Fault Diagnosis of Autonomous Underwater Vehicles
|
|
- Phillip Phelps
- 5 years ago
- Views:
Transcription
1 Research Journal of Appled Scences, Engneerng and Technology 5(6): , 03 SSN: ; e-ssn: Maxwell Scentfc Organzaton, 03 Submtted: March 3, 0 Accepted: January, 03 Publshed: Aprl 30, 03 Fault Dagnoss of Autonomous Underwater Vehcles Xao Lang, Jundong Zhang and 3 We L College of Traffc Equpment and Ocean Engneerng, College of Marne Engneerng, 3 College of Envronmental Scence and Engneerng, Dalan Martme Unversty, Dalan, Chna Abstract: n ths study, we propose the least dsturbance algorthm addng scale factor and shft factor. The dynamc learnng rato can be calculated to mnmze the scale factor and shft factor of wavelet functon and the varaton of net weghts and the algorthm mprove the stablty and the convergence of wavelet neural networ. t was appled to buld the dynamcal model of autonomous underwater vehcles and the resduals are generated by comparng the outputs of the dynamcal model wth the real state values n the condton of thruster fault. Fault detecton rules are dstlled by resdual analyss to execute thruster fault dagnoss. The results of smulaton prove the effectveness. Keywords: Autonomous underwater vehcle, least dsturbance, thruster fault dagnoss, wavelet neural networ NTRODUCTON Wth the development of the actvtes n deep ocean, the applcaton of underwater vehcles s wdespread (Xu and Xao, 007; Bldberg, 99). Underwater vehcles are frequently performng msson n unstructured, complcated and hazardous envronment (Adam, 985). For autonomous underwater vehcles, the ablty to detect and tolerate fault s a crucal ssue to realze ts autonomy. Model-based technque has the merts such as low cost, hgh relablty and easy realzaton for autonomous underwater vehcles, so t s a sutable approach. However, for the nfluence of model error, measurement nose, outer dsturbance and so on, t s dffcult enogh to buld up the accurate model for autonomous underwater vehcles. Neural networ has the characters of strong nput-output nonlnear mappng, dstrbuted store of nformaton, parallel process and especally strong self-organzng and selflearnng ablty, whch mae neural networ become an effectve method for fault dagnoss. Moreover, t has been appled n practce (Alessandr et al., 999). Wavelet neural networ s a new radal bass functon neural networ developed from wavelet transform. The orthonormalty of wavelet functon used as the hdden layer functon maes wavelet neural networ more sutable n learnng functon of local varaton and dscontnutes. By adustng scale factor and shft factor of wavelet functon and weghts of networ to affect output of networ, wavelet neural networ has strong ablty of dstllng sgnal detals and mappng nonlnear functon (L and We, 998; Zhao and Zhou, 003). n ths study, we propose a least dsturbance wavelet neural networ to buld up the dynamc model of underwater vehcles and add scale factor and shft factor of wavelet functon to dynamc learnng rate algorthm based on steepest descent method. Then, we compare the output of model wth the real state value to acheve resduals and dstll the fault nformaton from the resduals to detect fault. n ths study, we propose the least dsturbance algorthm addng scale factor and shft factor. The dynamc learnng rato can be calculated to mnmze the scale factor and shft factor of wavelet functon and the varaton of net weghts and the algorthm mprove the stablty and the convergence of wavelet neural networ. t was appled to buld the dynamcal model of autonomous underwater vehcles and the resduals are generated by comparng the outputs of the dynamcal model wth the real state values n the condton of thruster fault. Fault detecton rules are dstlled by resdual analyss to execute thruster fault dagnoss. The results of smulaton prove the effectveness. LEAST DSTURBANCE WAVELET NEURAL NETWORK The common tranng algorthm of wavelet neural networ s steepest descent method and learnng rate s extremely sgnfcant for the convergence and stablty of c networ. The hdden layer functon of wavelet neural networ s wavelet functon and ts scale factor and shft factor are also adusted to mnmze the least square error. For wavelet neural networ, outputs of neural networ are affected by both net weghts and scale factor and shft factor of wavelet functon. From the perspectve, we add scale factor and shft factor of wavelet functon to the least dsturbance dynamc learnng rate algorthm based on steepest descent method (Lu et al., 00). Correspondng Author: Xao Lang, Dalan Martme Unversty, Dalan 606, Chna 407
