The two-train separation problem on level track
|
|
|
- Mark Barnett
- 5 years ago
- Views:
Transcription
1 Energy-efficient train timetables The two-train separation problem on level track Amie Albrecht Phil Howlett Peter Pudney Xuan Vu Peng Zhou Scheduling and Control Group School of Information Technology and Mathematical Sciences, UniSA ARC Linkage Grant LP
2
3
4
5 reduce energy costs by up to 20% improve on-time arrivals by 10% reduce braking by up to 30%
6 Power Hold Coast Brake
7 Optimal control of a single train Equation of motion: dv dt = p q r(v) + g(x) v dx Elapsed distance: dt = v Journey cost: J = T 0 p dt Controls: Tractive power: 0 p(x) P Braking force: 0 q(x) Q Problem: Find controls p, q and an associated speed profile v(x) with v(0) = v(x ) = 0 and t(x) T that minimises energy.
8 Optimal control of a single train Equation of motion: dv dt = p q r(v) + g(x) v dx Elapsed distance: dt = v Journey cost: J = T 0 p dt Controls: Tractive power: 0 p(x) P Braking force: 0 q(x) Q Problem: Find controls p, q and an associated speed profile v(x) with v(0) = v(x ) = 0 and t(x) T that minimises energy.
9 Optimal control of a single train Equation of motion: dv dt = p q r(v) + g(x) v dx Elapsed distance: dt = v Journey cost: J = T 0 p dt Controls: Tractive power: 0 p(x) P Braking force: 0 q(x) Q
10 Optimal control of a single train Equation of motion: dv dt = p q r(v) + g(x) v dx Elapsed distance: dt = v Journey cost: J = T 0 p dt Controls: Tractive power: 0 p(x) P Braking force: 0 q(x) Q Problem: Find controls p, q and an associated speed profile v(x) with v(0) = v(x ) = 0 and t(x) T that minimises energy.
11 v x
12 What we know about single train control On level track the optimal strategy is power-speedhold-coast-brake.
13 What we know about single train control 30 V v 20 U px(0,v) pt(0,v) pj(0,v) x hx(a, b, V ) ht(a, b, V ) hj(a, b, V ) cx(v,u) ct(v,u) cj(v,u) bx(u, 0) bt(u, 0) bj(u, 0)
14 What we know about single train control 30 V v 20 U px(0,v) pt(0,v) pj(0,v) x hx(a, b, V ) ht(a, b, V ) hj(a, b, V ) cx(v,u) ct(v,u) cj(v,u) bx(u, 0) bt(u, 0) bj(u, 0)
15 What we know about single train control 30 V v 20 U px(0,v) pt(0,v) pj(0,v) x hx(a, b, V ) ht(a, b, V ) hj(a, b, V ) cx(v,u) ct(v,u) cj(v,u) bx(u, 0) bt(u, 0) bj(u, 0)
16 What we know about single train control 30 V v 20 U px(0,v) pt(0,v) pj(0,v) x hx(a, b, V ) ht(a, b, V ) hj(a, b, V ) cx(v,u) ct(v,u) cj(v,u) bx(u, 0) bt(u, 0) bj(u, 0)
17 What we know about single train control 30 V v 20 U px(0,v) pt(0,v) pj(0,v) x hx(a, b, V ) ht(a, b, V ) hj(a, b, V ) cx(v,u) ct(v,u) cj(v,u) bx(u, 0) bt(u, 0) bj(u, 0)
18 What we know about single train control On level track the optimal strategy is power-speedhold-coast-brake. As the hold speed increases the journey time decreases. There is a unique hold speed for each given journey time.
19 What we know about single train control On level track the optimal strategy is power-speedhold-coast-brake. As the hold speed increases the journey time decreases. There is a unique hold speed for each given journey time. We can also find the optimal strategy for: Piecewise-constant and continuously varying gradients Steep gradients Speed limits
20 What happens with multiple trains?
21 What happens with multiple trains? If there are no disruptions we can drive efficiently to the timetable.
22 What happens with multiple trains? If there are no disruptions we can drive efficiently to the timetable. That rarely happens! In practice, trains are often slowed or stopped at signals for safe separation. Instead, trains should be regulated so that they are less likely to encounter restrictive signals.
23 What happens with multiple trains? If there are no disruptions we can drive efficiently to the timetable. That rarely happens! In practice, trains are often slowed or stopped at signals for safe separation. Instead, trains should be regulated so that they are less likely to encounter restrictive signals. So, we want to determine optimal driving strategies for a fleet of trains.
24 The two-train separation problem Problem: Find driving strategies for two trains travelling on the same track in the same direction subject to given journey times so that an adequate separation is maintained and so that the total energy consumption is minimised.
