IPSJ SIG Technical Report Vol.2014-HPC-143 No /3/3 Identity Parareal 1,2,a) 3 Parareal-in-Time (identity) Identity Parareal, Parareal-in-Time,
|
|
|
- ぜんま ふじがわ
- 4 years ago
- Views:
Transcription
1 Identity Parareal 1,2,a) 3 Parareal-in-Time (identity) Identity Parareal, Parareal-in-Time, Identity Parareal, JST CREST 3 a) taami@cc.yushu-u.ac.jp 2001 Lions Pararealin-Time [1] [2], [3] [4], [5], [6], [7], [8], [9], [10] [11], [12], [13], [14] 2. Parareal-in-Time Parareal-in-Time t {x } (x +1 F (x )) ( ) G (x ) F (x ) x (r+1) +1 = G (x (r+1) )+F (x (r) ) G (x (r) ) (1) r {x (r) } r x (r) = x c 2014 Information Processing Society of Japan 1
2 1 (a) Parareal-in-Time (b) ( T g =1/M, T c = t ) Parareal-in-Time r 100% (1) 2.1 (1) G (x ) F (x ) G (x ) (1) (API) F (x ) G (x ) 2.2 ( dt M F (x ) Mdt G (x ) ) F (x ) G (x ) M Parareal-in-Time (1) 1 P R M S(P, R, M, t) = KM K(1 + Mt)+MR(1 + t) (2) t P + R 1 K K Parareal-in-Time M 3. Identity Parareal Parareal-in-Time Identity Parareal [15], [16] Parareal-in-Time G(x) G(x) G(x) G(x) c 2014 Information Processing Society of Japan 2
3 2 (a) Identity Parareal (b) G(x) =x Parareal-in-Time x +1 = F (x ) ( ) d x(t) =f(t, x(t)) (3) dt x(t + dt) =x(t)+ f x dt + O(dt2 ) (4) x(t) Parareal-in-Time x (r+1) +1 = x (r+1) + F (x (r) ) x(r) (5) (identity) Identity Parareal (iparareal) 3.2 [15], [16], [17] F x +1 = F x =[I +(F I)] x (6) I +(F I) 1 x = [I +(F I)]x 0 (7) =0 F F I F I I + (F I)+ >j(f I)(F j I)+... (8) r x (r) x (r+1) +1 = x (r+1) + F x (r) x (r) (9) x (r) x ( ) x (r) x [ρ(f I)] j (10) x 0 j j=r+1 ρ(a) A ρ(f I) ρ(f I) F {x } c 2014 Information Processing Society of Japan 3
4 (a) (b) (c) RMS Error (Angstrom) a=1 a=2 a=4 RMS Error (Angstrom) r=1 r=2 r=4 r=6 Symplectic 2 Symplectic 4 RMS Error (Angstrom) r=1 r=2 Symplectic 2 r=4 r=6 Symplectic Delta T (fs) Delta T (fs) Delta T (fs) 3 16 : (a) (SI) (b) F (x) 2 SI iparareal (c) F (x) 4 SI iparareal 3.3 (4) iparareal (Molecular Dynamics (MD)) (fs, sec) (µs, 10 6 sec) (ms, 10 3 sec) MD (Velocity Verlet) t +1 = t + dt 6N ( x j v j ) [ x j (t +1 )=x j (t )+ v j (t )+ f ] j(t ) dt dt 2m j (11) v j (t +1 )=v j (t )+ f j(t )+f j (t +1 ) dt 2m j (12) MD 3 iparareal 3(a) (SI) 2 SI (b) (a) 2 4 SI (c) (a) 4 MD r =4 r = Burgers Burgers u t + u u x = 1 2 u Re x 2 (13) Navier- Stoes Re 0 x<1 u(x) N u j u(x j )(x j = j/n, j =0, 1,...,N 1) u N = u 0 Euler [ 1 u δ 2 ] u j (t) δu j (t) dt j = u j (t)+ Re δx 2 u j (14) δx 2 [ ] 1 δ 2 u j u j (t + dt) =u j (t)+ Re δx 2 δu u j j dt (15) δx Re = 100 Euler (2nd Runge-Kutta) 4(a) u(x) =sin(2πx) t =0.25 Euler ( ) c 2014 Information Processing Society of Japan 4
5 (a) (b) 10-3 N= N=100 N=200 N=200 Error 10-5 N=500 Error 10-5 N= N= N= Delta T Delta T 4 (a) ( Euler ) (b) iparareal (P = 16) ( R =1 R =2 ) 4(b) F (x ) iparareal R =1 R =2 Paraeral-in-Time iparareal R =2 R =1 iparareal R =2 dt Re 2N 2 (16) 4(a) (b) dt dt iparareal Parareal-in-Time 3.4 iparareal 2(a) S(P, R, t) = K Kt + R(1 + t) (17) K = P + R 1 t 1 1 t MD 6N (48N Bytes) O(N 2 ) N t 1 O(N log N) FFT 8N Bytes O(N) t 1 Burgers 4. iparareal MPI iparareal (5) y (r+1) = F (x (r) ) x (r) (18) x (r+1) +1 = x (r+1) + y (r+1) (19) [19] MPI c 2014 Information Processing Society of Japan 5
![5 (a) (b) Procedure: 1. void bb(double *x, double *y, int n, int prev, int next) { 2. MPI Recv(x, n, MPI DOUBLE, prev,..); 3. for (int i = 0: i < n: i++) 4. x[i] += y[i]; 5.](/docs-images/103/157723256/images/6-2.jpg "MPI Send(x, n, MPI DOUBLE, next,..); 6. } 5(a) Reduce 5(b) 12 2 P0 P11 0.4msec 4.1 ( 6(a) ) [20] 4.2 ( 6(a) ) 6(b) 12 N m =2, 4,..., 64 0.")
6 5 (a) (b) Procedure: 1. void bb(double *x, double *y, int n, int prev, int next) { 2. MPI Recv(x, n, MPI DOUBLE, prev,..); 3. for (int i = 0: i < n: i++) 4. x[i] += y[i]; 5. MPI Send(x, n, MPI DOUBLE, next,..); 6. } 5(a) Reduce 5(b) 12 2 P0 P11 0.4msec 4.1 ( 6(a) ) [20] 4.2 ( 6(a) ) 6(b) 12 N m =2, 4,..., msec MBytes/sec N = 10 6 N = msec 40 msec 2 GBytes/sec (P0 11 ) 3 GFlops CPU c 2014 Information Processing Society of Japan 6
![6 (a) (b) 5. Parareal-in-Time Jülich Supercomputer Center [13], [14] iparareal [21] MPI [22] MPI Reduce 6.](/docs-images/103/157723256/images/7-0.jpg "(identity) Pararealin-Time (Identity Parareal ) Burgers iparareal (C) ( 23540454) CX400 12 [1] Lions, J., Maday, Y., and Turinici, G.: A parareal in time discretization of PDE s. C. R. Acad. Sci.")
