untitled
|
|
- しょうり ひろなが
- 5 years ago
- Views:
Transcription
1 c Data Envelopment Analysis DEA IT DHARMA Ltd DEA-AR (Assurance Region) 1 DEA 1 1 [1] [2] 2. [2] (RF : Range factor) 47240Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
2 RF RF RF 1 UZR (Ultimate Zone Rating) Defensive Runs Saved : DRS (1/5) 1 (1/7) 1 6 (1/10) 36 (6/7) 2 5 (1/8) 13 (2/13) 3 3 (1/6) 30 (3/7) 4 (1/7) 2 (1/4) 1 (1/19) 4 (2/21) 2 (2/19) 3 (1/21) 8 (3/19) 14 (7/21) (6/6) 24 (2/4) 1 19 (1/11) 14 (4/11) 2 7 (2/9) 7 (1/7) 3 21 (3/7) 44 (7/8) 8 (1/5) 2 (1/7) 2 (2/21) 4 (2/19) 1 (1/21) 5 (3/19) 5 (4/21) 1 (1/19) (a/b): 220 b a (DEA) DEA / / 2. / 3. / 4. / 5. / 6. / 7. / 8. / Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.41473
3 9. / x (x 0.194)/( ) DEA CCR [3] o F 1 n M 9G 1 x gh (=1): h g y jh: h j θ =max M u jy jo u jy jh G v gx gh 0 g=1 (h =1,...,n; h o ) G v gx go =1, u 2 2u j (j 2) g=1 v g 0(g =1,...,G), u j 0(j =1,...,M) 1 h o 1 3 (u 2 2 u j (j 2)) 2 1 (G =1) u jy jh 1 (h =1,...,n; h o), u j 0(j =1,...,M) v g i k S(i, k) DEA-AR SS I(i, k, j)= 1 : i k j 0: 6 6 Ce Pa 1: 2 :13 :24 :3 5: 6 :7 : A(i, j) =1: i j 0: A(i, j) S(i, k) [ DEA-AR ] I(i, k, j) k F 1 max I(i, k, j) S(i, k) i,k,j SS I(i, k, j) A(i, j) =c j; i,k c j =1(j 6),c 6 =3 3 1 I(i, k, j) =Q [Q =8,Q=9] i,k,j I(i, k, j) 1, I(i, k, j) {0, 1} k,j 4 I(i, k, j) A(i, j) =1; k i,j I(i, 6,j) A(i, j) =P [P =3,P =4] i,j 6 3 best9 DEA (DEA best9) Ce θ hih u j[j =1,...,M] i 4 h i 47442Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
4 3 4 θ hi 2012 Ce11 best9 DEA best Ce12 best9 DEA best Pa11 best9 DEA best Pa12 best9 DEA best θ =0.611 u j[j =1,...,M] θ hh<θ hi h i θ hh>θ hi h i best9 DEA best9 θ hh>θ hi h i i h DEA best9 () Ce11 Ce DEA best9 Pa11 Pa best9 DEA best9 best DEA best best9 5 6 DEA 2011 best DEA best9 (u 2 2u j (j 2)) 2 best DEA best Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.43475
5 7 best9 DEAbest9 Ce11 Ce12 Pa11 Pa (62 ) DEA best DEA best DEA best DEA u j o 220 (u 2 2u j (j 2)) DEA best9 10 Pa11 Pa (a, b) u a/u b L ab U ab 2 L ab U ab DEA-AR (a, b) u a/u b L ab U ab DEA-AR M 47644Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
6 n (DEA-AR) θ o =max M u jy jo u jy jh 1 (h =1,...,n; h o) u kl jk u j u ku jk ( j k) u j 0(j =1, 2,...,[M =9]) o θ o 3 F (1) DEA best best best (2) c 7 =1, I(i, 6,j) A(i, j) =4, I(i, k, j) =9 i,j i,k,j DEA best DEA best9 best [243] 2012 [252] (3) 108 (6) (6) 157 (5)* 75 2 (2)* 15 (6) (6) 184 (6)* 68 (1) 216 (3) 239 (6) 238 (1) 248 (4)* 54 (2) 231 (5)* 35 (4) 112 * 3 best9 () [] 10 DEA best9 best [207] 2012 [204] (6)* 3 (6)* 14 1 (3)* 26 (4) (2) 175 (6)* 5 3 (4) 142 (3)* 34 (6) 187 (6) 201 (6) 194 (6) 192 (1)* 7 (5) 101 (6) 129 (2)* 27 (5)* (1)* 54 + * 3 best 9 () [] best9 3 1 best9 best9 best DEA best9 best (DEA best9) (1) Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.45477
7 11 best9 SS0 3 3DEA 5, 6 8 9, 10 Ce Ce Pa Pa (2) (3) (4) (1) (2) DEA best9 best9 (4) SS 220 best9 SS best9 best UZR DRS [1] T. Ueda and H. Amatatsu, Determination of bounds in DEA assurance region method: Its application to evaluation of baseball players and chemical companies, Journal of the Operations Research Society of Japan, 52, , [2] [3] DEA Ce (5/6) RF DEA-AR (a, b) 47846Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
