Completed Project

연구과제 목록 [완료]

1. [코난테크놀로지], Advanced Domain Adaptation 솔루션 개발, 2023/12/18 ~ 2025/01/17 (종료)

2. [티라유텍], 패션 드레이핑 자동화를 위한 의복 패턴 매칭 알고리즘 개발, 2024/01/29 ~ 2024/06/30 (종료)

3. [SK Hynix], CD-SEM 장비 관리 고도화를 위한 Image 기반 HW Reponse Para 발굴, 2023/04/24 ~ 2024/04/23 (종료)

4. [LG에너지솔루션], 도메인적응을 통한 테스트베드 모델의 실제 설비 적용, 2023/04/01 ~ 2023/08/31 (종료)

Explainable Deep Learning on Multimodal Data for Boiling Crisis in Nuclear Reactors

1. Leverage multimodal data to ensure the model robustness and reliability. We are developing a heterogeneous data fusion technique to utilize the joint power of data from multi-modality

2. Extract hidden physics from modality-dependent features

Building Fault Detection Baseline Construction

1. Construct baselines by sampling from the multiple sensor readings collected from real building systems for fault test cases

2. A model-free decision metric, relying on the data to extract information on sample sufficiency and characterize heterogeneity of a dataset

Imaging-based Diagnosis

1. Feature Transfer Enabled Multi-Task Deep Learning Model offers 1) a fully automatic system handling detection, segmentation, and classification, 2) cross-view features transferring for improved model performance, and 3) a lower risk of negative transferring issues (transfer only within the same domain) 

2. Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis generates synthetic images by utilizing knowledge transfer between image modalities 

Sensor-based patient diagnosis for fall risk using a 10-meter walking test

Predicting fall risks among community-dwelling older adults using linear and nonlinear gait variability features and random decision forest framework

Smartphone-based Telemonitoring of Parkinson's Disease Patients

1. Developing the telemonitoring framework to leverage other patient's beneficial information (avoiding negative transfer) when building a predictive model for a target patient

2. Predicting severity of Parkinson's Disease from smartphone-based features using feature + instance selection in a semi-supervised regression

Exploring underlying multivariate characteristics of two high-dimensional datasets

Discovering characteristics of Post-Treatment Recurrent High-Grade Gliomas 1) identifies distinct gene clusters corresponding to the immunohistochemical stains and 2) maximizes multivariate correlation between two datasets (imaging and genetics)

Knowledge discovery in neuroimaging data fusion for migraine diagnosis

1. Identify changes in brain function and structure that correlate with the response to erenumab (medication)

2. Develop a domain adaptation technique that tackles the challenge of discrepancy in imaging parameters across different cohorts of patients, which causes significant difficulty in building a unified imaging-based diagnostic model