untitled

Size: px
Start display at page:

Download "untitled"

Transcription

1 SICE -- FL6_21_1 1

2 ( ) (ptimal cntrl) Fig :, -V (inite hrizn) receding hrizn (mving hrizn) 2

3 (inite hrizn) Fig : 15 ASA (ininite hrizn) (t ) Fig : F-8 [2] (receding hrizn cntrl) Fig : 3

4 ( k 1) ( (, ) ( ) M ( q) q&& C( q, q& ) q& g( q) τ (,) ( k 1) A ( B ( ) & && k 1 d m m & 1 u m Fig : d u m k Fig : 4

5 U ( X u 2 U u 1 2 X U X ( ) X 1m 1m u 2 U ( k 1) A ( B 2 u 1 X 1 U X 1V 1V 9 9 ( k 1) ( (, ) 5

6 V (, u) 1 i l( (, ) F( ( )) l( (, ) F( ( )) (stage cst) (terminal cst) l( (, ) >, l(,), F( ( )) > l( (, ) ( Q( R l( (, ) F( ( )) F( ( )) ( ) P ( ) Q, R > 6

7 ptimal cntrl prblem P U () u { V (, u) u U ( )} ( ) : min ptimal u { u (; ), u (1; ),, u ( 1; ) } ( ) L (trajectry) { (; ), (1; ),, ( 1; ), ( ; ) } ( ) L (value unctin) V ( ) V (, u ( )) i ( ; ) 3 2 u () ( ( 1; ), 1; )) 1 V 1 () () 7

8 ( ) : u (; ) u ( ; ) receding hrizn (mving hrizn) u { u (; ), u (1; ),, u ( 1; )} ( ) L u () u 1 u 1 u 1 8

9 X {} X ( ) V F( ( )) F( ( )) ( ) P ( ) (, u) 1 i Kwn and Pearsn (1977) l( (, ) P () X F( ( )) ( ) X X X F( ( )) 9

10 ( ( Q( u ( R ) V (, u) i l ( (, ) u ( K( ( ) 1 K R B PB B PA V ( V ( (, u ( ) ( P( V V ( k l ( k ( k 1) A( B ( ) P A PA A PB R B PB B PA Q 1) V 1) P( k ( (, ) ( 1) ( P( V ( u ( K( 1 ( 1

11 ( k 1) A ( B U ( X ( ) X X X K ( R B P ( ) k,..., 1 k,..., 1 V (, u) i ( ) ( Q( u ( R ( ) P ( ) F( ( )) ( ) P ( ) F( ) l(, ( )) B) 1 B P O A ( ) K 11

12 X ( ) K ( k 1) ( A BK) ( U ( X k,..., 1 k,..., 3. ( ) X X ( ) K X k 1 K ( A BK) U k,1..., k ( A BK) X k X (),1..., O X,. k k { K( A BK) U,( A BK ) X r k,1,..., } O O 12

13 (terminal cnstraint) ( ) u (, u) : (1), ( ) (stabilizing cntrl) u X X X (terminal cnstraint set) () u φ(, u) φ(, u) : φ( ) φ( ) φ() φ(, u) φ( ) 13

14 A1 : X X, ( ) U, A2 : A3 : A4 : X, X (, ( )) X, [ F l](, ( )), X X X A3 : X (psitive invariant set) A4 : F() ( ) (cntrl Lyapunv unctin) A1-A3 : (easibility) 14

15 2 3 1 P ( ) u ~ ( ) ( ; ) ( 1; ) u ( ; ) u ( 1; ) ( 1; ) 1 u ( ; ) ( 1; ) 1 u ~ ( ) : L )) () { u (1; ),, u ( 1; ), ( ( ; } ( ( ; )) U ( ( ; ), ( ( ; ))) ( ( ; ), ( ( ; ))) ( ( ; ), ( ( ; ))) X ( ( ; )) U X X ( ( ; )) X 15

16 ( ; ) ( 1; ) ( 1; ) ( ; ) u () 1 () () u ~ ( ) u ( ; ) u ( 1; ) ( 1; ) 1 ( ; ) ( 1; ) u ( ; ) u ( 1; ) 1 u ( 1; ) u ( ; ) ( ( ; ), ( ( ; ))) ( ( ; ), ( ( ; ))) X ( ( ; )) U ( 1; ) 1 X ( ( ; )) 16

17 ( ; ) ( 1; ) ( 1; ) ( ; ) () u () 1 () u ~ ( ) u ( ; ) u ( 1; ) ( 1; ) 1 ( ; ) ( 1; ) u ( ; ) u ( 1; ) 1 u ( 1; ) u ( ; ) ( ( ; ), ( ( ; ))) ( ( ; ), ( ( ; ))) X ( ( ; )) U ( 1; ) 1 X ( ( ; )) 17

18 u ~ ( ) () ( ; ) 1 1 V (, u) l( (, ) V ( 1; ) u ( ; ) u ( 1; ) 1 i ( 1; ) u ( ; ) ( 1; ) 1 ( ( ; ), ( ( ; ))) ( ( ; ), ( ( ; ))) X ( ( ; )) U X ( ( ; )) F( ( )) ~ ( (, u )) l( ( i; ), u ( i; )) F( ( ; )) i V ( (; ), u (; )) l( (; ), u (; )) F( ( ; )) l( ( ; ), ( ( ; ))) F( ( ( ; ), ( ( ; )))) 18

