AICLab
AI Convergence Lab | Chungbuk National University
Dream. Excellence. Teamwork.
Welcome to AICLab!
We are excited to embark on this journey of innovation and discovery in Applied AI and Computer Vision. Our lab brings together passionate researchers from diverse backgrounds, united by a common vision to create impactful solutions that bridge academic excellence with industry needs.
— Professor KIM Tae-Kyung
Principal Investigator, AICLab
Mission & Vision
Mission: AICLab was established with the mission of pursuing applied AI and computer vision for industry and academic purposes.
Vision: We aim to be the trusted problem solver for industry in terms of Applied AI and computer vision initiatives and solutions to improve pain points and operational efficiency. Together with our partners and clients, we strive to achieve excellence in our work.
To set our target as broad as the peak of the mountain. Pursue our dream while maintaining excellence in our work, and by collaborating with our highly talented team members who share the same vision and mission.
Latest News
Stay Updated with Our Recent Achievements and Milestones
Team Building Activities : Hiking at Goesan Trail
Our team went on a day trip of hiking at the Goesan trial to build our stamina, train our mind and bodies, and reenvision our dream and commitment towardds excellence.
A meeting with Cambodian Delegates from MPTC
AICLab met with Head of Cambodian Delegates from MPTC to re-emphasize our partnership and collaboration between CBNU-MPTC and Korea-Cambodia towards AI-driven digital transformation in Cambodia.
Team Building Activities : a trip to Mungyeong
Our team went on our first trip together to Mungyeong to collectively and collaboratively set our goals, enjoy the food and nature, and refocus before the starting of the Fall semester at CBNU.
Paper Published in PLOS ONE
Successfully published "PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos" in PLOS ONE journal
Lab Officially Established
AICLab officially launched at Chungbuk National University with a mission to pursue applied AI and computer vision research
Research Team
Talented Minds Pursuing Excellence Together
Big Data | AI | Software Education
WSN | Swarm Intelligence | IoT
Computer Vision, LLM, VLM
Data Analytics, LLM
Big Data
Computer Vision, LLM
Healthcare AI, Computer Vision
LLM, Cybersecurity
Publications
Building the Future of Applied AI
PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos
PLOSOne - View Paper
IntelliScrape: Automated Web Scraping via a Closed-Loop LLM Guided by Observation and Replay
BIGDAS Conference - View Conference
A Novel Hybrid Rule-based aggregation System for Detecting Abnormal Anomalies in Welfare Subsidy Transaction Operating in Real-time Scenarios
BIGDAS Conference - View Conference
AI-Scientist Web-Based System for Streamlined Scientific Paper Generation
BIGDAS Conference - View Conference
Research Projects
Bridging AI Research with Real-World Impact
Arm Robot for Bin-Picking in Unstructured Environments
The Project aims to develop an intelligent bin-picking system that enables robots to detect, localize, and grasp objects in cluttered, unstructured environments. By combining advanced vision techniques such as instance segmentation, graph-transformer reasoning, depth refinement, shape completion, and 6D pose estimation, the project seeks to overcome challenges like occlusion and sensor noise.
IntelliScrape: An AI-assisted Web Scraping Module Generator
The project aims to develop an AI-assisted module development system that partially automated COOCON’s scraping module creation process. Leveraging artificial intelligence, network traffic analysis, and browser automation technologies, the proposed system aims to reduce manual effort, minimize human error, and accelerate the overall development lifecycle of COOCON developer.
Fall Detection Systems
The project aims to develop a real-time fall detection system using YOLO for high-speed human pose estimation to extract skeletal keypoints from video streams. Through feature engineering, it computes a dozen biomechanical angles capturing body orientation and velocity, which are then fed into a classification-based model to accurately distinguish intentional movements from sudden, unintentional falls.
Member's Activities
Building Excellence Through Collaboration and Learning