2 Res. J. Appl. Sc. Eng. Technol., 5(6): , 03 Defne the nput-output relatonshp of wavelet neural networ as: ( ) y w h = () (( )/ ) h = f net b a () ( ) net w x = (3) The error functon s gven by: E = d y ( ) (4) where, y s the th component of an output vector and h denotes the output of th wavelet n hdden layer and net denotes the nput of th wavelet n hdden layer, x s the th component of nput vector. d s the th desred target output. a, b s scale factor and shft factor of wavelet functon n hdden layer. Mae dfference operaton for (4) as follows: ( ) E = ( d y) y = ( d y) h w ( ) ( ) ( ) + w / a f () x w w / a f () ( ) ( ) ( xw b ) a w / a f () b (5) f we mae ΔE = - E through the modfyng weght, nput-output relatonshp wll satsfy the need of the goal functon. However, owng to frst-order approxmaton of the dfference operaton; there wll be errors for nonlnear neural networ and t s very hard to get ΔE = - E through once modfcaton, so η s ntroduced whch s usually gven by 0< η <. ΔE. s defned as: E = η E = Ω (6) where η has the same meanng as the tranng rato of the steepest descent method, but the hgher certanty s obtaned compared wth the standard steepest descent method. f the tranng rato s selected n the adacent area of, the smlar results can be obtaned. From (5) and (6), the varaton of net weghts can be descrbed as: The soluton of (7) s ndefnte, namely, there are nfnte approprate solutons. To obtan the defnte soluton condton, we construct a performance functon: ( ) ( ) J = w + w + a + b,, (8) The sgnfcance to mnmze the performance functon s to adust net weghts and scale factor and shft factor of wavelet neural networ as small as possble and mae the current error be zero. The smaller the adustable value s, the less the dsturbance of the prevous learnng nowledge would be. Therefore, the performance functon s called least dsturbance functon. The equvalent expresson of (7) s: ( ) ( ) J = w + w + a,, ( ) + b λ ( ) d y h w ( ) ( ) ( ) + w / a f ( net ) x w w / a f () ( ) ( ) ( xw b ) a w / a ) b Ω (9) where, λ s an unnown coeffcent. We can use Lagrange extreme value theory to get the soluton whch satsfes (7) and mnmzes J. The algorthm n detal s as follows. To mae the dervatve of the weght modfed value be zero, we can obtan: J ( ) = w ( ) ( ) 0 λ d y h = w J ( ) ( ) = w ( ) ( ) / () 0 λ d y w a f x = w (0) () J ( ) = a +λ ( d y ) w / a f ( net ) a ( ) ( xw b ) = 0 J ( ) = b +λ d y w a f ( net ) = () ( ) / 0 (3) b ( ) ( ) d y h w w / a f + ( net ) ( ) ( ) ( ) ( ) ( ) x w w / a f net ( x w b ) ( ) a w / a ) b Ω= 0 (7) 407 (0) to (3) can be rewrtten as: ( ) ( ) () w = λ d y h = λδ h (4) ( ) (5) ( ) ( ) ( ) w = λ d y w / a ) x = λδ x / a
3 Res. J. Appl. Sc. Eng. Technol., 5(6): , 03 ( ) ( ) a = λ ( d y ) w / a ) ( xw b ) ( ) ( ) = λδ ( xw b )/ a (6) b = λ d y w a f net = λδ a ( ) ( ) ( ) / ( ) / (7) Substtutng (4), (5), (6) and (7) nto (7): That s: (9) ( ) ( ) xλδ x + w / a ) ( ) ( ) ( ) ( xw b ) λδ ( xw b )/ a ( ) ( ) + w / a ) λδ / a = Ω ( ) ( ) ( ) δ hλδ h + w / a ) ( ) ( ) λ δ h +λ δ x / a + ( ) ( ) ( ) λ δ [( xw b ) / a ] +λ δ / a =Ω We can obtan: Ω λ= ( ) ( ) δ h + δ x / a + ( ) ( ) ( ) δ (( xw b ) / a ) + δ / a (8) (0) Substtutng (0) nto (4), (5), (6) and (7), we can obtan the sutable varaton of net weghts, scale factor and shft factor. Consderng when error approaches zero, the numerator and denomnator of (0) wll both approach zero, we add a small value ε > 0 nto the denomnator, then we can obtan: Ω λ= ( ) ( ) δ h + δ x / a +ε+ ( ) ( ) ( ) δ (( xw b ) / a ) + δ / a () The varaton of net weghts, scale factor and shft factor also use steepest descent method, but snce λ wll change tmely wth the current state and the current nput of the system, t wll be of beneft to the convergence and robustness of the networ. From (), we can conclude that ths change wll lead to that the reducng of error s always at the sutable level whch s 4073 Fg. : Effect of traned networ n two methods determned by η of (6), that s, the reducng of error wll not be large to cause unnecessary oscllaton and wll not be small to cause the convergence too low. On the other hand, snce η s equal to the expected reducton rato of the error, whose value s a lttle less than whch wll not nfluence the convergence of networ serously, but n steepest descent method the convergence s senstve to the learnng rato. n the standard steepest descent method, when the number of hdden layer ponts s added, t s necessary to reduce the learnng rato to assure the convergence of the networ. n (), when Σ h s added, λ s reduced smultaneously. Whle addng the number of hdden layer ponts means to add Σ h and thus λ s reduced. Ths dynamc learnng rato s helpful for the convergence stablty of the networ. Wth the same ntal value of neural networ, the effect of two dfferent algorthms s shown as Fg.. The tranng data are from a certan yawng moton of one underwater vehcle. As can be seen, the convergence velocty of the networ usng least dsturbance algorthm s much better. MODELNG USNG LEAST DSTURBANCE WAVELET NEURAL NETWORK The proposed approach has been verfed on a certan autonomous underwater vehcle named ZS4 (made n HEU, Chna) for smulaton study. The vehcle has eght thrusters and they can be dvded nto four groups based on the functon of the thrusters: horzontal plane thrusters, vertcal plane thrusters, sde thrusters and vertcal thrusters. Each group has two thrusters. Horzontal and vertcal plane thrusters are the ducted thrusters and sde and vertcal thrusters are the tunnel thrusters. As the velocty ncreases n the surge drecton, thruster deducton becomes serous enough, thus we close four tunnel thrusters n the hgh velocty to save energy. So n ths study, we manly dscuss the thruster fault dagnoss for four man thrusters. Fgure shows the thruster confguraton of ZS4.
