& 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable),
|
|
- ぜんま のたけ
- 5 years ago
- Views:
Transcription
1 .... Deeping and Expansion of Large-Scale Random Fields and Probabilistic Image Processing Kazuyuki Tanaka The mathematical frameworks of probabilistic image processing are formulated by means of Markov random fields and Bayesian inference. Some practical algorithms are constructed by applying belief propagation methods. Markov random fields and belief propagation methods can be regarded as one of statistical-mechanical approaches to probabilistic information processing. In the present paper, we review some fundamental frameworks and statisticalmechanical approaches of probabilistic image processing.. (Genrrative Model).,,, Graduate School of Information Sciences, Tohoku University.,.,,,..,.,. (Bayes Formula).., (Compulational Complexity).,., (Belief Propagation) 98,,.,,, (Statistical-Mechanical Informatics). ) 3),..,,.., (Bayes Formula),,,. (Bayesian Inference). (Prior Probability),, (Posterior Probability). 9/6/ c 9 Information Processing Society of Japan
2 & 3 3 ' ' (., (Pixel), (Light Intensity) (Random Variable). (Joint Probability). V., V = {,,, V }. i x i x = (x, x,, x V ) T. x i i (State Variable), x (State Vector). i (Random Variable) X i, X = (X, X,, X V ) T, (Random Vector). x Pr{X = x}. Pr{X = x}...,..,. Pr{X = x},. Pr { X = x } {i,j} E exp ( α(x i x j ) ) () E {i, j}. V =,,, 3,. E = { {, }, {, 3}, {3, }, {5, 6}, {6, 7}, {7, 8}, {9, }, {, }, {, }, {, 5}, {, 6}, {3, 7}, {, 8}, {5, 9}, {6, }, {7, }, {8, } } (). V E (V, E) x = (x, x,, x ) T. (V, E) Pr{X = x}. &/3 5 '6 ' ( &/3 ) 3 * 3 + ( 3, ) ) 3 * * * ,, ) 3E- * 3F&G. + 3F&?&, 3H& - &/. && & 5 798:<;%=?> 7@;A=:<;B;%>C7@;B;A:<;BD> Lmn?opqb # $!!" #%$ IKJMLON5PFQRP<STPVURPFWXPZYOPZ[\PH]\PF^XP_N6`\P_NaN5PN5Q b cdjelxfn5pfq ghp@fiqrp<sjghp!fkstpvu ghp!fiwxphyrgp@fby\pz[xgp@fb[\pz]xgp@fb^\pn6`xgp@fnl`\pnmnoghp@fn6òpnmnoghp fnmnlp_n5q ghp@fnlpfwtghp!fqrphyrghp!fkstph[rgp@furpz]xgp@fbw\ph^tghp@fayopn6`xgp@fa[opnmnoghp!fb]\p_n5q gb x () Pr{X = x}. (Q = ). (). V = {, }, E = { {, } } x (V, E). x =, x,,. Pr{X = } = Pr{X = } > Pr{X = } = Pr{X = } (3) ( ).. 9/6/ V = {, }, E = {, } x = (, ) T, (, ) T, (, ) T, (, ) T () Pr{X = x}. c 9 Information Processing Society of Japan
3 9/6/ V = {,, 3,, 5, 6, 7, 8, 9} () E = { {, }, {, 3}, {, 5}, {5, 6}, {7, 8}, {8, 9}, {, }, {, 5}, {3, 6}, {, 7}, {5, 8}, {6, 9} } (5) (V, E) 5. 5 i, x i = ( i V \{5}) 5 x 5 = x 5 = Pr{X = x} x 5 = ( 3 ). 3 V = {,,, 9}, E (5) 5, x i = ( i V \{5}) 5 x 5 = x 5 = () Pr{X = x} x 5 =. V = {,,, } E (). (V, E) 6 7, ( x i ) = (i {,, ( 5, 9, ) }), x i = ( (i {3, ), 8,, }) 6 7 x 6 =,,, Pr{X = x} x 7 x 6 =., (), 6, 7 x 7.., V V = {,,, }, E () 6 7,! x i = (i {,!,! 5, 9, }),! x i! = (i {3,, 8,, }) 6 7 x 6 =,,, Pr{X = x} x 7!! x 6 =. x 7., Pr { X = x } x., x = x = = x V,., x = x = = x V x., E {i, j} x i = x j {i, j} (x i x j {i, j} ) x. () Pr { X = x } (Markov Chain Monte Carlo: MCMC) ),5), x = (x, x, x V ) T 5. 5 () Pr X = x x = (x, x, x V ) T. 5 α. 3 c 9 Information Processing Society of Japan
4 , x i x j {i, j} K(x) (x i x j) = ( δ xi,x j ) (6) {i,j} E {i,j} E. x K(x) Pr { X = x }. K(x) % x A = {x K(x)/ E.} Pr{A} Pr { X = x } Ā {x K(x)/ E >.} Pr{Ā} {x K(x)/ E.} {x K(x)/ E >.}. α Pr { X = x } Pr{A} > Pr{Ā} (7),,, α, Pr{A} < Pr{Ā} (8). A Pr{X = x}, A, Pr{A}. α x i = x j ({i, j} E) x, x i = x j({i, j} E) x. 5 α x. (7) (8) α.. 5 α = α α.... Cov[X i, X j ] (x i µ i )(x j µ j )Pr{X = x} ( {i, j} E) (9) x =x = x V =. µ i X i. µ i x =x = x V = x i Pr{X = x} () V + (9) Cov[X i, X j] α 6, α = α = 6 V + (9) Cov[X i, X j ].. 5 α =, ( 7 ).. x 8. Q = 56 () 3. 3 V \{5} 5. 9/6/ Pr{X 5 = x 5 X =, X =, X 3 =, X =, X 6 =, X 7 =, X 8 =, X 9 = } Pr{X =, X =, X3 =, X =, X5 = x5, X6 =, X7 =, X8 =, X9 = } = Pr{X =, X =, X 3 =, X =, X 6 =, X 7 =, X 8 =, X 9 = } () c 9 Information Processing Society of Japan
