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32 × 48 2015
![](/images/236.jpg)
32 48 32 -32 -128 ()_
2010531 32 48 32 -32 -128 ( ) 16 -16 -64 -96 X-32 -48 -32 Y 32,48,32,-32.。。。Y-(-32)=32,X= -64 。。。。(-192)
get price: 4![](/images/1.jpg)
Math Solver GeoGebra
123 f (x) ABC αβγ x y z π 7 8 9 × ÷ e 4 5 6 + − < > ≤ ≥ 1 2 3 = ( ),0 . Free math problem solver with steps from GeoGebra: solve equations, algebra, trigonometry, calculus, and
get price: 4![](/images/230.jpg)
()
2023927 32×5×10 648÷(48÷8) 44×(77+23) 432÷2÷3 (32) 、 300÷5= 45×2= 540÷6= 20×40= 4.1+5.2= 500×3= 420÷7= 22×40= 84÷2= 77×5= 96÷2= 3.6-2.2= 909÷9= 0×
get price![](/images/3.jpg)
2014618 32 × 32 × 20 42. 4 38 26. 9 25 48 48 450 × 450 × 200 457. 0 480 219. 1 219 343 298 32 × 32 × 25 42. 4 38 33. 7 32 48 48 450 × 450 × 250 457. 0 480 273. 0 273 343 308 40 × 40 × 15 48. 3 45 21. 3 18 57 57 450 × 450 ×
get price![](/images/114.jpg)
HAV 2×32×0.15
2023125 :13×32×0.15+4×48×0.2 :18±1mm :G1 2 :≥75% :33 、 : :、 :HAVP :2×32×0.15+2×
get price![](/images/130.jpg)
× (mm) 6×1 (kg/m) 0.125 × (mm) 27×3.5 6×1.5 0.168 28×2 8×1 0.174 28×3 8×1.5 0.243 30×2 10×1 0.224 30×2.5 10×1.5 0.318 30×3 12×1 0.274 32×2 12×1.5 0.392 32×2.5 12×2 0.498 32×3 14×1 0.
get price![](/images/145.jpg)
An attention residual u-net with differential preprocessing
202321 Mathematically, P R e L U (x) = m a x (0, x) + a × m i n (0, x), which can improve the model accuracy at a negligible extra computational cost. The proposed 3D ARU-Net was trained by using cropped patches of size 48 × 256 × 256 with a sliding window running over the input 3D NIfTI image of size 256 × 256 × 256 to reduce memory
get price![](/images/129.jpg)
favicon.ico,-CSDN
20201016 -favicon.ico -16×16、32×32、48×48、64×64128×128 -8,2432HTML favicon.ico,
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![](/images/236.jpg)
32 48 32 -32 -128 ()_
2010531 32 48 32 -32 -128 ( ) 16 -16 -64 -96 X-32 -48 -32 Y 32,48,32,-32.。。。Y-(-32)=32,X= -64 。。。。(-192)
get price![](/images/1.jpg)
Math Solver GeoGebra
123 f (x) ABC αβγ x y z π 7 8 9 × ÷ e 4 5 6 + − < > ≤ ≥ 1 2 3 = ( ),0 . Free math problem solver with steps from GeoGebra: solve equations, algebra, trigonometry, calculus, and
get price![](/images/230.jpg)
()
2023927 32×5×10 648÷(48÷8) 44×(77+23) 432÷2÷3 (32) 、 300÷5= 45×2= 540÷6= 20×40= 4.1+5.2= 500×3= 420÷7= 22×40= 84÷2= 77×5= 96÷2= 3.6-2.2= 909÷9= 0×
get price![](/images/3.jpg)
2014618 32 × 32 × 20 42. 4 38 26. 9 25 48 48 450 × 450 × 200 457. 0 480 219. 1 219 343 298 32 × 32 × 25 42. 4 38 33. 7 32 48 48 450 × 450 × 250 457. 0 480 273. 0 273 343 308 40 × 40 × 15 48. 3 45 21. 3 18 57 57 450 × 450 ×
get price![](/images/114.jpg)
HAV 2×32×0.15
2023125 :13×32×0.15+4×48×0.2 :18±1mm :G1 2 :≥75% :33 、 : :、 :HAVP :2×32×0.15+2×
get price![](/images/130.jpg)
× (mm) 6×1 (kg/m) 0.125 × (mm) 27×3.5 6×1.5 0.168 28×2 8×1 0.174 28×3 8×1.5 0.243 30×2 10×1 0.224 30×2.5 10×1.5 0.318 30×3 12×1 0.274 32×2 12×1.5 0.392 32×2.5 12×2 0.498 32×3 14×1 0.
get price![](/images/145.jpg)
An attention residual u-net with differential preprocessing
202321 Mathematically, P R e L U (x) = m a x (0, x) + a × m i n (0, x), which can improve the model accuracy at a negligible extra computational cost. The proposed 3D ARU-Net was trained by using cropped patches of size 48 × 256 × 256 with a sliding window running over the input 3D NIfTI image of size 256 × 256 × 256 to reduce memory
get price![](/images/129.jpg)
favicon.ico,-CSDN
20201016 -favicon.ico -16×16、32×32、48×48、64×64128×128 -8,2432HTML favicon.ico,
get price