25 The two-train separation problem Problem: Find driving strategies for two trains travelling on the same track in the same direction subject to given journey times so that an adequate separation is maintained and so that the total energy consumption is minimised. In general, the overall strategy may be better if neither train follows the single-train optimal profile.
26 Maintaining safe separation Do not allow two trains on the same track section at the same time.
27 Maintaining safe separation Do not allow two trains on the same track section at the same time. Specify intermediate signal times. A signal time is: the latest exit time for the leading train the earliest entry for the following train
28 Outline of a solution procedure 1. Find optimal journeys with prescribed intermediate signal times 2. Find optimal intermediate times
29 1a. An optimal journey for the leading train If the optimal unrestricted journey does not leave a particular section before the required time then it must go faster on the first part of the journey.
30 1a. An optimal journey for the leading train If the optimal unrestricted journey does not leave a particular section before the required time then it must go faster on the first part of the journey. A strategy of optimal type may use different hold speeds for different sections, but as the journey progresses the hold speeds will decrease. power-hold-coast }{{} first section coast-hold-coast }{{} for (n 2) sections coast-hold-coast-brake }{{} last section
31 1b. An optimal journey for the following train If the optimal unrestricted journey enters a particular section before the required time then it must go slower on the first part of the journey. A strategy of optimal type may use different hold speeds for different sections, but as the journey progresses the hold speeds will increase. power-hold-power }{{} for (n 1) sections power-hold-coast-brake }{{} last section
32 1. Optimal journeys with prescribed intermediate signal times 30 U 1 U 2 20 U 3 10 U 4 0 x 0 x 1 x 2 x 3 x 4 30 U 2,U 3 20 U 1 U x 0 x 1 x 2 x 3 x 4
33 2. Finding optimal intermediate times Calculate times taken to traverse sections (using p t, h t, c t, b t) Some requirements: s i=0 f i s 1 g i + T i=0 n f i T l i=0 n g i T f i=0 s = 1,..., n
34 2. Finding optimal intermediate times Form a Lagrangian function: J = J l + J f + constraints Compute the partial derivatives of J : f i V j g i Y j Arrive at a necessary condition for optimality This condition means we can check if prescribed times are optimal.
35 Future work Implement an automated search for the hold speeds Devise an efficient algorithm to optimise the prescribed intermediate times Solve the three-train problem (and then many trains) Tackle non-level track Integrate with scheduling systems (use timing windows rather than timing points)
Transportation Timetabling
Outline DM87 SCHEDULING, TIMETABLING AND ROUTING 1. Sports Timetabling Lecture 16 Transportation Timetabling Marco Chiarandini 2. Transportation Timetabling Tanker Scheduling Air Transport Train Timetabling
[f(t)] 2 + [g(t)] 2 + [h(t)] 2 dt. [f(u)] 2 + [g(u)] 2 + [h(u)] 2 du. The Fundamental Theorem of Calculus implies that s(t) is differentiable and
![[f(t)] 2 + [g(t)] 2 + [h(t)] 2 dt. [f(u)] 2 + [g(u)] 2 + [h(u)] 2 du. The Fundamental Theorem of Calculus implies that s(t) is differentiable and](/thumbs/92/108353356.jpg "[f(t)] 2 + [g(t)] 2 + [h(t)] 2 dt. [f(u)] 2 + [g(u)] 2 + [h(u)] 2 du. The Fundamental Theorem of Calculus implies that s(t) is differentiable and")
Midterm 2 review Math 265 Fall 2007 13.3. Arc Length and Curvature. Assume that the curve C is described by the vector-valued function r(r) = f(t), g(t), h(t), and that C is traversed exactly once as t
Goals: To study constrained optimization; that is, the maximizing or minimizing of a function subject to a constraint (or side condition).
Unit #23 : Lagrange Multipliers Goals: To study constrained optimization; that is, the maximizing or minimizing of a function subject to a constraint (or side condition). Constrained Optimization - Examples
The quantitative relationship between distance, time and speed
The quantitative relationship between distance, time and speed Introduction In order to understand motion, it is important to consider the basic definition in terms of distance and time. When we say a
A Fast Numerical Optimization Algorithm for Aircraft Continuous Descent Approach
ERCOFTAC 2006 DESIGN OPTIMISATION: METHODS & APPLICATIONS GRAN CANARIA, CANARY ISLANDS, SPAIN A Fast Numerical Optimization Algorithm for Aircraft Continuous Descent Approach J.M. Canino*, J. González
Lecture 19. Vector fields. Dan Nichols MATH 233, Spring 2018 University of Massachusetts. April 10, 2018.