5 9/6/ 情報処理学会研究報告 図 7 通常の自然画像を 値化した画像と図 5 の α = のときの生成画像の類似性. X x X x x X X3 x3 X3 x3 X x X x XV = X5, XV \{5} =, xv = x5, xv \{5} = X6 x6 X6 x6 X7 x7 X x 7 7 X8 x8 X8 x8 X X9 X9 x x9 (3) x9 という記号を導入する. この記号をつかうと式 ()-() は画素 5 以外の画素の輝度値が xi ( i V \{5}) であるときの画素 5 の輝度値に対する確率は次のように与えられる. Pr{X5 = x5 XV \{5} = xv \{5} } = Pr{XV \{5} = xv \{5} } = 図 8 式 () の確率分布におけるマルコフ連鎖モンテカルロ (Markov Chain Monte Carlo) 法による生成画像 x. X x5 = Pr{XV = xv } 式 ()-(5) に式 () を代入すると次の式が得られる. Y Pr{X5 = x5 XV \{5} = xv \{5} } exp α(x5 xj ) Pr{X =, X =, X3 =, X =, X6 =, X7 =, X8 =, X9 = } = X Pr{XV = xv } Pr{XV \{5} = xv \{5} } Pr{X =, X =, X3 =, X =, X5 = x5, X6 =, X7 =, X8 =, X9 = } () (5) (6) j 5 x5 = () 5 {,, 6, 8} (7) Pr{X5 = x5 XV \{5} = xv \{5} } = Pr{X5 = x5 Xk = xk, k 5} (8) 5 は画素 5 のすべての最近接画素の集合をあらわしている. 式 (6) 式が長いので が成り立つことを示している. 同様にして, 図 の例で V \{6, 7} の画素の状態が固定されている場合に 5 c 9 Information Processing Society of Japan
6 X V = X X X 3 X X 5 X 6 X 7 X 8 X 9 X X X, X V \{6,7} = X X X 3 X X 5 X 8 X 9 X X X, x V = x x x 3 x x 5 x 6 x 7 x 8 x 9 x x x, x V \{6,7} = x x x 3 x x 5 x 8 x 9 x x x (9), 6 7. Pr{X 6 = x 6, X 7 = x 7 X V \{6,7} = x V \{6,7} } = Pr{X V \{6,7} = x V \{6,7} } = x 6 =x 7 = ()-() (). Pr{X 6 = x 6, X 7 = x 7 X V \{6,7} = x V \{6,7} } ( exp ( α(x 6 x k ) )) k 6\{7} exp ( α(x 6 x 7 ) )( Pr{X V = x V } Pr{X V \{6,7} = x V \{6,7} } () Pr{X V = x V } () k 7\{6} exp ( α(x 7 x k ) )) () {6} {, 5, 7, }, {7} {3, 6, 8, } (3) Pr{X 6 = x 6, X 7 = x 7 X V \{6,7} = x V \{6,7} }. = Pr{X 6 = x 6, X 7 = x 7 X k = x k, k {6, 7}} () () (),. X V (Markov Random Field: MRF),. Pr{X i = x i X V \{i} = x V \{i} } = Pr{X i = x i X k = x k, k {i}} ( i V ) (5) i i., () () (V, E). Pr{X i = x i X V \{i} = x V \{i} } exp ( α(xi x k) ) (6) k i Pr{X i = x i, X j = x j X V \{i,j} = x V \{i,j} } ( exp ( α(x i x k ) )) k i\{j} exp ( α(xi xj))( 3. k j\{i} exp ( α(xj x k) )) (7) (),.. x. (Additive White Gaussian Noise). n i (i V ) n = (n, n,, n V ) y = (y, y,, y V ). y = x + n (8) y = (y, y,, y V ) Y = (Y, Y,, Y V ) ( 9 ). Pr{Y = y X = x} exp ( σ (y i x i ) ) (9) i V 9/6/ Pr{Y = y X = x} x y, y x Pr{X = x Y = y}. () (9) (Bayes 6 c 9 Information Processing Society of Japan
7 / -. / : 3>; 33 3 : 3<; 3=3 3 9/6/ #"! 9 "!#!#!$ % & %'& ()*+*!#!#!,+ % & %'& (9). %$'&, '&#()(*(+& -,- $. /&&#()()(+&, -,- (3). Pr{X = x Y = y},. X i ( ), E[X i] = Q Q x =x = Q x V = x ipr{x = x Y = y} (3) Formula). Pr{X = x Y = y} = ( i V (9) y. Pr{Y = y X = x}pr{x = x} Pr{Y = y} exp ( σ (y i x i ) ))( {i,j} E (3) Pr{X = x Y exp ( α(x i x j ) )) (3) = y} (Posterior Probability)., Pr{X = x} (Prior Probability). () Pr{X = x} (3) Pr{X = x Y 9. = y}. O(exp( V )). ( ) (Belief Propagation). {i, j} M i j (x j ) M j i (x i ) (Message). (3) (Message Propagation Rule) ( ). Q M j i (x i ) Constant exp ( α(x i z) σ (z y j) ) z= M k j (z) ( {i, j} E) (3) k j \{i} 7 c 9 Information Processing Society of Japan
8 Q M i j(x j) Constant exp ( α(xj z) (z yi)) σ z= M k j (z) ( {i, j} E) (33) k i \{i} j j, j \{i} j i. (3)-(33),, k j \{i} O()., O( V ). {M i j (x), M j i (x) {i, j} E}, ). 98,, (Low Density Parity Check: LDPC), (Code Division Multiple Access: CDMA), (Satisfiability: SAT). 3),6) 8). 3),6),9). (Sample Average)., () x, x (9) y., y Pr{ X Y = y} x x = x(y) x x d(x, x) = (xi xi) V i V,.,.,,., Q Q Q ( d ( x, x(y) ) x =x = x V = Pr{Y = y X = x}dy dy dy V )Pr{X = x} (3),. (3) (Spin Glass Theory), (Configuration Average).,,,, CDMA, 3),) ). 9/6/ 8 c 9 Information Processing Society of Japan
