untitled

Size: px
Start display at page:

Download "untitled"

Transcription

1 25 MATLAB 2 39

2 ( ) HSV y x 40

3 4

4 f (t) f ( t) = t f ( t + t) tt f ( t) 2 f ( t) g( t) = f ( τ ) g( t τ ) dτ f (τ ) t g(t τ ) 0 42

5 t f (τ ) f (τ ) g( t τ ) 0 g( t τ ) t High pass filter Low pass filter

6 clear all Img=imread( z: MATLAB character.bmp'); ImgG=double(Img); [xsize,ysize]=size(imgg); filter=(/3)*[- 0 ; - 0 ; - 0 ]; %x %filter=(/3)*[- - -; 0 0 0; ]; %y ImgOut=zeros(xsize, ysize); for i=2:xsize- for j=2:ysize- temp=imgg(i-:i+ :i+, j-:j+); result=temp.*filter; ImgOut(i, j)=sum(result(:)); ImgOut=abs(ImgOut); figure(), subplot(,2,), ) imagesc(imgg);axis([0 xsize 0 ysize]); subplot(,2,2), imagesc(uint8(imgout));axis([0 xsize 0 ysize]); colormap('gray'); 44

7 filter=(/9)*[ ; ; ]; filter=(/3)*[ ; ; ]; 2 filter=(/4)*[0 0; -4 ; 0 0];

8 CCD 46

9 clear all Img=imread( z: MATLAB Lenna.bmp'); ImgG=double((0.299*Img(: Img(:,:,)+0.687: 687*Img(: Img(:,:,2)+0.4: 4*Img(: Img(:,:,3))/3); : [xsize,ysize]=size(imgg); ImgNoise=ImgG; noise=rand(xsize,ysize); for i=:xsize for j=:ysize if (noise(i,j) < 0.02) ImgNoise(i,j)=0; figure(), subplot(2,,), ) imagesc(imgg);axis([0 xsize 0 ysize]); subplot(2,2,2), imagesc(uint8(imgnoise));axis([0 xsize 0 ysize]); colormap('gray'); 47

10 clear all Img=imread( z: MATLAB Lenna.bmp'); ImgG=double((0.299*Img(: Img(:,:,)+0.687: 687*Img(: Img(:,:,2)+0.4: 4*Img(: Img(:,:,3))/3); : [xsize,ysize]=size(imgg); ImgNoise=ImgG; noise=rand(xsize,ysize); for i=:xsize for j=:ysize if (noise(i,j) < 0.02) ImgNoise(i,j)=0; figure(), subplot(2,,), ) imagesc(imgg);axis([0 xsize 0 ysize]); subplot(2,2,2), imagesc(uint8(imgnoise));axis([0 xsize 0 ysize]); colormap('gray'); 48

11 filter=[ ; ; ]; ImgMedian=zeros(xsize, ysize); for i=2:xsize- for j=2:ysize- temp=imgnoise(i-:i+, j-:j+); result=temp.*filter; ImgMedian(i, j)=median(result(:)); ImgMedian=abs(ImgMedian); subplot(2,2,3), 2 imagesc(uint8(imgmedian));axis([0 xsize 0 ysize]); colormap('gray'); 49

12 X ( ω) = x t e dt x(t) ( ) jωt x( t) y( t) X ( ω) Y ( ω) FT X (ω) Y (ω) IFT 50

13 5

14 Low Pass clear all Img=imread( z: MATLAB Lenna.bmp'); ImgG=double((0.299*Img(:,:,)+0.687*Img(:,:,2)+0.4*Img(:,:,3))/3); ImgG=ImgG/max(max(ImgG)); [xsize,ysize]=size(imgg); % 2 Fsize=20; % ImgFFT=fft2(ImgG); ImgFFT=fftshift(ImgFFT); filter2d=zeros(xsize,ysize); filter2d(xsize/2-fsize:xsize/2+fsize, ysize/2-fsize:ysize/2+fsize)=; result=filter2d. *ImgFFT; ImgIFFT=ifft2(result); ImgIFFT=sqrt(ImgIFFT.*conj(ImgIFFT)); ImgOut=ImgIFFT/max(max(ImgIFFT)); figure(), subplot(2,2,), imagesc(uint8(imgg*256.0));axis([0 xsize 0 ysize]); subplot(2,2,2), imagesc(uint8(abs(imgfft)));axis([0 xsize 0 ysize]); subplot(2,2,3), 2 imagesc(uint8(abs(result)));axis([0 xsize 0 ysize]); subplot(2,2,4), imagesc(uint8(imgout*256.0));axis([0 xsize 0 ysize]); colormap('gray'); 52

15 High pass Fsize=20; % ImgFFT=fft2(ImgG); ImgFFT=fftshift(ImgFFT); filter2d=ones(xsize,ysize); filter2d(xsize/2-fsize:xsize/2+fsize, ysize/2-fsize:ysize/2+fsize)=0; Motion blur filter2d=zeros(xsize,ysize); %motion blur filter2d(:xsize, ysize/2-fsize/4:ysize/2+fsize/4)=; 53

