The University of Tokyo, Institute of Industrial Science (Information & System Division, Electrical Control System Engeneering) Ce-501, Komaba,
|
|
- はすな たかにし
- 4 years ago
- Views:
Transcription
1 The University of Tokyo, Institute of Industril Science (Informtion & System Division, Electricl Control System Engeneering) Ce-5, 4-6- Komb, Meguro-ku,Tokyo Jpn Phone: , Fx: E-Mil: [] A. ms,, ms, B.,,,,, C.,, [3] 8 [4],,,, ABS TCS,,,,,,,.2,, 2, 3, µ µ,,, 4,, 5 µ,, µ 6,, 6
2 µ,,,, 3, DC Wounded Motor Current Sensor motor velocity (8ppr) ccelertion commnd PC98 note motor current output commnd Qudrnt Chopper Counter Bord A/D, D/A converters Bttery rer tire velocity (2ppr) ,,, 2,,,, Conversion Bse Nissn Mrch (Micr) size [mm] weight [kg](btteries included) Motor Advnced D.C. Motors, Inc. type DC series wound rted power 9[kW](hr.), 32[kW](5min.) size/weight φ 232,length 397[mm], 65[kg] Controller Curtis Instruments, Inc. type MOSFET PWM Chopper opertion frequency 5[kHz] rted voltge/current 2[V]/4[A] Bttery Jpn Storge Bttery Co.,Ltd. GTX-3E4L type led cid voltge/cpcity 2[V]/92[Ah](5hr.) weight 27.5[kg] CPU PC98NS/T(i386SL, 2MHz) weight 3.2[kg] A/D nd D/A converters 2bit, 8ch/2bit, 2ch 2, [5][4][5], 4 F d F s, 2 F d, F s µ, N () F d = µ(λ)n () 2., 5 λ, 2
3 F d 4. N F s 3, λ opt, 6, V w >V [2][6],, V M w M V w F m N F d 5., V, V w (2) λ = V w V mx(v,v w ) (2) 2, 2, µ mx,, µ mx µ mx λ opt,,.5.2 λ λ opt, λ >λ opt, λ, λ = F s = µ(λ)n =,, µ-λ 6. 6, dv w M w = F m F d (3) dt M dv dt = F d (4), M w, F m, F d, M, (3), (4), F m F d F d = M w M dv w dv (5), V w >V, (2) dv w dv = V w V = λ (6) (5), () µ ( ) µ = F M w m M + M w M N + M w + M w M M λ (7), F m λ, F m /N, +M w /M, 3
4 (c) F m µ 4. Friction Coefficient µ B A C (b) () D E I Fd KN KN Q r Q J Js Jns ω Jn λ r ˆFd 7. 8., µ-λ,, 2, 7 (),(b),(c), F m () (b) (c) µ-λ, () (b) (c) (b) () A B E D A 2 (b) (c) (c) (b) E D,, λ opt,,, 4, µ (), F d N, [][][2],,,, DC, AC (3), (8) F d = dω (T J r dt ) (8), r, T, J, ω, F m = T/r, M w = J/r 2 (8), 8 ˆF d, 8 K, N, J n,,,,, 8 Q Low Pss Filter (LPF) 4.2, M (9) F d = M dv dt (9) 4
5 5 4 Current(A) Driving Force(N) 3 2 Velocities(m/s) Time(s) () Driving Wheel Chssis Time(s) (b) 9. Driving Force(N) Observed Reference Time(s).,, () {.5I 2 I<2 T =.252I.4 () I>2 9 Q, () Low Pss Filter Q = ( + τs) 2 (), τ =.5[s].2.4., 3[s], 9(b), 9(b),, 5 µ Friction Coefficient dµ dλ µ 2 µ-λ µ,, µ [7][8], µ 2 5. µ µ (2) 5
6 .5, (3) (6).45.4 λ(t) = α µ(t) β α = C µ(t)+c 2 (6) Texture mesure gmm s.2.5. s s s ii i i i i g g g g g g i (6) λ µ (6) β/α α, β, /α (6) slip slope k 3. µ ()sphlt, (s)snow, (i)ice, (g)grvel α = dµ dλ = dµ(t)/dt dλ(t)/dt (2) (3) µ(t) =αλ(t)+β (3) F. Gustfsson (3), [7], µ, λ = µ 3 λ = µ, λ = µ, λ = µ 3, µ α, ω v, r (4), (5),, (5) Vr e = ω v/r (4) γ =4Vr(e) (5) 3, γ α γ >.27 γ <.27, α>3 γ <.27, α<3, γ, α, µ Estimted slip slope Time [s] 4. µ 5.2 µ (8),, 2 µ µ,,, µ µ (7), (8) µ [][][2][3] A = dµ dλ = dµ/dt dλ/dt dµ dt = Adλ dt (7) (8),, κ, (9) (2) (2) y[k] =ˆθ T [k]φ[k] (9) P [k ]φ[k] ˆθ[k] =ˆθ[k ] +φ T [k]p [k ]φ[k] (ˆθ[k ]φ[k] y[k]) (2) 6
7 P [k] = [P [k ] κ P [k ]φ[k]φt [k]p [k ] ] (2) +φ T [k]p [k ]φ[k], κ γ =trp [k], κ (22) κ = +γ φ[k] 2 (22), (8) (9) (23) (25) µ φ[k] = dλ dt (23) y[k] = dµ dt (24) ˆθ[k] =Â (25),,.6, γ (22), φ[k],,,, µ 5 6() κ =.98, (b) γ =. 6(), t = 2[ms], 5(),,, λ µ,, 6(b) (), dλ/dt = κ = () Estimted Vlue of A 2 Forgetting Fctor.98" Driving Force[N] 5 Driving Force (b) Estimted Vlue of A () (κ =.98) gmm= (b) (γ =.) µ () 7
8 Driving Force[N] Estimted Vlue of A Estimted Vlue of A Slip rtio () Driving Force (b) Forgetting Fctor= () (κ =.98) gmm= (b) (γ =.) 8. µ,, dλ/dt, κ< 9,,, Forgetting Fctor gmm= (γ =.),,,, 2,, κ γ, 6 µ 6. λ = µ µ mx µ mx, [9][] [] 6.., 2 2 2, 22,, p l, p m (26), F z w (27) p =4p m l ( l ) (26) 8
