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Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

Marcio Trindade Guerreiro, Eliana Maria Andriani Guerreiro, Tathiana Mikamura Barchi, Juliana Biluca, Thiago Antonini Alves, Yara de Souza de Souza Tadano, Flávio Trojan, Hugo Valadares Siqueira
2021 Applied Sciences  
We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoids, Fuzzy C-Means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (  ...  In this investigation, we propose a solution for a real case study.  ...  Development (CNPq), processes number 40558/2018-5, and 315298/2020-0, and Araucaria Foundation, process number 51497, for their support.  ... 
doi:10.3390/app11219868 fatcat:pvf22abbcfg7liaqkqkbr6hegu

Innovative Design and Analysis of Production Systems by Multi-objective Optimization and Data Mining

Amos H.C. Ng, Sunith Bandaru, Marcus Frantzén
2016 Procedia CIRP  
The innovation lies on how these two methods using different computational intelligence algorithms can be synergistically integrated and used interactively by production systems designers to support their  ...  This paper presents an innovative approach for the design and analysis of production systems using multi-objective optimization and data mining.  ...  Acknowledgements This work is partially financed by Knowledge Foundation (KKS), Sweden, through the IDSS project and AB Volvo through their Academic Preferred Partnership in Virtual Manufacturing.  ... 
doi:10.1016/j.procir.2016.04.159 fatcat:czoe67cv4raappplelyjyxtgrq

A Bayesian Framework for Integrated Deep Metric Learning and Tracking of Vulnerable Road Users Using Automotive Radars

Anand Dubey, Avik Santra, Jonas Fuchs, Maximilian Lubke, Robert Weigel, Fabian Lurz
2021 IEEE Access  
In this work, we demonstrate the performance of the proposed Bayesian framework using several vulnerable user targets based on a 77 GHz automotive radar.  ...  Subsequently, feature embedding corresponding to target's micro-Doppler signature are learned using novel Bayesian based deep metric learning approaches.  ...  However, conventional automotive radar systems face a lot of challenges, especially in complex urban environments, where the sensor needs to detect, classify and track multiple targets, e.g.  ... 
doi:10.1109/access.2021.3077690 fatcat:6hbklgq6s5fn5me6utfu7sh4yi

Parallel driving in CPSS: a unified approach for transport automation and vehicle intelligence

Fei-Yue Wang, Nan-Ning Zheng, Dongpu Cao, Clara Marina Martinez, Li Li, Teng Liu
2017 IEEE/CAA Journal of Automatica Sinica  
The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation  ...  This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems (CPSS) framework aiming at synergizing connected automated driving.  ...  The design process consist of a first stage for relevant signals selection using k-means algorithm as included in Table I and a second stage of final clusters selection and algorithm definition using  ... 
doi:10.1109/jas.2017.7510598 fatcat:qsccnkayzfhazgkkixgl77m43u

Research on the Key Issues of Big Data Quality Management, Evaluation, and Testing for Automotive Application Scenarios

Yingzi Wang, Ce Yu, Jue Hou, Yongjia Zhang, Xiangyi Fang, Shuyue Wu
2021 Complexity  
To improve the operational efficiency of complex data quality management algorithms in large-scale data scenarios, corresponding parallelization algorithms are studied and implemented for detection and  ...  This paper provides an in-depth analysis and discussion of the key issues of quality management, evaluation, and detection contained in big data for automotive application scenarios.  ...  Acknowledgments is work was sponsored in part by Intelligent Manufacturing Project of TianJin (20193155).  ... 
doi:10.1155/2021/9996011 doaj:a324b518eb9c45c4bc6d6f22db8792ca fatcat:xwdmfx7c5zhnzlizgelr75qv4q

A data mining approach to forecast behavior

Nihat Altintas, Michael Trick
2012 Annals of Operations Research  
We consider a manufacturing environment in which forecasts of future orders are used as inputs for a series of decisions.  ...  We define the complexities that are captured from our data set, developing the daily flow analysis to obtain accuracy ratios of forecasts as a performance measure for customers.  ...  K-Medoid Analysis We introduce K-Medoid clustering technique, which is a modified version of the well-known K-Means clustering technique.  ... 
doi:10.1007/s10479-012-1236-9 fatcat:tn7urpuazngyvcdbxwxy3otcpu

Temperature drift modeling and compensation of micro-electro-mechanical system gyroscope based on improved support vector machine algorithms

Xinwang Wang, Huiliang Cao
2020 International Journal of Distributed Sensor Networks  
This article suggested two methods to compensate for the temperature drift of the micro-electro-mechanical system gyroscopes, which are support vector machine method and C-means support vector machine.  ...  The output of X axis which was ranged from −40°C to 60°C based on the micro-electro-mechanical system gyroscope is reduced and analyzed in this article.  ...  Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by the National Natural ORCID iD Xinwang  ... 
doi:10.1177/1550147720908195 fatcat:ignesjwnyrarpkvfdenhydfazi

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Fernando Castaño, Stanisław Strzelczak, Alberto Villalonga, Rodolfo E. Haber, Joanna Kossakowska
2019 Remote Sensing  
The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level.  ...  The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system.  ...  Acknowledgments: The authors would like to thank the AUTOPIA group located at the Center for Automation and Robotics, jointly owned by the Spanish National Research Council and Technical University of  ... 
doi:10.3390/rs11192252 fatcat:7q5kuvx6yjezpolh5reuhcssri

