Preprocessing Techniques to Enhance Data Quality for AI Models in EMC Analysis

In this paper Electromagnetic Compatibility (EMC) data is discussed and its effects on training a machine learning model. EMC data is inherently complex due to factors such as noise, high dimensionality and simulation data. Training a machine learning model is dependent on multiple factors. Among these many factors, data quality stands out as the most critical one. Data quality determines how effectively domain-specific information is encoded, prioritizing fairness, bias reduction, and increased robustness. Poor data quality can lead to skewed models that fail to generalize or accurately predict real-world behaviors. Proper representation of the data domain is crucial to achieving reliable learning outcomes while minimizing knowledge gaps, inconsistencies and errors. By identifying, ranking, and selecting key features that are most relevant to the target problem, feature extraction improves the efficiency and accuracy of AI models by focusing on the most informative aspects of the data. This paper discusses the treatment of data for EMC analysis and delves into a range of preprocessing techniques designed to enhance data quality. These methods include normalization techniques, noise reduction to filter irrelevant fluctuations, data augmentation to increase dataset diversity, dimensionality reduction and feature extraction to prioritize important attributes. Each technique plays a vital role in optimizing data for machine learning tasks. Finally, the impact of these preprocessing strategies are required to enhance the performance and accuracy of the AI models, highlighting best practices for achieving robust models in EMC applications.