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Improving the quality of supervised finite-state machine construction using real-valued variables

Igor Buzhinsky, Daniil Chivilikhin, Vladimir Ulyantsev, Fedor Tsarev
2014 Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14  
This paper improves this approach via the use of real-valued variables which are calculated using the FSM's input data.  ...  The use of finite-state machines (FSMs) is a reliable choice for control system design since they can be formally verified.  ...  ACKNOWLEDGEMENTS This work was financially supported by the Government of Russian Federation, Grant 074-U01, and by RFBR, research project No. 14-07-31244 mol a.  ... 
doi:10.1145/2598394.2605679 dblp:conf/gecco/BuzhinskyCUT14 fatcat:tdq7ywb4jrc4hpmm76amdyckii

Identifying Miscalculation Ratio and Missing Value of Missing Data Imputation Using DarbouX Variate

A. Finny Belwin
2019 International Journal for Research in Applied Science and Engineering Technology  
We have approached the data completion problem using two well-known supervised machine learning techniques -Booster Algorithm and DarbouX variate.  ...  Experimental results reveal a significant improvement of accuracy in the proposed approach.  ...  Missing value may generate bias and affect the quality of the supervised learning process.  ... 
doi:10.22214/ijraset.2019.2012 fatcat:ywftkefj6nhrxopuhnypughcyu

A Review of Predictive Quality of Experience Management in Video Streaming Services

Maria Torres Vega, Cristian Perra, Filip De Turck, Antonio Liotta
2018 IEEE transactions on broadcasting  
It requires not only managing the network Quality of Service but also to exert real-time control, addressing the user's Quality of Experience (QoE) expectations.  ...  Herein, we review the most significant 'predictive' QoE management methods for video streaming services, showing how different machine learning approaches may be used to perform proactive control.  ...  ACKNOWLEDGMENT This work has been partially supported by the Italian Ministry of University and Research (MIUR), within the Smart City framework (project: PON04a2 00381 "CAGLIARI2020".  ... 
doi:10.1109/tbc.2018.2822869 fatcat:tcdvi4ngbzcw5di5xre5escmiq

Combinatorial optimization and reasoning with graph neural networks [article]

Quentin Cappart, Didier Chételat, Elias Khalil, Andrea Lodi, Christopher Morris, Petar Veličković
2021 arXiv   pre-print
However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or by  ...  The inductive bias of GNNs effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity.  ...  Supervised learning Given a finite training set, i.e., a set of examples (e.g., graphs) together with target values (e.g., real values in the case of regression), supervised learning tries to adapt the  ... 
arXiv:2102.09544v2 fatcat:eweej3mq2bbohaifazeghswcpi

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Ziqi Huang, Yang Shen, Jiayi Li, Marcel Fey, Christian Brecher
2021 Sensors  
This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration  ...  As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service.  ...  Supervised learning algorithms refer to machine learning methods in which models are trained using labels.  ... 
doi:10.3390/s21196340 pmid:34640660 fatcat:qy3qiazvqrejvfuixudd6ywqsq

Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

Youwei Li, Huaiping Jin, Shoulong Dong, Biao Yang, Xiangguang Chen
2021 Sensors  
Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes.  ...  First, an improved supervised regression algorithm called NCLELM is developed, by integrating the philosophy of negative correlation learning into extreme learning machine (ELM).  ...  In most cases, the n quality variables in the industrial process o are real numbers, thus the decision variables yu = y1,1 u , yu , ···, yu , ···, yu , yu , ···, yu  ... 
doi:10.3390/s21248471 pmid:34960564 pmcid:PMC8708742 fatcat:exo4otim3nb7tatuixdvhgdlry

Industrial data science – a review of machine learning applications for chemical and process industries

Max Mowbray, Mattia Vallerio, Carlos Perez-Galvan, Dongda Zhang, Antonio Del Rio Chanona, Francisco J. Navarro-Brull
2022 Reaction Chemistry & Engineering  
Understand and optimize industrial processes via machine learning and chemical engineering principles.  ...  Acknowledgements The authors appreciate the support from JMP (SAS Institute Inc.) for facilitating the open access of this manuscript.  ...  however, ultimately the construction process itself is always subject to finite data.  ... 
doi:10.1039/d1re00541c fatcat:q7ielo4h2bgudlypi4vjd4aajm