7 6 (a) (b) 5. Parareal-in-Time Jülich Supercomputer Center [13], [14] iparareal [21] MPI [22] MPI Reduce 6. (identity) Pararealin-Time (Identity Parareal ) Burgers iparareal (C) ( ) CX [1] Lions, J., Maday, Y., and Turinici, G.: A parareal in time discretization of PDE s. C. R. Acad. Sci., Ser. I: Math. 332, (2001). [2] Baffico, L., Bernard, S., Maday, Y., Turinici, G., and Zérah, G.: Parallel-in-time molecular-dynamics simulations. Phys. Rev. E 66, (2002). [3] Maday, Y., Turinichi, G.: Parallel in Time Algorithms for Quantum Control: Parareal Time Discretization Scheme. IJQC 93, (2003). [4] Farhat, C. and Chandesris, M.: Time-decomposed parallel time-integrators: theory and feasibility studies for fluid, structure, and fluidstructure applications. Int. J. Numer. Meth. Engng. 58, (2003). [5] Fischer, P. F., Hecht, F., and Maday, Y.: A Parareal in c 2014 Information Processing Society of Japan 7
8 Time Semi-implicit Approximation of the Navier-Stoes Equations. LNCSE 40, (2005). [6] Gander, M. J. and Vandewalle, S.: On the Superlinear and Linear Convergence of the Parareal Algorithm. LNCSE 55, (Springer, 2007). [7] Gander, M. J. and Vandewalle, S.: Analysis of the parareal time-parallel time-integration method. SIAM J. Sci. Comput. 29, (2007). [8] Samaddar, D. and Newman, D. E., and Sánchez, R.: Parallelization in time of numerical simulations of fullydeveloped plasma turbulence using the parareal algorithm. J. Comp. Phys. 229, (2010). [9] Duarte, M., Massot, M., and Descombes, S.: Parareal operator splitting techniques for multi-scale reaction waves: Numerical analysis and strategies. ESAIM: Math. Mod. Num. Analysis 45, (2011). [10] Ruprecht, D. and Krause, R.: Explicit parallel-in-time integration of a linear acoustic-advection system. Computers & Fluids 59, (2012). [11] Aubanel, E.: Scheduling of tass in the parareal algorithm. Parallel Computing 37, (2011). [12] Elwasif, W. R., Foley, S. S., Bernholdt, D. E., Berry, L. A., Samaddar, D., Newman, D. E., and Sanchez, R.: A dependency-driven formulation of parareal: parallelin-time solution of PDEs as a many-tas application. in Proc. MTAGS 11, (2011). [13] Spec, R., Ruprecht, D., Krause, R., Emmett, M., Minion, M., Winel, M., and Gibbon, P.: A massively spacetime parallel N-Body Solver. in Proc. SC12, Technical Paper No. 92, 1 11 (2012). [14] Spec, R., Ruprecht, D., Emmett, M., Bolten, M., Krause, R.: A space-time parallel solver for the threedimensional heat equation. International Conference on Parallel Computing, B5-1 (Munich, 10-13, Sep. 2013). [15] :, Vol HPC-131, No.6, 1 8 (2011). [16] T. Taami and A. Nishida, Parareal Acceleration of Matrix Multiplication, Adv. Par. Comp. 22, (2012). [17] Bal, G.: On the Convergence and the Stability of the Parareal Algorithm to Solve Partial Differential Equations. LNCSE 40, (2005). [18] :, Vol HPC-138, No.12, 1 6 (2013). [19] Fuudome, D. and Taami, T.: Parallel Bucet-Brigade Communication Interface for Scientific Applications. in proc. EuroMPI 13, (2013). [20] Hoefler, T., Lumsdaine, A., and Rehm, W.: Implementation and Performance Analysis of Non-Blocing Collective Operations for MPI. in Proc. SC07 (IEEE Computer Society/ACM, 2007). [21] Kurza, J. and Dongarra, J.: Implementing Linear Algebra Routines on Multi-core Processors with Pipelining and a Loo Ahead. LNCS 4699, (2007). [22] Worringen, J.: Pipelining and overlapping for MPI collective operations. in Proc. IEEE Conference on Local Computer Networ, (2003). c 2014 Information Processing Society of Japan 8
IPSJ SIG Technical Report Vol.2011-HPC-131 No /10/6 1 1 Parareal-in-Time Applicability of Time-domain Parallelism to Iterative Linear Calculus T
1 1 Parareal-in-Time Applicability of Time-domain Parallelism to Iterative Linear Calculus Toshiya Taami 1 and Aira Nishida 1 The time-domain parallelism, nown as Parareal-in-Time algorithm, has been applied
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member
A Feasibility Study of Direct-Mapping-Type Parallel Processing Method to Solve Linear Equations in Load Flow Calculations Hiroaki Inayoshi, Non-member (University of Tsukuba), Yasuharu Ohsawa, Member (Kobe
Trapezoidal Rule θ = 1/ x n x n 1 t = 1 [f(t n 1, x n 1 ) + f(t n, x n )] (6) 1. dx dt = f(t, x), x(t 0) = x 0 (7) t [t 0, t 1 ] f t [t 0, t 1 ], x x
![Trapezoidal Rule θ = 1/ x n x n 1 t = 1 [f(t n 1, x n 1 ) + f(t n, x n )] (6) 1. dx dt = f(t, x), x(t 0) = x 0 (7) t [t 0, t 1 ] f t [t 0, t 1 ], x x](/thumbs/92/107866474.jpg "Trapezoidal Rule θ = 1/ x n x n 1 t = 1 [f(t n 1, x n 1 ) + f(t n, x n )] (6) 1. dx dt = f(t, x), x(t 0) = x 0 (7) t [t 0, t 1 ] f t [t 0, t 1 ], x x")
University of Hyogo 8 8 1 d x(t) =f(t, x(t)), dt (1) x(t 0 ) =x 0 () t n = t 0 + n t x x n n x n x 0 x i i = 0,..., n 1 x n x(t) 1 1.1 1 1 1 0 θ 1 θ x n x n 1 t = θf(t n 1, x n 1 ) + (1 θ)f(t n, x n )
kokyuroku.dvi
On Applications of Rigorous Computing to Dynamical Systems (Zin ARAI) Department of Mathematics, Kyoto University email: arai@math.kyoto-u.ac.jp 1 [12, 13] Lorenz 2 Lorenz 3 4 2 Lorenz 2.1 Lorenz E. Lorenz
IPSJ SIG Technical Report Vol.2015-HPC-150 No /8/6 I/O Jianwei Liao 1 Gerofi Balazs 1 1 Guo-Yuan Lien Prototyping F
I/O Jianwei Liao 1 Gerofi Balazs 1 1 Guo-Yuan Lien 1 1 1 1 1 30 30 100 30 30 2 Prototyping File I/O Arbitrator Middleware for Real-Time Severe Weather Prediction System Jianwei Liao 1 Gerofi Balazs 1 Yutaka
三石貴志.indd
流通科学大学論集 - 経済 情報 政策編 - 第 21 巻第 1 号,23-33(2012) SIRMs SIRMs Fuzzy fuzzyapproximate approximatereasoning reasoningusing using Lukasiewicz Łukasiewicz logical Logical operations Operations Takashi Mitsuishi
