Smart Grid Algorithm Engineering (SGAE)
|
|
- Garey Basil Lambert
- 6 years ago
- Views:
Transcription
1 Smart Grid Algorithm Engineering (SGAE) A research-oriented process model for the design of distributed algorithms for Smart Grid control Astrid Nieße, Martin Tröschel, Michael Sonnenschein niesse@offis.de, troeschel@offis.de, sonnenschein@uni-oldenburg.de June 2014
2 What is SGAE? SGAE is a cyclic process model that should help research teams in designing distributed Smart Grid control algorithms in such a way, that major traps on the way to the field are avoided. SGAE is a domainspecific extension of Algorithm Engineering [Sanders2009]. SGAE is compatible with the Design Science guidelines [Hevner2004]. SGAE should help in the development by documenting major insights in such a way, that researchers need in their daily work (not the same as scientific publishing). To this end, SGAE is a quality assurance instrument. SGAE gives hints on what to do in which phase, and who is needed for what (you will need Smart Grid experts to develop Smart Grid algorithms, but not for everything done in the process cycle). SGAE is a living model in itself, so improvements are made in an evolutionary manner. SGAE gives only recommendations to our daily work you will have to tailor it for your needs. 2
3 Who is needed for SGAE? Algorithm engineers (AE): As the main purpose is to develop distributed control concepts, we need personnel with a profound background in algorithms and the ability to detect an algorithmic categorisation of real-world problems. Domain Experts (DE): We need personnel with an expertise in the context of the problem to be solved. The needed expertise depends on the problem if our solution has to cover everything from market to field automation, from bulk generation to customer premises, it will be nearly impossible to find a DE with sufficient knowledge in all the areas. DEs may be energy market experts, DER experts, power system engineers, electrical engineers, etc. Experts in experimental engineering (EE): As Smart Grid algorithm development heavily depends on simulative evaluation, we need experts in this field. EE should have a background in model building, simulation, design of experiments and statistics. 3
4 Smart Grid Algorithm Engineering (SGAE) 4
5 Initial phase: Conceptualise Objective Define the problem to be solved within the application domain and thereby prepare the topic for non-domain experts. Specify hypotheses and scenarios to evaluate the performance regarding application-specific performance indicators. Input Industrial partners problem definition (if applicable/possible) Roles DE, AE (for review of problem definition) Procedure and output 1. Problem definition 2. Define domain performance indicators 3. Identify hard constraints for analytical evaluation 4. Define hypotheses for evaluation (to be refined) 5. Design scenarios for experimental evaluation (to be refined) 5
6 Design Objective Identify the algorithmic complexity (e.g. NP-hardness) and the proper class (e.g. DCOP) of the specified problem, develop a possible algorithmic solution for the problem, check whether the proposed algorithm is adequate for the application domain and whether it is based on the right assumptions on its technical environment. Input Problem definition, set of scenarios Roles AE, DE (for reviewing) Procedure and output 1. Identification of complexity and problem class 2. Development, usually based on generalized algorithm schemes 3. Check for applicability (e.g. needed data accessibility) 6
7 Analyse Objective The objective of this phase is to analyse whether the proposed solution fulfills hard constraints that might be part of the problem definition. Input Problem definition (conceptualisation phase) Domain PIs (conceptualisation phase) Hard constraints (conceptualisation phase) (Distributed) control algorithm (design phase) Roles AE Procedure and output The objective of this phase is to formally prove needed characteristics of the algorithm under development. So a method is needed that is appropriate for this task, e.g. model checking. Usually the task of formally proving characteristics is very complex and time-consuming, so the choice of an appropriate method is crucial here. 7
8 Implement Objective The objective of the implementation phase is to generate a prototypic implementation of the algorithms designed including code to generate data sets needed for the identified domain performance indicators. Input Designed algorithm (design phase) Domain performance indicators (conceptualisation phase) Existing software systems (if applicable) Roles AE Procedure and output Procedure depends on internal guidelines. Output: prototypic implementation, code needed to generate evaluation data for defined performance indicators. Important aspects: 1. Agent system architecture (like BDI, InteRRaP, ) (if needed) 2. Technological framework 3. Integration strategy (for teams) 8
