The application of high-resolution satellite images in deep learning-based change detection methods has become increasingly popular. However, the down-sampling and cropping strategies deployed to fit the GPU memory co...
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This paper proposes a specialized network model, Lightweight Dynamic Inverted Residual network (LDIRNet), for the detection of surface defects in ceramic bearings. The model integrates the iRMB module to improve featu...
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With the global population steadily increasing, ensuring food security is a major concern, especially in agriculture. Detecting plant diseases early can make farming more efficient and prevent significant food losses....
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The established SDN multi-controller architecture faces the challenge of locating controllers to boost the scalability, reliability, and security of the network. Reliability is one of the important metric to handle wh...
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This paper aims to use convolutional neural network (CNN) to develop an intelligent system, mainly to detect the helmet wearing and overload behavior when driving non-motor vehicles on the road. The intelligent non-mo...
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Garbage collection (GC) has long impacted the ability of solid state disks (SSD) to function properly and has become an important issue that many memory manufacturers are eager to address. In recent years, with the ad...
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A segmentation method combining Mask R-CNN (Mask Region-Based Convolutional Neural network) and monocular depth estimation is proposed to identify and segment the copper ear wire closest to the robotic arm on nickel a...
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Facial expression recognition (FER) plays a crucial role in domains such as healthcare and access security. Traditional models primarily utilize convolutional networks to extract features like facial landmarks and pos...
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Facial expression recognition (FER) plays a crucial role in domains such as healthcare and access security. Traditional models primarily utilize convolutional networks to extract features like facial landmarks and positions of facial features. However, these methods often result in feature maps with significant redundancy, contributing minimally to network performance enhancement. To address this limitation, we propose the DPConv module, which innovatively segments the channel dimension and applies dual convolutional kernel sizes. This module replaces several convolutional blocks within the POSTER++ (Mao et al. in POSTER++: A Simpler and Stronger Facial Expression Recognition network. arXiv:2301.12149, 2023) architecture, leading to a reduction in parameters while simultaneously enhancing network efficiency and accuracy. Moreover, we propose a sliding window multi-head cross-self-attention mechanism, which is based on the sliding window multi-head self-attention (Liu et al. in Proceedings of the IEEE/CVF internationalconference on computer Vision, 2021) mechanism, which substitutes the conventional attention mechanism, facilitating the modeling of global dependencies and further optimizing the network's overall performance. Our model, DPPOSTER, was tested on the RAF-DB, FERPlus and SFEW datasets, and experimental comparisons were conducted with different combinations of convolution kernel sizes and channel segmentation ratios. The results showed that DPPOSTER achieved performance improvements of 0.59%, 0.37% and 2.32% over POSTER++ on the RAF-DB, FERPlus and SFEW datasets, respectively.
With the development of network devices, the network traffic presents high-dimensional, enormous as well as complex characteristics, and the network threats and attacks continue to intensify. Existing network intrusio...
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In UAV networks, route discovery time is the duration from network deployment until the time when each UAV finds a route to every other UAV. It is a critical metric that shows how long a UAV network can be formed and ...
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