Master of Science in Engineering in Information Technology

Transcription

1 Master f Science in Engineering in Infrmatin Technlgy The Faculty f Engineering and Science Aalbrg University 2011 Enters int frce as f 1. September 2012

2 Preface Pursuant t Act 695 f June 22, 2011 n Universities (the University Act) with subsequent changes, the fllwing curriculum fr the Master's prgram in Infrmatin Technlgy in Esbjerg is stipulated. The prgram als fllws the Framewrk Prvisins and the Examinatin Plicies and Prcedures fr the Faculty f Engineering and Science. Table f Cntents Chapter 1: Legal Basis f the Curriculum, etc Basis in ministerial rders Faculty affiliatin Bard f Studies affiliatin... 2 Chapter 2: Admissin, Degree Designatin, Prgram Duratin and Cmpetence Prfile Admissin Degree designatin in Danish and English The prgram s specificatin in ECTS credits Cmpetence prfile n the diplma... 3 Chapter 3: Cntent and Organizatin f the Prgram Overview f the prgram Descriptins f mdules. 7 Chapter 4: Entry int Frce, Interim Prvisins and Revisin Chapter 5: Other Prvisins Rules cncerning written wrk, including the Master s thesis

3 Chapter 1: Legal Basis f the Curriculum, etc. 1.1 Basis in ministerial rders The Master s prgram in Infrmatin Technlgy is rganized in accrdance with the Ministry f Science, Technlgy and Innvatin s Ministerial Order n. 814 f June 29, 2010 n Bachelr s and Master s Prgrams at Universities (the Ministerial Order f the Study Prgrams) and Ministerial Order n. 857 f July 1, 2010 n University Examinatins (the Examinatin Order) with subsequent changes. Further reference is made t Ministerial Order n. 233 f March 24, 2011 (the Admissin Order) and Ministerial Order n. 250 f March 15, 2007 (the Grading Scale Order) with subsequent changes. 1.2 Faculty affiliatin Kandidatuddannelsen hører under Det Teknisk-Naturvidenskabelige Fakultet, Aalbrg Universitet. The Master s prgram falls under the Faculty f Engineering and Science, Aalbrg University. 1.3 Bard f Studies affiliatin The Master s prgram falls under the Bard f Studies fr Electrnics and Infrmatin Technlgy Chapter 2: Admissin, Degree Designatin, Prgram Duratin and Cmpetence Prfile 2.1 Admissin Admissin t the Master s prgram in Infrmatin Technlgy requires a Bachelr s degree in IT, Cmmunicatin and New Media (AAU) Electrnics and IT (AAU) Internet Technlgies and Cmputer Systems (AAU) Sftware Technlgy (DTU) IT & Cmmunicatin Technlgy (DTU) Internet Technlgy & Ecnmy (DTU) (BEng (diplm) degree) IT (DTU) (BEng (diplm) degree) IT & Cmmunicatin (IHK) (BEng (diplm) degree) Cmputer Science r the like. Students with anther Bachelr's degree, upn applicatin t the Bard f Studies, will be admitted after a specific academic assessment if the applicant is deemed t have cmparable educatinal prerequisites. The University can stipulate requirements cncerning cnducting additinal exams prir t the start f study. 2.2 Degree designatin in Danish and English The Master s prgramme entitles the graduate t ne f the fllwing designatins: Civilingeniør, cand.plyt. (candidatus/candidata plytechnices) i infrmatinsteknlgi med specialisering i intelligente pålidelige systemer. The English designatin is: Master f Science (MSc) in Engineering (Infrmatin Technlgy with specialisatin in Intelligent Reliable Systems). r 2

4 Civilingeniør, cand.plyt. (candidatus/candidata plytechnices) i infrmatinsteknlgi med specialisering i intelligente infrmatins systemer. The English designatin is: Master f Science (MSc) in Engineering (Infrmatin Technlgy with specialisatin in Intelligent Infrmatin Systems). 2.3 The prgram s specificatin in ECTS credits The Master s prgram is a 2-year, research-based, full-time study prgram. The prgram is set t 120 ECTS credits. 2.4 Cmpetence prfile n the diplma The fllwing cmpetence prfile will appear n the diplma: A graduate f the Master s prgram has cmpetencies acquired thrugh an educatinal prgram that has taken place in a research envirnment. The graduate f the Master s prgram can perfrm highly qualified functins n the labr market n the basis f the educatinal prgram. Mrever, the graduate has prerequisites fr research (a Ph.D. prgram). Cmpared t the Bachelr s degree, the graduate f the Master s prgram has develped her/his academic knwledge and independence, s that the graduate can independently apply scientific thery and methd in bth an academic and ccupatinal/prfessinal cntext. 2.5 Cmpetence prfile f the prgram: The graduate f the Master s prgram: : Has knwledge in ne r mre subject areas that, in selected areas within infrmatin technlgy, is based n the highest internatinal research in a subject area Can understand and, n a scientific basis, reflect ver subject area s related t infrmatin technlgy and identify scientific prblems within that area Demnstrate an understanding f research wrk and be able t becme a part f the research envirnment Specific fr students specialised in Intelligent Reliable Systems: Has knwledge and cmprehensin within cntrl thery and its applicatins Has a thrugh understanding f prbabilistic, statistics and stchastic theries and methds Has understanding f fault detectin, diagnsis and reliability analysis f engineering systems Specific fr students specialised in Intelligent Infrmatin Systems: Has in-depth knwledge and understanding n hw t apply methds and algrithms frm cmputatinal intelligence in areas such as: infrmatin search, scial netwrk analysis, business and industrial applicatins and engineering and scientific prblems. Can understand and, n a scientific basis, reflect n the applicability and efficiency f the cmputing methds emplyed t slve prblems in a specific applicatin dmain. Can reflect ver the technical aspects and applicability f technlgy in infrmatin systems, n its thery, methds and practice, and identify scientific prblems. Has knwledge f methds t design efficient scalable sftware systems Understands hw t mdel and handle cmplexity, uncertainty and imprecisin fr a variety f prblem dmains. 3

