Grund.dvi

Size: px
Start display at page:

Download "Grund.dvi"

Transcription

1 24

2 M133

3 i viterbi

4 ii 30 32

5 iii 2.1 Algorithm for the proposed method (NS chart) left-to-righthmm Timing of likelihood calculation The system of the small autonomous mobile robot MieC Joystick map Two routes of straight and slalom for the experiment The transition probability matrix The emission probability matrix (Output matrix) The emission probability matrix S = 100, n = The comparison between the learning data and the estimation data for slalom The comparison between the learning data and the estimation data for straight Transition of the likelihood on slalom Transition of the likelihood on straight Route for verifying the validity of the forcefeedback Joystick map in polar coordinate Transition of the likelihood for the curve course Choice of the course

6 iv

7 1 1 1 ( HMM) 1.1

8 1 2 [1] 2

9 1 3. ( HMM) HMM DNA ([2][3][4]) [5] [6][7] HMM HMM [8] HMM HMM 2 3

10 HMM HMM

11 2 5 NS Fig. 2.1 Time Loop Observe human operation Select the maximum likelihood HMM Low Learn as a new model The likelihood High generates a maximum likelihood route Force feedback Correction by human Fig. 2.1 Algorithm for the proposed method (NS chart) Forward-Probability Backward-Probability Viterbi

12 HMM HMM O = {o 1,...,o N } ( ) S = {s 1,...,s N } s n s n 1 p(s n s n 1 ) A i s i j s j a ij A = {a ij } (1 i,j N) s i Π = {π i } i o b i (o) B = b i (1 i,j N,1 t T) λ = (A,B,Π) left-toright HMM Fig. 2.2 s 1 Fig. 2.2 left-to-righthmm HMM Baum-Welch Baum-Welch Forward-Backward

13 2 7 Probability Backward-Probability α β λ α β α t (j) = P(o 1,o 2,...,o t,s t = j λ) (2.1) β t (i) = P(o t+1,o t+2,...,o T,s t = i λ) (2.2) λ t O = o 1,o 2,...,o t s j λ s i t O = o t+1,o t+2,...,o T α β Forward Probability α 1 (i) = π i (2.3) N α t+1 (j) = α t (i)a ij b j (o t+1 ) (2.4) i=1 Backward P robability β T (i) = 1 (2.5) β t (i) = N a ij b i (o t+1 )β t+1 (j) (2.6) j=1 i = 1,2,...,N j = 1,2,...,N t = 1,2,...,T α

14 2 8 ξ γ ξ t (i,j) = α t(i)a ij b j (o t+1 )β t+1 (j) P(O λ) (2.7) γ t (i) = N ξ t (i,j) (2.8) j=1 P(O λ) = N α T (i) (2.9) i=1 ξ γ t s i t+1 s j t s i λ = (A,B,Π) π i = γ 1 (i) (2.10) ā ij = bj (k) = T 1 t=1 ξ t(i,j) T 1 t=1 γ t(i) T t=1,s.t.o t=v k γ t (j) T t=1 γ t(j) (2.11) (2.12) v = {v 1,v 2,...,v k } λ gradient left-to-right Ergodic

15 viterbi viterbi viterbi O = {o 1,o 2,...,o T } Q = {q 1,q 2,...,q T } viterbi t q t = S i δ t (i) = max q1,q 2,...,q t 1 Pr[q 1,q 2,...,q t 1 = S i,o 1,O 2,...,O t λ] (2.13) t δ t (i) ψ t (i) δ 1 (i) = π i b i (O 1 ), i = 1,...,N (2.14) ψ 1 (i) = 0 (2.15) δ t (j) = max 1 i N [δ t 1 (i)a ij ]b j (O t ), t = 2,...,T, j = 1,...,N (2.16) ψ t (j) = argmax 1 i N [δ t 1 (i)a ij ], t = 2,...,T, j = 1,...,N (2.17) T P qt P = max 1 i N [δ T (i)] (2.18) q T = argmax 1 i N [δ T (i)] (2.19)

16 2 10 q t = ψ t (q t+1 ), t = T 1,T 2,...,1 (2.20) viterbi t+1 B 2.3 Fig. 2.3 Fig. 2.3 Timing of likelihood calculation