Lecture 19 Vector fields Dan Nichols nichols@math.umass.edu MATH 233, Spring 218 University of Massachusetts April 1, 218 (2) Chapter 16 Chapter 12: Vectors and 3D geometry Chapter 13: Curves and vector
2.1 Partial Derivatives
.1 Partial Derivatives.1.1 Functions of several variables Up until now, we have only met functions of single variables. From now on we will meet functions such as z = f(x, y) and w = f(x, y, z), which
ANSWER KEY. (a) For each of the following partials derivatives, use the contour plot to decide whether they are positive, negative, or zero.
Math 2130-101 Test #2 for Section 101 October 14 th, 2009 ANSWE KEY 1. (10 points) Compute the curvature of r(t) = (t + 2, 3t + 4, 5t + 6). r (t) = (1, 3, 5) r (t) = 1 2 + 3 2 + 5 2 = 35 T(t) = 1 r (t)
Link and Link Impedance 2018/02/13. VECTOR DATA ANALYSIS Network Analysis TYPES OF OPERATIONS
VECTOR DATA ANALYSIS Network Analysis A network is a system of linear features that has the appropriate attributes for the flow of objects. A network is typically topology-based: lines (arcs) meet at intersections
Exam 2 Review Sheet. r(t) = x(t), y(t), z(t)
Exam 2 Review Sheet Joseph Breen Particle Motion Recall that a parametric curve given by: r(t) = x(t), y(t), z(t) can be interpreted as the position of a particle. Then the derivative represents the particle
Independence of Path and Conservative Vector Fields
Independence of Path and onservative Vector Fields MATH 311, alculus III J. Robert Buchanan Department of Mathematics Summer 2011 Goal We would like to know conditions on a vector field function F(x, y)
Effect of Parameter Tuning on Performance of Cuckoo Search Algorithm for Optimal Reactive Power Dispatch
RESEARCH ARTICLE OPEN ACCESS Effect of Parameter Tuning on Performance of Cuckoo Search Algorithm for Optimal Reactive Power Dispatch Tejaswini Sharma Laxmi Srivastava Department of Electrical Engineering
Chapter 10 Digital PID
Chapter 10 Digital PID Chapter 10 Digital PID control Goals To show how PID control can be implemented in a digital computer program To deliver a template for a PID controller that you can implement yourself
4 to find the dimensions of the rectangle that have the maximum area. 2y A =?? f(x, y) = (2x)(2y) = 4xy
Optimization Constrained optimization and Lagrange multipliers Constrained optimization is what it sounds like - the problem of finding a maximum or minimum value (optimization), subject to some other
University of California, Berkeley Department of Mathematics 5 th November, 2012, 12:10-12:55 pm MATH 53 - Test #2
University of California, Berkeley epartment of Mathematics 5 th November, 212, 12:1-12:55 pm MATH 53 - Test #2 Last Name: First Name: Student Number: iscussion Section: Name of GSI: Record your answers
6.1 (CD-ROM TOPIC) USING THE STANDARDIZED NORMAL DISTRIBUTION TABLE
.1: (CD-ROM Topic) Using the Standardized Normal Distribution Table CD-1.1 (CD-ROM TOPIC) USING THE STANDARDIZED NORMAL DISTRIBUTION TABLE Any set of normally distributed data can be converted to its standardized
Scheduling. Radek Mařík. April 28, 2015 FEE CTU, K Radek Mařík Scheduling April 28, / 48
Scheduling Radek Mařík FEE CTU, K13132 April 28, 2015 Radek Mařík (marikr@fel.cvut.cz) Scheduling April 28, 2015 1 / 48 Outline 1 Introduction to Scheduling Methodology Overview 2 Classification of Scheduling
SATURN 101: Part 3 Improving Convergence
SATURN 101: Part 3 Improving Convergence 2018 User Group Meeting November 2018 Final 03/12/18 - UGM2018 SAT101 Part 3 Improving Convergence Dirck Van Vliet SATURN Assignment 101 Part 3 - Recap on SAVEIT
Elimination of the Eects of Mutual Coupling. in an Adaptive Nulling System with a Look. Direction Constraint. R.S. Adve and T.K.