16 Multi-sampling ImgFFT=fft2(ImgG); ImgFFT=fftshift(ImgFFT); Mag=2; X=zeros(2*xsize,2*ysize); for x=:2*xsize for y=:2*ysize if mod(x,2) 2)==0 && mod(y,2) 2)==0 oldx=(x-xsize)/mag; oldy=(y-ysize)/mag; oldx=oldx+xsize/2; oldy=oldy+ysize/2; oldy+ysize/2; if oldx>0 & oldx<=xsize & oldy>0 & oldy<=ysize; X(x,y)=ImgFFT(oldx,oldy); ScaledImgFFT=X( :256+28, :256+28); ImgIFFT=ifft2(ScaledImgFFT); ImgIFFT=sqrt(ImgIFFT.*conj(ImgIFFT)); ImgOut=ImgIFFT/max(max(ImgIFFT)); 54

17 2 Minkowski a b b,(x-b) A Morphology processing 55

18 Dilation ImgBw=[ ; ; 0 0 0; ; ]; [xsize,ysize]=size(imgbw); %Se=ones(3,3); %8 Matrix Se=[00;;00]; 0; ; 0 0]; %4 Matrix Ap=[2,2]; %active point Iout=zeros(size(ImgBw)); [Sxsize,Sysize]=size(Se); for i=:xsize for j=:ysize if(imgbw(i,j)>0) for m=:sxsize for n=:sysize tmp=i-ap(,)+m; tmp2=j-ap(,2)+n; if(se(m,n)>0 & tmp>0 & tmp2>0 & tmp<=xsize & tmp2<=ysize) Iout(tmp,tmp2)=Iout(tmp,tmp2)+Se(m,n); tmp2)=iout(tmp tmp2)+se(m for i=:xsize for j=:ysize if(iout(i,j)>) Iout(i,j)=; 56

19 Erosion ImgBw2=Iout; Iout2=ImgBw2; for i=:xsize for j=:ysize if(imgbw2(i,j)>0) for m=:sxsize for n=:sysize tmp=i-ap(,)+m; )+m; tmp2=j-ap(,2)+n; if(se(m,n)~=0 && tmp>0 && tmp2>0 && tmp<=xsize && tmp2<=ysize) if(imgbw2(tmp,tmp2)~= ) Iout2(i, j)=0; elseif(tmp>xsize tmp2>ysize) Iout2(i,j)=0; 57

20 Labeling

21 < > clear all; Img=imread( z: MATLAB shapes.bmp'); ImgG=double(Img(:,:,2))/255.0; ( [xsize,ysize]=size(imgg); avg= mean(mean(imgg(:,:))); for i=:xsize for j=:ysize if (ImgG(i,j) > avg) ImgBw(i,j)=0; else ImgBw(i,j)=; < > ImgLB=ImgBw; ImgLB(,:)=0; ImgLB(xsize,:)=0; ImgLB(:,)=0; ImgLB(:,ysize)=0; % label=0; for kx=2:xsize- for ky=2:ysize- W=zeros(3); W(:3,:3)=ImgLB(kx-:kx+, ky-:ky+); %8 maxw=max(w(:)); if ImgLB(kx,ky)== & ImgLB(kx,ky-)==0 & maxw== label=label+; l l b l ImgLB(kx,ky)=label; if ImgLB(kx,ky)== & maxw>= ImgLB(kx,ky)=maxW; k 59

22 < > figure() subplot(,3,), imagesc(img), title('input') subplot(,3,2), imagesc(imgbw), title('binay') subplot(,3,3), imagesc(uint8(imglb*255/max(max(imglb)))), title('label') colormap('gray'); 2 60

23 % % loop=; if flag==0 while loop break; flag=0; for kx=2:xsize- for ky=2:ysize- if ImgLB(kx,ky)~=0 & ImgLB(kx+,ky)~=0 & ImgLB(kx,ky)~=ImgLB(kx+,ky) old=imglb(kx,ky); new=imglb(kx+,ky); flag=; break; % for kx=2:xsize- for ky=2:ysize- if ImgLB(kx,ky)==old ImgLB(kx,ky)=new; < > figure() subplot(,3,), imagesc(img), title('input') subplot(,3,2), imagesc(imgbw), title('binay') subplot(,3,3), imagesc(uint8(imglb*255/max(max(imglb)))), title('label') colormap('gray'); 6

24 % curlb=[]; for kx=2:xsize- for ky=2:ysize- if ImgLB(kx,ky)~=0 curlb(imglb(kx,ky))=imglb(kx,ky); ( g (, y); count=; while count if curlb(count)==0 curlb(count)=[]; count=count-; count=count+; if count > length(curlb) break; for i=:length(curlb) for kx=2:xsize- for ky=2:ysize- if ImgLB(kx,ky)==curLB(i) ImgLB(kx,ky)=i; 62

25 % < > maxarea=0; figure() for k=:maxlb subplot(,3,), imagesc(img), title('input') if maxarea < sumimg(k) subplot(,3,2), imagesc(imgbw), title('binay') maxarea=sumimg(k); maxarealb=k; for i=:xsize for j=:ysize pixel=imglb(i,j); if pixel~=maxarealb; finalimg(i,j,:)=0; subplot(,3,3), 3) imagesc(uint8(finalimg)), title('maxarea') colormap('gray'); 63

26 Center F r N = r F (i,j) f(i,j) p+q) p q m i j f ( i, j ) = r 0 F m f ( i, j ) N = r F = r F pq 00 i = i = j j ( i j ) m if, 0 i j = i j ( i j) m jf, 0 G( i, j) m0 m, m 00 m 0 = 00 64