17 2 11 t O = {o 1,o 2,...,o t } P(o 1,o 2,...,o t λ) λ

18 MieC (Fig. 3.1)

19 3 13 Fig. 3.1 The system of the small autonomous mobile robot MieC MieC 2 CCD (Logicool QCAM-200R) LAN CPU FPGA CPU FPGA MieC (Logitech FORCE 3D PRO) Fig. 3.2 x,y [0-255] 8

20 3 14 Fig. 3.2 Joystick map Force-FeedBack Fig HMM HMM

21 3 15 Fig. 3.3 Two routes of straight and slalom for the experiment MieC t O = o 1,o 2,...,o t 2 HMM Foward-Probability [step](19.2[sec])

22 λ A i j (3.1) (3.2) a 11 a 12 a 1N. a.. 21 a2n A ij =. a ij., i = 1,...,N (3.1)..... a N1 a N2 a NN B j (v k ) = b 11 b 12 a 1K b a2k. b jk b N1 b N2 a NK, j = 1,...,N, k = 1,...,K(3.2) n 1 1 [0-255] Fig. 3.4 Fig. 3.5 S(j) S(j) S(j) S(i) S(i) S(i) (a)initial model (b)learned model (c)learned model n = 0 n = 5 n = 30 Fig. 3.4 The transition probability matrix

23 3 17 v(k) S(j) (a)initial model v(k) S(j) (b)learned model n = 5 v(k) S(j) (c)learned model n = 30 Fig. 3.5 The emission probability matrix (Output matrix) S(i) S(j) 20x20 i j i j

24 3 18 S(j) v(k) j k S(j) v(k) x 20x64 flat start left-to-right HMM Fig. 3.4(a) Fig. 3.5(a) 6 Fig. 3.4(b) Fig. 3.4(c) Fig S = 100 Fig. 3.6

25 3 19 v(k) S(j) Fig. 3.6 The emission probability matrix S = 100, n = 30 viterbi Fig. 3.7 Fig. 3.8

26 learning data output data symbol step Fig. 3.7 The comparison between the learning data and the estimation data for slalom learning data output data symbol step Fig. 3.8 The comparison between the learning data and the estimation data for straight HMM HMM 96.3% 97.9%

27 Fig. 3.9 Fig HMM HMM Fig. 3.9 HMM straighthmm slalomhmm 1e-06 likehood 1e-08 1e-10 1e-12 1e-14 1e step Fig Transition of the likelihood on slalom

28 straighthmm slalomhmm 1e-06 likehood 1e-08 1e-10 1e-12 1e-14 1e step Fig. 3.9 Transition of the likelihood on straight HMM Fig HMM 0 HMM HMM HMM

29 MieC Force-Feedback 4 0 [-5 +5] Fig cm 80cm 40cm Fig Route for verifying the validity of the forcefeedback

30 3 24 Table Table 3.1 [ ] Force-Feedback Fig [ ] 15[ ] Fig Joystick map in polar coordinate Table 3.2.3

31 3 25 Table 3.2 A B C D Force-Feedback

32 [step](16.0[sec]) Fig e-20 Curve model Straight model 1e-40 likelihood 1e-60 1e-80 1e-100 1e step Fig Transition of the likelihood for the curve course 40[step] Fig [cm] 60[cm]

33 3 27 Straight model Corve model Fig Choice of the course Table 3.3 A B C D

34 3 28

35 HMM HMM

36 30 [1] Vol. 13 No pp [2] Hidden Markov Model. MVE, 97(207), , [3] HMM GMM [4] DNA Vol.40 No pp [5] HMM (D-II) vol.j85-d-ii no.7 July 2002 pp [6] Vol.22 No.2 pp [7] vol.27 No.5

37 31 pp [8],, / (C ) (2001-4) No ( ) [9] C.M. : Pattern Recognition and Machine Learning.,pp ,2006 [10] :,,pp ,2007

38 32

3 3 i

3 3 i 00D8102021I 2004 3 3 3 i 1 ------------------------------------------------------------------------------------------------1 2 ---------------------------------------------------------------------------------------2

More information

ito.dvi

ito.dvi 1 2 1006 214 542 160 120 160 1 1916 49 1710 55 1716 1 2 1995 1 2 3 4 2 3 1950 1973 1969 1989 1 4 3 3.1 3.1.1 1989 2 3.1.2 214 542 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

