In this paper, a comparative study of conventional LQR controller, optimized PID controller and intelligent Fuzzy logic controller is demonstrated while achieving the desired pitch axis trajectories of a 2-DOF helicop...
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The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practic...
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Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection...
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Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models...
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With the continuous development of computer network technology, artificial intelligence stands out. Although its technology is not mature enough, artificial intelligence is also a kind of human intelligence technology...
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In order to create spaces that are secure, cozy, energy-efficient, and visually beautiful, smart buildings are becoming more and more sophisticated cyber-physical systems. However, as functional complexity, financial ...
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Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligentcontrol algorithms, such as fuzzy logic, neural networks, gene...
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In this study, a thorough hierarchical control structure that supports autonomous decision-making that arises in autonomous systems and robots is proposed. Distinct state and decision/control sets are frequently used ...
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The research objects for evaluating the reliability of the real-time intelligent seismic processing system (RISP) in the Inner Mongolia seismic network were 31 earthquake events that occurred in the region within 30 d...
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The research objects for evaluating the reliability of the real-time intelligent seismic processing system (RISP) in the Inner Mongolia seismic network were 31 earthquake events that occurred in the region within 30 days before and after the Helingeer M-L 4.5 earthquake on March 30, 2020. The manual cataloging was compared to the output results of the real-time intelligent seismic processing system (automatic catalog). It was observed that the number of events identified by the RISP system was approximately 2.5 times that of the manual ones, with 30 automatic catalogs matching the manual catalogs (31). The event recall rate was as high as 96.8%. The automatic catalog had a small deviation from the manual catalog in terms of earthquake occurrence time, epicenter location, magnitude, and P-wave and S-wave phase arrival time. When the results of the automatic and manual catalogs are compared, it is evident that the cataloging of the same events in both catalogs is consistent, with the earthquake occurrence time and epicenter deviation usually settling within +/- 2 s and +/- 10 km, respectively. The automatic catalog meets the error range requirements of the manual catalog. The Real-time intelligent Seismic Processing System (RISP) produces data that meets expected goals and supports scientific research, such as rapid aftershock sequence production and earthquake swarm trend judgment post-earthquake.
As society advances, computer vision will play an increasingly crucial role in digital and intelligent transformations. Known as deep learning models, Convolutional Neural Networks (CNNs) have emerged as a key compone...
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As society advances, computer vision will play an increasingly crucial role in digital and intelligent transformations. Known as deep learning models, Convolutional Neural Networks (CNNs) have emerged as a key component of computer vision due to their superior performance in automatically detecting image features, handling high-dimensional data and performing large-scale classification tasks. This paper examines the development of CNNs, leveraging the strengths of current mainstream image recognition methods, and proposes a Self-Distillation and Attention-based Convolutional Neural Network (SDACNN) model to further enhance CNN accuracy. Experimental results demonstrate that the proposed model effectively accomplishes image recognition tasks.
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