排序方式: 共有14条查询结果,搜索用时 15 毫秒
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针对小区居民用电数据挖掘效率低、数据量大等难题,进行了基于云计算和改进K-means算法的海量用电数据分析方法研究。针对传统K-means算法中存在初始聚类中心和K值难确定的问题,提出一种基于密度的K-means改进算法。首先,定义样本密度、簇内样本平均距离的倒数和簇间距离三者乘积为权值积,通过最大权值积法依次确定聚类中心,提高了聚类的准确率;然后,基于MapReduce模型实现改进算法的并行化,提高了聚类的效率;最后,以小区400户家庭用电数据为基础,进行海量电力数据的挖掘分析实验。以家庭为单位,提取出用户的峰时耗电率、负荷率、谷电负荷系数以及平段用电量百分比,建立聚类的数据维度特征向量,完成相似用户类型的聚类,同时分析出各类用户的行为特征。基于Hadoop集群的实验结果证明提出的改进K-means算法运行稳定、可靠,具有很好的聚类效果。 相似文献
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Deep learning (DL) requires massive volume of data to train the network. Insufficient training data will cause serious overfitting problem and degrade the classification accuracy. In order to solve this problem, a method for automatic modulation classification ( AMC) using AlexNet with data augmentation was proposed. Three data augmentation methods is considered, i. e. , random erasing, CutMix, and rotation. Firstly, modulated signals are converted into constellation representations. And all constellation representations are divided into training dataset and test dataset. Then, training dataset are augmented by three methods. Secondly, the optimal value of execution probability for random erasing and CutMix are determined. Simulation results show that both of them perform optimally when execution probability is 0.5. Thirdly, the performance of three data augmentation methods are evaluated. Simulation results demonstrate that all augmentation methods can improve the classification accuracy. Rotation improves the classification accuracy by 13.04% when signal noise ratio (SNR) is 2 dB. Among three methods, rotation outperforms random erasing and CutMix when SNR is greater than - 6 dB. Finally, compared with other classification algorithms, random erasing, CutMix, and rotation used in this paper achieved the performance significantly improved. It is worth mentioning that the classification accuracy can reach 90.5% with SNR at 10 dB. 相似文献
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以线性分组码和卷积码为基础构造出一类(2k,k,2)卷积码,并通过定义一种三维矩阵进行了状态转移描述.通过引入各种矩阵处理模块,构建出一种具有并行处理能力的维特比矩阵译码器,这种译码器的单一结构有利于对其进行分析和设计.仿真实验表明,该类卷积码的确具有高效的译码速度和优良的纠错能力. 相似文献
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脉冲雷达对回波的检测要求一定的回波信噪比,分析表明,脉冲同步积累能够有效提高信噪比。给出了脉冲积累的仿真结果,并设计了一种基于FPGA的同步积累器,测试结果表明,该积累器能有效提高接收脉冲信噪比,从而改善雷达的检测性能,且实现简单,有较强的工程应用价值。 相似文献