9 Experiment Objective Generation of data with a maximum of informational content regarding the evaluation of the control system under test using a minimum of (time-consuming) simulation runs. Input Implementation of the control system (implementation phase) Scenario definitions, domain-specific PIs and corresponding metrics (conceptualisation phase) Implemented and evaluated simulation models (especially from model library) Roles EE, DE Procedure and output 1. Development and/or adaption of simulation models 2. Design of scenarios 3. Design of experiments 4. Pre-processing of simulation results as input for evaluation phase 9
10 Evaluate Objective Evaluate the designed algorithms against the hypotheses defined early in the process (probably refined within SGAE iterations). Results are manifested in a knowledge base, giving input for new projects and research questions and delivering information for researchers new to the application domain. Input Problem definition (conceptualisation phase) Algorithmic categorisation (design phase) Domain PIs (conceptualisation phase) Hypotheses (conceptualisation phase) Algorithm design (design phase) Simulation results (data sets reflecting domain PIs) (experiment phase) Roles DE Procedure and output 1. Evaluation based on hypotheses, domain PIs and simulation results 2. Transfer to knowledge base 10
11 Some closing remarks SGAE is a cyclic model so several iterations of the phases from Design to Evaluate are part of the main idea, with the solution evolving over time. Transfer to the knowledge base is hard, especially for the ideas that did not lead to a solution but it will help in future projects. Last but not least: Feel free to contact us for any comments or ideas on SGAE! 11
Use of CIM in AEP Enterprise Architecture. Randy Lowe Director, Enterprise Architecture October 24, 2012
Use of CIM in AEP Enterprise Architecture Randy Lowe Director, Enterprise Architecture October 24, 2012 Introduction AEP Stats and Enterprise Overview AEP Project Description and Goals CIM Adoption CIM
More informationDesigning a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses
Designing a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses Thomas F.C. Woodhall Masters Candidate in Civil Engineering Queen s University at Kingston,
More informationnew research in learning and working
Research shows that colleges and universities are vying with competing institutions to attract and retain the brightest students and the best faculty. Second, learning and teaching styles are changing
More informationMajor Milestones, Team Activities, and Individual Deliverables
Major Milestones, Team Activities, and Individual Deliverables Milestone #1: Team Semester Proposal Your team should write a proposal that describes project objectives, existing relevant technology, engineering
More informationPurdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study
Purdue Data Summit 2017 Communication of Big Data Analytics New SAT Predictive Validity Case Study Paul M. Johnson, Ed.D. Associate Vice President for Enrollment Management, Research & Enrollment Information
More informationAGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016
AGENDA Advanced Learning Theories Alejandra J. Magana, Ph.D. admagana@purdue.edu Introduction to Learning Theories Role of Learning Theories and Frameworks Learning Design Research Design Dual Coding Theory
More informationIntroduction to Simulation
Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /
More informationSoftware Maintenance
1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories
More informationOCR LEVEL 3 CAMBRIDGE TECHNICAL
Cambridge TECHNICALS OCR LEVEL 3 CAMBRIDGE TECHNICAL CERTIFICATE/DIPLOMA IN IT SYSTEMS ANALYSIS K/505/5481 LEVEL 3 UNIT 34 GUIDED LEARNING HOURS: 60 UNIT CREDIT VALUE: 10 SYSTEMS ANALYSIS K/505/5481 LEVEL
More informationSpecification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments
Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,
More informationCircuit Simulators: A Revolutionary E-Learning Platform
Circuit Simulators: A Revolutionary E-Learning Platform Mahi Itagi Padre Conceicao College of Engineering, Verna, Goa, India. itagimahi@gmail.com Akhil Deshpande Gogte Institute of Technology, Udyambag,
More informationIs operations research really research?