9 5.. (),,. 5). ),). (No.879, No.8798) COE. ) K. Tanaka: Statistical-Mechanical Approach to Image Processing (Topical Review), Journal of Physics A: Mathematical and General, Vol.35, No.37, pp.r8- R5,. ) :,, 6. 3) ( ): SGC,, 6. ) :, (3). 5), : II,, 5. 6),,,, : I,, 3. 7) C. M. Bishop Pattern Recognition and Machine Learning, Springer, 6. 8) M. J. Wainwright and M. I. Jordan: Graphical Models, Exponential Families, and Variational Inference, now Publishing Inc, 8. 9) M. Opper and D. Saad (eds): Advanced Mean Field Methods Theory and Practice, MIT Press,. ) :,, 999. ) H. Nishimori: Statistical Physics of Spin Glasses and Information Processing, An Introduction, Oxford University Press,. ) : /,,. 3) Y. Kabashima and D. Saad: Statistial Mechanics of Low-Density Parity-Check Codes (Topical Review), Journal of Physics A: Mathematical and General, Vol. 37, No.6, pp.r-r3,. ) M. Mézard and A. Montanari: Information, Physics, and Computation, Oxford University Press, 9. 5) A. S. Willsky: Multiresolution Markov models for signal and image processing, Proceedings of IEEE, vol.9, no.8, pp ,. ( 5 9 ) ( ) ,,,, IEEE-CIS. 9/6/ 9 c 9 Information Processing Society of Japan
main.dvi
CDMA 1 CDMA ( ) CDMA CDMA CDMA 1 ( ) Hopfield [1] Hopfield 1 E-mail: okada@brain.riken.go.jp 1 1: 1 [] Hopfield Sourlas Hopfield [3] Sourlas 1? CDMA.1 DS/BPSK CDMA (Direct Sequence; DS) (Binary Phase-Shift-Keying;
More informationmain.dvi
1 10,.,,.,,,.,,, 2. 1,, [1].,,,.,,.,,,.. 100,,., [2]. [3,4,5]. [6,7,8,9,10,11]. [12, 13, 14]. 1 E-mail: kau@statp.is.tohoku.ac.jp CDMA [15, 16].. 1970, 1980 90, 1990 30,,. [17, 18]. [19, 20, 21]. [17,
More information4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
More informationばらつき抑制のための確率最適制御
( ) http://wwwhayanuemnagoya-uacjp/ fujimoto/ 2011 3 9 11 ( ) 2011/03/09-11 1 / 46 Outline 1 2 3 4 5 ( ) 2011/03/09-11 2 / 46 Outline 1 2 3 4 5 ( ) 2011/03/09-11 3 / 46 (1/2) r + Controller - u Plant y
More informationDirichlet process mixture Dirichlet process mixture 2 /40 MIRU2008 :
Dirichlet Process : joint work with: Max Welling (UC Irvine), Yee Whye Teh (UCL, Gatsby) http://kenichi.kurihara.googlepages.com/miru_workshop.pdf 1 /40 MIRU2008 : Dirichlet process mixture Dirichlet process
More informationx T = (x 1,, x M ) x T x M K C 1,, C K 22 x w y 1: 2 2
Takio Kurita Neurosceince Research Institute, National Institute of Advanced Indastrial Science and Technology takio-kurita@aistgojp (Support Vector Machine, SVM) 1 (Support Vector Machine, SVM) ( ) 2
More information( )/2 hara/lectures/lectures-j.html 2, {H} {T } S = {H, T } {(H, H), (H, T )} {(H, T ), (T, T )} {(H, H), (T, T )} {1
( )/2 http://www2.math.kyushu-u.ac.jp/ hara/lectures/lectures-j.html 1 2011 ( )/2 2 2011 4 1 2 1.1 1 2 1 2 3 4 5 1.1.1 sample space S S = {H, T } H T T H S = {(H, H), (H, T ), (T, H), (T, T )} (T, H) S
More informationQ [4] 2. [3] [5] ϵ- Q Q CO CO [4] Q Q [1] i = X ln n i + C (1) n i i n n i i i n i = n X i i C exploration exploitation [4] Q Q Q ϵ 1 ϵ 3. [3] [5] [4]
1,a) 2,3,b) Q ϵ- 3 4 Q greedy 3 ϵ- 4 ϵ- Comparation of Methods for Choosing Actions in Werewolf Game Agents Tianhe Wang 1,a) Tomoyuki Kaneko 2,3,b) Abstract: Werewolf, also known as Mafia, is a kind of
More informationMicrosoft PowerPoint - SSII_harada pptx
The state of the world The gathered data The processed data w d r I( W; D) I( W; R) The data processing theorem states that data processing can only destroy information. David J.C. MacKay. Information
More information25 11M15133 0.40 0.44 n O(n 2 ) O(n) 0.33 0.52 O(n) 0.36 0.52 O(n) 2 0.48 0.52
26 1 11M15133 25 11M15133 0.40 0.44 n O(n 2 ) O(n) 0.33 0.52 O(n) 0.36 0.52 O(n) 2 0.48 0.52 1 2 2 4 2.1.............................. 4 2.2.................................. 5 2.2.1...........................
