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