L
Ling Guan
Researcher at Ryerson University
Publications - 445
Citations - 6719
Ling Guan is an academic researcher from Ryerson University. The author has contributed to research in topics: Feature extraction & Image retrieval. The author has an hindex of 35, co-authored 438 publications receiving 5952 citations. Previous affiliations of Ling Guan include University of British Columbia & Zhengzhou University.
Papers
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Optimal Scheduling for Charging and Discharging of Electric Vehicles
TL;DR: A globally optimal scheduling Scheme and a locally optimal scheduling scheme for EV charging and discharging which is not only scalable to a large EV population but also resilient to the dynamic EV arrivals are proposed.
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A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films
Songyang Yu,Ling Guan +1 more
TL;DR: A computer-aided diagnosis (CAD) system for the automatic detection of clustered microcalcifications in digitized mammograms gives quite satisfactory detection performance.
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Recognizing Human Emotional State From Audiovisual Signals
Yongjin Wang,Ling Guan +1 more
TL;DR: A novel multiclassifier scheme is proposed to boost the recognition performance of human emotional state from audiovisual signals based on a comparative study of different classification algorithms and specific characteristics of individual emotion.
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Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues
TL;DR: A comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles examines the theory underlying self-driving vehicles from deep learning perspective and current implementations, followed by their critical evaluations.
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Appliance Scheduling Optimization in Smart Home Networks
F. A. Qayyum,Muhammad Naeem,Ahmed Shaharyar Khwaja,Alagan Anpalagan,Ling Guan,Bala Venkatesh +5 more
TL;DR: An optimization algorithm, which can provide a schedule for smart home appliance usage, is proposed based on the mixed-integer programming technique and shows that adding a PV system in the home results in the reduction of electricity bills and the export of energy to the national grid in times when solar energy production is more than the demand of the home.