More information23_02.dvi
Vol. 2 No. 2 10 21 (Mar. 2009) 1 1 1 Effect of Overconfidencial Investor to Stock Market Behaviour Ryota Inaishi, 1 Fei Zhai 1 and Eisuke Kita 1 Recently, the behavioral finance theory has been interested
More information(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
More informationカルマンフィルターによるベータ推定( )
β TOPIX 1 22 β β smoothness priors (the Capital Asset Pricing Model, CAPM) CAPM 1 β β β β smoothness priors :,,. E-mail: koiti@ism.ac.jp., 104 1 TOPIX β Z i = β i Z m + α i (1) Z i Z m α i α i β i (the
More informationConvolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolution
Convolutional Neural Network 2014 3 A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolutional Neural Network Fukui Hiroshi 1940 1980 [1] 90 3
More information2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
a) Change Detection Using Joint Intensity Histogram Yasuyo KITA a) 2 (0 255) (I 1 (x),i 2 (x)) I 2 = CI 1 (C>0) (I 1,I 2 ) (I 1,I 2 ) 2 1. [1] 2 [2] [3] [5] [6] [8] Intelligent Systems Research Institute,
More informationL. S. Abstract. Date: last revised on 9 Feb translated to Japanese by Kazumoto Iguchi. Original papers: Received May 13, L. Onsager and S.
L. S. Abstract. Date: last revised on 9 Feb 01. translated to Japanese by Kazumoto Iguchi. Original papers: Received May 13, 1953. L. Onsager and S. Machlup, Fluctuations and Irreversibel Processes, Physical
More informationInput image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L
1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives
More informationMicrosoft Word - toyoshima-deim2011.doc
DEIM Forum 2011 E9-4 252-0882 5322 252-0882 5322 E-mail: t09651yt, sashiori, kiyoki @sfc.keio.ac.jp CBIR A Meaning Recognition System for Sign-Logo by Color-Shape-Based Similarity Computations for Images
More information確率論と統計学の資料
5 June 015 ii........................ 1 1 1.1...................... 1 1........................... 3 1.3... 4 6.1........................... 6................... 7 ii ii.3.................. 8.4..........................
More informationTHE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. E-mail: {ytamura,takai,tkato,tm}@vision.kuee.kyoto-u.ac.jp Abstract Current Wave Pattern Analysis for Anomaly
More information2 1,2, , 2 ( ) (1) (2) (3) (4) Cameron and Trivedi(1998) , (1987) (1982) Agresti(2003)
3 1 1 1 2 1 2 1,2,3 1 0 50 3000, 2 ( ) 1 3 1 0 4 3 (1) (2) (3) (4) 1 1 1 2 3 Cameron and Trivedi(1998) 4 1974, (1987) (1982) Agresti(2003) 3 (1)-(4) AAA, AA+,A (1) (2) (3) (4) (5) (1)-(5) 1 2 5 3 5 (DI)
More information130 Oct Radial Basis Function RBF Efficient Market Hypothesis Fama ) 4) 1 Fig. 1 Utility function. 2 Fig. 2 Value function. (1) (2)
Vol. 47 No. SIG 14(TOM 15) Oct. 2006 RBF 2 Effect of Stock Investor Agent According to Framing Effect to Stock Exchange in Artificial Stock Market Zhai Fei, Shen Kan, Yusuke Namikawa and Eisuke Kita Several
More information149 (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]
More informationX X X Y R Y R Y R MCAR MAR MNAR Figure 1: MCAR, MAR, MNAR Y R X 1.2 Missing At Random (MAR) MAR MCAR MCAR Y X X Y MCAR 2 1 R X Y Table 1 3 IQ MCAR Y I
(missing data analysis) - - 1/16/2011 (missing data, missing value) (list-wise deletion) (pair-wise deletion) (full information maximum likelihood method, FIML) (multiple imputation method) 1 missing completely
More informationOptical 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
More information5 Armitage x 1,, x n y i = 10x i + 3 y i = log x i {x i } {y i } 1.2 n i i x ij i j y ij, z ij i j 2 1 y = a x + b ( cm) x ij (i j )
5 Armitage. x,, x n y i = 0x i + 3 y i = log x i x i y i.2 n i i x ij i j y ij, z ij i j 2 y = a x + b 2 2. ( cm) x ij (i j ) (i) x, x 2 σ 2 x,, σ 2 x,2 σ x,, σ x,2 t t x * (ii) (i) m y ij = x ij /00 y
More informationIsogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206,
H28. (TMU) 206 8 29 / 34 2 3 4 5 6 Isogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206, http://link.springer.com/article/0.007/s409-06-0008-x
More informationuntitled
c 645 2 1. GM 1959 Lindsey [1] 1960 Howard [2] Howard 1 25 (Markov Decision Process) 3 3 2 3 +1=25 9 Bellman [3] 1 Bellman 1 k 980 8576 27 1 015 0055 84 4 1977 D Esopo and Lefkowitz [4] 1 (SI) Cover and
More information情報処理学会研究報告 IPSJ SIG Technical Report Vol.2011-MBL-57 No.27 Vol.2011-UBI-29 No /3/ A Consideration of Features for Fatigue Es
1 1 1 1 1 5 1 2 1 A Consideration of Features for Fatigue Estimation by Gait Analysis Using Accelerometer Hidekazu Higashi, 1 Tadashi Shigeoka, 1 Tsuyoshi Itokawa, 1 Teruaki Kitasuka 1 and Masayoshi Aritsugi
More information,.,. 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
More informationii 3.,. 4. F. (), ,,. 8.,. 1. (75%) (25%) =7 20, =7 21 (. ). 1.,, (). 3.,. 1. ().,.,.,.,.,. () (12 )., (), 0. 2., 1., 0,.