19 V u ~ ( ) () ( ; ) ( 1; ) u ( ; ) u ( 1; ) ( 1; ) u ( ; ) ( 1; ) ( ( ; ), ( ( ; ))) ( ( ; ), ( ( ; ))) X ( ( ; )) U ( ( ; )) X 1 1 ~ ( (, u )) V ( (; ), u (; )) l( (; ), u (; )) F( ( ; )) V () () l( ( ; ), ( ( ; ))) F( ( ( ; ), ( ( ; )))) V ( ) l(, ( )) F( ( ; ), ( ( ; ))) l( ( ; ), ( ( ; ))) Q F(, u) : F( ) F( ) 19

20 V () u ~ ( ) ( ; ) 1 1 ~ ( (, u )) V ( ) l(, ( )) F( ( ; ), ( ( ; ))) l( ( ; ), ( ( ; ))) V V ( ) l(, ( )) [ F l]( ( ; ), ( ( ; ))) ( ) l(, ( )) Q A4 : ( 1; ) u ( ; ) u ( 1; ) ( 1; ) u ( ; ) ( 1; ) [ F l](, ( )) ( ( ; ), ( ( ; ))) ( ( ; ), ( ( ; ))) X ( ( ; )) U ( ( ; )), X X 2

21 u ~ ( ) () ( ; ) ( 1; ) u ( ; ) u ( 1; ) ( 1; ) 1 1 ~ ( V (, u )) V ( ) l(, ( )) u ( ; ) ( 1; ) ( ( ; ), ( ( ; ))) ( ( ; ), ( ( ; ))) X ( ( ; )) U ( ( ; )) X u ~ ( ) : L )) V V { u (1; ),, u ( 1; ), ( ( ; } ( ) V (, u~ ( )) ~ ( ( ) V (, u )) V ( ) l(, ( )) 21

22 V u ~ ( ) () ( ; ) V ( 1; ) u ( ; ) u ( 1; ) ( 1; ) 1 1 ( ) V ( ) l(, ( )) (, ( )) l(, ( )) u ( 1; ) Q V ( ; ) ( ( ; ), ( ( ; ))) ( ( ; ), ( ( ; ))) X ( ( ; )) U ( ( ; )) (, u) : V ( ) V ( ) value unctin V ( ) > value unctin V ( ) ( ) V X 22

23 (ininite hrizn) (t ) (receding hrizn cntrl) l( (, ) V (, u) 1 i l( (, ) F( ( )) F( ( )) (terminal cst) F( ( )) 23

24 ( k 1) A( B y ( C( 1 V (, u) { ( Q( u ( R } i [ F l](, ( )) A4 : P ( ) k, X A P A Q P ( A k ) Q A k? ( ) P ( ) A4 A : A BK : Q K Q ( ) : K RK 24

25 1 V (, u) { ( Q( u ( R } i 1 i i { { F( ( ( Q( u ( Q( u ( R } i ( ) P ( ) { ( Q( u ( R } ( R } )) P ( A k k ) Q A k (ininite hrizn) A. Jadbabaie, J. Yu and J. Hauser, Uncnstrained Receding-Hrizn Cntrl nlinear Systems, IEEE rans. Autmatic Cntrl, Vl.46,.5, pp , 21. cntrl Lyapunv unctin 25

26 ( k 1) A( B V ( ( Q( u ( R ) (, u) i 1 V i (, u) 1 i ( ( Q( u ( R ) u ( K( ( ) ( ( Q( u ( R ( Q( u ( R ) U ( X ( ) X X k,..., 1 k,..., i K ~ ( ) 1 R B PB B PA ( ) P ( )! ( ) X ( ) X ( ) K( K ( ) 1 R B P B B P A 26

27 l( (, ) F ( ( )) (, u) l( (, ) V 1 i l( (, ) F( ( )) l( (, ) Hrizn G. Grimm, M. J. Messina, S. E. una and A. R. eel, Mdel Predictive Cntrl: Fr Want a Lcal Cntrl Lyapunv Functin, All is t Lst, IEEE rans. Autmatic Cntrl, Vl.5,.5, pp , 25. A. Jadbabaie and J. Hauser, On the Stability Receding Hrizn Cntrl With a General erminal ky Institute Cst, echnlgy IEEE rans. Autmatic Cntrl, Vl.5,.5, pp ,

28 Hver Crat ( ) Visual Feedback System () Fig : Hver Crat Fig : Visual Feedback System 28

29 Hver Crat (Hver Crat) Hver Crat, d cs dt 3 1/ L sin 3 cs 3 u 1/ L sin 3 [ ] 1, 2, 3 [ ] u u1, u2. Ohthuka and A. Kdama, Autmatin Cde Generatin System r nlinear Receding Hrizn Cntrl,, Vl.38,.7, pp Fig : Hver Crat Fig : Hver Crat ~htsuka/paper/cca2hp_hver.pd 29

30 (Hver Crat) U u ~ u ma ma X ~ ma ma t ( ( )) L( ( t ), t ) J ϕ ) dt ϕ t {( ) ( ) u u} 1 L p( t) ( t) Q p( t) ( t) 2 1 ( p( t) ( t) ) S ( p( t) ( t) ) 2 Q > > p(t) S π [ ] 2 ( t) (1 e αt ) (stage cst) (terminal cst) C/GMRES 3

31 (Hver Crat) 31

32 Visual Feedback Cntrl (Visual Feedback Cntrl) & 1 1 ξ M ( q) C( q, q& ) ξ M ( q) u & sλ R z w wc J ξ b sλ R z w wc J αj t J u, t) l ( τ ), τ ) dτ RHC b b R wc arget Object R wc Camera R& wc ( ( ) M ( ( t )) t 1 l( ( τ ), τ )) 4 Q u R u ( ( t )) 4V ( ( t )) Planar Manipulatr Fig : Visual Feedback System Q > R 1 > V () (stage cst) M (terminal cst) 32