9 N = 2 3 p mwl (27) 23, σ (), k x (28), σ (s) µ mx, (29) µ mx p Sliding Are Adhesive Are l σ () = k x λ (28) σ (s) = µ mx p (29) 23. = (3) Rod Friction Coefficient Ground Contct Are Adhesive Are Sliding Are σ () = σ (s) (3) (28), (29) (3), S n (3) S n = l = C sλ 3µ mx N (3), C s, (32) 2. C s = 2 wk xl 2 (32) T F d l Adhesive Are Sliding Are 2. p p m w l 22. =,, F d (33) F d = = l σ wd σ () wd + l σ (s) wd = C s λl 2 n + µ mx N( 3l 2 n +2l 3 n) =3µ mx NS n ( S n + S2 n 3 ) (33), (3) S n, F d (34) F d = µn = C s λ (C sλ) 2 3µ mx N + (C sλ) 3 27(µ mx N) 2 (34) (34) µ mx N 2,, µ mx N (35) µ mx N = 3(C sλ) 2 + 3(C s λ) 3 (4F d C s λ) 8(C s λ F d ) (35) (35), F d, λ, C s 9
10 ,,, (32) C s, C s C s 2,, (36), λ = C s C s = df d dλ (36) λ=, 5 µ,,, S n = (3), (37) C s = 3µ mxn λ opt (37) 2 C s 6..2,, (35) µ-λ, [] 5.2 µ,,,,, y[k] =ˆθ T [k]φ[k], (38) (4), 5.2 (2), (2) y[k] =3(C s λ) 2 + 3(C s λ) 3 (4F d C s λ) (38) ˆθ[k] =µ x W (39) φ[k] = 8(C s λ F d ) (4) λ (4) φ[k],, λ, φ[k],, Driving Force(N) () (b) 24. Driving Force(N) Mx Driving Force
11 , C s,, (37), (4) C s =7 4 [kn] (4),,,, C s, (4) Vehicle Direction Driving Force(N) Driving Force Mx sphlt () wetplte -.2 sphlt wet plte 6[m] x[m] Wet Plte Asphlt (b) 28. Driving Force(N) Driving Force Mx 2 3 () (b) , 2[s], γ = 25, Low Pss Filter 4.2 (), τ =.4[s],,,, µ, 26,,, 27 27, [s],.6[s], 2[N],,.6[s],
12 , 2[N],,, 26 3[m] 28 27,,, , r (42) r = ˆF d µ mx N (42) (42) ˆF d µ mx N (42),, r, r 6..3, ,, (),,, LED Driving Force(N) Adhesion Rte Mx Driving Force sphlt wet plte () sphlt wet plte (b) λ opt, µ A Â,,,,, [4][5] µ-λ µ-λ λ opt µ, 3 A, A 2 µ, 2
13 , µ-λ, µ mx µ mx, 6., µ mx,, λ µ, ASPHALT, GRAVEL, SNOW, ICE 4, µ mx, 4, µ λ 3, ASPHALT = ^ λ µ λ µ Â λ, 4 λ opt, ˆλ opt (43), K A K I ASPHALT ICE, ˆλ opta ˆλ opti λ = Smll µ ICE SNOW GRAVEL ASPHALT m 35. λ =Middle-Smll, Middle-Big, Big µ ˆλ opt = K Aˆλ opta +K GˆλoptG +K S ˆλoptS +K I ˆλoptI K A + K G + K S + K I (43) λ 33, µ λ =MS MB B µ 34 35, λ =SS µ-λ 36. L 3
14 Â, µ A λ λ opt. λ  (44) L, 36, 3 L λ opt /λ 2 L = log  (44) 2. L λ opt /λ Rod Conditon L(=log Â) PB PS ZO NS NB ASPHALT GRAVEL SNOW ICE , Â, λ opt, (45) ˆλ opt if A = Negtive then ˆλ opt [k+] =.9ˆλ opt [k](45), , 37 λ opt λ, 38 λ opt, ˆλ opt ˆλ opt ,, µ-λ (46) Mgic-Formul 39. µ = C sin(d rctn(eλ)) (46) 3, λ opt µ mx 5 µ-λ 4, 5[s], λ. < Â<.5, λ µ Low Pss Filter 4. µ-λ 4
15 4 43 4, 42, 2[s] λ λ opt, λ opt 43 2[s], 42, 43 44, 45 [s] F d,, [s] 4. A A A B B A,,,
16 ABS,,.6.4 Adhesion.2 A Skid B Adhesion Time[s] () Grdient of F d -F m curve g Theoreticl F d / Fm for dhesive wheel. Experiment Skid Simultion Time [s] (b) 46. C [] Y.Hori, Future Vehicle driven by Electricity nd Control -Reserch on 4 Wheel Motored UOT Mrch II, Proc. of AMC 22, invited pper, pp.-4, Mribor, Sloveni, 22. [2] Y.Hori, Y.Toyod nd Y.Tsuruok, Trction Control of Electric Vehicle -Bsic Experimentl Results using the Test EV UOT Electric Mrch, IEEE Trns. on Industry Applictions, 34, 5, pp.3-38, 998. [3] S. Ski, H. Sdo nd Y. Hori, Motion Control in n Electric Vehicle with Four Independently Driven In-Wheel Motors, IEEE Trns. on Mechtronics, 4,, pp.9-6, 999. [4] H. Shimizu, K. Kwkmi, Y. Kkizki, S. Mtsugur nd M. Ohnishi, KAZ The super electric vehicle, Proc. of EVS8, Berlin, 2. [5], ABS,, 995. [6],,,, D, Vol.8-D, No., pp [7] F. Gustfsson, Slip-bsed Tire-Rod Friction Estimtion, IFAC Automtic, Vol. 33, No. 6, pp.87-99, 997. [8] M. Sugi, H. Ymguchi, M. Miyshit, T. Umeno nd K. Asno, New Control Technique for Mximizing Breking Force on Antilock Breking System, Proc. AVEC 98, pp , 998. [9],,, Vol. 5, No., pp.58-62, 997. [],,,,, Vol.2, pp.87-9, 999. [],,,, 5, pp.45-46, 999. [2] H. Sdo, S. Ski nd Y. Hori, Rod Condition Estimtion for Trction Control in Electric Vehicle, in Proc. of the 999 IEEE Interntionl Symposium on Industril Electronics, Bled. Sloveni, 99TH8465,Vol.2, pp , 999. [3],,,, 4, pp.93-94, 998. [4],,,,, D, Vol.2-D, No.4, pp , 2. [5],,,,,, pp.43-44, 999. [6],,,, D, Vol.2-D, No.2, pp , 2. 6
.. F x) = x ft)dt ), fx) : PDF : probbility density function) F x) : CDF : cumultive distribution function F x) x.2 ) T = µ p), T : ) p : x p p = F x
203 7......................................2................................................3.....................................4 L.................................... 2.5.................................