8 1 (a/b) Ce11 32 (5/6) 29 (7/7) 37 (4/6) 45 (1/5) 44 (5/6) 6 (6/20) 7 (7/20) 10 (10/20) 19 (2/6) 21 (1/7) 14 (3/6) 3 (3/5) 6 (1/6) 42 (12/20) 45 (15/20) 40 (10/20) 11 (3/6) 17 (4/7) 32 (4/6) 15 (1/5) 27 (2/6) 30 (12/20) 33 (14/20) 31 (13/20) 27 (1/6) 20 (7/7) 9 (3/6) 19 (1/5) 4 (4/6) 39 (10/20) 43 (14/20) 42 (13/20) RF 14 (5/6) 1 (1/7) 22 (6/6) 30 (4/5) 25 (6/6) 29 (1/20) 35 (5/20) 32 (2/20) (2/6) Ce12 34 (4/9) 30 (6/7) 41 (1/8) 60 (6/8) 56 (7/7) 17 (16/24) 19 (18/24) 13 (13/24) 28 (5/9) 32 (2/7) 23 (8/8) 3 (3/8) 7 (1/7) 47 (8/24) 44 (5/24) 51 (12/24) 9 (1/9) 21 (5/7) 14 (2/8) 49 (2/8) 20 (1/7) 42 (16/24) 31 (12/24) 36 (14/24) 32 (2/9) 16 (2/7) 7 (2/8) 23 (2/8) 1 (1/7) 43 (5/24) 37 (1/24) 47 (8/24) RF 11 (5/9) 1 (1/7) 12 (1/8) 35 (3/8) 23 (3/7) 34 (1/24) 45 (7/24) 37 (2/24) (6/9) Pa11 21 (1/9) 18 (2/7) 41 (3/7) 55 (4/6) 47 (2/6) 6 (6/22) 20 (18/22) 9 (9/22) 32 (8/9) 36 (6/7) 17 (5/7) 1 (1/6) 10 (4/6) 48 (13/22) 40 (5/22) 45 (10/22) 1 (1/9) 10 (1/7) 22 (1/7) 52 (2/6) 40 (2/6) 1 (1/22) 48 (22/22) 1 (1/22) 40 (7/9) 13 (1/7) 7 (3/7) 17 (1/6) 1 (1/6) 49 (15/22) 48 (14/22) 34 (5/22) RF 9 (3/9) 5 (5/7) 17 (3/7) 30 (1/6) 21 (1/6) 31 (1/22) 37 (3/22) 40 (6/22) (3/9) Pa12 34 (8/9) 19 (2/6) 36 (1/5) 50 (3/5) 40 (1/7) 7 (7/20) 16 (16/20) 2 (2/20) 19 (2/9) 32 (5/6) 17 (5/5) 2 (2/5) 13 (7/7) 46 (14/20) 40 (8/20) 50 (18/20) 7 (3/9) 5 (1/6) 22 (1/5) 48 (2/5) 47 (6/7) 21 (9/20) 40 (16/20) 20 (8/20) 26 (2/9) 13 (1/6) 9 (5/5) 21 (3/5) 4 (3/7) 44 (12/20) 46 (14/20) 45 (13/20) RF 9 (3/9) 3 (3/6) 13 (1/5) 31 (3/5) 21 (2/7) 27 (2/20) 35 (5/20) 33 (4/20) (6/9) (a/b) : 60 b a u a/u b L ab U ab (m =) Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.47479
9 k t k 7 t 7 1 t k t k i j k t k i j f ij =log e t k ln t k [1] N M j μ j μ N N μ j = f ij/n, μ = f ij/(nm) i=1 i=1 D B D W D T D B = μ j μ /M N D W = { f ij μ j /N }/M i=1 N D T = f ij μ /(NM) i=1 = D B + D W D T t k =10 kd B + D W C 2 D B + D W = C 2 D B D W t k v v +(s 1 s 2)=0,s 1 0, s 2 0 (A1) C(1 b) s 1 0, C b s 2 0, b {0, 1} (A2) C: (A3) (s 1 + s 2) v (A2) s 1 s 2 =0s 1 s 2b D B + D W = C 2 D B t k F 2 t k F 2 max (s 1j + s 2j)/M D B N N : μ = f ij/(nm), μ j = f ij/n i=1 i=1 μ j μ + s 1j s 2j =0; j =1, 2,...,M μ j μ = s 1j + s 2j[ (A1)] (s 1j + s 2j)/M D B =0 D B M C 2(1 b j) s 1j 0, M C 2 b j s 2j 0, b j {0, 1} [ (A2) MC 2 (A3) C ] f ij μ j + t (1) ij t (2) ij =0 f ij μ j = t (1) ij + t (2) ij N i=1 (t (1) ij + t (2) ij )/(NM) D W =0 D W t (1) ij 0, t (2) ij 0: i =1,...,N; j =1,...,M D B + D W = C 2 : ln(t k) ln(t k+1) C 0 : k =1,...,m; t m+1 =1 s 1j,s 2j,t (1) ij,t (2) ij (i =1,...,N; j =1,...,M), t k(k =1,...,m+1),D W,D B N =10, M =9, m =6, C 0 = (ln 9)/20 ln t 1 =ln9 ln t 7 =0 (ln 9)/6 C 0 k t k 4 j μ j = 10 fij/10 5 i= (t 1) 5 2 (t 2) 4 3 (t 3) Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
10 μ 2 = {5ln(t 1)+4ln(t 2)+ln(t 3)} /10 = 2.10 μ j 1 μ j h j k t k =exp(f hj) [ln t k = f hj] t a b r ab L ab U ab 1 h 2 4 r r 24 =7.78 2/6.97 4= L 24 =0.90 U 24 =1.55 (a, b) L abu ab 0.4 (max k μ k) >μ j j (max k μ k)= = [] 0.4 L abu ab DEA-AR F 2 t k t 1 t 2 t 3 t 4 t 5 t j μ j Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.49481
11 6 1 h j exp(f hj) h 2 4 r h r Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
untitled
c 816 Web 1. 30 [1] [2] [3, 4] [5] 10 [6] 185 8540 2 8 38 [5] [5, 7] [5] 3 (1) 608 18 Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited. (2) (3) 2. 2.1 Web 2014 1 2013 12 2.2
More informationuntitled
c Society5.0 Society5.0 Society5.0 Society5.0 2017 Society5.0 SDGs SIP PRISM Society5.0 2017 SIP ImPACT PRISM SDGs 1. Society5.0 2016 9 Society5.0 OR [1] Society5.0 2. Society5.0 2.1 Society5.0 Society5.0
More informationor57_4_175.dvi
