KSB Convergence System Research Section
KSB Convergence System Research Section is performing the KSB Convergence Research Department's tasks, establishing strategies for utilization and deploy, and performing system engineering roles, including R&D process and quality control, including defining system requirements. In addition, we have developed domain-specific machine learning/deep learning models and application services for energy and health areas with the goal of providing domain knowledge convergence intelligence services for solving national and social issues and presenting effectiveness.
Major R&D technologies include the Autonomous distributed building management system technology to analyze the patterns of occupant and energy consumption by area through machine learning/deep learning, to autonomously optimize the occupant environment of heating and lighting, etc., and to predict the energy management and demand according to the situation in each area, and also the IoE based health(stroke) monitoring system technology for elderly to enable them to detect stroke risk and emergency situations in advance and respond quickly through machine learning/deep learning and medical knowledge-base, based on monitoring vital signal with wearable devices during their daily lives, such as walking, driving and sleeping. In addition we are researching the IoE intelligent context aware and collaboration technology to minimize human intervention by recognizing and cooperating with the surrounding situation.
⬥ Main Research Areas
- Development of autonomous distributed energy management and health monitoring artificial intelligence services for the elderly
- Building Energy Data Collection, Preprocessing, Re-occupancy Prediction, Energy Demand Prediction and Optimal Control Machine Learning/Deep Learning Model Development
- Develop the elderly bio-signal collection, Preprocessing, stroke pre-detection machine learning/deep learning models and build stroke knowledge bases;
- Develop IoT Intelligent cognitive/cooperative technology, supervising tasks and establishing strategies for diffusion
Director KIM, Nae Soo