Reliability and Failure Analysis of Devices and Systems
Session organizers
- Mohamed Ali BELAID, Tunisia
- Jahangir H. SARKER, Canada
- Hamza KAOUACH, France
Description of the session thematic
The special session content will feature the presentation of contributed papers focusing on reliability, assessment techniques and methods for devices and systems. This session includes main reliability aspects and skills in the field of electronics and microelectronics, for present and future applications. It practically focuses on recent developments and future perspectives in innovative techniques and failure analysis methods (devices and circuits) for reliability issue. The authors should contribute to reliability concepts, the understanding and analysis of failure mechanisms by means of innovative methods in failure analysis, measurement, characterization, simulation or modelling.
Topics and Keywords
A- Assessment Techniques and Methods for Devices and Circuits :
Design for reliability; advanced reliability simulation; limits to accelerated tests; reliability relationship; counterfeit detection on system level; condition monitoring; reliability assessment techniques.
B-Semiconductor and Technologies
Process-related issues, passivation stability; hot carriers injection, NBTI, TDDB; high-K dielectrics and gate stacks; mechanical and thermal aspects; silicon on insulator devices.
C-Progress in Failure Analysis Methods
Electron, ion and optical beam techniques; scanning probe techniques; static or dynamic techniques; electric or magnetic field-based techniques; electrical, thermal and thermo-mechanical characterization; construction analysis; failure analysis: case studies.
D-Power Devices and Systems
Smart power devices, IGBT, Thyristors, wide bandgap power devices, power electronic systems.
E-Extreme Environments and Radiation
ESD-EOS, latchup; EMC-EMI (integrated circuits, power electronic systems); radiation impact on circuits and systems reliability; …
Special Session description
Reliability and Failure Analysis of Devices and Systems.pdf
Artificial Intelligence for Intelligent Transportation Systems
Session organizers
- Dr. Fares BOURIACHI, Algeria
Description of the session thematic
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are all advanced technologies used in Intelligent Transportation Systems (ITS) to improve traffic flow, reduce congestion, and enhance safety. AI can help make transportation systems more efficient, reliable, and safe, ultimately improving quality of life for commuters and reducing the environmental impact of transportation. We welcome submissions that explore the application of machine learning, deep learning, natural language processing, computer vision, and other AI techniques in ITS. We encourage interdisciplinary research that integrates AI with other fields, such as transportation engineering, urban planning, and public policy.
Topics and Keywords
AI, Machine Learning and Deep learning for ITS, Smart Traffic Lights, Intelligent traffic signal control (TSC),Traffic Prediction and Management, Traffic flow modeling and prediction, Autonomous vehicles and their control systems, Cyber security in Connected and Autonomous Vehicles (CAVs), New technological trends in ITS, Smart cities and transportation, V2X communications in ITS
Special Session description
Artificial Intelligence for Intelligent Transportation Systems.pdf
Data Analytics, Smart Energy and Storage Systems: Survey of Technologies, Techniques, and Applications
Session organizers
Prof. Chokri BEN SALAH, Tunisia
Ferdaws BEN NACEUR, Tunisia
Description of the session thematic
This special session addresses the following thematic and applications:
- Applications integrating smart tools such as data analysis and smart technologies, management and control of smart energy storage systems, smart city, smart grid and electric vehicle.
- Predictive techniques for estimating the load, energy prices, renewable energy inputs, load status, fault diagnosis, etc.
- Information technology applications, cloud usage, IoT systems, information management systems, systems modeling and their contributions to energy management system.
- Smart meters and information extraction tools, which provide various opportunities associated with the big data collected. This includes acquisition, statistics, transmission, processing, visualization, interpretation and intervention.
Topics and Keywords
Smart grids; Smart energy, Data analytics; sustainable energy generation; intelligent Storage systems; Data-intensive computing; Data processing systems; Energy Data Statistics
Special Session description
SS_smart_energy.pdf
Machine Learning, Deep Learning and Optimization Techniques for Sensor Devices
Session organizers
- Fraj ECHOUCHENE, Tunisia
- Sameh KAZIZ, Tunisia
- Houcine BARHOUMI, Tunisia
Description of the session thematic
This special session aims to present and discuss the recent research works related to sensor devices. It addresses the aspects and skills in biosensors, modeling, simulation and optimization techniques for present and future applications. More precisely, it practically focuses on recent developments and prospects of innovative techniques of detection methods in different fields such as biomedical, environmental, agro-food technologies, etc.
Researchers interested in this topic are kindly invited to contribute to the understanding and analysis of different detection systems, considering their sensitivity and efficiency.
Topics and Keywords
Sensor devices :
- Micro-electromechanical systems / Nano-electromechanical systems for Sensing Applications
- Sensor Networks
- Simulation and optimization of sensor devices
- Biosensors
- Machine learning based advanced biosensors
- SPR biosensors
- Physical and Chemical Sensors
- Microfluidics and Lab-on-a-chip
Special Session description
Reliability and Failure Analysis of Devices and Systems.pdf
Artificial Intelligence for Electrical Networks and Microgrids
Session organizers
- Najiba MRABET BELLAAJ, Tunisia
- Zina BOUSSAADA, France
Description of the session thematic
This special session addresses the application of artificial intelligence (AI) techniques to electric networks and microgrids. It will particularly focus on the modeling, identification, and prediction of energy generation, storage, and management systems. It also aims to discuss and raise the challenges and opportunities associated with the use of AI in these areas and highlight the potential benefits of improving efficiency in the energy sector and especially in microgrids. Additionally, the session will address the crucial issue of energy transition and emphasize the need for a shift towards sustainable and renewable sources of energy. The key objective is to discuss and highlight the role that AI can play in accelerating this transition process.
On the other hand, fault diagnosis and reliability of microgrids are key topics in this special session. AI showed their effectiveness and strong potential to provide accurate identification and isolation of faulty components. Works addressing this issue through advanced AI tools are also welcome.
Moreover, a critical aspect related to this thematic is the challenge of accurately predicting renewable energy production, which is vital for an efficient management of renewable energy systems and microgrids. In this regard, AI techniques such as machine learning algorithms and many other AI tools can generate an accurate prediction of renewable energy output and load demand. However, many challenges still exist and are under investigation study such as the development of accurate prediction models, including the need for high-quality data, sophisticated algorithms, and robust validation techniques.
Topics and Keywords
A: Predicting curve load and renewable energy production
Energy forecasting; Production prediction; Time series analysis; Machine learning; Solar energy; Wind energy; weather data; supervisory control and data acquisition (SCADA)
B: Energy management Systems (EMS)
Energy Management System; Artificial intelligence; Machine learning; Deep learning; Fuzzy logic; Multi Agent Systems, Micro Grid, distributed systems, Reinforcement learning; Expert systems; Energy efficiency; Energy consumption; Energy demand; Energy supply; Energy optimization; Energy policy; Grid Stability
C: Fault diagnosis in microgrid components
Supervision, fault diagnosis, energy efficiency; microgrid protection; fault detection; Fault isolation; node sensors faults; DC Microgrid Protection; Short Circuit Fault; Fault Diagnosis.
Special Session description
Artificial Intelligence for Electrical Networks and Microgrids.pdf