c Excel Excel Excel Excel Microsoft Excel 1. OR Microsoft Excel Excel 1 Excel Excel Excel or 2007 Excel OR Excel Excel LP Excel LP Excel 112 8551 1 13 27 1 Excel Excel Excel 2010 Excel OpenOffice Calc
More informationor58_11_651.dvi
c 1. 2. 480 1195 1 1 OECD 2010 [1] 33 OECD 2009 3,265 3,035 8,233 913 1 1 4 OECD 2 3 OECD 1,000 2.2 OECD 3.1 34 5 OECD 14 [2] 1 2013 11 Copyright c by ORSJ. Unauthorized reproduction of this article is
More informationuntitled
c Twitter 1. Twitter 140 SNS 1,392 Facebook 2 14 [4]. 2011 Twitter 58 1 [1]. Twitter Twitter [4] Twitter SNS [5]. [1]. 432 8561 3 5 1 13.5.22 14.2.10 [6] Web [13] SIR [10] SIR SIR 2 2014 4 Copyright c
More information行列代数2010A
(,) A (,) B C = AB a 11 a 1 a 1 b 11 b 1 b 1 c 11 c 1 c a A = 1 a a, B = b 1 b b, C = AB = c 1 c c a 1 a a b 1 b b c 1 c c i j ij a i1 a i a i b 1j b j b j c ij = a ik b kj b 1j b j AB = a i1 a i a ik
More informationor58_8_455.dvi
c Voice of CustomerVOC CS VOC Facebook Twitter SNS VOC SNS 1 VOC 1. WEB 2. 151 8583 2 2 1 VOC SNS 3. 3.1 4 (1) FAX (2) HP Twitter (3) (4) (1) (3) (4) 1 WEB 2013 8 Copyright c by ORSJ. Unauthorized reproduction
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(2018 2Q C) [ ] R 2 2 P = (a, b), Q = (c, d) Q P QP = ( ) a c b d (a c, b d) P = (a, b) O P ( ) a p = b P = (a, b) p = ( ) a b R 2 {( ) } R 2 x = x, y
(2018 2Q C) [ ] R 2 2 P = (a, b), Q = (c, d) Q P QP = a c b d (a c, b d) P = (a, b) O P a p = b P = (a, b) p = a b R 2 { } R 2 x = x, y R y 2 a p =, c q = b d p + a + c q = b + d q p P q a p = c R c b
More informationLINEAR ALGEBRA I Hiroshi SUZUKI Department of Mathematics International Christian University
LINEAR ALGEBRA I Hiroshi SUZUKI Department of Mathematics International Christian University 2002 2 2 2 2 22 2 3 3 3 3 3 4 4 5 5 6 6 7 7 8 8 9 Cramer 9 0 0 E-mail:hsuzuki@icuacjp 0 3x + y + 2z 4 x + y
More informationuntitled
c OR 21 OR 1. 21 21 IoT OR OR OR 260 8672 1 8 1 OR 2. 2.1 public health [1] communicable (infectious) diseases vehicle burden HIV/AIDS (SARS) 258 60 Copyright c by ORSJ. Unauthorized reproduction of this
More information1 1.1 H = µc i c i + c i t ijc j + 1 c i c j V ijklc k c l (1) V ijkl = V jikl = V ijlk = V jilk () t ij = t ji, V ijkl = V lkji (3) (1) V 0 H mf = µc
013 6 30 BCS 1 1.1........................ 1................................ 3 1.3............................ 3 1.4............................... 5 1.5.................................... 5 6 3 7 4 8
More information弾性定数の対称性について
() by T. oyama () ij C ij = () () C, C, C () ij ji ij ijlk ij ij () C C C C C C * C C C C C * * C C C C = * * * C C C * * * * C C * * * * * C () * P (,, ) P (,, ) lij = () P (,, ) P(,, ) (,, ) P (, 00,
More informationuntitled
c OR&SA OR&SA 2 OR&SA (Polarity) OR&SA 1. 1) 2) OR&SA 2 3) 2 OR&SA 2014 7 [1] 1) 2) 3) 153 8648 2 2 1 4) 5) 6) 1980 1990 2000 2. 234 36 Copyright c by ORSJ. Unauthorized reproduction of this article is
More informationor57_12_673.dvi
c ID ID ID 1 POS ID-POS ID-POS ID ID RFM LTV 8 7 ID KPI ID ID-POS ID KPI 1. ID 1.1 ID ID ID-POS ID IC ID IC ID nanaco Edy Suica PASMO IC 1 ID ID 100 0005 1 6 5 ID ID POS Web IC 1 ID 1 Twitter Facebook
More information... 1... 2... 9... 20... 23 - i - ... 30... 34... 38 - ii - - 1 - - 2 - - 3 - - 4 - - 5 - - 6 - - 7 - - 8 - - 9 - - 10 - 10 (A) (B) (A)(B) (C) (E) (C)(E) 16 3,096 3,700 6,796 975 5,821 17 3,002 4,082 7,084
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 information(2016 2Q H) [ ] R 2 2 P = (a, b), Q = (c, d) Q P QP = ( ) a c b d (a c, b d) P = (a, b) O P ( ) a p = b P = (a, b) p = ( ) a b R 2 {( ) } R 2 x = x, y