IPSJ SIG Technical Report Vol.2013-ARC-207 No.23 Vol.2013-HPC-142 No /12/17 1,a) 1,b) 1,c) 1,d) OpenFOAM OpenFOAM A Bottleneck and Cooperation
1,a) 1,b) 1,c) 1,d) OpenFOAM OpenFOAM A Bottleneck and Cooperation with the Post Processes in Numerical Calculation of Transient Phenomena Taizo Kobayashi 1,a) Yoshiyuki Morie 1,b) Toshiya Takami 1,c)
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
,a),2,3 3,4 CG 2 2 2 An Interpolation Method of Different Flow Fields using Polar Interpolation Syuhei Sato,a) Yoshinori Dobashi,2,3 Tsuyoshi Yamamoto Tomoyuki Nishita 3,4 Abstract: Recently, realistic
Bulletin of JSSAC(2014) Vol. 20, No. 2, pp (Received 2013/11/27 Revised 2014/3/27 Accepted 2014/5/26) It is known that some of number puzzles ca
Bulletin of JSSAC(2014) Vol. 20, No. 2, pp. 3-22 (Received 2013/11/27 Revised 2014/3/27 Accepted 2014/5/26) It is known that some of number puzzles can be solved by using Gröbner bases. In this paper,
CPU Levels in the memory hierarchy Level 1 Level 2... Increasing distance from the CPU in access time Level n Size of the memory at each level 1: 2.2
FFT 1 Fourier fast Fourier transform FFT FFT FFT 1 FFT FFT 2 Fourier 2.1 Fourier FFT Fourier discrete Fourier transform DFT DFT n 1 y k = j=0 x j ω jk n, 0 k n 1 (1) x j y k ω n = e 2πi/n i = 1 (1) n DFT
1 Fourier Fourier Fourier Fourier Fourier Fourier Fourier Fourier Fourier analog digital Fourier Fourier Fourier Fourier Fourier Fourier Green Fourier
Fourier Fourier Fourier etc * 1 Fourier Fourier Fourier (DFT Fourier (FFT Heat Equation, Fourier Series, Fourier Transform, Discrete Fourier Transform, etc Yoshifumi TAKEDA 1 Abstract Suppose that u is
(1970) 17) V. Kucera: A Contribution to Matrix Ouadratic Equations, IEEE Trans. on Automatic Control, AC- 17-3, 344/347 (1972) 18) V. Kucera: On Nonnegative Definite Solutions to Matrix Ouadratic Equations,
Design of highly accurate formulas for numerical integration in weighted Hardy spaces with the aid of potential theory 1 Ken ichiro Tanaka 1 Ω R m F I = F (t) dt (1.1) Ω m m 1 m = 1 1 Newton-Cotes Gauss
Numerical Analysis II, Exam End Term Spring 2017
H. Ammari W. Wu S. Yu Spring Term 2017 Numerical Analysis II ETH Zürich D-MATH End Term Spring 2017 Problem 1 Consider dx = f(t, x), t [0, T ] dt x(0) = x 0 R [28 Marks] with f C subject to the Lipschitz
1 4 1 ( ) ( ) ( ) ( ) () 1 4 2
7 1995, 2017 7 21 1 2 2 3 3 4 4 6 (1).................................... 6 (2)..................................... 6 (3) t................. 9 5 11 (1)......................................... 11 (2)
Relaxation scheme of Besse t t n = n t, u n = u(t n ) (n = 0, 1,,...)., t u(t) = F (u(t)) (1). (1), u n+1 u n t = F (u n ) u n+1 = u n + tf (u n )., t
RIMS 011 5 3 7 relaxation sheme of Besse splitting method Scilab Scilab http://www.scilab.org/ Google Scilab Scilab Mathieu Colin Mathieu Colin 1 Relaxation scheme of Besse t t n = n t, u n = u(t n ) (n
error_g1.eps
Runge-Kutta Method Runge-Kutta Method x n+ = x n +hf(t n x n ) dx dt dx x(t+h) x(t) (t) = lim dt h h t = t n h > dx dt (t x(t n +h) x(t n ) n) = lim x(t n+) x(t n ) h h h x = f(tx) x(t n+ ) x(t n ) f(t
EGunGPU
Super Computing in Accelerator simulations - Electron Gun simulation using GPGPU - K. Ohmi, KEK-Accel Accelerator Physics seminar 2009.11.19 Super computers in KEK HITACHI SR11000 POWER5 16 24GB 16 134GFlops,
Feynman Encounter with Mathematics 52, [1] N. Kumano-go, Feynman path integrals as analysis on path space by time slicing approximation. Bull
![Feynman Encounter with Mathematics 52, [1] N. Kumano-go, Feynman path integrals as analysis on path space by time slicing approximation. Bull](/thumbs/94/118365979.jpg "Feynman Encounter with Mathematics 52, [1] N. Kumano-go, Feynman path integrals as analysis on path space by time slicing approximation. Bull")
Feynman Encounter with Mathematics 52, 200 9 [] N. Kumano-go, Feynman path integrals as analysis on path space by time slicing approximation. Bull. Sci. Math. vol. 28 (2004) 97 25. [2] D. Fujiwara and
133 1.,,, [1] [2],,,,, $[3],[4]$,,,,,,,,, [5] [6],,,,,, [7], interface,,,, Navier-Stokes, $Petr\dot{o}$v-Galerkin [8], $(,)$ $()$,,
![133 1.,,, [1] [2],,,,, $[3],[4]$,,,,,,,,, [5] [6],,,,,, [7], interface,,,, Navier-Stokes, $Petr\dot{o}$v-Galerkin [8], $(,)$ $()$,,](/thumbs/84/90503973.jpg "133 1.,,, [1] [2],,,,, $[3],[4]$,,,,,,,,, [5] [6],,,,,, [7], interface,,,, Navier-Stokes, $Petr\dot{o}$v-Galerkin [8], $(,)$ $()$,,")
836 1993 132-146 132 Navier-Stokes Numerical Simulations for the Navier-Stokes Equations in Incompressible Viscous Fluid Flows (Nobuyoshi Tosaka) (Kazuhiko Kakuda) SUMMARY A coupling approach of the boundary
IPSJ SIG Technical Report Vol.2014-HPC-143 No /3/3 1 1 t-fpc 1 bit SCALE t-fpc TIME-SERIES DATA COMPRESSION METHOD FOR TIME EVOLUTION SIMULATION
1 1 t-fpc 1 bit SCALE t-fpc TIME-SERIES DATA COMPRESSION METHOD FOR TIME EVOLUTION SIMULATIONS Yuki Matsuo 1 Yutaka Ishikawa 1 Abstract: Time evolution simulations generate a large amount of data periodically
untitled
3 3. (stochastic differential equations) { dx(t) =f(t, X)dt + G(t, X)dW (t), t [,T], (3.) X( )=X X(t) : [,T] R d (d ) f(t, X) : [,T] R d R d (drift term) G(t, X) : [,T] R d R d m (diffusion term) W (t)
4 2016 3 8 2.,. 2. Arakawa Jacobin., 2 Adams-Bashforth. Re = 80, 90, 100.. h l, h/l, Kármán, h/l 0.28,, h/l.., (2010), 46.2., t = 100 t = 2000 46.2 < Re 46.5. 1 1 4 2 6 2.1............................