5 : Excels in scientific methds, tls and general skills within infrmatin technlgy Can evaluate and select amng the subject area s(s ) scientific theries, methds, tls and general skills and, n a scientific basis, advance new analyses and slutins Can cmmunicate research-based knwledge and discuss prfessinal and scientific prblems with bth peers and nn-specialists Have btained skills which are related t the emplyment area within infrmatin technlgy Specific fr students specialised in Intelligent Reliable Systems: Can design and develp intelligent reliable systems using state f the art theries and methds within cntrl engineering Able t apply systematic methds fr mdelling cmplex mechanical structures dynamically in bth planar and spatial cases. Specific fr students specialised in Intelligent Infrmatin Systems: can design and develp intelligent infrmatin systems using state f the art cmputer languages and sftware technlgies Cmpetencies Can manage wrk and develpment situatins that are cmplex, unpredictable and require new slutins within the area f infrmatin technlgy Can independently initiate and implement discipline-specific and interdisciplinary cperatin and assume prfessinal respnsibility. Can independently take respnsibility fr wn prfessinal develpment and specializatin Has cmpetencies in design, develpment and test f infrmatin technlgy Specific fr students specialised in Intelligent Reliable Systems: Has cmpetencies within system identificatin, fault detectin, reliability and diagnsis Can cntribute t the scientific develpment within intelligent reliable systems Can priritize and build ptinal cmpetencies in: mdeling f mechanical structures, Kalman filtering, adaptive cntrl, supervised/unsupervised learning and artificial intelligence Specific fr students specialised in Intelligent Infrmatin Systems: has cmpetencies in applying cmputing techniques that invlve searching fr ptimal slutins in large slutin spaces can cntribute creatively and innvatively t identify and prpse new business slutins that are scalable and invlve sme frm f cmputatinal intelligence 4

6 Chapter 3: Cntent and Organizatin f the Prgram The prgram has tw specialisatins: Intelligent Reliable Systems (IRS) Intelligent Infrmatin Systems (IIS) The prgram is structured in mdules and rganized as a prblem-based study. A mdule is a prgram element r a grup f prgram elements, which aims t give students a set f prfessinal skills within a fixed time frame specified in ECTS credits, and cncluding with ne r mre examinatins within specific exam perids. Examinatins are defined in the curriculum. The prgram is based n a cmbinatin f academic, prblem-riented and interdisciplinary appraches and rganized based n the fllwing wrk and evaluatin methds that cmbine skills and reflectin: lectures classrm instructin prject wrk wrkshps exercises (individually and in grups) teacher feedback reflectin prtfli wrk 5

7 3.1 Overview f the prgram: An verview f the ECTS credit breakdwn fr the varius semesters by mdules is shwn in the table belw. Sem. P/C Mdule IIS IRS ECTS Assessment Exam 1st C Prbability Thery, Statistics Elective 1 Mandatry 5 7-pint scale Internal and Stchastic Prcesses C System Identificatin and Mandatry 5 7-pint scale Internal Diagnsis C Advanced Mdelling f Elective pint scale Internal Dynamic Systems C Kalman Filter Thery and its Elective pint scale Internal Applicatins C Prject Organized Prblem- Elective pint scale Internal Based Learning C Fuzzy Lgic Elective pint scale Internal Mandatry C Intelligent Systems Mandatry 5 7-pint scale Internal 5 7-pint scale Internal C Representatin and Management Elective 1 P Intelligent Infrmatin Systems Mandatry 15 7-pint scale External P System Identificatin and Mandatry 15 7-pint scale External Estimatin 2nd C Cntrl and Surveillance Mandatry 5 7-pint scale Internal Prcesses and Systems C Fault Detectin and Diagnsis Mandatry 5 7-pint scale Internal Techniques C Reliability Mdeling and Mandatry 5 7-pint scale Internal Analysis C Infrmatin Retrieval and Mandatry 5 7-pint scale Internal Search Engines C Scalable Infrmatin Systems Mandatry 5 7-pint scale Internal C Scially Intelligent Cmputing Mandatry 5 7-pint scale Internal P Fault Diagnsis and Reliability - Mandatry 15 7-pint scale External Analysis P Infrmatin Retrieval and Mandatry 15 7-pint scale External Mining 3 rd C Adaptive and Optimal Cntrl Elective pint scale Internal C Intelligent Cntrl and Elective pint scale Internal Reliability Oriented Design C Machine Learning Elective 2 Elective pint scale Internal C Machine Intelligence Elective 2 Elective pint scale Internal C State-f-the-art within Intelligent Elective 2 Elective pint scale Internal Infrmatin Systems P Design f Intelligent Reliable Mandatry 20 7-pint scale External Systems P Applied Intelligent Infrmatin Mandatry 20 7-pint scale External Systems 4 th P Master s Thesis Mandatry 3 Mandatry 3 30/50 7-pint scale External Ttal 120 IRS: Intelligent Reliable Systems IIS: Intelligent Infrmatin Systems C/P: Curse/Prject mdule 1 : One curse must be chsen (ttal: 5 ECTS) 2 : Tw curses must be chsen (ttal: 10 ECTS) 3 : Students may chse either a 30 ECTS r a 50 ETCS thesis prject. In the latter case the learning bjectives fr the thesis include bth the learning bjectives fr the prjects n 3 rd and 4 th semester! Table 1 Structure f Master s prgram 6

