The multilayer architecture of the global input-output network and its properties

Grassi, Rosanna; Bartesaghi, Paolo; Clemente, Gian Paolo; Luu, Duc Thi

doi:10.1016/j.jebo.2022.10.029

Cited by 10 publications

(5 citation statements)

References 50 publications

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“…In formula (16), y u (x, y) represents the numerical relationship between area visibility and background brightness, L op represents the background illumination of low illumination area, and θ represents parameters. Under the above illuminance numerical relationship, the background illuminance parameter is taken as the enhancement object, and an image subtraction model is used to construct a numerical enhancement process.…”

Section: Construction Of Region Hierarchical Enhancement Methods For ...mentioning

confidence: 99%

“…In order to speed up the recovery and correction of fuzzy defect image, a fast correction algorithm based on neural network can be designed for fast correction of image fuzzy defects. It only needs to go through three levels, starting from the pixel input of the input layer, through the correction and summation of the hidden layer, and finally output from the output layer as demonstrated [16]. An activation function is required in the connection weight to process the summation information of offset.…”

Section: Design a Fast Correction Algorithm For Fuzzy Defect Image Re...mentioning

confidence: 99%

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Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network

Zhan-Peng Cui

2023

Journal of Computers

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<p>In contrast enhancement of fuzzy defect image, details loss and noise expansion are east to occur, which brings difficulties to subsequent image analysis and defect recognition. Therefore, a fuzzy defect image restoration and enhancement method based on neural network is proposed. A double fusion neural network composed of a depth generation network and a discrimination network is designed. The residual of the denoised fuzzy image and the real image is output by the network, which is input into the discrimination network together with the real image, and the difference between the two is judged by the total loss function. To solve the problem of pixel coordinate value of fuzzy defect image, neural network is used to build a fast correction algorithm. Therefore, a fuzzy image restoration and enhancement method based on neural network is proposed to improve the image quality. By reconstructing the resolution of fuzzy defect image, a hierarchical enhancement method of fuzzy defect image region is constructed to achieve fuzzy defect image restoration and enhancement. The results show that the proposed method has high image processing ability in restoration and enhancement of fuzzy defect images. The fitting value of neural network is 0.92, which is significantly higher than that of the other two methods, indicating that the image restoration and enhancement method based on neural network has higher accuracy. Therefore, the restoration and enhancement method of fuzzy defect image based on neural network has a good restoration and enhancement effect, and can effectively meet the actual needs of people for high-quality images.</p> <p> </p>

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Section: Construction Of Region Hierarchical Enhancement Methods For ...mentioning

confidence: 99%

Section: Design a Fast Correction Algorithm For Fuzzy Defect Image Re...mentioning

confidence: 99%

Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network

Zhan-Peng Cui

2023

Journal of Computers

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“…The intermediate flow matrix records the economic exchanges among 17 sectors and 34 provinces in Indonesia, reflecting their intricate economic relationships (e.g., supply and demand) as well as their interdependence and mutual constraints. A multilayer network is defined as 𝑀𝑀 = (𝐺𝐺, C) [9][10][11]. 𝐺𝐺 is a set of weighted directed graphs representing each economic sector layer, 𝐺𝐺 = �𝐺𝐺 𝛼𝛼 ; 𝛼𝛼 ∈ {1, … , 𝑘𝑘}�.…”

Section: Datamentioning

confidence: 99%

Exploring The Spatial Structure of Interregional Supply Chain: A Multilayer Network Approach

Situngkir,

Maulana

2024

Preprint

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This research aims to elucidate the organizational patterns of interregional economic interdependence to enhance our comprehension of the national economy's structure at a regional scale. Employing a multilayer network model, this study represents economic interdependence among Indonesian regions, utilizing the InterRegional Input-Output (IRIO) table. Through the application of various metrics, such as degree and strength distribution, assortativity coefficient, and global and local rich club coefficient, to the multilayer IRIO network, we uncover the organizational patterns of economic exchanges between provinces and economic sectors within Indonesia. Our findings demonstrate that a multilayer network approach reveals the heterogeneous and complex structure of the national economy at the regional level. By analyzing the assortativity pattern and global rich-club coefficient, we illustrate that the IRIO network exhibits a hierarchical organization, where significant provincial-sector nodes are interconnected and form dense rich clubs, extending from a few structural cores to peripheral regions. Additionally, we identify distinct connectivity patterns of non-rich nodes based on their incoming and outgoing relations. The insights gained from this study have implications for the macro-control of regional development.

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“…In general, not only their internal composition is changing, but also the ranking of the central members within. These changes in large international clusters as well as regional ones will reflect the results and dynamics of cooperation, choice of partners, and competition between industries and countries [7]. We believe that this method advantage is especially important in modern conditions of changing the structure of the economy, areas of cooperation of territories, countries.…”

Section: Introductionmentioning

confidence: 99%

Solving problems of agricultural production development by modeling using the cluster analysis method

Lubova,

Araslanbayev,

Salimova

et al. 2023

BIO Web Conf.

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The article outlines the need to apply a systematic approach to the analysis and solution of agricultural production development problems. The system approach implementation is carried out using a multidimensional data analysis method – the cluster analysis method. A system of indicators characterizing the conditions and results of agricultural enterprises in the regions of the Central and Volga Federal Districts of the Russian Federation has been built, a multidimensional grouping of regions has been carried out to identify problems and directions for their solution. The multidimensional approach to the study allowed an objective analysis and formulation of well-founded specific conclusions and recommendations for the regions of each cluster. As a result, two clusters of regions were identified according to the level of agricultural production development. Subclusters are also allocated within clusters to detail and concretize proposals. Recommendations on agricultural production development are made to the regions of each cluster. The directions for the formation of the strategy for the development of agriculture of the corresponding groups of regions and territories are outlined.

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The multilayer architecture of the global input-output network and its properties

Cited by 10 publications

References 50 publications

Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network

Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network

Exploring The Spatial Structure of Interregional Supply Chain: A Multilayer Network Approach

Solving problems of agricultural production development by modeling using the cluster analysis method

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