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Name: Jared Calbert
Type: User
Bio: My name is Jared Calbert, I am a college student and most of the time I have to study. I enjoy rap and dancing, as well as playing basketball.
Name: Jared Calbert
Type: User
Bio: My name is Jared Calbert, I am a college student and most of the time I have to study. I enjoy rap and dancing, as well as playing basketball.
为了解决当前NILM方法在噪声条件下准确识别具有相似特征和微小负载变化的电器所面临的挑战,本研究引入了一种新的深度学习框架,用于非侵入式负载监测,称为amsam -NILM模型。该模型将选择性核(SK)单元的动态感受野调节能力与高效多尺度注意(EMA)模块的高效多尺度注意机制相结合,增强了模型对不同尺度信息的适应性和处理能力。我们使用基准UK-DALE数据集进行的实验表明,AMSAIM-NILM模型在准确性和F1分数方面都优于现有方法,特别是在需要细粒度负载检测的场景中。实验验证结果表明,与现有方法相比,amsam - nilm模型显著提高了识别效率,在处理各种设备负载的时间模式时具有更高的准确性和鲁棒性。
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MUSENILM model, which is a non-intrusive load decomposition model that incorporates a parallel multi-scale attention mechanism
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