27 Center maxlb=max(imglb(:)); sumimg=zeros(,maxlb); xgra=zeros(,maxlb); ygra=zeros(,maxlb); for i=:xsize for j=:ysize pixel=imglb(i,j); if pixel~=0; xgra(pixel)=xgra(pixel)+i*pixel; ygra(pixel)=ygra(pixel)+j*pixel; sumimg(pixel)=sumimg(pixel)+pixel; xgra=round(xgra./sumimg); ygra=round(ygra./sumimg); < > finalimg(:,:,:)=imgbw*28; for k=:maxlb finalimg(xgra(k)-:xgra(k)+, ygra(k)-:ygra(k)+, :)=255; figure() subplot(,3,), ) imagesc(img), title('input') subplot(,3,2), imagesc(imgbw), title('binay') subplot(,3,3), imagesc(uint8(finalimg)), title('gravity') colormap('gray'); 65

untitled

untitled 24 MATLAB 1 17 MATLAB 18 MATLAB download http://www.ess.ic.kanagawa-it.ac.jp/app_images_j.html Lena http://ndevilla.free.fr/lena/ f /l / Z: matlab matlab (color.zip ) Matlab current Directry z: matlab

More information

2 7 V 7 {fx fx 3 } 8 P 3 {fx fx 3 } 9 V 9 {fx fx f x 2fx } V {fx fx f x 2fx + } V {{a n } {a n } a n+2 a n+ + a n n } 2 V 2 {{a n } {a n } a n+2 a n+

2 7 V 7 {fx fx 3 } 8 P 3 {fx fx 3 } 9 V 9 {fx fx f x 2fx } V {fx fx f x 2fx + } V {{a n } {a n } a n+2 a n+ + a n n } 2 V 2 {{a n } {a n } a n+2 a n+ R 3 R n C n V??,?? k, l K x, y, z K n, i x + y + z x + y + z iv x V, x + x o x V v kx + y kx + ky vi k + lx kx + lx vii klx klx viii x x ii x + y y + x, V iii o K n, x K n, x + o x iv x K n, x + x o x

More information

< F2D F B834E2E6A7464>

< F2D F B834E2E6A7464> ランダムウォーク [Java アプレット ] [Java アプレリケーョン ] 1. はじめに 酔っぱらいは前後左右見境なくふらつきます 酔っぱらいは目的地にたどり着こうと歩き回っているうちに何度も同じところに戻って来てしまったりするものです 今 酔っぱらいが数直線上の原点にいるとします 原点を出発して30 回ふらつくとき 30 回目に酔っぱらいがいる位置は 出発点である原点からどれくらい離れてしまっているのでしょうか

More information

1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0

1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0 1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0 0 < t < τ I II 0 No.2 2 C x y x y > 0 x 0 x > b a dx

More information

1003shinseihin.pdf

1003shinseihin.pdf 1 1 1 2 2 3 4 4 P.14 2 P.5 3 P.620 6 7 8 9 10 11 13 14 18 20 00 P.21 1 1 2 3 4 5 2 6 P7 P14 P13 P11 P14 P13 P11 3 P13 7 8 9 10 Point! Point! 11 12 13 14 Point! Point! 15 16 17 18 19 Point! Point! 20 21

More information

ヤフー株式会社 株主通信VOL.16

ヤフー株式会社 株主通信VOL.16 01 260,602264,402 122,795125,595 64,84366,493 107110 120,260123,060 0 500 300 400 200 100 700 600 800 39.8% 23.7% 36.6% 26.6% 21.1% 52.4% 545 700 0 50 200 150 100 250 300 350 312 276 151 171 02 03 04 POINT

More information

p01.qxd

p01.qxd 2 s 1 1 2 6 2 POINT 23 23 32 15 3 4 s 1 3 2 4 6 2 7003800 1600 1200 45 5 3 11 POINT 2 7003800 7 11 7003800 8 12 9 10 POINT 2003 5 s 45700 3800 5 6 s3 1 POINT POINT 45 2700 3800 7 s 5 8 s3 1 POINT POINT

More information

株主通信:第18期 中間

株主通信:第18期 中間 19 01 02 03 04 290,826 342,459 1,250,678 276,387 601,695 2,128,760 31,096 114,946 193,064 45,455 18,478 10,590 199,810 22,785 2,494 3,400,763 284,979 319,372 1,197,774 422,502 513,081 2,133,357 25,023

More information

ワタベウェディング株式会社

ワタベウェディング株式会社 1 2 3 4 140,000 100,000 60,000 20,000 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 5 6 71 2 13 14 7 8 9 10 11 12 1 2 2 point 1 point 2 1 1 3 point 3 4 4 5 6 point 4 point 5 point 6 13 14 15 16 point 17

More information

untitled

untitled 1 2 3 4 5 6 7 Point 60,000 50,000 40,000 30,000 20,000 10,000 0 29,979 41,972 31,726 45,468 35,837 37,251 24,000 20,000 16,000 12,000 8,000 4,000 0 16,795 22,071 20,378 14 13 12 11 10 0 12.19 12.43 12.40

More information

株主通信 第16 期 報告書

株主通信 第16 期 報告書 10 15 01 02 1 2 3 03 04 4 05 06 5 153,476 232,822 6,962 19,799 133,362 276,221 344,360 440,112 412,477 846,445 164,935 422,265 1,433,645 26,694 336,206 935,497 352,675 451,321 1,739,493 30,593 48,894 153,612

More information

-- 0 500 1000 1500 2000 2500 3000 () 0% 20% 40% 60%23 47.5% 16.0% 26.8% 27.6% 10,000 -- 350 322 300 286 250 200 150 100 50 0 20 21 22 23 24 25 26 27 28 29 -- ) 300 280 260 240 163,558 165,000 160,000

More information

2004 2005 2 2 1G01P038-0 1 2 1.1.............................. 2 1.2......................... 2 1.3......................... 3 2 4 2.1............................ 4 2.2....................... 4 2.3.......................