(2016 2Q H) [ ] R 2 2 P = (a, b), Q = (c, d) Q P QP = a c b d (a c, b d) P = (a, b) O P a p = b P = (a, b) p = a b R 2 { } R 2 x = x, y R y 2 a p =, c q = b d p + a + c q = b + d q p P q a p = c R c b
More information行列代数2010A
a ij i j 1) i +j i, j) ij ij 1 j a i1 a ij a i a 1 a j a ij 1) i +j 1,j 1,j +1 a i1,1 a i1,j 1 a i1,j +1 a i1, a i +1,1 a i +1.j 1 a i +1,j +1 a i +1, a 1 a,j 1 a,j +1 a, ij i j 1,j 1,j +1 ij 1) i +j a
More informationMC-440 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 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67
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)
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 information1. 2 P 2 (x, y) 2 x y (0, 0) R 2 = {(x, y) x, y R} x, y R P = (x, y) O = (0, 0) OP ( ) OP x x, y y ( ) x v = y ( ) x 2 1 v = P = (x, y) y ( x y ) 2 (x
. P (, (0, 0 R {(,, R}, R P (, O (0, 0 OP OP, v v P (, ( (, (, { R, R} v (, (, (,, z 3 w z R 3,, z R z n R n.,..., n R n n w, t w ( z z Ke Words:. A P 3 0 B P 0 a. A P b B P 3. A π/90 B a + b c π/ 3. +
More information<303288C991BD946797C797592E696E6464>
175 71 5 19 / 100 20 20 309 133 72 176 62 3 8 2009 2002 1 2 3 1 8 1 20 1 2 + 0.4952 1 2 http://www.mtwa.or.jp/ h19mokuji.html 20 100 146 0 6,365 359 111 0 38,997 11,689 133,960 36,830 76,177 155,684 18,068
More informationor58_10_599.dvi
c 1. 450 m 14 26 1 =1.852 km/h 300 m 1 2 34 m 1933 2 30 m 135 8533 2 1 6 1 2009 12 29 m 1 (Weather Routing) [1] 2013 10 Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited. 27
More informationlinearal1.dvi
19 4 30 I 1 1 11 1 12 2 13 3 131 3 132 4 133 5 134 6 14 7 2 9 21 9 211 9 212 10 213 13 214 14 22 15 221 15 222 16 223 17 224 20 3 21 31 21 32 21 33 22 34 23 341 23 342 24 343 27 344 29 35 31 351 31 352
More informationv v = v 1 v 2 v 3 (1) R = (R ij ) (2) R (R 1 ) ij = R ji (3) 3 R ij R ik = δ jk (4) i=1 δ ij Kronecker δ ij = { 1 (i = j) 0 (i
1. 1 1.1 1.1.1 1.1.1.1 v v = v 1 v 2 v 3 (1) R = (R ij ) (2) R (R 1 ) ij = R ji (3) R ij R ik = δ jk (4) δ ij Kronecker δ ij = { 1 (i = j) 0 (i j) (5) 1 1.1. v1.1 2011/04/10 1. 1 2 v i = R ij v j (6) [
More information1. 4cm 16 cm 4cm 20cm 18 cm L λ(x)=ax [kg/m] A x 4cm A 4cm 12 cm h h Y 0 a G 0.38h a b x r(x) x y = 1 h 0.38h G b h X x r(x) 1 S(x) = πr(x) 2 a,b, h,π
. 4cm 6 cm 4cm cm 8 cm λ()=a [kg/m] A 4cm A 4cm cm h h Y a G.38h a b () y = h.38h G b h X () S() = π() a,b, h,π V = ρ M = ρv G = M h S() 3 d a,b, h 4 G = 5 h a b a b = 6 ω() s v m θ() m v () θ() ω() dθ()
More information+ 1 ( ) I IA i i i 1 n m a 11 a 1j a 1m A = a i1 a ij a im a n1 a nj a nm.....
+ http://krishnathphysaitama-uacjp/joe/matrix/matrixpdf 1 ( ) I IA i i i 1 n m a 11 a 1j a 1m A = a i1 a ij a im a n1 a nj a nm (1) n m () (n, m) ( ) n m B = ( ) 3 2 4 1 (2) 2 2 ( ) (2, 2) ( ) C = ( 46
More informationMicrosoft Word - 演習5_蒸発装置
1-4) q T sat T w T S E D.N.B.(Departure from Nucleate Boiling)h(=q/ T sat ) F q B q D 3) 1 5-7) 4) T W [K] T B [K]T BPR [K] B.P.E.Boiling Pressure Rising T B T W T BPR (3.1) Dühring T BPR T W T W T B T
More information20 9 19 1 3 11 1 3 111 3 112 1 4 12 6 121 6 122 7 13 7 131 8 132 10 133 10 134 12 14 13 141 13 142 13 143 15 144 16 145 17 15 19 151 1 19 152 20 2 21 21 21 211 21 212 1 23 213 1 23 214 25 215 31 22 33
More informationn ξ n,i, i = 1,, n S n ξ n,i n 0 R 1,.. σ 1 σ i .10.14.15 0 1 0 1 1 3.14 3.18 3.19 3.14 3.14,. ii 1 1 1.1..................................... 1 1............................... 3 1.3.........................
