Industrial AI: Machine Intelligence in Industrial applications

Development of data enhancement and dataset correction based on data quality evaluation

• In this study, we focus on the development of data quality evaluation, quality assessment metric, and quality indicator system considering various fields, types of data characteristics. Based on the features of the datasets in various industry fields, research on outlier detection and correction of missing values is also performed.

In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This research presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy.

산업AI, 머신러닝, 딥러닝 및 강화학습, 앙상블 기반 딥러닝 응용 기술 연구