Soft Sensors and Intelligent Algorithms for Data Fusion
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: closed (30 September 2017) | Viewed by 86001
Special Issue Editors
Interests: system identification; soft sensors; soft computing; machine learning; neural networks; nonlinear control; complex systems; industrial automation; process monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: computational intelligence; evolutionary computation; intelligent control; neural networks; swarm intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: biometrics; handwriting recognition; decision classifiers; embedded systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Over the past few years, Soft Sensors have been established as valuable tools for estimating the value of a physical quantity, without actually measuring it. Soft sensors present some remarkable advantages over traditional hardware-based sensors, not only improving the availability of measurements, but also increasing their accuracy and reliability, and reducing the associated cost and time delays. As soft sensors are usually data-driven, relying on data fusion to provide a mathematical model for the physical quantity to be estimated, their development is catalysed by recent advancements in scientific fields like machine learning and computational intelligence.
Soft sensor implementations cover a broad spectrum of fields, ranging from industrial applications, to non-destructive testing and multimodal biometrics. The aim of this Special Issue is to bring together innovative developments in soft sensors, including advancements in intelligent algorithms and data fusion techniques for soft sensor development, as well as succesfull soft sensor applications. Papers addressing the wide range of aspects of this technology are invited, including, but not limited to:
- Computational intelligence methods for soft sensors design
- Soft sensor learning algorithms
- Semi-supervised learning for soft sensor design
- Hard and soft sensor data fusion
- Adaptive soft sensors
- Feature selection in soft sensors
- Industrial soft sensors
- Soft sensors for inferential control
- Soft sensors for monitoring and fault detection
- Chemical soft sensors
- Data fusion of implantable and/or wearable sensors
- Multi-modal biometrics (including face, handwriting, fingerprint, gait, iris/retina, voice, typing rhythm, hand, etc.)
- Soft sensors for environmental applications
- Soft sensors for non-destructive testing
- Soft sensors for social sensing
- Soft sensors in medical applications
- Soft sensors in the smart grid context
Prof. Maria Gabriella Xibilia
Dr. Alex Alexandridis
Dr. Elias N. Zois
Guest Editors
Manuscript Submission Information
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Keywords
- Computational intelligence
- Data fusion
- Fault detection
- Inferential sensors
- Machine learning
- Multimodal biometrics
- Non-destructive testing
- Soft computing
- Soft sensors
- Virtual sensors
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