8 3.2 Descriptins f mdules Curse mdule Prbability Thery, Statistics and Stchastic Prcesses Sandsynlighedsregning, statistik g stkastiske prcesser Prerequisites: Mathematics frm a relevant Bachelr f Science. Students wh cmplete the mdule must: Have knwledge and cmprehensin abut prbability and statistics thery in general Have knwledge f discrete time stchastic prcesses and mdels fr real life signals Have knwledge abut simple descriptins fr stchastic prcesses in time and frequency dmain Have knwledge abut linear filtering f stchastic prcesses Have a cmprehensin f spectral estimatin techniques Be able t apply prbability and statistics thery fr signal analysis and filtering. Be able t apply detectin and estimatin methds in cnnectin with statinary stchastic prcesses within simple prblems Cmpetencies Independently be able t define and analyse scientific prblems within the area f prbabilistic, statistic and/r stchastic prcesses Cntents: Basic cncepts and analysis f prbability thery Basic cncepts and methds f statistics thery Definitin f stchastic prcesses, stchastic sequences, stchastic vectrs Simple 2nd rder descriptin f stchastic prcesses: Expected value, (aut/crss) crrelatin and (aut/crss) cvariance functins and matrices Overview f cmmenly encuntered stchastic prcesses Statinarity Ergdicity Pwer spectral density: its prprties and its relatin t the autcrrelatin Linear filtering f stchastic prcesses AR, MA and ARMA prcesses Binary and multi- hypthesis testing Mean square errr filtering and predictin Intrductin t ptimum filters, e.g., the Wiener filter and Kalman filter Spectral estimatin As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As stated in the Framewrk Prvisins 7

9 Curse mdule System Identificatin and Diagnsis Systemidentifikatin g diagnsticering Prerequisites: Numerical methds, cntrl thery, prbability, statistics and stchastic prcesses, state-space methds.. Students wh cmplete the mdule must: Have cmprehensin f the fundamental principles f typical methds f system identificatin Have cmprehensin f the fundamental cncepts, terms and methdlgies f abnrmal diagnsis Have cmprehensin f sme typical mdel-based and signal-based diagnsis Be able t apply the learned knwledge t handle sme simple system identificatin prblems under assistance f a cmmercial sftware Be able t apply and analyze different diagnsis methds Cmpetencies Independently be able t define and analyse scientific prblems within the area f system identificatin and diagnsis. Independently be able t be a part f prfessinal and interdisciplinary develpment wrk within the area f system identificatin and diagnsis. Cntents: System Identificatin General intrductin t mdelling and system identificatin Typical mdeling methds: physics-based and experiment-based Parametric and nn-parametric mdels General prcedures f system identificatin Nn-recursive methds Least-Square methd and its variants Instrumental variable methds Predictin errr methds Recursive methds Recursive Least-Square methds Recursive instrumental variable methds Recursive predictin errr methds Frgetting factr techniques and time-varying systems identificatin Intrductin t subspace methds MIMO system identificatin Practical cnsideratins Input signals and persistent excitatin Mdel structure selectin Mdel validatin Cmmercial sftware and examples Fault Detectin and Diagnsis Fundamental cncepts, terms and principles f FDD Terminlgy Fundamental principles General verview f typical methds FDD mdelling and analysis 8

10 Fault types and classificatin Fault mdelling Fault delectability Fault diagnsability Parameter identificatin based diagnsis methds State estimatin based diagnsis methds As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As stated in the Framewrk Prvisins 9

11 Curse mdule Advanced Mdeling f Dynamic Systems Advanceret mdellering af dynamiske systemer Prerequisites: Purpse: t cntribute t students attainment f knwledge and cmprehensin f systematic methds fr mdelling cmplex mechanical structures and nn-rigid (flexible) mechanical structures, and further t achieve knwledge and cmprehensin abut advanced dynamics equatins and slutins fr mtin f systems with rigid r nn-rigid bdies. t cntribute t students attainment f knwledge and cmprehensin f fluid pwer systems and cmpnents and enable them t analyse and mdel such systems. Students wh cmplete the mdule must: Have knwledge and cmprehensin fr cmplex mechanical structures Have knwledge f mdelling nn-rigid (flexible) mechanical structures and frictin between tw mving parts. Have knwledge and cmprehensin fr advanced dynamics fr mtin f systems with rigid r nn-rigid bdies. Have knwledge and cmprehensin fr 3-dimensinal kinematic prblems. Have cmprehensin f the characteristics f the pressure media and its influence n the system dynamics Be able t apply systematic methds fr mdelling cmplex mechanical structures dynamically in bth planar and spatial cases. Be able t analyze and mdel the dynamics f fluid pwer cmpnents and systems Be able t judge the usefulness f the set up methds Be able t relate the methds t applicatins in the industry Cmpetencies Independently be able t define and analyse scientific prblems within the area f advanced mechanic systems Independently be able t be a part f prfessinal and interdisciplinary develpment wrk within fluid pwer and advanced mechanic systems. Cntents: Advanced mechanic systems: Planar and spatial rigid bdy kinematics Cartesian crdinates and Euler parameters Transfrmatin matrices Cinematic cnstraints fr plane and spatial jints and actuatrs Cinematic cnstraints fr a cinematically determined system Psitin, velcity and acceleratin analysis Energy methds Lagrange multipliers Reactin frces and trques Rigid bdy mtin (equatins f mtin) fr planar and spatial cases Mdelling flexible mechanical bdies and jints Advanced frictin mdels 10

12 Fluid pwer: Intrductin t dynamic hydraulic systems Prperties f the pressure media and the stiffness influence n the system dynamics Cntinuity and mmentum equatins Systematic apprach fr deriving dynamic lumped parameter mdels f system cmpnents such as: cylinders, pumps, mtrs, valves and flw and pressure regulating cmpnents Flw frces in valves Fluid pwer (serv) drives Mdelling and simulatin f selected characteristic cmpnent(s) Examples f cntrl system design fr fluid pwer systems As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As stated in the Framewrk Prvisins 11