More informationProposal of Driving Torque Control Method for Electric Vehicle with In-Wheel Motors Masataka Yoshimura (Yokohama National University) Hiroshi Fujimoto
Propoal of Control Method for Electric ehicle with In-Wheel Motor Maataka Yohimura (Yokohama National Univerity) Hirohi Fujimoto (The Univerity of Tokyo) Abtract The anti-lip control or the lip ratio control
More information.. ( )T p T = p p = T () T x T N P (X < x T ) N = ( T ) N (2) ) N ( P (X x T ) N = T (3) T N P T N P 0
20 5 8..................................................2.....................................3 L.....................................4................................. 2 2. 3 2. (N ).........................................
More informationN cos s s cos ψ e e e e 3 3 e e 3 e 3 e
3 3 5 5 5 3 3 7 5 33 5 33 9 5 8 > e > f U f U u u > u ue u e u ue u ue u e u e u u e u u e u N cos s s cos ψ e e e e 3 3 e e 3 e 3 e 3 > A A > A E A f A A f A [ ] f A A e > > A e[ ] > f A E A < < f ; >
More informationLLG-R8.Nisus.pdf
d M d t = γ M H + α M d M d t M γ [ 1/ ( Oe sec) ] α γ γ = gµ B h g g µ B h / π γ g = γ = 1.76 10 [ 7 1/ ( Oe sec) ] α α = λ γ λ λ λ α γ α α H α = γ H ω ω H α α H K K H K / M 1 1 > 0 α 1 M > 0 γ α γ =
More informationnews
ETL NEWS 1999.9 ETL NEWS 1999.11 Establishment of an Evaluation Technique for Laser Pulse Timing Fluctuations Optoelectronics Division Hidemi Tsuchida e-mail:tsuchida@etl.go.jp A new technique has been
More information,, 2. Matlab Simulink 2018 PC Matlab Scilab 2
(2018 ) ( -1) TA Email : ohki@i.kyoto-u.ac.jp, ske.ta@bode.amp.i.kyoto-u.ac.jp : 411 : 10 308 1 1 2 2 2.1............................................ 2 2.2..................................................
More information鉄鋼協会プレゼン
NN :~:, 8 Nov., Adaptive H Control for Linear Slider with Friction Compensation positioning mechanism moving table stand manipulator Point to Point Control [G] Continuous Path Control ground Fig. Positoining
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(1.2) T D = 0 T = D = 30 kn 1.2 (1.4) 2F W = 0 F = W/2 = 300 kn/2 = 150 kn 1.3 (1.9) R = W 1 + W 2 = = 1100 N. (1.9) W 2 b W 1 a = 0
1 1 1.1 1.) T D = T = D = kn 1. 1.4) F W = F = W/ = kn/ = 15 kn 1. 1.9) R = W 1 + W = 6 + 5 = 11 N. 1.9) W b W 1 a = a = W /W 1 )b = 5/6) = 5 cm 1.4 AB AC P 1, P x, y x, y y x 1.4.) P sin 6 + P 1 sin 45
More information1 (Berry,1975) 2-6 p (S πr 2 )p πr 2 p 2πRγ p p = 2γ R (2.5).1-1 : : : : ( ).2 α, β α, β () X S = X X α X β (.1) 1 2
2005 9/8-11 2 2.2 ( 2-5) γ ( ) γ cos θ 2πr πρhr 2 g h = 2γ cos θ ρgr (2.1) γ = ρgrh (2.2) 2 cos θ θ cos θ = 1 (2.2) γ = 1 ρgrh (2.) 2 2. p p ρgh p ( ) p p = p ρgh (2.) h p p = 2γ r 1 1 (Berry,1975) 2-6
More information概況
2 4 6 2 2 2 3 2 4 22 5 23 27 34 37 44 45 46 2 78.67 85.77 2.6. 7. 2 2, 65 85,464 93,8 65 85.5 93.2 8 56.2 77.9 2 8.87 88.8 3 () 65 3 6 2 2 2 2 2 22 3 2 2 2 2 2 2 2 2 28.58 28.74 29.9 8.8 8.84 2.63 65 28.3
More informationIIC Proposal of Range Extension Control System by Drive and Regeneration Distribution Based on Efficiency Characteristic of Motors for Electric
IIC-1-19 Proposal of Range Extension Control System by Drive and Regeneration Distribution Based on Efficiency Characteristic of Motors for Electric Vehicle Toru Suzuki, Hiroshi Fujimoto (Yokohama National
More information= hυ = h c λ υ λ (ev) = 1240 λ W=NE = Nhc λ W= N 2 10-16 λ / / Φe = dqe dt J/s Φ = km Φe(λ)v(λ)dλ THBV3_0101JA Qe = Φedt (W s) Q = Φdt lm s Ee = dφe ds E = dφ ds Φ Φ THBV3_0102JA Me = dφe ds M = dφ ds
More information第86回日本感染症学会総会学術集会後抄録(I)
κ κ κ κ κ κ μ μ β β β γ α α β β γ α β α α α γ α β β γ μ β β μ μ α ββ β β β β β β β β β β β β β β β β β β γ β μ μ μ μμ μ μ μ μ β β μ μ μ μ μ μ μ μ μ μ μ μ μ μ β
More informationuntitled
1 SS 2 2 (DS) 3 2.1 DS................................ 3 2.2 DS................................ 4 2.3.................................. 4 2.4 (channel papacity)............................ 6 2.5........................................