More information

課題

課題 int starttime_msec; boolean counting = false; size(400,200); smooth(); //font は各自のものに変更してください font = loadfont("serif-48.vlw"); void mouseclicked(){ counting = true; starttime_msec = millis(); int t=0;

More information

II Karel Švadlenka * [1] 1.1* 5 23 m d2 x dt 2 = cdx kx + mg dt. c, g, k, m 1.2* u = au + bv v = cu + dv v u a, b, c, d R

II Karel Švadlenka * [1] 1.1* 5 23 m d2 x dt 2 = cdx kx + mg dt. c, g, k, m 1.2* u = au + bv v = cu + dv v u a, b, c, d R II Karel Švadlenka 2018 5 26 * [1] 1.1* 5 23 m d2 x dt 2 = cdx kx + mg dt. c, g, k, m 1.2* 5 23 1 u = au + bv v = cu + dv v u a, b, c, d R 1.3 14 14 60% 1.4 5 23 a, b R a 2 4b < 0 λ 2 + aλ + b = 0 λ =

More information

数値計算:常微分方程式

数値計算:常微分方程式 ( ) 1 / 82 1 2 3 4 5 6 ( ) 2 / 82 ( ) 3 / 82 C θ l y m O x mg λ ( ) 4 / 82 θ t C J = ml 2 C mgl sin θ θ C J θ = mgl sin θ = θ ( ) 5 / 82 ω = θ J ω = mgl sin θ ω J = ml 2 θ = ω, ω = g l sin θ = θ ω ( )

More information

<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63>

<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63> 通信方式第 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/072662 このサンプルページの内容は, 第 2 版発行当時のものです. i 2 2 2 2012 5 ii,.,,,,,,.,.,,,,,.,,.,,..,,,,.,,.,.,,.,,.. 1990 5 iii 1 1

More information

5. [1 ] 1 [], u(x, t) t c u(x, t) x (5.3) ξ x + ct, η x ct (5.4),u(x, t) ξ, η u(ξ, η), ξ t,, ( u(ξ,η) ξ η u(x, t) t ) u(x, t) { ( u(ξ, η) c t ξ ξ { (

5. [1 ] 1 [], u(x, t) t c u(x, t) x (5.3) ξ x + ct, η x ct (5.4),u(x, t) ξ, η u(ξ, η), ξ t,, ( u(ξ,η) ξ η u(x, t) t ) u(x, t) { ( u(ξ, η) c t ξ ξ { ( 5 5.1 [ ] ) d f(t) + a d f(t) + bf(t) : f(t) 1 dt dt ) u(x, t) c u(x, t) : u(x, t) t x : ( ) ) 1 : y + ay, : y + ay + by : ( ) 1 ) : y + ay, : yy + ay 3 ( ): ( ) ) : y + ay, : y + ay b [],,, [ ] au xx

More information

< F2D BCA82CC978E89BA82CC8EC08CB12E6A7464>

< F2D BCA82CC978E89BA82CC8EC08CB12E6A7464> パチンコ玉の落下の実験 [Java アプレット ] [Java アプリケーション ] 1. はじめに 1 個のパチンコ玉が釘に当たって左右に分かれながら落下するとき パチンコ玉はどこに落下するのでしょうか ただし パチンコ玉が釘に当たって左右に分かれるとき その分かれ方は左右半々であるとします パチンコ玉が落下し易い場所はあるのでしょうか それとも どこの場所も同じなのでしょうか シミュレーションソフト

More information

IA hara@math.kyushu-u.ac.jp Last updated: January,......................................................................................................................................................................................

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

gr09.dvi

gr09.dvi .1, θ, ϕ d = A, t dt + B, t dtd + C, t d + D, t dθ +in θdϕ.1.1 t { = f1,t t = f,t { D, t = B, t =.1. t A, tdt e φ,t dt, C, td e λ,t d.1.3,t, t d = e φ,t dt + e λ,t d + dθ +in θdϕ.1.4 { = f1,t t = f,t {

More information

5 H Boltzmann Einstein Brown 5.1 Onsager [ ] Tr Tr Tr = dγ (5.1) A(p, q) Â 0 = Tr Âe βĥ0 Tr e βĥ0 = dγ e βh 0(p,q) A(p, q) dγ e βh 0(p,q) (5.2) e βĥ0

5 H Boltzmann Einstein Brown 5.1 Onsager [ ] Tr Tr Tr = dγ (5.1) A(p, q) Â 0 = Tr Âe βĥ0 Tr e βĥ0 = dγ e βh 0(p,q) A(p, q) dγ e βh 0(p,q) (5.2) e βĥ0 5 H Boltzmann Einstein Brown 5.1 Onsager [ ] Tr Tr Tr = dγ (5.1) A(p, q) Â = Tr Âe βĥ Tr e βĥ = dγ e βh (p,q) A(p, q) dγ e βh (p,q) (5.2) e βĥ A(p, q) p q Â(t) = Tr Â(t)e βĥ Tr e βĥ = dγ() e βĥ(p(),q())

More information

December 28, 2018

December 28, 2018 e-mail : kigami@i.kyoto-u.ac.jp December 28, 28 Contents 2............................. 3.2......................... 7.3..................... 9.4................ 4.5............. 2.6.... 22 2 36 2..........................