13 Curse mdule Kalman Filter Thery and its Applicatin Kalman filterteri g anvendelse Prerequisites: Purpse: t cntribute t students attainment f knwledge and cmprehensin f Kalman filter thery. t cntribute t students attainment f knwledge and cmprehensin f hw t apply Kalman filter thery fr engineering prblems, such as abnrmal diagnsis and multiple target tracking etc.. Students wh cmplete the mdule must: Have knwledge and cmprehensin fr Kalman filter thery Have knwledge and cmprehensin fr extended Kalman filter techniques Have knwledge and cmprehensin fr vectr-based kalman filter thery. Have cmprehensin f the applicatin f Kalman filter thery t abnrmal scenari diagnsis Have cmprehensin f the applicatin f Kalman filter thery t multiple target tracking Be able t apply Kalman filter thery fr state estimatin prblem in the presence f nises. Be able t apply Kalman filter thery fr abnrmal diagnsis prblem Be able t apply Kalman filter thery fr multiple target tracking prblem Be able t judge the usefulness f the set up methds Be able t relate the methds t applicatins in the industry Cmpetencies Independently be able t define and analyse scientific prblems using Kalman filter thery Independently be able t apply Kalman filter thery fr different engineering prblems Cntents: Cnventinal Kalman filter thery: Scale Kalman filter Vectr-based Kalman filter Cnvergence and precnditins Extended Kalman filter thery: Extended Kalman filter (EKF) Uncended Kalman filter (UKF) Multi-mde Kalman filter Applicatin f KF thery Fault detectin using KF thery Fault diagnsis using KF thery Multiple target tracking Multi-mde system estimatin As described in the intrductin t Chapter 3. 12

14 Exam frmat: Individual ral r written examinatin As stated in the Framewrk Prvisins 13

15 Curse mdule Prject Organized Prblem-Based Learning Prjekt rganiseret prblem-baseret læring Prerequisites: A relevant Bachelr s degree Purpse: T intrduce students t the Aalbrg Mdel in Prblem Based Learning. In additin students will learn abut the a numerical cmputing using Matlab Students wh cmplete the mdule must: Have knwledge and understanding f prject rganized prblem based learning Have knwledge abut grup wrk/cnflicts and ways t slve cnflicts Have knwledge and cmprehensin f planning and structuring the dcumentatin f a prject Be able t cmprehend time-dmain analysis f cntinuus-time systems Be able t cmprehend frequency respnse analysis f cntinuus-time systems Be able t apply the basic rules in discrete cntrl thery including having knwledge abut sampling systems, zer-rder-hld and the influence f time delays. Have knwledge and cmprehensin f the basic features f MATLAB as a prgramming language Be able t apply the prject rganized learning t actual prblem related wrk in grups f up t 6 persns Be able t apply systematic methds Be able t analyze and t design time-invariant linear cntinuus-time cntrl systems using classical methds Be able t analyze different design and cmpensatin methds in cntrl engineering Be able t apply discrete equivalents fr cntinuus transfer functins. Be able t analyze, design and implement digital cntrl systems Be able t use cmmercial simulatin sftware as a cntrl system design tl Be able t use the simple pltting facilities in MATLAB Be able t use data analysis rutines in MATLAB Cmpetencies Independently be able t define and analyse scientific prblems As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As stated in the Framewrk Prvisins 14

16 Curse mdule Fuzzy Lgic Fuzzy lgik Prerequisites Basic knwledge in set thery, algebra and calculus. Data structures and algrithms. Students wh cmplete the mdule: Must have knwledge abut fuzzy sets, fuzzy lgic peratrs, fuzzy relatins, pssibility thery, fuzzy cntrllers, type I and II fuzzy sets, fuzzy numbers, fuzzy expert systems. Must be able t understand hw t mdel imprecisin and vagueness using fuzzy sets, fuzzy inference, hw t use fuzzy relatins, imprtance weighted aggregatin, and applicatins in pattern matching and decisin-making. Must be able t mdel mathematically a prblem that invlves imprecisin and vagueness using fuzzy sets. Must be able t develp a fuzzy lgic based infrmatin system. Must be able t measure the perfrmance f a fuzzy lgic based system Cmpetencies Must be able t apply fuzzy lgic t slve prblems that invlve vagueness and imprecisin in different applicatins dmains such as decisin making systems, structured and unstructured infrmatin retrieval, cntrl systems, and natural language prcessing amng thers. Must be able t evaluate the strengths and weaknesses f fuzzy systems, the perfrmance btained by a fuzzy lgic-based system As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As stated in the Framewrk Prvisins 15

17 Curse mdule Intelligent Systems Intelligente systemer Prerequisites Basic knwledge in prbability thery, set thery, algebra and calculus. Data structures and algrithms. Students wh cmplete the mdule: Must have knwledge abut the use f heuristics t slve prblems where n exact slutin is knwn, abut searching efficiently in large search spaces fr ptimal slutins, the cnnectinist and evlutinary cmputing mdels, basic prbabilistic graphical mdels, and ptimizatin techniques. Must be able t understand cmputatinal cmplexity, and hybrid systems that cmbine several methds and heuristics. Must be able t design and implement infrmatin systems that can be applied effectively in predictin, ptimizatin, r decisin making. Must be able t apply intelligent heuristics in a variety f applicatin dmains such as predictin, ptimizatin, decisin making. Cmpetencies Must be able t decide which heuristics r cmputatinally intelligent mdels shuld be used in a specific applicatin and argue abut his/her decisin. Must be able t assess the applicability f an intelligent system in a specific dmain. Must be able t evaluate the perfrmance f an intelligent system As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins 16

18 Curse mdule Representatin and Management Repræsentatin g håndtering af viden Prerequisites Data structures and algrithms. Students wh cmplete the mdule: Must have an understanding f knwledge representatin techniques and their apprpriate uses. Must have an understanding f the prcess f managing and maintaining knwledge and the variety f techniques fr ding s. Must be able t design and implement a knwledge representatin and management system using a static r dynamic representatin f knwledge. Must be able t explain, cmpare, and cntrast cncepts f data, infrmatin, and knwledge. Must be able t explain knwledge representatin strategies and their relative advantages/disadvantages regarding: static knwledge representatin structures dynamic knwledge representatin structure Must be able t explain apprpriate strategies fr managing, maintaining, and expliting knwledge using: user directed knwledge management autmated knwledge management Must be able t explain the evlutin and mtivatin fr the knwledge management prcess. Cmpetencies Must have cmpetencies in evaluating a knwledge representatin system. As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins 17

