Real-Time Tracking Algorithm for Aerial Vehicles Using Improved Convolutional Neural Network and Transfer Learning

Li, Xiaofeng; Jin, Wei; Jiao, Hongshuang

doi:10.1109/tits.2021.3072872

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…If a person makes a wrong decision, then he can reduce the next mistakes through continuous learning, and the neural network is a learning method that simulates people. When the network judges wrong, it can reduce the next mistakes through continuous learning [15][16][17]. To study the nonlinear dynamic properties of neural network, the dynamic system theory, nonlinear programming theory, and statistical theory are mainly used.…”

Section: Neural Network Structure and Fitness Functionmentioning

confidence: 99%

[Retracted] Influence of Sports Biomechanics on Martial Arts Sports and Comprehensive Neuromuscular Control under the Background of Artificial Intelligence

Zhang

et al. 2022

Contrast Media & Molecular Imaging

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Neuromuscular control refers to the reflexes of nerves that affect muscle balance and function. In addition, there are interactions between joint structure, muscle function, and the central nervous system. In the integration with other intelligent control methods and optimization algorithms, such as fuzzy control/expert verification and genetic algorithm, it provides nonparametric object models, optimization parameters, reasoning models, and fault diagnosis. The central nervous system is the main research object of neuromuscular control. Martial arts often cause injuries or affect the progress of martial arts because of some irregular movements. Chinese traditional martial arts is another name for “martial arts” in the late Qing Dynasty in China. It is mainly reflected in the individual’s application and attainments in martial arts traditional teaching methods and personal cultivation. Therefore, this paper proposes an analysis of the influence of sports biomechanics on martial arts sports and comprehensive neuromuscular control in the context of artificial intelligence. In this paper, the specific research of Wushu sports is carried out mainly in two aspects: sports biomechanics and neuromuscular control. It uses a variety of algorithms, successively using particle swarm algorithm, neural network structure, fitness function, and so on. This paper compares and analyzes their accuracy and then selects the optimal algorithm. It then conducts experimental research on the martial arts movements of professional martial arts Sanda players. The final experimental conclusion shows that, regarding lower limb selective response time and the middle left lower limb prereaction time (L-PMT) of the elite athlete group and the ordinary athlete group, the average movement value of the elite group of 2.336 is significantly greater than that of the ordinary group of 1.938. This shows that, within a certain range, the larger the knee angle and the smaller the hip angle, the stronger the ability to buffer the impact of the ground, without causing greater damage to the muscles and joints.

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Section: Neural Network Structure and Fitness Functionmentioning

confidence: 99%

[Retracted] Influence of Sports Biomechanics on Martial Arts Sports and Comprehensive Neuromuscular Control under the Background of Artificial Intelligence

Zhang

et al. 2022

Contrast Media & Molecular Imaging

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“…is sudden language change plays a subtle and irreplaceable role while shaping and shaping the characters [23,24]. Due to differences in thinking and culture, English and Chinese language expressions are sometimes untranslatable.…”

Section: Corpus Experiments On Code-switching In Novelsmentioning

confidence: 99%

Cognitive Feature Extraction of Puns Code-Switching Based on Neural Network Optimization Algorithm

Zhang

Liao

2022

International Transactions on Electrical Energy Systems

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Code-switching is the choice of a language, a variant of using multiple languages in the same conversation. Broadly speaking, code-switching refers to adjusting one’s language style, appearance, behavior, and expression in order to improve the comfort of others in exchange for fair treatment, quality service, and employment opportunities. “Besieged City” is considered a masterpiece of 20th-century China. From the data point of view, this work has a total of 110 code shifts, but there are many studies on this language phenomenon, but none of them involve the perspective of register. However, language translation research based on register theory is of great significance. It is generally believed that the human brain’s thinking is divided into three basic ways: abstract (logical) thinking, image (intuitive) thinking, and inspiration (awareness) thinking. Artificial neural networks are the second way to simulate human thinking. Therefore, this paper proposes research on cognitive feature extraction of pun code-switching based on a neural network optimization algorithm. It mainly introduces code-switching under cognitive language and also briefly analyzes code-switching and speech feature extraction and uses a neural network optimization algorithm to conduct an in-depth analysis of code-switching. Finally, in the experimental part, the experimental analysis of the famous novel “Besieged City” is carried out, the application of 89 language code-switching in the text is deeply analyzed, and the data analysis of its three variables is carried out from the perspective of register. The experimental results show that: in novels, there are two types of code-switching: preparation and improvisation. 24 code-switches are prepared, accounting for 26.9%, and 10 code-switches are improvisation, accounting for 8.9%. As for the verbal code-change, there are both preparatory and impromptu ones. 36 code-switching cases were improvised, accounting for 40.4%, and 19 code-switching cases were prepared, accounting for 21.3%. The analysis also confirms that the more formal the text, the less linguistic transformation it contains.

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“…This can be achieved by duplicating samples from rare classes, generating synthetic samples, or employing other methods. RCS helps improve the model's performance on imbalanced datasets, reducing classification errors for rare classes, and thus better adapting to real-world applications [14].…”

Section: Rare Class Samplingmentioning

confidence: 99%

Road Scene Semantic Segmentation Based on Deep Learning

2023

IJFET

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This study aims to address the problem of semantic segmentation in complex road scenes, which has significant applications in fields such as autonomous driving, traffic monitoring, and urban planning. The methods investigated in our research primarily include key steps such as data collection, preprocessing, and annotation. We employ CNN models for data augmentation and introduce the DAFormer semantic segmentation algorithm. In the end, this paper proposes an enhanced DAFormer network architecture, incorporating techniques such as rare class sampling, Object Category ImageNet Feature Distance (FD), and learning rate warm-up. The application of these techniques enables DAFormer to better understand image content in complex road scenarios, providing a powerful tool to tackle real-world challenges. We evaluate its performance in this challenging task by comparing it with four traditional algorithms. Experimental results demonstrate a significant performance improvement in the enhanced DAFormer algorithm in complex road environments, achieving an average intersection over union (MIoU) of 0.82, pixel accuracy (PA) of up to 89%, and improved timeliness. Compared to other algorithms, the enhanced DAFormer exhibits superior performance in terms of accuracy, stability, and timeliness.

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Real-Time Tracking Algorithm for Aerial Vehicles Using Improved Convolutional Neural Network and Transfer Learning

Cited by 11 publications

References 33 publications

[Retracted] Influence of Sports Biomechanics on Martial Arts Sports and Comprehensive Neuromuscular Control under the Background of Artificial Intelligence

[Retracted] Influence of Sports Biomechanics on Martial Arts Sports and Comprehensive Neuromuscular Control under the Background of Artificial Intelligence

Cognitive Feature Extraction of Puns Code-Switching Based on Neural Network Optimization Algorithm

Road Scene Semantic Segmentation Based on Deep Learning

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