IEC 62196-2-2016标准内容高清可复制,非扫描版。 Plugs, socket-outlets, vehicle connectors and vehicle inlets – Conductive charging of electric vehicles – Part 2: Dimensional compatibility and interchangeability requirements for a.c.pin and contact-tube accessories 插头、插座、车辆连接器和车辆入口 – 电动汽车的传导充电 第 2 部分:交流引脚和接触管附件的尺寸兼容性和互换性要求
2022-02-08 14:03:03 8.55MB IEC62196 IEC62196-2 欧标充电标准
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Two Dimensional Phase Unwrapping theory经典书,我的是不要积分的
2022-02-02 22:58:58 14MB TwoDimensional PhaseUnwrapping
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介绍了二维相关光谱的原理,并给出了常用的方法及应用领域。
2022-01-10 19:36:20 4.71MB 二维光谱,相关分析
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英国Liverpool John Moores University.,二维相位解包的博士论文
2022-01-08 21:00:01 2.88MB Phase Unwrapping
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Abstract On-chip interconnects are predicted to be a fundamental issue in designing multi-core chip multiprocessors (CMPs) and system-on-chip (SoC) architectures with numerous homogeneous and heterogeneous cores and functional blocks. To mitigate the interconnect crisis, one promising option is network-on-chip (NoC), where a general purpose on-chip interconnection network replaces the traditional design-specific global on-chip wiring by using switching fabrics or routers to connect IP cores or processing elements. Such packet-based communication networks have been gaining wide acceptance due to their scalability and have been proposed for future CMPs and SoC design. In this chapter, we study the combination of both three-dimensional integrated circuits and NoCs, since both are proposed as solutions to mitigate the interconnect scaling challenges. This chapter will start with a brief introduction on network-on-chip architecture and then discuss design space exploration for various network topologies in 3D NoC design, as well as different techniques on 3D on-chip router design. Finally, it describes a design example of using 3D NoC with memory stacked on multi-core CMPs.
2021-12-17 08:14:32 1.48MB Yuan Xie Narayanan Vijaykrishnan
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matlab导入excel代码utl_max_values_from_a_three_Dimension_array 三维数组最后一维的最大值。 关键字:sas sql join合并大数据分析宏oracle teradata mysql sas社区stackoverflow statistics人工智慧AI Python R Java Javascript WPS Matlab SPSS Scala Perl CC#Excel MS Access JSON图形映射NLP自然语言处理机器学习igraph DOSUBL DOW循环stackoverfl SAS社区。 三维数组最后一维的最大值 github https://github.com/rogerjdeangelis/utl_max_values_from_a_three_dimensional_array Same results with SAS and WPS Two solutions 1. Base SAS/WPS 2. WPS/PROC R or SAS/IML/R 3. Rick Wicklin IML solutio
2021-12-13 21:54:43 5KB 系统开源
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This comprehensive book presents a rigorous and state-of-the-art treatment of variational inequalities and complementarity problems in finite dimensions. This class of mathematical programming problems provides a powerful framework for the unified analysis and development of efficient solution algorithms for a wide range of equilibrium problems in economics, engineering, finance, and applied sciences. New research material and recent results, not otherwise easily accessible, are presented in a self-contained and consistent manner. The book is published in two volumes, with the first volume concentrating on the basic theory and the second on iterative algorithms. Both volumes contain abundant exercises and feature extensive bibliographies. Written with a wide range of readers in mind, including graduate students and researchers in applied mathematics, optimization, and operations research as well as computational economists and engineers, this book will be an enduring reference on the subject and provide the foundation for its sustained growth
2021-11-15 18:43:05 5.42MB VI CP
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This book brings together methodological concepts, computational algorithms, a few applications and mathematical theory for high-dimensional statistics.
2021-11-13 22:55:41 8.51MB Statistics
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挤 多维监控异常根因分析,复现论文ISSRE 2019 REG文件“多维根本原因的通用和鲁棒性本地化”。 数据 数据集A,B0,B1,B2,B3,B4,D在上可用。 基本事实根本原因集在每个子文件夹的injection_info.csv中。 引文 @inproceedings {squeeze,title = {多维根源的通用且鲁棒的本地化},作者= {Li,Zeyan和Luo,Chengyang and Zhao,Yiwei and Sun,Yongqian and Sui,Kaixin and Wang,Xiping and Liu,Dapeng ,书名= {2019 IEEE第30届软件可靠性工程国际研讨会(ISSRE)},年份= {2019},组织= {IEEE}}
2021-10-14 19:33:53 12KB Python
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【ICML2021】基于稀疏标签编码的多维分类 在多维分类中,输出空间中存在多个类变量,每个类变量对应一个异构类空间。由于类空间的异质性,在从MDC示例中学习时,考虑类变量之间的依赖关系非常具有挑战性。本文提出了一种新的多目标预测方法,即SLEM方法,它在编码的标签空间中学习预测模型,而不是在异构的标签空间中学习预测模型。具体来说,SLEM在编码-训练-解码框架中工作。在编码阶段,通过成对分组、一次热转换和稀疏线性编码三种级联操作,将每个类向量映射为实值向量。在训练阶段,在编码标签空间内学习多输出回归模型。在解码阶段,通过对学习的多输出回归模型的输出进行正交匹配追踪,得到预测的类向量。实验结果清楚地验证了SLEM相对于最先进的MDC方法的优越性。
2021-10-08 23:19:35 443KB 多维分类
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