Volume 22 (2), pp. 155 180 http://www.orssa.org.za ORiON ISSN 0529-191-X c 2006 Is operations research really research? NJ Manson Received: 2 October 2006; Accepted: 1 November 2006 Abstract This paper
More informationIAT 888: Metacreation Machines endowed with creative behavior. Philippe Pasquier Office 565 (floor 14)
IAT 888: Metacreation Machines endowed with creative behavior Philippe Pasquier Office 565 (floor 14) pasquier@sfu.ca Outline of today's lecture A little bit about me A little bit about you What will that
More informationAn Industrial Technologist s Core Knowledge: Web-based Strategy for Defining Our Discipline
Volume 17, Number 2 - February 2001 to April 2001 An Industrial Technologist s Core Knowledge: Web-based Strategy for Defining Our Discipline By Dr. John Sinn & Mr. Darren Olson KEYWORD SEARCH Curriculum
More informationDIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits.
DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE Sample 2-Year Academic Plan DRAFT Junior Year Summer (Bridge Quarter) Fall Winter Spring MMDP/GAME 124 GAME 310 GAME 318 GAME 330 Introduction to Maya
More informationImplementing a tool to Support KAOS-Beta Process Model Using EPF
Implementing a tool to Support KAOS-Beta Process Model Using EPF Malihe Tabatabaie Malihe.Tabatabaie@cs.york.ac.uk Department of Computer Science The University of York United Kingdom Eclipse Process Framework
More informationWhat is PDE? Research Report. Paul Nichols
What is PDE? Research Report Paul Nichols December 2013 WHAT IS PDE? 1 About Pearson Everything we do at Pearson grows out of a clear mission: to help people make progress in their lives through personalized
More informationOn the Combined Behavior of Autonomous Resource Management Agents
On the Combined Behavior of Autonomous Resource Management Agents Siri Fagernes 1 and Alva L. Couch 2 1 Faculty of Engineering Oslo University College Oslo, Norway siri.fagernes@iu.hio.no 2 Computer Science
More informationECE-492 SENIOR ADVANCED DESIGN PROJECT
ECE-492 SENIOR ADVANCED DESIGN PROJECT Meeting #3 1 ECE-492 Meeting#3 Q1: Who is not on a team? Q2: Which students/teams still did not select a topic? 2 ENGINEERING DESIGN You have studied a great deal
More informationEECS 571 PRINCIPLES OF REAL-TIME COMPUTING Fall 10. Instructor: Kang G. Shin, 4605 CSE, ;
EECS 571 PRINCIPLES OF REAL-TIME COMPUTING Fall 10 Instructor: Kang G. Shin, 4605 CSE, 763-0391; kgshin@umich.edu Number of credit hours: 4 Class meeting time and room: Regular classes: MW 10:30am noon
More informationLIBRARY AND RECORDS AND ARCHIVES SERVICES STRATEGIC PLAN 2016 to 2020
LIBRARY AND RECORDS AND ARCHIVES SERVICES STRATEGIC PLAN 2016 to 2020 THE UNIVERSITY CONTEXT In 2016 there are three key drivers that are influencing the University s strategic planning: 1. The strategy
More informationKnowledge Elicitation Tool Classification. Janet E. Burge. Artificial Intelligence Research Group. Worcester Polytechnic Institute
Page 1 of 28 Knowledge Elicitation Tool Classification Janet E. Burge Artificial Intelligence Research Group Worcester Polytechnic Institute Knowledge Elicitation Methods * KE Methods by Interaction Type
More informationPROCESS USE CASES: USE CASES IDENTIFICATION
International Conference on Enterprise Information Systems, ICEIS 2007, Volume EIS June 12-16, 2007, Funchal, Portugal. PROCESS USE CASES: USE CASES IDENTIFICATION Pedro Valente, Paulo N. M. Sampaio Distributed
More informationData Fusion Models in WSNs: Comparison and Analysis
Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) Data Fusion s in WSNs: Comparison and Analysis Marwah M Almasri, and Khaled M Elleithy, Senior Member,
More informationDesigning a Computer to Play Nim: A Mini-Capstone Project in Digital Design I
Session 1793 Designing a Computer to Play Nim: A Mini-Capstone Project in Digital Design I John Greco, Ph.D. Department of Electrical and Computer Engineering Lafayette College Easton, PA 18042 Abstract
More informationRunning Head: STUDENT CENTRIC INTEGRATED TECHNOLOGY
SCIT Model 1 Running Head: STUDENT CENTRIC INTEGRATED TECHNOLOGY Instructional Design Based on Student Centric Integrated Technology Model Robert Newbury, MS December, 2008 SCIT Model 2 Abstract The ADDIE
More informationeportfolios in Education - Learning Tools or Means of Assessment?
