Building efficient acoustic models for dialects is a major challenge in Automatic Speech Recognition (ASR) systems. In this paper, we investigate the Moroccan Fessi dialect speech recognition system based on phoneme m...
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At the moment, power supply systems without centralized power supply, using autonomous sources, are becoming increasingly popular. In this paper, the issue of the effectiveness of the introduction of a DC-based power ...
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The measuring test lead is an important part of high voltage bushings with capacitor-type insulation. The test lead is connected to the outer capacitor lining of the frame, dividing the input capacitance into two: C1 ...
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Semantic segmentation plays an important role in computer perception tasks. Integrating the rich details of RGB images with the illumination robustness of thermal infrared (TIR) images is a promising approach for achi...
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Semantic segmentation plays an important role in computer perception tasks. Integrating the rich details of RGB images with the illumination robustness of thermal infrared (TIR) images is a promising approach for achieving reliable semantic scene understanding. Current approaches for RGB-Thermal semantic segmentation often overlook the unique characteristics exhibited by each modality at different encoding layers and underutilize the complementary information between the two modalities during decoding. To acquire complementary cross-modality encoding and decoding features, we propose a multi-branch differential bidirectional fusion network known as MDBFNet. Firstly, it models the dependencies between the modality-specific characteristics and the different encoding layers, and designs a TIR-led detail enhancement module (TDE) and an RGB-led semantic enhancement module (RSE) to guide distinguishable fusion for different layer features. Secondly, a three-branch fusion decoder with three supervision (TFDS) is proposed to thoroughly explore the complementary decoding features between two modalities. Experiments on MFNet and PST900 datasets show that our method surpasses state-of-the-art methods by a clear margin. IEEE
Control frameworks for legged robots often rely on accurate dynamic models. However, these models often proves to be inaccurate due to factors such as mechanical wear and tear, and unforeseen changes such as the addit...
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Text detection in natural images is a crucial task for extracting and recognizing valuable information, but it comes with significant challenges. Traditional image processing methods often rely on synthetic features, ...
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Industrial furnaces are often used in siderurgy and metallurgy applications. Most of them are based on burning different fuels or use electrical resistors to bring the temperature to the desired working conditions. Th...
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The paper proposes a solution for modelling and controlling the operation of a DC/DC boost converter. The converter model is based on a neural network structure. The feedforward fully connected neural networks generat...
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To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus alg...
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The article discusses the theoretical foundations of the design of a single-channel ultrahigh frequency moisture meter with direct measurement of the moisture content of bulk materials. In accordance with the requirem...
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