Deep Reinforcement Learning for Communication Flow Control in Wireless Mesh Networks

Liu, Qingzhi; Cheng, Long; Jia, Adele Lu; Liu, Cong

doi:10.1109/mnet.011.2000303

Cited by 70 publications

(42 citation statements)

References 14 publications

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“…ey designed a classifier based on a self-associated neural network to classify sports videos. Based on the combination of SVM and neural networks [19][20][21][22][23], the classification accuracy is high. However, the algorithm complexity of the joint classifier is high.…”

Section: Related Workmentioning

confidence: 99%

AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification

Cai

Liu

2021

Journal of Healthcare Engineering

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As a hot research topic, sports video classification research has a wide range of applications in switched TV, video on demand, smart TV, and other fields and is closely related to people’s lives. Under this background, sports video classification research has aroused great interest in people. However, the existing methods usually use manual video classification, which the workers themselves often influence. It is challenging to ensure the accuracy of the results, leading to the wrong classification. Due to these limitations, we introduce neural network technology to the automatic classification of sports. This paper proposed a novel attention-based graph convolution-guided third-order hourglass network (AGTH-Net) classification model. First, we designed a kind of figure convolution model based on the attention mechanism. The model is the key to introduce the attention mechanism for neighborhood node weights’ allocation. It reduces the impact of error nodes in the neighborhood while avoiding manual weight assignment. Second, according to the sports complex video image characteristics, we use the third-order hourglass network structure. It is used for the extraction and fusion of multiscale characteristics of sports. In addition, in the hourglass, internal network residual-intensive modules are introduced, realizing characteristics in different levels of network transfer and reuse. It is helpful for maximum details to feature extracting and enhancing the network expression ability. Comparison and ablation experiments are also carried out to prove the effectiveness and superiority of the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification

Cai

Liu

2021

Journal of Healthcare Engineering

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“…It is mainly responsible for realtime monitoring of the wireless data received in the serial port of the computer [11], receiving the data distributed in each key sensor node of the human body in real time, distinguishing the nodes of each sensor data, and sending these data to the data preprocessing module.…”

Section: Serial Data Receivingmentioning

confidence: 99%

Health Analysis of Footballer Using Big Data and Deep Learning

Yang

Yuan

Yan

2021

Scientific Programming

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With the development of information technology, health management and big data have risen and developed in recent years. Big data need proper analysis and shape in order to extract meaningful information from it. This paper analyzes the application status and prospects of big data in the field of health management. The results show that the most widely used big data in health management are intelligent wearable devices. Big data applications in football players’ mental health monitoring systems and chronic disease health management systems also have a good prospect. The intelligent wearable device is applied to several aspects of sports work: teaching and sports training, real-time monitoring of football players’ physical exercise process, collecting football players’ heart rate, calorie consumption, exercise steps, and track, blood pressure, blood oxygen, and other physical exercise data; through monitoring the heart rate, we can get the intensity and duration of football players’ physical exercise in school; through the calculation, we can also get the football players’ time energy consumption and understand the overall situation of football players’ physical exercise in school; through step counting and track monitoring, we can master the number of steps and track of football players; by monitoring the changes of blood oxygen and blood pressure of football players, we need to build a third-party residents’ health information storage and analysis system and further realize the marketization of residents’ health big data. The experimental results of the proposed study show the effectiveness of the proposed work.

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“…is method utilizes two networks [32], derived from VGG19 and ResNet45, for structural damage classification and semantic segmentation, respectively. By parallel, the two networks can simultaneously process multiple types of damage classification and pixel-level segmentation.…”

Section: Related Workmentioning

confidence: 99%

Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

Meng

2021

Scientific Programming

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Crack is the early expression form of the concrete pavement disease. Early discovery and treatment of it can play an important role in the maintenance of the pavement. With ongoing advancements in computer hardware technology, continual optimization of deep learning algorithms, as compared to standard digital image processing algorithms, utilizing automation of crack detection technology has a deep learning algorithm that is more exact. As a result of the benefits of greater robustness, the study of concrete pavement crack picture has become popular. In view of the poor effect and weak generalization ability of traditional image processing technology on image segmentation of concrete cracks, this paper studies the image segmentation algorithm of concrete cracks based on convolutional neural network and designs an end-to-end segmentation model based on ResNet101. It integrates more low-level features, which make the fracture segmentation results more refined and closer to the practical application scenarios. Compared with other methods, the algorithm in this paper has achieved higher detection accuracy and generalization ability.

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Deep Reinforcement Learning for Communication Flow Control in Wireless Mesh Networks

Cited by 70 publications

References 14 publications

AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification

AGTH-Net: Attention-Based Graph Convolution-Guided Third-Order Hourglass Network for Sports Video Classification

Health Analysis of Footballer Using Big Data and Deep Learning

Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

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