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Energy Management Services based on Wireless Sensors and Home Gateway
Jongwoon Hwang,Jeangha Sea,Waong Hee Kim 한국IT서비스학회 2010 한국IT서비스학회 학술대회 논문집 Vol.2010 No.1
In this paper, we propose an energy monitoring and management service based on wireless sensors and a home gateway for homes and buildings. Homes and buildings have a significant energy saving potential compared with other sectors. Sensing, monitoring, and managing of the information on the energy consumption are required for an efficient energy saving service. The proposed system is composed of two main components, wireless sensor and an intelligent home gateway. Wireless sensors have the ZigBee communication interface for communication, and the intelligent home gateway is an energy portal. We expect that energy saving could be achieved with this system. As a further work, we will analyze the practical impact of the proposed service.
Visual Control of Automated COVID-19 Swab Sampling Robot
Guebin Hwang(황규빈),Jongwoon Lee(이종원),Sungwook Yang(양성욱) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
As coronavirus disease 2019 (COVID-19) has become a global pandemic, the initial screening of patients is regarded as the most effective way to prevent the spread of COVID-19. However, enormous swap sampling has led to the high workload of medical professionals as the number of infection has increased. Therefore, we propose a swab sampling robot systems in a fully automated fashion to address these issues. For automation of nasopharyngeal swab, the proposed robot system is designed to automatically insert a sampling swab through one nostril and collect sample via visual feedback. A prototype of the robot system incorporates a 6-DOF manipulator and a RGB-D sensor to investigate the feasibility of a visual-servo control scheme in automatic swab sampling. For accurate and safe operation, a deep learning-based nostril detection algorithm is adopted, which offers an accuracy of 99% at 60 FPS. Given the nostril position in 3D, the robot is planned to reach the target within a specific time. Since the kinematic control of the robot is prone to failure in reaching the target, we introduce the visual-servo control of the robot via the detection and tracking of error between the target position and the current swab tip. As a result, the visual-servo controlled robot could successfully reach the nostril target within an error of 1.11 mm on average for 30 trails trials different swabs, while the non-visual control with a 10.58-mm positioning error failed to reach the target.
Foreground Object Extraction from Stereo Images with Layer Quantization
Woong Hee Kim,Jongwoon Hwang 대한전자공학회 2010 ICEIC:International Conference on Electronics, Inf Vol.1 No.1
In this paper, we present a method of extracting foreground object from stereo images with layer quantization using the PSO (Particle Swarm Optimization) algorithm. Many foreground object extraction or background modeling methods use a statistical model like mixture Gaussian model. The parameters of it are usually adaptively updated for robustness. However, they need a series of images or image sequences for accurate modeling, and they have still some problems. With an additional information, disparity or depth, a foreground object could be extracted without any statistical background modeling in our approach.