In the context of engineering education, constructing a curriculum system with an Outcome-Based Education (OBE) approach using reverse thinking can effectively motivate students to learn proactively. However, due to t...
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Federated Learning (FL) has emerged as a promising training framework that enables a server to effectively train a global model by coordinating multiple devices, i.e., clients, without sharing their raw data. Keeping ...
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This paper delves into the interleaved hysteresis control method based on soft-switching power amplifiers. Firstly, through modeling and analysis, the basic topology and mode transition of the soft-switching power amp...
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This research aims to explore and optimize multimodal emotion recognition to enhance its performance. Multimodal emotion recognition involves analyzing information from different modalities - speech, vision, and text ...
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In response to the issues of high computation and large model parameters in current smoking detection algorithms, making them difficult to deploy on edge devices, this paper proposes an improved lightweight YOLOv8 alg...
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Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recogni...
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In pursuit of the goal of reducing the wastage of renewable energy resources and enhancing the flexibility of the power system, this paper introduces a coordinated optimization scheduling strategy, incorporating distr...
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In view of the existing security products limited use scenarios, deployment and use is not flexible, limited performance and other problems, design and implementation of intelligent video surveillance system based on ...
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Purpose: This paper presents a theoretical analysis of the DynaTrans algorithm, a novel approach for dynamic optimization of urban transportation networks. Design/methodology/approach: We introduce an Adaptive Closene...
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Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image ***,researchers have found a connection between convol...
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Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image ***,researchers have found a connection between convolutional neural networks(CNNs)and Gabor ***,some Gabor-based CNN methods have been proposed for HSI ***,most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning ***,these methods require patch cubes as network *** patch cubes may contain interference pixels,which will negatively affect the classification *** address these problems,in this paper,we propose a learnable three-dimensional(3D)Gabor convolutional network with global affinity attention for HSI *** precisely,the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter,which can be learned and updated during the training ***,spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube,thus alleviating the interfering pixels *** results on three well-known HSI datasets(including two natural crop scenarios and one urban scenario)have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.
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