A Fast Environmental Pollution Emergency Plan Generation System Integrating Various Methods

Size: px
Start display at page:

Download "A Fast Environmental Pollution Emergency Plan Generation System Integrating Various Methods"

Transcription

1 Research Joural of ppled Sceces, Egeerg ad Techology 5(0): , 203 ISSN: ; e-issn: Maxwell Scetfc Orgazato, 203 Submtted: ugust 7, 202 ccepted: September 3, 202 Publshed: March 25, 203 Fast Evrometal Polluto Emergecy Pla Geerato System Itegratg Varous Methods Lfeg Yag ad Zmg Kou Tayua Uversty of Techology, Tayua , Cha bstract: Itegratg methods ad techques of CBR, RBR, R-HP ad GIS etc.,ths study desged ad developed a fast evrometal polluto emergecy pla geerato system. The study aalyzes the workg prcple ad process that geerate emergecy pla combg CBR ad RBR. I CBR system, R-HP s used to costruct the case dex system of evrometal polluto emergeces ad calculate the weght of dex; smlarty betwee cases s measured by earest-eghbour algorthm. Rule-based reasog s performed accordace wth the rules the kowledge base ad GIS s used to acheve the vsual expresso of the dyamc emergecy pla. Itegrato of varous methods optmzes the respose tme ad complexty of system, also ehaces the practcal operablty. Ths study has great sgfcace researchg of evrometal emergecy maagemet ad mprovg emergecy respose capablty. Keywords: CBR, evrometal polluto, emergecy pla, GIS, RBR, R-HP INTRODUCTION I recet years, wth the rapd developmet of Chese soco-ecoomc, evrometal polluto cdets occur frequetly, whch have brought great harm ad loss to the ecologcal evromet, huma health, soco-ecoomc. Compared wth the geeral polluto cdets, evrometal polluto emergeces hold characters as short reacto tme, low level of formato, hgher cotrol costs. fter the outbreak, respods eed to de doe at the frst tme, effectve coutermeasures should bee take to prevet the further expaso of the evet. t preset, Cha's evrometal polluto emergecy respose system s stll the tal applcato stage ad emergecy pla almost exsts as statc text or smple maagemet formato system (Chag et al., 997). Such emergecy pla lacks of adaptablty, operablty ad tmeless the emergecy respose process. Therefore, research of fast emergecy pla geerato techque wth the support of kowledge has mportat ecoomc value ad socal sgfcace. The tradtoal method s usg the expert system (Rule-Based Reasog, RBR) to geerate the decso method. However, ths approach s ofte slow ad t s also hard to geerate the reasog rules sometmes (Zhag ad Lu, 2002). CBR (Case-based Reasog) has broke through the bottleeck for kowledge acqusto. It s a reasog mode for solvg the curret problem based o the experece got from the solvg of smlar problems the past. CBR s partcularly applcable to the areas whch a quck decso-makg s eeded but the mechasm s ot etrely clear, or the stuato s too complex to establsh a rule model (Qu ad Hao, 2004). However, due to the lack of deductve ablty, the sgle CBR reasog s the lack of systematc. I ths study, the RBR ad CBR are combed, supplemeted by R-HP (Reformed- alytc Herarchy Process) ad wth the expresso techology based o GIS (Geographc Iformato System) for the dyamc pla. 3BSTUDIES OF VRIOUS METHODS The workg prcple ad the process of the system: Whe a ew emergecy evet happes, the case base of CBR system s frst retreved to fd the smlar solutos (Koloder, 992). If there s a sutable case, t ca be appled drectly or slghtly modfed as a emergecy pla. Oly whe the soluto cases caot be obtaed from the case base or the case solutos are ot satsfactory, the RBR system s used to reaso for a soluto by users ad the the reasog result s stored the case base for future use after evaluatg of mplemetato results (Jewe et al., 2007). The works ad the process of the system are show Fg.. Wth RBR system, users ca smulate all kds of evets advace ad geerates the approprate plas, or the process to solve the cdet, record the results of reasog. ccdet report the system ca be used to keep records of the cdet hadlg progress. The accdet report ad the case base have the same orgazatoal structure. Therefore, the valuable Correspodg uthor: Lfeg Yag, Tayua Uversty of Techology, Tayua , Cha 2897

