2010 : M
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1 2010 M
2 2010 : M
3 I
4 II
5 1 1.1 Second Life [1] [2] 1
6 [3] [4][5] [6][7][8] [9] [10] Pelechano [11] Pelechano ( ) 2
7
8 [12]
9 2.1: : 2 1 5
10 :
11 : 7
12 2.5: : 8
13 ( )
![3.1.1 R [13] c](/docs-images/91/106027272/images/14-0.jpg "c 1 3.1 3.1: 10")
14 3.1.1 R [13] c c : 10
![3.1.2 [6] ( ) 3.](/docs-images/91/106027272/images/15-0.jpg "2 3.2: [14] [15]")
15 3.1.2 [6] ( ) : [14] [15] 11
16 W(x, y) W(x, y) : 3.4: 12
17 3.1.3 [6] [16] A i A i A i V i A j W(x, y) V i r θ a V i R c a A i (3.1) A i A j (3.2)
18 A i = A i V i (cr + a) (3.1) V i A j A i < r, and A j A i A j A i > cos θ a (3.2) 3.5: A i V i A j V j θ v 2 V i V j (3.3) A j A i
19 V j V i V j V i < cos θ v (3.3) 3.6: [17] [18] W(x, y) W(x, y) A i E i 15
20 Q e Q e W(x, y) E i A i A i A j A j T ij Q f E i T ij θ f A i A j F ij (3.4) A i (3.5) F i A i 3.7 F ij = ( QT T ij 2 ) Tij T ij cos θ f (3.4) F i = j F ij (3.5) 3.7: 16
21 A i t V i V i E i F i (3.6) A i t P i P i V i [19] (3.7) V i = V i + (E i F i ) t (3.6) P i = P i + V i t (3.7) 3.2 [9] [20] o A i l P o r P o lp o r P o A j A i A j
22 3.8: 3.9: [21] A i A j R (3.8) S = {S 0, S 1,...} A i D ij (3.9) (3.10) A i B i 18
23 3.10 S = { j A j A i < 2R } (3.8) D ij = A j A i 2R (3.9) B i = A i + j S A j A i A j A i D ij (3.10) 3.10:
24 3.11: (3.10)
25 D FK ToolKit System[22] : (n) 500 (R) 0.5 (c) 1.2 (a) 1.0 (θ a ) 120 (r) 5.0 (Q e ) 0.1 (Q f )
26 4.1:
27 4.2: 4.3:
28 4.4: 4.5:
29 4.6: 4.7:
30 5 3D FK ToolKit System[22] 26
31 27
32 [1] Second Life. [2]. [3] : :. k4no.info/index j.html. [4],,,, : (2008). [5],, (2009). [6],, Master s thesis (2006). [7],, Master s thesis (2006). [8],,, 22 (2010). 28
33 [9],, (432), (1992). [10],,,,,, (2005). [11] N. Pelechano, J.M. Allbeck and N.I.Badler, Controlling individual agents in high-density crowd simulation, Proceedings of the 2007 ACM SIGGRAPH (2007). [12]. [13], [ ],, (1990). [14],,,,, 9, (1999). [15] David M. Bourg Glenn Seemann, [ AI ], O REILLY (2005). [16],, Master s thesis (2003). [17],, Master s thesis (2005). [18],, Master s thesis (2001). [19], [ CG ], (2008). 29
34 [20], [ ], (2001). [21] David M. Bourg Glenn Seemann, [ AI ], O REILLY (2005). [22] Fine Kernel Tool Kit System. 30
2010 : M CG 3DCG 3 3
2010 M0107432 2010 : M0107432 3 3 CG 3DCG 3 3 1 1 1.1............................ 1 1.2.............................. 2 2 3 2.1................................. 3 2.2 3........................ 4 2.3.................................
2009 : M (CG)
2009 M0106262 2009 : M0106262 (CG) 2 2 2 2 2 2 1 1 1.1............................. 1 1.2.............................. 3 2 4 2.1............................... 4 2.2............................... 6 2.3.............................
弾性定数の対称性について
() by T. oyama () ij C ij = () () C, C, C () ij ji ij ijlk ij ij () C C C C C C * C C C C C * * C C C C = * * * C C C * * * * C C * * * * * C () * P (,, ) P (,, ) lij = () P (,, ) P(,, ) (,, ) P (, 00,
2018 M
2018 M0115001 2018 9 2018 : M0115001 1 3D 3D 1 3D 3D 1 1 1.1......................................... 1 1.2......................................... 3 2 4 2.1...................................... 4 2.2................................
2007 3DCG : M DCG 3DCG 3DCG 3D (huristic method) C++
2007 3DCG M0104402 2007 3DCG : M0104402 3DCG 3DCG 3DCG 3D (huristic method) C++ 1 1 1.1............................ 1 1.2.............................. 3 2 4 2.1......................... 4 2.2....................
2009 : M DCG 3 4 3
2009 M0106121 2009 : M0106121 3DCG 3 4 3 1 1 1.1............................. 1 1.2.............................. 4 2 5 2.1.............................. 5 2.2............................. 6 2.2.1...........................