33 (Visual Feedback Cntrl) 33

34 (nnlinear mdel predictive cntrl) (rbust mdel predictive cntrl) (ast systems) (hybrid systems) rajectry Generatin Path Planning 34

2.2 h h l L h L = l cot h (1) (1) L l L l l = L tan h (2) (2) L l 2 l 3 h 2.3 a h a h (a, h)

2.2 h h l L h L = l cot h (1) (1) L l L l l = L tan h (2) (2) L l 2 l 3 h 2.3 a h a h (a, h) 1 16 10 5 1 2 2.1 a a a 1 1 1 2.2 h h l L h L = l cot h (1) (1) L l L l l = L tan h (2) (2) L l 2 l 3 h 2.3 a h a h (a, h) 4 2 3 4 2 5 2.4 x y (x,y) l a x = l cot h cos a, (3) y = l cot h sin a (4) h a

More information

Chap9.dvi

Chap9.dvi .,. f(),, f(),,.,. () lim 2 +3 2 9 (2) lim 3 3 2 9 (4) lim ( ) 2 3 +3 (5) lim 2 9 (6) lim + (7) lim (8) lim (9) lim (0) lim 2 3 + 3 9 2 2 +3 () lim sin 2 sin 2 (2) lim +3 () lim 2 2 9 = 5 5 = 3 (2) lim

More information

() x + y + y + x dy dx = 0 () dy + xy = x dx y + x y ( 5) ( s55906) 0.7. (). 5 (). ( 6) ( s6590) 0.8 m n. 0.9 n n A. ( 6) ( s6590) f A (λ) = det(a λi)

() x + y + y + x dy dx = 0 () dy + xy = x dx y + x y ( 5) ( s55906) 0.7. (). 5 (). ( 6) ( s6590) 0.8 m n. 0.9 n n A. ( 6) ( s6590) f A (λ) = det(a λi) 0. A A = 4 IC () det A () A () x + y + z = x y z X Y Z = A x y z ( 5) ( s5590) 0. a + b + c b c () a a + b + c c a b a + b + c 0 a b c () a 0 c b b c 0 a c b a 0 0. A A = 7 5 4 5 0 ( 5) ( s5590) () A ()

More information

K E N Z OU

K E N Z OU K E N Z OU 11 1 1 1.1..................................... 1.1.1............................ 1.1..................................................................................... 4 1.........................................

More information

LLG-R8.Nisus.pdf

LLG-R8.Nisus.pdf d M d t = γ M H + α M d M d t M γ [ 1/ ( Oe sec) ] α γ γ = gµ B h g g µ B h / π γ g = γ = 1.76 10 [ 7 1/ ( Oe sec) ] α α = λ γ λ λ λ α γ α α H α = γ H ω ω H α α H K K H K / M 1 1 > 0 α 1 M > 0 γ α γ =

More information

振動工学に基礎

振動工学に基礎 Ky Words. ω. ω.3 osω snω.4 ω snω ω osω.5 .6 ω osω snω.7 ω ω ( sn( ω φ.7 ( ω os( ω φ.8 ω ( ω sn( ω φ.9 ω anφ / ω ω φ ω T ω T s π T π. ω Hz ω. T π π rad/s π ω π T. T ω φ 6. 6. 4. 4... -... -. -4. -4. -6.

More information

飽和分光

飽和分光 3 Rb 1 1 4 1.1 4 1. 4 5.1 LS 5. Hyperfine Structure 6 3 8 3.1 8 3. 8 4 11 4.1 11 5 14 5.1 External Cavity Laser Diode: ECLD 14 5. 16 5.3 Polarization Beam Splitter: PBS 17 5.4 Photo Diode: PD 17 5.5 :

More information

untitled

untitled 8- My + Cy + Ky = f () t 8. C f () t ( t) = Ψq( t) () t = Ψq () t () t = Ψq () t = ( q q ) ; = [ ] y y y q Ψ φ φ φ = ( ϕ, ϕ, ϕ,3 ) 8. ψ Ψ MΨq + Ψ CΨq + Ψ KΨq = Ψ f ( t) Ψ MΨ = I; Ψ CΨ = C; Ψ KΨ = Λ; q

More information

吸収分光.PDF

吸収分光.PDF 3 Rb 1 1 4 1.1 4 1. 4 5.1 5. 5 3 8 3.1 8 4 1 4.1 External Cavity Laser Diode: ECLD 1 4. 1 4.3 Polarization Beam Splitter: PBS 13 4.4 Photo Diode: PD 13 4.5 13 4.6 13 5 Rb 14 6 15 6.1 ECLD 15 6. 15 6.3

More information

xia2.dvi

xia2.dvi Journal of Differential Equations 96 (992), 70-84 Melnikov method and transversal homoclinic points in the restricted three-body problem Zhihong Xia Department of Mathematics, Harvard University Cambridge,

More information

4‐E ) キュリー温度を利用した消磁:熱消磁

4‐E ) キュリー温度を利用した消磁:熱消磁 ( ) () x C x = T T c T T c 4D ) ) Fe Ni Fe Fe Ni (Fe Fe Fe Fe Fe 462 Fe76 Ni36 4E ) ) (Fe) 463 4F ) ) ( ) Fe HeNe 17 Fe Fe Fe HeNe 464 Ni Ni Ni HeNe 465 466 (2) Al PtO 2 (liq) 467 4G ) Al 468 Al ( 468

More information

9 2 1 f(x, y) = xy sin x cos y x y cos y y x sin x d (x, y) = y cos y (x sin x) = y cos y(sin x + x cos x) x dx d (x, y) = x sin x (y cos y) = x sin x

9 2 1 f(x, y) = xy sin x cos y x y cos y y x sin x d (x, y) = y cos y (x sin x) = y cos y(sin x + x cos x) x dx d (x, y) = x sin x (y cos y) = x sin x 2009 9 6 16 7 1 7.1 1 1 1 9 2 1 f(x, y) = xy sin x cos y x y cos y y x sin x d (x, y) = y cos y (x sin x) = y cos y(sin x + x cos x) x dx d (x, y) = x sin x (y cos y) = x sin x(cos y y sin y) y dy 1 sin

More information

() Remrk I = [0, ] [x i, x i ]. (x : ) f(x) = 0 (x : ) ξ i, (f) = f(ξ i )(x i x i ) = (x i x i ) = ξ i, (f) = f(ξ i )(x i x i ) = 0 (f) 0.