2 Res. J. ppl. Sc. Eg. Techol., 5(0): , 203 Table : Idex system ad weght calculato Sub-layer Sub-layer dex Idex weght ttrbute type Cotamat Name Character Qualty Number Evet characters tcs Type Character Durato Number Deaths Number Ijures Number Locato Source Character Compay Character the HP ad adds the optmal trasfer matrx, thereby makes the calculato of the weghts s more objectve. Through the aalyss of the emergecy alarm formato, the system costructs the dex system of ar polluto evets, as show Table. Fg. : The workg prcple ad the process Weght s calculated as follows: accdet report ca be as a vald case to add to the case base after the ed of the emergecy. Because cdet formato s complete, the case dex system the CBR system s costructed solely based o the ecessary alarm formato. R- HP s used to represet the case dex system ad to calculate the dex weght. The earest-eghbor algorthm s used to measure the smlarty of the cases. CBR system has a self-optmzg fucto. Therefore, the effcecy ad accuracy of the system wll also be greatly mproved wth the crease of emergecy plas case base. Due to the combato of RBR, CBR, HP ad GIS, the system has the followg advatages. Frstly, t mproves the performace ad solves the kowledge acqusto bottleeck of tradtoal RBR system. Secodly, t also solves the automatc acqusto of the tal case CBR system. Fally, dyamc results of the emergecy pla are plotted o the map of GIS, such as the atmospherc pollutat dffused rego, affected sesg receptors, evacuato routes of receptor ad the optmal route of the emergecy rescue, etc. ll of these provde strog support to deal wth evrometal polluto emergeces. t preset, the system has bee used a cty emergecy platform. The effect of mplemetato shows that t has obvous advatages performace ad operablty. Step : Calculatg sub-layer weght wth 0,, 2 scale method. Frst, some matrxes ca be costructed the order of comparso matrx j, judgmet matrx B j, trasfer matrx C j, optmal trasfer matrx D j ad quas-optmal cosstet matrx B j. The the egevectors of B j are solved ad ormalzato. Fally, the weght of three sub-layers ca be calculated, as show Table. Each matrx s calculated as follows Eq. (-5): Comparso matrx j : = 2 Kowledge represetato of the case base CBR where, r system: The system uses R-HP to costruct case s sortg dex for the degree of mportace, dex system ad calculate dex weght. The prcple of HP cludes of aalyzg of the ma elemets of r the target, groupg the elemets for establshg the = j, rmax = max{ r }, j= herarchy, determg the relatve mportace of the sub-layers ad the varous elemets the herarchy, establshmet of judgmet matrx ad gettg the fal rm = m{ r }, k m = r max rm weght of the elemet (Sughoo et al., 2007). R-HP ameds cosstet judgmet matrx, whch s proe to Trasfer matrx C j : where, j = ad Jugemet matrx B j : B r r r r rmax r 2 s more mportat tha j j are equally mportat js more mportat tha ( k ) ( k ) +, r +, r () m (2) max m j = m m

3 C (, j =,2, ) Res. J. ppl. Sc. Eg. Techol., 5(0): , 203 = lg B, (3) j j, Optmal trasfer matrx D j : Dj = (4) ( Ck C jk ) k= Quas-optmal cosstet matrx B j : ' j D j B = 0 (5) Step 2: Calculatg sub-layer dex weght respectvely. Method of calculato s the same way as the step. The value of weght s show Table. Step 3: Sub-layer dex weght multply by a sub-layer weght ad fally the dex weght s show Table. Case retreval CBR system: The earest-eghbor algorthm s used to measure the smlarty of the cases (López De Mátaras et al., 2005). The type of case dex s show Table. Step : calculatg the smlarty betwee dexes. Defg X s a source case case base,t s target case, the the smlarty betwee X ad T whch are two values of the dex s expressed as: sm ( X, T ), X = T,character = 0, X T,character X T, umber max m (6) where, max s the maxmum value of all the dex values of the dex ad m s the mmum value of all the property values of the dex. Step 2: Calculatg the smlarty betwee cases. The smlarty betwee the cases s expressed as: Sm ( X T ) =, ω sm( X, T ) (7) = where, s the total umber of case dex, ω s the value of the dex weght. The key techologes of the RBR system: Usually, RBR system cossts of two parts: the kowledge base ad ferece ege. ccordg to the rules the kowledge base ad the put parameters from the users, ferece ege executes query, matchg, effcecy of the system calculato, reasog ad fally deduced the emergecy pla. O the oe had, ths pla s as the decso-makg of the evet; o the other had, t s stored case base. Oce a smlar stuato occurs, the pla ca be drectly retreved from the case base wthout reasog RBR aga. Reasog process of the ferece ege of the system s as follows: Step : Geeratg reasog codtos RC accordg to the evet. RC s expressed as RC = (RC RC 2 RC 3 RC ) ad t s used to match wth the prerequste of the rules reasog. Step 2: Selectg RC. Step 3: Matchg rules kowledge case utl RC be to obta. Step 4: Implemetg rules coclusos ad storg the results. Step 5: Repeat the secod step to the fourth step utl RC. Step 6: Output the results of all the rules of reasog. The results of the system are gve the way of text ad GIS. IMPLEMENTTION SYSTEM ND DISCUSSION rchtecture, desg ad developmet of the system are based o the techcal specfcatos such as SO (Servce Oreted rchtecture) ad Web Servces. The structure of the system s B/S ad programmg laguage s JV. The clet s developed by JSP ad JX, whch supports the remote asychroous calls ad the server s developed by the Servlet ad Hberate. I addto, WEB GIS s used as a applcato support, to acheve a dyamc pla (X et al., 2008). For example, a atmospherc polluto emergecy was caused by chlore leakage ad the fast geerato process of the emergecy pla s show Fg. 2, 3, 4 ad 5. Pla geerato speed of ths system depeds largely o the sze of the case base. If oly a small umber of cases the case base, we ofte caot get the case wth hgh smlarty as a pla, so, we have to use the RBR method as a pla geerato tool. Wth the accumulato of cases case base, the success rate of the CBR method crease ad the average tme of the pla s geerato be reduced accordgly. However, wth the growg umber of cases case lbrary, case retreval tme s gettg loger ad the respose tme of the system also grows. Therefore, the case base mateace s a mportat factor to affect the