1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15. 1. 2. 3. 16 17 18 ( ) ( 19 ( ) CG PC 20 ) I want some rice. I want some lice. 21 22 23 24 2001 9 18 3 2000 4 21 3,. 13,. Science/Technology, Design, Experiments,
IEEE HDD RAID MPI MPU/CPU GPGPU GPU cm I m cm /g I I n/ cm 2 s X n/ cm s cm g/cm
Neutron Visual Sensing Techniques Making Good Use of Computer Science J-PARC CT CT-PET TB IEEE HDD RAID MPI MPU/CPU GPGPU GPU cm I m cm /g I I n/ cm 2 s X n/ cm s cm g/cm cm cm barn cm thn/ cm s n/ cm
200708_LesHouches_02.ppt
Numerical Methods for Geodynamo Simulation Akira Kageyama Earth Simulator Center, JAMSTEC, Japan Part 2 Geodynamo Simulations in a Sphere or a Spherical Shell Outline 1. Various numerical methods used
最新耐震構造解析 ( 第 3 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 3 版 1 刷発行時のものです.
最新耐震構造解析 ( 第 3 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/052093 このサンプルページの内容は, 第 3 版 1 刷発行時のものです. i 3 10 3 2000 2007 26 8 2 SI SI 20 1996 2000 SI 15 3 ii 1 56 6
Title 混合体モデルに基づく圧縮性流体と移動する固体の熱連成計算手法 Author(s) 鳥生, 大祐 ; 牛島, 省 Citation 土木学会論文集 A2( 応用力学 ) = Journal of Japan Civil Engineers, Ser. A2 (2017), 73 Issue
Title 混合体モデルに基づく圧縮性流体と移動する固体の熱連成計算手法 Author(s) 鳥生, 大祐 ; 牛島, 省 Citation 土木学会論文集 A2( 応用力学 ) = Journal of Japan Civil Engineers, Ser. A2 (2017), 73 Issue Date 2017 URL http://hdl.handle.net/2433/229150 Right
Optical Flow t t + δt 1 Motion Field 3 3 1) 2) 3) Lucas-Kanade 4) 1 t (x, y) I(x, y, t)
http://wwwieice-hbkborg/ 2 2 4 2 -- 2 4 2010 9 3 3 4-1 Lucas-Kanade 4-2 Mean Shift 3 4-3 2 c 2013 1/(18) http://wwwieice-hbkborg/ 2 2 4 2 -- 2 -- 4 4--1 2010 9 4--1--1 Optical Flow t t + δt 1 Motion Field
IPSJ SIG Technical Report Vol.2014-ARC-213 No.24 Vol.2014-HPC-147 No /12/10 GPU 1,a) 1,b) 1,c) 1,d) GPU GPU Structure Of Array Array Of
GPU 1,a) 1,b) 1,c) 1,d) GPU 1 GPU Structure Of Array Array Of Structure 1. MPS(Moving Particle Semi-Implicit) [1] SPH(Smoothed Particle Hydrodynamics) [] DEM(Distinct Element Method)[] [] 1 Tokyo Institute
Wikipedia YahooQA MAD 4)5) MAD Web 6) 3. YAMAHA 7) 8) 2 3 4 5 6 2. Vocaloid2 2006 1 PV 2009 1 1100 200 YouTube 1 minato minato ussy 3D MAD F EDis ussy
1, 2 3 1, 2 Web Fischer Social Creativity 1) Social Creativity CG Network Analysis of an Emergent Massively Collaborative Creation Community Masahiro Hamasaki, 1, 2 Hideaki Takeda 3 and Takuichi Nishimura
: u i = (2) x i Smagorinsky τ ij τ [3] ij u i u j u i u j = 2ν SGS S ij, (3) ν SGS = (C s ) 2 S (4) x i a u i ρ p P T u ν τ ij S c ν SGS S csgs
![: u i = (2) x i Smagorinsky τ ij τ [3] ij u i u j u i u j = 2ν SGS S ij, (3) ν SGS = (C s ) 2 S (4) x i a u i ρ p P T u ν τ ij S c ν SGS S csgs](/thumbs/91/106610562.jpg ": u i = (2) x i Smagorinsky τ ij τ [3] ij u i u j u i u j = 2ν SGS S ij, (3) ν SGS = (C s ) 2 S (4) x i a u i ρ p P T u ν τ ij S c ν SGS S csgs")
15 C11-4 Numerical analysis of flame propagation in a combustor of an aircraft gas turbine, 4-6-1 E-mail: tominaga@icebeer.iis.u-tokyo.ac.jp, 2-11-16 E-mail: ntani@iis.u-tokyo.ac.jp, 4-6-1 E-mail: itoh@icebeer.iis.u-tokyo.ac.jp,
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
II ( ) (7/31) II ( [ (3.4)] Navier Stokes [ (6/29)] Navier Stokes 3 [ (6/19)] Re
![II ( ) (7/31) II ( [ (3.4)] Navier Stokes [ (6/29)] Navier Stokes 3 [ (6/19)] Re](/thumbs/94/118770263.jpg "II ( ) (7/31) II ( [ (3.4)] Navier Stokes [ (6/29)] Navier Stokes 3 [ (6/19)] Re")
II 29 7 29-7-27 ( ) (7/31) II (http://www.damp.tottori-u.ac.jp/~ooshida/edu/fluid/) [ (3.4)] Navier Stokes [ (6/29)] Navier Stokes 3 [ (6/19)] Reynolds [ (4.6), (45.8)] [ p.186] Navier Stokes I Euler Navier
Black-Scholes [1] Nelson [2] Schrödinger 1 Black Scholes [1] Black-Scholes Nelson [2][3][4] Schrödinger Nelson Parisi Wu [5] Nelson Parisi-W
![Black-Scholes [1] Nelson [2] Schrödinger 1 Black Scholes [1] Black-Scholes Nelson [2][3][4] Schrödinger Nelson Parisi Wu [5] Nelson Parisi-W](/thumbs/97/132191371.jpg "Black-Scholes [1] Nelson [2] Schrödinger 1 Black Scholes [1] Black-Scholes Nelson [2][3][4] Schrödinger Nelson Parisi Wu [5] Nelson Parisi-W")