24(2012) (1 C106) 4 11 (2 C206) 4 12 http://www.math.is.tohoku.ac.jp/~obata,.,,,.. 1. 2. 3. 4. 5. 6. 7.,,. 1., 2007 (). 2. P. G. Hoel, 1995. 3... 1... 2.,,. ii 3.,. 4. F. (),.. 5... 6.. 7.,,. 8.,. 1. (75%)
More information³ÎΨÏÀ
2017 12 12 Makoto Nakashima 2017 12 12 1 / 22 2.1. C, D π- C, D. A 1, A 2 C A 1 A 2 C A 3, A 4 D A 1 A 2 D Makoto Nakashima 2017 12 12 2 / 22 . (,, L p - ). Makoto Nakashima 2017 12 12 3 / 22 . (,, L p
More informationKobe University Repository : Kernel タイトル Title 著者 Author(s) 掲載誌 巻号 ページ Citation 刊行日 Issue date 資源タイプ Resource Type 版区分 Resource Version 権利 Rights DOI
Kobe University Repository : Kernel タイトル Title 著者 Author(s) 掲載誌 巻号 ページ Citation 刊行日 Issue date 資源タイプ Resource Type 版区分 Resource Version 権利 Rights DOI 平均に対する平滑化ブートストラップ法におけるバンド幅の選択に関する一考察 (A Study about
More informationIPSJ SIG Technical Report An Evaluation Method for the Degree of Strain of an Action Scene Mao Kuroda, 1 Takeshi Takai 1 and Takashi Matsuyama 1
1 1 1 An Evaluation Method for the Degree of of an Action Scene Mao Kuroda, 1 Takeshi Takai 1 and Takashi Matsuyama 1 The purpose of our research is to investigate structure of an action scene scientifically.
More informationIPSJ 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
More information18 2 20 W/C W/C W/C 4-4-1 0.05 1.0 1000 1. 1 1.1 1 1.2 3 2. 4 2.1 4 (1) 4 (2) 4 2.2 5 (1) 5 (2) 5 2.3 7 3. 8 3.1 8 3.2 ( ) 11 3.3 11 (1) 12 (2) 12 4. 14 4.1 14 4.2 14 (1) 15 (2) 16 (3) 17 4.3 17 5. 19
More informationDesign 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
More information28 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
More informationNo. 3 Oct The person to the left of the stool carried the traffic-cone towards the trash-can. α α β α α β α α β α Track2 Track3 Track1 Track0 1
ACL2013 TACL 1 ACL2013 Grounded Language Learning from Video Described with Sentences (Yu and Siskind 2013) TACL Transactions of the Association for Computational Linguistics What Makes Writing Great?
More information(a) (b) (c) Canny (d) 1 ( x α, y α ) 3 (x α, y α ) (a) A 2 + B 2 + C 2 + D 2 + E 2 + F 2 = 1 (3) u ξ α u (A, B, C, D, E, F ) (4) ξ α (x 2 α, 2x α y α,
[II] Optimization Computation for 3-D Understanding of Images [II]: Ellipse Fitting 1. (1) 2. (2) (edge detection) (edge) (zero-crossing) Canny (Canny operator) (3) 1(a) [I] [II] [III] [IV ] E-mail sugaya@iim.ics.tut.ac.jp
More informationIMES DISCUSSION PAPER SERIES Discussion Paper No. 99-J- 9 -J-19 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN
IMES DISCUSSION PAPER SERIES Discussion Paper No. 99-J- 9 -J-19 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN 100-8630 03 IMES Discussion Paper Series 99-J- 9 -J-19 1999 6 * * [1999] *(E-mail:
More informationturbo 1993code Berrou 1) 2[dB] SNR 05[dB] 1) interleaver parallel concatenated convolutional code ch
1 -- 2 6 LDPC 2012 3 1993 1960 30 LDPC 2 LDPC LDPC LDPC 6-1 LDPC 6-2 6-3 c 2013 1/(13) 1 -- 2 -- 6 6--1 2012 3 turbo 1993code Berrou 1) 2[dB] SNR 05[dB] 1) 6 1 2 1 1 interleaver 2 2 2 parallel concatenated
More information1 Web [2] Web [3] [4] [5], [6] [7] [8] S.W. [9] 3. MeetingShelf Web MeetingShelf MeetingShelf (1) (2) (3) (4) (5) Web MeetingShelf
1,a) 2,b) 4,c) 3,d) 4,e) Web A Review Supporting System for Whiteboard Logging Movies Based on Notes Timeline Taniguchi Yoshihide 1,a) Horiguchi Satoshi 2,b) Inoue Akifumi 4,c) Igaki Hiroshi 3,d) Hoshi
More informationletter by letter reading read R, E, A, D 1
3 2009 10 14 1 1.1 1 1.2 1 letter by letter reading read R, E, A, D 1 1.3 1.4 Exner s writing center hypergraphia, micrographia hypergraphia micrographia 2 3 phonological dyslexia surface dyslexia deep
More informationi Version 1.1, (2012/02/22 24),.,..,.,,. R-space,, ( R- space),, Kahler (Kähler C-space)., R-space,., R-space, Hermite,.
R-space ( ) Version 1.1 (2012/02/29) i Version 1.1, (2012/02/22 24),.,..,.,,. R-space,, ( R- space),, Kahler (Kähler C-space)., R-space,., R-space, Hermite,. ii 1 Lie 1 1.1 Killing................................
More information,,.,.,,.,.,.,.,,.,..,,,, i
22 A person recognition using color information 1110372 2011 2 13 ,,.,.,,.,.,.,.,,.,..,,,, i Abstract A person recognition using color information Tatsumo HOJI Recently, for the purpose of collection of
More information/22 R MCMC R R MCMC? 3. Gibbs sampler : kubo/
2006-12-09 1/22 R MCMC R 1. 2. R MCMC? 3. Gibbs sampler : kubo@ees.hokudai.ac.jp http://hosho.ees.hokudai.ac.jp/ kubo/ 2006-12-09 2/22 : ( ) : : ( ) : (?) community ( ) 2006-12-09 3/22 :? 1. ( ) 2. ( )
More information60 (W30)? 1. ( ) 2. ( ) web site URL ( :41 ) 1/ 77
60 (W30)? 1. ( ) kubo@ees.hokudai.ac.jp 2. ( ) web site URL http://goo.gl/e1cja!! 2013 03 07 (2013 03 07 17 :41 ) 1/ 77 ! : :? 2013 03 07 (2013 03 07 17 :41 ) 2/ 77 2013 03 07 (2013 03 07 17 :41 ) 3/ 77!!