4 Res. J. Appl. Sc. Eng. Technol., 5(6): , 03 Fg. : Thruster confguraton of ZS4 The autonomous underwater vehcle s a hgh couplng system and for dentfyng each degree, wavelet neural networ should be a mult-nput and mult-output system. The hdden layer functon s defned as the Morlet wavelet: Fg. 3: North velocty output of traned networ ( /) = () gx ( ) cos(.75 xe ) x ZS4 autonomous underwater vehcle s equpped wth Doppler Velocty Log (DVL) to measure threedmensonal lnear velocty and compass to measure three-dmensonal angles. Consderng the sensors and the executon equpments and smplfyng the approxmatng model of neural networ, we defne the nput and output as: Fg. 4: South velocty output of traned networ [ ] u( ) = X ( ) Y ( ) Z ( ) M ( ) N ( ) K ( ) T T T T T T y( ) = [ u( ) v( ) w( ) roll( ) ptch( ) yaw( )] T (3) where, u, v w are lnear velocty n surge, sway and heave drecton. roll, ptch, yaw are angular velocty n roll, ptch and yaw drecton. X T, Y T, Z T, N T, K T, are force/moment n surge, sway, heave, roll, ptch and yaw drecton. From the nput and output, we can realze 6- DOF nonlnear model dentfcaton. The neural networ structure s We tran wavelet neural networ offlne. ntalzng the parameters of wavelet neural networ s an mportant ssue and for the parameter learnng rate, we use the dynamc approach above. For the scale factor and shft factor, we apply the approach n Zhao and Zhou (003). Accordng to the wavelet theory, gven t*as the tme doman center, Δψ as the mother wavelet radus. The tme doman s gven by: * * [ b at b, b at b ] + ψ + + ψ (4) Fg. 5: Yaw output of traned networ n order to guarantee that the wavelet extends ntally over the whole nput doman, the scale factor and shft factor should satsfy the followng equatons: Thus: b at a w x b + at + a ψ = w x * + ψ = mn = * max = w x max w x mn = = a = ψ * * w x max ( ψ t ) w x mn ( ψ + t ) = = b = ψ (5) (6) 4074
5 Res. J. Appl. Sc. Eng. Technol., 5(6): , 03 Fg. 6: North velocty of left thruster fault experment Fg. 7: Yaw of left thruster fault experment The net weghts s less crtcal and the parameters are ntalzed to small random value between [-, + ]. The tranng data are from the smulaton experments such as surgng, yawng, swayng and so on. The traned neural networ can smulate the moton of the autonomous underwater vehcle very well. The Fg. 3, 4 and 5 show the results and we can see that the least dsturbance wavelet neural networ can approxmate the whole functon outlne and also dstll the change detal. ANALYSS OF SMULATON RESULTS Comparng the outputs of wavelet neural networ wth the real state values, we can obtan the resduals and analyze them to dstll fault nformaton. To mnmze the effect of envronment nose, the resduals are analyzed as: n a sold perod of gatherng a group of resduals, max value and mn value are cut and the mean valve of the left data s used as the threshold value. The perod and the threshold value are based on plenty of experments. f the resdual s beyond the threshold value, we consder there occurs a fault. n smulaton, the faults of the thrusters are consdered as zero outputs of the controller. Smulaton s shown as the example of settng a fault of a certan thruster n certan tme and then analyzng the resduals. Fgure 6, 7, 8 and 9 show the real state values and the outputs of wavelet neural networ n the fault of the left or the rght thruster when the autonomous underwater vehcle surges. We can see when the man thruster has a fault, the surge velocty changes and the change ncreases to a steady value as tme goes. Meanwhle, the yaw value ncreases nfntely. For the fault of the left thruster, the yaw s negatve. For the fault of the rght thruster, the yaw s postve. So we can set one threshold value for each degree. f both the resdual of the real velocty and the estmated velocty and the resdual of the real yaw and the estmated yaw are beyond the separate threshold value and the yaw resdual s negatve, the left thruster has a fault. f both 4075 Fg. 8: North velocty of rght thruster fault experment Fg. 9: Yaw of rght thruster fault experment the resdual of the real velocty and the estmated velocty and the resdual of the real yaw and the estmated yaw are beyond the separate threshold value and the yaw resdual s postve, the rght thruster has a fault. For the other thrusters, the approach s the same. CONCLUSON Amng at the character that hdden layer wavelet functon of wavelet neural networ can adust scale factor and shft factor to affect output of the networ, the least dsturbance algorthm addng scale factor and shft factor s proposed. The algorthm can calculate the dynamcal learnng rato to mprove the stablty and the
