Localization of Wireless Sensors Networks Using Dynamic Cell Structures Neural Networks

Othmani, Mohsen; Ezzedine, Tahar

doi:10.1109/iintec.2018.8695306

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The number of nodes that can be used for input and output is determined by constructing a network, and the network is formed by using sample data in the form of input vector classification. Data compression: determine the number of input and output nodes by building a network, and use sample data to form a network with dimensions that can reduce output [7][8].…”

Section: Bp Neural Networkmentioning

confidence: 99%

Construction of electric emergency materials storage system based on BP neural network

Song

Chen

Liu

et al. 2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

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The demand forecasting of power supply is an indispensable part of China's energy production and consumption. The scale of power grid construction in China has been expanding and the load growth rate has accelerated. Accidents, equipment failures and personnel unemployment often occur in the power system. BP neural network has certain algorithm advantages. Therefore, in order to ensure the safety and demand of residents and buildings, it is necessary to study the emergency materials storage system of power grid based on BP neural network. This paper mainly uses experimental testing and quantitative analysis methods to study the advantages and methods of BP neural network algorithm in the construction and application of electric emergency materials storage system. The experimental data shows that the learning rate of 0.06 selected by BP neural network is the most appropriate in the construction of storage system.

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Section: Bp Neural Networkmentioning

confidence: 99%

Construction of electric emergency materials storage system based on BP neural network

Song

Chen

Liu

et al. 2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

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“…Due to the variable contour features of sandy land areas in remote sensing images and the easy confusion with targets such as Gobi and bare land, the deep learning-based target extraction methods using the target contour as the main feature still have a lot of room for accuracy improvement in the long time series and sandy land extraction tasks. Furthermore, the classic static neural network is trained using a large quantity of data, yielding only a static network model with set parameters, which can only employ fixed model parameters in the model inference and cannot effectively handle many types of input images [20,21]. If a typical static inference model is used in the desertification monitoring task, the model would be disturbed by the complex sandy land characteristics, and the extraction accuracy would suffer as a result of the multi-scale, multi-type, and complicated sandy land characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Study of Sandy Land Changes in the Chifeng Region from 1990 to 2020 Based on Dynamic Convolution

Zhu,

Zhang,

Chang

et al. 2023

Sustainability

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Desertification is the process of land degradation and the reduction or destruction of biological potential in arid, semi-arid, and semi-humid areas, and its impact on agricultural development and the ecological environment cannot be ignored. Therefore, many researchers have aimed to achieve the acquisition of large-scale sandy land areas using sandy land extraction algorithms based on remote sensing images. However, the sandy land extraction accuracy needs to be improved because of the variable contour features in the remote sensing images and the easy confusion with targets such as the Gobi and bare ground areas. In this study, we combine the dynamic convolution with a U-Net model and propose the DU-Net sandy land extraction model. The method is based on dynamic convolution, which can adaptively learn the complex features of the target and build a dynamic convolutional neural network to achieve high-accuracy extraction of complex targets. DU-Net achieved 86.32% in IoU, 93.22% in precision, 94.5% in recall, and 92.66% in F1-score in sandy land extraction accuracy, which are 4.68%, 2.33%, 3.09%, and 2.76% improvements, respectively, compared with the U-Net static neural network. Based on this, we obtained the spatial and temporal evolution trends of sandy land areas based on Landsat images in the Chifeng region in the Inner Mongolia Autonomous Region, China. Meanwhile, in order to investigate the mechanism of spatial and temporal changes in the sandy land area in the study region over the past 30 years, the direct and indirect effects of seven climatic and human socioeconomic activity factors on the changes in the sandy land area in the study region were evaluated based on a structural equation model. The results show that the sandy area in the Chifeng region tended to first increase and then decrease over the study period, with the sandy land area reaching its maximum around the year 2000. In addition, the main driving factor for the change in the sandy land area in the Chifeng region has been human socioeconomic activities, with climatic conditions as the secondary driving factor. The method proposed in this paper realizes the rapid extraction of sandy land areas with high accuracy at a large scale and with a long time series and provides a basis for assessing the effectiveness of ecosystem restoration projects.

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Study on Using Signal Filtering Techniques for Machine Learning-based Indoor Positioning Systems(IPS)

Jayathissa

Maduranga

2022

2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)

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Localization of Wireless Sensors Networks Using Dynamic Cell Structures Neural Networks

Cited by 3 publications

References 6 publications

Construction of electric emergency materials storage system based on BP neural network

Construction of electric emergency materials storage system based on BP neural network

A Study of Sandy Land Changes in the Chifeng Region from 1990 to 2020 Based on Dynamic Convolution

Study on Using Signal Filtering Techniques for Machine Learning-based Indoor Positioning Systems(IPS)

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