Elimination of the Eects of Mutual Coupling in an Adaptive Nulling System with a Look Direction Constraint R.S. Adve and T.K. Sarkar Department of Electrical and Computer Engineering, Introduction Syracuse
Autonomous Self-deployment of Wireless Access Networks in an Airport Environment *
Autonomous Self-deployment of Wireless Access Networks in an Airport Environment * Holger Claussen Bell Labs Research, Swindon, UK. * This work was part-supported by the EU Commission through the IST FP5
physicsandmathstutor.com
ADVANCED GCE MATHEMATICS 4737 Decision Mathematics 2 Candidates answer on the answer booklet. OCR supplied materials: 8 page answer booklet (sent with general stationery) Insert for Questions 4 and 6 (inserted)
Honors Algebra 2 Assignment Sheet - Chapter 1
Assignment Sheet - Chapter 1 #01: Read the text and the examples in your book for the following sections: 1.1, 1., and 1.4. Be sure you read and understand the handshake problem. Also make sure you copy
1SL SlewGuard User Manual
1SL SlewGuard User Manual v1.2 04/04/2016 Change History Issue Date Changes Job V1.0 10/08/2013 First draft V1.1 20/06/2014 Update product identification V1.2 04/04/2016 General update and reformat of
SYDE 112, LECTURE 34 & 35: Optimization on Restricted Domains and Lagrange Multipliers
SYDE 112, LECTURE 34 & 35: Optimization on Restricted Domains and Lagrange Multipliers 1 Restricted Domains If we are asked to determine the maximal and minimal values of an arbitrary multivariable function
Two-stage column generation and applications in container terminal management
Two-stage column generation and applications in container terminal management Ilaria Vacca Matteo Salani Michel Bierlaire Transport and Mobility Laboratory EPFL 8th Swiss Transport Research Conference
Electric Circuit Fall 2015 Pingqiang Zhou. ShanghaiTech University. School of Information Science and Technology. Professor Pingqiang Zhou
Electric Circuit Fall 5 Pingqiang Zhou ShanghaiTech University School of Information Science and Technology Professor Pingqiang Zhou LABORATORY Gyrator Guide. Objective In this laboratory measurement you
A NEW TOOL PATH STRATEGY TAPS THE TRUE POTENTIAL OF CNC MILLING MACHINES
volume 9 issue 33 A NEW TOOL PATH STRATEGY TAPS THE TRUE POTENTIAL OF CNC MILLING MACHINES There s no denying that CNC milling machines represent a quantum leap in productivity over their manual brethren.
Topic 7f Time Domain FDM
Course Instructor Dr. Raymond C. Rumpf Office: A 337 Phone: (915) 747 6958 E Mail: rcrumpf@utep.edu Topic 7f Time Domain FDM EE 4386/5301 Computational Methods in EE Topic 7f Time Domain FDM 1 Outline
Decision Mathematics D1 Advanced/Advanced Subsidiary. Friday 17 May 2013 Morning Time: 1 hour 30 minutes
Paper Reference(s) 6689/01R Edexcel GCE Decision Mathematics D1 Advanced/Advanced Subsidiary Friday 17 May 2013 Morning Time: 1 hour 30 minutes Materials required for examination Nil Items included with
CHAPTER 11 PARTIAL DERIVATIVES
CHAPTER 11 PARTIAL DERIVATIVES 1. FUNCTIONS OF SEVERAL VARIABLES A) Definition: A function of two variables is a rule that assigns to each ordered pair of real numbers (x,y) in a set D a unique real number
Understanding slope and y-intercept Student Activity Sheet 2; use with Exploring Connecting rate of change and slope
1. The y-value of the point at which a graph crosses the y-axis is called the. 2. is a measure of the steepness of a line. 3. Calculate the rate of change by analyzing the differences in the y-values and
SCHMITT TRIGGER. Typical ``real world'' signals consist of a superposition of a ``noise'' signal and a
SCHMITT TRIGGER Typical ``real world'' signals consist of a superposition of a ``noise'' signal and a signal or signals of interest. For example, the signal at the bottom of Figure 19 shows a superposition
As the Planimeter s Wheel Turns
As the Planimeter s Wheel Turns December 30, 2004 A classic example of Green s Theorem in action is the planimeter, a device that measures the area enclosed by a curve. Most familiar may be the polar planimeter
There is another online survey for those of you (freshman) who took the ALEKS placement test before the semester. Please follow the link at the Math 165 web-page, or just go to: https://illinois.edu/sb/sec/2457922
Review guide for midterm 2 in Math 233 March 30, 2009
Review guide for midterm 2 in Math 2 March, 29 Midterm 2 covers material that begins approximately with the definition of partial derivatives in Chapter 4. and ends approximately with methods for calculating
Computing Permutations with Stacks and Deques
Michael Albert 1 Mike Atkinson 1 Steve Linton 2 1 Department of Computer Science, University of Otago 2 School of Computer Science, University of St Andrews 7th Australia New Zealand Mathematics Convention
Autonomous Underwater Vehicle Navigation.
Autonomous Underwater Vehicle Navigation. We are aware that electromagnetic energy cannot propagate appreciable distances in the ocean except at very low frequencies. As a result, GPS-based and other such
Gauss and AGM. Burton Rosenberg. January 30, 2004
Gauss and AGM Burton Rosenberg January 3, 24 Introduction derivation of equation. what has it to do w/ the lemniscate agm properties of I elliptic integrals The Elliptic Integral of the First Kind Define
Relationship to theory: This activity involves the motion of bodies under constant velocity.
