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1 (Zin ARAI) 1 dynamical systems ( mechanics ) dynamical systems 3 G X Ψ:G X X, (g, x) Ψ(g, x) =:Ψ g (x) Ψ id (x) =x, Ψ gh (x) =Ψ h (Ψ g (x)) ( x X g, h G) G X Ψ x X G O(x) := Ψ(G, x) G Z f =Ψ 1 : X X O(x) ={f k (x) k Z} x k f k (x) 1
2 f k f f k = f f }{{} k R Ψ R Z R 2 L λ (x) =λx L λ : R R λ 0 L λ x λ X = R G = Z Ψ k = L k λ 1 x λ =1.01 x <0 x λ λ Ψ(k, x) =L k λ (x) =λk x k λ > 1 λ < 1 Z k 2
3 3 λ 0 λ =0 L λ k <0 Ψ k Z Z Z 0 := {k Z k 0} 4 L λ Q λ (x) =λx(1 x) x Q λ L λ (1 x) x =1 Q λ (1) = 0 Q λ R Q λ ([0, 1]) [0, 1] Q λ :[0, 1] [0, 1] [0, 1] L λ 2 Q λ Q k λ (x) 3
4 1 5 1 dx dt = λx λ R x λ <0 t =0 x = x 0 x(t) =x 0 e λt Ψ:R R R Ψ(t, x) :=xe λt 4
5 M C 1 G = R X = M Ψ:R M M C 1 M ξ dψ(t, x) ξ x := dt Ψ Z Z 0 G R flow flow G = Z flow Ψ : R X X t R f(x) =Ψ(t, x) f : X X f Ψ -t f flow t t=0 S P(x) x 2 flow Ψ p X T T Ψ T (p) =p T>0 p p S p x S S T x S f(x) 5
6 p S U f : U S f flow S 2 flow S X flow flow f : X X X [0, 1] X {1} X {0} (x, 1) (f(x), 0) X = X [0, 1]/ (x, s) (x, s), (x, 1) (y, 0) y = f(x) flow Ψ Ψ t (x, s) =(f t+s (x), t+ s t + s ) f suspension flow x := max{k Z k x} f suspension flow Ψ S = X {0} f flow flow flow 6 G = Z, Z 0 R 100 6
7 3 F. Diacu and P. Holmes, Celestial Encounters, Springer L λ y X x X ω- t k Ψ t k (x) y R Z y X x X α- t k Ψ t k (x) y ω- α- ω(x) = Ψ t (x), α(x) = Ψ t (x) ω-α- T 0 t T T 0 t T 7
8 7 R 2. X = R 2 S 2 C 1 x X ω(x) α(x) flow α ω limit cycle 3 3 Van der Pol R 2 3 genus 3 genus T 2 flow x T 2 O(x) x T 2 O(x) =T 2 8
9 8 Lorenz ẋ = σx + σy ẏ = ρx y xz ż = βz + xy R 3 σ, ρ, β Lorenz (σ, ρ, β) =(10, 28, 8/3) z y x 4 Lorenz
10 [1] X. Ψ:G X X C>0 x X U y U t G t x y C x X U : x y U t G such that d(ψ t (x), Ψ t (y)) >C d X 9 ( ( ) x a x H a,b : R 2 R 2 : y) 2 + by x M. Hénon 10
11 a =1.4, b =0.3 5 amazon [6] [7] 11
12 [1],, [2] B. Hasselblatt and A. Katok, A First Course in Dynamics, Cambridge University Press, [3] R. L. Devaney, An Introduction to Chaotic Dynamical System, 2nd ed., Perseus Books Publishing, ,, [4] M. W. Hirsch, S. Smale and R. L. Devaney, Differential Equations, Dynamical Systems and an Introduction to Chaos, 2nd ed., Academic Press, 2004.,, [5]J.PalisandW.deMelo,Geometric Theory of Dynamical Systems, Springer, [6] C. Robinson, Dynamical Systems, CRC Press, 1999.,, [7] A. Katok and B. Hasselblatt, Introduction to the Modern Theory of Dynamical Systems, Cambridge University Press, [10] 600 [8] J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical System, and Bifurcations of Vector Fields, Springer, [9] K. T. Alligood, T. D. Sauer and J. A. Yorke, Chaos. An Introduction to Dynamical Systems Springer, , 2, 3, [10] P. Cvitanović, et al., Chaos: classical and quantum, Handbooks Handbook 3 Volume 1A, 1B Z R 12
