With the widespread adoption of smart farms and continuous advancements in IoT (Internet of Things) technology, acquiring diverse additional data has become increasingly convenient. Consequently, studies relevant to deep learning models that leverage multimodal data for crop disease diagnosis and associated data augmentation methods are significantly growing. We propose a comprehensive deep learning model that predicts crop type, detects disease presence, and assesses disease severity at the same time. We utilize multimodal data comprising crop images and environmental variables such as temperature, humidity, and dew points. We confirmed that the results of diagnosing crop diseases using multimodal data improved 2.58%p performance compared to using crop images only. We also propose a multimodal-based mixup augmentation method capable of utilizing both image and environmental data. In this study, multimodal data refer to data from multiple sources, and multimodal mixup is a data augmentation technique that combines multimodal data for training. This expands the conventional mixup technique that was originally applied solely to image data. Our multimodal mixup augmentation method showcases a performance improvement of 1.33%p compared to the original mixup method.

한국멀티미디어학회(The Journal of Curriculum Evaluation)

Hwang, M.S.; Park, M.K., and Lee, H.S., 2023. A study on the visual effects production process for efficient underwater explosion CG visualization. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 518-522. Charlotte (North Carolina), ISSN 0749-0208. The creation of visual effects is rooted in the simulation of real-world physical phenomena, with the goal of enhancing the sense of immersion in the contents. One such effect, the visualization of underwater explosions using Computer Graphics (CG), poses a significant challenge due to a lack of readily available academic or reference data. This study is an experimental examination of technology for the realization of 3D computer graphics, specifically addressing the challenges of underwater explosion effects. The research process for the production of underwater explosion CG, previously comprising four steps, has been modified and expanded to six steps in this study. Through the identification and resolution of shortcomings in existing experimental research and optimization of the production process, this study aims to provide an efficient method for the creation of underwater explosion effects in CG.

Detecting pipeline leaks in nuclear power plants is important to ensuring operational safety and minimizing economic losses. Although supervised deep learning models have shown promising performance in leak detection, they face two major challenges: the difficulty of obtaining sufficient leak data and the degradation of performance when applied to unseen environments. To address these challenges, we propose one-class prototypical networks that trains exclusively on normal state data without requiring labeled leak data. Our method leverages only normal state data from multiple domains for training and effectively adapts to new environments with minimal support data. To further enhance performance, a self-supervised anomaly detection strategy is introduced by augmenting normal data and treating them as pseudo-leak data. Experimental evaluations in multiple simulated nuclear power plant environments demonstrate that the proposed method consistently achieves high detection performance, achieving an average AUC-ROC score of 0.994 across all environments, even in unseen domains. Furthermore, explainability analysis using 1D Grad-CAM confirms that the model correctly identifies leak-relevant frequency bands, highlighting its ability to capture domain-invariant features. These results demonstrate the practicality and robustness of self-supervised one-class prototypical networks as an effective solution for real-world industrial anomaly detection tasks where fault data are unavailable.