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Bo-Yeong Kang
School of Mechanical Engineering, Kyungpook National University, Daegu, Korea

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Journal article
Published: 30 November 2020 in Journal of Korea Robotics Society
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ACS Style

Yewon Kim; Bo-Yeong Kang. Cooperative Robot for Table Balancing Using Q-learning. Journal of Korea Robotics Society 2020, 15, 404 -412.

AMA Style

Yewon Kim, Bo-Yeong Kang. Cooperative Robot for Table Balancing Using Q-learning. Journal of Korea Robotics Society. 2020; 15 (4):404-412.

Chicago/Turabian Style

Yewon Kim; Bo-Yeong Kang. 2020. "Cooperative Robot for Table Balancing Using Q-learning." Journal of Korea Robotics Society 15, no. 4: 404-412.

Journal article
Published: 14 July 2020 in IEEE Access
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Sleepiness detection system that evaluates driver’s sleepiness level has always been the primary interest of researchers. There are a number of systems like electroencephalography-based sleepiness detection system (ESDS), vehicle based sleepiness estimator system, image acquisition technology and bio-mathematical models to detect drowsiness of drivers. However there has been less research on hybrid of these systems that detect sleepiness of drivers. In order to overcome the above limitation we propose a neural network based hybrid multimodal system that detects driver fatigue using electroencephalography(EEG) data, gyroscope data and image processing data. It was found that the proposed hybrid system performed well with a detection accuracy of 93.91% in identifying the drowsiness state of the driver.

ACS Style

Naveen Senniappan Karuppusamy; Bo-Yeong Kang. Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing. IEEE Access 2020, 8, 129645 -129667.

AMA Style

Naveen Senniappan Karuppusamy, Bo-Yeong Kang. Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing. IEEE Access. 2020; 8 (99):129645-129667.

Chicago/Turabian Style

Naveen Senniappan Karuppusamy; Bo-Yeong Kang. 2020. "Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing." IEEE Access 8, no. 99: 129645-129667.

Review article
Published: 28 November 2019 in Intelligent Service Robotics
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Human–robot interaction was always based on estimation of human emotions from human facial expressions, voice and gestures. Human emotions were always categorized in a discretized manner, while we estimate facial images from common datasets for continuous emotions. Linear regression was used in this study which numerically quantizes human emotions as valence and arousal by displaying the raw images on the two-respective coordinate axis. The face image datasets from the Japanese female facial expression (JAFFE) dataset and the extended Cohn–Kanade (CK+) dataset were used in this experiment. Human emotions for the above-mentioned datasets were interpreted by 85 participants who were used in the experimentation. The best result from a series of experiments shows that the minimum of root mean square error for the JAFFE dataset was 0.1661 for valence and 0.1379 for arousal. The proposed method has been compared with previous methods such as songs, sentences, and it is observed that the proposed method for common datasets testing showed an outstanding emotion estimation performance.

ACS Style

Hyun-Soon Lee; Bo-Yeong Kang. Continuous emotion estimation of facial expressions on JAFFE and CK+ datasets for human–robot interaction. Intelligent Service Robotics 2019, 13, 15 -27.

AMA Style

Hyun-Soon Lee, Bo-Yeong Kang. Continuous emotion estimation of facial expressions on JAFFE and CK+ datasets for human–robot interaction. Intelligent Service Robotics. 2019; 13 (1):15-27.

Chicago/Turabian Style

Hyun-Soon Lee; Bo-Yeong Kang. 2019. "Continuous emotion estimation of facial expressions on JAFFE and CK+ datasets for human–robot interaction." Intelligent Service Robotics 13, no. 1: 15-27.

Journal article
Published: 10 October 2018 in Sensors
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This paper proposes a system for estimating the level of danger when a driver accesses the center console of a vehicle while driving. The proposed system uses a driver monitoring platform to measure the distance between the driver’s hand and the center console during driving, as well as the time taken for the driver to access the center console. Three infrared sensors on the center console are used to detect the movement of the driver’s hand. These sensors are installed in three locations: the air conditioner or heater (temperature control) button, wind direction control button, and wind intensity control button. A driver’s danger level is estimated to be based on a linear regression analysis of the distance and time of movement between the driver’s hand and the center console, as measured in the proposed scenarios. In the experimental results of the proposed scenarios, the root mean square error of driver H using distance and time of movement between the driver’s hand and the center console is 0.0043, which indicates the best estimation of a driver’s danger level.

ACS Style

Hyun-Soon Lee; Sunyoung Oh; DaeSeong Jo; Bo-Yeong Kang. Estimation of Driver’s Danger Level when Accessing the Center Console for Safe Driving. Sensors 2018, 18, 3392 .

AMA Style

Hyun-Soon Lee, Sunyoung Oh, DaeSeong Jo, Bo-Yeong Kang. Estimation of Driver’s Danger Level when Accessing the Center Console for Safe Driving. Sensors. 2018; 18 (10):3392.

Chicago/Turabian Style

Hyun-Soon Lee; Sunyoung Oh; DaeSeong Jo; Bo-Yeong Kang. 2018. "Estimation of Driver’s Danger Level when Accessing the Center Console for Safe Driving." Sensors 18, no. 10: 3392.