ARINC653 is an important standard for IMA integrated modular avionics systems. In today's rapidly developing world, single core ARINC653 can no longer meet the increasingly diverse tasks and complex systems. There...
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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.
Deep learning-primarily based aid allocation algorithms for 6G networks enable enhanced community overall performance via close to most useful schedulers. those algorithms leverage deep neural network architectures to...
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Air pollution can affect human health, so it is necessary to predict the air quality index (AQI) in advance. In this work, air quality data collected by the Internet of Drone Things (IoDT) is predicted and analyzed to...
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In order to dynamically create a sequence of textual descriptions for images, image description models often make use of the attention mechanism, which involves an automatic focus on different regions within an image....
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This paper introduces a novel approach for assessing piano performance through video analysis using Dynamic Time Warping (DTW). Traditional methods of evaluating piano playing often rely on auditory cues or sheet musi...
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Wildfire in the forest leads to a huge amount of financial and human losses. That is it causes damage to the forest and the life of firefighters. To reduce the amount of such damage, unmanned aerial vehicles are among...
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In this paper, a cloud platform-based power system energy load forecasting method is proposed to improve the efficiency and accuracy of power system energy load forecasting. Firstly, the architecture of distributed po...
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Modern software systems generate a large amount of logs during runtime, which reflect the system's operational status. The reliability of system services relies on automatic log anomaly detection. Researchers have...
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Underwater fish detection is important for underwater ecological conservation and underwater species monitoring. In this study, an underwater fish recognition method, MT-YOLO, is proposed based on the YOLOv8s model an...
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