19 Prject mdule Intelligent Infrmatin Systems Intelligente Infrmatinsesystemer Prerequisites Data structures and algrithms. Students wh cmplete the mdule: Must have knwledge abut the use f mdels, methds and techniques used in fuzzy lgic, prbabilistic mdels, evlutinary cmputing and ptimizatin t slve prblems invlving uncertainty, imprecisin, large search spaces, r finding the ptimal slutin t a prblem. Must be able t understand hybrid systems that cmbine different cmputatinal intelligence techniques. Must be able t select the mst apprpriate knwledge representatin fr a specific applicatin dmain Must be able t apply cmputatinal intelligence and an apprpriate knwledge representatin t slve cmplex prblems where n traditinal mathematical mdel is knwn r where knwn mathematical mdels are difficult t apply. Must be able t design and develp an intelligent infrmatin system fr a specific applicatin dmain. Cmpetencies Must have cmpetencies in evaluating and assessing the perfrmance f an intelligent infrmatin system in a specific applicatin dmain. Prject wrk Exam frmat: Individual ral examinatin based n a prject reprt As are stated in the Framewrk Prvisins 18

20 Prject mdule System Identificatin and Estimatin Systemidentifikatin g estimering Prerequisites Bachelr f Science in EE, CSE r alike. Students wh cmplete the mdule: Have knwledge and cmprehensin fr the system identificatin techniques. Have fundamental knwledge and cmprehensin f prbability, statistics and stchastic prcesses. Have knwledge and cmprehensin f Kalman filter thery and its typical applicatins.. Be able t chse different system identificatin/estimatin methds and algrithms fr different identificatin and/r estimatin engineering prblems. Be able t evaluate the results using the prbabilistic and/r statistic sense. Be able t verify the analytical and numerical appraches by means f simple labratry experiments Be able t cmmunicate scientific results by use f papers, psters and ral presentatins Cmpetencies Be able t cntrl the wrking and develpment prcess within the prject theme, and be able t develp new slutins within identificatin/estimatin technlgy Independently be able t define and analyse scientific prblems f identificatin /estimatin fr engineering systems, and based n that make and state the reasns fr decisins made fr selecting crrespnding methd. Independently be able t cntinue wn develpment in cmpetences and specializatin Cntent: The prject unit fcuses n the identificatin and/r estimatin f engineering systems fr the cntrl design purpse. The cnsidered systems can cme frm (petr-)chemical prcess industry, ffshre il and gas industry, mechanical systems, rbts r ther engineering systems with the requirements fr identificatin and/r estimatin. The cnsidered prblem shuld be frmulated and analyzed, then sme prper identificatin/estimatin methd needs t be selected and implemented. The designed/cnstructed system is assessed thrugh simulatin and practical test as well. Prject wrk Exam frmat: Individual ral examinatin based n a prject reprt As are stated in the Framewrk Prvisins 19

21 Curse mdule Cntrl and Surveillance Prcesses and Systems Regulerings g vervågningsprcesser g -systemer Prerequisites Cntrl thery and digital micrprcessrs. Purpse: The curse purpse cnsists f tw parts: T cntribute t students attainment f cmprehensin f sme typical industrial cntrl and surveillance prcesses/systems, such as cntrl f AC-machines, PLC prgramming and implementatin and SCADA systems. T cntribute t students attainment f cmprehensin f fundamental knwledge f nnlinear cntrl systems and the feedback linearizatin design methd Students wh cmplete the mdule: Have cmprehensin f sme typical industrial autmatin prcesses/systems including the cntrl f AC-machines, PLC systems and SCADA systems Have cmprehensin f fundamental cncepts and terms f nnlinear cntrl thery. Have cmprehensin f Lyapunv s methds fr stability analysis and stabilizatin cntrl design. Be able t apply the learned knwledge t handle sme small-sized industrial autmatin systems. Be able t apply the feedback linearizatin methd fr nn-linear cntrl design. Be able t judge the usefulness f the set up methds Be able t relate the methds t applicatins in the industry Cmpetencies Independently be able t define and analyse scientific prblems within the area f cntrl and surveillance systems. Independently be able t be a part f prfessinal and interdisciplinary develpment wrk within the area f cntrl and surveillance systems. Cntents: Industrial autmatin systems: Intrductin t industrial autmatin systems Overview f typical energy- industrial autmatin systems Cntrl f AC machines AC machine mdels, e.g., dynamic mdels, space-vectr mdels AC machine statinary characteristics Mtring vs. generating mde Speed-trque-current-vltage-flux characteristics Inductin machine cntrl Variable frequency peratin (V/Hz cntrl) Small-signal stability analysis during V/Hz cntrl Vltage-vectr cntrl Cmpensatin fr resistive vltage drps Lad cmpensatin (slip frequency) Permanent-magnet machine cntrl Trque prductin mechanisms Rtr-flux riented cntrl principles 20

22 Current cntrl Principles f field-weakening peratin Prgrammable Lgic Cntrllers (PLC s) Architecture f PLC systems, includes the micrprcessr unit, I/O mdules, cmmunicatins and user interface PLC prgramming using IEC standard Intrductin t Prgrammable Autmatin Cntrllers (PAC s) Examples f vendr PLC s and fieldbus interfaces t PLC s Supervisry Cntrl And Data Acquisitin (SCADA) systems System cncepts and features Human Machine Interface (HMI) Remte Terminal Unit (RTU) Supervisry statin Cmmunicatin infrastructure and methds SCADA architectures, e.g., mnlithic, distributed, netwrked cnfiguratins Reliability and security issues Redundancy Reliability statistic calculatin Netwrk security Applicatin examples f SCADA in energy systems Nnlinear cntrl Thery Intrductin t nnlinear cntrl Phase plane analysis Lyapunv stability thery Lyapunv Stability Linearizatin and lcal stability Lyapunv s direct methd Stabilizatin cntrl design based n Lyapunv methd Feedback linearizatin Lie derivatives and Lie brackets Diffemrphisms and state transfrmatins Frbenius therem Input-state linearizatin f SISO systems Input-utput linearizatin f SISO systems As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins 21