13 random dynamical systems Volume 2 [11] B. Hasselblatt and A. Katok (ed.), Handbook of Dynamical Systems, Volume 1A, Elsevier Science, [12] B. Hasselblatt and A. Katok (ed.), Handbook of Dynamical Systems, Volume 1B, Elsevier Science, [13] B. Fiedler (ed.), Handbook of Dynamical Systems, Volume 2, Elsevier Science, S. Smale Differential Dynamical Systems [14] [6] [15] [16] [17] [14] S. Smale, The Mathematics of Time: Essays on Dynamical Systems, Economic Processes, and Related Topics, Springer, [15] M. Shub, Global Stability of Dynamical Systems, Springer, [16] J. Palis and F. Takens, Hyperbolicity and Sensitive Chaotic Dynamics at Homoclinic Bifurcations, Cambridge University Press, [17] C. Bonatti, L. Diaz and M. Viana, Dynamics Beyond Uniform Hyperbolicity, Springer, [18] [19] [18] V. I. Arnold, Mathematical Methods of Classical Mechanics, 2nd ed., Springer, ,, [19],, [11] 13
14 Franks Misiurewicz 2 [20] C. Conley, Isolated Invariant Sets and the Morse Index, American Mathematical Society, [21] J. Franks, Homology and Dynamical Systems, American Mathematical Society, [22] smooth ergodic theory [7] [25] [22] V. I. Arnold and A. Avez, Probrèmes Ergodiques de la Mécanique Classique, Gauthier-Villars, 1967.,, [23] M. Pollicott and M. Yuri, Dynamical Systems and Ergodic Theory, Cambridge University Press, [24] P. Walters, An Introduction to Ergodic Theory, Springer, [25] M. Pollicott, Lectures on Ergodic Theory and Pesin Theory on Compact Manifolds, Cambridge University Press, [26] [27] [28] [29] [30] [26] A. F. Beardon, Iteration of Rational Functions, Springer, [27] J. Milnor, Dynamics in One Complex Variable, 3rd ed., Princeton University Press, 2006 [28] S. Morosawa, Y. Nishimura, M. Taniguchi and T. Ueda, Holomorphic Dynamics, Cambridge University Press,
15 [29],, [30] M. Braverman and M. Yampolsky, Computability of Julia Sets, Springer, [31, 32] [33] [31] D. Lind and B. Marcus, Symbolic Dynamics and Coding, Cambridge University Press, [32] B. Kitchens, Symbolic Dynamics, Springer, [33] H. Xie, Grammatical Complexity and One-dimensional Dynamical Systems, World Scientific,
kokyuroku.dvi
On Applications of Rigorous Computing to Dynamical Systems (Zin ARAI) Department of Mathematics, Kyoto University email: [email protected] 1 [12, 13] Lorenz 2 Lorenz 3 4 2 Lorenz 2.1 Lorenz E. Lorenz
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Shunsuke Kobayashi [6] [] [7] u t = D 2 u x 2 + fu, v + s L ut, xdx, L x 0.L, t > 0, Neumann 0 v t = D 2 v 2 + gu, v, x 0, L, t > 0. x2 u u v t, 0 = t, L = 0, x x. v t, 0 = t, L = 0.2 x x ut, x R vt, x
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2006 11 28 1. (1) ẋ = ax = x(t) =Ce at C C>0 a0 x(t) 0(t )!! 1 0.8 0.6 0.4 0.2 2 4 6 8 10-0.2 (1) a =2 C =1 1. (1) τ>0 (2) ẋ(t) = ax(t τ) 4 2 2 4 6 8 10-2 -4 (2) a =2 τ =1!! 1. (2) A. (2)
2016 Course Description of Undergraduate Seminars (2015 12 16 ) 2016 12 16 ( ) 13:00 15:00 12 16 ( ) 1 21 ( ) 1 13 ( ) 17:00 1 14 ( ) 12:00 1 21 ( ) 15:00 1 27 ( ) 13:00 14:00 2 1 ( ) 17:00 2 3 ( ) 12
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医系の統計入門第 2 版 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 第 2 版 1 刷発行時のものです.