4 Res. J. ppl. Sc. Eg. Techol., 5(0): , 203 Fg. 2: Results of case retreval CBR Fg. 3: Iput of reasog RBR Fg. 4: Dsplay of dyamc pla 2900

5 Res. J. ppl. Sc. Eg. Techol., 5(0): , 203 Fg. 5: Example of accdet report or case CONCLUSION It s very mportat to geerate respose pla the shortest possble tme to deal wth the emergeces, whch wll greatly reduce the loss of lves ad property. Ths study descrbes the desg ad mplemetato of a fast evrometal polluto emergecy pla geerato system from theoretcal ad egeerg aspects, whch tegrates a varety of methods of RBR, CBR, R-HP, GIS etc. t. O the theoretcal sde, the system uses the alarm formato as the retreval codtos of plas, so t ca reduce the complexty of CBR system ad applcato. Ths system uses a R-HP method to costruct the case dex system ad the weght. The pla matchg s realzed wth the weghted smlarty retreval method. O the egeerg sde, we costruct a fast evrometal polluto emergecy pla geerato system. Ths system combes the RBR system ad the CBR system. GIS techology s used to do the vsualzato of the cotets of the pla. d ow the system has bee appled to a cty emergecy platform. The effect of mplemetato shows that t has obvous advatages performace ad operablty. CKNOWLEDGMENT Ths study was supported by the Natoal Hgh Techology Research ad Developmet Program of Cha ( ). REFERENCES Chag, N.B., Y.L. We, C.C. Tseg ad C.Y.J. Kao, 997. The desg of a GIS-based decso support system for chemcal emergecy preparedess ad respose a urba evromet. Comp. Ev. Urba Syst., 2(): Jewe, L., Z. Sh, Q. He ad Z. Sh, quck emergecy respose pla geerato system combg CBR ad RBR. J. Comp. Res. Dev., 4: Koloder, J.L., 992. troducto to case based reasog. rtf. Itell. Rev., 6(): López De Mátaras, R., D.M.G. McSherry, D. Brdge, D. Leake, B. Smyth, et al., Retreval, reuse, revso ad reteto case-based reasog. Kowl. Eg. Rev., 20(03): Qu, M. ad H. Hao, Desg ad mplemetato of real-tme expert system combg CBR ad RBR. Comput. Eg., 30(8): Sughoo,., K. Gwaghee ad K. Kyug-I, case-based reasog cost estmatg model usg experece by aalytc herarchy process. Buld. Ev., 42: X, Y., Z. We ad L. Gog, Emergecy preparedess ad respose system of evrometal polluto accdet based o GIS. Comp. ppl., 28: Zhag, J. ad Z. Lu, Case-based reasog ad rule-based reasog for emergecy preparedess formato system. J. Toga Uerst, 30(7):

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

Journal of Chemical and Pharmaceutical Research, 2014, 6(7): Research Article

Journal of Chemical and Pharmaceutical Research, 2014, 6(7): Research Article Avalable ole www.jocpr.com Joural of Chemcal ad Pharmaceutcal Research, 2014, 6(7):1035-1041 Research Artcle ISSN : 0975-7384 CODEN(SA) : JCPRC5 Desg ad developmet of kowledge maagemet platform for SMEs

More information

A New Method for Decision Making Based on Soft Matrix Theory

A New Method for Decision Making Based on Soft Matrix Theory Joural of Scetfc esearch & eports 3(5): 0-7, 04; rtcle o. JS.04.5.00 SCIENCEDOMIN teratoal www.scecedoma.org New Method for Decso Mag Based o Soft Matrx Theory Zhmg Zhag * College of Mathematcs ad Computer

More information

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. Research on scheme evaluation method of automation mechatronic systems

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. Research on scheme evaluation method of automation mechatronic systems [ype text] [ype text] [ype text] ISSN : 0974-7435 Volume 0 Issue 6 Boechology 204 Ida Joural FULL PPER BIJ, 0(6, 204 [927-9275] Research o scheme evaluato method of automato mechatroc systems BSRC Che