More information

「産業上利用することができる発明」の審査の運用指針(案)

「産業上利用することができる発明」の審査の運用指針(案) 1 1.... 2 1.1... 2 2.... 4 2.1... 4 3.... 6 4.... 6 1 1 29 1 29 1 1 1. 2 1 1.1 (1) (2) (3) 1 (4) 2 4 1 2 2 3 4 31 12 5 7 2.2 (5) ( a ) ( b ) 1 3 2 ( c ) (6) 2. 2.1 2.1 (1) 4 ( i ) ( ii ) ( iii ) ( iv)

More information

一般演題(ポスター)

一般演題(ポスター) 6 5 13 : 00 14 : 00 A μ 13 : 00 14 : 00 A β β β 13 : 00 14 : 00 A 13 : 00 14 : 00 A 13 : 00 14 : 00 A β 13 : 00 14 : 00 A β 13 : 00 14 : 00 A 13 : 00 14 : 00 A β 13 : 00 14 : 00 A 13 : 00 14 : 00 A

More information

204 / CHEMISTRY & CHEMICAL INDUSTRY Vol.69-1 January 2016 047

204 / CHEMISTRY & CHEMICAL INDUSTRY Vol.69-1 January 2016 047 9 π 046 Vol.69-1 January 2016 204 / CHEMISTRY & CHEMICAL INDUSTRY Vol.69-1 January 2016 047 β γ α / α / 048 Vol.69-1 January 2016 π π π / CHEMISTRY & CHEMICAL INDUSTRY Vol.69-1 January 2016 049 β 050 Vol.69-1

More information

44 4 I (1) ( ) (10 15 ) ( 17 ) ( 3 1 ) (2)

44 4 I (1) ( ) (10 15 ) ( 17 ) ( 3 1 ) (2) (1) I 44 II 45 III 47 IV 52 44 4 I (1) ( ) 1945 8 9 (10 15 ) ( 17 ) ( 3 1 ) (2) 45 II 1 (3) 511 ( 451 1 ) ( ) 365 1 2 512 1 2 365 1 2 363 2 ( ) 3 ( ) ( 451 2 ( 314 1 ) ( 339 1 4 ) 337 2 3 ) 363 (4) 46

More information

i ii i iii iv 1 3 3 10 14 17 17 18 22 23 28 29 31 36 37 39 40 43 48 59 70 75 75 77 90 95 102 107 109 110 118 125 128 130 132 134 48 43 43 51 52 61 61 64 62 124 70 58 3 10 17 29 78 82 85 102 95 109 iii

More information

特-3.indd

特-3.indd Development of Automation Technology for Precision Finishing Works Employing a Robot Arm There is demand for the automation of finishing processes that require technical skills in the manufacturing of

More information

WPA(Win Probability Added) 1 WPA WPA ( ) WPA WPA WPA WPA WPA

WPA(Win Probability Added) 1 WPA WPA ( ) WPA WPA WPA WPA WPA 21 4 25 1 31 WPA(Win Probability Added) 1 WPA WPA ( ) WPA WPA WPA WPA WPA 1 1 2 WPA 3 2.1 WPA(Win Probability Added)................................. 3 2.2........................... 3 2.2.1...................................

More information

第86回日本感染症学会総会学術集会後抄録(II)

第86回日本感染症学会総会学術集会後抄録(II) χ μ μ μ μ β β μ μ μ μ β μ μ μ β β β α β β β λ Ι β μ μ β Δ Δ Δ Δ Δ μ μ α φ φ φ α γ φ φ γ φ φ γ γδ φ γδ γ φ φ φ φ φ φ φ φ φ φ φ φ φ α γ γ γ α α α α α γ γ γ γ γ γ γ α γ α γ γ μ μ κ κ α α α β α

More information

第85 回日本感染症学会総会学術集会後抄録(III)

第85 回日本感染症学会総会学術集会後抄録(III) β β α α α µ µ µ µ α α α α γ αβ α γ α α γ α γ µ µ β β β β β β β β β µ β α µ µ µ β β µ µ µ µ µ µ γ γ γ γ γ γ µ α β γ β β µ µ µ µ µ β β µ β β µ α β β µ µµ β µ µ µ µ µ µ λ µ µ β µ µ µ µ µ µ µ µ

