reinforcement learning

AETERNA Quantum Matrix Acceleration & Zero-Copy Sovereign IPC: Sub-Microsecond Lock-Free Shared Memory Ring Buffers, Aharonov-Bohm Topological Phase Protection on NVIDIA Ada Lovelace sm_89, and Zen 4 AVX-512 Concurrency

AETERNA QUANTUM MATRIX ACCELERATION & ZERO-COPY SOVEREIGN IPC: Sub-Microsecond Lock-Free Shared Memory Ring Buffers, Aharonov-Bohm Topological Phase Protection on NVIDIA Ada Lovelace sm_89, and Zen 4 AVX-512 Concurrency Author: Dimitar Stavrev Prodromov (AETERNA Technologies HQ, Pomorie, Bulgaria)Authority Signature: 0...

PIPO: Physics-informed deep reinforcement learning for pareto-optimal control for active suspension system

Stable, high-temperature acoustic sonoluminescence using learning-based control

Abstract Achieving high collapse temperatures under stable operating conditions is a central challenge in acoustic cavitation and sonoluminescence, as instabilities typically limit energy focusing. Conventional high-amplitude, low-frequency driving favors high collapse temperatures but simultaneously triggers shape ins...

Code for "Multi-criteria prioritized reinforcement learning for autonomous underwater vehicle navigation in 3D multi-node environments"

Artificial Intelligence–Driven Intrusion Detection Systems: A Systematic Review of Machine Learning, Deep Learning, and Adaptive Ensemble Approaches

This is a critical systematic review of 40 peer-reviewed articles published between 2021 and 2025 on artificial intelligence-enhanced Intrusion Detection Systems (IDS).To maintain rigour and transparency in the review, the PRISMA 2020 and Kitchenham guidelines are adhered to.The appropriate literature was also searched...

sruthijanga/EANAS-GreenAI: EANAS-GreenAI

EANAS-GreenAI v1.0.0 EANAS-GreenAI is a research implementation of Energy-Aware Neural Architecture Search for Edge-AI Deployments. It combines CNN architecture search, reinforcement learning, and evolutionary optimization to balance model accuracy, inference latency, and energy consumption. Key Features: Five-block CN...

Editorial: The role of cellular subtypes in striatal activity

The basal ganglia are central to the selection and execution of actions, integrating diverse information to shape motor, cognitive, and motivational behaviors. Within this network, the striatum occupies a key position where convergent cortical, thalamic, and neuromodulatory inputs are transformed into patterns of activ...

A hierarchical reinforcement learning framework for high-efficiency control of turbine-assisted fuel cell air supply systems

Event-Based Reinforcement Learning for Cyber-Physical Energy Systems – Smart Buildings, Smart Grid, and Smart Cities

DiffLyap-MEC: Diffusion-Guided Lyapunov Optimization for Joint Offloading, Semantic Compression, and Resource Allocation in ISAC-Enabled 6G Edge Computing

Integrated sensing and communication (ISAC), semantic communication, and mobile edge computing (MEC) jointly create a high-dimensional, non-convex, and time-coupled resource allocation problem in sixth-generation (6G) networks that existing heuristics and deep reinforcement learning (DRL) methods solve either slowly or...

ENGINEERING DECISION SYSTEMS FOR METAL FABRICATION: VYACHESLAV SHARGAEV'S APPLIED AI APPROACH

Closed-loop artificial intelligence is increasingly claimed for metal-fabrication systems that combine reinforcement-learning-based process control with vision-based quality inspection. This review organizes the reinforcement-learning, computer-vision, and production-economics literature bearing on metal cutting around...

Topology-aware graph reinforcement learning for generalizable cascading failure mitigation in hybrid AC/DC power systems

Cascading failures in hybrid AC/DC power systems can propagate rapidly through DC-side disturbances, AC branch overloads, topology changes, and islanding, making emergency mitigation a constrained sequential decision-making problem under evolving network structures. This paper proposes a topology-aware graph reinforcem...