Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model....
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作者:
Zhang, ZhibingSchool of Mathematics and Physics
Key Laboratory of Modeling Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Education Institutes Anqing Normal University Anqing246133 China
In this paper, we study Liouville type results for the three-dimensional stationary incompressible MHD equations and Hall-MHD equations. By a new iteration argument, we establish Liouville type theorems if the velocit...
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In radar automatic target recognition (RATR), inverse synthetic aperture radar (ISAR) image recognition shows its advantages. Due to the limited sample size of ISAR images, support vector machine (SVM), known for its ...
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ISBN:
(纸本)9798400709753
In radar automatic target recognition (RATR), inverse synthetic aperture radar (ISAR) image recognition shows its advantages. Due to the limited sample size of ISAR images, support vector machine (SVM), known for its robustness in small sample classification, is often used for ISAR image recognition. For ISAR images of different targets, the single kernel SVM algorithm might lose its robustness. Therefore, this paper applies multiple kernel learning (MKL) to ISAR ship target recognition. The process begins with the preprocessing of the ISAR images to suppress Gaussian white noise. Then, principal component analysis (PCA) is employed to extract features from the ISAR images. Finally, the Simple-MKL method is used to recognize the samples. Experiments based on simulation data indicate that the method used in this paper improves the accuracy compared to other single-kernel SVM algorithms with different kernel functions.
Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then clus...
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ISBN:
(纸本)9798400712456
Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then cluster articles into stories based on similarity. However, the results obtained by these methods are one-time, and clustered news cannot adaptively update in a continuous news stream. Additionally, the inadequate quality of article encoding and the presence of noise data deteriorate the performance of story discovery. To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. Furthermore, HRSTORY captures better article embeddings through modeling multi-layer relational dependencies within the text. By using sentence-level noise masking, HRSTORY improves the relevance of news article representation to core topics and reduces the interference of noise data. Experiments on real news datasets show that HRSTORY outperforms the state-of-the-art algorithms in unsupervised online story discovery performance.
In the field of optics, infrared and visible images are often required for use in different situations. In particular, in the field of optical sensors, infrared and visible sensors are mainly used to obtain different ...
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The present study compares the Atlantic Meridional Overturning Circulation (AMOC) in the North Atlantic from two simulations by an oceanic general circulation model with 1° × 1° and 0.1° × 0.1...
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By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Nevertheless, they are usually parameter-...
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In this paper, we study the Liouville type theorems for the stationary tropical climate model in three dimension. With the help of the delicate estimates of several integrals and an iteration argument, we establish Li...
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A vine copula is a flexible method for multivariate dependence simulations that assumes stationarity. However, only a few studies have focused on non-stationarity and comprehensively developed nonstationary vine copul...
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In this paper, we proposed a fully discrete projection method with modular grad-div stabilization for solving the time-dependent inductionless magnetohydrodynamic equations. The method incorporates a minimally intrusi...
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