Hypervector-Driven Optimization of Kr Gas Discharge Lamp Efficiency Through Dynamic Spectral Control

This research introduces Hypervector Spectral Analysis & Dynamic Lamp Control (HSDLC), a novel system to optimize the efficiency and lifespan of Krypton (Kr) gas discharge lamps. The system addresses a key challenge: performance variability caused by subtle manufacturing inconsistencies in Kr gas cylinders, which traditional static ballast controls cannot mitigate.

The HSDLC methodology is an intelligent feedback loop. First, a high-resolution spectrometer continuously captures the lamp's unique light spectrum. This complex spectral data is then converted into a compact, high-dimensional "hypervector" using a Discrete Cosine Transform (DCT), creating a unique fingerprint of the lamp's current state. This fingerprint is compared against a database of known gas cylinder profiles using cosine similarity to identify its specific characteristics.

Based on this real-time analysis, a reinforcement learning agent (a Deep Q-Network) dynamically adjusts the lamp’s operating parameters, such as voltage and current. The agent’s goal is to learn the optimal settings to maximize luminous efficacy (lumens per watt) while simultaneously preventing fluctuations that could shorten the lamp's life.

Experimental results demonstrated the system's profound impact. Across 10 different lamps, HSDLC achieved an average luminous efficacy increase of 15.4% (ranging from 12-18%) and extended operational lifespan by approximately 12% compared to conventional control methods. The results were statistically significant (p < 0.001).

By integrating advanced spectral analysis with AI-driven control, HSDLC represents a significant advancement in sustainable lighting. It offers substantial energy savings and reduced maintenance costs, presenting a powerful, adaptive solution for demanding applications like architectural lighting.