More information2011de.dvi
211 ( 4 2 1. 3 1.1............................... 3 1.2 1- -......................... 13 1.3 2-1 -................... 19 1.4 3- -......................... 29 2. 37 2.1................................ 37
More information24 I ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x
24 I 1.1.. ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x 1 (t), x 2 (t),, x n (t)) ( ) ( ), γ : (i) x 1 (t),
More informationGauss Gauss ɛ 0 E ds = Q (1) xy σ (x, y, z) (2) a ρ(x, y, z) = x 2 + y 2 (r, θ, φ) (1) xy A Gauss ɛ 0 E ds = ɛ 0 EA Q = ρa ɛ 0 EA = ρea E = (ρ/ɛ 0 )e
7 -a 7 -a February 4, 2007 1. 2. 3. 4. 1. 2. 3. 1 Gauss Gauss ɛ 0 E ds = Q (1) xy σ (x, y, z) (2) a ρ(x, y, z) = x 2 + y 2 (r, θ, φ) (1) xy A Gauss ɛ 0 E ds = ɛ 0 EA Q = ρa ɛ 0 EA = ρea E = (ρ/ɛ 0 )e z
More information64 3 g=9.85 m/s 2 g=9.791 m/s 2 36, km ( ) 1 () 2 () m/s : : a) b) kg/m kg/m k
63 3 Section 3.1 g 3.1 3.1: : 64 3 g=9.85 m/s 2 g=9.791 m/s 2 36, km ( ) 1 () 2 () 3 9.8 m/s 2 3.2 3.2: : a) b) 5 15 4 1 1. 1 3 14. 1 3 kg/m 3 2 3.3 1 3 5.8 1 3 kg/m 3 3 2.65 1 3 kg/m 3 4 6 m 3.1. 65 5
More information9 1. (Ti:Al 2 O 3 ) (DCM) (Cr:Al 2 O 3 ) (Cr:BeAl 2 O 4 ) Ĥ0 ψ n (r) ω n Schrödinger Ĥ 0 ψ n (r) = ω n ψ n (r), (1) ω i ψ (r, t) = [Ĥ0 + Ĥint (
9 1. (Ti:Al 2 O 3 ) (DCM) (Cr:Al 2 O 3 ) (Cr:BeAl 2 O 4 ) 2. 2.1 Ĥ ψ n (r) ω n Schrödinger Ĥ ψ n (r) = ω n ψ n (r), (1) ω i ψ (r, t) = [Ĥ + Ĥint (t)] ψ (r, t), (2) Ĥ int (t) = eˆxe cos ωt ˆdE cos ωt, (3)
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_0212_68<5A66><4EBA><79D1>_<6821><4E86><FF08><30C8><30F3><30DC><306A><3057><FF09>.pdf
More information
Note.tex 2008/09/19( )
1 20 9 19 2 1 5 1.1........................ 5 1.2............................. 8 2 9 2.1............................. 9 2.2.............................. 10 3 13 3.1.............................. 13 3.2..................................
More information1 12 ( )150 ( ( ) ) x M x 0 1 M 2 5x 2 + 4x + 3 x 2 1 M x M 2 1 M x (x + 1) 2 (1) x 2 + x + 1 M (2) 1 3 M (3) x 4 +
( )5 ( ( ) ) 4 6 7 9 M M 5 + 4 + M + M M + ( + ) () + + M () M () 4 + + M a b y = a + b a > () a b () y V a () V a b V n f() = n k= k k () < f() = log( ) t dt log () n+ (i) dt t (n + ) (ii) < t dt n+ n
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 information5 1.2, 2, d a V a = M (1.2.1), M, a,,,,, Ω, V a V, V a = V + Ω r. (1.2.2), r i 1, i 2, i 3, i 1, i 2, i 3, A 2, A = 3 A n i n = n=1 da = 3 = n=1 3 n=1
4 1 1.1 ( ) 5 1.2, 2, d a V a = M (1.2.1), M, a,,,,, Ω, V a V, V a = V + Ω r. (1.2.2), r i 1, i 2, i 3, i 1, i 2, i 3, A 2, A = 3 A n i n = n=1 da = 3 = n=1 3 n=1 da n i n da n i n + 3 A ni n n=1 3 n=1
More information医系の統計入門第 2 版 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 第 2 版 1 刷発行時のものです.
医系の統計入門第 2 版 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/009192 このサンプルページの内容は, 第 2 版 1 刷発行時のものです. i 2 t 1. 2. 3 2 3. 6 4. 7 5. n 2 ν 6. 2 7. 2003 ii 2 2013 10 iii 1987
More information201711grade1ouyou.pdf
2017 11 26 1 2 52 3 12 13 22 23 32 33 42 3 5 3 4 90 5 6 A 1 2 Web Web 3 4 1 2... 5 6 7 7 44 8 9 1 2 3 1 p p >2 2 A 1 2 0.6 0.4 0.52... (a) 0.6 0.4...... B 1 2 0.8-0.2 0.52..... (b) 0.6 0.52.... 1 A B 2
More informationpp d 2 * Hz Hz 3 10 db Wind-induced noise, Noise reduction, Microphone array, Beamforming 1
72 12 2016 pp. 739 748 739 43.60.+d 2 * 1 2 2 3 2 125 Hz 0.3 0.8 2 125 Hz 3 10 db Wind-induced noise, Noise reduction, Microphone array, Beamforming 1. 1.1 PSS [1] [2 4] 2 Wind-induced noise reduction
More information: 2005 ( ρ t +dv j =0 r m m r = e E( r +e r B( r T 208 T = d E j 207 ρ t = = = e t δ( r r (t e r r δ( r r (t e r ( r δ( r r (t dv j =
72 Maxwell. Maxwell e r ( =,,N Maxwell rot E + B t = 0 rot H D t = j dv D = ρ dv B = 0 D = ɛ 0 E H = μ 0 B ρ( r = j( r = N e δ( r r = N e r δ( r r = : 2005 ( 2006.8.22 73 207 ρ t +dv j =0 r m m r = e E(
