reinforcement learning

EH-SWADS: energy-harvesting smart weather-aware drone sink for agricultural WSNs

EH-SWADS is proposed, an energy-harvesting extension of the previously proposed weather-aware UAV-assisted WSN architecture, SWADS, designed for sustainable, long-term agricultural monitoring and explicitly incorporates the harvested-to-consumed energy balance into the learning process.

The Role of AI in Digital Circuit Design

Artificial Intelligence (AI) is increasingly being applied to digital circuit design to assist with circuit analysis, optimization, and automation. This study examines the role of AI in digital circuit design, focusing on Graph Neural Networks (GNNs), Reinforcement Learning (RL), and Generative AI. These approaches can...

Digital twin-enabled reinforcement learning for adaptive and degradation-aware control of high-temperature electrolysis

Rationality Randomization With Maximum Entropy for Robust Dynamic Obstacle Avoidance on Legged Robots

Reinforcement learning (RL) for legged robots has recently gained significant attention due to its ability to enable traversal of complex terrains and navigation in cluttered environments. However, most existing approaches focus on static or predictably moving obstacles and rely on handcrafted curricula or manual task...

A novel heterogeneous coupled neuron network and its application in mechanical fault feature enhancement

In practical engineering, fault characteristic signals are often overwhelmed by strong background noise and complex working conditions, which poses a serious challenge for timely detection and accurate identification of faults. Therefore, a novel heterogeneous coupled neuron network (HCNN) is proposed and the network u...

Adaptive and Learning Robots

In the past, robots have been required to be explicitly programmed to carry out tasks. Learning robots differ from this notion by allowing them to learn from experience, which allows them to get better the more they perform a task. This chapter will covers the basics of learning in robotics and give an overview of how...

A controlled benchmark of differentiable optimization and reinforcement learning for traffic engineering in transmission backhaul networks

Simulation framework and reference implementations for the paper "A controlled benchmark of differentiable optimization and reinforcement learning for traffic engineering in transmission backhaul networks". The archive contains the implementations of all nine routing methods evaluated in the paper — a multi-commodity f...

Organizational Phenomenology: Artificial F1 and the Geometry of Coherent Agency

This paper introduces Artificial F1, a computational framework that formalises a previously under-specified control layer in intelligent systems: pre-representational signal admission and valence structuring. Unlike standard approaches in reinforcement learning and attention-based architectures, which operate over a fi...

Graph neural network-based service migration decision in vehicular networks

Abstract The rapid development of the Internet of Vehicles (IoV) has led to an exponential increase in the number of latency sensitive and computationally intensive tasks. Due to the limitations of onboard computing resources in vehicles, offloading these tasks generated during vehicle operation to a remote cloud for p...

BOOK - The Architecture of Permanence From the Riemann Hypothesis to Deterministic Cognitive Engineering

📚 THE ARCHITECTURE OF PERMANENCE: From the Riemann Hypothesis to Deterministic Cognitive Engineering Overview Author: Frank Morales Aguilera, BEng, MEng, SMIEEE Affiliation: Sovereign Machine Laboratory (SOMALA), Montréal, Canada Year: 2026 Deterministic Seed: 123 This manuscript documents a single mathematical princi...

Intelligent classification of smart city sectors from IoT-based energy consumption data using AI-based methods

The findings demonstrate that combining deep representation learning with adaptive optimization improves classification accuracy and stability, offering practical value for sector-aware energy planning, load prioritization, and data-driven decision support in smart city energy management.

Knowledge-Driven reinforcement learning for Prefabricated construction supply Chain Scheduling: A Two-Phase large language model integration framework

Prefabricated construction supply chains require coordinated scheduling across production, transportation, and assembly under transport uncertainty, storage constraints, and assembly precedence requirements. However, existing approaches rely on stage-isolated optimization, static heuristics, or black-box AI models that...