More information

main

main 14 1. 12 5 main 1.23 3 1.230000 3 1.860867 1 2. 1988 1925 1911 1867 void JPcalendar(int x) 1987 1 64 1 1 1 while(1) Ctrl C void JPcalendar(int x){ if (x > 1988) printf(" %d %d \n", x, x-1988); else if(x

More information

A (1) = 4 A( 1, 4) 1 A 4 () = tan A(0, 0) π A π

A (1) = 4 A( 1, 4) 1 A 4 () = tan A(0, 0) π A π 4 4.1 4.1.1 A = f() = f() = a f (a) = f() (a, f(a)) = f() (a, f(a)) f(a) = f 0 (a)( a) 4.1 (4, ) = f() = f () = 1 = f (4) = 1 4 4 (4, ) = 1 ( 4) 4 = 1 4 + 1 17 18 4 4.1 A (1) = 4 A( 1, 4) 1 A 4 () = tan

More information

(1) (2) (3) (4) HB B ( ) (5) (6) (7) 40 (8) (9) (10)

(1) (2) (3) (4) HB B ( ) (5) (6) (7) 40 (8) (9) (10) 2017 12 9 4 1 30 4 10 3 1 30 3 30 2 1 30 2 50 1 1 30 2 10 (1) (2) (3) (4) HB B ( ) (5) (6) (7) 40 (8) (9) (10) (1) i 23 c 23 0 1 2 3 4 5 6 7 8 9 a b d e f g h i (2) 23 23 (3) 23 ( 23 ) 23 x 1 x 2 23 x

More information

1 3 3 5 6 9 10 12 13 14 15 17 18 18 19 20 22 24 25 28 28 29 30 31 31 32 32 33 33 2 34 35 35 36 36 36 39 40 41 41 42 43 43 44 44 45 48 50 50 51 51 51 52 53 55 57 59 61 63 65 67 69 71 73 75 77 3 82 84 86

More information

2014 3 10 5 1 5 1.1..................................... 5 2 6 2.1.................................... 6 2.2 Z........................................ 6 2.3.................................. 6 2.3.1..................

More information

[1] 1.1 x(t) t x(t + n ) = x(t) (n = 1,, 3, ) { x(t) : : 1 [ /, /] 1 x(t) = a + a 1 cos πt + a cos 4πt + + a n cos nπt + + b 1 sin πt + b sin 4πt = a

[1] 1.1 x(t) t x(t + n ) = x(t) (n = 1,, 3, ) { x(t) : : 1 [ /, /] 1 x(t) = a + a 1 cos πt + a cos 4πt + + a n cos nπt + + b 1 sin πt + b sin 4πt = a 13/7/1 II ( / A: ) (1) 1 [] (, ) ( ) ( ) ( ) etc. etc. 1. 1 [1] 1.1 x(t) t x(t + n ) = x(t) (n = 1,, 3, ) { x(t) : : 1 [ /, /] 1 x(t) = a + a 1 cos πt + a cos 4πt + + a n cos nπt + + b 1 sin πt + b sin

More information

Appendix A BASIC BASIC Beginner s All-purpose Symbolic Instruction Code FORTRAN COBOL C JAVA PASCAL (NEC N88-BASIC Windows BASIC (1) (2) ( ) BASIC BAS

Appendix A BASIC BASIC Beginner s All-purpose Symbolic Instruction Code FORTRAN COBOL C JAVA PASCAL (NEC N88-BASIC Windows BASIC (1) (2) ( ) BASIC BAS Appendix A BASIC BASIC Beginner s All-purpose Symbolic Instruction Code FORTRAN COBOL C JAVA PASCAL (NEC N88-BASIC Windows BASIC (1 (2 ( BASIC BASIC download TUTORIAL.PDF http://hp.vector.co.jp/authors/va008683/

More information

認識行動システム論

認識行動システム論 05/5/ レポートメールエラー インタラクティブシステム論第 7 回 梶本裕之 Twitter ID kajimoto ハッシュタグ #ninshiki 第 5 回分のレポートメールがエラーになった可能性があります.( メールボックスのサイズオーバー ) 再度送信してください. 日程 4/9 イントロダクション 4/6 Scilab の紹介 ( 西 6 号館 3 階 PC ルーム ) 4/3 出張により休講

More information

1 2 2 (POC)

1 2 2 (POC) 2007 Taichi Kawasaki Kinki University School of Engineerings 1 2 2 (POC) 6 3 7 4 12 5 13 6 18 7 24 8 26 9 27 10 31 1 1 [1]. 1 [2] P.Chazal [3] 2 3 2 1: 3 2: 4 3: 5 2 (POC) f(n 1, n 2 ) g(n 1, n 2 ) 2 F

More information

sp3.dvi

sp3.dvi 3 15 5 22 1 2 1.1... 2 1.2... 4 1.3... 5 1.4... 8 1.5 (Matlab )... 11 2 15 2.1... 15 2.2... 16 2.3... 17 3 19 3.1... 19 3.2... 2 3.3... 21 3.4... 22 3.5... 23 3.6... 24 3.7... 25 3.8 Daubechies... 26 4

More information

Sample function Re random process Flutter, Galloping, etc. ensemble (mean value) N 1 µ = lim xk( t1) N k = 1 N autocorrelation function N 1 R( t1, t1