6 Res. J. Appl. Sc. Eng. Technol., 5(6): , 03 convergence of the wavelet neural networ. Meanwhle, for strong ablty of dstllng sgnal detals and mappng nonlnear functon of wavelet neural networ, the algorthm s appled to buld the dynamcal model of the autonomous underwater vehcle and the resduals are acheved by comparng the outputs of the neural networ wth the real state values. Fault detecton rules are dstlled from the resduals to execute actuator fault dagnoss. The results of smulaton prove the approach s effectve. ACKNOWLEDMENT Ths study s supported by the Natonal Natural Scence Foundaton of Chna (Grant No ) and the Chna Postdoctoral Scence Foundaton (Grant No ) and Fundamental Research Funds for the Central Unverstes of Chna (Grant No. 0QN30) and Specal Fund for Marne Scentfc Research n the Publc nterest (Grant No ). REFERENCES Adam, J.A., 985. Probng beneath the sea. EEE Spectrum, (4): Alessandr, A., M. Cacca and G. Veruggo, 999. Fault detecton of actuator fault n unmanned underwater vehcles. Control Eng. Practce, 7(): Bldberg, D.R., 99. Autonomous underwater vehcles: A tool for the ocean. Unmanned Syst., 9(): 0-5. L, X. and G. We, 998. Dynamc system dentfcaton and ts applcaton usng wavelet neural networ. Control Theory Appl., 5(4): Lu, X., J. Lu and Y. Xu, 00. Moton control of underwater vehcle based on least dsturbance bp algorthm. J. Harbn Eng. Unv., (9): 0-3. Xu, Y. and K. Xao, 007. Technology development of autonomous ocean vehcle. Robot, 33(5): Zhao, X. and C. Zhou, 003. A research on ntalng parameter of wavelet neural networ. J. South Chna Unv. Technol., 3():
EEE 241: Linear Systems
EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they
More informationChapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems
Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons
More informationLecture Notes on Linear Regression
Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume
More informationRBF Neural Network Model Training by Unscented Kalman Filter and Its Application in Mechanical Fault Diagnosis
Appled Mechancs and Materals Submtted: 24-6-2 ISSN: 662-7482, Vols. 62-65, pp 2383-2386 Accepted: 24-6- do:.428/www.scentfc.net/amm.62-65.2383 Onlne: 24-8- 24 rans ech Publcatons, Swtzerland RBF Neural
More informationAssessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion
Assessment of Ste Amplfcaton Effect from Input Energy Spectra of Strong Ground Moton M.S. Gong & L.L Xe Key Laboratory of Earthquake Engneerng and Engneerng Vbraton,Insttute of Engneerng Mechancs, CEA,
More informationCHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE
CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng
More informationOperating conditions of a mine fan under conditions of variable resistance
Paper No. 11 ISMS 216 Operatng condtons of a mne fan under condtons of varable resstance Zhang Ynghua a, Chen L a, b, Huang Zhan a, *, Gao Yukun a a State Key Laboratory of Hgh-Effcent Mnng and Safety
More informationNumerical Heat and Mass Transfer
Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and
More informationReport on Image warping
Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.
More informationDesign and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm
Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:
More informationParameter Estimation for Dynamic System using Unscented Kalman filter
Parameter Estmaton for Dynamc System usng Unscented Kalman flter Jhoon Seung 1,a, Amr Atya F. 2,b, Alexander G.Parlos 3,c, and Klto Chong 1,4,d* 1 Dvson of Electroncs Engneerng, Chonbuk Natonal Unversty,
More informationMMA and GCMMA two methods for nonlinear optimization
MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons
More informationThe Two-scale Finite Element Errors Analysis for One Class of Thermoelastic Problem in Periodic Composites
7 Asa-Pacfc Engneerng Technology Conference (APETC 7) ISBN: 978--6595-443- The Two-scale Fnte Element Errors Analyss for One Class of Thermoelastc Problem n Perodc Compostes Xaoun Deng Mngxang Deng ABSTRACT
More informationGeneralized Linear Methods
Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set
More informationCOEFFICIENT DIAGRAM: A NOVEL TOOL IN POLYNOMIAL CONTROLLER DESIGN
Int. J. Chem. Sc.: (4), 04, 645654 ISSN 097768X www.sadgurupublcatons.com COEFFICIENT DIAGRAM: A NOVEL TOOL IN POLYNOMIAL CONTROLLER DESIGN R. GOVINDARASU a, R. PARTHIBAN a and P. K. BHABA b* a Department
More informationMultilayer Perceptrons and Backpropagation. Perceptrons. Recap: Perceptrons. Informatics 1 CG: Lecture 6. Mirella Lapata
Multlayer Perceptrons and Informatcs CG: Lecture 6 Mrella Lapata School of Informatcs Unversty of Ednburgh mlap@nf.ed.ac.uk Readng: Kevn Gurney s Introducton to Neural Networks, Chapters 5 6.5 January,
More informationPower law and dimension of the maximum value for belief distribution with the max Deng entropy
Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng
More informationAn Improved multiple fractal algorithm
Advanced Scence and Technology Letters Vol.31 (MulGraB 213), pp.184-188 http://dx.do.org/1.1427/astl.213.31.41 An Improved multple fractal algorthm Yun Ln, Xaochu Xu, Jnfeng Pang College of Informaton
More informationREAL-TIME DETERMINATION OF INDOOR CONTAMINANT SOURCE LOCATION AND STRENGTH, PART II: WITH TWO SENSORS. Beijing , China,
REAL-TIME DETERMIATIO OF IDOOR COTAMIAT SOURCE LOCATIO AD STREGTH, PART II: WITH TWO SESORS Hao Ca,, Xantng L, Wedng Long 3 Department of Buldng Scence, School of Archtecture, Tsnghua Unversty Bejng 84,
More informationLinear Approximation with Regularization and Moving Least Squares
Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...
