Pohang University of Science and Technology · 工学
Professor Sang Woo Kim's research lab specializes in intelligent fault diagnosis and condition monitoring, with a strong focus on advanced signal processing, machine learning, and deep learning techniques for industrial applications. The lab develops innovative methods for defect detection in steel surfaces using optimized lighting and filtering, diagnostic systems for lithium-ion batteries combining capacity and fault co-diagnosis, and novelty detection frameworks for soft fault identification in electrical systems. Their work bridges theoretical advancements in algorithms—such as recursive prototype reduction and analysis of convolutional neural network behavior—with practical solutions in manufacturing and energy systems.
Figures are computed from collected data and may differ slightly.
In this paper a novel filtering scheme combined with a lighting method is proposed for defect detection in steel surfaces. A steel surface has non-uniform brightness and various shaped defects, which cause difficulties in defect detection. To solve this problem we propose a sub-optimal filtering that is combined with a switching-lighting method. First, dual-light switching lighting (DLSL) is explained, which decreases the effect of non-uniformity of surface brightness and improves the detection
Most of the prototype reduction schemes (PRS), which have been reported in the literature, process the data in its entirety to yield a subset of prototypes that are useful in nearest-neighbor-like classification. Foremost among these are the prototypes for nearest neighbor classifiers, the vector quantization technique, and the support vector machines. These methods suffer from a major disadvantage, namely, that of the excessive computational burden encountered by processing all the data. In thi
Accurate health diagnostics of lithium-ion batteries are indispensable for efficient utilization. A decrease in battery capacity not only diminishes the energy efficiency but also causes several detrimental effects , such as an internal short circuit (ISC) fault; these fault can lead to thermal runaway. However, the simultaneous impact of aging and ISC faults complicates the ability to distinguish between two factors within a singular discharging or charging process. This study focuses on the co
Deep neural networks have been used in various fields, but their internal behavior in how they understand images is not well known. In this study, we discuss two counterintuitive properties of convolutional neural networks (CNNs). First, we evaluated the size of the receptive field of CNNs with their classification accuracy. Previous studies have attempted to increase the size of the receptive field for performance gain. However, we observed that some CNNs with a smaller receptive field can achi
Two lower bounds for the trace of the solution of the discrete algebraic Riccati equation (DARE) are presented. It is shown that in many cases, these trace bounds are tighter than those in the literature and greater than the trace of the state weighting matrix even when the system matrix is singular. The results are illustrated by an example.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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