Sample function Re random process Flutter, Galloping, etc. ensemble (mean value) N 1 µ = lim xk( t1) N k = 1 N autocorrelation function N 1 R( t1, t1 Sample function Re random process Flutter, Galloping, etc. ensemble (mean value) µ = lim xk( k = autocorrelation function R( t, t + τ) = lim ( ) ( + τ) xk t xk t k = V p o o R p o, o V S M R realization

More information

v_-3_+2_1.eps

v_-3_+2_1.eps I 9-9 (3) 9 9, x, x (t)+a(t)x (t)+b(t)x(t) = f(t) (9), a(t), b(t), f(t),,, f(t),, a(t), b(t),,, x (t)+ax (t)+bx(t) = (9),, x (t)+ax (t)+bx(t) = f(t) (93), b(t),, b(t) 9 x (t), x (t), x (t)+a(t)x (t)+b(t)x(t)

More information

all.dvi

all.dvi 29 4 Green-Lagrange,,.,,,,,,.,,,,,,,,,, E, σ, ε σ = Eε,,.. 4.1? l, l 1 (l 1 l) ε ε = l 1 l l (4.1) F l l 1 F 30 4 Green-Lagrange Δz Δδ γ = Δδ (4.2) Δz π/2 φ γ = π 2 φ (4.3) γ tan γ γ,sin γ γ ( π ) γ tan

More information

x A Aω ẋ ẋ 2 + ω 2 x 2 = ω 2 A 2. (ẋ, ωx) ζ ẋ + iωx ζ ζ dζ = ẍ + iωẋ = ẍ + iω(ζ iωx) dt dζ dt iωζ = ẍ + ω2 x (2.1) ζ ζ = Aωe iωt = Aω cos ωt + iaω sin

x A Aω ẋ ẋ 2 + ω 2 x 2 = ω 2 A 2. (ẋ, ωx) ζ ẋ + iωx ζ ζ dζ = ẍ + iωẋ = ẍ + iω(ζ iωx) dt dζ dt iωζ = ẍ + ω2 x (2.1) ζ ζ = Aωe iωt = Aω cos ωt + iaω sin 2 2.1 F (t) 2.1.1 mẍ + kx = F (t). m ẍ + ω 2 x = F (t)/m ω = k/m. 1 : (ẋ, x) x = A sin ωt, ẋ = Aω cos ωt 1 2-1 x A Aω ẋ ẋ 2 + ω 2 x 2 = ω 2 A 2. (ẋ, ωx) ζ ẋ + iωx ζ ζ dζ = ẍ + iωẋ = ẍ + iω(ζ iωx) dt dζ

More information

QMI_09.dvi

QMI_09.dvi 25 3 19 Erwin Schrödinger 1925 3.1 3.1.1 3.1.2 σ τ 2 2 ux, t) = ux, t) 3.1) 2 x2 ux, t) σ τ 2 u/ 2 m p E E = p2 3.2) E ν ω E = hν = hω. 3.3) k p k = p h. 3.4) 26 3 hω = E = p2 = h2 k 2 ψkx ωt) ψ 3.5) h

More information

dx dt = f x,t ( ) t

dx dt = f x,t ( ) t MATLAB 1. 2. Runge-Kutta 3. 1. 2. 3. dx dt = f x,t ( ) t dx( t) dt = lim Δt x( t + Δt) x( t) Δt t = nδt n = 0,1,2,3,4,5, x = x( nδt) n Δt dx ( ) dt = f x,t x n +1 x n Δt = f ( x,nδt) n 1 x n = x 0 n =

More information

調査結果(施設用).PDF

調査結果(施設用).PDF 19 1 2 328 65 501 18 12 328 65.5 2. 173 35 1. 328 65 N=501 «1» 1 501 328 65.5 2 1. 010 46 14.0% 14.4% 14.4% 2. 1120 58 17.7% 18.2% 32.6% 3. 2130 52 15.9% 16.3% 48.9% 4. 3140 25 7.6% 7.8% 56.7% 5. 4150

More information

QMI_10.dvi

QMI_10.dvi 25 3 19 Erwin Schrödinger 1925 3.1 3.1.1 σ τ x u u x t ux, t) u 3.1 t x P ux, t) Q θ P Q Δx x + Δx Q P ux + Δx, t) Q θ P u+δu x u x σ τ P x) Q x+δx) P Q x 3.1: θ P θ Q P Q equation of motion P τ Q τ σδx

More information

85 4

85 4 85 4 86 Copright c 005 Kumanekosha 4.1 ( ) ( t ) t, t 4.1.1 t Step! (Step 1) (, 0) (Step ) ±V t (, t) I Check! P P V t π 54 t = 0 + V (, t) π θ : = θ : π ) θ = π ± sin ± cos t = 0 (, 0) = sin π V + t +V

More information

ランダムウォークの境界条件・偏微分方程式の数値計算

ランダムウォークの境界条件・偏微分方程式の数値計算 B L06(2018-05-22 Tue) : Time-stamp: 2018-05-22 Tue 21:53 JST hig,, 2, multiply transf http://hig3.net L06 B(2018) 1 / 38 L05-Q1 Quiz : 1 M λ 1 = 1 u 1 ( ). M u 1 = u 1, u 1 = ( 3 4 ) s (s 0)., u 1 = 1