More information<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63>
確率的手法による構造安全性の解析 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/55271 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 25 7 ii Benjamin &Cornell Ang & Tang Schuëller 1973 1974 Ang Mathematica
More informationst.dvi
9 3 5................................... 5............................. 5....................................... 5.................................. 7.........................................................................
More informationHilbert, von Neuman [1, p.86] kt 2 1 [1, 2] 2 2
hara@math.kyushu-u.ac.jp 1 1 1.1............................................... 2 1.2............................................. 3 2 3 3 5 3.1............................................. 6 3.2...................................
More informationVol. 48 No. 4 Apr LAN TCP/IP LAN TCP/IP 1 PC TCP/IP 1 PC User-mode Linux 12 Development of a System to Visualize Computer Network Behavior for L
Vol. 48 No. 4 Apr. 2007 LAN TCP/IP LAN TCP/IP 1 PC TCP/IP 1 PC User-mode Linux 12 Development of a System to Visualize Computer Network Behavior for Learning to Associate LAN Construction Skills with TCP/IP
More informationsakigake1.dvi
(Zin ARAI) arai@cris.hokudai.ac.jp http://www.cris.hokudai.ac.jp/arai/ 1 dynamical systems ( mechanics ) dynamical systems 3 G X Ψ:G X X, (g, x) Ψ(g, x) =:Ψ g (x) Ψ id (x) =x, Ψ gh (x) =Ψ h (Ψ g (x)) (
More information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
More informationVol.-ICS-6 No.3 /3/8 Input.8.6 y.4 Fig....5 receptive field x 3 w x y Machband w(x =
DOG(Difference of two Gaussians 8 A feedback model for the brightness illusion Shoji Nodasaka and Asaki Saito We consider mechanism of the Hermann grid. The mechanism is usually explained by effects of
More information2 4 (four-dimensional variational(4dvar))(talagrand and Courtier(1987), Courtier et al.(1994)) (Ensemble Kalman Filter( EnKF))(Evensen(1994), Evensen(
1,3 2,3 2,3 ; ; ; 1. (Wunsch(1996), Daley(1991), Bennett(2002), (1997)) 1 106-8569 4-6-7 2 106-8569 4-6-7 3 (JST) (CREST) 2 4 (four-dimensional variational(4dvar))(talagrand and Courtier(1987), Courtier
More information12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? ( :51 ) 2/ 71
2010-12-02 (2010 12 02 10 :51 ) 1/ 71 GCOE 2010-12-02 WinBUGS kubo@ees.hokudai.ac.jp http://goo.gl/bukrb 12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? 2010-12-02 (2010 12
More information1 IDC Wo rldwide Business Analytics Technology and Services 2013-2017 Forecast 2 24 http://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h24/pdf/n2010000.pdf 3 Manyika, J., Chui, M., Brown, B., Bughin,
More informationAutumn II III Zon and Muysken 2005 Zon and Muysken 2005 IV II 障害者への所得移転の経済効果 分析に用いるデータ
212 Vol. 44 No. 2 I はじめに 2008 1 2 Autumn 08 213 II III Zon and Muysken 2005 Zon and Muysken 2005 IV II 障害者への所得移転の経済効果 17 18 1 分析に用いるデータ 1 2005 10 12 200 2 2006 9 12 1 1 2 129 35 113 3 1 2 6 1 2 3 4 4 1
More information1 : ( ) ( ) ( ) ( ) ( ) etc (SCA)
START: 17th Symp. Auto. Decentr. Sys., Jan. 28, 2005 Symplectic cellular automata as a test-bed for research on the emergence of natural systems 1 : ( ) ( ) ( ) ( ) ( ) etc (SCA) 2 SCA 2.0 CA ( ) E.g.
More informationii 3.,. 4. F. ( ), ,,. 8.,. 1. (75% ) (25% ) =7 24, =7 25, =7 26 (. ). 1.,, ( ). 3.,...,.,.,.,.,. ( ) (1 2 )., ( ), 0., 1., 0,.
(1 C205) 4 10 (2 C206) 4 11 (2 B202) 4 12 25(2013) http://www.math.is.tohoku.ac.jp/~obata,.,,,..,,. 1. 2. 3. 4. 5. 6. 7. 8. 1., 2007 ( ).,. 2. P. G., 1995. 3. J. C., 1988. 1... 2.,,. ii 3.,. 4. F. ( ),..
More information,, Andrej Gendiar (Density Matrix Renormalization Group, DMRG) 1 10 S.R. White [1, 2] 2 DMRG ( ) [3, 2] DMRG Baxter [4, 5] 2 Ising 2 1 Ising 1 1 Ising
,, Andrej Gendiar (Density Matrix Renormalization Group, DMRG) 1 10 S.R. White [1, 2] 2 DMRG ( ) [3, 2] DMRG Baxter [4, 5] 2 Ising 2 1 Ising 1 1 Ising Model 1 Ising 1 Ising Model N Ising (σ i = ±1) (Free
More information01.Œk’ì/“²fi¡*
AIC AIC y n r n = logy n = logy n logy n ARCHEngle r n = σ n w n logσ n 2 = α + β w n 2 () r n = σ n w n logσ n 2 = α + β logσ n 2 + v n (2) w n r n logr n 2 = logσ n 2 + logw n 2 logσ n 2 = α +β logσ
More informationAHP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 informationFig. 1 Relative delay coding.