More information

Journal of Chemical and Pharmaceutical Research, 2014, 6(7): Research Article

Journal of Chemical and Pharmaceutical Research, 2014, 6(7): Research Article Avalable ole www.jocpr.com Joural of Chemcal ad Pharmaceutcal Research, 04, 6(7):4-47 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Predcto of CNG automoble owershp by usg the combed model Ku Huag,

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Consistency test of martial arts competition evaluation criteria based on mathematical ahp model

Consistency test of martial arts competition evaluation criteria based on mathematical ahp model ISSN : 0974-7435 Volume 8 Issue 2 BoTechology BoTechology A Ida Joural Cosstecy test of martal arts competto evaluato crtera based o mathematcal ahp model Hu Wag Isttute of Physcal Educato, JagSu Normal

More information

Research on SVM Prediction Model Based on Chaos Theory

Research on SVM Prediction Model Based on Chaos Theory Advaced Scece ad Techology Letters Vol.3 (SoftTech 06, pp.59-63 http://dx.do.org/0.457/astl.06.3.3 Research o SVM Predcto Model Based o Chaos Theory Sog Lagog, Wu Hux, Zhag Zezhog 3, College of Iformato

More information

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR Pot Patter Aalyss Part I Outle Revst IRP/CSR, frst- ad secod order effects What s pot patter aalyss (PPA)? Desty-based pot patter measures Dstace-based pot patter measures Revst IRP/CSR Equal probablty:

More information

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

Study on Risk Analysis of Railway Signal System

Study on Risk Analysis of Railway Signal System Yuayua L, Youpeg Zhag, Rag Hu Study o Rsk Aalyss of Ralway Sgal System YUANYUAN LI, YOUPENG ZHANG, RANG HU School of Automato ad Electrcal Egeerg Lazhou Jaotog Uversty NO.88 ANg West Aeue, Lazhou, GaSu

More information

Dynamic Analysis of Axially Beam on Visco - Elastic Foundation with Elastic Supports under Moving Load

Dynamic Analysis of Axially Beam on Visco - Elastic Foundation with Elastic Supports under Moving Load Dyamc Aalyss of Axally Beam o Vsco - Elastc Foudato wth Elastc Supports uder Movg oad Saeed Mohammadzadeh, Seyed Al Mosayeb * Abstract: For dyamc aalyses of ralway track structures, the algorthm of soluto

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Evaluation on Ecological Environment of Scientific and Technological Innovation Talents in China

Evaluation on Ecological Environment of Scientific and Technological Innovation Talents in China AMSE JOURNALS-2016-Seres: Modellg C; Vol. 77; N 1; pp 108-118 Submtted July 2016; Revsed Oct. 15, 2016, Accepted Dec. 10, 2016 Evaluato o Ecologcal Evromet of Scetfc ad Techologcal Iovato Talets Cha Nabg

More information

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK

ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK ABOUT ONE APPROACH TO APPROXIMATION OF CONTINUOUS FUNCTION BY THREE-LAYERED NEURAL NETWORK Ram Rzayev Cyberetc Isttute of the Natoal Scece Academy of Azerbaa Republc ramrza@yahoo.com Aygu Alasgarova Khazar

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture) CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.

More information

A New Family of Transformations for Lifetime Data

A New Family of Transformations for Lifetime Data Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

Ranking Bank Branches with Interval Data By IAHP and TOPSIS

Ranking Bank Branches with Interval Data By IAHP and TOPSIS Rag Ba Braches wth terval Data By HP ad TPSS Tayebeh Rezaetazaa Departmet of Mathematcs, slamc zad Uversty, Badar bbas Brach, Badar bbas, ra Mahaz Barhordarahmad Departmet of Mathematcs, slamc zad Uversty,

More information

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall

More information

COMPARISON OF ANALYTIC HIERARCHY PROCESS AND SOME NEW OPTIMIZATION PROCEDURES FOR RATIO SCALING

COMPARISON OF ANALYTIC HIERARCHY PROCESS AND SOME NEW OPTIMIZATION PROCEDURES FOR RATIO SCALING Please cte ths artcle as: Paweł Kazbudzk, Comparso of aalytc herarchy process ad some ew optmzato procedures for rato scalg, Scetfc Research of the Isttute of Mathematcs ad Computer Scece, 0, Volume 0,

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted

More information

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates

More information

MA/CSSE 473 Day 27. Dynamic programming

MA/CSSE 473 Day 27. Dynamic programming MA/CSSE 473 Day 7 Dyamc Programmg Bomal Coeffcets Warshall's algorthm (Optmal BSTs) Studet questos? Dyamc programmg Used for problems wth recursve solutos ad overlappg subproblems Typcally, we save (memoze)

More information

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter A Robust otal east Mea Square Algorthm For Nolear Adaptve Flter Ruxua We School of Electroc ad Iformato Egeerg X'a Jaotog Uversty X'a 70049, P.R. Cha rxwe@chare.com Chogzhao Ha, azhe u School of Electroc