More informationuntitled
( 9:: 3:6: (k 3 45 k F m tan 45 k 45 k F m tan S S F m tan( 6.8k tan k F m ( + k tan 373 S S + Σ Σ 3 + Σ os( sin( + Σ sin( os( + sin( os( p z ( γ z + K pzdz γ + K γ K + γ + 9 ( 9 (+ sin( sin { 9 ( } 4
More informationH.Haken Synergetics 2nd (1978)
27 3 27 ) Ising Landau Synergetics Fokker-Planck F-P Landau F-P Gizburg-Landau G-L G-L Bénard/ Hopfield H.Haken Synergetics 2nd (1978) (1) Ising m T T C 1: m h Hamiltonian H = J ij S i S j h i S
More information1 (1) () (3) I 0 3 I I d θ = L () dt θ L L θ I d θ = L = κθ (3) dt κ T I T = π κ (4) T I κ κ κ L l a θ L r δr δl L θ ϕ ϕ = rθ (5) l
1 1 ϕ ϕ ϕ S F F = ϕ (1) S 1: F 1 1 (1) () (3) I 0 3 I I d θ = L () dt θ L L θ I d θ = L = κθ (3) dt κ T I T = π κ (4) T I κ κ κ L l a θ L r δr δl L θ ϕ ϕ = rθ (5) l : l r δr θ πrδr δf (1) (5) δf = ϕ πrδr
More informationO x y z O ( O ) O (O ) 3 x y z O O x v t = t = 0 ( 1 ) O t = 0 c t r = ct P (x, y, z) r 2 = x 2 + y 2 + z 2 (t, x, y, z) (ct) 2 x 2 y 2 z 2 = 0
9 O y O ( O ) O (O ) 3 y O O v t = t = 0 ( ) O t = 0 t r = t P (, y, ) r = + y + (t,, y, ) (t) y = 0 () ( )O O t (t ) y = 0 () (t) y = (t ) y = 0 (3) O O v O O v O O O y y O O v P(, y,, t) t (, y,, t )
More information1. z dr er r sinθ dϕ eϕ r dθ eθ dr θ dr dθ r x 0 ϕ r sinθ dϕ r sinθ dϕ y dr dr er r dθ eθ r sinθ dϕ eϕ 2. (r, θ, φ) 2 dr 1 h r dr 1 e r h θ dθ 1 e θ h
IB IIA 1 1 r, θ, φ 1 (r, θ, φ)., r, θ, φ 0 r
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 information29 1 6 1 1 1.1 1.1 1.1( ) 1.1( ) 1.1: 2 1.2 1.2( ) 4 4 1 2,3,4 1 2 1 2 1.2: 1,2,3,4 a 1 2a 6 2 2,3,4 1,2,3,4 1.2( ) 4 1.2( ) 3 1.2( ) 1.3 1.3 1.3: 4 1.4 1.4 1.4: 1.5 1.5 1 2 1 a a R = l a l 5 R = l a +
More informationma22-9 u ( v w) = u v w sin θê = v w sin θ u cos φ = = 2.3 ( a b) ( c d) = ( a c)( b d) ( a d)( b c) ( a b) ( c d) = (a 2 b 3 a 3 b 2 )(c 2 d 3 c 3 d
A 2. x F (t) =f sin ωt x(0) = ẋ(0) = 0 ω θ sin θ θ 3! θ3 v = f mω cos ωt x = f mω (t sin ωt) ω t 0 = f ( cos ωt) mω x ma2-2 t ω x f (t mω ω (ωt ) 6 (ωt)3 = f 6m ωt3 2.2 u ( v w) = v ( w u) = w ( u v) ma22-9
More informationδ ij δ ij ˆx ˆx ŷ ŷ ẑ ẑ 0, ˆx ŷ ŷ ˆx ẑ, ŷ ẑ ẑ ŷ ẑ, ẑ ˆx ˆx ẑ ŷ, a b a x ˆx + a y ŷ + a z ẑ b x ˆx + b
23 2 2.1 n n r x, y, z ˆx ŷ ẑ 1 a a x ˆx + a y ŷ + a z ẑ 2.1.1 3 a iˆx i. 2.1.2 i1 i j k e x e y e z 3 a b a i b i i 1, 2, 3 x y z ˆx i ˆx j δ ij, 2.1.3 n a b a i b i a i b i a x b x + a y b y + a z b
More informationy = x x R = 0. 9, R = σ $ = y x w = x y x x w = x y α ε = + β + x x x y α ε = + β + γ x + x x x x' = / x y' = y/ x y' =
y x = α + β + ε =,, ε V( ε) = E( ε ) = σ α $ $ β w ( 0) σ = w σ σ y α x ε = + β + w w w w ε / w ( w y x α β ) = α$ $ W = yw βwxw $β = W ( W) ( W)( W) w x x w x x y y = = x W y W x y x y xw = y W = w w
More informationx V x x V x, x V x = x + = x +(x+x )=(x +x)+x = +x = x x = x x = x =x =(+)x =x +x = x +x x = x ( )x = x =x =(+( ))x =x +( )x = x +( )x ( )x = x x x R
V (I) () (4) (II) () (4) V K vector space V vector K scalor K C K R (I) x, y V x + y V () (x + y)+z = x +(y + z) (2) x + y = y + x (3) V x V x + = x (4) x V x + x = x V x x (II) x V, α K αx V () (α + β)x
More informationI ( ) 2019
I ( ) 2019 i 1 I,, III,, 1,,,, III,,,, (1 ) (,,, ), :...,, : NHK... NHK, (YouTube ),!!, manaba http://pen.envr.tsukuba.ac.jp/lec/physics/,, Richard Feynman Lectures on Physics Addison-Wesley,,,, x χ,
More informationMD ,RM ,VT Aircraft Yaw-rate Suppression Method Using Driving Force Control by Electrically Driven Wheel for One-wheel Landing Tosh
MD-17-071,RM-17-054,VT-17-008 Aircraft Yaw-rate Suppression Method Using Driving Force Control by Electrically Driven Wheel for One-wheel Landing Toshiki Niinomi, Hiroshi Fujimoto, Yasumasa Watanabe (The
More information最新耐震構造解析 ( 第 3 版 ) サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 第 3 版 1 刷発行時のものです.