UNIFORM MOTION Lab format: this lab is a remote lab activity Relationship to theory: This activity involves the motion of bodies under constant velocity. LEARNING OBJECTIVES Read and understand these instructions
DESIGN & CREATIVE TECHNOLOGIES FINAL EXAM TIMETABLE SEMESTER
Wednesday 24 October DESIGN & CREATIVE TECHNOLOGIES FINAL EXAM TIMETABLE SEMESTER 2 2018 PHOTO ID IS REQUIRED FOR ALL EXAMINATIONS The Exam Timetable is subject to change, please check back regularly for
Designing with Parametric Sketches
Designing with Parametric Sketches by Cory McConnell In the world of 3D modeling, one term that comes up frequently is parametric sketching. Parametric sketching, the basis for 3D modeling in Autodesk
Resistance and Ohm s Law
Resistance and Ohm s Law Textbook pages 290 301 Section 8.3 Summary Before You Read Do you think electrons can move through all conducting substances equally well? Give your reasons why or why not on the
v1.0 ASSEMBLY GUIDE Edgar Tall Chest of Drawers
v1.0 ASSEMBLY GUIDE Edgar Tall Chest of Drawers Components Upon unpacking your tall chest of drawers from it s delivery box, you should have the separate pieces shown below. Follow the steps on the next
Control of the Contract of a Public Transport Service
Control of the Contract of a Public Transport Service Andrea Lodi, Enrico Malaguti, Nicolás E. Stier-Moses Tommaso Bonino DEIS, University of Bologna Graduate School of Business, Columbia University SRM
Name: Date: Block: Mid-Unit 4 Test Review All work must be shown for full credit.
Name: Date: Block: Mid-Unit 4 Test Review All work must be shown for full credit. 1) How do you have to walk so the motion detector graphs a straight line? Explain as clearly as you can. 2) What determines
FUNCTIONS OF SEVERAL VARIABLES AND PARTIAL DIFFERENTIATION
FUNCTIONS OF SEVERAL VARIABLES AND PARTIAL DIFFERENTIATION 1. Functions of Several Variables A function of two variables is a rule that assigns a real number f(x, y) to each ordered pair of real numbers
3V 10-Tap Silicon Delay Line DS1110L
XX-XXXX; Rev 1; 11/3 3V 1-Tap Silicon Delay Line General Description The 1-tap delay line is a 3V version of the DS111. It has 1 equally spaced taps providing delays from 1ns to ns. The series delay lines
Capacity Estimation Principles and Methods
Capacity Estimation Principles and Methods Prof. Dr.-Ing. Ingo A. Hansen 27-11-2017 Delft University of Technology Challenge the future Content 1. Principles 2. Classification of capacity estimation models
14.4. Tangent Planes. Tangent Planes. Tangent Planes. Tangent Planes. Partial Derivatives. Tangent Planes and Linear Approximations
14 Partial Derivatives 14.4 and Linear Approximations Copyright Cengage Learning. All rights reserved. Copyright Cengage Learning. All rights reserved. Suppose a surface S has equation z = f(x, y), where
VectorPlot[{y^2-2x*y,3x*y-6*x^2},{x,-5,5},{y,-5,5}]
![VectorPlot[{y^2-2x*y,3x*y-6*x^2},{x,-5,5},{y,-5,5}]](/thumbs/88/116610289.jpg "VectorPlot[{y^2-2x*y,3x*y-6*x^2},{x,-5,5},{y,-5,5}]")
hapter 16 16.1. 6. Notice that F(x, y) has length 1 and that it is perpendicular to the position vector (x, y) for all x and y (except at the origin). Think about drawing the vectors based on concentric
Milling PCBs. Jonathan Bachrach. September 14, EECS UC Berkeley
Milling PCBs Jonathan Bachrach EECS UC Berkeley September 14, 2016 Last Time 1 PCBs wisegeek Today 2 Milling PCBs CNC 3 Computerized Numeric Control Benefits 4 Automation Precision Repeatability Flexibility
