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FS3_Bdot1
:
function [M_bdot,Mdet_sta,ThetaInDegrees] = fcn(B,B_old,K_bdot,K_theta,Alignb, sampleTime,wb, normV)
%% B&Bdot
b = B/norm(B);
b_old = B_old/norm(B);
bdot = (b-b_old)/sampleTime/norm(b);
Bdot = (B-B_old)/sampleTime;
%% 6U Control Therom
if(normV<0.08& bdot~=0)
%stabilization mode
temp1 = cross(transpose(wb),B);
Mdet_sta = transpose(temp1);%ori
else
if(bdot~=0)
%detunbling mode
Mc = -transpose([0,0,0.01]);
K = transpose([10000,10000,10000]);
Mdet_sta = -K.*(bdot)-Mc;
else
Mdet_sta = transpose([0 0 0]);
end
end
CosTheta = dot(Alignb,B)/(norm(Alignb)*norm(B));
ThetaInDegrees = acosd(CosTheta);
Parameter: norm<0.08 go into stablization
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FS3_Bdot1
:
function [M_bdot,Mdet_sta,ThetaInDegrees] = fcn(B,B_old,K_bdot,K_theta,Alignb, sampleTime,wb, normV)
%% B&Bdot
b = B/norm(B);
b_old = B_old/norm(B);
bdot = (b-b_old)/sampleTime/norm(b);
Bdot = (B-B_old)/sampleTime;
%% 6U Control Therom
%detunbling mode
Mc = -transpose([0,0,0.01]);
K = transpose([10000,10000,10000]);
Mdet = -K.*(bdot)-Mc;
CosTheta = dot(Alignb,B)/(norm(Alignb)*norm(B));
ThetaInDegrees = acosd(CosTheta);
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Misson :
keep doing
A Feature Pyramid Network, or FPN, is a feature extractor that takes a single-scale image of an arbitrary size as input, and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion. This process is independent of the backbone convolutional architectures. It therefore acts as a generic solution for building feature pyramids inside deep convolutional networks to be used in tasks like object detection.
Jun 13, 2024image
Jun 6, 2024image
Jun 6, 2024Getting started %matplotlib inline import cv2 import random import numpy as np import matplotlib.pyplot as plt from google.colab.patches import cv2_imshow ]: %%capture ! wget -O img1.jpg "https://drive.google.com/uc?,→export=download&id=1omMydL6ADxq_vW5gl_1EFhdzT9kaMhUt" ! wget -O img2.jpg "https://drive.google.com/uc?,→export=download&id=12lxB1ArAlwGn97XgBgt-SFyjE7udMGvf"
Jun 6, 2024or
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