() Remrk I = [0, ] [x i, x i ]. (x : ) f(x) = 0 (x : ) ξ i, (f) = f(ξ i )(x i x i ) = (x i x i ) = ξ i, (f) = f(ξ i )(x i x i ) = 0 (f) 0. () 6 f(x) [, b] 6. Riemnn [, b] f(x) S f(x) [, b] (Riemnn) = x 0 < x < x < < x n = b. I = [, b] = {x,, x n } mx(x i x i ) =. i [x i, x i ] ξ i n (f) = f(ξ i )(x i x i ) i=. (ξ i ) (f) 0( ), ξ i, S, ε >

More information

4 小川/小川

4 小川/小川 B p p B pp M p T p M p Tp T pt T p T p T p p Tp T T p T p T pt p Tp p p p p p p p p T p p T T M M p p p p p p T p p p T T p T B T T p T T p T p T T T p T p p T p Tp T p p Tp T p T Tp T T p T p T p T p

More information

[Ver. 0.2] 1 2 3 4 5 6 7 1 1.1 1.2 1.3 1.4 1.5 1 1.1 1 1.2 1. (elasticity) 2. (plasticity) 3. (strength) 4. 5. (toughness) 6. 1 1.2 1. (elasticity) } 1 1.2 2. (plasticity), 1 1.2 3. (strength) a < b F

More information

Untitled

Untitled II 14 14-7-8 8/4 II (http://www.damp.tottori-u.ac.jp/~ooshida/edu/fluid/) [ (3.4)] Navier Stokes [ 6/ ] Navier Stokes 3 [ ] Reynolds [ (4.6), (45.8)] [ p.186] Navier Stokes I 1 balance law t (ρv i )+ j

More information

( ) e + e ( ) ( ) e + e () ( ) e e Τ ( ) e e ( ) ( ) () () ( ) ( ) ( ) ( )

( ) e + e ( ) ( ) e + e () ( ) e e Τ ( ) e e ( ) ( ) () () ( ) ( ) ( ) ( ) n n (n) (n) (n) (n) n n ( n) n n n n n en1, en ( n) nen1 + nen nen1, nen ( ) e + e ( ) ( ) e + e () ( ) e e Τ ( ) e e ( ) ( ) () () ( ) ( ) ( ) ( ) ( n) Τ n n n ( n) n + n ( n) (n) n + n n n n n n n n

More information

4 Mindlin -Reissner 4 δ T T T εσdω= δ ubdω+ δ utd Γ Ω Ω Γ T εσ (1.1) ε σ u b t 3 σ ε. u T T T = = = { σx σ y σ z τxy τ yz τzx} { εx εy εz γ xy γ yz γ

4 Mindlin -Reissner 4 δ T T T εσdω= δ ubdω+ δ utd Γ Ω Ω Γ T εσ (1.1) ε σ u b t 3 σ ε. u T T T = = = { σx σ y σ z τxy τ yz τzx} { εx εy εz γ xy γ yz γ Mindlin -Rissnr δ εσd δ ubd+ δ utd Γ Γ εσ (.) ε σ u b t σ ε. u { σ σ σ z τ τ z τz} { ε ε εz γ γ z γ z} { u u uz} { b b bz} b t { t t tz}. ε u u u u z u u u z u u z ε + + + (.) z z z (.) u u NU (.) N U

More information

6 2 2 x y x y t P P = P t P = I P P P ( ) ( ) ,, ( ) ( ) cos θ sin θ cos θ sin θ, sin θ cos θ sin θ cos θ y x θ x θ P

6 2 2 x y x y t P P = P t P = I P P P ( ) ( ) ,, ( ) ( ) cos θ sin θ cos θ sin θ, sin θ cos θ sin θ cos θ y x θ x θ P 6 x x 6.1 t P P = P t P = I P P P 1 0 1 0,, 0 1 0 1 cos θ sin θ cos θ sin θ, sin θ cos θ sin θ cos θ x θ x θ P x P x, P ) = t P x)p ) = t x t P P ) = t x = x, ) 6.1) x = Figure 6.1 Px = x, P=, θ = θ P

More information

#A A A F, F d F P + F P = d P F, F y P F F x A.1 ( α, 0), (α, 0) α > 0) (x, y) (x + α) 2 + y 2, (x α) 2 + y 2 d (x + α)2 + y 2 + (x α) 2 + y 2 =

#A A A F, F d F P + F P = d P F, F y P F F x A.1 ( α, 0), (α, 0) α > 0) (x, y) (x + α) 2 + y 2, (x α) 2 + y 2 d (x + α)2 + y 2 + (x α) 2 + y 2 = #A A A. F, F d F P + F P = d P F, F P F F A. α, 0, α, 0 α > 0, + α +, α + d + α + + α + = d d F, F 0 < α < d + α + = d α + + α + = d d α + + α + d α + = d 4 4d α + = d 4 8d + 6 http://mth.cs.kitmi-it.c.jp/

