This paper presents a comparative analysis of two developed Generative Adversarial Network (GAN) architectures for synthesizing sensor data in predictive maintenance (PdM) applications within high-radiation environments. The study ad-dresses the challenge of data scarcity in such settings, where experimental runs are constrained by the risk of device failure and economic considerations. The two GAN models: GAN-1 uses the Conditional Tabular GAN (CTGAN) architecture, and GAN-2 employs a custom network. These models generated synthetic datasets that were used to train and evaluate three machine learning algorithms: Random Forest, k-Nearest Neighbours, and eXtreme Gradient Boosting. The performance of these PdM models trained on synthetic data was compared against models trained on the original limited dataset. Results demonstrate that GAN-1 produced synthetic data closely mirroring the characteristics of the original dataset, enabling PdM models to achieve comparable performance levels. This study highlights the potential of GAN-based data synthesis in enhancing PdM model development for high-radiation environments, offering a viable solution to the challenges of limited data availability in such harsh settings. The findings have significant implications for improving operational reliability and safety in nuclear and other extreme environments where electronic systems are deployed.

Energy efficiency can be improved significantly by monitoring and optimizing utilization patterns, which is essential due to the substantial impact of human behavior on domestic energy consumption. Thus, this study describes developing and assessing a comprehensive plug incorporating numerous sensors to monitor household power consumption and environmental factors. The plug collects temperature, humidity, human presence, CO2 levels, and power consumption data, which is subsequently processed and visualized within the Home-Assistant platform. The evaluation demonstrates that the HLW8012 energy meter, which is embedded in the plug, obtains a high level of accuracy of 98.16%, surpassing commercially available smart switches like the Sonoff POW. The system offers consumers a comprehensive understanding of their energy consumption, which enables them to make more informed and efficient energy use decisions. This innovative solution illustrates the potential of sophisticated monitoring tools to substantially contribute to reducing energy waste in domestic environments.