最新耐震構造解析 ( 第 3 版 ) サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/052093 このサンプルページの内容は, 第 3 版 1 刷発行時のものです. i 3 10 3 2000 2007 26 8 2 SI SI 20 1996 2000 SI 15 3 ii 1 56 6
More informationanalog-control-mod : 2007/2/4(8:44) 2 E8 P M () r e K P ( ) T I u K M T M K D E8.: DC PID K D E8. (E8.) P M () E8.2 K P D () ( T ) (E8.2) K M T M K, T
analog-control-mod : 2007/2/4(8:44) E8 E8. PID DC. PID 2. DC PID 3. E8.2 DC PID C8 E8. DC PID E6 DC P M () K M ( T M ) (E8.) DC PID C8 E8. r e u E8.2 PID E8. PID analog-control-mod : 2007/2/4(8:44) 2 E8
More informationCVMに基づくNi-Al合金の
CV N-A (-' by T.Koyama ennard-jones fcc α, β, γ, δ β α γ δ = or α, β. γ, δ α β γ ( αβγ w = = k k k ( αβγ w = ( αβγ ( αβγ w = w = ( αβγ w = ( αβγ w = ( αβγ w = ( αβγ w = ( αβγ w = ( βγδ w = = k k k ( αγδ
More information.5 z = a + b + c n.6 = a sin t y = b cos t dy d a e e b e + e c e e e + e 3 s36 3 a + y = a, b > b 3 s363.7 y = + 3 y = + 3 s364.8 cos a 3 s365.9 y =,
[ ] IC. r, θ r, θ π, y y = 3 3 = r cos θ r sin θ D D = {, y ; y }, y D r, θ ep y yddy D D 9 s96. d y dt + 3dy + y = cos t dt t = y = e π + e π +. t = π y =.9 s6.3 d y d + dy d + y = y =, dy d = 3 a, b
More informationAC Modeling and Control of AC Motors Seiji Kondo, Member 1. q q (1) PM (a) N d q Dept. of E&E, Nagaoka Unive
AC Moeling an Control of AC Motors Seiji Kono, Member 1. (1) PM 33 54 64. 1 11 1(a) N 94 188 163 1 Dept. of E&E, Nagaoka University of Technology 163 1, Kamitomioka-cho, Nagaoka, Niigata 94 188 (a) 巻数
More informationrenshumondai-kaito.dvi
3 1 13 14 1.1 1 44.5 39.5 49.5 2 0.10 2 0.10 54.5 49.5 59.5 5 0.25 7 0.35 64.5 59.5 69.5 8 0.40 15 0.75 74.5 69.5 79.5 3 0.15 18 0.90 84.5 79.5 89.5 2 0.10 20 1.00 20 1.00 2 1.2 1 16.5 20.5 12.5 2 0.10
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 information1 (1997) (1997) 1974:Q3 1994:Q3 (i) (ii) ( ) ( ) 1 (iii) ( ( 1999 ) ( ) ( ) 1 ( ) ( 1995,pp ) 1
1 (1997) (1997) 1974:Q3 1994:Q3 (i) (ii) ( ) ( ) 1 (iii) ( ( 1999 ) ( ) ( ) 1 ( ) ( 1995,pp.218 223 ) 1 2 ) (i) (ii) / (iii) ( ) (i ii) 1 2 1 ( ) 3 ( ) 2, 3 Dunning(1979) ( ) 1 2 ( ) ( ) ( ) (,p.218) (
More information.2 ρ dv dt = ρk grad p + 3 η grad (divv) + η 2 v.3 divh = 0, rote + c H t = 0 dive = ρ, H = 0, E = ρ, roth c E t = c ρv E + H c t = 0 H c E t = c ρv T
NHK 204 2 0 203 2 24 ( ) 7 00 7 50 203 2 25 ( ) 7 00 7 50 203 2 26 ( ) 7 00 7 50 203 2 27 ( ) 7 00 7 50 I. ( ν R n 2 ) m 2 n m, R = e 2 8πε 0 hca B =.09737 0 7 m ( ν = ) λ a B = 4πε 0ħ 2 m e e 2 = 5.2977
More information研修コーナー
l l l l l l l l l l l α α β l µ l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l
More information2 1) 2) 3) 4) 5) 6) Development of Second Generation Wireless In-Wheel Motor with Dynamic Wireless Power Transfer Hiroshi Fujimoto Takuma Takeuchi Kat
2 1) 2) 3) 4) 5) 6) Development of Second Generation Wireless In-Wheel Motor with Dynamic Wireless Power Transfer Hiroshi Fujimoto Takuma Takeuchi Katsuhiro Hata Takehiro Imura Motoki Sato Daisuke Gunji
More informationi
009 I 1 8 5 i 0 1 0.1..................................... 1 0.................................................. 1 0.3................................. 0.4........................................... 3
More informationTOP URL 1
TOP URL http://amonphys.web.fc.com/ 3.............................. 3.............................. 4.3 4................... 5.4........................ 6.5........................ 8.6...........................7
More informationDPA,, ShareLog 3) 4) 2.2 Strino Strino STRain-based user Interface with tacticle of elastic Natural ObjectsStrino 1 Strino ) PC Log-Log (2007 6)
1 2 1 3 Experimental Evaluation of Convenient Strain Measurement Using a Magnet for Digital Public Art Junghyun Kim, 1 Makoto Iida, 2 Takeshi Naemura 1 and Hiroyuki Ota 3 We present a basic technology
More information) ] [ h m x + y + + V x) φ = Eφ 1) z E = i h t 13) x << 1) N n n= = N N + 1) 14) N n n= = N N + 1)N + 1) 6 15) N n 3 n= = 1 4 N N + 1) 16) N n 4
1. k λ ν ω T v p v g k = π λ ω = πν = π T v p = λν = ω k v g = dω dk 1) ) 3) 4). p = hk = h λ 5) E = hν = hω 6) h = h π 7) h =6.6618 1 34 J sec) hc=197.3 MeV fm = 197.3 kev pm= 197.3 ev nm = 1.97 1 3 ev
More informationkeisoku01.dvi
2.,, Mon, 2006, 401, SAGA, JAPAN Dept. of Mechanical Engineering, Saga Univ., JAPAN 4 Mon, 2006, 401, SAGA, JAPAN Dept. of Mechanical Engineering, Saga Univ., JAPAN 5 Mon, 2006, 401, SAGA, JAPAN Dept.
