笔者提出应用ART算法根据测量得到的两个垂直方向上的弧根线性电流分布重建弧根电流密度分布的方法。对Drouet M G实验测量的弧根在2个互相垂直方向的电流分布进行计算,获得了2-D弧根电流密度分布并加以讨论。结果表明,ART重建算法结合...笔者提出应用ART算法根据测量得到的两个垂直方向上的弧根线性电流分布重建弧根电流密度分布的方法。对Drouet M G实验测量的弧根在2个互相垂直方向的电流分布进行计算,获得了2-D弧根电流密度分布并加以讨论。结果表明,ART重建算法结合分离极板法所测的弧根电流分布是获得其电流密度分布的一个切实可行而有效的途径。展开更多
The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Con...The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.展开更多
文摘笔者提出应用ART算法根据测量得到的两个垂直方向上的弧根线性电流分布重建弧根电流密度分布的方法。对Drouet M G实验测量的弧根在2个互相垂直方向的电流分布进行计算,获得了2-D弧根电流密度分布并加以讨论。结果表明,ART重建算法结合分离极板法所测的弧根电流分布是获得其电流密度分布的一个切实可行而有效的途径。
文摘The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.
