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Distribution Reliability and Automation01:25

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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多级分布对齐为多源通用域调整的多级分布对齐

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    本研究引入了一种新的多源通用域适应 (MSUDA) 方法,用于对来自多个来源的数据进行分类,即使是未知的类别. 该方法有效地识别出已知和未知样本,同时对齐特征分布.

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    科学领域:

    • 计算机科学 计算机科学
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 多源通用域适应 (MSUDA) 旨在将知识从多个标记的源域转移到一个未标记的目标域.
    • 关键的挑战包括处理任意数量的源域,目标域中的私有 (未知) 类别和域差异.
    • 现有的方法难以识别未知的目标样本并提取强大的域不变特征.

    研究的目的:

    • 提出一种新型网络,即多代表性DA网络 (MRDAN),用于使用多个源域与非相同的标签集对未标记的目标数据进行分类.
    • 解决目标域中识别已知和未知样本的挑战.
    • 为了有效地提取域不变特征,尽管跨域的分布差异.

    主要方法:

    • 引入了一个基于冲突的,没有值的,具有不确定性的预测 (CPU) 模块,通过利用源域的互补知识,同时识别已知和未知样本.
    • 制定了多层分布对齐 (MLDA) 策略,以逐步减少具有非相同类别空间的多个域之间的分布差异.
    • 在拟议的MRDAN分类框架内利用这些模块.

    主要成果:

    • 拟议的MRDAN有效地通过利用来自多个源域的知识来对未标记的目标数据进行分类.
    • 该CPU模块成功地识别了已知和未知样本,而不需要预定义的值.
    • MLDA策略有助于精确提取域不变特征,改善已知和未知样本的识别.

    结论:

    • 在多源通用域适应方面,MRDAN框架表现出显著的有效性,特别是在未知目标类别的场景中.
    • CPU 和 MLDA 模块的组合为处理域差异和私有类别提供了强大的解决方案.
    • 三个基准数据集的实验结果验证了拟议方法在识别已知和未知样本方面的优越性.