Fuzzy control of a multiple hearth furnace

Ramirez, M. C. Garcia; Haber, Rodolfo E.; Peña, Víctor H. de la; Rodriguez, Ivan D.

doi:10.1016/j.compind.2003.05.001

Cited by 37 publications

(15 citation statements)

References 4 publications

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“…AI techniques also provide cognitive abilities, so that performance may be improved by increasing network lifetime and reliability [75]. Those techniques include ANN and fuzzy inference systems [76,77]. Zhang et al proposed a soft-computing system based on Genetic Algorithm-Support Vector Regression (GA-SVR), in order to facilitate the reliability and survivability of the Structural Health Monitoring (SHM) system faced with, for example, an invalid fiber link in the sensor network [78].…”

Section: Statistical and Artificial Intelligence-based Methodsmentioning

confidence: 99%

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

et al. 2019

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Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). 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. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. 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 study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

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Section: Statistical and Artificial Intelligence-based Methodsmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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“…AI techniques also provide cognitive abilities, so that performance may be improved by increasing network life-time and reliability [19]. Some of those techniques are ANN and fuzzy inference system [20,21]. Zhang…”

Section: Statistical and Artificial Intelligence-based Methodsmentioning

confidence: 99%

Towards Sensor Reliability Using Internet-of-Things LiDAR Data in a Cyber-Physical System

Castaño¹,

Villalonga²,

Haber³

et al. 2019

Preprint

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Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

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“…Then, the data generated by each individual component may impact on other sub-systems as it will contribute to the generation of the whole CPSoS knowledge information. However, the strategy to deal with complexity and heterogeneity of CPSoS is one of the main bottlenecks [17,18].…”

Section: Introductionmentioning

confidence: 99%

Towards the Adoption of Cyber-Physical Systems of Systems Paradigm in Smart Manufacturing Environments

Ferrer

Mohammed

Lastra

et al. 2018

2018 IEEE 16th International Conference on Industrial Informatics (INDIN)

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Cyber-physical Systems (CPS) in industrial manufacturing facilities demand a continuous interaction with different and a large amount of distributed and networked computing nodes, devices and human operators. These systems are critical to ensure the quality of production and the safety of persons working at the shop floor level. Furthermore, this situation is similar in other domains, such as logistics that, in turn, are connected and affect the overall production efficiency. In this context, this article presents some key steps for integrating three pillars of CPS (production line, logistics and facilities) into the current smart manufacturing environments in order to adopt an industrial Cyber-Physical Systems of Systems (CPSoS) paradigm. The approach is focused on the integration in several digital functionalities in a cloud-based platform to allow a real time multiple devices interaction, data analytics/sharing and machine learning-based global reconfiguration to increase the management and optimization capabilities for increasing the quality of facility services, safety and energy efficiency and industrial productivity. Conceptually, isolated systems may enhance their capabilities by accessing to information of other systems. The approach introduces particular vision, main components, potential and challenges of the envisioned CPSoS. In addition, the description of one scenario for realizing the CPSoS vision is presented. The results herein presented will pave the way for the adoption of CPSoS that can be used as a pilot for further research on this emerging topic.

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Fuzzy control of a multiple hearth furnace

Cited by 37 publications

References 4 publications

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

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

Towards Sensor Reliability Using Internet-of-Things LiDAR Data in a Cyber-Physical System

Towards the Adoption of Cyber-Physical Systems of Systems Paradigm in Smart Manufacturing Environments

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