Trends and applications of machine learning in water supply networks management

Alicia Robles Velasco, Jesús Muñuzuri, Luis Onieva, María Rodríguez Palero
2021 Journal of Industrial Engineering and Management  
Machine learning is a field in constant development, and it has a great potential and capability to attain improvements in real industries.  ...  The objective of this work is to define the stages and main characteristics of machine learning systems, focusing on supervised learning methods.  ...  Acknowledgments The authors wish to acknowledge to EMASESA, Empresa Metropolitana de Abastecimiento y Saneamiento de Aguas de Sevilla, and to the Universidad de Sevilla (VI PPIT-US) because of their financial  ... 
doi:10.3926/jiem.3280 fatcat:62bp2pgt5bbv3knymknlhawk74

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Ziqi Huang, Yang Shen, Jiayi Li, Marcel Fey, Christian Brecher
2021 Sensors 21(19)  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY  ...  The authors would also like to thank Altair Engineering, Inc., Fraunhofer IPT, as well as the Chair of Production Metrology and Quality Management, and Production Engineering of the Laboratory for Machine  ...  Acknowledgments: The authors would like to thank the German Research Foundation DFG for the support within the Cluster of Excellence "Internet of Production"-390621612.  ... 
doi:10.18154/rwth-2021-09877 fatcat:yjhprcascvfutisat7olust3kq

Context-aware Dynamic Data-driven Pattern Classification

Shashi Phoha, Nurali Virani, Pritthi Chattopadhyay, Soumalya Sarkar, Brian Smith, Asok Ray
2014 Procedia Computer Science  
This work aims to mathematically formalize the notion of context, with the purpose of allowing contextual decision-making in order to improve performance in dynamic data driven classification systems.  ...  Improvements in context-aware classification have been quantified and validated in an unattended sensor-fence application for US Border Monitoring.  ...  (ii) Construction of probabilistic finite state automata (PFSA).  ... 
doi:10.1016/j.procs.2014.05.119 fatcat:oh3sh2ocyrbzzpieiemrvbzmry

Bayesian semi-supervised learning with support vector machine

Sounak Chakraborty
2011 Statistical Methodology  
Our simulation study and real life data analysis show considerable improvement in prediction quality for our semi-supervised learning over supervised learning methods when we have a high learning rate  ...  The likelihood is constructed using a special type of hinge loss function which also involves the unlabeled data. A penalty term is added for the likelihood part constructed from the unlabeled data.  ...  Some additional comparisons In this section we present results using three more recent semi-supervised methods [11] .  ... 
doi:10.1016/j.stamet.2009.09.002 fatcat:qphtnr2m65as5fig7rc35ormhm

Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

Alsharif, Kelechi, Yahya, Chaudhry
2020 Symmetry  
This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis.  ...  This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/sym12010088 fatcat:darnmyhy2jbinlxy7aj5vlc57i

Machine Learning: An Indispensable Tool in Bioinformatics [chapter]

Iñaki Inza, Borja Calvo, Rubén Armañanzas, Endika Bengoetxea, Pedro Larrañaga, José A. Lozano
2009 Msphere  
The use of machine learning techniques has been extended to a wide spectrum of bioinformatics applications.  ...  We make the interested reader aware of a set of popular web resources, open source software tools, and benchmarking data repositories that are frequently used by the machine learning community.  ...  -00018 projects (Spanish Ministry of Education and Science), and the COMBIOMED network in computational biomedicine (Carlos III Health Institute).  ... 
doi:10.1007/978-1-60327-194-3_2 pmid:19957143 fatcat:gu4mkicphnezxho7inqda62f2m

Role of Machine Learning in Manufacturing Sector

2019 International journal of recent technology and engineering  
This paper discusses the various areas where machine learning can improve the process of manufacturing like predictive maintenance, process optimisation, quality control, scheduling of resources among  ...  The relationship of how machines and humans can co-exist and work together to improve the efficiency of production is also discussed.  ...  One such example of quality control using machine learning is the application of supervised learning and clustering to the process states as well as deep learning. D.  ... 
doi:10.35940/ijrte.d8191.118419 fatcat:xwn446vhezdm7bj3opzslnjcte

Machine-Learning-Based Optimization of Energy Management in a Novel Hybrid Powertrain of Concrete Truck Mixers

Ying Huang, Fachao Jiang, Haiming Xie
2021 World Electric Vehicle Journal  
two control variables, and the driving data analysis through machine learning and data-driven methods.  ...  Firstly, an optimal control database is constructed, which benefits from a global optimization algorithm with dimension reduction for the constrained time-varying two-point boundary value problems with  ...  Conflicts of Interest: Haiming Xie is an employee of Fengzhi Technology Co., Ltd. The paper reflects the views of the scientists, and not the company.  ... 
doi:10.3390/wevj12040175 fatcat:73huw2brzrae5gnaxmewg3gfpe
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