Intelligent Systems for Industry, Supply Chain and Environment
A.Y. 2018/2019
Learning objectives
The course presents methodologies and techniques for intelligent systems for monitoring and control of industrial, environmental and supply chain applications, typically based on computational intelligence approaches.
Expected learning outcomes
Undefined
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Milan
Responsible
Lesson period
Second semester
Course syllabus
· Intelligent sensors: Heterogeneous multi-sensor systems. Sensor data analysis. Diagnosys. Fault tolerance. Self-calibration. Adaptivity. Management.
· Sensor networks: Structure. Functions. Adaptivity. Management. Distributed data analysis. Fault tolerance. Diagnosys.
· Measurements: Acquisition and processing of sensor measurement in advanced adaptive infrastructures.
· Sensor signal and image processing: Feature extraction. Multi-sensorial data fusion. Adaptivity of measurement representation, operations and functions to the application needs. Virtual sensors. Information compression.
· Classification and clustering: Classification and clustering of sensor signals. Sensitività analysis. Class robustness.
· Data mining and knowledge extraction: Adaptive knowledge extraction from sensor data and system information. Knowledge representation.
· Monitoring: Applications of intelligent system sto complex system monitoring. Applications to industrial process monitoring. Qualità monitoring. Applications to environmental monitoring.
· Prediction: Applications of intelligent system for prediction in the industry and the environment and the supply chain. Quality prediction.
· Control: Applications of intelligent system sto control of industrial processes, industrial automation, robotic systems, complex products, power distribution grids, automotive and transport systems and the supply chain.
· Sensor networks: Structure. Functions. Adaptivity. Management. Distributed data analysis. Fault tolerance. Diagnosys.
· Measurements: Acquisition and processing of sensor measurement in advanced adaptive infrastructures.
· Sensor signal and image processing: Feature extraction. Multi-sensorial data fusion. Adaptivity of measurement representation, operations and functions to the application needs. Virtual sensors. Information compression.
· Classification and clustering: Classification and clustering of sensor signals. Sensitività analysis. Class robustness.
· Data mining and knowledge extraction: Adaptive knowledge extraction from sensor data and system information. Knowledge representation.
· Monitoring: Applications of intelligent system sto complex system monitoring. Applications to industrial process monitoring. Qualità monitoring. Applications to environmental monitoring.
· Prediction: Applications of intelligent system for prediction in the industry and the environment and the supply chain. Quality prediction.
· Control: Applications of intelligent system sto control of industrial processes, industrial automation, robotic systems, complex products, power distribution grids, automotive and transport systems and the supply chain.
Professor(s)
Reception:
By appointment (via email)
Computer Science Department, Via Celoria 18 - 20133 Milano (MI), Italy