003 7 14 Black-Scholes [1] Nelson [] Schrödinger 1 Black Scholes [1] Black-Scholes Nelson [][3][4] Schrödinger Nelson Parisi Wu [5] Nelson Parisi-Wu Nelson e-mail: takatoshi-tasaki@nifty.com kabutaro@mocha.freemail.ne.jp
86 6 r (6) y y d y = y 3 (64) y r y r y r ϕ(x, y, y,, y r ) n dy = f(x, y) (6) 6 Lipschitz 6 dy = y x c R y(x) y(x) = c exp(x) x x = x y(x ) = y (init
8 6 ( ) ( ) 6 ( ϕ x, y, dy ), d y,, dr y r = (x R, y R n ) (6) n r y(x) (explicit) d r ( y r = ϕ x, y, dy ), d y,, dr y r y y y r (6) dy = f (x, y) (63) = y dy/ d r y/ r 86 6 r (6) y y d y = y 3 (64) y
橡固有値セミナー2_棚橋改.PDF
1 II. 2003 5 14 2... Arnoldi. Lanczos. Jacobi-Davidson . 3 4 Ax = x A A Ax = Mx M: M 5 Householder ln ln-1 0 l3 0 l2 l1 6 Lanczos Lanczos, 1950 Arnoldi Arnoldi, 1951 Hessenberg Jacobi-Davidson Sleijpen
II
II 2016 7 21 computer-assisted proof 1 / 64 1. 2. 3. Siegfried M. Rump : [1] I,, 14:3 (2004), pp. 214 223. [2] II,, 14:4 (2004), pp. 346 359. 2 / 64 Risch 18 3 / 64 M n = 2 n 1 (n = 1, 2,... ) 2 2 1 1
日本数学会・２０１１年度年会（早稲田大学）・企画特別講演
日本数学会 2011 年度年会 ( 早稲田大学 ) 企画特別講演 MSJMEETING-2011-0 1. 2., (1) ρ t + (ρw) x = 0, (ρw) t + (ρw 2 + p) x = (µw x ) x, (ρ(e + w2 2 )) t + ((ρ(e + w2 2 ) + p)w) x = (κθ x + µww x ) x., ρ, w, θ, µ κ, p e, p,
2). 3) 4) 1.2 NICTNICT DCRA Dihedral Corner Reflector micro-arraysdcra DCRA DCRA DCRA 3D DCRA PC USB PC PC ON / OFF Velleman K8055 K8055 K8055
1 1 1 2 DCRA 1. 1.1 1) 1 Tactile Interface with Air Jets for Floating Images Aya Higuchi, 1 Nomin, 1 Sandor Markon 1 and Satoshi Maekawa 2 The new optical device DCRA can display floating images in free
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. TV A310
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. TV 367 0035 1011 A310 E-mail kawamura@suou.waseda.jp Total Variation Total Variation Total Variation Abstract
149 (Newell [5]) Newell [5], [1], [1], [11] Li,Ryu, and Song [2], [11] Li,Ryu, and Song [2], [1] 1) 2) ( ) ( ) 3) T : 2 a : 3 a 1 :
![149 (Newell [5]) Newell [5], [1], [1], [11] Li,Ryu, and Song [2], [11] Li,Ryu, and Song [2], [1] 1) 2) ( ) ( ) 3) T : 2 a : 3 a 1 :](/thumbs/92/109497872.jpg "149 (Newell [5]) Newell [5], [1], [1], [11] Li,Ryu, and Song [2], [11] Li,Ryu, and Song [2], [1] 1) 2) ( ) ( ) 3) T : 2 a : 3 a 1 :")
Transactions of the Operations Research Society of Japan Vol. 58, 215, pp. 148 165 c ( 215 1 2 ; 215 9 3 ) 1) 2) :,,,,, 1. [9] 3 12 Darroch,Newell, and Morris [1] Mcneil [3] Miller [4] Newell [5, 6], [1]
<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63>
MATLAB/Simulink による現代制御入門 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/9241 このサンプルページの内容は, 初版 1 刷発行当時のものです. i MATLAB/Simulink MATLAB/Simulink 1. 1 2. 3. MATLAB/Simulink
Nosé Hoover 1.2 ( 1) (a) (b) 1:
1 watanabe@cc.u-tokyo.ac.jp 1 1.1 Nosé Hoover 1. ( 1) (a) (b) 1: T ( f(p x, p y, p z ) exp p x + p y + p ) z (1) mk B T p x p y p = = z = 1 m m m k BT () k B T = 1.3 0.04 0.03 0.0 0.01 0-5 -4-3 - -1 0
[6] G.T.Walker[7] 1896 P I II I II M.Pascal[10] G.T.Walker A.P.Markeev[11] M.Pascal A.D.Blackowiak [12] H.K.Moffatt T.Tokieda[15] A.P.Markeev M.Pascal
![[6] G.T.Walker[7] 1896 P I II I II M.Pascal[10] G.T.Walker A.P.Markeev[11] M.Pascal A.D.Blackowiak [12] H.K.Moffatt T.Tokieda[15] A.P.Markeev M.Pascal](/thumbs/91/107524944.jpg "[6] G.T.Walker[7] 1896 P I II I II M.Pascal[10] G.T.Walker A.P.Markeev[11] M.Pascal A.D.Blackowiak [12] H.K.Moffatt T.Tokieda[15] A.P.Markeev M.Pascal")
viscous 1 2002 3 Nature Moffatt & Shimomura [1][2] 2005 [3] [4] Ueda GBC [5] 1 2 1 1: 2: Wobble stone 1 [6] G.T.Walker[7] 1896 P I II I II M.Pascal[10] G.T.Walker A.P.Markeev[11] M.Pascal A.D.Blackowiak
MS#sugaku(ver.2).dvi
1 1,,,.,,,,,.,.,,,.,, Computer-Aided Design).,,, Boltzmann,, [1]., Anderson, []., Anderson, Schrödinger [3],[4]., nm,,.,,,.,, Schrödinger.,, [5],[6].,,.,,, 1 .,, -. -, -., -,,,. Wigner-Boltzmann Schrödinger,
Vol.1( ) No JASCOME Trefftz ( ) SIMULATION OF SLOSHING PHENOMENON BY INDIRECT TREFFTZ METHOD (EXTENSION OF SIMULATION SCHEME) 1), 2),
Vol.1( 2001 7 ) No.01-070611 JASCOME Trefftz ( ) SIMULATION OF SLOSHING PHENOMENON BY INDIRECT TREFFTZ METHOD (EXTENSION OF SIMULATION SCHEME) 1), 2), 3) 4), Yoichi IKEDA, Jun ichi KATSURAGAWA, Eisuke