More information

Median as a Weighted Arithmetic Mean of All Sample Observations

Median as a Weighted Arithmetic Mean of All Sample Observations Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of

More information

Laboratory I.10 It All Adds Up

Laboratory I.10 It All Adds Up Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

Department of Agricultural Economics. PhD Qualifier Examination. August 2011

Department of Agricultural Economics. PhD Qualifier Examination. August 2011 Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly

More information

An Analysis of the Drivers Affecting the Implementation of Cost Control in Hydropower Construction Project Based on ISM-FAHP

An Analysis of the Drivers Affecting the Implementation of Cost Control in Hydropower Construction Project Based on ISM-FAHP A Aalyss of the Drvers Affectg the Implemetato of Cost Cotrol Hydropower Costructo Project Based o ISM-FAHP Yua Wu, Qg Ba School of Ecoomcs ad Maagemet, North Cha Electrc Power Uversty, Bejg, Cha bq0911@cepu.edu.c;

More information

Research Article A New Derivation and Recursive Algorithm Based on Wronskian Matrix for Vandermonde Inverse Matrix

Research Article A New Derivation and Recursive Algorithm Based on Wronskian Matrix for Vandermonde Inverse Matrix Mathematcal Problems Egeerg Volume 05 Artcle ID 94757 7 pages http://ddoorg/055/05/94757 Research Artcle A New Dervato ad Recursve Algorthm Based o Wroska Matr for Vadermode Iverse Matr Qu Zhou Xja Zhag

More information

Systematic Selection of Parameters in the development of Feedforward Artificial Neural Network Models through Conventional and Intelligent Algorithms

Systematic Selection of Parameters in the development of Feedforward Artificial Neural Network Models through Conventional and Intelligent Algorithms THALES Project No. 65/3 Systematc Selecto of Parameters the developmet of Feedforward Artfcal Neural Network Models through Covetoal ad Itellget Algorthms Research Team G.-C. Vosakos, T. Gaakaks, A. Krmpes,

More information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

More information

Analysis of Variance with Weibull Data

Analysis of Variance with Weibull Data Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad

More information

Finite Difference Approximations for Fractional Reaction-Diffusion Equations and the Application In PM2.5

Finite Difference Approximations for Fractional Reaction-Diffusion Equations and the Application In PM2.5 Iteratoal Symposum o Eergy Scece ad Chemcal Egeerg (ISESCE 5) Fte Dfferece Appromatos for Fractoal Reacto-Dffuso Equatos ad the Applcato I PM5 Chagpg Xe, a, Lag L,b, Zhogzha Huag,c, Jya L,d, PegLag L,e

More information

Application of Improved Grey Correlative Method in Safety Evaluation on Fully Mechanized Mining Faces

Application of Improved Grey Correlative Method in Safety Evaluation on Fully Mechanized Mining Faces Avalable ole at www.scecedrect.com Proceda Earth ad Plaetary Scece 2 ( 2011 ) 58 63 The Secod Iteratoal Coferece o Mg Egeerg ad Metallurgcal Techology Applcato of Improved Grey Correlatve Method Safety

More information

8.1 Hashing Algorithms

8.1 Hashing Algorithms CS787: Advaced Algorthms Scrbe: Mayak Maheshwar, Chrs Hrchs Lecturer: Shuch Chawla Topc: Hashg ad NP-Completeess Date: September 21 2007 Prevously we looked at applcatos of radomzed algorthms, ad bega

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems Char for Network Archtectures ad Servces Prof. Carle Departmet of Computer Scece U Müche Aalyss of System Performace IN2072 Chapter 5 Aalyss of No Markov Systems Dr. Alexader Kle Prof. Dr.-Ig. Georg Carle

More information

Processing of Information with Uncertain Boundaries Fuzzy Sets and Vague Sets

Processing of Information with Uncertain Boundaries Fuzzy Sets and Vague Sets Processg of Iformato wth Ucerta odares Fzzy Sets ad Vage Sets JIUCHENG XU JUNYI SHEN School of Electroc ad Iformato Egeerg X'a Jaotog Uversty X'a 70049 PRCHIN bstract: - I the paper we aalyze the relatoshps

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

A Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies

A Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies ISSN 1684-8403 Joural of Statstcs Volume 15, 008, pp. 44-53 Abstract A Combato of Adaptve ad Le Itercept Samplg Applcable Agrcultural ad Evrometal Studes Azmer Kha 1 A adaptve procedure s descrbed for

More information

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:

More information

We have already referred to a certain reaction, which takes place at high temperature after rich combustion.