1. Vector Fields. f 1 (x, y, z)i + f 2 (x, y, z)j + f 3 (x, y, z)k.
HAPTER 14 Vector alculus 1. Vector Fields Definition. A vector field in the plane is a function F(x, y) from R into V, We write F(x, y) = hf 1 (x, y), f (x, y)i = f 1 (x, y)i + f (x, y)j. A vector field
Separately Excited DC Motor for Electric Vehicle Controller Design Yulan Qi
6th International Conference on Sensor etwork and Computer Engineering (ICSCE 2016) Separately Excited DC Motor for Electric Vehicle Controller Design ulan Qi Wuhan Textile University, Wuhan, China Keywords:
A Simple Real-Time People Counter with Device Management System Using Digital Logic Design
International Journal of Scientific & Engineering Research Volume 3, Issue 8, August-2012 1 A Simple Real-Time People Counter with Device Management System Using Digital Logic Design Sani Md. Ismail, Shaikh
Routing Messages in a Network
Routing Messages in a Network Reference : J. Leung, T. Tam and G. Young, 'On-Line Routing of Real-Time Messages,' Journal of Parallel and Distributed Computing, 34, pp. 211-217, 1996. J. Leung, T. Tam,
ANT Channel Search ABSTRACT
ANT Channel Search ABSTRACT ANT channel search allows a device configured as a slave to find, and synchronize with, a specific master. This application note provides an overview of ANT channel establishment,
3/23/2015. Chapter 11 Oscillations and Waves. Contents of Chapter 11. Contents of Chapter Simple Harmonic Motion Spring Oscillations
Lecture PowerPoints Chapter 11 Physics: Principles with Applications, 7 th edition Giancoli Chapter 11 and Waves This work is protected by United States copyright laws and is provided solely for the use
TRAFFIC ENGINEERING SAB3843. CHE ROS BIN ISMAIL and OTHMAN BIN CHE PUAN
TRAFFIC ENGINEERING SAB3843 GEOMETRIC DESIGN OF ROADS CHE ROS BIN ISMAIL and OTHMAN BIN CHE PUAN SOME MAJOR ELEMENTS OF ROAD GEOMETRY: Horizontal alignment, Vertical alignment, Sight distances, Crosssection,
LAB 5: Mobile robots -- Modeling, control and tracking
LAB 5: Mobile robots -- Modeling, control and tracking Overview In this laboratory experiment, a wheeled mobile robot will be used to illustrate Modeling Independent speed control and steering Longitudinal
I II III IV V VI VII VIII IX X Total
1 of 16 HAND IN Answers recorded on exam paper. DEPARTMENT OF MATHEMATICS AND STATISTICS QUEEN S UNIVERSITY AT KINGSTON MATH 121/124 - APR 2018 Section 700 - CDS Students ONLY Instructor: A. Ableson INSTRUCTIONS:
The Evolution of Waveform Relaxation for Circuit and Electromagnetic Solvers
The Evolution of Waveform Relaxation for Circuit and Electromagnetic Solvers Albert Ruehli, Missouri S&T EMC Laboratory, University of Science & Technology, Rolla, MO with contributions by Giulio Antonini,
Intelligent Agents & Search Problem Formulation. AIMA, Chapters 2,
Intelligent Agents & Search Problem Formulation AIMA, Chapters 2, 3.1-3.2 Outline for today s lecture Intelligent Agents (AIMA 2.1-2) Task Environments Formulating Search Problems CIS 421/521 - Intro to
Railway disruption management
Railway disruption management 4 5 6 7 8 Delft Center for Systems and Control Railway disruption management For the degree of Master of Science in Systems and Control at Delft University of Technology
6. FUNDAMENTALS OF CHANNEL CODER
82 6. FUNDAMENTALS OF CHANNEL CODER 6.1 INTRODUCTION The digital information can be transmitted over the channel using different signaling schemes. The type of the signal scheme chosen mainly depends on
Functions of more than one variable
Chapter 3 Functions of more than one variable 3.1 Functions of two variables and their graphs 3.1.1 Definition A function of two variables has two ingredients: a domain and a rule. The domain of the function
Relay Placement in Sensor Networks
Relay Placement in Sensor Networks Jukka Suomela 14 October 2005 Contents: Wireless Sensor Networks? Relay Placement? Problem Classes Computational Complexity Approximation Algorithms HIIT BRU, Adaptive
Instruction Manual. 1) Starting Amnesia
Instruction Manual 1) Starting Amnesia Launcher When the game is started you will first be faced with the Launcher application. Here you can choose to configure various technical things for the game like
SAMPLE COURSE OUTLINE AUTOMOTIVE ENGINEERING AND TECHNOLOGY GENERAL YEAR 12
SAMPLE COURSE OUTLINE AUTOMOTIVE ENGINEERING AND TECHNOLOGY GENERAL YEAR 12 Copyright School Curriculum and Standards Authority, 2015 This document apart from any third party copyright material contained
Questions and Answers
EKS Technology Development Program EKR-DT Process Process Optimisation Program COSIA Presentation Questions and Answers May 30, 2017 This document addresses some of outstanding questions that are related
SCHEDULING Giovanni De Micheli Stanford University
SCHEDULING Giovanni De Micheli Stanford University Outline The scheduling problem. Scheduling without constraints. Scheduling under timing constraints. Relative scheduling. Scheduling under resource constraints.