IDRstab(s, L) GBiCGSTAB(s, L) 2. AC-GBiCGSTAB(s, L) Ax = b (1) A R n n x R n b R n 2.1 IDR s L r k+1 r k+1 = b Ax k+1 IDR(s) r k+1 = (I ω k A)(r k dr
1 2 IDR(s) GBiCGSTAB(s, L) IDR(s) IDRstab(s, L) GBiCGSTAB(s, L) Verification of effectiveness of Auto-Correction technique applied to preconditioned iterative methods Keiichi Murakami 1 Seiji Fujino 2
28 Horizontal angle correction using straight line detection in an equirectangular image
28 Horizontal angle correction using straight line detection in an equirectangular image 1170283 2017 3 1 2 i Abstract Horizontal angle correction using straight line detection in an equirectangular image
磁性物理学 - 遷移金属化合物磁性のスピンゆらぎ理論
email: takahash@sci.u-hyogo.ac.jp May 14, 2009 Outline 1. 2. 3. 4. 5. 6. 2 / 262 Today s Lecture: Mode-mode Coupling Theory 100 / 262 Part I Effects of Non-linear Mode-Mode Coupling Effects of Non-linear
(a) (b) (c) (d) 1: (a) (b) (c) (d) (a) (b) (c) 2: (a) (b) (c) 1(b) [1 10] 1 degree k n(k) walk path 4
![(a) (b) (c) (d) 1: (a) (b) (c) (d) (a) (b) (c) 2: (a) (b) (c) 1(b) [1 10] 1 degree k n(k) walk path 4](/thumbs/91/105665060.jpg "(a) (b) (c) (d) 1: (a) (b) (c) (d) (a) (b) (c) 2: (a) (b) (c) 1(b) [1 10] 1 degree k n(k) walk path 4")
1 vertex edge 1(a) 1(b) 1(c) 1(d) 2 (a) (b) (c) (d) 1: (a) (b) (c) (d) 1 2 6 1 2 6 1 2 6 3 5 3 5 3 5 4 4 (a) (b) (c) 2: (a) (b) (c) 1(b) [1 10] 1 degree k n(k) walk path 4 1: Zachary [11] [12] [13] World-Wide
,,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3
![,,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3](/thumbs/91/104771717.jpg ",,, 2 ( ), $[2, 4]$, $[21, 25]$, $V$,, 31, 2, $V$, $V$ $V$, 2, (b) $-$,,, (1) : (2) : (3) : $r$ $R$ $r/r$, (4) : 3")
1084 1999 124-134 124 3 1 (SUGIHARA Kokichi),,,,, 1, [5, 11, 12, 13], (2, 3 ), -,,,, 2 [5], 3,, 3, 2 2, -, 3,, 1,, 3 2,,, 3 $R$ ( ), $R$ $R$ $V$, $V$ $R$,,,, 3 2 125 1 3,,, 2 ( ), $[2, 4]$, $[21, 25]$,
takei.dvi
0 Newton Leibniz ( ) α1 ( ) αn (1) a α1,...,α n (x) u(x) = f(x) x 1 x n α 1 + +α n m 1957 Hans Lewy Lewy 1970 1 1.1 Example 1.1. (2) d 2 u dx 2 Q(x)u = f(x), u(0) = a, 1 du (0) = b. dx Q(x), f(x) x = 0
特集_03-07.Q3C
3-7 Error Detection and Authentication in Quantum Key Distribution YAMAMURA Akihiro and ISHIZUKA Hirokazu Detecting errors in a raw key and authenticating a private key are crucial for quantum key distribution
JFE.dvi
,, Department of Civil Engineering, Chuo University Kasuga 1-13-27, Bunkyo-ku, Tokyo 112 8551, JAPAN E-mail : atsu1005@kc.chuo-u.ac.jp E-mail : kawa@civil.chuo-u.ac.jp SATO KOGYO CO., LTD. 12-20, Nihonbashi-Honcho
,.,. NP,., ,.,,.,.,,, (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., , tatsukaw
,.,. NP,.,. 1 1.1.,.,,.,.,,,. 2. 1.1.1 (PCA)...,,. Tipping and Bishop (1999) PCA. (PPCA)., (Ilin and Raiko, 2010). PPCA EM., 152-8552 2-12-1, tatsukawa.m.aa@m.titech.ac.jp, 190-8562 10-3, mirai@ism.ac.jp
163 KdV KP Lax pair L, B L L L 1/2 W 1 LW = ( / x W t 1, t 2, t 3, ψ t n ψ/ t n = B nψ (KdV B n = L n/2 KP B n = L n KdV KP Lax W Lax τ KP L ψ τ τ Cha
63 KdV KP Lax pair L, B L L L / W LW / x W t, t, t 3, ψ t n / B nψ KdV B n L n/ KP B n L n KdV KP Lax W Lax τ KP L ψ τ τ Chapter 7 An Introduction to the Sato Theory Masayui OIKAWA, Faculty of Engneering,
非線形長波モデルと流体粒子法による津波シミュレータの開発 I_ m ρ v p h g a b a 2h b r ab a b Fang W r ab h 5 Wendland 1995 q= r ab /h a d W r ab h
土木学会論文集 B2( 海岸工学 ) Vol. 70, No. 2, 2014, I_016-I_020 非線形長波モデルと流体粒子法による津波シミュレータの開発 Development of a Tsunami Simulator Integrating the Smoothed-Particle Hydrodynamics Method and the Nonlinear Shallow Water
( ) 1 1.1? ( ) ( ) ( ) 1.1(a) T m ( ) 1.1(a) T g ( ) T g T g 500 74% ( ) T K ( 1.1(b) 15 T g T g 10 13 T g T g T g [ ] A ( ) exp (1.1) T T 0 Vogel-Fulcher T 0 T 0 T K T K Ortho-Terphenil (OTP) SiO 2 (1.1)
5D1 SY0004/14/ SICE 1, 2 Dynamically Consistent Motion Design of Humanoid Robots even at the Limit of Kinematics Kenya TANAKA 1 and Tomo
5D1 SY4/14/-485 214 SICE 1, 2 Dynamically Consistent Motion Design of Humanoid Robots even at the Limit of Kinematics Kenya TANAKA 1 and Tomomichi SUGIHARA 2 1 School of Engineering, Osaka University 2-1