More informationOrientation Model of Elite Education and Mass Education
Proceedngs of the 8th Internatonal Conference on Innovaton & Management 723 Orentaton Model of Elte Educaton and Mass Educaton Ye Peng Huanggang Normal Unversty, Huanggang, P.R.Chna, 438 (E-mal: yepeng@hgnc.edu.cn)
More informationWeek3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity
Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle
More informationGlobal Sensitivity. Tuesday 20 th February, 2018
Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values
More informationNeural networks. Nuno Vasconcelos ECE Department, UCSD
Neural networs Nuno Vasconcelos ECE Department, UCSD Classfcaton a classfcaton problem has two types of varables e.g. X - vector of observatons (features) n the world Y - state (class) of the world x X
More informationA Network Intrusion Detection Method Based on Improved K-means Algorithm
Advanced Scence and Technology Letters, pp.429-433 http://dx.do.org/10.14257/astl.2014.53.89 A Network Intruson Detecton Method Based on Improved K-means Algorthm Meng Gao 1,1, Nhong Wang 1, 1 Informaton
More informationPulse Coded Modulation
Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal
More informationNon-linear Canonical Correlation Analysis Using a RBF Network
ESANN' proceedngs - European Smposum on Artfcal Neural Networks Bruges (Belgum), 4-6 Aprl, d-sde publ., ISBN -97--, pp. 57-5 Non-lnear Canoncal Correlaton Analss Usng a RBF Network Sukhbnder Kumar, Elane
More informationDe-noising Method Based on Kernel Adaptive Filtering for Telemetry Vibration Signal of the Vehicle Test Kejun ZENG
6th Internatonal Conference on Mechatroncs, Materals, Botechnology and Envronment (ICMMBE 6) De-nosng Method Based on Kernel Adaptve Flterng for elemetry Vbraton Sgnal of the Vehcle est Kejun ZEG PLA 955
More informationHigh resolution entropy stable scheme for shallow water equations
Internatonal Symposum on Computers & Informatcs (ISCI 05) Hgh resoluton entropy stable scheme for shallow water equatons Xaohan Cheng,a, Yufeng Ne,b, Department of Appled Mathematcs, Northwestern Polytechncal
More informationCOMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
More information(Online First)A Lattice Boltzmann Scheme for Diffusion Equation in Spherical Coordinate
Internatonal Journal of Mathematcs and Systems Scence (018) Volume 1 do:10.494/jmss.v1.815 (Onlne Frst)A Lattce Boltzmann Scheme for Dffuson Equaton n Sphercal Coordnate Debabrata Datta 1 *, T K Pal 1
More informationStudy on Non-Linear Dynamic Characteristic of Vehicle. Suspension Rubber Component
Study on Non-Lnear Dynamc Characterstc of Vehcle Suspenson Rubber Component Zhan Wenzhang Ln Y Sh GuobaoJln Unversty of TechnologyChangchun, Chna Wang Lgong (MDI, Chna [Abstract] The dynamc characterstc
More informationGeneral viscosity iterative method for a sequence of quasi-nonexpansive mappings
Avalable onlne at www.tjnsa.com J. Nonlnear Sc. Appl. 9 (2016), 5672 5682 Research Artcle General vscosty teratve method for a sequence of quas-nonexpansve mappngs Cuje Zhang, Ynan Wang College of Scence,
More informationChapter 11: Simple Linear Regression and Correlation
Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests
More informationTransfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system
Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng
More informationModule 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
More informationGrid Generation around a Cylinder by Complex Potential Functions
Research Journal of Appled Scences, Engneerng and Technolog 4(): 53-535, 0 ISSN: 040-7467 Mawell Scentfc Organzaton, 0 Submtted: December 0, 0 Accepted: Januar, 0 Publshed: June 0, 0 Grd Generaton around
More informationApplication research on rough set -neural network in the fault diagnosis system of ball mill
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(4):834-838 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Applcaton research on rough set -neural network n the
More informationSolving Nonlinear Differential Equations by a Neural Network Method
Solvng Nonlnear Dfferental Equatons by a Neural Network Method Luce P. Aarts and Peter Van der Veer Delft Unversty of Technology, Faculty of Cvlengneerng and Geoscences, Secton of Cvlengneerng Informatcs,
More informationSupport Vector Machines. Vibhav Gogate The University of Texas at dallas
Support Vector Machnes Vbhav Gogate he Unversty of exas at dallas What We have Learned So Far? 1. Decson rees. Naïve Bayes 3. Lnear Regresson 4. Logstc Regresson 5. Perceptron 6. Neural networks 7. K-Nearest
