Complex Variables and Applications
2022-06-05 21:10:26 10.09MB Complex Variables and Applications
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数学经典教材
2022-06-02 14:18:23 39.97MB 数学
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complex_yolov4_pytorch预训练模型,详细介绍和使用方式请参考csdn博客:https://blog.csdn.net/suiyingy/article/details/125087396。
2022-06-02 09:11:19 229.01MB 人工智能 python 深度学习 complex_yolov4
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An MBSE modeling approach to efficiently address complex systems
2022-05-20 19:00:15 804KB MBSE
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该文档介绍了如何在复数域通过有理函数进行插值和逼近。
2022-05-17 11:13:15 60.7MB 插值 逼近 有理函数 复数域
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CMIF - 复杂模式指标函数 输入: H - 全复数 H 矩阵(频率响应函数),包括至少 2 个参考点测量值: 【频点数×DOF数×参考点数】 W - 频率矢量 [Hz] 参考: DJ Ewins,“模态测试:理论、实践和应用”,p。 300
2022-05-07 20:13:19 675B matlab
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Essential Issues in SOC Design Designing Complex Systems-on-Chip 是SOC设计必备的专业书籍。
2022-05-05 10:14:02 5.11MB hardware accelerator FPGA design
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for Complex Variables and Applications
2022-05-02 01:16:50 4.33MB math
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Complex Networks Toolbox for MatLab is designed to analyze large-scale graphs, model them, explore with simulations of dynamic processes and generate appealing and insightful layouts. example: function [BetweenneessCentrality, varargout]= GraphBetweennessCentrality(Graph,SourceNodes) % Computes betweenneess centrality of each node. % % Receives: % Graph - Graph Struct - the graph loaded with GraphLoad % SourceNodes - array of double - (optional) nodes, from which passes start. Default: [] (all nodes). % % Returns: % BetweenneessCentrality - array of double - Betweenneess Centrality for each node. % Nodes - array of double - (optional)List of all nodes for which betweennessn centrality is computed % % Algorithm: % http://www.boost.org/libs/graph/doc/betweenness_centrality.html % % See Also: % mexGraphAllNodeShortestPasses % warning('Use the more optimized mexGraphBetweennessCentrality.dll'); error(nargchk(1,2,nargin)); error(nargoutchk(0,2,nargout)); if ~exist('SourceNodes') | isempty(SourceNodes) SourceNodes = unique(Graph.Data(:,1)); end Nodes = unique(Graph.Data(:,1:2)); %TotalPasses = zeros(GraphCountNumberOfNodes(Graph),GraphCountNumberOfNodes(Graph)); Betweenness = zeros(GraphCountNumberOfNodes(Graph),1); for Node = Nodes(:).' [ShortesPasses PassesHistogram]= mexGraphAllNodeShortestPasses(Graph,Node); %TotalPasses = TotalPasses + sum(PassesHistogram(2:end)); tic for i = 1 : numel(ShortesPasses) %T = ShortesPasses(i).Passes(end); %TotalPasses(Node,ShortesPasses(i).Passes(end)) = size(ShortesPasses(i).Passes,2); % compute total number of shortes passes from Node to some other node. Passes = ShortesPasses(i).Passes(2:end-1,:); NodesOnTheWay = unique(Passes); if numel(NodesOnTheWay)==1 Count = 1; % hist behaves differently in this case. else Count = hist(Passes(:),NodesOnTheWay);
2022-04-26 20:46:45 24.47MB 复杂网络 连锁故障
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