Euler, Yang-Mills Clebsch variable Helicity ( Tosiaki Kori ) School of Sciences and Technology, Waseda Uiversity (i) Yang-Mills 3 A T (T A) Poisson Ha
Euler, Yang-ills Clebsch variable Helicity Tosiaki Kori ) School of Sciences and Technology, Waseda Uiversity i) Yang-ills 3 A T T A) Poisson Hamilton ii) Clebsch parametrization iii) Y- Y-iv) Euler,v)
D-brane K 1, 2 ( ) 1 K D-brane K K D-brane Witten [1] D-brane K K K K D-brane D-brane K RR BPS D-brane
![D-brane K 1, 2 ( ) 1 K D-brane K K D-brane Witten [1] D-brane K K K K D-brane D-brane K RR BPS D-brane](/thumbs/94/118614691.jpg "D-brane K 1, 2 ( ) 1 K D-brane K K D-brane Witten [1] D-brane K K K K D-brane D-brane K RR BPS D-brane")
D-brane K 1, 2 E-mail: sugimoto@yukawa.kyoto-u.ac.jp (2004 12 16 ) 1 K D-brane K K D-brane Witten [1] D-brane K K K K D-brane D-brane K RR BPS D-brane D-brane RR D-brane K D-brane K D-brane K K [2, 3]
a m 1 mod p a km 1 mod p k<s 1.6. n > 1 n 1= s m, (m, = 1 a n n a m 1 mod n a km 1 mod n k<sn a 1.7. n > 1 n 1= s m, (m, = 1 r n ν = min ord (p 1 (1 B
10 004 Journal of the Institute of Science and Engineering. Chuo University Euler n > 1 p n p ord p n n n 1= s m (m B psp = {a (Z/nZ ; a n 1 =1}, B epsp = { ( a (Z/nZ ; a n 1 a }, = n B spsp = { a (Z/nZ
1. ( ) 1.1 t + t [m]{ü(t + t)} + [c]{ u(t + t)} + [k]{u(t + t)} = {f(t + t)} (1) m ü f c u k u 1.2 Newmark β (1) (2) ( [m] + t ) 2 [c] + β( t)2
![1. ( ) 1.1 t + t [m]{ü(t + t)} + [c]{ u(t + t)} + [k]{u(t + t)} = {f(t + t)} (1) m ü f c u k u 1.2 Newmark β (1) (2) ( [m] + t ) 2 [c] + β( t)2](/thumbs/80/80934335.jpg "1. ( ) 1.1 t + t [m]{ü(t + t)} + [c]{ u(t + t)} + [k]{u(t + t)} = {f(t + t)} (1) m ü f c u k u 1.2 Newmark β (1) (2) ( [m] + t ) 2 [c] + β( t)2")
212 1 6 1. (212.8.14) 1 1.1............................................. 1 1.2 Newmark β....................... 1 1.3.................................... 2 1.4 (212.8.19)..................................
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
IPSJ SIG Technical Report Vol.2009-DPS-141 No.20 Vol.2009-GN-73 No.20 Vol.2009-EIP-46 No /11/27 1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Spe
1. MIERUKEN 1 2 MIERUKEN MIERUKEN MIERUKEN: Speech Visualization System Based on Augmented Reality Yuichiro Nagano 1 and Takashi Yoshino 2 As the spread of the Augmented Reality(AR) technology and service,
1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2
CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for
多孔質弾性体と流体の連成解析 (非線形現象の数理解析と実験解析)
1748 2011 48-57 48 (Hiroshi Iwasaki) Faculty of Mathematics and Physics Kanazawa University quasi-static Biot 1 : ( ) (coup iniury) (contrecoup injury) 49 [9]. 2 2.1 Navier-Stokes $\rho(\frac{\partial
x(t) + t f(t, x) = x(t) + x (t) t x t Tayler x(t + t) = x(t) + x (t) t + 1 2! x (t) t ! x (t) t 3 + (15) Eular x t Teyler 1 Eular 2 Runge-Kutta
6 Runge-KuttaEular Runge-Kutta Runge-Kutta A( ) f(t, x) dx dt = lim x(t + t) x(t) t 0 t = f(t, x) (14) t x x(t) t + dt x x(t + dt) Euler 7 t 1 f(t, x(t)) x(t) + f(t + dt, x(t + dt))dt t + dt x(t + dt)
1 capillary-gravity wave 1) 2) 3, 4, 5) (RTI) RMI t 6, 7, 8, 9) RMI RTI RMI RTI, RMI 10, 11) 12, 13, 14, 15) RMI 11) RTI RTI y = η(x, t) η t
1 capillary-gravity wave 1) ) 3, 4, 5) (RTI) RMI t 6, 7, 8, 9) RMI RTI RMI RTI, RMI 10, 11) 1, 13, 14, 15) RMI 11) RTI RTI 3 3 4 y = η(x, t) η t + ϕ 1 η x x = ϕ 1 y, η t + ϕ η x x = ϕ y. (1) 1 ϕ i i (i
1
1 Borel1956 Groupes linéaire algébriques, Ann. of Math. 64 (1956), 20 82. Chevalley1956/58 Sur la classification des groupes de Lie algébriques, Sém. Chevalley 1956/58, E.N.S., Paris. Tits1959 Sur la classification
fiš„v8.dvi
(2001) 49 2 333 343 Java Jasp 1 2 3 4 2001 4 13 2001 9 17 Java Jasp (JAva based Statistical Processor) Jasp Jasp. Java. 1. Jasp CPU 1 106 8569 4 6 7; fuji@ism.ac.jp 2 106 8569 4 6 7; nakanoj@ism.ac.jp
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,
IA hara@math.kyushu-u.ac.jp Last updated: January,......................................................................................................................................................................................