More information

1 P2 P P3P4 P5P8 P9P10 P11 P12

1 P2 P P3P4 P5P8 P9P10 P11 P12 1 P2 P14 2 3 4 5 1 P3P4 P5P8 P9P10 P11 P12 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 & 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1! 3 2 3! 4 4 3 5 6 I 7 8 P7 P7I P5 9 P5! 10 4!! 11 5 03-5220-8520

More information

SOM SOM(Self-Organizing Maps) SOM SOM SOM SOM SOM SOM i

SOM SOM(Self-Organizing Maps) SOM SOM SOM SOM SOM SOM i 20 SOM Development of Syllabus Vsualization System using Spherical Self-Organizing Maps 1090366 2009 3 5 SOM SOM(Self-Organizing Maps) SOM SOM SOM SOM SOM SOM i Abstract Development of Syllabus Vsualization

More information

16 16 16 1 16 2 16 3 24 4 24 5 25 6 33 7 33 33 1 33 2 34 3 34 34 34 34 34 34 4 34-1 - 5 34 34 34 1 34 34 35 36 36 2 38 38 41 46 47 48 1 48 48 48-2 - 49 50 51 2 52 52 53 53 1 54 2 54 54 54 56 57 57 58 59

More information

2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2

2003/3 Vol. J86 D II No.3 2.3. 4. 5. 6. 2. 1 1 Fig. 1 An exterior view of eye scanner. CCD [7] 640 480 1 CCD PC USB PC 2 334 PC USB RS-232C PC 3 2.1 2 Curved Document Imaging with Eye Scanner Toshiyuki AMANO, Tsutomu ABE, Osamu NISHIKAWA, Tetsuo IYODA, and Yukio SATO 1. Shape From Shading SFS [1] [2] 3 2 Department of Electrical and Computer Engineering,

More information

Accuracy Improvement by Compound Discriminant Functions for Resembling Character Recognition Takashi NAKAJIMA, Tetsushi WAKABAYASHI, Fumitaka KIMURA,

Accuracy Improvement by Compound Discriminant Functions for Resembling Character Recognition Takashi NAKAJIMA, Tetsushi WAKABAYASHI, Fumitaka KIMURA, Journal Article / 学 術 雑 誌 論 文 混 合 識 別 関 数 による 類 似 文 字 認 識 の 高 精 度 化 Accuracy improvement by compoun for resembling character recogn 中 嶋, 孝 ; 若 林, 哲 史 ; 木 村, 文 隆 ; 三 宅, 康 二 Nakajima, Takashi; Wakabayashi,

More information

診療ガイドライン外来編2014(A4)/FUJGG2014‐01(大扉)

診療ガイドライン外来編2014(A4)/FUJGG2014‐01(大扉) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

More information

i

i 14 i ii iii iv v vi 14 13 86 13 12 28 14 16 14 15 31 (1) 13 12 28 20 (2) (3) 2 (4) (5) 14 14 50 48 3 11 11 22 14 15 10 14 20 21 20 (1) 14 (2) 14 4 (3) (4) (5) 12 12 (6) 14 15 5 6 7 8 9 10 7

More information

- - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8...

- - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8... 取 扱 説 明 書 - - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8...9...11 - - - - - - - - - - - - - - - - -

More information

受賞講演要旨2012cs3

受賞講演要旨2012cs3 アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート アハ ート α β α α α α α

More information

1 3 1.1............................... 3 1.2.............................. 4 1.3.............................. 6 2 Self-Organizing Map:SOM 7 2.1 SOM..

1 3 1.1............................... 3 1.2.............................. 4 1.3.............................. 6 2 Self-Organizing Map:SOM 7 2.1 SOM.. 2016 3 1 3 1.1............................... 3 1.2.............................. 4 1.3.............................. 6 2 Self-Organizing Map:SOM 7 2.1 SOM................................ 7 2.2 SOM..............................