Shuttle Planning for Link Closures in Urban Public Transport Networks
Downloaded from orbit.dtu.dk on: Jan 02, 2019 Shuttle Planning for Link Closures in Urban Public Transport Networks van der Hurk, Evelien; Koutsopoulos, Haris N.; Wilson, Nigel; Kroon, Leo G.; Maroti,
Definitions and claims functions of several variables
Definitions and claims functions of several variables In the Euclidian space I n of all real n-dimensional vectors x = (x 1, x,..., x n ) the following are defined: x + y = (x 1 + y 1, x + y,..., x n +
UNIVERSITY OF SOUTHAMPTON
UNIVERSITY OF SOUTHAMPTON ELEC6014W1 SEMESTER II EXAMINATIONS 2007/08 RADIO COMMUNICATION NETWORKS AND SYSTEMS Duration: 120 mins Answer THREE questions out of FIVE. University approved calculators may
WESI 205 Workbook. 1 Review. 2 Graphing in 3D
1 Review 1. (a) Use a right triangle to compute the distance between (x 1, y 1 ) and (x 2, y 2 ) in R 2. (b) Use this formula to compute the equation of a circle centered at (a, b) with radius r. (c) Extend
Optimal Resource Allocation for OFDM Uplink Communication: A Primal-Dual Approach
Optimal Resource Allocation for OFDM Uplink Communication: A Primal-Dual Approach Minghua Chen and Jianwei Huang The Chinese University of Hong Kong Acknowledgement: R. Agrawal, R. Berry, V. Subramanian
Pearson Edexcel GCE Decision Mathematics D2. Advanced/Advanced Subsidiary
Pearson Edexcel GCE Decision Mathematics D2 Advanced/Advanced Subsidiary Wednesday 29 June 2016 Morning Time: 1 hour 30 minutes Paper Reference 6690/01 You must have: D2 Answer Book Candidates may use
2016 Motorized Shades Basic Programming
About Motorized Shades: 2016 Motorized Shades Basic Programming A. THE (DUAL VOLTAGE) TWO WIRE 25TE MOTOR REQUIRES 120V AC OR 240V AC. B. BATTERIES ARE (NOT INCLUDED) FOR 12V 25CE BATTERY MOTORS. EACH
MA Lesson 16 Sections 2.3 and 2.4
MA 1500 Lesson 16 Sections.3 and.4 I Piecewise Functions & Evaluating such Functions A cab driver charges $4 a ride for a ride less than 5 miles. He charges $4 plus $0.50 a mile for a ride greater than
Using Nested Column Generation & Generic Programming to solve Staff Scheduling Problems:
Using Nested Column Generation & Generic Programming to solve Staff Scheduling Problems: Using Compile-time Customisation to create a Flexible C++ Engine for Staff Rostering Andrew Mason & Ed Bulog Department
Numerical Oscillations in EMTP-Like Programs
Session 19; Page 1/13 Spring 18 Numerical Oscillations in EMTP-Like Programs 1 Causes of Numerical Oscillations The Electromagnetic transients program and its variants all use the the trapezoidal rule
GF Machining Solutions Speed of Development : The Future of Machine Building. Sergei Schurov 23/06/2016
GF Machining Solutions Speed of Development : The Future of Machine Building Sergei Schurov 23/06/2016 Heritage Innovation Outlook Machine Tools Industry: Journey Through the Time Heritage Swiss Trains
Convolution Pyramids. Zeev Farbman, Raanan Fattal and Dani Lischinski SIGGRAPH Asia Conference (2011) Julian Steil. Prof. Dr.