An Architecture of Small-scaled Neuro-hardware Using Probabilistically-coded Pulse Neurons Takeshi Kawashima, Non-member (DENSO CORPORATION), Akio Ishiguro, Member (Nagoya University), Shigeru Okuma, Member
More informationIPSJ SIG Technical Report Vol.2017-SLP-115 No /2/18 1,a) 1 1,2 Sakriani Sakti [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Scie
1,a) 1 1,2 Sakriani Sakti 1 1 1 1. [1][2] [3][4] [5][6][7] [8] [9] 1 Nara Institute of Science and Technology 2 Japan Science and Technology Agency a) ishikawa.yoko.io5@is.naist.jp 2. 1 Belief-Desire theory
More informationIPSJ SIG Technical Report Vol.2010-CVIM-170 No /1/ Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Ta
1 1 1 1 2 1. Visual Recognition of Wire Harnesses for Automated Wiring Masaki Yoneda, 1 Takayuki Okatani 1 and Koichiro Deguchi 1 This paper presents a method for recognizing the pose of a wire harness
More informationtokei01.dvi
2. :,,,. :.... Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN 4 3. (probability),, 1. : : n, α A, A a/n. :, p, p Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN
More informationMicrosoft Word doc
. 正規線形モデルのベイズ推定翠川 大竹距離減衰式 (PGA(Midorikawa, S., and Ohtake, Y. (, Attenuation relationships of peak ground acceleration and velocity considering attenuation characteristics for shallow and deeper earthquakes,
More informationAbstract 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
More informationPart () () Γ Part ,
Contents a 6 6 6 6 6 6 6 7 7. 8.. 8.. 8.3. 8 Part. 9. 9.. 9.. 3. 3.. 3.. 3 4. 5 4.. 5 4.. 9 4.3. 3 Part. 6 5. () 6 5.. () 7 5.. 9 5.3. Γ 3 6. 3 6.. 3 6.. 3 6.3. 33 Part 3. 34 7. 34 7.. 34 7.. 34 8. 35
More information1 3DCG [2] 3DCG CG 3DCG [3] 3DCG 3 3 API 2 3DCG 3 (1) Saito [4] (a) 1920x1080 (b) 1280x720 (c) 640x360 (d) 320x G-Buffer Decaudin[5] G-Buffer D
3DCG 1) ( ) 2) 2) 1) 2) Real-Time Line Drawing Using Image Processing and Deforming Process Together in 3DCG Takeshi Okuya 1) Katsuaki Tanaka 2) Shigekazu Sakai 2) 1) Department of Intermedia Art and Science,
More information? (EM),, EM? (, 2004/ 2002) von Mises-Fisher ( 2004) HMM (MacKay 1997) LDA (Blei et al. 2001) PCFG ( 2004)... Variational Bayesian methods for Natural
SLC Internal tutorial Daichi Mochihashi daichi.mochihashi@atr.jp ATR SLC 2005.6.21 (Tue) 13:15 15:00@Meeting Room 1 Variational Bayesian methods for Natural Language Processing p.1/30 ? (EM),, EM? (, 2004/
More information解説 査読の虎の巻 山里敬也通信ソサイエティ副編集長 Takaya Yamazato 佐波孝彦通信ソサイエティ和文論文誌編集副委員長 Takahiko Saba 塩田茂雄通信ソサイエティ英文論文誌編集副委員長 Shigeo Shiota 太田能 IEICE Communications Expres
解説 査読の虎の巻 山里敬也通信ソサイエティ副編集長 Takaya Yamazato 佐波孝彦通信ソサイエティ和文論文誌編集副委員長 Takahiko Saba 塩田茂雄通信ソサイエティ英文論文誌編集副委員長 Shigeo Shiota 太田能 IEICE Communications Express 編集副委員長 Chikara Ota 1. モナリザの瞳と LDPC 1 LV 50 1963 Gallager
More information3. ( 1 ) Linear Congruential Generator:LCG 6) (Mersenne Twister:MT ), L 1 ( 2 ) 4 4 G (i,j) < G > < G 2 > < G > 2 g (ij) i= L j= N
RMT 1 1 1 N L Q=L/N (RMT), RMT,,,., Box-Muller, 3.,. Testing Randomness by Means of RMT Formula Xin Yang, 1 Ryota Itoi 1 and Mieko Tanaka-Yamawaki 1 Random matrix theory derives, at the limit of both dimension
More informationIPSJ SIG Technical Report Vol.2009-CVIM-167 No /6/10 Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing
Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing number of HOG Features based on Real AdaBoost Chika Matsushima, 1 Yuji Yamauchi, 1 Takayoshi Yamashita 1, 2 and
More information応用数学III-4.ppt
III f x ( ) = 1 f x ( ) = P( X = x) = f ( x) = P( X = x) =! x ( ) b! a, X! U a,b f ( x) =! " e #!x, X! Ex (!) n! ( n! x)!x! " x 1! " x! e"!, X! Po! ( ) n! x, X! B( n;" ) ( ) ! xf ( x) = = n n!! ( n
More informationφ 4 Minimal subtraction scheme 2-loop ε 2008 (University of Tokyo) (Atsuo Kuniba) version 21/Apr/ Formulas Γ( n + ɛ) = ( 1)n (1 n! ɛ + ψ(n + 1)
φ 4 Minimal subtraction scheme 2-loop ε 28 University of Tokyo Atsuo Kuniba version 2/Apr/28 Formulas Γ n + ɛ = n n! ɛ + ψn + + Oɛ n =,, 2, ψn + = + 2 + + γ, 2 n ψ = γ =.5772... Euler const, log + ax x
More information., White-Box, White-Box. White-Box.,, White-Box., Maple [11], 2. 1, QE, QE, 1 Redlog [7], QEPCAD [9], SyNRAC [8] 3 QE., 2 Brown White-Box. 3 White-Box
White-Box Takayuki Kunihiro Graduate School of Pure and Applied Sciences, University of Tsukuba Hidenao Iwane ( ) / Fujitsu Laboratories Ltd. / National Institute of Informatics. Yumi Wada Graduate School
More information2 / 5 Auction: Theory and Practice 3 / 5 (WTO) 1 SDR 27 1,6 Auction: Theory and Practice 4 / 5 2
stakagi@econ.hokudai.ac.jp June 22, 212 2................................................................ 3...................................................... 4............................................................
