A data-driven intelligent planning model for UAVs routing networks in mobile Internet of Things

Meng, Dian; Xiao, Yang; Guo, Zhiwei; Jolfaei, Alireza; Qin, Lanxia; Lu, Xinting; Xiang, Qiao

doi:10.1016/j.comcom.2021.08.014

Cited by 63 publications

(21 citation statements)

References 24 publications

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“…e complexity of the transformation matrix is related to transformation steps, that is, the more steps, the more transformation matrix, which can produce the transformation of the complex graph [39,40].…”

Section: Discussionmentioning

confidence: 99%

A Theoretical Scientific Programming Framework for Application of Linear Matrix Transformation in Plane Computer Graphics

Chen

2022

Scientific Programming

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Plane computer graphics are basic information carriers in many industrial scenarios, such as engineering simulation, automatic control, and software design. Plane computer graphics are generally a kind of digital signals guided by mathematical symbols, and each vertex of a plane computer graph forms a graph matrix. Therefore, linear matrix transformation serves as the most common algorithmic unit to realize various information processing operations. To improve ease of graph matrix computing in practical engineering scenarios, this paper proposes a theoretical scientific programming framework for application of linear matrix transformation in plane computer graphics. Firstly, theoretical basis of linear matrix transformation in homogeneous plane coordinates is displayed and analyzed. Then, the universal theorem about linear transformation of graph matrices is deduced, and corresponding proofs are also given. Finally, a case study is set up to demonstrate the main workflow of the proposed theoretical scientific programming framework. The simulative results reveal feasibility of the proposal.

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Section: Discussionmentioning

confidence: 99%

A Theoretical Scientific Programming Framework for Application of Linear Matrix Transformation in Plane Computer Graphics

Chen

2022

Scientific Programming

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“…At the last edition of the competition in 2017, ImageNet announced the end of a large vision challenge based on this dataset and will henceforth work on solving unsolved vision challenges. In this competition, SENet took the top spot in this image recognition with a top-5 error rate of 2.25%, which has far surpassed human ability and shows the maturity of image recognition technology [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Convolutional Neural Network-Based Scene Image Recognition Method

Wang

Qiao

Liu

2022

Computational Intelligence and Neuroscience

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To solve the problems existing in the research of scene recognition, this paper studies a new convolutional neural network target detection model to achieve a better balance between the accuracy and speed of high-speed scene image recognition. First, aiming at the problem that the image is easy to be disturbed by impurities and poor quality in fine-grained image recognition, a preprocessing method based on the Canny edge detection is designed and the Canny operator is introduced to process the gray image. Second, the L2 regularization algorithm is used to optimize the basic network framework of the convolutional neural network, enhance the stability of the model in a complex environment, improve the generalization ability of the model, and improve the recognition accuracy of the algorithm to a certain extent. Finally, by collecting the campus environment datasets under different environmental conditions, the location recognition experiment and heat map visualization experiment are carried out. Experiments show that compared with the basic convolution neural network algorithm, the algorithm has better recognition performance and good generalization ability. The research of this study realizes the effective combination of multiframe convolution neural network and batch normalization algorithm and has a good practical effect on scene image recognition.

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“…These advantages will be an innovation in the construction safety evaluation of high-rise buildings. Its superiority lies in overcoming the defects of the old safety evaluation method, bringing a new evaluation data processing mode, and bringing accuracy and speed into the results of high-rise building construction safety evaluation [ 10 , 11 ]. Secondly, through the research on the relevant content of the safety evaluation theory, the shortcomings of the commonly used evaluation methods are analyzed, and the safety evaluation research of high-rise building construction based on fuzzy mathematics is established.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Neural Networks-Based Safety Evaluation for Smart Construction Devices

Wang

Zhou

2022

Computational Intelligence and Neuroscience

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Based on the theory of lightweight neural networks, this paper presents a safety evaluation model for smart construction devices. The model index system includes the internal logical relationship between the input and output indexes, and the input indexes are specifically refined. According to the safety evaluation results, the article observes what type of accidents will occur at the construction site. According to the detailed and specific output index system, the six input factor layer indicators correspond to the indicators of several multiple network index layers, respectively. In the simulation process, MATLAB software was used to write the multiple neural network model program for the safety evaluation of the construction site, and the appropriate multiple network structure and related parameters were selected. The experimental results show that the multiple neural networks are trained by collecting 10 expert evaluation samples, and the trained multiple neural networks are applied to real construction cases. Comparing the two sets of data, it can be seen that the gap is relatively small, and the sample training is better. The multiple neural networks have relatively good evaluation performance. The method has a fast calculation speed and effectively improves the efficiency and practical value of safety evaluation.

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A data-driven intelligent planning model for UAVs routing networks in mobile Internet of Things

Cited by 63 publications

References 24 publications

A Theoretical Scientific Programming Framework for Application of Linear Matrix Transformation in Plane Computer Graphics

A Theoretical Scientific Programming Framework for Application of Linear Matrix Transformation in Plane Computer Graphics

An Improved Convolutional Neural Network-Based Scene Image Recognition Method

Lightweight Neural Networks-Based Safety Evaluation for Smart Construction Devices

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