More informationII ( ) (7/31) II ( [ (3.4)] Navier Stokes [ (6/29)] Navier Stokes 3 [ (6/19)] Re
II 29 7 29-7-27 ( ) (7/31) II (http://www.damp.tottori-u.ac.jp/~ooshida/edu/fluid/) [ (3.4)] Navier Stokes [ (6/29)] Navier Stokes 3 [ (6/19)] Reynolds [ (4.6), (45.8)] [ p.186] Navier Stokes I Euler Navier
More informationhttp://www.ns.kogakuin.ac.jp/~ft13389/lecture/physics1a2b/ pdf I 1 1 1.1 ( ) 1. 30 m µm 2. 20 cm km 3. 10 m 2 cm 2 4. 5 cm 3 km 3 5. 1 6. 1 7. 1 1.2 ( ) 1. 1 m + 10 cm 2. 1 hr + 6400 sec 3. 3.0 10 5 kg
More informationKeysight Technologies 誘電体測定の基礎
Keysight Technologies Application Note LCR1.1 THz (MUT) 1. PCB PCB SAR RAM IC 3 Keysight - Application Note... 2... 4... 4... 7... 8... 1... 11... 11... 12 Debye... 12 Cole-Cole... 13... 13... 14... 15...
More information2
σ γ l σ ο 4..5 cos 5 D c D u U b { } l + b σ l r l + r { r m+ m } b + l + + l l + 4..0 D b0 + r l r m + m + r 4..7 4..0 998 ble4.. ble4.. 8 0Z Fig.4.. 0Z 0Z Fig.4.. ble4.. 00Z 4 00 0Z Fig.4.. MO S 999
More information修士論文
SAW 14 2 M3622 i 1 1 1-1 1 1-2 2 1-3 2 2 3 2-1 3 2-2 5 2-3 7 2-3-1 7 2-3-2 2-3-3 SAW 12 3 13 3-1 13 3-2 14 4 SAW 19 4-1 19 4-2 21 4-2-1 21 4-2-2 22 4-3 24 4-4 35 5 SAW 36 5-1 Wedge 36 5-1-1 SAW 36 5-1-2
More information( ) ( )
20 21 2 8 1 2 2 3 21 3 22 3 23 4 24 5 25 5 26 6 27 8 28 ( ) 9 3 10 31 10 32 ( ) 12 4 13 41 0 13 42 14 43 0 15 44 17 5 18 6 18 1 1 2 2 1 2 1 0 2 0 3 0 4 0 2 2 21 t (x(t) y(t)) 2 x(t) y(t) γ(t) (x(t) y(t))
More informationII (No.2) 2 4,.. (1) (cm) (2) (cm) , (
II (No.1) 1 x 1, x 2,..., x µ = 1 V = 1 k=1 x k (x k µ) 2 k=1 σ = V. V = σ 2 = 1 x 2 k µ 2 k=1 1 µ, V σ. (1) 4, 7, 3, 1, 9, 6 (2) 14, 17, 13, 11, 19, 16 (3) 12, 21, 9, 3, 27, 18 (4) 27.2, 29.3, 29.1, 26.0,
More informationII 2 II
II 2 II 2005 yugami@cc.utsunomiya-u.ac.jp 2005 4 1 1 2 5 2.1.................................... 5 2.2................................. 6 2.3............................. 6 2.4.................................
More informationAFO AFO 4 2.3AFO 5 3 AFO 3.1 AFO
17 1060126 1 1 2 2 AFO 2.1 3 2.2AFO 4 2.3AFO 5 3 AFO 3.1 AFO 6 3.2 6 3.3 7 3.4 8 3.5 9 4.1 14 4.2 17 4.3 18 4.4 18 5.1 19 5.2 19 5.3 19 5.4 21 6.1 22 23 24 1 1 (Ankle-foot orthosis AFO) 1) AFO(Fig.1) AFO
More information(1) (2) (3) (4) 1
8 3 4 3.................................... 3........................ 6.3 B [, ].......................... 8.4........................... 9........................................... 9.................................
More information1 2 2 (Dielecrics) Maxwell ( ) D H
2003.02.13 1 2 2 (Dielecrics) 4 2.1... 4 2.2... 5 2.3... 6 2.4... 6 3 Maxwell ( ) 9 3.1... 9 3.2 D H... 11 3.3... 13 4 14 4.1... 14 4.2... 14 4.3... 17 4.4... 19 5 22 6 THz 24 6.1... 24 6.2... 25 7 26
More information18 I ( ) (1) I-1,I-2,I-3 (2) (3) I-1 ( ) (100 ) θ ϕ θ ϕ m m l l θ ϕ θ ϕ 2 g (1) (2) 0 (3) θ ϕ (4) (3) θ(t) = A 1 cos(ω 1 t + α 1 ) + A 2 cos(ω 2 t + α
18 I ( ) (1) I-1,I-2,I-3 (2) (3) I-1 ( ) (100 ) θ ϕ θ ϕ m m l l θ ϕ θ ϕ 2 g (1) (2) 0 (3) θ ϕ (4) (3) θ(t) = A 1 cos(ω 1 t + α 1 ) + A 2 cos(ω 2 t + α 2 ), ϕ(t) = B 1 cos(ω 1 t + α 1 ) + B 2 cos(ω 2 t
More information第5章 偏微分方程式の境界値問題
October 5, 2018 1 / 113 4 ( ) 2 / 113 Poisson 5.1 Poisson ( A.7.1) Poisson Poisson 1 (A.6 ) Γ p p N u D Γ D b 5.1.1: = Γ D Γ N 3 / 113 Poisson 5.1.1 d {2, 3} Lipschitz (A.5 ) Γ D Γ N = \ Γ D Γ p Γ N Γ
More informationuntitled
PGF 17 6 1 11 1 12 1 2 21 2 22 2 23 3 1 3 1 3 2 3 3 3 4 3 5 4 6 4 2 4 1 4 2 4 3 4 4 4 5 5 3 5 1 5 2 5 5 5 5 4 5 1 5 2 5 3 6 5 6 1 6 2 6 6 6 24 7 1 7 1 7 2 7 3 7 4 8 2 8 1 8 2 8 3 9 4 9 5 9 6 9 3 9 1 9
More informationuntitled
17 5 13 1 2 1.1... 2 1.2... 2 1.3... 3 2 3 2.1... 3 2.2... 5 3 6 3.1... 6 3.2... 7 3.3 t... 7 3.4 BC a... 9 3.5... 10 4 11 1 1 θ n ˆθ. ˆθ, ˆθ, ˆθ.,, ˆθ.,.,,,. 1.1 ˆθ σ 2 = E(ˆθ E ˆθ) 2 b = E(ˆθ θ). Y 1,,Y