23 Curse mdule Fault Detectin and Diagnsis Techniques Fejlfinding g diagnsticeringsteknikker Prerequisites Prbability, statistics and stchastic prcesses, system identificatin and estimatin, Purpse: The curse purpse cnsists f tw parts: T cntribute t students attainment f cmprehensin f sme typical fault detectin and diagnsis techniques. Students wh cmplete the mdule: Have cmprehensin f sme typical mdel-free fault detectin and diagnsis methds Have cmprehensin f sme typical mdel-based fault detectin and diagnsis methds Have cmprehensin f Lyapunv s methds fr stability analysis and stabilizatin cntrl design Be able t apply the learned knwledge t handle sme industrial autmatin systems. Be able t judge the usefulness f the set up methds Be able t relate the methds t applicatins in the industry Cmpetencies Independently be able t define and analyse scientific prblems within the area f fault detectin and diagnsis. Independently be able t be a part f prfessinal and interdisciplinary develpment wrk within the area f fault detectin and diagnsis. Cntents: Fundamental cncepts, terms and principles f FDD Fault mdelling and analysis Fault types and classificatin Fault mdelling Fault delectability Fault diagnsability Residual generatin (I): Observer based FDD methds fr deterministic systems Review f bserver thery Fault detectin using single bserver Fault diagnsis using a bank f bservers Residual generatin (II): Kalman filter based FDD methds fr stchastic systems Review f prbability and stchastic prcesses Kalman filter thery Extended Kalman filter Fault detectin using single Kalman filter Fault diagnsis using a bank f Kalman filters (Multiple Mdel (MM) methd) Fault diagnsis using a bank interactive Kalman filters (Interactive Multiple Mdel (IMM) methd) Fault diagnsis using a tw-stage Kalman filter fr additive and multiplicative faults Rbust residual generatin (I): Unknwn Input Observer (UIO) methd 22

24 (cmplete) Disturbance decupling principle UIO thery Rbust FDD using UIO methd Rbust residual generatin (II): Rbust filtering methd Disturbance attenuatin principle Mdelling uncertainties Intrductin t rbust filtering thery (H_infty ptimal cntrl thery) Rbust FDD using H_infty filtering methd Residual evaluatin Simple vting techniques Statistical testing appraches Likelihd functin methds Prbabilities f false alarm and miss FDD using Parity space appraches Delectability and diagnsability Parity space methds fr FDD Parameter estimatin based FDD methds Parametric fault characteristics FDD using parameter estimatin (least-square methds) FDD using recursive system identificatin methds Signal-based (mdel-free) FDD methds FDD using spectrum analysis FDD using shrt-timed Furier transfrm and wavelet transfrm FDD using Principal Cmpnent Analysis (PCA) FDD using sme artificial intelligence methds As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins 23

25 Curse mdule Reliability Mdeling and Analysis Pålidelighedsmdellering g analyse Prerequisites Prbability, statistics and stchastic prcesses Purpse: The curse purpse cnsists f tw parts: T cntribute t students attainment f cmprehensin f fundamental principles fr reliability mdelling T cntribute t students attainment f cmprehensin f fundamental principles fr reliability analysis Students wh cmplete the mdule: Have cmprehensin f fundamental principles fr reliability mdelling and analysis Have cmprehensin f reliability analysis using lgic diagrams Have cmprehensin f Bayesian methds fr simple reliability mdelling and analysis Be able t apply prbabilistic methds fr reliability mdelling and analysis. Be able t judge the usefulness f the set up methds Be able t relate the methds t applicatins in the industry Cmpetencies Independently be able t define and analyse scientific prblems within the area f reliability mdelling and analysis. Independently be able t be a part f prfessinal and interdisciplinary develpment wrk within the area f reliability mdelling and analysis. Cntents: Principles f reliability mdelling Quality and reliability Creating reliability vs. measuring reliability Failure mdes, causes and mechanisms Prbabilistic mdels f failure phenmena Essentials f prbability thery Prbabilistic definitin f reliability Cmpnent reliability Cmmn distributin in cmpnent reliability Cmpnent reliability mdel selectin System reliability analysis Structure analysis and design Reliability blck diagram methd Fault mdes and effects analysis Fault tree analysis Hazard and risk analysis 24

26 Reliability analysis f dynamic systems Markv thery and applicatins Simulatin methds (Mnte Carl methds) Analysis f fault tlerant systems Bayesian analysis Fundatins f Bayesian statistical inference Bayesian inference in reliability Perfrming Bayesian reliability analysis Bayesian decisin and estimatin thery Uncertainty analysis and prpagatin methds Measuring uncertainty Uncertainty prpagatin Reliability in cmputer systems Hardware reliability vs. sftware reliability Sftware reliability imprvement methds Sftware reliability assessment methds As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins 25

27 Curse mdule Infrmatin Retrieval and Search Engines Infrmatinssøgning g søgemaskiner Prerequisites Fuzzy Lgic, Intelligent Systems Students wh cmplete the mdule: Must have knwledge abut infrmatin retrieval mdels, questin answering systems, search engines, enterprise search systems, indexing, infrmatin extractin, entity recgnitin, semantic web, ntlgies, infrmatin fusin, visualizatin, basic natural language prcessing, and human factrs in infrmatin access,. Must be able t understand hw t build search engines, hw metasearch engines wrk, hw t manage structured, semistructured and unstructured data, hw t rganize search indexes, hw t visualize retrieved infrmatin, and hw t derive, mdel, and apply user cntext and behavir in interpretatin f user queries. Mst knw the relevant state-f-art in infrmatin access technlgy. Must be able t apply his/her knwledge f the thery and mdels used in infrmatin retrieval and search engines in the design and evaluatin f an infrmatin retrieval system. Must be able t develp tls fr search engines and t design and develp infrmatin access slutins with end-users. Cmpetencies Must have cmpetencies in evaluating the use and applicatin f natural language prcessing tls t develp infrmatin retrieval systems. As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins 26

