid, 8, 738
Starke, 2018, Toward resilient smart grid communications using distributed sdn with ml-based anomaly detection, 83
Llanos, 2017, Load estimation for microgrid planning based on a self-organizing map methodology, Appl. Soft Comput., 53, 323, 10.1016/j.asoc.2016.12.054
Tong, 2018, An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders, J. Parall. distrib. Comput., 117, 267, 10.1016/j.jpdc.2017.06.007
Lu, 2018, Electric load data characterising and forecasting based on trend index and auto-encoders, J. Eng., 2018, 1915, 10.1049/joe.2018.8350
Yang, 2018
Ahmed, 2019, Mitigating the impacts of covert cyber attacks in smart grids via reconstruction of measurement data utilizing deep denoising autoencoders, Energies, 12, 3091, 10.3390/en12163091
Zheng, 2017, Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids, IEEE Trans. Ind. Inf., 14, 1606, 10.1109/TII.2017.2785963
Zahid, 2019, Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids, Electronics, 8, 122, 10.3390/electronics8020122
Kuo, 2018, A high precision artificial neural networks model for short-term energy load forecasting, Energies, 11, 213, 10.3390/en11010213
Kong, 2017, Short-term residential load forecasting based on LSTM recurrent neural network, IEEE Trans. Smart Grid, 10, 841, 10.1109/TSG.2017.2753802
Ouyang, 2017, Using LSTM networks to identify false data of smart terminals in the smart grid, 765
Hasan, 2019, Electricity theft detection in smart grid systems: a CNN-LSTM based approach, Energies, 12, 3310, 10.3390/en12173310
Abdel-Nasser, 2019, Accurate photovoltaic power forecasting models using deep LSTM-RNN, Neural Comput. Appl., 31, 2727, 10.1007/s00521-017-3225-z
Marino, 2016, Building energy load forecasting using deep neural networks, 7046
He, 2017, Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism, IEEE Trans. Smart Grid, 8, 2505, 10.1109/TSG.2017.2703842
Menon, 2016, A secure deep belief network architecture for intrusion detection in smart grid home area network, IIOAB J., 7, 479
He, 2017, Short-term power load forecasting with deep belief network and copula models, 191
Hamedani, 2017, Reservoir computing meets smart grids: Attack detection using delayed feedback networks, IEEE Trans. Ind. Inf., 14, 734, 10.1109/TII.2017.2769106
Moon, 2018, Hybrid short-term load forecasting scheme using random forest and multilayer perceptron, Energies, 11, 3283, 10.3390/en11123283
Alimi, 2019, Real time security assessment of the power system using a hybrid support vector machine and multilayer perceptron neural network algorithms, Sustainability, 11, 3586, 10.3390/su11133586
Wahid, 2017, Prediction of energy consumption in the buildings using multi-layer perceptron and random forest, IJAST, 101, 13, 10.14257/ijast.2017.101.02
P. Radoglou-Grammatikis, P. Sarigiannidis, G. Efstathopoulos, P.-A. Karypidis, A. Sarigiannidis, DIDEROT: an intrusion detection and prevention system for DNP3-based SCADA systems, in: Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020, pp. 1–8.
Grammatikis, 2020, ARIES: A novel multivariate intrusion detection system for smart grid, Sensors, 20, 5305, 10.3390/s20185305
Ge, 2017, Data mining and analytics in the process industry: The role of machine learning, Ieee Access, 5, 20590, 10.1109/ACCESS.2017.2756872
Medjaher, 2012, Remaining useful life estimation of critical components with application to bearings, IEEE Trans. Reliab., 61, 292, 10.1109/TR.2012.2194175
Shi, 2016, Edge computing: Vision and challenges, IEEE Internet Things J., 3, 637, 10.1109/JIOT.2016.2579198
Pang, 2004, Authenticating query results in edge computing, 560
F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things, in: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, 2012, pp. 13–16.
Hunkeler, 2008, MQTT-s—A publish/subscribe protocol for wireless sensor networks, 791
Vinoski, 2006, Advanced message queuing protocol, IEEE Internet Comput., 10, 87, 10.1109/MIC.2006.116
Pardo-Castellote, 2003, Omg data-distribution service: Architectural overview, 200
Nolan, 2016, An evaluation of low power wide area network technologies for the internet of things, 439
Ratasuk, 2016, NB-IoT system for M2m communication, 1
Bekara, 2014, Security issues and challenges for the IoT-based smart grid., 532
Grammatikis, 2019, Securing the internet of things: Challenges, threats and solutions, Internet Things, 5, 41, 10.1016/j.iot.2018.11.003
Triantafyllou, 2018, Network protocols, schemes, and mechanisms for internet of things (iot): Features, open challenges, and trends, Wirel. commun. Mob. Comput., 2018, 10.1155/2018/5349894
Namvar, 2016, Jamming in the internet of things: A game-theoretic perspective, 1
Tang, 2018, Jamming mitigation via hierarchical security game for IoT communications, IEEE Access, 6, 5766, 10.1109/ACCESS.2018.2793280
Chen, 2018, DQN-based power control for IoT transmission against jamming, 1
Hao, 2020, Efficient and privacy-enhanced federated learning for industrial artificial intelligence, IEEE Trans. Ind. Inform., 16, 6532, 10.1109/TII.2019.2945367
Savazzi, 2020, Federated learning with cooperating devices: A consensus approach for massive IoT networks, IEEE Internet Things J., 7, 4641, 10.1109/JIOT.2020.2964162
Yang, 2019, Federated machine learning: Concept and applications, ACM Trans. Intell. Syst. Technol. (TIST), 10, 1, 10.1145/3298981
Mohri, 2019
R. Shokri, V. Shmatikov, Privacy-preserving deep learning, in: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 2015, pp. 1310–1321.
Xu, 2020, Verifynet: Secure and verifiable federated learning, IEEE Trans. Inform. Forensics Secur., 15, 911, 10.1109/TIFS.2019.2929409
Qu, 2020, A blockchained federated learning framework for cognitive computing in industry 4.0 networks, IEEE Trans. Ind. Inform., 1