知能科学：ニューラルネットワーク
2 3 4 (Neural Network) (Deep Learning) (Deep Learning) ( x x = ax + b x x x ? x x x w σ b = σ(wx + b) x w b w b .2.8.6 σ(x) = + e x.4.2 -.2 - -5 5 x w x2 w2 σ x3 w3 b = σ(w x + w 2 x 2 + w 3 x 3 + b) x,
知能科学：ニューラルネットワーク
2 3 4 (Neural Network) (Deep Learning) (Deep Learning) ( x x = ax + b x x x ? x x x w σ b = σ(wx + b) x w b w b .2.8.6 σ(x) = + e x.4.2 -.2 - -5 5 x w x2 w2 σ x3 w3 b = σ(w x + w 2 x 2 + w 3 x 3 + b) x,
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平成１８年度「商品先物取引に関する実態調査」報告書
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23 15961615 1659 1657 14 1701 1711 1715 11 15 22 15 35 18 22 35 23 17 17 106 1.25 21 27 12 17 420,845 23 32 58.7 32 17 11.4 71.3 17.3 32 13.3 66.4 20.3 17 10,657 k 23 20 12 17 23 17 490,708 420,845 23
2010 : M0107189 3DCG 3 (3DCG) 3DCG 3DCG 3DCG S
2010 M0107189 2010 : M0107189 3DCG 3 (3DCG) 3DCG 3DCG 3DCG S 1 1 1.1............................ 1 1.2.............................. 4 2 5 2.1............................ 5 2.2.............................
AHPを用いた大相撲の新しい番付編成
5304050 2008/2/15 1 2008/2/15 2 42 2008/2/15 3 2008/2/15 4 195 2008/2/15 5 2008/2/15 6 i j ij >1 ij ij1/>1 i j i 1 ji 1/ j ij 2008/2/15 7 1 =2.01/=0.5 =1.51/=0.67 2008/2/15 8 1 2008/2/15 9 () u ) i i i
24 21 21115025 i 1 1 2 5 2.1.................................. 6 2.1.1........................... 6 2.1.2........................... 7 2.2...................................... 8 2.3............................
1. 2 P 2 (x, y) 2 x y (0, 0) R 2 = {(x, y) x, y R} x, y R P = (x, y) O = (0, 0) OP ( ) OP x x, y y ( ) x v = y ( ) x 2 1 v = P = (x, y) y ( x y ) 2 (x
. P (, (0, 0 R {(,, R}, R P (, O (0, 0 OP OP, v v P (, ( (, (, { R, R} v (, (, (,, z 3 w z R 3,, z R z n R n.,..., n R n n w, t w ( z z Ke Words:. A P 3 0 B P 0 a. A P b B P 3. A π/90 B a + b c π/ 3. +
2009 3DCG : M0106423 3DCG,,,, 3DCG 2D 3DCG 2D 3DCG 3DCG
2009 3DCG M0106423 2009 3DCG : M0106423 3DCG,,,, 3DCG 2D 3DCG 2D 3DCG 3DCG 1 1 1.1................................. 1 1.2................................. 1 1.3............................... 3 1.4.................................
3 65 1 4 5 67 1 2 5 5 3 6 68 23 69 2 6 8m 10m 1. 2. 3. 70 66 600km 11 3 16 21 3 0 3m 2m 0 5m 71 11 3 17 0 5 0 0 72 73 74 75 3 76 77 4 78 79 5 80 81 82 83 2 83 . 84 6 a b c d e f g a b c 3 85 16 86 87 7
2003 : 00P249,,, CG
2003 Web3D 00P249 2003 : 00P249,,, CG 1 1 1.1................................. 1 1.2................................. 3 1.3.............................. 3 2 4 2.1............................... 4 2.2.....................
Taro11-接遇のしおり（一太郎版）
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- k k k = y. k = ky. y du dx = ε ux ( ) ux ( ) = ax+ b x u() = ; u( ) = AE u() = b= u () = a= ; a= d x du ε x = = = dx dx N = σ da = E ε da = EA ε A x A x x - σ x σ x = Eε x N = EAε x = EA = N = EA k =
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1. 1 1.1 1.1.1 1.1.1.1 v v = v 1 v 2 v 3 (1) R = (R ij ) (2) R (R 1 ) ij = R ji (3) R ij R ik = δ jk (4) δ ij Kronecker δ ij = { 1 (i = j) 0 (i j) (5) 1 1.1. v1.1 2011/04/10 1. 1 2 v i = R ij v j (6) [
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145 13 13.1 13.1.1 0 m mg S 13.1 F 13.1 F /m S F F 13.1 F mg S F F mg 13.1: m d2 r 2 = F + F = 0 (13.1) 146 13 F = F (13.2) S S S S S P r S P r r = r 0 + r (13.3) r 0 S S m d2 r 2 = F (13.4) (13.3) d 2
IPSJ SIG Technical Report Vol.2014-CG-155 No /6/28 1,a) 1,2,3 1 3,4 CG An Interpolation Method of Different Flow Fields using Polar Inter
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2012 M0109218 2012 : M0109218 36 1 1 1.1............................. 1 1.2................................. 5 2 6 2.1................... 6 2.2................ 8 2.3............ 12 3 15 3.1...................
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量子力学 問題
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yoshi@image.med.osaka-u.ac.jp http://www.image.med.osaka-u.ac.jp/member/yoshi/ II Excel, Mathematica Mathematica Osaka Electro-Communication University (2007 Apr) 09849-31503-64015-30704-18799-390 http://www.image.med.osaka-u.ac.jp/member/yoshi/
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