Zeev Farbman, Raanan Fattal and Dani Lischinski SIGGRAPH Asia Conference (2011) presented by: Julian Steil supervisor: Prof. Dr. Joachim Weickert Fig. 1.1: Gradient integration example Seminar - Milestones
Optimal Rhode Island Hold em Poker
Optimal Rhode Island Hold em Poker Andrew Gilpin and Tuomas Sandholm Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 {gilpin,sandholm}@cs.cmu.edu Abstract Rhode Island Hold
Embedded Systems. 9. Power and Energy. Lothar Thiele. Computer Engineering and Networks Laboratory
Embedded Systems 9. Power and Energy Lothar Thiele Computer Engineering and Networks Laboratory General Remarks 9 2 Power and Energy Consumption Statements that are true since a decade or longer: Power
MULTI-VARIABLE OPTIMIZATION NOTES. 1. Identifying Critical Points
MULTI-VARIABLE OPTIMIZATION NOTES HARRIS MATH CAMP 2018 1. Identifying Critical Points Definition. Let f : R 2! R. Then f has a local maximum at (x 0,y 0 ) if there exists some disc D around (x 0,y 0 )
NI-MH BATTERY MODELLING FOR AMBIENT INTELLIGENCE APPLICATIONS. D. Szente-Varga, Gy. Horvath, M. Rencz
Stresa, Italy, 25-27 April 2007 NI-MH BATTERY MODELLING FOR AMBIENT INTELLIGENCE APPLICATIONS D. Szente-Varga, Gy. Horvath, M. Rencz (szvdom horvath rencz@eet.bme.hu) Budapest University of Technology
Partial Differentiation 1 Introduction
Partial Differentiation 1 Introduction In the first part of this course you have met the idea of a derivative. To recap what this means, recall that if you have a function, z say, then the slope of the
ESTIMATING ROAD TRAFFIC PARAMETERS FROM MOBILE COMMUNICATIONS
ESTIMATING ROAD TRAFFIC PARAMETERS FROM MOBILE COMMUNICATIONS R. Bolla, F. Davoli, A. Giordano Department of Communications, Computer and Systems Science (DIST University of Genoa Via Opera Pia 13, I-115
Testing Sensors & Actors Using Digital Oscilloscopes
Testing Sensors & Actors Using Digital Oscilloscopes APPLICATION BRIEF February 14, 2012 Dr. Michael Lauterbach & Arthur Pini Summary Sensors and actors are used in a wide variety of electronic products
47. Conservative Vector Fields
47. onservative Vector Fields Given a function z = φ(x, y), its gradient is φ = φ x, φ y. Thus, φ is a gradient (or conservative) vector field, and the function φ is called a potential function. Suppose
Artificial Neural Network based Mobile Robot Navigation
Artificial Neural Network based Mobile Robot Navigation István Engedy Budapest University of Technology and Economics, Department of Measurement and Information Systems, Magyar tudósok körútja 2. H-1117,
M14/5/MATME/SP1/ENG/TZ1/XX MATHEMATICS STANDARD LEVEL PAPER 1. Candidate session number. Tuesday 13 May 2014 (afternoon) Examination code
M4/5/MATME/SP/ENG/TZ/XX MATHEMATICS STANDARD LEVEL PAPER Tuesday 3 May 04 (afternoon) hour 30 minutes Candidate session number Examination code 4 7 3 0 3 INSTRUCTIONS TO CANDIDATES Write your session number
Adaptive Power Control for UMTS
Adaptive Power Control for UMTS Rachod Patachaianand Supervisor: Dr Kumbesan Sandrasegaran March 2007 A thesis submitted in part fulfillment of the degree of Master of Engineering by Research at The Faculty
A proposal of intelligent operating method considering the change of constraint conditions
A proposal of intelligent operating method considering the change of constraint conditions Shutaro TAKAHASHI and Seiji YASUNOBU Intelligent Control System Laboratory, Department of Intelligent Interaction
FUZZY LOGIC CONTROL FOR NON-LINEAR MODEL OF THE BALL AND BEAM SYSTEM
11th International DAAAM Baltic Conference INDUSTRIAL ENGINEERING 20-22 nd April 2016, Tallinn, Estonia FUZZY LOGIC CONTROL FOR NON-LINEAR MODEL OF THE BALL AND BEAM SYSTEM Moezzi Reza & Vu Trieu Minh
MATH 253 Page 1 of 7 Student-No.: Midterm 2 November 16, 2016 Duration: 50 minutes This test has 4 questions on 7 pages, for a total of 40 points.
MATH 253 Page 1 of 7 Student-No.: Midterm 2 November 16, 2016 Duration: 50 minutes This test has 4 questions on 7 pages, for a total of 40 points. Read all the questions carefully before starting to work.
33. Riemann Summation over Rectangular Regions
. iemann Summation over ectangular egions A rectangular region in the xy-plane can be defined using compound inequalities, where x and y are each bound by constants such that a x a and b y b. Let z = f(x,
Department of Electrical and Computer Systems Engineering
Department of Electrical and Computer Systems Engineering Technical Report MECSE-31-2005 Asynchronous Self Timed Processing: Improving Performance and Design Practicality D. Browne and L. Kleeman Asynchronous
High Speed Speculative Multiplier Using 3 Step Speculative Carry Save Reduction Tree
High Speed Speculative Multiplier Using 3 Step Speculative Carry Save Reduction Tree Alfiya V M, Meera Thampy Student, Dept. of ECE, Sree Narayana Gurukulam College of Engineering, Kadayiruppu, Ernakulam,
Adaptive Touch Sampling for Energy-Efficient Mobile Platforms
Adaptive Touch Sampling for Energy-Efficient Mobile Platforms Kyungtae Han Intel Labs, USA Alexander W. Min, Dongho Hong, Yong-joon Park Intel Corporation, USA April 16, 2015 Touch Interface in Today s