Special Session Title: AI for Industrial and Environmental Applications
Organisers: Pasquale Coscia, Universitá degli Studi di Milano, Italy
Hang Zhao, Tsinghua University, China
Artificial intelligence (AI) represents a core component of many industrial production processes to guarantee high quality standards and advance legacy manufacturing. Low-powered and cost-effective sensors paired with advanced technologies have enabled the development of smart factories able to manage big data, perform predictive maintenance, and benefit from real-time monitoring of industrial instrumentation. Traditional rule-based techniques are, in fact, being rapidly replaced by more efficient and intelligent solutions empowering a transition to semi- or fully-automated operations reducing human risks or repetitive tasks. In this field, AI-powered manufacturing represents a valuable aid to monitor production lines for increasing efficiency, accuracy, and being compliant with up-to-date environmental regulations requiring the adoption of environmentally friendly strategies.
This special session aims at sharing and discussing innovative solutions for both scientific and professional communities about recent results and advances of AI methods for industrial and environmental applications. The scope of this session comprehends, but it is not limited to:
• Deep Learning for big data in industrial applications • Anomaly detection
• Computational intelligence for ambient intelligence • Fault and defect analysis
• Adaptive systems
• Smart interfaces
• Human tracking and biometric recognition in industrial applications • Touchless measurement systems
• Image processing and virtual sensors
• Mobile sensors
• Environmental monitoring
• Industrial Internet of Things (IIoT)
• Quality assessment
• Industry 4.0
• In-line process monitoring
• Low-power devices
• Sensor fusion
• Wearable sensors
Biography of Organisers:
Pasquale Coscia is an Assistant Professor (RTDa) at the Department of Computer Science of University of Milan (Italy) and member of the Industrial, Environmental and Biometric Informatics (IEBI) Laboratory. He received both his M.Sc. degree in Computer Engineering and Ph.D. degree in Industrial and Information Engineering from University of Campania “Luigi Vanvitelli” (Italy) in 2014 and 2019, respectively. From 2019 to 2022, he was a post-doctoral researcher in the Visual Intelligence and Machine Perception (VIMP) group at University of Padova (Italy). In 2017, he was a visiting student at University of Florence (Italy) and University of Padova. In 2016, he was involved in the Visiting Research Program (VRP) at the NATO STO-CMRE in La Spezia (Italy). He also co-organized the Benchmarking Trajectory Forecasting Models (BTFM) workshop series at ECCV’20 and ICCV’21. His main research interests are in the areas of computer vision, neural networks and machine learning.
Hang Zhao is an Assistant Professor at IIIS, Tsinghua University, Principle Investigator of MARS Lab. His research interests are multi-modal machine learning, autonomous driving and computer vision. He was a Research Scientist at Waymo (known as Google’s self-driving project). Before that, he got his Ph.D. degree at MIT under Professor Antonio Torralba, and his M.S. under Professor Ramesh Raskar. Before MIT, he received his B.S. from CKC Honors College, Zhejiang University.