Ulsan National Institute of Science and Technology · Engineering
Professor Gyeongho Kim's research lab specializes in intelligent manufacturing and predictive analytics, focusing on developing advanced machine learning and deep learning models for real-time condition monitoring and fault prediction in industrial processes. The lab's main research directions include uncertainty-aware tool wear prediction in machining, multimodal fault detection in plastic injection molding, and maritime accident prediction using interpretable machine learning. These efforts are aligned with sustainable manufacturing and zero-defect production, emphasizing practical deployability and robustness under diverse operating conditions. The lab also explores innovative electrochemical biosensing techniques for sensitive protein detection, demonstrating interdisciplinary applications in both industrial and biomedical fields.
Figures are computed from collected data and may differ slightly.
Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency’s volatility and unique characteristics, accurate price prediction is essential for developing successful investment strategies. To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, th
Tool wear negatively affects machined surfaces and causes surface cracking, therefore increasing manufacturing costs and degrading product quality. Titanium alloys, which are widely used because of their desirable mechanical properties, have problems associated with tool wear due to poor thermal properties, such as specific heat capacity and thermal conductivity. Therefore, the accurate prediction of tool wear is necessary during the titanium alloy end-milling process to improve product quality
The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neur
Accurate tool wear prediction is an essential task in machining processes because it helps to schedule efficient tool maintenance and maximise the tool's useful life, thus contributing to sustainable production via zero defect manufacturing (ZDM). However, there are limitations to existing methods; these cannot be used under multiple machining conditions, which is common practice. This problem not only hinders accurate tool wear monitoring but also necessitates the use of multiple models, which
Direct electron transfer (DET) between a redox label and an electrode has been used for sensitive and selective sandwich-type detection without a wash step. However, applying DET is still highly challenging in protein detection, and a single redox label per probe is insufficient to obtain a high electrochemical signal. Here, we report a wash-free, sandwich-type detection of thrombin using DET and catalytic signal amplification of multiple redox labels. The detection scheme is based on (i) the re
Every year, maritime accidents cause severe damages not only to humans but also to maritime instruments like vessels. The authors of this work therefore propose a machine learning-based maritime accident prediction system that can be used to prevent maritime accidents from happening by predicting and interpreting the accidents. This work overcomes the limitations of the existing works that lack practicability in the sense that the ex-post analyses are conducted to suggest accident prevention str
This study explores the potential of seven ferrocenyl (Fc) compounds with cross-linking groups as alternative redox labels to methylene blue (MB) for electrochemical aptamer-based (E-AB) sensors. The cross-linking efficiency, formal potential (<i>E</i><sup>0</sup>'), and electrochemical durability of these compounds were evaluated. Compound <b>Fc1a-X</b> exhibited superior performance, characterized by efficient cross-linking, a moderate and pH-insensitive <i>E</i><sup>0</sup>', and enhanced dur
This work proposes an overview of the recent semi-supervised learning approaches and related works. Despite the remarkable success of neural networks in various applications, there exist a few formidable constraints, including the need for a large amount of labeled data. Therefore, semi-supervised learning, which is a learning scheme in which scarce labels and a larger amount of unlabeled data are utilized to train models (e.g., deep neural networks), is getting more important. Based on the key
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