More information46 4 E E E E E 0 0 E E = E E E = ) E =0 2) φ = 3) ρ =0 1) 0 2) E φ E = grad φ E =0 P P φ = E ds 0
4 4.1 conductor E E E 4.1: 45 46 4 E E E E E 0 0 E E = E E E =0 4.1.1 1) E =0 2) φ = 3) ρ =0 1) 0 2) E φ E = grad φ E =0 P P φ = E ds 0 4.1 47 0 0 3) ε 0 div E = ρ E =0 ρ =0 0 0 a Q Q/4πa 2 ) r E r 0 Gauss
More information1 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)
More information閨75, 縺5 [ ィ チ573, 縺 ィ ィ
39ィ 8 998 3. 753 68, 7 86 タ7 9 9989769 438 縺48 縺55 3783645 タ5 縺473 タ7996495 ィ 59754 8554473 9 8984473 3553 7. 95457357, 4.3. 639745 5883597547 6755887 67996499 ィ 597545 4953473 9 857473 3553, 536583, 89573,
More information数学Ⅱ演習(足助・09夏)
II I 9/4/4 9/4/2 z C z z z z, z 2 z, w C zw z w 3 z, w C z + w z + w 4 t R t C t t t t t z z z 2 z C re z z + z z z, im z 2 2 3 z C e z + z + 2 z2 + 3! z3 + z!, I 4 x R e x cos x + sin x 2 z, w C e z+w
More informationMicrosoft Word - Œ{Ł¶.doc
17 59.0% 41.0% 60.8% 76.0%71.9% 65.3% 17 2.6% 3.5% 25.9% 57.3% 16.7% 28.1% 52.2% 11.1% 2.6% =270 18 2 (=199) 1 17 71.0% 76.0% 44.2% 71.9% 36.2% 18.1% 65.3% 16.7% 34.1% 16.3% 47.1% 14.9% 13.8% 5.0% 3.6%
More informationuntitled
c 2020 70 800 1. 1 1 1,600 1 [1, 2] 112 8551 1 13 27 taguchi@ise.chuo-u.ac.jp 1 1.1 17 [1] 14 30 1,600 1.2 [2] 54 37 2017 1 Copyright c by ORSJ. Unauthorized reproduction of this article is prohibited.
More information(1) (2) (3) (4) 1
8 3 4 3.................................... 3........................ 6.3 B [, ].......................... 8.4........................... 9........................................... 9.................................
More information(1) (2) (1) (2) 2 3 {a n } a 2 + a 4 + a a n S n S n = n = S n
. 99 () 0 0 0 () 0 00 0 350 300 () 5 0 () 3 {a n } a + a 4 + a 6 + + a 40 30 53 47 77 95 30 83 4 n S n S n = n = S n 303 9 k d 9 45 k =, d = 99 a d n a n d n a n = a + (n )d a n a n S n S n = n(a + a n
More informationJapan Research Review 1998年7月号
Japan Research Review 1998.7 Perspectives ****************************************************************************************** - 1 - Japan Research Review 1998.7-2 - Japan Research Review 1998.7-3
More information(2) ( 61129) 117, ,678 10,000 10,000 6 ( 7530) 149, ,218 10,000 10,000 7 ( 71129) 173, ,100 10,000 10,000 8 ( 8530) 14
16 8 26 MMF 23 25 5 16 7 16 16 8 1 7 16 8 2 16 8 26 16 8 26 P19 (1) 16630 16,999,809,767 55.69 4,499,723,571 14.74 9,024,172,452 29.56 30,523,705,790 100.00 1 (2) 16 6 30 1 1 5 ( 61129) 117,671 117,678
More information\\ \Data_in4\TeX\OR\63-7\07\or63_7_401.dvi
c CO 2 2 CO 2 CO 2 CO 2 IPCC 1. CO 2 2015 400 ppm CO 2 CO 2 2 2.5 16.2 8.2 [1] CO 2 305 0005 1 1 1 3F 1134 mamoru@sk.tsukuba.ac.jp 206 000 626 2 2 507 brother.hide10@gmail.com 305 005 1 1 1 IIIS 4F kojima.kazunori.ga@un.tsukuba.ac.jp
More informationuntitled
47 48 10 49 2005.6.1 17 500 50 1988 1994.1.1 16 22 51 18 1989 2005 17 2006 18 4 12 18 2007 19 1 12 2007 19 H18.8. J.H. 20 19 52 53 42.9 54 50 50 3080 55 30 100 56 57 22 96 6.8 9.4 31.44 58 10 780 250 59
More information情報理論 第5回 情報量とエントロピー
5 () ( ) ( ) ( ) p(a) a I(a) p(a) p(a) I(a) p(a) I(a) (2) (self information) p(a) = I(a) = 0 I(a) = 0 I(a) a I(a) = log 2 p(a) = log 2 p(a) bit 2 (log 2 ) (3) I(a) 7 6 5 4 3 2 0 0.5 p(a) p(a) = /2 I(a)
More information(Basics of Proability Theory). (Probability Spacees ad Radom Variables,, (Ω, F, P ),, X,. (Ω, F, P ) (probability space) Ω ( ω Ω ) F ( 2 Ω ) Ω σ (σ-fi
II (Basics of Probability Theory ad Radom Walks) (Preface),.,,,.,,,...,,.,.,,.,,. (Basics of Proability Theory). (Probability Spacees ad Radom Variables...............2, (Expectatios, Meas).............................