More informationIPSJ SIG Technical Report 1, Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1
1, 2 1 1 1 Instrument Separation in Reverberant Environments Using Crystal Microphone Arrays Nobutaka ITO, 1, 2 Yu KITANO, 1 Nobutaka ONO 1 and Shigeki SAGAYAMA 1 This paper deals with instrument separation
More information(Basic of Proability Theory). (Probability Spacees ad Radom Variables , (Expectatios, Meas) (Weak Law
I (Radom Walks ad Percolatios) 3 4 7 ( -2 ) (Preface),.,,,...,,.,,,,.,.,,.,,. (,.) (Basic of Proability Theory). (Probability Spacees ad Radom Variables...............2, (Expectatios, Meas).............................
More information258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
More information1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +
3 3D 1,a) 1 1 Kinect (X, Y) 3D 3D 1. 2010 Microsoft Kinect for Windows SDK( (Kinect) SDK ) 3D [1], [2] [3] [4] [5] [10] 30fps [10] 3 Kinect 3 Kinect Kinect for Windows SDK 3 Microsoft 3 Kinect for Windows
More informationThe 15th Game Programming Workshop 2010 Magic Bitboard Magic Bitboard Bitboard Magic Bitboard Bitboard Magic Bitboard Magic Bitboard Magic Bitbo
Magic Bitboard Magic Bitboard Bitboard Magic Bitboard Bitboard Magic Bitboard 64 81 Magic Bitboard Magic Bitboard Bonanza Proposal and Implementation of Magic Bitboards in Shogi Issei Yamamoto, Shogo Takeuchi,
More informationわが国企業による資金調達方法の選択問題
* takeshi.shimatani@boj.or.jp ** kawai@ml.me.titech.ac.jp *** naohiko.baba@boj.or.jp No.05-J-3 2005 3 103-8660 30 No.05-J-3 2005 3 1990 * E-mailtakeshi.shimatani@boj.or.jp ** E-mailkawai@ml.me.titech.ac.jp
More informationuntitled
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)
More informationwaseda2010a-jukaiki1-main.dvi
November, 2 Contents 6 2 8 3 3 3 32 32 33 5 34 34 6 35 35 7 4 R 2 7 4 4 9 42 42 2 43 44 2 5 : 2 5 5 23 52 52 23 53 53 23 54 24 6 24 6 6 26 62 62 26 63 t 27 7 27 7 7 28 72 72 28 73 36) 29 8 29 8 29 82 3
More information2 22006 2 e-learning e e 2003 1 4 e e e-learning 2 Web e-leaning 2004 2005 2006 e 4 GP 4 e-learning e-learning e-learning e LMS LMS Internet Navigware
2 2 Journal of Multimedia Aided Education Research 2006, Vol. 2, No. 2, 19 e 1 1 2 2 1 1 GP e 2004 e-learning 2004 e-learning 2005 e-learning e-learning e-learning e-learning 2004 e-learning HuWeb 2005
More informationSample 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 informationFA $*1$ $*$ 1, $*$2 : $*2$ : Takehiro Takano $*$ 1, Katsunori Ano*2 $*1$ : Graduate School of Engineering and Science, Shibaura Ins
Title マルコフ連鎖に基づく最適打順モデルによる FA 打者獲得戦略 ( 不確実 不確定性の下での数理意思決定モデルとその周辺 ) Author(s) 高野, 健大 ; 穴太, 克則 Citation 数理解析研究所講究録 (2016), 1990: 89-96 Issue Date 2016-04 URL http://hdl.handle.net/2433/224603 Right Type
More information(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc
1,a) 1,b) Obstacle Detection from Monocular On-Vehicle Camera in units of Delaunay Triangles Abstract: An algorithm to detect obstacles by using a monocular on-vehicle video camera is developed. Since
More informationIPSJ SIG Technical Report Vol.2017-MUS-116 No /8/24 MachineDancing: 1,a) 1,b) 3 MachineDancing MachineDancing MachineDancing 1 MachineDan
MachineDancing: 1,a) 1,b) 3 MachineDancing 2 1. 3 MachineDancing MachineDancing 1 MachineDancing MachineDancing [1] 1 305 0058 1-1-1 a) s.fukayama@aist.go.jp b) m.goto@aist.go.jp 1 MachineDancing 3 CG
More informationbag-of-words bag-of-keypoints Web bagof-keypoints Nearest Neighbor SVM Nearest Neighbor SIFT Nearest Neighbor bag-of-keypoints Nearest Neighbor SVM 84
Bag-of-Keypoints Web G.Csurka bag-of-keypoints Web Bag-of-keypoints SVM 5.% Web Image Classification with Bag-of-Keypoints Taichi joutou and Keiji yanai Recently, need for generic image recognition is
More information2/50 Auction: Theory and Practice 3 / 50 (WTO) 10 SDR ,600 Auction: Theory and Practice 4 / 50 2
stakagi@econ.hokudai.ac.jp June 24, 2011 2.... 3... 4... 7 8... 9.... 10... 11... 12 IPV 13 SPSB... 15 SPSB.... 17 SPSB.... 19 FPSB... 20 FPSB.... 22 FPSB.... 23... 24 Low Price Auction.... 27 APV 29...
More information1 Tokyo Daily Rainfall (mm) Days (mm)
( ) r-taka@maritime.kobe-u.ac.jp 1 Tokyo Daily Rainfall (mm) 0 100 200 300 0 10000 20000 30000 40000 50000 Days (mm) 1876 1 1 2013 12 31 Tokyo, 1876 Daily Rainfall (mm) 0 50 100 150 0 100 200 300 Tokyo,
More information空力騒音シミュレータの開発
41 COSMOS-V, an Aerodynamic Noise Simulator Nariaki Horinouchi COSMOS-V COSMOS-V COSMOS-V 3 The present and future computational problems of the aerodynamic noise analysis using COSMOS-V, our in-house
More information1 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
More informationdvi
2017 65 2 185 200 2017 1 2 2016 12 28 2017 5 17 5 24 PITCHf/x PITCHf/x PITCHf/x MLB 2014 PITCHf/x 1. 1 223 8522 3 14 1 2 223 8522 3 14 1 186 65 2 2017 PITCHf/x 1.1 PITCHf/x PITCHf/x SPORTVISION MLB 30
More information