医系の統計入門第 2 版 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/009192 このサンプルページの内容は, 第 2 版 1 刷発行時のものです. i 2 t 1. 2. 3 2 3. 6 4. 7 5. n 2 ν 6. 2 7. 2003 ii 2 2013 10 iii 1987
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c Koichi Suga, 4 4 6 5 ISBN 978-4-64-6445- 4 ( ) x(t) t u(t) t {u(t)} {x(t)} () T, (), (3), (4) max J = {u(t)} V (x, u)dt ẋ = f(x, u) x() = x x(t ) = x T (), x, u, t ẋ x t u u ẋ = f(x, u) x(t ) = x T x(t
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1 13 6 8 3.6.3 - Aharonov-BohmAB) S 1/ 1/ S t = 1/ 1/ 1/ 1/, 1.1) 1/ 1/ *1 AB ) e iθ AB S AB = e iθ, AB θ π ϕ = e ϕ ϕ ) ϕ 1.) S S ) e iθ S w = e iθ 1.3) θ θ AB??) S t = 4 sin θ 1 + e iθ AB e iθ AB + e
) a + b = i + 6 b c = 6i j ) a = 0 b = c = 0 ) â = i + j 0 ˆb = 4) a b = b c = j + ) cos α = cos β = 6) a ˆb = b ĉ = 0 7) a b = 6i j b c = i + 6j + 8)
4 4 ) a + b = i + 6 b c = 6i j ) a = 0 b = c = 0 ) â = i + j 0 ˆb = 4) a b = b c = j + ) cos α = cos β = 6) a ˆb = b ĉ = 0 7) a b = 6i j b c = i + 6j + 8) a b a b = 6i j 4 b c b c 9) a b = 4 a b) c = 7
19 σ = P/A o σ B Maximum tensile strength σ % 0.2% proof stress σ EL Elastic limit Work hardening coefficient failure necking σ PL Proportional
19 σ = P/A o σ B Maximum tensile strength σ 0. 0.% 0.% proof stress σ EL Elastic limit Work hardening coefficient failure necking σ PL Proportional limit ε p = 0.% ε e = σ 0. /E plastic strain ε = ε e
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1 9 v.0.1 c (2016/10/07) Minoru Suzuki T µ 1 (7.108) f(e ) = 1 e β(e µ) 1 E 1 f(e ) (Bose-Einstein distribution function) *1 (8.1) (9.1)
1 9 v..1 c (216/1/7) Minoru Suzuki 1 1 9.1 9.1.1 T µ 1 (7.18) f(e ) = 1 e β(e µ) 1 E 1 f(e ) (Bose-Einstein distribution function) *1 (8.1) (9.1) E E µ = E f(e ) E µ (9.1) µ (9.2) µ 1 e β(e µ) 1 f(e )
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2. :,,,. :.... Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN 4 3. (probability),, 1. : : n, α A, A a/n. :, p, p Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN
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x () g(x) = f(t) dt f(x), F (x) 3x () g(x) g (x) f(x), F (x) (3) h(x) = x 3x tf(t) dt.9 = {(x, y) ; x, y, x + y } f(x, y) = xy( x y). h (x) f(x), F (x
[ ] IC. f(x) = e x () f(x) f (x) () lim f(x) lim f(x) x + x (3) lim f(x) lim f(x) x + x (4) y = f(x) ( ) ( s46). < a < () a () lim a log xdx a log xdx ( ) n (3) lim log k log n n n k=.3 z = log(x + y ),
1 Abstract 2 3 n a ax 2 + bx + c = 0 (a 0) (1) ( x + b ) 2 = b2 4ac 2a 4a 2 D = b 2 4ac > 0 (1) 2 D = 0 D < 0 x + b 2a = ± b2 4ac 2a b ± b 2
1 Abstract n 1 1.1 a ax + bx + c = 0 (a 0) (1) ( x + b ) = b 4ac a 4a D = b 4ac > 0 (1) D = 0 D < 0 x + b a = ± b 4ac a b ± b 4ac a b a b ± 4ac b i a D (1) ax + bx + c D 0 () () (015 8 1 ) 1. D = b 4ac
ver F = i f i m r = F r = 0 F = 0 X = Y = Z = 0 (1) δr = (δx, δy, δz) F δw δw = F δr = Xδx + Y δy + Zδz = 0 (2) δr (2) 1 (1) (2 n (X i δx
ver. 1.0 18 6 20 F = f m r = F r = 0 F = 0 X = Y = Z = 0 (1 δr = (δx, δy, δz F δw δw = F δr = Xδx + Y δy + Zδz = 0 (2 δr (2 1 (1 (2 n (X δx + Y δy + Z δz = 0 (3 1 F F = (X, Y, Z δr = (δx, δy, δz S δr δw
& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable),
.... Deeping and Expansion of Large-Scale Random Fields and Probabilistic Image Processing Kazuyuki Tanaka The mathematical frameworks of probabilistic image processing are formulated by means of Markov
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( 9 5 6 ) ) AGD ) 7) S. ψ (r, t) ψ(r, t) (r, t) Ĥ ψ(r, t) = e iĥt/ħ ψ(r, )e iĥt/ħ ˆn(r, t) = ψ (r, t)ψ(r, t) () : ψ(r, t)ψ (r, t) ψ (r, t)ψ(r, t) = δ(r r ) () ψ(r, t)ψ(r, t) ψ(r, t)ψ(r, t) = (3) ψ (r,
http://www.ns.kogakuin.ac.jp/~ft13389/lecture/physics1a2b/ pdf I 1 1 1.1 ( ) 1. 30 m µm 2. 20 cm km 3. 10 m 2 cm 2 4. 5 cm 3 km 3 5. 1 6. 1 7. 1 1.2 ( ) 1. 1 m + 10 cm 2. 1 hr + 6400 sec 3. 3.0 10 5 kg
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