More informationCVaR
CVaR 20 4 24 3 24 1 31 ,.,.,. Markowitz,., (Value-at-Risk, VaR) (Conditional Value-at-Risk, CVaR). VaR, CVaR VaR. CVaR, CVaR. CVaR,,.,.,,,.,,. 1 5 2 VaR CVaR 6 2.1................................................
More information1 食品安全を主な目的とする取組
--a 2003 7 26 3. 3.1-1- 16 2 27 0227012-2-a 23 7 1 82 2 1 7 9 2 ( ) -2- -2-b 19 3 28 18 14701-2-c ) 15 5 2-3- 26 21 7 2 2 7 2 3 7 2 4 10 83 23 3 1 7 2 5 7 2 5-2-d -4- -5 - -3-a -6- -4-a -7- -4-b -8- -5-a
More information<4D F736F F D B B BB2D834A836F815B82D082C88C60202D B2E646F63>
例題で学ぶはじめての塑性力学 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/066721 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. http://www.morikita.co.jp/support/ 03 3817 5670 FAX 03 3815 8199 i 1
More informationn (1.6) i j=1 1 n a ij x j = b i (1.7) (1.7) (1.4) (1.5) (1.4) (1.7) u, v, w ε x, ε y, ε x, γ yz, γ zx, γ xy (1.8) ε x = u x ε y = v y ε z = w z γ yz
1 2 (a 1, a 2, a n ) (b 1, b 2, b n ) A (1.1) A = a 1 b 1 + a 2 b 2 + + a n b n (1.1) n A = a i b i (1.2) i=1 n i 1 n i=1 a i b i n i=1 A = a i b i (1.3) (1.3) (1.3) (1.1) (ummation convention) a 11 x
More information(2004 ) 2 (A) (B) (C) 3 (1987) (1988) Shimono and Tachibanaki(1985) (2008) , % 2 (1999) (2005) 3 (2005) (2006) (2008)
,, 23 4 30 (i) (ii) (i) (ii) Negishi (1960) 2010 (2010) ( ) ( ) (2010) E-mail:fujii@econ.kobe-u.ac.jp E-mail:082e527e@stu.kobe-u.ac.jp E-mail:iritani@econ.kobe-u.ac.jp 1 1 16 (2004 ) 2 (A) (B) (C) 3 (1987)
More information(Basics of Proability Theory). (Probability Spacees ad Radom Variables,, (Ω, F, P ),, X,. (Ω, F, P ) (probability space) Ω ( ω Ω ) F ( 2 Ω ) Ω σ (σ-fi
I (Basics of Probability Theory ad Radom Walks) 25 4 5 ( 4 ) (Preface),.,,,.,,,...,,.,.,,.,,. (,.) (Basics of Proability Theory). (Probability Spacees ad Radom Variables...............2, (Expectatios,
More information(2 X Poisso P (λ ϕ X (t = E[e itx ] = k= itk λk e k! e λ = (e it λ k e λ = e eitλ e λ = e λ(eit 1. k! k= 6.7 X N(, 1 ϕ X (t = e 1 2 t2 : Cauchy ϕ X (t
6 6.1 6.1 (1 Z ( X = e Z, Y = Im Z ( Z = X + iy, i = 1 (2 Z E[ e Z ] < E[ Im Z ] < Z E[Z] = E[e Z] + ie[im Z] 6.2 Z E[Z] E[ Z ] : E[ Z ] < e Z Z, Im Z Z E[Z] α = E[Z], Z = Z Z 1 {Z } E[Z] = α = α [ α ]
More informationRF_1
RF_1 10/04/16 10:32 http://rftechno.web.infoseek.co.jp/rf_1.html 1/12 RF_1 10/04/16 10:32 http://rftechno.web.infoseek.co.jp/rf_1.html 2/12 RF_1 10/04/16 10:32 http://rftechno.web.infoseek.co.jp/rf_1.html
More information13,825,228 3,707,995 26.8 4.9 25 3 8 9 1 50,000 0.29 1.59 70,000 0.29 1.74 12,500 0.39 1.69 12,500 0.55 10,000 20,000 0.13 1.58 30,000 0.00 1.26 5,000 0.13 1.58 25,000 40,000 0.13 1.58 50,000 0.00 1.26
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 information31 33
17 3 31 33 36 38 42 45 47 50 52 54 57 60 74 80 82 88 89 92 98 101 104 106 94 1 252 37 1 2 2 1 252 38 1 15 3 16 6 24 17 2 10 252 29 15 21 20 15 4 15 467,555 14 11 25 15 1 6 15 5 ( ) 41 2 634 640 1 5 252
More information(, Goo Ishikawa, Go-o Ishikawa) ( ) 1
(, Goo Ishikawa, Go-o Ishikawa) ( ) 1 ( ) ( ) ( ) G7( ) ( ) ( ) () ( ) BD = 1 DC CE EA AF FB 0 0 BD DC CE EA AF FB =1 ( ) 2 (geometry) ( ) ( ) 3 (?) (Topology) ( ) DNA ( ) 4 ( ) ( ) 5 ( ) H. 1 : 1+ 5 2
More information1 [ 1] (1) MKS? (2) MKS? [ 2] (1) (42.195k) k 2 (2) (3) k/hr [ 3] t = 0 10 ( 1 velocity [/s] 8 4 O
: 2014 4 10 1 2 2 3 2.1...................................... 3 2.2....................................... 4 2.3....................................... 4 2.4................................ 5 2.5 Free-Body
More information離散最適化基礎論 第 11回 組合せ最適化と半正定値計画法
11 okamotoy@uec.ac.jp 2019 1 25 2019 1 25 10:59 ( ) (11) 2019 1 25 1 / 38 1 (10/5) 2 (1) (10/12) 3 (2) (10/19) 4 (3) (10/26) (11/2) 5 (1) (11/9) 6 (11/16) 7 (11/23) (11/30) (12/7) ( ) (11) 2019 1 25 2