28 Curse Mdule Scalable Infrmatin Systems Skalérbare infrmatins systemer Prerequisites Data structures and algrithms Students wh cmplete the mdule: Must have knwledge abut distributed systems, the design f scalable systems, hw t use web services, abut mashup pages, abut clud and grid cmputing. Must understand cache systems, state and stateless systems, scalability bttlenecks, SOA, web services, REST, parallel and distributed cmputing. Must understand hw parallel and distributed databases wrk. Must have knwledge n languages and libraries used t implement parallelizatin. Must understand basic scalability cncepts and parallelizatin techniques such as the scalability cube, sftware pipelines and MapReduce t implement efficiently an algrithm. Must be able t apply scalability techniques t design reliable scalable distributed systems. Cmpetencies Must be able t evaluate scalable systems and determine the perfrmance f a parallel prcessing system. Must be able t evaluate the feasibility f mving partially r fully a whle scalable distributed applicatin t the clud and/r the grid. As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins 27

29 Curse Mdule Scially Intelligent Cmputing Beregning i intelligente sciale netværk Prerequisites Fuzzy Lgic, Intelligent Systems, Students wh cmplete the mdule: Must have knwledge abut cmputatinal methds applied in scial netwrks, n cllabrative wrkgrups, n methds fr mining scial netwrk sites fr pinins and sentiment analysis and classificatin. Must understand hw t btain infrmatin f scial netwrks frm the web, the metrics used in scial netwrks, abut web 2.0 and scial netwrk visualizatin tls. Must understand basic cncepts f security and privacy in scial netwrks Must understand cmplex netwrks, its features and applicatins. Must understand the principles f dynamic and static netwrks Must understand basic graph thery, the algrithms and representatins used in graph thery, and hw it can be used t mdel scial netwrks r ther cmplex netwrks. Must be able t apply methds and algrithms frm scial netwrk analysis t discver patterns f cllabratin and rles, pinins, sentiment and trends in scial netwrks. Must be able t apply the metrics used in scial netwrks effectively and the metrics used t measure the perfrmance f algrithms fr sentiment analysis and classificatin Must be able t apply and develp algrithms fr scial netwrk analysis Must be able t apply visualizatin techniques in scial netwrk analysis Cmpetencies Must be able t evaluate the applicability f the metrics and mdels used in scial netwrk analysis within a given prblem dmain. As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins 28

30 Prject mdule Fault Diagnsis and Reliability Analysis Fejldiagnsticering g pålidelighedsanalyse Prerequisites 1. Semester Purpse: The purpse f the prject unit is t cntribute t students attainment f knwledge abut fault detectin, diagnsis and relevant reliability analysis f engineering systems Students wh cmplete the mdule: Have knwledge and cmprehensin fr hw t design, analyse and mdel different fault diagnsis systems fr different typical engineering systems Have knwledge and cmprehensin f fundamental reliability analysis and mdelling. Be able t apply prbabilistic methds fr reliability mdelling, analysis and assessment. Be able t apply different fault diagnsis methds fr develping mnitring and surveilliance system. Be able t verify the analytical and numerical appraches either by means f labratry experiments r simulatin study Cmpetences Be able t cntrl the wrking and develpment prcess within the prject theme, and be able t develp new slutins within mnitring and surveilliance system. Independently be able t define and analyse mnitring/diagnsis prblems frm the reliability pint f view, and based n that make and state the reasns fr decisins made fr methd selectin. Independently be able t cntinue wn develpment in cmpetences and specializatin Cntent: The prject is based n a prblem t mnitr a prcess system, which can be a chemical prcess, mechanical system, r any ther safety-critical systems. The reliability f the cnsidered system as well as individual cmpnents shuld be analysed and assessed using the prbabilistic methds. The strategies and methds fr Fault Detectin and Diagnsis (FDD) shuld be determined fr the cnsidered system by taking sme intelligent methds int cnsideratin. The chsen FDD slutin has t be implemented n a real-time platfrm and tested, either by the cmputer simulatins r a dedicated hardware Prject wrk Exam frmat: Individual ral examinatin based n a prject reprt As are stated in the Framewrk Prvisins 29

31 Prject mdule Infrmatin Retrieval and Mining Infrmatinshentning g søgning Prerequisites 1. Semester Students wh cmplete the mdule: Must have knwledge n methds and mdels f infrmatin retrieval fr structured, unstructured and semistructured data. Must have knwledge n methds and mdels fr mining and extracting infrmatin frm diverse surces f data such as cllectin f dcuments, databases r scial netwrks. Must have knwledge n efficient implementatin techniques fr scalable infrmatin retrieval r extractin systems Must be able t apply infrmatin retrieval techniques t design and implement infrmatin retrieval systems and search engines Must be able t apply infrmatin extractin techniques int dcument cllectins r scial netwrks Cmpetences Must be able t evaluate the applicatin f infrmatin retrieval and infrmatin extractin techniques int dcument cllectins r scial netwrks Prject wrk Exam frmat: Individual ral examinatin based n a prject reprt As are stated in the Framewrk Prvisins 30

