Grad School Projects

갤러리 보기

Search

Abstract:

Automated Verification of Neural Network-Based Image Classifier for Stability Assurance in Graphics Rendering

We introduce a hybrid emotion recognition model that combines a pre-trained EfficientNet backbone with LSTM to capture the temporal dynamics of facial expressions. Unlike traditional static FER approaches, our model processes video sequences to classify valence and arousal into 21-level categories (from -10 to 10), enabling more accurate emotion estimation over time. The system is trained on the AFEW-VA dataset and outperforms baseline CNN-LSTM architectures, demonstrating the benefits of integrating spatio-temporal modeling in affective computing.

Results

 Compared to ResNet-50 baseline, our model improved the CCC score by over 60% (Valence: 0.31 → 0.97)

 EfficientNetLSTM achieved superior spatio-temporal representation despite smaller batch sizes and longer training time (7 hrs vs. 6 hrs)

Sequence-Based Facial Emotion Recognition using EfficientNet and LSTM

Compressible Flow Simulation using FEniCS

I implemented a simulation of compressible fluid flow using the finite element framework FEniCS. The governing equations include the full Navier–Stokes equations, accounting for variable density, momentum conservation, and thermal energy transport:

Mass conservation: ∂ρ/∂t + ∇·(ρu) = 0

Momentum equation includes both viscous and pressure forces with variable density

Energy conservation: accounts for heat convection and conduction

I derived and implemented the weak forms of these equations to suit finite element analysis.

During simulation, we encountered numerical instability, particularly in the advection term. To address this, we plan to incorporate SUPG (Streamline Upwind Petrov-Galerkin) stabilization, which mitigates oscillations and enhances robustness in advection-dominated flows.

This approach is expected to improve both convergence and physical realism of the compressible flow model.

Compressible Flow Implementation with FeniCS

Diffusion Model

GAN

Regression

Machine Learning Algorithms Implementation

Texture Volume Rendering with GLSL

This project implements GPU-based volume rendering using GLSL. Rays are cast through a 3D texture volume, with AABB (Axis-Aligned Bounding Box) intersection used to compute entry and exit points for ray traversal. The renderer supports alpha blending for accumulating semi-transparent voxel colors and isocontouring for visualizing surfaces of constant intensity. The combined use of ray marching and transfer functions allows for flexible and efficient real-time rendering of volumetric datasets.

Volume Rendering using GLSL