matlab 做模式地形，處理生成grid網(wǎng)格
上傳時間： 2018-01-05
上傳用戶：tingchong
%球體 close all; G=6.67e-11; R=2;%球體半徑 p=4.0;%密度 D=10.0;%深度 M=(4/3)*pi*R^3*p;%質(zhì)量 x=-20:1:20; g=G*M*D./((x.^2+D^2).^(3/2)); Vxz=-3*G*M*D.*x./((x.^2+D^2).^(5/2)); Vzz=G*M.*(2*D^2-x.^2)./((x.^2+D^2).^(5/2)); Vzzz=3*G*M.*(2*D^2-3.*x.^2)./((x.^2+D^2).^(7/2)); subplot(2,2,1) plot(x,g,'k-'); xlabel('水平距離(m)'); ylabel('重力異常值'); title('球體重力異常Δg'); grid on subplot(2,2,2) plot(x,Vxz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vxz'); grid on subplot(2,2,3) plot(x,Vzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzz'); grid on subplot(2,2,4); plot(x,Vzzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzzz'); grid on %% %水平圓柱體 close all G=6.67e-11; p=10.0;%線密度 D=100.0;%深度 x=-200:1:200; g=G*2*p*D./(x.^2+D^2); Vxz=4*G*p*D.*x./(x.^2+D^2).^2; Vzz=2*G*p.*(D^2-x.^2)./(x.^2+D^2).^2; Vzzz=4*G*p.*(D^2-3.*x.^2)./((x.^2+D^2).^3); subplot(2,2,1) plot(x,g,'k-'); xlabel('水平距離(m)'); ylabel('重力異常值'); title('水平圓柱體重力異常Δg'); grid on subplot(2,2,2) plot(x,Vxz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vxz'); grid on subplot(2,2,3) plot(x,Vzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzz'); grid on subplot(2,2,4); plot(x,Vzzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzzz'); grid on %% %垂直臺階 G=6.67e-11; p=4.0;%密度 h1=50.0;%下層深度 h2=40.0;%上層深度 x=-100:1:100; g=G*p.*(pi*(h1-h2)+x.*log((x.^2+h1^2)./(x.^2+h2^2))+2*h1.*atan(x./h1)-2*h2.*atan(x./h2)); Vxz=G*p.*log((h1^2+x.^2)./(h2^2+x.^2)); Vzz=2*G*p.*atan((x.*(h1-h2))./(x.^2+h1*h2)); Vzzz=2*G*p.*x*(h1^2-h2^2)./((h1^2+x.^2).*(x.^2+h2^2)); subplot(2,2,1) plot(x,g,'k-'); xlabel('水平距離(m)'); ylabel('重力異常值'); title('垂直臺階重力異常Δg'); grid on subplot(2,2,2) plot(x,Vxz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vxz'); grid on subplot(2,2,3) plot(x,Vzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzz'); grid on subplot(2,2,4); plot(x,Vzzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzzz'); grid on %% %傾斜臺階 G=6.67e-11; p=4.0;%密度 h1=50.0;%下層深度 h2=40.0;%上層深度 a=pi/6;%傾斜角度 x=-500:1:500; g=G*p.*(pi*(h1-h2)+2*h1.*atan((x+h1*cot(a))./h1)-2*h2.*atan((x+h2*cot(a))./h1)+x.*sin(a)^2.*log(((h1+x.*sin(a).*cos(a)).^2+x.^2.*sin(a)^4)./((h2+x.*(sin(a)*cos(a))).^2+x.^2.*sin(a)^4))); Vxz=G*p.*(sin(a)^2.*log(((h1*cot(a)+x).^2+h1^2)./((h2*cot(a)+x).^2+h2^2))-2*sin(2*a).*(atan((h1/sin(a)+x.*cos(a))./(x.*sin(a)))-atan((h2/sin(a)+x.^cos(a))./(sin(a).*x)))); Vzz=G*p.*(0.5*sin(2*a)^2.*log(((h1*cot(a)+x).^2+h1^2)./((h2*cot(a)+x).^2+h2^2))+2*sin(a)^2.*(atan((h1/sin(a)+x.*cos(a))./(x.*sin(a)))-atan((h2/sin(a)+x.*cos(a))./(x.*sin(a))))); Vzzz=2*G*p*sin(a)^2.*((x+2*h2*cot(a))./((h2*cot(a)+x).^2+h2^2)-(x+2*h1*cot(a))./((h1*cot(a)+x).^2+h1^2)); subplot(2,2,1) plot(x,g,'k-'); xlabel('水平距離(m)'); ylabel('重力異常值'); title('傾斜臺階重力異常Δg'); grid on subplot(2,2,2) plot(x,Vxz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vxz'); grid on subplot(2,2,3) plot(x,Vzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzz'); grid on subplot(2,2,4); plot(x,Vzzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzzz'); grid on %% %鉛錘柱體 G=6.67e-11; p=4.0;%密度 h1=50.0;%下層深度 h2=40.0;%上層深度 a=3;%半徑 x=-500:1:500; g=G*p.*((x+a).*log(((x+a).^2+h1^2)./((x+a).^2+h2^2))-(x-a).*log(((x-a).^2+h1^2)./((x-a).^2+h2^2))+2*h1.*(atan((x+a)./h1)-atan((x-a)./h1))-2*h2.*(atan((x+a)./h2)-atan((x-a)./h2))); Vxz=G*p.*log((((x+a).^2+h1^2).*((x-a).^2+h2^2))./(((x+a).^2+h2^2).*((x-a).^2+h1^2))); Vzz=2*G*p.*(atan(h1./(x+a))-atan(h2./(x+a))-atan(h1./(x-a))+atan(h2./(x-a))); Vzzz=2*G*p.*((x+a)./((x+a).^2+h2^2)-(x+a)./((x+a).^2+h1^2)-(x-a)./((x-a).^2+h2^2)+(x-a)./((x-a).^2+h1^2)); subplot(2,2,1) plot(x,g,'k-'); xlabel('水平距離/m') ylabel('重力異常值') title('鉛垂柱體重力異常') grid on subplot(2,2,2) plot(x,Vxz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vxz'); grid on subplot(2,2,3) plot(x,Vzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzz'); grid on subplot(2,2,4); plot(x,Vzzz); xlabel('水平距離(m)'); ylabel('導數(shù)值'); title('Vzzz'); grid on
上傳時間： 2019-05-10
上傳用戶：xiajiang
Accurate pose estimation plays an important role in solution of simultaneous localization and mapping (SLAM) problem, required for many robotic applications. This paper presents a new approach called R-SLAM, primarily to overcome systematic and non-systematic odometry errors which are generally caused by uneven floors, unexpected objects on the floor or wheel-slippage due to skidding or fast turns.The hybrid approach presented here combines the strengths of feature based and grid based methods to produce globally consistent high resolution maps within various types of environments.