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
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
第 55 回自動制御連合講演会 2012 年 11 月 17 日,18 日京都大学 1K403 ( ) Interpolation for the Gas Source Detection using the Parameter Estimation in a Sensor Network S. T
第 55 回自動制御連合講演会 212 年 11 月 日, 日京都大学 1K43 () Interpolation for the Gas Source Detection using the Parameter Estimation in a Sensor Network S. Tokumoto, T. Namerikawa (Keio Univ. ) Abstract The purpose of
IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,
![IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,](/thumbs/94/119099134.jpg "IPSJ SIG Technical Report 1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai,")
1,a) 1,b) 1,c) 1,d) 2,e) 2,f) 2,g) 1. [1] [2] 2 [3] 1 599 8531 1 1 Osaka Prefecture University 1 1, Gakuencho, Naka, Sakai, Osaka 599 8531, Japan 2 565 0871 Osaka University 1 1, Yamadaoka, Suita, Osaka
2009 4
2009 4 LU QR Cholesky A: n n A : A = IEEE 754 10 100 = : 1 / 36 A A κ(a) := A A 1. = κ(a) = Ax = b x := A 1 b Ay = b + b y := A 1 (b + b) x = y x x x κ(a) b b 2 / 36 IEEE 754 = 1 : u 1.11 10 16 = 10 16
A MATLAB Toolbox for Parametric Rob TitleDesign based on symbolic computatio Design of Algorithms, Implementatio Author(s) 坂部, 啓 ; 屋並, 仁史 ; 穴井, 宏和 ; 原
A MATLAB Toolbox for Parametric Rob TitleDesign based on symbolic computatio Design of Algorithms, Implementatio Author(s) 坂部, 啓 ; 屋並, 仁史 ; 穴井, 宏和 ; 原, 辰次 Citation 数理解析研究所講究録 (2004), 1395: 231-237 Issue
Abstract This paper concerns with a method of dynamic image cognition. Our image cognition method has two distinguished features. One is that the imag
2004 RGB A STUDY OF RGB COLOR INFORMATION AND ITS APPLICATION 03R3237 Abstract This paper concerns with a method of dynamic image cognition. Our image cognition method has two distinguished features. One
2.2 ( y = y(x ( (x 0, y 0 y (x 0 (y 0 = y(x 0 y = y(x ( y (x 0 = F (x 0, y(x 0 = F (x 0, y 0 (x 0, y 0 ( (x 0, y 0 F (x 0, y 0 xy (x, y (, F (x, y ( (
(. x y y x f y = f(x y x y = y(x y x y dx = d dx y(x = y (x = f (x y = y(x x ( (differential equation ( + y 2 dx + xy = 0 dx = xy + y 2 2 2 x y 2 F (x, y = xy + y 2 y = y(x x x xy(x = F (x, y(x + y(x 2
2. Eades 1) Kamada-Kawai 7) Fruchterman 2) 6) ACE 8) HDE 9) Kruskal MDS 13) 11) Kruskal AGI Active Graph Interface 3) Kruskal 5) Kruskal 4) 3. Kruskal
1 2 3 A projection-based method for interactive 3D visualization of complex graphs Masanori Takami, 1 Hiroshi Hosobe 2 and Ken Wakita 3 Proposed is a new interaction technique to manipulate graph layouts
ver.1 / c /(13)
1 -- 11 1 c 2010 1/(13) 1 -- 11 -- 1 1--1 1--1--1 2009 3 t R x R n 1 ẋ = f(t, x) f = ( f 1,, f n ) f x(t) = ϕ(x 0, t) x(0) = x 0 n f f t 1--1--2 2009 3 q = (q 1,..., q m ), p = (p 1,..., p m ) x = (q,
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,
A (4.5mW) self (0.5mW) B(3mW) C(1mw) B1(1mW) B2(2mW) C1(1mw) PowerScope 4) SystemMoniter EnergyMonitor EnergyAnalyzer 46 Android 2.2
Android 1 2 2 4 3 3 AndroidOS 2 An Enegy Profiling for Android Application Syuhei Hiya, 1 Kenji Hisazumi, 1 Toru Ishihara, 1 Takeshi Kamiyama, 4 Tsuneo Nakanishi 1 and Fukuda Akirra 1 This paper proposes
IV (2)
COMPUTATIONAL FLUID DYNAMICS (CFD) IV (2) The Analysis of Numerical Schemes (2) 11. Iterative methods for algebraic systems Reima Iwatsu, e-mail : iwatsu@cck.dendai.ac.jp Winter Semester 2007, Graduate
316 on One Hundred Years of Boundary Layer Research, Proceedings of the IUTAM Symposium held at DLR-Göttingen, Germany, 2004, (eds. G. E. A. Meier and
316 on One Hundred Years of Boundary Layer Research, Proceedings of the IUTAM Symposium held at DLR-Göttingen, Germany, 2004, (eds. G. E. A. Meier and K. R. Sreenivasan), Solid Mech. Appl., 129, Springer,
(MIRU2008) HOG Histograms of Oriented Gradients (HOG)
(MIRU2008) 2008 7 HOG - - E-mail: katsu0920@me.cs.scitec.kobe-u.ac.jp, {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human
2014 x n 1 : : :
2014 x n 1 : : 2015 1 30 : 5510113 1 x n 1 n x 2 1 = (x 1)(x+1) x 3 1 = (x 1)(x 2 +x+1) x 4 1 = (x 1)(x + 1)(x 2 + 1) x 5 1 = (x 1)(x 4 + x 3 + x 2 + x + 1) 1, 1,0 n = 105 2 1 n x n 1 Maple 1, 1,0 n 2
3 m/sec 8.35 39.06 3.22 2.15 13.72 52.78 15.00 2.12 2.69 12.62 27.62 3 m/ 772 79 68 263 410 1,182 3 m/sec 3.87 0.63 8.00 3.12 1.38 12.50 12.50 2.00 2.50 1.00 5.50 5.50 m/ 105 122 20 247 247 3 m/sec 0.23
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
S I. dy fx x fx y fx + C 3 C dy fx 4 x, y dy v C xt y C v e kt k > xt yt gt [ v dt dt v e kt xt v e kt + C k x v + C C k xt v k 3 r r + dr e kt S dt d
S I.. http://ayapin.film.s.dendai.ac.jp/~matuda /TeX/lecture.html PDF PS.................................... 3.3.................... 9.4................5.............. 3 5. Laplace................. 5....
WATOC2005 Best Poster Diamond Certificate α -(BEDT-TTF) 2 IBr 2 H.turkmenica X H C H O Brønsted Al K Electronic structure of CeNiGe 2 x Si x studied by Ce 4d-4f resonant and angleresolved photoemission
２
1 1 http://www.jpo.go.jp/indexj.htm International Patent Classification European Patent Classification JST Online Information System Derwent World Patent Index The Scientific and Technical Information
programmingII2019-v01
II 2019 2Q A 6/11 6/18 6/25 7/2 7/9 7/16 7/23 B 6/12 6/19 6/24 7/3 7/10 7/17 7/24 x = 0 dv(t) dt = g Z t2 t 1 dv(t) dt dt = Z t2 t 1 gdt g v(t 2 ) = v(t 1 ) + g(t 2 t 1 ) v v(t) x g(t 2 t 1 ) t 1 t 2