More information

日本糖尿病学会誌第58巻第3号

日本糖尿病学会誌第58巻第3号 l l μ l l l l l μ l l l l μ l l l l μ l l l l l l l l l l l l l μ l l l l μ Δ l l l μ Δ μ l l l l μ l l μ l l l l l l l l μ l l l l l μ l l l l l l l l μ l μ l l l l l l l l l l l l μ l l l l β l l l μ

More information

86 7 I ( 13 ) II ( )

86 7 I ( 13 ) II ( ) 10 I 86 II 86 III 89 IV 92 V 2001 93 VI 95 86 7 I 2001 6 12 10 2001 ( 13 ) 10 66 2000 2001 4 100 1 3000 II 1988 1990 1991 ( ) 500 1994 2 87 1 1994 2 1000 1000 1000 2 1994 12 21 1000 700 5 800 ( 97 ) 1000

More information

B000 B913 B913 S000 S500 L500 L913 B400 B913 B933 S320 L000 L913 492 498 P 38 5 P591 P595 P596 900 911 913 913 913 913 914 916 930 493 498 P 528 P594 P596 P597 910 913 913 913 913 913 914 918 700 723 746

More information

日本糖尿病学会誌第58巻第2号

日本糖尿病学会誌第58巻第2号 β γ Δ Δ β β β l l l l μ l l μ l l l l α l l l ω l Δ l l Δ Δ l l l l l l l l l l l l l l α α α α l l l l l l l l l l l μ l l μ l μ l l μ l l μ l l l μ l l l l l l l μ l β l l μ l l l l α l l μ l l

More information

2797 4 5 6 7 2. 2.1 COM COM 4) 5) COM COM 3 4) 5) 2 2.2 COM COM 6) 7) 10) COM Bonanza 6) Bonanza 6 10 20 Hearts COM 7) 10) 52 4 3 Hearts 3 2,000 4,000

2797 4 5 6 7 2. 2.1 COM COM 4) 5) COM COM 3 4) 5) 2 2.2 COM COM 6) 7) 10) COM Bonanza 6) Bonanza 6 10 20 Hearts COM 7) 10) 52 4 3 Hearts 3 2,000 4,000 Vol. 50 No. 12 2796 2806 (Dec. 2009) 1 1, 2 COM TCG COM TCG COM TCG Strategy-acquisition System for Video Trading Card Game Nobuto Fujii 1 and Haruhiro Katayose 1, 2 Behavior and strategy of computers

More information

1 1 3 1.1 (Frequecy Tabulatios)................................ 3 1........................................ 8 1.3.....................................

1 1 3 1.1 (Frequecy Tabulatios)................................ 3 1........................................ 8 1.3..................................... 1 1 3 1.1 (Frequecy Tabulatios)................................ 3 1........................................ 8 1.3........................................... 1 17.1................................................

More information

第1部 一般的コメント

第1部 一般的コメント (( 2000 11 24 2003 12 31 3122 94 2332 508 26 a () () i ii iii iv (i) (ii) (i) (ii) (iii) (iv) (a) (b)(c)(d) a) / (i) (ii) (iii) (iv) 1996 7 1996 12

More information

表1票4.qx4

表1票4.qx4 iii iv v 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 22 23 10 11 24 25 26 27 10 56 28 11 29 30 12 13 14 15 16 17 18 19 2010 2111 22 23 2412 2513 14 31 17 32 18 33 19 34 20 35 21 36 24 37 25 38 2614

More information

第1章 国民年金における無年金

第1章 国民年金における無年金 1 2 3 4 ILO ILO 5 i ii 6 7 8 9 10 ( ) 3 2 ( ) 3 2 2 2 11 20 60 12 1 2 3 4 5 6 7 8 9 10 11 12 13 13 14 15 16 17 14 15 8 16 2003 1 17 18 iii 19 iv 20 21 22 23 24 25 ,,, 26 27 28 29 30 (1) (2) (3) 31 1 20

More information

o 2o 3o 3 1. I o 3. 1o 2o 31. I 3o PDF Adobe Reader 4o 2 1o I 2o 3o 4o 5o 6o 7o 2197/ o 1o 1 1o

o 2o 3o 3 1. I o 3. 1o 2o 31. I 3o PDF Adobe Reader 4o 2 1o I 2o 3o 4o 5o 6o 7o 2197/ o 1o 1 1o 78 2 78... 2 22201011... 4... 9... 7... 29 1 1214 2 7 1 8 2 2 3 1 2 1o 2o 3o 3 1. I 1124 4o 3. 1o 2o 31. I 3o PDF Adobe Reader 4o 2 1o 72 1. I 2o 3o 4o 5o 6o 7o 2197/6 9. 9 8o 1o 1 1o 2o / 3o 4o 5o 6o

