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Summary: The study proposed an integrated real-time detect-track method for monitoring the bolt rotation angle, achieving over 90% accuracy by combining an object detector and an optical flow tracking algorithm. Extensive parameter studies were conducted to enhance tracking performance against background noise and illumination changes, revealing the potential of the RTDT-bolt method for real-world applications.
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Summary: This study has developed a new framework that allows robots to intelligently recognize object surface materials and adapt their disinfection methods accordingly. By utilizing a deep learning network to classify materials on contaminated surfaces, the appropriate disinfection modes and parameters can be chosen based on the computed infection risk. The proposed method achieved high accuracy in experiments and successfully implemented adaptive robotic disinfection in healthcare facilities.
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Computer Science, Artificial Intelligence
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Summary: This paper proposes an improved mesh subdivision method that coordinates the gap between smoothness and the number of faces by introducing a variable threshold and a new crack-solving method. Extensive experiments demonstrate that the proposed method consistently outperforms existing mesh subdivision methods in different settings.
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Article
Computer Science, Artificial Intelligence
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Summary: This paper surveys the state-of-the-art techniques in action recognition and prediction, covering existing models, popular algorithms, technical difficulties, popular action databases, evaluation protocols, and promising future directions.
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Summary: This study developed an instrumentation based on low-cost cameras and modern machine learning techniques for vehicle recognition and counting, which could be integrated with existing ITS for updating noise maps and action plans. By evaluating the acoustic impact of ITS installation in road traffic management, it was confirmed that ITS system can effectively reduce noise impact on citizens.
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Computer Science, Interdisciplinary Applications
Allen A. Zhang et al.
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Computer Science, Artificial Intelligence
Lu Wang et al.
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Computer Science, Artificial Intelligence
Jahongir Azimjonov et al.
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Summary: Computer vision has emerged as a key technology for applications like traffic surveillance, particularly in detecting anomalies in public places. However, there is a lack of systematic survey on vision-guided anomaly detection techniques. This study aims to address the gap by analyzing various methods and providing future directions for research in this area.
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Computer Science, Artificial Intelligence
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INTEGRATED COMPUTER-AIDED ENGINEERING
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Computer Science, Interdisciplinary Applications
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INTEGRATED COMPUTER-AIDED ENGINEERING
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Jie Shen et al.
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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
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Summary: The study utilized deep learning and transfer learning methods to detect epileptic seizures using EEG signals, achieving 100% accuracy without requiring additional feature extraction steps. This automatic identification and classification model can aid in early diagnosis of epilepsy, providing effective early treatment opportunities.
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(2020)
Review
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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Proceedings Paper
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ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II
(2017)
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IET INTELLIGENT TRANSPORT SYSTEMS
(2015)
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Stefan Atev et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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Claudio Piciarelli et al.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2008)