More informationI-2 (100 ) (1) y(x) y dy dx y d2 y dx 2 (a) y + 2y 3y = 9e 2x (b) x 2 y 6y = 5x 4 (2) Bernoulli B n (n = 0, 1, 2,...) x e x 1 = n=0 B 0 B 1 B 2 (3) co
16 I ( ) (1) I-1 I-2 I-3 (2) I-1 ( ) (100 ) 2l x x = 0 y t y(x, t) y(±l, t) = 0 m T g y(x, t) l y(x, t) c = 2 y(x, t) c 2 2 y(x, t) = g (A) t 2 x 2 T/m (1) y 0 (x) y 0 (x) = g c 2 (l2 x 2 ) (B) (2) (1)
More informationR R 16 ( 3 )
(017 ) 9 4 7 ( ) ( 3 ) ( 010 ) 1 (P3) 1 11 (P4) 1 1 (P4) 1 (P15) 1 (P16) (P0) 3 (P18) 3 4 (P3) 4 3 4 31 1 5 3 5 4 6 5 9 51 9 5 9 6 9 61 9 6 α β 9 63 û 11 64 R 1 65 13 66 14 7 14 71 15 7 R R 16 http://wwwecoosaka-uacjp/~tazak/class/017
More informationuntitled
(a) (b) (c) (d) Wunderlich 2.5.1 = = =90 2 1 (hkl) {hkl} [hkl] L tan 2θ = r L nλ = 2dsinθ dhkl ( ) = 1 2 2 2 h k l + + a b c c l=2 l=1 l=0 Polanyi nλ = I sinφ I: B A a 110 B c 110 b b 110 µ a 110
More informationMicrosoft Word - 11問題表紙(選択).docx
A B A.70g/cm 3 B.74g/cm 3 B C 70at% %A C B at% 80at% %B 350 C γ δ y=00 x-y ρ l S ρ C p k C p ρ C p T ρ l t l S S ξ S t = ( k T ) ξ ( ) S = ( k T) ( ) t y ξ S ξ / t S v T T / t = v T / y 00 x v S dy dx
More information20 4 20 i 1 1 1.1............................ 1 1.2............................ 4 2 11 2.1................... 11 2.2......................... 11 2.3....................... 19 3 25 3.1.............................
More information微分積分 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 初版 1 刷発行時のものです.
微分積分 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. ttp://www.morikita.co.jp/books/mid/00571 このサンプルページの内容は, 初版 1 刷発行時のものです. i ii 014 10 iii [note] 1 3 iv 4 5 3 6 4 x 0 sin x x 1 5 6 z = f(x, y) 1 y = f(x)
More informationgr09.dvi
.1, θ, ϕ d = A, t dt + B, t dtd + C, t d + D, t dθ +in θdϕ.1.1 t { = f1,t t = f,t { D, t = B, t =.1. t A, tdt e φ,t dt, C, td e λ,t d.1.3,t, t d = e φ,t dt + e λ,t d + dθ +in θdϕ.1.4 { = f1,t t = f,t {
More information( ) 2017 2 23 : 1998 1 23 ii All Rights Reserved (c) Yoichi OKABE 1998-present. ( ) ( ) Web iii iv ( ) (G) 1998 1 23 : 1998 12 30 : TeX 2007 1 27 : 2011 9 26 : 2012 4 15 : 2012 5 6 : 2015 4 25 : v 1 1
More information<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63>
マイクロメカトロニクス 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/077331 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. 1984.10 1986.7 1995 60 1991 Piezoelectric Actuators and Ultrasonic Motors
More informationELCB TEM2 PRICE LIST 2007_Dorai on 7th Feb 07.xls
Erth-lekge Circuit Brekers Economicl Series Frme Size (A) 0 Type ZE-NF ZE-NF ZE-NF ZE-NF ZE-NF ZE0-NF Number of poles Phse nd φw wires φw, φw φw - - RATINGS Rted impulse withstnd voltge [Uimp] kv Rted
More informationa) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a
a) Extraction of Similarities and Differences in Human Behavior Using Singular Value Decomposition Kenichi MISHIMA, Sayaka KANATA, Hiroaki NAKANISHI a), Tetsuo SAWARAGI, and Yukio HORIGUCHI 1. Johansson
More informationB 1 B.1.......................... 1 B.1.1................. 1 B.1.2................. 2 B.2........................... 5 B.2.1.......................... 5 B.2.2.................. 6 B.2.3..................
More informationD d d c b a c x n cε c sε c c σ c sσ c n a c a t sε t sσ t n a t cε t cσ t S n = 0 ( ) 2 bd + n a 2 cdc + atd xn = bd + n ( ac + at ) n = n 1 I M = E
D b σ σ σ σ S ( ) bd bd ( ) I M E I b ( ) ( ) E.56 F E D ( D ) ( ) M E I.56 F b ( D ) D b σ σ σ S b b ( ) ( ) I M E I b ( ) ( ) E y E M E I ( ) ( ) E y F ( ) ( ) ( ) ( ) ( ) ( ) ( ) y y y y y y A E T SGN
More informationIA September 25, 2017 ( ) I = [a, b], f (x) I = (a 0 = a < a 1 < < a m = b) I ( ) (partition) S (, f (x)) = w (I k ) I k a k a k 1 S (, f (x)) = I k 2
IA September 5, 7 I [, b], f x I < < < m b I prtition S, f x w I k I k k k S, f x I k I k [ k, k ] I I I m I k I j m inf f x w I k x I k k m k sup f x w I k x I k inf f x w I S, f x S, f x sup f x w I
More information