More information

0.,,., m Euclid m m. 2.., M., M R 2 ψ. ψ,, R 2 M.,, (x 1 (),, x m ()) R m. 2 M, R f. M (x 1,, x m ), f (x 1,, x m ) f(x 1,, x m ). f ( ). x i : M R.,,

0.,,., m Euclid m m. 2.., M., M R 2 ψ. ψ,, R 2 M.,, (x 1 (),, x m ()) R m. 2 M, R f. M (x 1,, x m ), f (x 1,, x m ) f(x 1,, x m ). f ( ). x i : M R.,, 2012 10 13 1,,,.,,.,.,,. 2?.,,. 1,, 1. (θ, φ), θ, φ (0, π),, (0, 2π). 1 0.,,., m Euclid m m. 2.., M., M R 2 ψ. ψ,, R 2 M.,, (x 1 (),, x m ()) R m. 2 M, R f. M (x 1,, x m ), f (x 1,, x m ) f(x 1,, x m ).

More information

- 2 -

- 2 - - 2 - - 3 - (1) (2) (3) (1) - 4 - ~ - 5 - (2) - 6 - (1) (1) - 7 - - 8 - (i) (ii) (iii) (ii) (iii) (ii) 10 - 9 - (3) - 10 - (3) - 11 - - 12 - (1) - 13 - - 14 - (2) - 15 - - 16 - (3) - 17 - - 18 - (4) -

More information

2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4 4 4 2 5 5 2 4 4 4 0 3 3 0 9 10 10 9 1 1

2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4 4 4 2 5 5 2 4 4 4 0 3 3 0 9 10 10 9 1 1 1 1979 6 24 3 4 4 4 4 3 4 4 2 3 4 4 6 0 0 6 2 4 4 4 3 0 0 3 3 3 4 3 2 4 3? 4 3 4 3 4 4 4 4 3 3 4 4 4 4 2 1 1 2 15 4 4 15 0 1 2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4

More information

1 (1) (2)

1 (1) (2) 1 2 (1) (2) (3) 3-78 - 1 (1) (2) - 79 - i) ii) iii) (3) (4) (5) (6) - 80 - (7) (8) (9) (10) 2 (1) (2) (3) (4) i) - 81 - ii) (a) (b) 3 (1) (2) - 82 - - 83 - - 84 - - 85 - - 86 - (1) (2) (3) (4) (5) (6)

More information

Fig. 4. Configuration of fatigue test specimen. Table I. Mechanical property of test materials. Table II. Full scale fatigue test conditions and test

Fig. 4. Configuration of fatigue test specimen. Table I. Mechanical property of test materials. Table II. Full scale fatigue test conditions and test (J. Soc. Mat. Sci., Japan), Vol. 52, No. 11, pp. 1351-1356, Nov. 2003 Fatigue Life Prediction of Coiled Tubing by Takanori KATO*, Miyuki YAMAMOTO*, Isao SAWAGUCHI** and Tetsuo YONEZAWA*** Coiled tubings,

More information

日本分子第4巻2号_10ポスター発表.indd

日本分子第4巻2号_10ポスター発表.indd JSMI Report 62 63 JSMI Report γ JSMI Report 64 β α 65 JSMI Report JSMI Report 66 67 JSMI Report JSMI Report 68 69 JSMI Report JSMI Report 70 71 JSMI Report JSMI Report 72 73 JSMI Report JSMI Report 74

More information

ÿþ

ÿþ I O 01 II O III IV 02 II O 03 II O III IV III IV 04 II O III IV III IV 05 II O III IV 06 III O 07 III O 08 III 09 O III O 10 IV O 11 IV O 12 V O 13 V O 14 V O 15 O ( - ) ( - ) 16 本 校 志 望 の 理 由 入 学 後 の

More information

エクセルカバー入稿用.indd

エクセルカバー入稿用.indd i 1 1 2 3 5 5 6 7 7 8 9 9 10 11 11 11 12 2 13 13 14 15 15 16 17 17 ii CONTENTS 18 18 21 22 22 24 25 26 27 27 28 29 30 31 32 36 37 40 40 42 43 44 44 46 47 48 iii 48 50 51 52 54 55 59 61 62 64 65 66 67 68