More information

p12.dvi

p12.dvi 301 12 (2) : 1 (1) dx dt = f(x,t) ( (t 0,t 1,...,t N ) ) h k = t k+1 t k. h k k h. x(t k ) x k. : 2 (2) :1. step. 1 : explicit( ) : ξ k+1 = ξ k +h k Ψ(t k,ξ k,h k ) implicit( ) : ξ k+1 = ξ k +h k Ψ(t k,t

More information

e a b a b b a a a 1 a a 1 = a 1 a = e G G G : x ( x =, 8, 1 ) x 1,, 60 θ, ϕ ψ θ G G H H G x. n n 1 n 1 n σ = (σ 1, σ,..., σ N ) i σ i i n S n n = 1,,

e a b a b b a a a 1 a a 1 = a 1 a = e G G G : x ( x =, 8, 1 ) x 1,, 60 θ, ϕ ψ θ G G H H G x. n n 1 n 1 n σ = (σ 1, σ,..., σ N ) i σ i i n S n n = 1,, 01 10 18 ( ) 1 6 6 1 8 8 1 6 1 0 0 0 0 1 Table 1: 10 0 8 180 1 1 1. ( : 60 60 ) : 1. 1 e a b a b b a a a 1 a a 1 = a 1 a = e G G G : x ( x =, 8, 1 ) x 1,, 60 θ, ϕ ψ θ G G H H G x. n n 1 n 1 n σ = (σ 1,

More information

DVIOUT

DVIOUT A. A. A-- [ ] f(x) x = f 00 (x) f 0 () =0 f 00 () > 0= f(x) x = f 00 () < 0= f(x) x = A--2 [ ] f(x) D f 00 (x) > 0= y = f(x) f 00 (x) < 0= y = f(x) P (, f()) f 00 () =0 A--3 [ ] y = f(x) [, b] x = f (y)

More information

agora04.dvi

agora04.dvi Workbook E-mail: kawahira@math.nagoya-u.ac.jp 2004 8 9, 10, 11 1 2 1 2 a n+1 = pa n + q x = px + q a n better 2 a n+1 = aan+b ca n+d 1 (a, b, c, d) =(p, q, 0, 1) 1 = 0 3 2 2 2 f(z) =z 2 + c a n+1 = a 2

More information

D = [a, b] [c, d] D ij P ij (ξ ij, η ij ) f S(f,, {P ij }) S(f,, {P ij }) = = k m i=1 j=1 m n f(ξ ij, η ij )(x i x i 1 )(y j y j 1 ) = i=1 j

D = [a, b] [c, d] D ij P ij (ξ ij, η ij ) f S(f,, {P ij }) S(f,, {P ij }) = = k m i=1 j=1 m n f(ξ ij, η ij )(x i x i 1 )(y j y j 1 ) = i=1 j 6 6.. [, b] [, d] ij P ij ξ ij, η ij f Sf,, {P ij } Sf,, {P ij } k m i j m fξ ij, η ij i i j j i j i m i j k i i j j m i i j j k i i j j kb d {P ij } lim Sf,, {P ij} kb d f, k [, b] [, d] f, d kb d 6..

More information

FR 34 316 13 303 54

FR 34 316 13 303 54 FR 34 316 13 303 54 23 ( 1 14 ) ( 3 10 ) 8/4 8/ 100% 8 22 7 12 1 9 8 45 25 28 17 19 14 3/1 6/27 5000 8/4 12/2930 1 66 45 43 35 49 25 22 20 23 21 17 13 20 6 1 8 52 1 50 4 11 49 3/4/5 75 6/7/8 46 9/10/11

More information

1 3 1.1.......................... 3 1............................... 3 1.3....................... 5 1.4.......................... 6 1.5........................ 7 8.1......................... 8..............................

More information

1

1 1 5% 4% 11% 8% 13% 12% 10% 6% 17% 6% 8% 4% 6% 6% 2% 17% 17% 12% 14% 16% 6% 37% 11% 17% 35% 2 (N=6,239) 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 1,585 1,126 950 494 345 296 242 263 191 150 131 116

More information

211 kotaro@math.titech.ac.jp 1 R *1 n n R n *2 R n = {(x 1,..., x n ) x 1,..., x n R}. R R 2 R 3 R n R n R n D D R n *3 ) (x 1,..., x n ) f(x 1,..., x n ) f D *4 n 2 n = 1 ( ) 1 f D R n f : D R 1.1. (x,

More information

基礎数学I

基礎数学I I & II ii ii........... 22................. 25 12............... 28.................. 28.................... 31............. 32.................. 34 3 1 9.................... 1....................... 1............

More information

高校生の就職への数学II

高校生の就職への数学II II O Tped b L A TEX ε . II. 3. 4. 5. http://www.ocn.ne.jp/ oboetene/plan/ 7 9 i .......................................................................................... 3..3...............................

More information

The Physics of Atmospheres CAPTER :

The Physics of Atmospheres CAPTER : The Physics of Atmospheres CAPTER 4 1 4 2 41 : 2 42 14 43 17 44 25 45 27 46 3 47 31 48 32 49 34 41 35 411 36 maintex 23/11/28 The Physics of Atmospheres CAPTER 4 2 4 41 : 2 1 σ 2 (21) (22) k I = I exp(

More information

f(x) = f(x ) + α(x)(x x ) α(x) x = x. x = f (y), x = f (y ) y = f f (y) = f f (y ) + α(f (y))(f (y) f (y )) f (y) = f (y ) + α(f (y)) (y y ) ( (2) ) f

f(x) = f(x ) + α(x)(x x ) α(x) x = x. x = f (y), x = f (y ) y = f f (y) = f f (y ) + α(f (y))(f (y) f (y )) f (y) = f (y ) + α(f (y)) (y y ) ( (2) ) f 22 A 3,4 No.3 () (2) (3) (4), (5) (6) (7) (8) () n x = (x,, x n ), = (,, n ), x = ( (x i i ) 2 ) /2 f(x) R n f(x) = f() + i α i (x ) i + o( x ) α,, α n g(x) = o( x )) lim x g(x) x = y = f() + i α i(x )

