Computational Intelligence in Data-Driven Soft Sensors: Current Methodologies and Practical Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 524
Special Issue Editors
Interests: intelligent data analysis; learning under uncertainty; computational intelligence; fuzzy sets; mathematical models; signal processing; dimensional metrology; industrial applications (ecoefficiency, rechargeable batteries, clean energy)
Special Issues, Collections and Topics in MDPI journals
Interests: multibody simulation; vehicle dynamics and virtual tests; vehicle, system and component testing; investigation of traffic accidents; transport and traffic engineering; vehicle engineering
Interests: multibody simulation; vehicle dynamics and virtual tests; vehicle, system and component testing; investigation of traffic accidents; transport and traffic engineering; vehicle engineering
Special Issue Information
Dear Colleagues,
Soft sensors, also called virtual sensors or state observers, are used to synthesize the value of latent variables from observable variables. In very complex systems (large number of variables and/or complicated dependencies between them), it is common to use data-driven soft sensors, for which only partial knowledge of the process or of the relationships between its variables is required, and the sensor learns from the available data using machine learning techniques. It is also common to combine different sources of information (first principles, expert knowledge, measured data) in the design of these sensors. In this sense, computational intelligence (CI), which relies on neural networks, fuzzy systems and evolutionary computation, plays an important role in the development of data-driven sensors, especially when the observable signals are of uncertain nature (observation noise, digitized values, censored values, incomplete measurements, etc.).
The application of CI to data-driven sensors is a hot topic, as shown by the following (non-exhaustive) list of issues, which is composed of different CI applications to sensor systems reported since 2020:
- autonomous vehicles;
- motion assessment, accelerometric data;
- energy optimized sensors and computational sustainability;
- condition monitoring and anomaly detection;
- power quality and energy management;
- wireless sensors and actuators;
- batteries and fuel cells;
- aerial vehicles and image processing;
- iot, cyber-physical systems, edge devices;
- smart grids, smart cities;
- biomedical and health care;
- emotion sensing.
The Special Issue will publish original research, reviews and applications in the field of computational intelligence techniques (fuzzy logic, artificial neural networks, evolutionary computing, learning theory and probabilistic methods) applied to data-driven sensor systems.
Prof. Dr. Luciano Sánchez
Prof. Dr. Daniel Álvarez Mántaras
Prof. Dr. Pablo Luque Rodríguez
Guest Editors
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Keywords
- soft sensors
- computational intelligence
- soft computing
- computational sustainability
- anomaly detection
- vehicles
- energy efficiency
- batteries
- human activity recognition
- internet of things
- cyber physical systems
- biomedical sensors
- social sensing
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