We have already referred to a certain reaction, which takes place at high temperature after rich combustion. ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a

More information

This lecture and the next. Why Sorting? Sorting Algorithms so far. Why Sorting? (2) Selection Sort. Heap Sort. Heapsort

This lecture and the next. Why Sorting? Sorting Algorithms so far. Why Sorting? (2) Selection Sort. Heap Sort. Heapsort Ths lecture ad the ext Heapsort Heap data structure ad prorty queue ADT Qucksort a popular algorthm, very fast o average Why Sortg? Whe doubt, sort oe of the prcples of algorthm desg. Sortg used as a subroute

More information

Bayes (Naïve or not) Classifiers: Generative Approach

Bayes (Naïve or not) Classifiers: Generative Approach Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg

More information

An Optimization Approach for Intersection Signal Timing Based on Multi-Objective Particle Swarm Optimization

An Optimization Approach for Intersection Signal Timing Based on Multi-Objective Particle Swarm Optimization A Approach for Itersecto Sgal Tmg Based o Mult-Objectve Partcle Swarm Hao Pag Feg Che Departmet of Automato Uversty of Scece ad Techology of Cha Hefe, Ahu, 230027, Cha shamrock@mal.ustc.edu.c chefeg@ustc.edu.c

More information

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations.

III-16 G. Brief Review of Grand Orthogonality Theorem and impact on Representations (Γ i ) l i = h n = number of irreducible representations. III- G. Bref evew of Grad Orthogoalty Theorem ad mpact o epresetatos ( ) GOT: h [ () m ] [ () m ] δδ δmm ll GOT puts great restrcto o form of rreducble represetato also o umber: l h umber of rreducble

More information

Analyzing Fuzzy System Reliability Using Vague Set Theory

Analyzing Fuzzy System Reliability Using Vague Set Theory Iteratoal Joural of Appled Scece ad Egeerg 2003., : 82-88 Aalyzg Fuzzy System Relablty sg Vague Set Theory Shy-Mg Che Departmet of Computer Scece ad Iformato Egeerg, Natoal Tawa versty of Scece ad Techology,

More information

NP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer

NP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer NP!= P By Lu Ra Table of Cotets. Itroduce 2. Prelmary theorem 3. Proof 4. Expla 5. Cocluso. Itroduce The P versus NP problem s a major usolved problem computer scece. Iformally, t asks whether a computer

More information

About a Fuzzy Distance between Two Fuzzy Partitions and Application in Attribute Reduction Problem

About a Fuzzy Distance between Two Fuzzy Partitions and Application in Attribute Reduction Problem BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND IORMATION TECHNOLOGIES Volume 6, No 4 Sofa 206 Prt ISSN: 3-9702; Ole ISSN: 34-408 DOI: 0.55/cat-206-0064 About a Fuzzy Dstace betwee Two Fuzzy Parttos ad Applcato

More information

Bounds for the Connective Eccentric Index

Bounds for the Connective Eccentric Index It. J. Cotemp. Math. Sceces, Vol. 7, 0, o. 44, 6-66 Bouds for the Coectve Eccetrc Idex Nlaja De Departmet of Basc Scece, Humates ad Socal Scece (Mathematcs Calcutta Isttute of Egeerg ad Maagemet Kolkata,

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

More information

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions.

Multi Objective Fuzzy Inventory Model with. Demand Dependent Unit Cost and Lead Time. Constraints A Karush Kuhn Tucker Conditions. It. Joural of Math. Aalyss, Vol. 8, 204, o. 4, 87-93 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/jma.204.30252 Mult Objectve Fuzzy Ivetory Model wth Demad Depedet Ut Cost ad Lead Tme Costrats A

More information

General Method for Calculating Chemical Equilibrium Composition

General Method for Calculating Chemical Equilibrium Composition AE 6766/Setzma Sprg 004 Geeral Metod for Calculatg Cemcal Equlbrum Composto For gve tal codtos (e.g., for gve reactats, coose te speces to be cluded te products. As a example, for combusto of ydroge wt

More information

A New Method for Consistency Correction of Judgment Matrix in AHP

A New Method for Consistency Correction of Judgment Matrix in AHP Ne Method for Cosstecy Correcto of Judgmet Matrx HP Hao Zhag Haa Normal UverstyHakou 5758Cha E-mal:74606560@qq.com Ygb We 2 Haa College of Softare TechologyQogha 57400Cha E-mal:W6337@63.com Gahua Yu 3

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

ESTIMATION OF MISCLASSIFICATION ERROR USING BAYESIAN CLASSIFIERS

ESTIMATION OF MISCLASSIFICATION ERROR USING BAYESIAN CLASSIFIERS Producto Systems ad Iformato Egeerg Volume 5 (2009), pp. 4-50. ESTIMATION OF MISCLASSIFICATION ERROR USING BAYESIAN CLASSIFIERS PÉTER BARABÁS Uversty of Msolc, Hugary Departmet of Iformato Techology barabas@t.u-msolc.hu

More information

Bezier curve and its application

Bezier curve and its application , 49-55 Receved: 2014-11-12 Accepted: 2015-02-06 Ole publshed: 2015-11-16 DOI: http://dx.do.org/10.15414/meraa.2015.01.02.49-55 Orgal paper Bezer curve ad ts applcato Duša Páleš, Jozef Rédl Slovak Uversty

