This paper presents a comparative study of the performances of arithmetic units, based on different number systems like Residue Number System (RNS), Double Base Number System (DBNS), Triple Base Number System (TBNS) a...This paper presents a comparative study of the performances of arithmetic units, based on different number systems like Residue Number System (RNS), Double Base Number System (DBNS), Triple Base Number System (TBNS) and Mixed Number System (MNS) for DSP applications. The performance analysis is carried out in terms of the hardware utilization, timing complexity and efficiency. The arithmetic units based on these number systems were employed in designing various modulation schemes like Binary Frequency Shift Keying (BFSK) modulator/demodulator. The analysis of the performance of the proposed modulator on above mentioned number systems indicates the superiority of other number systems over binary number system.展开更多
用户用电行为异常检测是保障电网安全运行、减少电力企业经济损失的关键环节。用户用电行为具有高度动态性和复杂性:不同用户、季节及时段的负荷曲线存在显著差异,正常波动与异常模式往往边界模糊,导致异常行为判断存在一定难度。为此,...用户用电行为异常检测是保障电网安全运行、减少电力企业经济损失的关键环节。用户用电行为具有高度动态性和复杂性:不同用户、季节及时段的负荷曲线存在显著差异,正常波动与异常模式往往边界模糊,导致异常行为判断存在一定难度。为此,本文提出一种基于动态时间规整(Dynamic Time Warping,DTW)算法的用户用电行为异常检测方法。利用欧氏距离定义电力用户用电数据类簇中心,并对类簇中心不断更新,实现用户用电行为数据的聚类。基于此,利用基于深度信念网络(Deep Belief Networks,DBN),提取用户用电行为特征,将该特征作为DTW算法的输入依据,度量用户用电行为的时间序列相似性,判断用户用电是否存在异常,最终完成用户用电行为异常检测。实验测试结果表明:所提方法检测出的异常用电行为数量与实际数量一致,且异常检测的召回率、F1分数指标均显著高于现有方法。展开更多
文摘This paper presents a comparative study of the performances of arithmetic units, based on different number systems like Residue Number System (RNS), Double Base Number System (DBNS), Triple Base Number System (TBNS) and Mixed Number System (MNS) for DSP applications. The performance analysis is carried out in terms of the hardware utilization, timing complexity and efficiency. The arithmetic units based on these number systems were employed in designing various modulation schemes like Binary Frequency Shift Keying (BFSK) modulator/demodulator. The analysis of the performance of the proposed modulator on above mentioned number systems indicates the superiority of other number systems over binary number system.
文摘用户用电行为异常检测是保障电网安全运行、减少电力企业经济损失的关键环节。用户用电行为具有高度动态性和复杂性:不同用户、季节及时段的负荷曲线存在显著差异,正常波动与异常模式往往边界模糊,导致异常行为判断存在一定难度。为此,本文提出一种基于动态时间规整(Dynamic Time Warping,DTW)算法的用户用电行为异常检测方法。利用欧氏距离定义电力用户用电数据类簇中心,并对类簇中心不断更新,实现用户用电行为数据的聚类。基于此,利用基于深度信念网络(Deep Belief Networks,DBN),提取用户用电行为特征,将该特征作为DTW算法的输入依据,度量用户用电行为的时间序列相似性,判断用户用电是否存在异常,最终完成用户用电行为异常检测。实验测试结果表明:所提方法检测出的异常用电行为数量与实际数量一致,且异常检测的召回率、F1分数指标均显著高于现有方法。
