Road extraction from high-resolution remote sensing images can provide vital data support for applications in urban and rural planning, traffic control, and environmental protection. However, roads in many remote sens...
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Road extraction from high-resolution remote sensing images can provide vital data support for applications in urban and rural planning, traffic control, and environmental protection. However, roads in many remote sensing images are densely distributed with a very small proportion of road information against a complex background, significantly impacting the integrity and connectivity of the extracted road network structure. To address this issue, we propose a method named StripUnet for dense road extraction from remote sensing images. The designed Strip Attention Learning Module (SALM) enables the model to focus on strip-shaped roads;the designed Multi-Scale Feature Fusion Module (MSFF) is used for extracting global and contextual information from deep feature maps;the designed Strip Feature Enhancement Module (SFEM) enhances the strip features in feature maps transmitted through skip connections;and the designed Multi-Scale Snake Decoder (MSSD) utilizes dynamic snake convolution to aid the model in better reconstructing roads. The designed model is tested on the public datasets DeepGlobe and Massachusetts, achieving F1 scores of 83.75% and 80.65%, and IoUs of 73.04% and 67.96%, respectively. Compared to the latest state-of-the-art models, F1 scores improve by 1.07% and 1.11%, and IoUs increase by 1.28% and 1.07%, respectively. Experiments demonstrate that StripUnet is highly effective in dense road network extraction. IEEE
While reinforcement learning has shown promising abilities to solve continuous control tasks from visual inputs, it remains a challenge to learn robust representations from high-dimensional observations and generalize...
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Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social *** social robot detection methods based on graph neural net...
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Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social *** social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social *** paper proposes a social robot detection method with the use of an improved neural ***,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships ***,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the ***,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph ***,social robots can be more accurately identified by combining user behavioral and relationship *** carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,*** with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two *** results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.
Generative adversarial networks(GANs) have drawn enormous attention due to their simple yet efective training mechanism and superior image generation quality. With the ability to generate photorealistic high-resolutio...
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Generative adversarial networks(GANs) have drawn enormous attention due to their simple yet efective training mechanism and superior image generation quality. With the ability to generate photorealistic high-resolution(e.g., 1024 × 1024) images, recent GAN models have greatly narrowed the gaps between the generated images and the real ones. Therefore, many recent studies show emerging interest to take advantage of pre-trained GAN models by exploiting the well-disentangled latent space and the learned GAN priors. In this study, we briefly review recent progress on leveraging pre-trained large-scale GAN models from three aspects, i.e.,(1) the training of large-scale generative adversarial networks,(2) exploring and understanding the pre-trained GAN models, and(3) leveraging these models for subsequent tasks like image restoration and editing.
Software, hardware, data, and computing power can be abstracted and encapsulated as services authorised to users in a paid or free manner for on demand deployment. Service composition combines multiple existing servic...
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We propose a scheme for generating high-quality single-photon sources utilizing the conventional photon blockade(CPB)effect in a cavity optomagnonic system with Kerr *** realization of the CPB effect depends on both t...
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We propose a scheme for generating high-quality single-photon sources utilizing the conventional photon blockade(CPB)effect in a cavity optomagnonic system with Kerr *** realization of the CPB effect depends on both the Kerr nonlinearity and Kerr-like nonlinearity of the optical cavity,which is converted using magneto-optical *** CPB effect can be realized in a cavity optomagnonic system with weak magneto-optical coupling by modulating the strength of the Kerr ***,our scheme supports photon blockade in both the strong and weak Kerr nonlinear regimes,which broadens the range of experimental ***,we explored the parameter regimes where the CPB effect could not be achieved because of the combined effects of the magneto-optical coupling and Kerr *** also determined the optimal driving amplitude region for generating high-quality single-photon *** work not only provides a novel route for realizing the CPB effect but also establishes a versatile platform for producing single-photon sources with high purity and brightness.
Amid the global shift of smart manufacturing towards greener and more intelligent paradigms, the spatiotemporal coupling characteristics of dynamic heat conduction networks pose significant challenges for optimizing t...
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In the scenario of a steam generator tube rupture accident in a lead-cooled fast reactor,secondary circuit subcooled water under high pressure is injected into an ordinary-pressure primary vessel,where a molten lead-b...
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In the scenario of a steam generator tube rupture accident in a lead-cooled fast reactor,secondary circuit subcooled water under high pressure is injected into an ordinary-pressure primary vessel,where a molten lead-based alloy(typically pure lead or lead-bismuth eutectic(LBE))is used as the *** clarify the pressure build-up characteristics under water-jet injection,this study conducted several experiments by injecting pressurized water into a molten LBE pool at Sun Yat-sen *** obtain a further understanding,several new experimental parameters were adopted,including the melt temperature,water subcooling,injection pressure,injection duration,and nozzle *** detailed analyses,it was found that the pressure and temperature during the water-melt interaction exhibited a consistent variation trend with our previous water-droplet injection mode LBE ***,the existence of a steam explosion was confirmed,which typically results in a much stronger pressure *** the non-explosion cases,increasing the injection pressure,melt-pool temperature,nozzle diameter,and water subcooling promoted pressure build-up in the melt ***,a limited enhancement effect was observed when increasing the injection duration,which may be owing to the continually rising pressure in the interaction vessel or the isolation effect of the generated steam *** of whether a steam explosion occurred,the calculated mechanical and kinetic energy conversion efficiencies of the melt were relatively small(not exceeding 4.1%and 0.7%,respectively).Moreover,the range of the conversion efficiency was similar to that of previous water-droplet experiments,although the upper limit of the jet mode was slightly lower.
In recent years, significant progress has been made in knowledge graph representation learning, which has shown promising results in knowledge computing applications such as relation extraction and knowledge reasoning...
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In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem(CTSP). When solving large-scale CTSP with a scale of more than 1000d...
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In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem(CTSP). When solving large-scale CTSP with a scale of more than 1000dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid IT?(HIT?) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard IT?. In the iteration process of HIT?, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HIT? is superior to the known comparison algorithms in terms of the quality solution.
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