More information

1. 1 A : l l : (1) l m (m 3) (2) m (3) n (n 3) (4) A α, β γ α β + γ = 2 m l lm n nα nα = lm. α = lm n. m lm 2β 2β = lm β = lm 2. γ l 2. 3

1. 1 A : l l : (1) l m (m 3) (2) m (3) n (n 3) (4) A α, β γ α β + γ = 2 m l lm n nα nα = lm. α = lm n. m lm 2β 2β = lm β = lm 2. γ l 2. 3 1. 1 A : l l : (1) l m (m 3) (2) m (3) n (n 3) (4) A 2 1 2 1 2 3 α, β γ α β + γ = 2 m l lm n nα nα = lm. α = lm n. m lm 2β 2β = lm β = lm 2. γ l 2. 3 4 P, Q R n = {(x 1, x 2,, x n ) ; x 1, x 2,, x n R}

More information

1 1. x 1 (1) x 2 + 2x + 5 dx d dx (x2 + 2x + 5) = 2(x + 1) x 1 x 2 + 2x + 5 = x + 1 x 2 + 2x x 2 + 2x + 5 y = x 2 + 2x + 5 dy = 2(x + 1)dx x + 1

1 1. x 1 (1) x 2 + 2x + 5 dx d dx (x2 + 2x + 5) = 2(x + 1) x 1 x 2 + 2x + 5 = x + 1 x 2 + 2x x 2 + 2x + 5 y = x 2 + 2x + 5 dy = 2(x + 1)dx x + 1 . ( + + 5 d ( + + 5 ( + + + 5 + + + 5 + + 5 y + + 5 dy ( + + dy + + 5 y log y + C log( + + 5 + C. ++5 (+ +4 y (+/ + + 5 (y + 4 4(y + dy + + 5 dy Arctany+C Arctan + y ( + +C. + + 5 ( + log( + + 5 Arctan

More information

64 3 g=9.85 m/s 2 g=9.791 m/s 2 36, km ( ) 1 () 2 () m/s : : a) b) kg/m kg/m k

64 3 g=9.85 m/s 2 g=9.791 m/s 2 36, km ( ) 1 () 2 () m/s : : a) b) kg/m kg/m k 63 3 Section 3.1 g 3.1 3.1: : 64 3 g=9.85 m/s 2 g=9.791 m/s 2 36, km ( ) 1 () 2 () 3 9.8 m/s 2 3.2 3.2: : a) b) 5 15 4 1 1. 1 3 14. 1 3 kg/m 3 2 3.3 1 3 5.8 1 3 kg/m 3 3 2.65 1 3 kg/m 3 4 6 m 3.1. 65 5

More information

Σ A Σ B r Σ A (Σ A ): A r = [ A r A x r A y r z ] T Σ B : B r = [ B r B x r B y r z ] T A r = A x B B r x + A y B B r y + A z B B r z A r = A R B B r

Σ A Σ B r Σ A (Σ A ): A r = [ A r A x r A y r z ] T Σ B : B r = [ B r B x r B y r z ] T A r = A x B B r x + A y B B r y + A z B B r z A r = A R B B r 3 : Σ A = O A {X A, Y A, Z A } : Σ B = O B {X B, Y B, Z B } O B : A p B X B, Y B, Z B Σ A : A x B, A y B, A z B Σ A : A p B Σ A : { A x B, A y B, A z B } A R B = [ A x A B y A B z B ] ( A R B ) T ( A R

More information

φ s i = m j=1 f x j ξ j s i (1)? φ i = φ s i f j = f x j x ji = ξ j s i (1) φ 1 φ 2. φ n = m j=1 f jx j1 m j=1 f jx j2. m

φ s i = m j=1 f x j ξ j s i (1)? φ i = φ s i f j = f x j x ji = ξ j s i (1) φ 1 φ 2. φ n = m j=1 f jx j1 m j=1 f jx j2. m 2009 10 6 23 7.5 7.5.1 7.2.5 φ s i m j1 x j ξ j s i (1)? φ i φ s i f j x j x ji ξ j s i (1) φ 1 φ 2. φ n m j1 f jx j1 m j1 f jx j2. m j1 f jx jn x 11 x 21 x m1 x 12 x 22 x m2...... m j1 x j1f j m j1 x

More information

73 p.1 22 16 2004p.152

73 p.1 22 16 2004p.152 1987 p.80 72 73 p.1 22 16 2004p.152 281895 1930 1931 12 28 1930 10 27 12 134 74 75 10 27 47.6 1910 1925 10 10 76 10 11 12 139 p.287 p.10 11 pp.3-4 1917 p.284 77 78 10 13 10 p.6 1936 79 15 15 30 80 pp.499-501

More information

2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226. 1893 B pp. 1 2. p. 3.

2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226. 1893 B pp. 1 2. p. 3. 1 73 72 1 1844 11 9 1844 12 18 5 1916 1 11 72 1 73 2 1862 3 1870 2 1862 6 1873 1 3 4 3 4 7 2 3 4 5 3 5 4 2007 p. 117. 2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226.