More informationDr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur
Analyss of Varance and Desgn of Experment-I MODULE VII LECTURE - 3 ANALYSIS OF COVARIANCE Dr Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Any scentfc experment s performed
More informationDesign and Analysis of Landing Gear Mechanic Structure for the Mine Rescue Carrier Robot
Sensors & Transducers 214 by IFSA Publshng, S. L. http://www.sensorsportal.com Desgn and Analyss of Landng Gear Mechanc Structure for the Mne Rescue Carrer Robot We Juan, Wu Ja-Long X an Unversty of Scence
More informationADVANCED MACHINE LEARNING ADVANCED MACHINE LEARNING
1 ADVANCED ACHINE LEARNING ADVANCED ACHINE LEARNING Non-lnear regresson technques 2 ADVANCED ACHINE LEARNING Regresson: Prncple N ap N-dm. nput x to a contnuous output y. Learn a functon of the type: N
More informationAppendix B. The Finite Difference Scheme
140 APPENDIXES Appendx B. The Fnte Dfference Scheme In ths appendx we present numercal technques whch are used to approxmate solutons of system 3.1 3.3. A comprehensve treatment of theoretcal and mplementaton
More informationAn identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites
IOP Conference Seres: Materals Scence and Engneerng PAPER OPE ACCESS An dentfcaton algorthm of model knetc parameters of the nterfacal layer growth n fber compostes o cte ths artcle: V Zubov et al 216
More informationJournal of Chemical and Pharmaceutical Research, 2014, 6(5): Research Article
Avalable onlne www.jocpr.com Journal of Chemcal and Pharmaceutcal Research, 014, 6(5):1683-1688 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Multple mode control based on VAV ar condtonng system
More informationPrinciples of Food and Bioprocess Engineering (FS 231) Solutions to Example Problems on Heat Transfer
Prncples of Food and Boprocess Engneerng (FS 31) Solutons to Example Problems on Heat Transfer 1. We start wth Fourer s law of heat conducton: Q = k A ( T/ x) Rearrangng, we get: Q/A = k ( T/ x) Here,
More informationHongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k)
ISSN 1749-3889 (prnt), 1749-3897 (onlne) Internatonal Journal of Nonlnear Scence Vol.17(2014) No.2,pp.188-192 Modfed Block Jacob-Davdson Method for Solvng Large Sparse Egenproblems Hongy Mao, College of
More informationThe equation of motion of a dynamical system is given by a set of differential equations. That is (1)
Dynamcal Systems Many engneerng and natural systems are dynamcal systems. For example a pendulum s a dynamcal system. State l The state of the dynamcal system specfes t condtons. For a pendulum n the absence
More information9 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations
Physcs 171/271 - Chapter 9R -Davd Klenfeld - Fall 2005 9 Dervaton of Rate Equatons from Sngle-Cell Conductance (Hodgkn-Huxley-lke) Equatons We consder a network of many neurons, each of whch obeys a set
More informationMultigradient for Neural Networks for Equalizers 1
Multgradent for Neural Netorks for Equalzers 1 Chulhee ee, Jnook Go and Heeyoung Km Department of Electrcal and Electronc Engneerng Yonse Unversty 134 Shnchon-Dong, Seodaemun-Ku, Seoul 1-749, Korea ABSTRACT
More informationA Robust Method for Calculating the Correlation Coefficient
A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal
More informationNON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS
IJRRAS 8 (3 September 011 www.arpapress.com/volumes/vol8issue3/ijrras_8_3_08.pdf NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS H.O. Bakodah Dept. of Mathematc
More informationON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION
Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION
More informationApplied Mathematics Letters
Appled Matheatcs Letters 2 (2) 46 5 Contents lsts avalable at ScenceDrect Appled Matheatcs Letters journal hoepage: wwwelseverco/locate/al Calculaton of coeffcents of a cardnal B-splne Gradr V Mlovanovć
More informationThe Study of Teaching-learning-based Optimization Algorithm
Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute
More informationon the improved Partial Least Squares regression
Internatonal Conference on Manufacturng Scence and Engneerng (ICMSE 05) Identfcaton of the multvarable outlers usng T eclpse chart based on the mproved Partal Least Squares regresson Lu Yunlan,a X Yanhu,b
More informationMath1110 (Spring 2009) Prelim 3 - Solutions
Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.
