When the aircraft is moving at high speed in the atmosphere,aero-optical imaging deviation will appear due to the influence of aero-optical *** order to achieve real-time compensation during the flight of the aircraft...
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When the aircraft is moving at high speed in the atmosphere,aero-optical imaging deviation will appear due to the influence of aero-optical *** order to achieve real-time compensation during the flight of the aircraft,it is necessary to analyze and predict the obtained imaging deviation *** order to improve the search speed and accuracy of the prediction algorithm and the ability to jump out of local optimum,in this paper,an improved sparrow search algorithm optimized extreme learning machine(ISSA-ELM) neural network model is proposed to predict the aero-optical imagine ***,the performance of ISSA-ELM,ELM neural network and SSA-ELM neural network was *** results showed that compared with ELM and SSA-ELM algorithms,the convergence speed of ISSA-ELM was significantly enhanced,and the accuracy of data prediction was also significantly improved.
Spandex is a widely used material in many industrial applications. However, the production of spandex products is often affected by the occurrence of defects during the manufacturing process, which may lead to product...
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This study explores the combination of unmanned aerial vehicle (UAV) and ground monitoring for real-time carbon footprint mapping technology in low-carbon smart parks. Addressing the conflict between sampling rate and...
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An adaptive fuzzy closed-loop control method using linear feedback technology was proposed to inhibit epileptiform activity simulated by a neural mass model. In this study, the problem of epileptic suppression was con...
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Emotion recognition is an important research directions in the field of artificial intelligence. In this paper, we adopted deep learning methods to try to improve the performance of emotion recognition. First, we desi...
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Stable control of the iron-making process through the adjustment of operating parameters is essential to improve blast furnace productivity. However, due to the complex and dynamic nature of the reaction process, it i...
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Fatigue driving is a major contributor to traffic accidents, as it reduces alertness and can even be fatal. To investigate alertness changes during prolonged driving, we first built a simulated driving experiment plat...
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Emotions are an essential part of human physiological performance. Therefore, the study of emotion recognition is extremely relevant in both practical applications and theoretical research. Based on the electroencepha...
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Accidental falls are one of the major threats to the elderly population. Older adults who are not caught in time after a fall may miss the best time to be rescued. We propose an improved YOLOv5 fall detection algorith...
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This paper aims to solve an optimal tracking control(OTC) problem of large-scale systems with multitime scales and coupled subsystems using singular perturbation(SP) theory and reinforcement learning(RL) techniques. A...
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This paper aims to solve an optimal tracking control(OTC) problem of large-scale systems with multitime scales and coupled subsystems using singular perturbation(SP) theory and reinforcement learning(RL) techniques. A considerable contribution of this paper is the development of a data-driven SP-based RL method for the OTC of unknown large-scale systems with multitime scales. To achieve this, a multitime scale tracking problem was decomposed into a linear quadratic tracker problem for slow subsystems and a dynamical game problem for fast subsystems using the SP theory. Then, the distributed composite feedback controllers were found using a distributed off-policy integral RL algorithm that uses only measured data from the system in real time. Thus, the operational index can follow its prescribed target value via an approximately optimal approach. Theoretical analysis and proof are presented to demonstrate that the sum of the performances of reduced-order subsystems is approximately equal to the performance of the original large-scale system. Finally, numerical and practical examples are provided to validate the effectiveness of the proposed method.
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