Jaekwang Kim

Hyo-Jin Kim, Jaekwang Kim, Hyung-Jun Park

In this study, we propose a graph neural network (GNN) model for efficiently predicting the flow behavior of non-Newtonian fluids with free surface dynamics. The numerical analysis of non-Newtonian fluids presents significant challenges, as traditional algorithms designed for Newtonian fluids with constant viscosity often struggle to converge when applied to non-Newtonian cases, where rheological properties vary dynamically with flow conditions. Among these, power-law fluids exhibit viscosity that decreases exponentially as the shear rate increases, making numerical simulations particularly difficult. The complexity further escalates in free surface flow scenarios, where computational challenges intensify. In such cases, particle-based methods like smoothed particle hydrodynamics (SPH) provide advantages over traditional grid-based techniques, such as the finite element method (FEM). Building on this approach, we introduce a novel GNN-based numerical model to enhance the computational efficiency of non-Newtonian power-law fluid flow simulations. Our model is trained on SPH simulation data, learning the effects of particle accelerations in the presence of SPH interactions based on the fluid's power-law parameters. The GNN significantly accelerates computations while maintaining reliable accuracy in benchmark tests, including dam-break and droplet impact simulations. The results underscore the potential of GNN-based simulation frameworks for efficiently modeling non-Newtonian fluid behavior, paving the way for future advancements in data-driven fluid simulations.

Minji Kang, Seongho Kim, Eunseo Go et al.

Precise monitoring of etch depth and the thickness of insulating materials, such as Silicon dioxide and silicon nitride, is critical to ensuring device performance and yield in semiconductor manufacturing. While conventional ex-situ analysis methods are accurate, they are constrained by time delays and contamination risks. To address these limitations, this study proposes a non-contact, in-situ etch depth prediction framework based on machine learning (ML) techniques. Two scenarios are explored. In the first scenario, an artificial neural network (ANN) is trained to predict average etch depth from process parameters, achieving a significantly lower mean squared error (MSE) compared to a linear baseline model. The approach is then extended to incorporate variability from repeated measurements using a Bayesian Neural Network (BNN) to capture both aleatoric and epistemic uncertainty. Coverage analysis confirms the BNN's capability to provide reliable uncertainty estimates. In the second scenario, we demonstrate the feasibility of using RGB data from digital image colorimetry (DIC) as input for etch depth prediction, achieving strong performance even in the absence of explicit process parameters. These results suggest that the integration of DIC and ML offers a viable, cost-effective alternative for real-time, in-situ, and non-invasive monitoring in plasma etching processes, contributing to enhanced process stability, and manufacturing efficiency.

Donggeun Ko, Dongjun Lee, Namjun Park et al.

Neural networks struggle with image classification when biases are learned and misleads correlations, affecting their generalization and performance. Previous methods require attribute labels (e.g. background, color) or utilizes Generative Adversarial Networks (GANs) to mitigate biases. We introduce DiffuBias, a novel pipeline for text-to-image generation that enhances classifier robustness by generating bias-conflict samples, without requiring training during the generation phase. Utilizing pretrained diffusion and image captioning models, DiffuBias generates images that challenge the biases of classifiers, using the top-$K$ losses from a biased classifier ($f_B$) to create more representative data samples. This method not only debiases effectively but also boosts classifier generalization capabilities. To the best of our knowledge, DiffuBias is the first approach leveraging a stable diffusion model to generate bias-conflict samples in debiasing tasks. Our comprehensive experimental evaluations demonstrate that DiffuBias achieves state-of-the-art performance on benchmark datasets. We also conduct a comparative analysis of various generative models in terms of carbon emissions and energy consumption to highlight the significance of computational efficiency.

Donggeun Ko, Sangwoo Jo, Dongjun Lee et al.

Dataset bias is a significant challenge in machine learning, where specific attributes, such as texture or color of the images are unintentionally learned resulting in detrimental performance. To address this, previous efforts have focused on debiasing models either by developing novel debiasing algorithms or by generating synthetic data to mitigate the prevalent dataset biases. However, generative approaches to date have largely relied on using bias-specific samples from the dataset, which are typically too scarce. In this work, we propose, DiffInject, a straightforward yet powerful method to augment synthetic bias-conflict samples using a pretrained diffusion model. This approach significantly advances the use of diffusion models for debiasing purposes by manipulating the latent space. Our framework does not require any explicit knowledge of the bias types or labelling, making it a fully unsupervised setting for debiasing. Our methodology demonstrates substantial result in effectively reducing dataset bias.

Jiun Lee, Jaekwang Kim, Inyong Yun

In this paper, we propose Edge Profile Super Resolution(EPSR) method to preserve structure information and to restore texture. We make EPSR by stacking modified Fractal Residual Network(mFRN) structures hierarchically and repeatedly. mFRN is made up of lots of Residual Edge Profile Blocks(REPBs) consisting of three different modules such as Residual Efficient Channel Attention Block(RECAB) module, Edge Profile(EP) module, and Context Network(CN) module. RECAB produces more informative features with high frequency components. From the feature, EP module produce structure informed features by generating edge profile itself. Finally, CN module captures details by exploiting high frequency information such as texture and structure with proper sharpness. As repeating the procedure in mFRN structure, our EPSR could extract high-fidelity features and thus it prevents texture loss and preserves structure with appropriate sharpness. Experimental results present that our EPSR achieves competitive performance against state-of-the-art methods in PSNR and SSIM evaluation metrics as well as visual results.

Hyunsung Lee, Yeongjae Jang, Jaekwang Kim et al.

A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K items with high scores. While sorting and ranking items are integral for this recommendation procedure, it is nontrivial to incorporate them in the process of end-to-end model training since sorting is nondifferentiable and hard to optimize with gradient descent. This incurs the inconsistency issue between existing learning objectives and ranking metrics of recommenders. In this work, we present DRM (differentiable ranking metric) that mitigates the inconsistency and improves recommendation performance by employing the differentiable relaxation of ranking metrics. Via experiments with several real-world datasets, we demonstrate that the joint learning of the DRM objective upon existing factor based recommenders significantly improves the quality of recommendations, in comparison with other state-of-the-art recommendation methods.