How to deal with damage to the power grid caused by natural disasters has become one of the main research focus of disaster prevention and mitigation. This paper collates two main approaches to distribution network da...
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How to deal with damage to the power grid caused by natural disasters has become one of the main research focus of disaster prevention and mitigation. This paper collates two main approaches to distribution network damage prediction and assessment under typhoon disasters: based on physical model-driven and data model-driven. Research based on physical models is divided into three parts by considering real-time wind speed correction, meteorological forecast information, and non-meteorological information. The field of data-driven models includes single-algorithm models and multi-algorithm models. These methods are known for their high accuracy, but have a strong dependence on data. Finally, the paper explores the potential benefits of integrating physical and data model-driven approaches, as well as implementing additional defensive measures based on predictive results.
Although artificial intelligence (AI) has been successful in many domains, fully grasping the concept for ordinary people is still challenging. Also, the convolutional neural network (CNN) is state-of-the-art model in...
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ISBN:
(纸本)9798350386851;9798350386844
Although artificial intelligence (AI) has been successful in many domains, fully grasping the concept for ordinary people is still challenging. Also, the convolutional neural network (CNN) is state-of-the-art model in computer vision research due to its fundamental properties linked to visual perceptions. For the sake of popular science, we crafted the CNN model design and training to make an award-winning installation artwork to interpret CNN. In each of the three installations, seven clear acrylic plates are lined up, showing the end-to-end tensor transitions from an animal image to a specific Chinese pictogram to reveal the layered structures, feature maps, and weights belonging to the CNN. We deeply hope to help pave the path toward a better understanding of modern advances in AI for the public.
The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and...
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Traditional path-switching methods are primarily designed for static or conventional network environments, rendering them inadequate for the dynamic nature of heterogeneous industrial networks. This paper aims to tack...
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network protection is a crucial region of generation and PC technology, and numerous equipment and techniques help ensure comfy conversation and records switch. Mum or Dad Communications software program (GCS) is a pr...
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In this study, we presente digital solution for training master's students in engineering sciences, using a bimodal learning approach at the Higher Institute of technology of Bangui's University. This proposal...
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As a crucial component of the digital economy, the market price fluctuations of Non-Fungible Tokens (NFTs) are influenced by various factors, making accurate prediction extremely important. This paper leverages a Grap...
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Traffic accidents are one of the top ten causes of death, with driver fatigue accounting for a significant proportion. Fatigue can lead to reduced attention and slower reaction times. Many professions require frequent...
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ISBN:
(纸本)9783031709654;9783031709661
Traffic accidents are one of the top ten causes of death, with driver fatigue accounting for a significant proportion. Fatigue can lead to reduced attention and slower reaction times. Many professions require frequent nighttime driving, such as truck and taxi drivers, making them particularly a high risk group. Therefore, preventing accidents caused by driver fatigue, especially during nighttime, is a crucial task. Currently, there is limited research and datasets focused on fatigue driving in nighttime scenes. To address this gap we collect nighttime fatigue driving data using an infrared (IR) camera, and propose Unbalanced LocalCNNs for fatigue driving detection in this work. The network architecture can effectively direct the network's attention to different regions based on specific actions caused by fatigue, and result in a 1.6% improvement in accuracy compared to the original models. Furthermore, an adversarial learning mechanism is introduced to enhance the network's robustness, ensuring effective feature extraction in both day and night scenarios. Compared to models without adversarial learning, the overall accuracy is improved by 1.5%. The code is available at https://***/KaiChun-Tu/slow fastDrowsyDriver.
Accurate classification of blood cells is very important for clinical diagnosis, but traditional classification methods are not only time-consuming, inefficient, but also easy to make subjective judgments. With the de...
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In recent years, stereo matching employing neural networks has emerged as a crucial research direction within the domain of computer vision. Stereoscopic depth estimation hinges on the optimal correspondence between t...
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