More information

provider_020524_2.PDF

provider_020524_2.PDF 1 1 1 2 2 3 (1) 3 (2) 4 (3) 6 7 7 (1) 8 (2) 21 26 27 27 27 28 31 32 32 36 1 1 2 2 (1) 3 3 4 45 (2) 6 7 5 (3) 6 7 8 (1) ii iii iv 8 * 9 10 11 9 12 10 13 14 15 11 16 17 12 13 18 19 20 (2) 14 21 22 23 24

More information

1.0, λ. Holt-Winters t + h,ỹ t ỹ t+h t = ỹ t + hf t.,,.,,,., Hassan [5],,,.,,,,,,Hassan EM,, [6] [8].,,,,Stenger [9]. Baum-Welch, Baum-Welch (Incremen

1.0, λ. Holt-Winters t + h,ỹ t ỹ t+h t = ỹ t + hf t.,,.,,,., Hassan [5],,,.,,,,,,Hassan EM,, [6] [8].,,,,Stenger [9]. Baum-Welch, Baum-Welch (Incremen DEIM Forum 2009 E8-4 HMM 184 8584 3-7-2 E-mail: kei.wakabayashi.bq@gs-eng.hosei.ac.jp, miurat@k.hosei.ac.jp, (HMM)., EM HMM, Baum-Welch,,,, Forecasting Time-Series on Data Stream using Incremental Hidden

More information

01_.g.r..

01_.g.r.. I II III IV V VI VII VIII IX X XI I II III IV V I I I II II II I I YS-1 I YS-2 I YS-3 I YS-4 I YS-5 I YS-6 I YS-7 II II YS-1 II YS-2 II YS-3 II YS-4 II YS-5 II YS-6 II YS-7 III III YS-1 III YS-2

More information

,..,,.,,.,.,..,,.,,..,,,. 2

,..,,.,,.,.,..,,.,,..,,,. 2 A.A. (1906) (1907). 2008.7.4 1.,.,.,,.,,,.,..,,,.,,.,, R.J.,.,.,,,..,.,. 1 ,..,,.,,.,.,..,,.,,..,,,. 2 1, 2, 2., 1,,,.,, 2, n, n 2 (, n 2 0 ).,,.,, n ( 2, ), 2 n.,,,,.,,,,..,,. 3 x 1, x 2,..., x n,...,,

More information

A9R799F.tmp

A9R799F.tmp !!!!! !!! " !!! ! "!!" " " ! ! " "!! "! " "!! !! !!! !!! ! !!!!! α ! "α!! "!! ! "α!! !! " " ! "! β ! ! "β " "! " " ! α λ !!!! ! """ ""! ! "!β"!!" ! ! "" ""! "!! !!!! ! " !! ! ! !"! "!! " ! ! α"!

More information

第89回日本感染症学会学術講演会後抄録(I)

第89回日本感染症学会学術講演会後抄録(I) ! ! ! β !!!!!!!!!!! !!! !!! μ! μ! !!! β! β !! β! β β μ! μ! μ! μ! β β β β β β μ! μ! μ!! β ! β ! ! β β ! !! ! !!! ! ! ! β! !!!!! !! !!!!!!!!! μ! β !!!! β β! !!!!!!!!! !! β β β β β β β β !!

More information

Support Vector Machine (SVM) 4 SVM SVM 2 80% 100% SVM SVM SVM 4 SVM 2 2 SVM 4

Support Vector Machine (SVM) 4 SVM SVM 2 80% 100% SVM SVM SVM 4 SVM 2 2 SVM 4 Analysis of Groove Feelings of Drums Plays 47 56340 19 1 31 Support Vector Machine (SVM) 4 SVM SVM 2 80% 100% SVM SVM SVM 4 SVM 2 2 SVM 4 1 1 1.1........................................ 1 1.1.1.............................