More informationNormally, in one phase reservoir simulation we would deal with one of the following fluid systems:
TPG4160 Reservor Smulaton 2017 page 1 of 9 ONE-DIMENSIONAL, ONE-PHASE RESERVOIR SIMULATION Flud systems The term sngle phase apples to any system wth only one phase present n the reservor In some cases
More informationOutline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]
DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm
More informationBearing Remaining Useful Life Prediction Based on an Improved Back Propagation Neural Network
nternatonal Journal of Performablty Engneerng, Vol. 10, No. 6, September 2014, pp.653-657. RAMS Consultants Prnted n nda Bearng Remanng Useful Lfe Predcton Based on an mproved Back Propagaton Neural Network
More informationModelling of the precise movement of a ship at slow speed to minimize the trajectory deviation risk
Computatonal Methods and Expermental Measurements XIV 29 Modellng of the precse movement of a shp at slow speed to mnmze the trajectory devaton rsk J. Maleck Polsh Naval Academy, Poland Faculty of Mechancs
More informationAPPLICATION OF RBF NEURAL NETWORK IMPROVED BY PSO ALGORITHM IN FAULT DIAGNOSIS
Journal of Theoretcal and Appled Informaton Technology 005-01 JATIT & LLS. All rghts reserved. ISSN: 199-8645 www.jatt.org E-ISSN: 1817-3195 APPLICATION OF RBF NEURAL NETWORK IMPROVED BY PSO ALGORITHM
More informationLinear Feature Engineering 11
Lnear Feature Engneerng 11 2 Least-Squares 2.1 Smple least-squares Consder the followng dataset. We have a bunch of nputs x and correspondng outputs y. The partcular values n ths dataset are x y 0.23 0.19
More informationChapter 13: Multiple Regression
Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to
More informationAtmospheric Environmental Quality Assessment RBF Model Based on the MATLAB
Journal of Envronmental Protecton, 01, 3, 689-693 http://dxdoorg/10436/jep0137081 Publshed Onlne July 01 (http://wwwscrporg/journal/jep) 689 Atmospherc Envronmental Qualty Assessment RBF Model Based on
More informationPARTICIPATION FACTOR IN MODAL ANALYSIS OF POWER SYSTEMS STABILITY
POZNAN UNIVE RSITY OF TE CHNOLOGY ACADE MIC JOURNALS No 86 Electrcal Engneerng 6 Volodymyr KONOVAL* Roman PRYTULA** PARTICIPATION FACTOR IN MODAL ANALYSIS OF POWER SYSTEMS STABILITY Ths paper provdes a
More informationA new Approach for Solving Linear Ordinary Differential Equations
, ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of
More informationSection 8.3 Polar Form of Complex Numbers
80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the
More informationErrors for Linear Systems
Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch
More informationNeuro-Adaptive Design II:
Lecture 37 Neuro-Adaptve Desgn II: A Robustfyng Tool for Any Desgn Dr. Radhakant Padh Asst. Professor Dept. of Aerospace Engneerng Indan Insttute of Scence - Bangalore Motvaton Perfect system modelng s
More informationCSE 252C: Computer Vision III
CSE 252C: Computer Vson III Lecturer: Serge Belonge Scrbe: Catherne Wah LECTURE 15 Kernel Machnes 15.1. Kernels We wll study two methods based on a specal knd of functon k(x, y) called a kernel: Kernel
More information829. An adaptive method for inertia force identification in cantilever under moving mass
89. An adaptve method for nerta force dentfcaton n cantlever under movng mass Qang Chen 1, Mnzhuo Wang, Hao Yan 3, Haonan Ye 4, Guola Yang 5 1,, 3, 4 Department of Control and System Engneerng, Nanng Unversty,
More informationSupporting Information
Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to
More informationKernels in Support Vector Machines. Based on lectures of Martin Law, University of Michigan
Kernels n Support Vector Machnes Based on lectures of Martn Law, Unversty of Mchgan Non Lnear separable problems AND OR NOT() The XOR problem cannot be solved wth a perceptron. XOR Per Lug Martell - Systems
More informationGrover s Algorithm + Quantum Zeno Effect + Vaidman
Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the
More informationNegative Binomial Regression
STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...