32 Curse mdule Adaptive and Optimal Cntrl Adaptiv g ptimal regulering Prerequisites Linear cntrl thery, numerical methds, ptimizatin thery Purpse: The curse purpse is t cntribute t students attainment f knwledge and cmprehensin f the fundamental knwledge f advanced cntrl with adaptive mechanisms and ptimal cntrl techniques. Students wh cmplete the mdule: Have cmprehensin f the fundamental principles f typical adaptive cntrl methds Have cmprehensin f the fundamental principles f typical ptimal cntrl methds Be able t use different adaptive and ptimal cntrl algrithms. Be able t apply sme typical adaptive/ptimal cntrl methds t slve sme specific linear cntrl prblems under the assistance f available cmputatin sftware Cmpetencies Independently be able t define and analyse scientific prblems within the area f adaptive and ptimal cntrl. Independently be able t be a part f prfessinal and interdisciplinary develpment wrk within the area f adaptive and ptimal cntrl. Cntents: Adaptive cntrl: Intrductin t adaptive cntrl Typical adaptive cntrl principles and methds Feed-frward adaptive cntrl and feedback adaptive cntrl Feedback adaptive cntrl Gain scheduling Mdel Reference Adaptive Cntrl (MRAC) Gradient ptimizatin MRAC s Stability ptimized MRAC s Mdel identificatin adaptive cntrl Parametric adaptive cntrl Explicit parameter adaptive cntrl Implicit parameter adaptive cntrl Multiple mdel adaptive cntrl Self-tuning regulatrs Optimal Cntrl: Review f ptimal cntrl principles Infinite hrizn ptimizatin: Linear Quadratic (LQ) cntrl Standard prblem frmulatin Slutins and Riccati equatins Discrete-time LQ cntrl Linear quadratic Gaussian (LQG)cntrl Applicatin examples Finite hrizn ptimizatin (I): Minimum Variance Cntrl (MVC) Prblem frmulatin fr SISO systems 31

33 Slutin and its prperties Generalized MVC Offset prblem Self-tuning MVC Finite hrizn ptimizatin (II): Mdel predictive Cntrl (MPC) Principles f MPC Typical MPC schemes based n different mdels Numerical cmputatin algrithms Nnlinear MPC Cmmercial sftware and examples Adaptive MPC Principles f adaptive MPC Typical algrithms As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins 32

34 Curse mdule Intelligent Cntrl and Reliability Oriented Design Intelligent regulering g design af pålidelige systemer Prerequisites Linear cntrl thery, numerical methds, ptimizatin thery Purpse: The curse purpse is t cntribute t students attainment f knwledge abut sme typical intelligent cntrl methds with cnsideratin f reliability Students wh cmplete the mdule: Have cmprehensin f the fundamental principles f typical intelligent cntrl methds Have cmprehensin f the fundamental principles f reliability riented design Be able t apply different intelligent cntrl algrithms fr different engineering prblems Be able t apply reliability riented design t slve sme specific reliable cntrl prblems under the assistance f available cmputatin sftware Cmpetencies Independently be able t define and analyse scientific prblems within the area f intelligent and reliable cntrl Independently be able t be a part f prfessinal and interdisciplinary develpment wrk within the area f intelligent and reliable cntrl. Cntents: Intelligent cntrl based n fuzzy lgic and neural netwrks Blean lgic, fuzzy thery f sets, membership functins, fuzzy lgic Fuzzy relatins, fuzzy rule bases, defuzzicatin Fuzzy mdelling and fuzzy cntrl Neurn mdel, learning, back prpagatin errr, gradient methds, Nn-parametric regressin and classificatin Nn-parametric estimatin, Parzen estimatrs, cmpetitive learning, winner takes all, K-means clustering The cherence between regressin and defuzzificatin, neural-fuzzy systems, learning in rule bases, extractin f rules frm neural netwrk Supervisry cntrl Discrete event systems and mdels Languages and autmata Safety, blcking, state estimatin and diagnsis Cntrllability therem Observability therem Supervisry cntrl prblem and their slutins Hybrid cntrl systems Terminlgy f hybrid systems Cntrl architectures f hybrid systems Mdelling f hybrid systems, Hybrid autmatn and its peratin Reachability and cntrllability analysis Stability f hybrid systems Multiple Lyapunv functin methd 33

35 Cntrl synthesis fr linear switched hybrid systems Active fault-tlerant (recnfigurable) cntrl General structure f active FTCS Classificatin f existing design strategies Incrpratin f perfrmance degradatin in designing FTCS Reliability assessment f FTCS Recnfigurable cntrller design techniques Statistic estimatin f reliability Reliability evaluatin f FDD methds As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin As are stated in the Framewrk Prvisins Curse Mdule Machine Learning Maskinlæring Prerequisites Basic knwledge in prbability thery, statistics, and linear algebra. Students wh cmplete the mdule: Must have knwledge abut supervised learning methds including K-nearest neighburs, decisin trees, linear discriminant analysis, and neural netwrks Must have knwledge abut unsupervised learning methds including: K-means, Gaussian mixture mdel, hidden Markv mdel, EM algrithm, and principal cmpnent analysis Must have knwledge abut algrithm-independent machine learning: Bayesian decisin thery, bias and variance trade-ff, and crss-validatin Must be able t understand reinfrcement learning Must be able t implement the fundamental methds either frm scratch r by using existing tls Cmpetencies Must have cmpetencies in analyzing a given prblem and identifying apprpriate machine learning methds t the prblem Must have cmpetencies in understanding the strengths and weaknesses f the methds Must be able t evaluate and cmpare the methds fr a specific applicatin prblem in a scientific way As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin. Are stated in the Framewrk Prvisins 34

36 Curse Mdule Machine Intelligence Maskinintelligens Prerequisites Algrithms and data structures, cmputer prgramming Students wh cmplete the mdule: Must have knwledge abut fundamental cncepts f prbability thery(reasning), decisin thery and machine learning, prbabilistic graphical mdels. Must be able t apply artificial intelligence and machine learning techniques t slve prblems invlving cmplex prcessing and analysis f cmplex data. Many f these techniques are applied in multi-agent systems, datamining, biinfrmatics and ther cmputer science subjects. Cmpetencies Must have cmpetencies in evaluating the applicability f prbabilistic graphical mdels int different applicatin dmains. As described in the intrductin t Chapter 3. Exam frmat: Individual ral r written examinatin. Are stated in the Framewrk Prvisins 35