More information

Autumn 06 1 2005 100 100 1 100 1 2003 2005 10 2003 2005 2

Autumn 06 1 2005 100 100 1 100 1 2003 2005 10 2003 2005 2 2005 25-2 17 395.6 149.1 1 2004 2 2003p.13 3 Autumn 06 1 2005 100 100 1 100 1 2003 2005 10 2003 2005 2 Vol. 42 No. 2 2 100 20052005 2002 20052005 3 2005 10 1 II III IV 1 1 1 2 1 4 15-2 30 4,091 54.5 2

More information

i ii iii iv v vi vii ( ー ー ) ( ) ( ) ( ) ( ) ー ( ) ( ) ー ー ( ) ( ) ( ) ( ) ( ) 13 202 24122783 3622316 (1) (2) (3) (4) 2483 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) 11 11 2483 13

More information

4

4 4 5 6 7 + 8 = ++ 9 + + + + ++ 10 + + 11 12 WS LC VA L WS = LC VA = LC L L VA = LC L VA L 13 i LC VA WS WS = LC = VA LC VA VA = VA α WS α = VA VA i WS = LC VA i t t+1 14 WS = α WS + WS α WS = WS WS WS =

More information

困ったときのQ&A

困ったときのQ&A ii iii iv NEC Corporation 1997 v P A R T 1 vi vii P A R T 2 viii P A R T 3 ix x xi 1P A R T 2 1 3 4 1 5 6 1 7 8 1 9 1 2 3 4 10 1 11 12 1 13 14 1 1 2 15 16 1 2 1 1 2 3 4 5 17 18 1 2 3 1 19 20 1 21 22 1

More information

ii

ii I05-010 : 19 1 ii k + 1 2 DS 198 20 32 1 1 iii ii iv v vi 1 1 2 2 3 3 3.1.................................... 3 3.2............................. 4 3.3.............................. 6 3.4.......................................

More information

P001-040(表1表4).ai

P001-040(表1表4).ai 3-STEP SYSTEM THE NEW STYLE OF Solution MODEL STEP.1 STEP.3 STEP.2 Ubiquitous Solutions 1 2 3 P5 P6 P7 P8 P9 P10 P11 P13 P14 P15 P16 4 P17 P18 P19 P20 5 P21 P22 P23 6 P24 P29 P30 7 P31 8 P32 9 P33 P34

More information

Fig. 1 Relative delay coding.

Fig. 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 information

Fig. 1. Example of characters superimposed on delivery slip.

Fig. 1. Example of characters superimposed on delivery slip. Extraction of Handwritten Character String Superimposed on Delivery Slip Data Ken-ichi MATSUO, Non-member, Katsuhiko UEDA, Non-member (Nara National College of Technology), Michio UMEDA, Member (Osaka

More information

178 5 I 1 ( ) ( ) 10 3 13 3 1 8891 8 3023 6317 ( 10 1914 7152 ) 16 5 1 ( ) 6 13 3 13 3 8575 3896 8 1715 779 6 (1) 2 7 4 ( 2 ) 13 11 26 12 21 14 11 21

178 5 I 1 ( ) ( ) 10 3 13 3 1 8891 8 3023 6317 ( 10 1914 7152 ) 16 5 1 ( ) 6 13 3 13 3 8575 3896 8 1715 779 6 (1) 2 7 4 ( 2 ) 13 11 26 12 21 14 11 21 I 178 II 180 III ( ) 181 IV 183 V 185 VI 186 178 5 I 1 ( ) ( ) 10 3 13 3 1 8891 8 3023 6317 ( 10 1914 7152 ) 16 5 1 ( ) 6 13 3 13 3 8575 3896 8 1715 779 6 (1) 2 7 4 ( 2 ) 13 11 26 12 21 14 11 21 4 10 (

More information

(interval estimation) 3 (confidence coefficient) µ σ/sqrt(n) 4 P ( (X - µ) / (σ sqrt N < a) = α a α X α µ a σ sqrt N X µ a σ sqrt N 2

(interval estimation) 3 (confidence coefficient) µ σ/sqrt(n) 4 P ( (X - µ) / (σ sqrt N < a) = α a α X α µ a σ sqrt N X µ a σ sqrt N 2 7 2 1 (interval estimation) 3 (confidence coefficient) µ σ/sqrt(n) 4 P ( (X - µ) / (σ sqrt N < a) = α a α X α µ a σ sqrt N X µ a σ sqrt N 2 (confidence interval) 5 X a σ sqrt N µ X a σ sqrt N - 6 P ( X

More information