More informationNeuro-Adaptive Design - I:
Lecture 36 Neuro-Adaptve Desgn - I: A Robustfyng ool for Dynamc Inverson Desgn Dr. Radhakant Padh Asst. Professor Dept. of Aerospace Engneerng Indan Insttute of Scence - Bangalore Motvaton Perfect system
More informationResearch on the Fuzzy Control for Vehicle Semi-active Suspension. Xiaoming Hu 1, a, Wanli Li 1,b
Advanced Materals Research Onlne: 0-0- ISSN: -9, Vol., pp -9 do:0.0/www.scentfc.net/amr.. 0 Trans Tech Publcatons, Swterland Research on the Fuy Control for Vehcle Sem-actve Suspenson Xaomng Hu, a, Wanl
More informationThe Chaotic Robot Prediction by Neuro Fuzzy Algorithm (2) = θ (3) = ω. Asin. A v. Mana Tarjoman, Shaghayegh Zarei
The Chaotc Robot Predcton by Neuro Fuzzy Algorthm Mana Tarjoman, Shaghayegh Zare Abstract In ths paper an applcaton of the adaptve neurofuzzy nference system has been ntroduced to predct the behavor of
More informationThe Minimum Universal Cost Flow in an Infeasible Flow Network
Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran
More informationMultilayer Perceptron (MLP)
Multlayer Perceptron (MLP) Seungjn Cho Department of Computer Scence and Engneerng Pohang Unversty of Scence and Technology 77 Cheongam-ro, Nam-gu, Pohang 37673, Korea seungjn@postech.ac.kr 1 / 20 Outlne
More informationA Fast Computer Aided Design Method for Filters
2017 Asa-Pacfc Engneerng and Technology Conference (APETC 2017) ISBN: 978-1-60595-443-1 A Fast Computer Aded Desgn Method for Flters Gang L ABSTRACT *Ths paper presents a fast computer aded desgn method
More informationELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM
ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look
More informationRolling Bearing Fault Diagnosis Based on EMD-TEO and Mahalanobis Distance
Rollng Bearng Fault Dagnoss Based on EMD-TE and Mahalanobs Dstance Lu Chen, Hu Jameng, Lu Hongme and Wang Jng RLLG BEARG FAULT DAGSS BASED EMD-TE AD MAHALABS DSTACE. LU CHE, HU JAMEG, LU HGME AD WAG JG
More informationCopyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for U Charts. Dr. Wayne A. Taylor
Taylor Enterprses, Inc. Adjusted Control Lmts for U Charts Copyrght 207 by Taylor Enterprses, Inc., All Rghts Reserved. Adjusted Control Lmts for U Charts Dr. Wayne A. Taylor Abstract: U charts are used
More informationPHYS 705: Classical Mechanics. Calculus of Variations II
1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary
More informationA Hybrid Variational Iteration Method for Blasius Equation
Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method
More informationIrregular vibrations in multi-mass discrete-continuous systems torsionally deformed
(2) 4 48 Irregular vbratons n mult-mass dscrete-contnuous systems torsonally deformed Abstract In the paper rregular vbratons of dscrete-contnuous systems consstng of an arbtrary number rgd bodes connected
More informationUncertainty and auto-correlation in. Measurement
Uncertanty and auto-correlaton n arxv:1707.03276v2 [physcs.data-an] 30 Dec 2017 Measurement Markus Schebl Federal Offce of Metrology and Surveyng (BEV), 1160 Venna, Austra E-mal: markus.schebl@bev.gv.at
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationStudy on Active Micro-vibration Isolation System with Linear Motor Actuator. Gong-yu PAN, Wen-yan GU and Dong LI
2017 2nd Internatonal Conference on Electrcal and Electroncs: echnques and Applcatons (EEA 2017) ISBN: 978-1-60595-416-5 Study on Actve Mcro-vbraton Isolaton System wth Lnear Motor Actuator Gong-yu PAN,
More informationCSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography
CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve
More informationParabola Model with the Application to the Single-Point Mooring System
Journal of Multdscplnary Engneerng Scence and Technology (JMEST) ISSN: 58-93 Vol. Issue 3, March - 7 Parabola Model wth the Applcaton to the Sngle-Pont Moorng System Chunle Sun, Yuheng Rong, Xn Wang, Mengjao
More informationPedersen, Ivar Chr. Bjerg; Hansen, Søren Mosegaard; Brincker, Rune; Aenlle, Manuel López
Downloaded from vbn.aau.dk on: Aprl 2, 209 Aalborg Unverstet Load Estmaton by Frequency Doman Decomposton Pedersen, Ivar Chr. Bjerg; Hansen, Søren Mosegaard; Brncker, Rune; Aenlle, Manuel López Publshed
More informationDETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM
Ganj, Z. Z., et al.: Determnaton of Temperature Dstrbuton for S111 DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM by Davood Domr GANJI
More informationAir Age Equation Parameterized by Ventilation Grouped Time WU Wen-zhong
Appled Mechancs and Materals Submtted: 2014-05-07 ISSN: 1662-7482, Vols. 587-589, pp 449-452 Accepted: 2014-05-10 do:10.4028/www.scentfc.net/amm.587-589.449 Onlne: 2014-07-04 2014 Trans Tech Publcatons,
More informationOdd/Even Scroll Generation with Inductorless Chua s and Wien Bridge Oscillator Circuits
Watcharn Jantanate, Peter A. Chayasena, Sarawut Sutorn Odd/Even Scroll Generaton wth Inductorless Chua s and Wen Brdge Oscllator Crcuts Watcharn Jantanate, Peter A. Chayasena, and Sarawut Sutorn * School
More informationUnknown input extended Kalman filter-based fault diagnosis for satellite actuator
Internatonal Conference on Computer and Automaton Engneerng (ICCAE ) IPCSI vol 44 () () IACSI Press, Sngapore DOI: 776/IPCSIV44 Unnown nput extended Kalman flter-based fault dagnoss for satellte actuator
More informationCopyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor
Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data
More information