More information() 1 1 2 2 3 2 3 308,000 308,000 308,000 199,200 253,000 308,000 77,100 115,200 211,000 308,000 211,200 62,200 185,000 308,000 154,000 308,000 2 () 308,000 308,000 253,000 308,000 77,100 211,000 308,000
More information151021slide.dvi
: Mac I 1 ( 5 Windows (Mac Excel : Excel 2007 9 10 1 4 http://asakura.co.jp/ books/isbn/978-4-254-12172-8/ (1 1 9 1/29 (,,... (,,,... (,,, (3 3/29 (, (F7, Ctrl + i, (Shift +, Shift + Ctrl (, a i (, Enter,
More information10 4 2
1 10 4 2 92 11 3 8 20 10 2 10 20 10 28 3 B 78 111 104 1021 95 10 2 4 10 8 95 18 10 30 11 13 104 20 105 105 105 105 107 5 1 11 26 13301500 6 GH 1 GH 34 7 11 27 9301030 8 4 9 GH 1 23 10 20 60 --------------------------------------------------------------------------------------------------------------------------
More information2001 Miller-Rabin Rabin-Solovay-Strassen self-contained RSA RSA RSA ( ) Shor RSA RSA 1 Solovay-Strassen Miller-Rabin [3, pp
200 Miller-Rabin 2002 3 Rabin-Solovay-Strassen self-contained RSA RSA RSA ( ) Shor 996 2 RSA RSA Solovay-Strassen Miller-Rabin [3, pp. 8 84] Rabin-Solovay-Strassen 2 Miller-Rabin 3 4 Miller-Rabin 5 Miller-Rabin
More informationII 2 3.,, A(B + C) = AB + AC, (A + B)C = AC + BC. 4. m m A, m m B,, m m B, AB = BA, A,, I. 5. m m A, m n B, AB = B, A I E, 4 4 I, J, K
II. () 7 F 7 = { 0,, 2, 3, 4, 5, 6 }., F 7 a, b F 7, a b, F 7,. (a) a, b,,. (b) 7., 4 5 = 20 = 2 7 + 6, 4 5 = 6 F 7., F 7,., 0 a F 7, ab = F 7 b F 7. (2) 7, 6 F 6 = { 0,, 2, 3, 4, 5 },,., F 6., 0 0 a F
More information0226_ぱどMD表1-ol前
No. MEDIA DATA 0 B O O K 00-090-0 0 000900 000 00 00 00 0000 0900 000900 AREA MAP 0,000 0,000 0,000 0,000 00,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 00,000 0,000
More information( 28 ) ( ) ( ) 0 This note is c 2016, 2017 by Setsuo Taniguchi. It may be used for personal or classroom purposes, but not for commercial purp
( 28) ( ) ( 28 9 22 ) 0 This ote is c 2016, 2017 by Setsuo Taiguchi. It may be used for persoal or classroom purposes, but ot for commercial purposes. i (http://www.stat.go.jp/teacher/c2epi1.htm ) = statistics
More informationall.dvi
5,, Euclid.,..,... Euclid,.,.,, e i (i =,, ). 6 x a x e e e x.:,,. a,,. a a = a e + a e + a e = {e, e, e } a (.) = a i e i = a i e i (.) i= {a,a,a } T ( T ),.,,,,. (.),.,...,,. a 0 0 a = a 0 + a + a 0
More information1 1.1 [ 1] velocity [/s] 8 4 (1) MKS? (2) MKS? 1.2 [ 2] (1) (42.195k) k 2 (2) (3) k/hr [ 3] t = 0
: 2016 4 1 1 2 1.1......................................... 2 1.2................................... 2 2 2 2.1........................................ 2 2.2......................................... 3 2.3.........................................
More information...v..&.{..&....
8 pp.- 2006 * h ** *** An empirical analysis on the efficiency of the M&A among the non-financial companies based on DEA Hidetoshi KOKUBO*, Koichi MIYAZAKI**, Tomohiko TAKAHASHI*** Abstract Using DEA (Data
More informationlimit&derivative
- - 7 )................................................................................ 5.................................. 7.. e ).......................... 9 )..........................................
More informationuntitled
2010 58 1 39 59 c 2010 20 2009 11 30 2010 6 24 6 25 1 1953 12 2008 III 1. 5, 1961, 1970, 1975, 1982, 1992 12 2008 2008 226 0015 32 40 58 1 2010 III 2., 2009 3 #3.xx #3.1 #3.2 1 1953 2 1958 12 2008 1 2
More informationD = [a, b] [c, d] D ij P ij (ξ ij, η ij ) f S(f,, {P ij }) S(f,, {P ij }) = = k m i=1 j=1 m n f(ξ ij, η ij )(x i x i 1 )(y j y j 1 ) = i=1 j
6 6.. [, b] [, d] ij P ij ξ ij, η ij f Sf,, {P ij } Sf,, {P ij } k m i j m fξ ij, η ij i i j j i j i m i j k i i j j m i i j j k i i j j kb d {P ij } lim Sf,, {P ij} kb d f, k [, b] [, d] f, d kb d 6..