More information

Based on GIS Technology of Urban Gardening and Greening Layout Optimization Model

Based on GIS Technology of Urban Gardening and Greening Layout Optimization Model Research Joural of Appled Sceces, Egeerg ad Techology 6(2): 266-270, 203 ISSN: 2040-7459; e-issn: 2040-7467 Maxwell Scetfc Orgazato, 203 Submtted: December 07, 202 Accepted: Jauary 07, 203 Publshed: July

More information

to the estimation of total sensitivity indices

to the estimation of total sensitivity indices Applcato of the cotrol o varate ate techque to the estmato of total sestvty dces S KUCHERENKO B DELPUECH Imperal College Lodo (UK) skuchereko@mperalacuk B IOOSS Electrcté de Frace (Frace) S TARANTOLA Jot

More information

KLT Tracker. Alignment. 1. Detect Harris corners in the first frame. 2. For each Harris corner compute motion between consecutive frames

KLT Tracker. Alignment. 1. Detect Harris corners in the first frame. 2. For each Harris corner compute motion between consecutive frames KLT Tracker Tracker. Detect Harrs corers the frst frame 2. For each Harrs corer compute moto betwee cosecutve frames (Algmet). 3. Lk moto vectors successve frames to get a track 4. Itroduce ew Harrs pots

More information

A Sequential Optimization and Mixed Uncertainty Analysis Method Based on Taylor Series Approximation

A Sequential Optimization and Mixed Uncertainty Analysis Method Based on Taylor Series Approximation 11 th World Cogress o Structural ad Multdscplary Optmsato 07 th -1 th, Jue 015, Sydey Australa A Sequetal Optmzato ad Med Ucertaty Aalyss Method Based o Taylor Seres Appromato aoqa Che, We Yao, Yyog Huag,

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

DATE: 21 September, 1999 TO: Jim Russell FROM: Peter Tkacik RE: Analysis of wide ply tube winding as compared to Konva Kore CC: Larry McMillan

DATE: 21 September, 1999 TO: Jim Russell FROM: Peter Tkacik RE: Analysis of wide ply tube winding as compared to Konva Kore CC: Larry McMillan M E M O R A N D U M DATE: 1 September, 1999 TO: Jm Russell FROM: Peter Tkack RE: Aalyss of wde ply tube wdg as compared to Kova Kore CC: Larry McMlla The goal of ths report s to aalyze the spral tube wdg

More information

Decision-Making Optimization of TMT: A Simulated Annealing Algorithm Analysis

Decision-Making Optimization of TMT: A Simulated Annealing Algorithm Analysis J. Servce Scece & Maagemet, 010, 3, 363-368 do:10.436/ssm.010.3304 Publshed Ole September 010 (http://www.scrp.org/oural/ssm) 363 Decso-Makg Optmzato of TMT: A Smulated Aealg Algorthm Aalyss Yuemg Che,

More information

Evaluation model of young basketball players physical quality and basic technique based on rbf neural network

Evaluation model of young basketball players physical quality and basic technique based on rbf neural network ISSN : 0974-7435 Volume 8 Issue 9 BoTechology BoTechology A Ida Joural Evaluato model of youg basketball players physcal qualty ad basc techque based o rbf eural etwork Guag Lu Wuha Isttute Of Physcal

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

STRATIFIED SAMPLING IN AGRICULTURAL SURVEYS

STRATIFIED SAMPLING IN AGRICULTURAL SURVEYS 3 STRATIFIED SAMPLIG I AGRICULTURAL SURVEYS austav Adtya Ida Agrcultural Statstcs Research Isttute, ew Delh-00 3. ITRODUCTIO The prme objectve of a sample survey s to obta fereces about the characterstc

More information

An Introduction to. Support Vector Machine

An Introduction to. Support Vector Machine A Itroducto to Support Vector Mache Support Vector Mache (SVM) A classfer derved from statstcal learg theory by Vapk, et al. 99 SVM became famous whe, usg mages as put, t gave accuracy comparable to eural-etwork

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

NP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer

NP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer NP!= P By Lu Ra Table of Cotets. Itroduce 2. Strategy 3. Prelmary theorem 4. Proof 5. Expla 6. Cocluso. Itroduce The P vs. NP problem s a major usolved problem computer scece. Iformally, t asks whether

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

Combining Gray Relational Analysis with Cumulative Prospect Theory for Multi-sensor Target Recognition

Combining Gray Relational Analysis with Cumulative Prospect Theory for Multi-sensor Target Recognition Sesors & Trasducers, Vol 172, Issue 6, Jue 2014, pp 39-44 Sesors & Trasducers 2014 by IFSA Publshg, S L http://wwwsesorsportalcom Combg Gray Relatoal Aalyss wth Cumulatve Prospect Theory for Mult-sesor

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

L5 Polynomial / Spline Curves

L5 Polynomial / Spline Curves L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a

More information

(Monte Carlo) Resampling Technique in Validity Testing and Reliability Testing

(Monte Carlo) Resampling Technique in Validity Testing and Reliability Testing Iteratoal Joural of Computer Applcatos (0975 8887) (Mote Carlo) Resamplg Techque Valdty Testg ad Relablty Testg Ad Setawa Departmet of Mathematcs, Faculty of Scece ad Mathematcs, Satya Wacaa Chrsta Uversty

More information

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices.

( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices. 4.3 - Modal Aalyss Physcal coordates are ot always the easest to work Egevectors provde a coveet trasformato to modal coordates Modal coordates are lear combato of physcal coordates Say we have physcal

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL DISTRIBUTION

BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL DISTRIBUTION Iteratoal Joural of Mathematcs ad Statstcs Studes Vol.4, No.3, pp.5-39, Jue 06 Publshed by Europea Cetre for Research Trag ad Developmet UK (www.eajourals.org BAYESIAN INFERENCES FOR TWO PARAMETER WEIBULL

More information

Chapter 8. Inferences about More Than Two Population Central Values

Chapter 8. Inferences about More Than Two Population Central Values Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha

More information

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed

Analysis of a Repairable (n-1)-out-of-n: G System with Failure and Repair Times Arbitrarily Distributed Amerca Joural of Mathematcs ad Statstcs. ; (: -8 DOI:.593/j.ajms.. Aalyss of a Reparable (--out-of-: G System wth Falure ad Repar Tmes Arbtrarly Dstrbuted M. Gherda, M. Boushaba, Departmet of Mathematcs,

More information

Bayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study

Bayes Interval Estimation for binomial proportion and difference of two binomial proportions with Simulation Study IJIEST Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue 5, July 04. Bayes Iterval Estmato for bomal proporto ad dfferece of two bomal proportos wth Smulato Study Masoud Gaj, Solmaz hlmad

More information

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter

The number of observed cases The number of parameters. ith case of the dichotomous dependent variable. the ith case of the jth parameter LOGISTIC REGRESSION Notato Model Logstc regresso regresses a dchotomous depedet varable o a set of depedet varables. Several methods are mplemeted for selectg the depedet varables. The followg otato s

More information

Confidence Intervals for Double Exponential Distribution: A Simulation Approach

Confidence Intervals for Double Exponential Distribution: A Simulation Approach World Academy of Scece, Egeerg ad Techology Iteratoal Joural of Physcal ad Mathematcal Sceces Vol:6, No:, 0 Cofdece Itervals for Double Expoetal Dstrbuto: A Smulato Approach M. Alrasheed * Iteratoal Scece

More information

A Planning Approach of Engineering Characteristics Based on QFD-TRIZ Integrated

A Planning Approach of Engineering Characteristics Based on QFD-TRIZ Integrated A Plag Approach of Egeerg Characterstcs Based o QFD-TRIZ Itegrated Shag Lu, Dogya Sh,, ad Yg Zhag College Mechacal ad Electrcal Egeerg, Harb Egeerg Uversty, Harb 5, Cha Postdoctoral Research Stato of Istrumet

More information

Study on a Fire Detection System Based on Support Vector Machine

Study on a Fire Detection System Based on Support Vector Machine Sesors & Trasducers, Vol. 8, Issue, November 04, pp. 57-6 Sesors & Trasducers 04 by IFSA Publshg, S. L. http://www.sesorsportal.com Study o a Fre Detecto System Based o Support Vector Mache Ye Xaotg, Wu

More information

An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model

An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model Sesors & Trasducers, Vol. 59, Issue, November, pp. 77-8 Sesors & Trasducers by IFSA http://www.sesorsportal.com A Improved Dfferetal Evoluto Algorthm Based o Statstcal Log-lear Model Zhehuag Huag School

More information

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis) We have covered: Selecto, Iserto, Mergesort, Bubblesort, Heapsort Next: Selecto the Qucksort The Selecto Problem - Varable Sze Decrease/Coquer (Practce wth algorthm aalyss) Cosder the problem of fdg the

More information

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design Authors: Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud Applcato of Calbrato Approach for Regresso Coeffcet Estmato uder Two-stage Samplg Desg Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud

More information

GENERATE FUZZY CONCEPTS BASED ON JOIN-IRREDUCIBLE ELEMENTS

GENERATE FUZZY CONCEPTS BASED ON JOIN-IRREDUCIBLE ELEMENTS GENERATE FUZZY CONCEPTS BASED ON JOIN-IRREDUCIBLE ELEMENTS Hua Mao ad *Zhe Zheg Departmet of Mathematcs ad Iformato Scece Hebe Uversty Baodg 071002 Cha *Author for Correspodece: 373380431@qq.com ABSTRACT

More information