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

sikepuri.dvi

sikepuri.dvi 2009 2 2 2. 2.. F(s) G(s) H(s) G(s) F(s) H(s) F(s),G(s) H(s) : V (s) Z(s)I(s) I(s) Y (s)v (s) Z(s): Y (s): 2: ( ( V V 2 I I 2 ) ( ) ( Z Z 2 Z 2 Z 22 ) ( ) ( Y Y 2 Y 2 Y 22 ( ) ( ) Z Z 2 Y Y 2 : : Z 2 Z

More information

A(6, 13) B(1, 1) 65 y C 2 A(2, 1) B( 3, 2) C 66 x + 2y 1 = 0 2 A(1, 1) B(3, 0) P 67 3 A(3, 3) B(1, 2) C(4, 0) (1) ABC G (2) 3 A B C P 6

A(6, 13) B(1, 1) 65 y C 2 A(2, 1) B( 3, 2) C 66 x + 2y 1 = 0 2 A(1, 1) B(3, 0) P 67 3 A(3, 3) B(1, 2) C(4, 0) (1) ABC G (2) 3 A B C P 6 1 1 1.1 64 A6, 1) B1, 1) 65 C A, 1) B, ) C 66 + 1 = 0 A1, 1) B, 0) P 67 A, ) B1, ) C4, 0) 1) ABC G ) A B C P 64 A 1, 1) B, ) AB AB = 1) + 1) A 1, 1) 1 B, ) 1 65 66 65 C0, k) 66 1 p, p) 1 1 A B AB A 67

More information

(1) 1 y = 2 = = b (2) 2 y = 2 = 2 = 2 + h B h h h< h 2 h

(1) 1 y = 2 = = b (2) 2 y = 2 = 2 = 2 + h B h h h< h 2 h 6 6.1 6.1.1 O y A y y = f() y = f() b f(b) B y f(b) f() = b f(b) f() f() = = b A f() b AB O b 6.1 2 y = 2 = 1 = 1 + h (1 + h) 2 1 2 (1 + h) 1 2h + h2 = h h(2 + h) = h = 2 + h y (1 + h) 2 1 2 O y = 2 1

More information

08-Note2-web

08-Note2-web r(t) t r(t) O v(t) = dr(t) dt a(t) = dv(t) dt = d2 r(t) dt 2 r(t), v(t), a(t) t dr(t) dt r(t) =(x(t),y(t),z(t)) = d 2 r(t) dt 2 = ( dx(t) dt ( d 2 x(t) dt 2, dy(t), dz(t) dt dt ), d2 y(t) dt 2, d2 z(t)

More information

HPC146

HPC146 2 3 4 5 6 int array[16]; #pragma xmp nodes p(4) #pragma xmp template t(0:15) #pragma xmp distribute t(block) on p #pragma xmp align array[i] with t(i) array[16] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Node

More information

CRA2656-A

CRA2656-A FH-401 2 3 4 C O N T E N T S 5 1 0 0 2 3 0 6 7 1 1 2 8 3 9 2 1 2 2 3 3 2 10 q 3 4 0 11 3 1 2 12 q 3 4 0 13 4 1 2 2 3 3 2 14 q 3 4 0 15 5 1 2 16 3 2 3 3 2 4 5 q 17 1 2 18 1 3 2 19 2 3 1 20 2 3 2 2 3 21

More information

,,,17,,, ( ),, E Q [S T F t ] < S t, t [, T ],,,,,,,,

,,,17,,, ( ),, E Q [S T F t ] < S t, t [, T ],,,,,,,, 14 5 1 ,,,17,,,194 1 4 ( ),, E Q [S T F t ] < S t, t [, T ],,,,,,,, 1 4 1.1........................................ 4 5.1........................................ 5.........................................

More information

x ( ) x dx = ax

x ( ) x dx = ax x ( ) x dx = ax 1 dx = a x log x = at + c x(t) = e at C (C = e c ) a > 0 t a < 0 t 0 (at + b ) h dx = lim x(t + h) x(t) h 0 h x(t + h) x(t) h x(t) t x(t + h) x(t) ax(t) h x(t + h) x(t) + ahx(t) 0, h, 2h,

More information

p _

p _ Index ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap. ap.

More information

AHPを用いた大相撲の新しい番付編成

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

More information

1.1 ft t 2 ft = t 2 ft+ t = t+ t 2 1.1 d t 2 t + t 2 t 2 = lim t 0 t = lim t 0 = lim t 0 t 2 + 2t t + t 2 t 2 t + t 2 t 2t t + t 2 t 2t + t = lim t 0

1.1 ft t 2 ft = t 2 ft+ t = t+ t 2 1.1 d t 2 t + t 2 t 2 = lim t 0 t = lim t 0 = lim t 0 t 2 + 2t t + t 2 t 2 t + t 2 t 2t t + t 2 t 2t + t = lim t 0 A c 2008 by Kuniaki Nakamitsu 1 1.1 t 2 sin t, cos t t ft t t vt t xt t + t xt + t xt + t xt t vt = xt + t xt t t t vt xt + t xt vt = lim t 0 t lim t 0 t 0 vt = dxt ft dft dft ft + t ft = lim t 0 t 1.1

More information

1. A0 A B A0 A : A1,...,A5 B : B1,...,B

1. A0 A B A0 A : A1,...,A5 B : B1,...,B 1. A0 A B A0 A : A1,...,A5 B : B1,...,B12 2. 3. 4. 5. A0 A, B Z Z m, n Z m n m, n A m, n B m=n (1) A, B (2) A B = A B = Z/ π : Z Z/ (3) A B Z/ (4) Z/ A, B (5) f : Z Z f(n) = n f = g π g : Z/ Z A, B (6)