標簽： localization environments challenging Resilient mapping R-SLAM and in
上傳時間： 2019-09-15
上傳用戶：zhudx2007
Smart Grids provide many benefits for society. Reliability, observability across the energy distribution system and the exchange of information between devices are just some of the features that make Smart Grids so attractive. One of the main products of a Smart Grid is to data. The amount of data available nowadays increases fast and carries several kinds of information. Smart metres allow engineers to perform multiple measurements and analyse such data. For example, information about consumption, power quality and digital protection, among others, can be extracted. However, the main challenge in extracting information from data arises from the data quality. In fact, many sectors of the society can benefit from such data. Hence, this information needs to be properly stored and readily available. In this chapter, we will address the main concepts involving Technology Information, Data Mining, Big Data and clustering for deploying information on Smart Grids.
標簽： Processing Cities Smart Data in
上傳時間： 2020-05-23
上傳用戶：shancjb
Smart Grids provide many benefits for society. Reliability, observability across the energy distribution system and the exchange of information between devices are just some of the features that make Smart Grids so attractive. One of the main products of a Smart Grid is to data. The amount of data available nowadays increases fast and carries several kinds of information. Smart metres allow engineers to perform multiple measurements and analyse such data. For example, information about consumption, power quality and digital protection, among others, can be extracted. However, the main challenge in extracting information from data arises from the data quality. In fact, many sectors of the society can benefit from such data. Hence, this information needs to be properly stored and readily available. In this chapter, we will address the main concepts involving Technology Information, Data Mining, Big Data and clustering for deploying information on Smart Grids.
標簽： Processing Cities Smart Data
上傳時間： 2020-05-25
上傳用戶：shancjb
Broadband powerline communication systems are continuing to gain significant market adoption worldwide for applications ranging from IPTV delivery to the Smart Grid. The suite of standards developed by the HomePlug Powerline Alliance plays an important role in the widespread deployment of broadband PLC. To date, more than 100 million HomePlug modems are deployed and these numbers continue to rise.
上傳時間： 2020-05-26
上傳用戶：shancjb
Broadband powerline communication systems are continuing to gain significant market adoption worldwide for applications ranging from IPTV delivery to the Smart Grid. The suite of standards developed by the HomePlug Powerline Alliance plays an important role in the widespread deployment of broadband PLC. To date, more than 100 million HomePlug modems are deployed and these numbers continue to rise.
上傳時間： 2020-06-06
上傳用戶：shancjb
Resource allocation is an important issue in wireless communication networks. In recent decades, cognitive radio technology and cognitive radio-based networks have obtained more and more attention and have been well studied to improve spectrum utilization and to overcomethe problem of spectrum scarcity in future wireless com- munication systems. Many new challenges on resource allocation appear in cogni- tive radio-based networks. In this book, we focus on effective solutions to resource allocation in several important cognitive radio-based networks, including a cogni- tive radio-basedopportunisticspectrum access network, a cognitiveradio-basedcen- tralized network, a cognitive radio-based cellular network, a cognitive radio-based high-speed vehicle network, and a cognitive radio-based smart grid.
上傳時間： 2020-06-07
上傳用戶：shancjb
n its Framework and Roadmap for Smart Grid Interoperability Standards, the US National Institute of Standards and Technology declares that a twenty-first-century clean energy economy demands a twenty-first-century electric grid. 1 The start of the twenty-first century marked the acceleration of the Smart Grid evolution. The goals of this evolution are broad, including the promotion of widespread and distributed deployment of renewable energy sources, increased energy efficiency, peak power reduction, automated demand response, improved reliability, lower energy delivery costs, and consumer participation in energy management.
上傳時間： 2020-06-07
上傳用戶：shancjb
The large-scale deployment of the smart grid (SG) paradigm could play a strategic role in supporting the evolution of conventional electrical grids toward active, flexible and self- healing web energy networks composed of distributed and cooperative energy resources. From a conceptual point of view, the SG is the convergence of information and operational technologies applied to the electric grid, providing sustainable options to customers and improved security. Advances in research on SGs could increase the efficiency of modern electrical power systems by: (i) supporting the massive penetration of small-scale distributed and dispersed generators; (ii) facilitating the integration of pervasive synchronized metering systems; (iii) improving the interaction and cooperation between the network components; and (iv) allowing the wider deployment of self-healing and proactive control/protection paradigms.
標簽： Computational Intelligence
上傳時間： 2020-06-07
上傳用戶：shancjb
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