Special Issue "Virtual Sensors with Neurocomputing and Machine Learning Techniques"
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 August 2021.
Special Issue Editors
Interests: intelligent transport systems; telecommunications; neuro-computing; machine learning and pattern recognition; nonlinear dynamics
Special Issues and Collections in MDPI journals
Interests: machine learning; pattern recognition; image processing; data mining; video understanding; cognitive modeling and recognition
Special Issues and Collections in MDPI journals
Interests: machine learning; cognitive neuroscience; applied mathematics; machine vision
Special Issues and Collections in MDPI journals
Interests: dynamic systems in engineering; neurocomputing and applications; optimization and inverse problems; intelligent transportation systems
Special Issues and Collections in MDPI journals
Interests: Computational Intelligence; Fuzzy Logics; Nonlinear Dynamics; Complex Systems; Chaos Theory; Power Electronics
Special Issues and Collections in MDPI journals
Interests: internet-of-things; artificial intelligence; blockchain technologies; next generation networks
Special Issues and Collections in MDPI journals
Special Issue Information
Dear Colleagues,
The idea of a virtual sensor is to extract information or parameter values that cannot be measured directly, or at least would require very costly sensors, by only using and appropriately processing the available information. Virtual sensing does therefore inherently help overcoming/alleviating a series of limitations of “physical sensors” through context related abstraction, modelling and either mathematical or machine learning related processing. Those limitations are cost, energy consumption, special accessibility of some part of the physical context, time-related latency of physical sensors, low resolution, low accuracy, etc.
Virtual sensors also have the potential of significantly enhancing accuracy. Indeed, virtual sensors takes into account all data that real sensors measure (e.g., temperature, pression, position, etc.). A virtual sensor now takes all available data from the real/physical sensors and calculates the exact parameters.
The complexity of virtual models significantly increases with the complexity of the process that they describe, and thus new methods for their development are constantly evaluated. Among many others, data-driven techniques and machine learning offer promising results, creating, for example, deep neural networks that are capable to map complex input-output relations.
Selected keywords (not limited to):
- Theoretical foundations of virtual sensors
- State-of-the-art review of virtual sensors, related challenges and technical solutions
- Sensor clouds
- Autonomous virtual actors in relation to virtual sensors
- Virtual sensors for diagnosis and fault detection
- Neurocomputing technologies for virtual sensors
- Machine learning techniques for virtual sensors
- Bayesian models for virtual sensors
- Creating virtual sensors using learning based super resolution and data fusion
- Stacked auto-encoder techniques for data-driven virtual sensing of relevant variables
- Virtual sensors for predicting fuel consumption (for automobiles or aircrafts)
- Virtual sensors in intelligent transportation systems
- Virtual sensors in body sensor networks
- Virtual sensors in video surveillance systems
- Virtual sensors for semiconductor manufacturing
- Mobile virtual sensors
- Virtual sensing for autonomous vehicles
- Sensor aggregation and virtual sensors
- Virtual sensors for service oriented virtual environments
- Virtual sensors to support model calibration
- Cost-benefit analysis of virtual sensors
- Forecasting product quality in industrial processes with virtual sensors
- Etc.
Prof. Dr. Kyandoghere Kyamakya
Dr. Fadi Al-Machot
Dr. Ahmad Haj Mosa
Dr. Jean Chamberlain Chedjou
Prof. Dr. Zhong Li
Prof. Dr. Antoine Bagula
Guest Editors
Manuscript Submission Information
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Keywords
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.