More information

< F2D82B682E182F182AF82F12E6A7464>

< F2D82B682E182F182AF82F12E6A7464> 3 人のじゃんけん [Java アプレット ] [Java アプリケーション ] 1. はじめに A 君 B 君 C 君の 3 人でじゃんけんを 1 回するときの勝ち負けを考えてみましょう あいこの場合は A 君 B 君 C 君の順に グー グー グー チョキ チョキ チョキ パー パー パー グー チョキ パー グー パー チョキ チョキ グー パー チョキ パー グー パー グー チョキ パー

More information

1. A0 A B A0 A : A1,...,A5 B : B1,...,B12 2. 5 3. 4. 5. A0 (1) A, B A B f K K A ϕ 1, ϕ 2 f ϕ 1 = f ϕ 2 ϕ 1 = ϕ 2 (2) N A 1, A 2, A 3,... N A n X N n X N, A n N n=1 1 A1 d (d 2) A (, k A k = O), A O. f

More information

ẍ = kx, (k > ) (.) x x(t) = A cos(ωt + α) (.). d/ = D. d dt x + k ( x = D + k ) ( ) ( ) k k x = D + i D i x =... ( ) k D + i x = or ( ) k D i x =.. k.

ẍ = kx, (k > ) (.) x x(t) = A cos(ωt + α) (.). d/ = D. d dt x + k ( x = D + k ) ( ) ( ) k k x = D + i D i x =... ( ) k D + i x = or ( ) k D i x =.. k. K E N Z OU 8 9 8. F = kx x 3 678 ẍ = kx, (k > ) (.) x x(t) = A cos(ωt + α) (.). d/ = D. d dt x + k ( x = D + k ) ( ) ( ) k k x = D + i D i x =... ( ) k D + i x = or ( ) k D i x =.. k. D = ±i dt = ±iωx,

More information

keisoku01.dvi

keisoku01.dvi 2.,, Mon, 2006, 401, SAGA, JAPAN Dept. of Mechanical Engineering, Saga Univ., JAPAN 4 Mon, 2006, 401, SAGA, JAPAN Dept. of Mechanical Engineering, Saga Univ., JAPAN 5 Mon, 2006, 401, SAGA, JAPAN Dept.

More information

(3) (2),,. ( 20) ( s200103) 0.7 x C,, x 2 + y 2 + ax = 0 a.. D,. D, y C, C (x, y) (y 0) C m. (2) D y = y(x) (x ± y 0), (x, y) D, m, m = 1., D. (x 2 y

(3) (2),,. ( 20) ( s200103) 0.7 x C,, x 2 + y 2 + ax = 0 a.. D,. D, y C, C (x, y) (y 0) C m. (2) D y = y(x) (x ± y 0), (x, y) D, m, m = 1., D. (x 2 y [ ] 7 0.1 2 2 + y = t sin t IC ( 9) ( s090101) 0.2 y = d2 y 2, y = x 3 y + y 2 = 0 (2) y + 2y 3y = e 2x 0.3 1 ( y ) = f x C u = y x ( 15) ( s150102) [ ] y/x du x = Cexp f(u) u (2) x y = xey/x ( 16) ( s160101)

More information

< F2D92DE82E8914B82CC977088D32E6A7464>

< F2D92DE82E8914B82CC977088D32E6A7464> 釣り銭の用意の実験 [Java アプレット ] [Java アプリケーション ] 1. はじめに クラス会などの幹事を務めることはありませんか 幹事になったつもりで考えてみてください 仮に クラス会への参加者人数は 35 人で 会費は 3500 円であるとします また 参加者は 1000 円札 4 枚でお釣りを必要とする人と 1000 円札 3 枚と 500 円玉 1 個でお釣りの要らない人の 2

More information

() n C + n C + n C + + n C n n (3) n C + n C + n C 4 + n C + n C 3 + n C 5 + (5) (6 ) n C + nc + 3 nc n nc n (7 ) n C + nc + 3 nc n nc n (

() n C + n C + n C + + n C n n (3) n C + n C + n C 4 + n C + n C 3 + n C 5 + (5) (6 ) n C + nc + 3 nc n nc n (7 ) n C + nc + 3 nc n nc n ( 3 n nc k+ k + 3 () n C r n C n r nc r C r + C r ( r n ) () n C + n C + n C + + n C n n (3) n C + n C + n C 4 + n C + n C 3 + n C 5 + (4) n C n n C + n C + n C + + n C n (5) k k n C k n C k (6) n C + nc

More information

c 2006 Yoneda norimasa All rights reserved

c 2006 Yoneda norimasa All rights reserved LEGO 011242 2006 2 Copyright c 2006 by Yoneda norimasa c 2006 Yoneda norimasa All rights reserved , LEGO,., Matlab Simlink,,., LEGO., 2, 2.,, LEGOMINDSTORMS, LOBOLAB.,,., Matlab,.,,,,,.,,,,, 2.,. i 1

More information

2003 12 11 1 http://www.sml.k.u-tokyo.ac.jp/members/nabe/lecture2003 http://www.sml.k.u-tokyo.ac.jp/members/nabe/lecture2002 nabe@sml.k.u-tokyo.ac.jp 2 1. 10/ 9 2. 10/16 3. 10/23 ( ) 4. 10/30 5. 11/ 6

More information