Automotive Industrial Supply Chain Performance Evaluation under Uncertain Constraints on Cloud Computing System [chapter]

Suthep Butdee
2020 Concepts, Applications and Emerging Opportunities in Industrial Engineering [Working Title]  
Performance evaluation is a critical and complex task as well as uncertain demands for automotive supply chain.  ...  Cloud computing system is capable to monitor real-time production processes for every subcontractor to assist the 1st tier to make decision and respond customer effectively.  ...  In automotive industry, a subcontractor is used as a major player in supply chain manufacturing cluster. The subcontractor is normally SME companies which makes contract for long period of time.  ... 
doi:10.5772/intechopen.93679 fatcat:bkvtmlqfb5dgti246mi25lt7te

A Cluster-Based Weighted Feature Similarity Moving Target Tracking Algorithm for Automotive FMCW Radar [article]

Rongqian Chen, Yingquan Zou, Anyong Gao, Leshi Chen
2021 arXiv   pre-print
For autonomous driving scenarios, we constructed a method that uses its motion parameters to extract and correct the trajectory of a moving target, which solves the problem of moving target detection and  ...  Aiming at the cluster matching in the target tracking stage, a new weighted feature similarity algorithm is proposed, which increases the matching rate of the same target in adjacent frames under strong  ...  The plot-level features are scarce, so millimeter-wave (MMW) radar in an autonomous driving they are hard to use to support complex intelligent algorithms. environment.  ... 
arXiv:2112.06388v1 fatcat:aslweitnyfe5doabljlmhkvl6i

A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing Industry

Micheal Omotayo Alabi, Ken Nixon, Ionel Botef
2018 American Journal of Engineering and Applied Sciences  
Machine Learning is a growing field of Artificial Intelligence (AI) that allows systems to learn from data, identify patterns and make decisions with very little human involvement.  ...  This paper explores recent applications of Machine Learning with Big Data in the field of additive manufacturing, for instance, application of machine learning in detecting defect or anomaly during build  ...  There is no conflict of interest to disclose in the cause writing this paper. Funding Information The authors have no support or funding to report this paper.  ... 
doi:10.3844/ajeassp.2018.1114.1124 fatcat:sjohrqgje5gsxmt7sbeps5rr5y

Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems

Edwin Lughofer, Vicente Macián, Carlos Guardiola, Erich Peter Klement
2011 Applied Soft Computing  
These models are of great use in the system calibration phase, and also can be integrated for the engine control and on-board diagnosis tasks.  ...  by an evolving version of vector quantization (eVQ) and estimates the consequent parameters of Takagi-Sugeno fuzzy systems with the local learning approach in order to optimize the least squares functional  ...  Furthermore, we acknowledge PSA for providing the engine and partially supporting our investigation. Special thanks are given to PO Calendini, P Gaillard and C.  ... 
doi:10.1016/j.asoc.2010.10.004 fatcat:wkma3kacnbbphliad5myzhiigu

Artificial Intelligence and Data Science in the Automotive Industry [article]

Martin Hofmann, Florian Neukart, Thomas Bäck
2017 arXiv   pre-print
Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future.  ...  It also uses examples to explain the way that these technologies are currently being used in the automotive industry on the basis of the major subprocesses in the automotive value chain (development, procurement  ...  The same applies to the use of online data mining, in which, for example, forecast models (e.g., for forecasting product quality) are not only constantly used for a production process, but also adapted  ... 
arXiv:1709.01989v1 fatcat:ddxespcumrb5rjmvlujg2nysw4

CONCEPTUAL FOUNDATIONS OF THE REGIONAL INDUSTRIAL CLUSTER FORMATION BASED ONEUROPEAN EXPERIENCE AND LEADING WORLD TENDENCIES

M. Р. Butko, N. V. Ivanova, O. V. Popelo, G. M. Samiilenko
2020 Financial and credit activity problems of theory and practice  
The presence of a large number of scientific achievements in the world and in Ukraine on these issues is revealed, which testifies to the complexity and multidimensionality of the studied clustering process  ...  For a more thorough understanding of the mechanism of introduction and development of the cluster principle of different European countries, the article analyzed cluster experience in such ones as Italy  ...  Wei [6] , Q. Liu [13] , J. Wan [13] , K. Zhou [13] , H. Cheng [4] , M.-S. Niu [4] , K.-H. Niu [4] , S. Hartono [16] , A. Sobari [16] , C. Sylvie and A. l Henrick [1] , T. Sonobe [15] , O.  ... 
doi:10.18371/fcaptp.v1i32.200528 fatcat:3q3r3en2iveapbbc45amthmnqe

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

Alessandro Davoli, Giorgio Guerzoni, Giorgio M. Vitetta
2021 IEEE Access  
In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided.  ...  The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems.  ...  ACKNOWLEDGMENT The authors would like to thank CNH Industrial Italia S.p.A. and CNH Industrial Belgium NV for funding this research work.  ... 
doi:10.1109/access.2021.3061424 fatcat:sm22tomcdfat5hpvyjdsvnnoe4
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