PARIS 7th International Congress on Electrical, Electronics and Computational Intelligence: PEECI-27

1. Core Domains

Electrical Engineering (Power & Systems):

• Power Systems and Energy Markets: Generation (thermal, hydro, nuclear), transmission grid stability, high-voltage DC (HVDC) systems, and power economics.

• Renewable Energy Sources: Solar photovoltaics, wind energy conversion systems, grid integration, and energy storage technology.

• Electrical Machines and Drives: Transformers, induction and synchronous motors, permanent magnet machines, and industrial motor control.

• Electromagnetics and Field Theory: Wave propagation, transmission lines, antennas, and electromagnetic compatibility (EMC).

Electronics Engineering (Devices & Circuits)

Focuses on the processing of signals and the design of low-power semiconductor devices and circuits.

• Solid-State Devices and Microelectronics: Semiconductor physics, MOSFET/FinFET fabrication, quantum dots, and optoelectronics.

• Analog and Digital Circuit Design: Operational amplifiers, filters, mixed-signal ICs, FPGA architectures, and ASIC design.

• Communication Systems: Radiofrequency (RF) engineering, modulation techniques, fiber optics, and wireless communication protocols.

• Consumer Electronics: Power management ICs, display technologies, and audio/video processing hardware.

Computational Intelligence (Algorithms & Cognition)

Focuses on nature-inspired, biologically-plausible, and data-driven algorithmic approaches to solve complex, non-linear problems.

• Artificial Neural Networks (ANNs) & Deep Learning: Multi-layer perceptrons, Convolutional Neural Networks (CNNs), Transformers, and Generative Adversarial Networks (GANs).

• Evolutionary Computation: Genetic algorithms, particle swarm optimization (PSO), ant colony optimization, and differential evolution.

• Fuzzy Logic Systems: Fuzzy sets, linguistic variables, fuzzy inference systems (Mamdani and Sugeno), and defuzzification methods.

• Probabilistic Reasoning and Cognitive Computing: Bayesian networks, hidden Markov models, and knowledge representation.

2. Interrelated and Cross-Disciplinary Domains

These fields exist precisely at the intersection of two or all three of the major disciplines, where hardware meets intelligent software.

Smart Grid and Intelligent Energy Systems

Intersection of: Electrical Engineering + Computational Intelligence

• Demand-Side Management: Utilizing machine learning to predict consumer energy loads and optimize distribution.

• Self-Healing Grids: Automated fault detection, isolation, and service restoration using fuzzy logic and multi-agent systems.

• Microgrid Management: AI-driven optimization for balancing localized renewable energy generation with battery storage.

Embedded Systems and Edge AI

Intersection of: Electronics Engineering + Computational Intelligence

• TinyML: Deploying ultra-low-power deep learning models directly on microcontrollers and sensor nodes.

• Hardware Acceleration for AI: Design of Neuromorphic chips, Tensor Processing Units (TPUs), and FPGA-based neural network accelerators.

• Intelligent Internet of Things (IoT): Smart sensor nodes that perform on-device data filtering, anomaly detection, and decentralized decision-making.

Power Electronics and Intelligent Control

Intersection of: Electrical Engineering + Electronics Engineering

• Converter Topologies: Inverters, rectifiers, and DC-DC converters used to interface renewable energy with the main grid.

• Electric Vehicle (EV) Powertrains: High-power battery management systems (BMS), traction inverters, and fast-charging infrastructure.

• Smart Lighting and Actuation: Electronic ballasts, LED drivers, and solid-state switching for industrial automation.

Robotics, Mechatronics, and Cyber-Physical Systems

Intersection of: Electrical + Electronics + Computational Intelligence

• Autonomous Navigation: Simultaneous Localization and Mapping (SLAM), computer vision for obstacle avoidance, and sensor fusion (LiDAR, Radar, Ultrasonics).

• Intelligent Robotic Actuation: Combining electric motors and electronic motor drivers with reinforcement learning for precise, adaptive robotic limb movement.

• Industrial Automation (Industry 4.0): Programmable Logic Controllers (PLCs) integrated with predictive maintenance algorithms to foresee machine failures before they occur