More informationa, b a bc c b a a b a a a a p > p p p 2, 3, 5, 7,, 3, 7, 9, 23, 29, 3, a > p a p [ ] a bp, b p p cq, c, q, < q < p a bp bcq q a <
22 9 8 5 22 9 29 0 2 2 5 2.............................. 5 2.2.................................. 6 2.3.............................. 8 3 8 4 9 4............................. 9 4.2 S(, a)..............................
More information20 6 4 1 4 1.1 1.................................... 4 1.1.1.................................... 4 1.1.2 1................................ 5 1.2................................... 7 1.2.1....................................
More information(H8) 1,412 (H9) 40,007 (H15) 30,103 851
(H8) 1,412 (H9) 40,007 (H15) 30,103 851 (H3) 1,466 (H3) 9,862 (H15) 4,450 704 9,795 1,677 18,488 402 44,175 3,824 8,592 853 7,635 1,695 2,202 179 5,127 841 27,631 452 35,173 177 123,797 186 45,727 1,735
More information22 25 34 44 10 12 14 15 11 12 16 18 19 20 21 11 12 22 10 23 24 12 25 11 12 2611 27 11 28 10 12 29 10 30 10 31 32 10 11 12 33 10 11 12 34
22 25 34 44 10 12 14 15 11 12 16 18 19 20 21 11 12 22 10 23 24 12 25 11 12 2611 27 11 28 10 12 29 10 30 10 31 32 10 11 12 33 10 11 12 34 35 10 12 36 10 12 37 10 38 10 11 12 39 10 11 12 40 11 12 41 10 11
More information- 1 - - 2 - - 3 - - 4 - H19 H18-5 - H19.7H20.3 8,629 11,600-6 - - 7 - - 8 - - 9 - H20.7 20 / - 10 - - 11 - 1 8,000 16,000 4,000 2 50 12 80-12 - 20 3040 50 18a 19a - 13 - - 14 - 1,000-15 - 3,000 4,500 560
More information17 12 12 13301515 2F1 P2 1 22 P19 160
136 17 12 12 13301515 2F1 P2 1 22 P19 160 161 15 87 15 P5 26 4 162 10 3 60 1/3 3 1 163 137 138 139 % %.%. (. ) ( ) 48 32 13 40 43 30 42 50 13 99 140 39 12 12 42 55 35 6 79 2004 16 17 39 37 53 13 1 1.2
More informationタ 縺29135 タ 縺5 [ y 1 x i R 8 x j 1 7,5 2 x , チ7192, (2) チ41299 f 675
139ィ 48 1995 3. 753 165, 2 6 86 タ7 9 998917619 4381 縺48 縺55 317832645 タ5 縺4273 971927, 95652539358195 45 チ5197 9 4527259495 2 7545953471 129175253471 9557991 3.9. タ52917652 縺1874ィ 989 95652539358195 45
More informationX G P G (X) G BG [X, BG] S 2 2 2 S 2 2 S 2 = { (x 1, x 2, x 3 ) R 3 x 2 1 + x 2 2 + x 2 3 = 1 } R 3 S 2 S 2 v x S 2 x x v(x) T x S 2 T x S 2 S 2 x T x S 2 = { ξ R 3 x ξ } R 3 T x S 2 S 2 x x T x S 2
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 information2 (2016 3Q N) c = o (11) Ax = b A x = c A n I n n n 2n (A I n ) (I n X) A A X A n A A A (1) (2) c 0 c (3) c A A i j n 1 ( 1) i+j A (i, j) A (i, j) ã i
[ ] (2016 3Q N) a 11 a 1n m n A A = a m1 a mn A a 1 A A = a n (1) A (a i a j, i j ) (2) A (a i ca i, c 0, i ) (3) A (a i a i + ca j, j i, i ) A 1 A 11 0 A 12 0 0 A 1k 0 1 A 22 0 0 A 2k 0 1 0 A 3k 1 A rk
More information1 8, : 8.1 1, 2 z = ax + by + c ax by + z c = a b +1 x y z c = 0, (0, 0, c), n = ( a, b, 1). f = n i=1 a ii x 2 i + i<j 2a ij x i x j = ( x, A x), f =
1 8, : 8.1 1, z = ax + by + c ax by + z c = a b +1 x y z c = 0, (0, 0, c), n = ( a, b, 1). f = a ii x i + i
More informationnewmain.dvi
数論 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/008142 このサンプルページの内容は, 第 2 版 1 刷発行当時のものです. Daniel DUVERNEY: THÉORIE DES NOMBRES c Dunod, Paris, 1998, This book is published
More informationuntitled
1 5,000 Copyright 1 5,000...4...4...4...4...4...4...4...5...5...5...5...5...5...6...6...6...7...8...9...9...9...10...12...12...12...12...13...14...14...14...15...15...15 Copyright 1 5,000...16...16...16...16...17...18
More informationtomo_sp1
1....2 2....7...14 4....18 5....24 6....30 7....37 8....44...50 http://blog.garenet.com/tomo/ Copyright 2008 1. Copyright 2008 Copyright 2008 Copyright 2008 Copyright 2008 Copyright 2008 Copyright 2008
More informationuntitled
Copyright 1 Copyright 2 Copyright 3 Copyright 4 Copyright 5 Copyright 6 Copyright 7 Copyright 8 Copyright 9 Copyright 10 Copyright 11 Copyright 12 Copyright 13 Copyright 14 Copyright 15 Copyright 16 Copyright
More informationuntitled
Copyright 3269 34 Copyright 3269 34 Copyright 3269 34 Copyright 3269 34 Copyright 3269 34 Copyright 3269 34 Copyright 3269 34 Copyright 3269 34 Copyright 3269 34 Copyright 3269 34 Copyright 3269 34 Copyright
More information