More information

29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23

29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23 29 2011 3 pp.55 86 19 1886 2 13 1 1 21 1888 1 13 2 3,500 3 5 5 50 4 1959 6 p.241 21 1 13 2 p.14 1988 p.2 21 1 15 29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23 1

More information

308 ( ) p.121

308 ( ) p.121 307 1944 1 1920 1995 2 3 4 5 308 ( ) p.121 309 10 12 310 6 7 ( ) ( ) ( ) 50 311 p.120 p.142 ( ) ( ) p.117 p.124 p.118 312 8 p.125 313 p.121 p.122 p.126 p.128 p.156 p.119 p.122 314 p.153 9 315 p.142 p.153

More information

日経テレコン料金表(2016年4月)

日経テレコン料金表(2016年4月) 1 2 3 4 8,000 15,000 22,000 29,000 5 6 7 8 36,000 42,000 48,000 54,000 9 10 20 30 60,000 66,000 126,000 166,000 50 100 246,000 396,000 1 25 8,000 7,000 620 2150 6,000 4,000 51100 101200 3,000 1,000 201

More information

122011pp.139174 18501933

122011pp.139174 18501933 122011pp.139174 18501933 122011 1850 3 187912 3 1850 8 1933 84 4 1871 12 1879 5 2 1 9 15 1 1 5 3 3 3 6 19 9 9 6 28 7 7 4 1140 9 4 3 5750 58 4 3 1 57 2 122011 3 4 134,500,000 4,020,000 11,600,000 5 2 678.00m

More information

Microsoft Word - 映画『東京裁判』を観て.doc

Microsoft Word - 映画『東京裁判』を観て.doc 1 2 3 4 5 6 7 1 2008. 2 2010, 3 2010. p.1 4 2008 p.202 5 2008. p.228 6 2011. 7 / 2008. pp.3-4 1 8 1 9 10 11 8 2008, p.7 9 2011. p.41 10.51 11 2009. p. 2 12 13 14 12 2008. p.4 13 2008, p.7-8 14 2008. p.126

More information

() L () 20 1

() L () 20 1 () 25 1 10 1 0 0 0 1 2 3 4 5 6 2 3 4 9308510 4432193 L () 20 1 PP 200,000 P13P14 3 0123456 12345 1234561 2 4 5 6 25 1 10 7 1 8 10 / L 10 9 10 11 () ( ) TEL 23 12 7 38 13 14 15 16 17 18 L 19 20 1000123456

More information

戦後の補欠選挙

戦後の補欠選挙 1 2 11 3 4, 1968, p.429., pp.140-141. 76 2005.12 20 14 5 2110 25 6 22 7 25 8 4919 9 22 10 11 12 13 58154 14 15 1447 79 2042 21 79 2243 25100 113 2211 71 113 113 29 p.85 2005.12 77 16 29 12 10 10 17 18

More information

K E N Z U 01 7 16 HP M. 1 1 4 1.1 3.......................... 4 1.................................... 4 1..1..................................... 4 1...................................... 5................................

More information

it-ken_open.key

it-ken_open.key 深層学習技術の進展 ImageNet Classification 画像認識 音声認識 自然言語処理 機械翻訳 深層学習技術は これらの分野において 特に圧倒的な強みを見せている Figure (Left) Eight ILSVRC-2010 test Deep images and the cited4: from: ``ImageNet Classification with Networks et

More information

画像工学特論

画像工学特論 .? (x i, y i )? (x(t), y(t))? (x(t)) (X(ω)) Wiener-Khintchine 35/97 . : x(t) = X(ω)e jωt dω () π X(ω) = x(t)e jωt dt () X(ω) S(ω) = lim (3) ω S(ω)dω X(ω) : F of x : [X] [ = ] [x t] Power spectral density

More information

7 π L int = gψ(x)ψ(x)φ(x) + (7.4) [ ] p ψ N = n (7.5) π (π +,π 0,π ) ψ (σ, σ, σ )ψ ( A) σ τ ( L int = gψψφ g N τ ) N π * ) (7.6) π π = (π, π, π ) π ±

7 π L int = gψ(x)ψ(x)φ(x) + (7.4) [ ] p ψ N = n (7.5) π (π +,π 0,π ) ψ (σ, σ, σ )ψ ( A) σ τ ( L int = gψψφ g N τ ) N π * ) (7.6) π π = (π, π, π ) π ± 7 7. ( ) SU() SU() 9 ( MeV) p 98.8 π + π 0 n 99.57 9.57 97.4 497.70 δm m 0.4%.% 0.% 0.8% π 9.57 4.96 Σ + Σ 0 Σ 89.6 9.46 K + K 0 49.67 (7.) p p = αp + βn, n n = γp + δn (7.a) [ ] p ψ ψ = Uψ, U = n [ α

More information

知能科学:ニューラルネットワーク

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,

More information

知能科学:ニューラルネットワーク

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,

More information

ohp_06nov_tohoku.dvi

ohp_06nov_tohoku.dvi 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)

More information

微分積分 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, 初版 1 刷発行時のものです.

微分積分 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, 初版 1 刷発行時のものです. 微分積分 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. ttp://www.morikita.co.jp/books/mid/00571 このサンプルページの内容は, 初版 1 刷発行時のものです. i ii 014 10 iii [note] 1 3 iv 4 5 3 6 4 x 0 sin x x 1 5 6 z = f(x, y) 1 y = f(x)

More information

K E N Z U 2012 7 16 HP M. 1 1 4 1.1 3.......................... 4 1.2................................... 4 1.2.1..................................... 4 1.2.2.................................... 5................................

More information

http://www.ike-dyn.ritsumei.ac.jp/ hyoo/wave.html 1 1, 5 3 1.1 1..................................... 3 1.2 5.1................................... 4 1.3.......................... 5 1.4 5.2, 5.3....................

