Research
TEAMLAB pursues AI-driven digital transformation across three interconnected domains.
조선해양 분야의 Digital Transformation
Shipbuilding & Maritime Digital Transformation
We apply AI and digital twin technologies to transform shipyard operations. Our work includes predictive maintenance, demand forecasting for maintenance vehicles, and smart manufacturing systems.
Selected Publications
- •SYnet: 4D CNN-Based Maintenance Lift Vehicle Demand Prediction (IEMS 2024)
- •Machine learning for forklift vehicles with irregular movement in a shipyard (Computers in Industry 2021)
Digital TwinSmart ManufacturingPredictive MaintenanceCNNSimulation
AI in Patent
Patent Intelligence & Analysis
We develop NLP and graph-based deep learning methods for patent analysis — including patent landscaping, document clustering, citation recommendation, and text classification.
Selected Publications
- •Deep learning for patent landscaping using transformer and graph embedding (TFSC 2022)
- •Two-stage deep learning system for patent citation recommendation (Scientometrics 2022)
- •Patent document clustering with deep embeddings (Scientometrics 2020)
NLPPatent LandscapingGraph EmbeddingsTransformersCitation Analysis
AI in Education
Educational AI & Assessment
We research automated scoring systems, spelling correction, and learning analytics using NLP for educational assessment across Korean and multilingual contexts.
Selected Publications
- •Spelling Errors in Korean Students and Efficacy of Automatic Spelling Correction (TKL 2021)
- •WA3I 프로젝트: 학습 지원 도구로서의 서술형 평가와 인공지능 (현장과학교육 2019)
NLPAutomated ScoringSpelling CorrectionLearning Analytics
연구 방법론
Research Methodologies
ML/DL AI Approach
TransformersCapsule Networks4D CNNGNNDiffusion ModelsLLMs
Digital Twin
Real-time MonitoringProcess SimulationPredictive Analytics
Simulation
Demand ForecastingScenario AnalysisProcess Optimization