More information

a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a

a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a), Tetsuo SAWARAGI, and Yukio HORIGUCHI 1. Johansson

More information

Tricorn

Tricorn Triorn 016 3 1 Mandelbrot Triorn Mandelbrot Robert L DevaneyAn introdution to haoti dynamial Systems Addison-Wesley, 1989 Triorn 1 W.D.Crowe, R.Hasson, P.J.Rippon, P.E.D.Strain- Clark, On the struture

More information

Z: Q: R: C: sin 6 5 ζ a, b

Z: Q: R: C: sin 6 5 ζ a, b Z: Q: R: C: 3 3 7 4 sin 6 5 ζ 9 6 6............................... 6............................... 6.3......................... 4 7 6 8 8 9 3 33 a, b a bc c b a a b 5 3 5 3 5 5 3 a a a a p > p p p, 3,

More information

Sgr.A 2 saida@daido-it.a.jp Sgr.A 1 3 1.1 2............................................. 3 1.2.............................. 4 2 1 6 2.1................................. 6 2.2...................................

More information

Note.tex 2008/09/19( )

Note.tex 2008/09/19( ) 1 20 9 19 2 1 5 1.1........................ 5 1.2............................. 8 2 9 2.1............................. 9 2.2.............................. 10 3 13 3.1.............................. 13 3.2..................................

More information

2 (March 13, 2010) N Λ a = i,j=1 x i ( d (a) i,j x j ), Λ h = N i,j=1 x i ( d (h) i,j x j ) B a B h B a = N i,j=1 ν i d (a) i,j, B h = x j N i,j=1 ν i

2 (March 13, 2010) N Λ a = i,j=1 x i ( d (a) i,j x j ), Λ h = N i,j=1 x i ( d (h) i,j x j ) B a B h B a = N i,j=1 ν i d (a) i,j, B h = x j N i,j=1 ν i 1. A. M. Turing [18] 60 Turing A. Gierer H. Meinhardt [1] : (GM) ) a t = D a a xx µa + ρ (c a2 h + ρ 0 (0 < x < l, t > 0) h t = D h h xx νh + c ρ a 2 (0 < x < l, t > 0) a x = h x = 0 (x = 0, l) a = a(x,

More information

pdf

pdf 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

More information

) 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 = 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

More information

Super perfect numbers and Mersenne perefect numbers /2/22 1 m, , 31 8 P = , P =

Super perfect numbers and Mersenne perefect numbers /2/22 1 m, , 31 8 P = , P = Super perfect numbers and Mersenne perefect numbers 3 2019/2/22 1 m, 2 2 5 3 5 4 18 5 20 6 25 7, 31 8 P = 5 35 9, 38 10 P = 5 39 1 1 m, 1: m = 28 m = 28 m = 10 height48 2 4 3 A 40 2 3 5 A 2002 2 7 11 13

More information

r d 2r d l d (a) (b) (c) 1: I(x,t) I(x+ x,t) I(0,t) I(l,t) V in V(x,t) V(x+ x,t) V(0,t) l V(l,t) 2: 0 x x+ x 3: V in 3 V in x V (x, t) I(x, t

r d 2r d l d (a) (b) (c) 1: I(x,t) I(x+ x,t) I(0,t) I(l,t) V in V(x,t) V(x+ x,t) V(0,t) l V(l,t) 2: 0 x x+ x 3: V in 3 V in x V (x, t) I(x, t 1 1 2 2 2r d 2r d l d (a) (b) (c) 1: I(x,t) I(x+ x,t) I(0,t) I(l,t) V in V(x,t) V(x+ x,t) V(0,t) l V(l,t) 2: 0 x x+ x 3: V in 3 V in x V (x, t) I(x, t) V (x, t) I(x, t) V in x t 3 4 1 L R 2 C G L 0 R 0

More information

(1) (2) (1) (2) 2 3 {a n } a 2 + a 4 + a a n S n S n = n = S n

(1) (2) (1) (2) 2 3 {a n } a 2 + a 4 + a a n S n S n = n = S n . 99 () 0 0 0 () 0 00 0 350 300 () 5 0 () 3 {a n } a + a 4 + a 6 + + a 40 30 53 47 77 95 30 83 4 n S n S n = n = S n 303 9 k d 9 45 k =, d = 99 a d n a n d n a n = a + (n )d a n a n S n S n = n(a + a n

More information

重力方向に基づくコントローラの向き決定方法

重力方向に基づくコントローラの向き決定方法 ( ) 2/Sep 09 1 ( ) ( ) 3 2 X w, Y w, Z w +X w = +Y w = +Z w = 1 X c, Y c, Z c X c, Y c, Z c X w, Y w, Z w Y c Z c X c 1: X c, Y c, Z c Kentaro Yamaguchi@bandainamcogames.co.jp 1 M M v 0, v 1, v 2 v 0 v

More information

(5) 75 (a) (b) ( 1 ) v ( 1 ) E E 1 v (a) ( 1 ) x E E (b) (a) (b)

(5) 75 (a) (b) ( 1 ) v ( 1 ) E E 1 v (a) ( 1 ) x E E (b) (a) (b) (5) 74 Re, bondar laer (Prandtl) Re z ω z = x (5) 75 (a) (b) ( 1 ) v ( 1 ) E E 1 v (a) ( 1 ) x E E (b) (a) (b) (5) 76 l V x ) 1/ 1 ( 1 1 1 δ δ = x Re x p V x t V l l (1-1) 1/ 1 δ δ δ δ = x Re p V x t V

More information

S. Yamauchi

S. Yamauchi S. Yamauchi 2017 7 22 1 1 2 2 2.1............................................. 2 2.2............................................. 3 2.3.......................................... 5 3 7 4 () 9 5 11 5.1.............................................

More information