eportfolios in Education - Learning Tools or Means of Assessment? Christian Dorninger, Christian Schrack Federal Ministry for Education, Art and Culture, Austria Federal Pedagogical University Vienna,
More informationLEARNING THROUGH INTERACTION AND CREATIVITY IN ONLINE LABORATORIES
xi LEARNING THROUGH INTERACTION AND CREATIVITY IN ONLINE LABORATORIES Michael E. Auer Professor of Electrical Engineering Carinthia University of Applied Sciences Villach, Austria My Thoughts about the
More informationelearning OVERVIEW GFA Consulting Group GmbH 1
elearning OVERVIEW 23.05.2017 GFA Consulting Group GmbH 1 Definition E-Learning E-Learning means teaching and learning utilized by electronic technology and tools. 23.05.2017 Definition E-Learning GFA
More informationTHE DEPARTMENT OF DEFENSE HIGH LEVEL ARCHITECTURE. Richard M. Fujimoto
THE DEPARTMENT OF DEFENSE HIGH LEVEL ARCHITECTURE Judith S. Dahmann Defense Modeling and Simulation Office 1901 North Beauregard Street Alexandria, VA 22311, U.S.A. Richard M. Fujimoto College of Computing
More informationAutomating the E-learning Personalization
Automating the E-learning Personalization Fathi Essalmi 1, Leila Jemni Ben Ayed 1, Mohamed Jemni 1, Kinshuk 2, and Sabine Graf 2 1 The Research Laboratory of Technologies of Information and Communication
More informationA GENERIC SPLIT PROCESS MODEL FOR ASSET MANAGEMENT DECISION-MAKING
A GENERIC SPLIT PROCESS MODEL FOR ASSET MANAGEMENT DECISION-MAKING Yong Sun, a * Colin Fidge b and Lin Ma a a CRC for Integrated Engineering Asset Management, School of Engineering Systems, Queensland
More informationDeploying Agile Practices in Organizations: A Case Study
Copyright: EuroSPI 2005, Will be presented at 9-11 November, Budapest, Hungary Deploying Agile Practices in Organizations: A Case Study Minna Pikkarainen 1, Outi Salo 1, and Jari Still 2 1 VTT Technical
More informationExercise Format Benefits Drawbacks Desk check, audit or update
Guidance Note 6 Exercising for Resilience With critical activities, resources and recovery priorities established, and preparations made for crisis management, all preparations and plans should be tested
More informationActivities, Exercises, Assignments Copyright 2009 Cem Kaner 1
Patterns of activities, iti exercises and assignments Workshop on Teaching Software Testing January 31, 2009 Cem Kaner, J.D., Ph.D. kaner@kaner.com Professor of Software Engineering Florida Institute of
More informationTelekooperation Seminar
Telekooperation Seminar 3 CP, SoSe 2017 Nikolaos Alexopoulos, Rolf Egert. {alexopoulos,egert}@tk.tu-darmstadt.de based on slides by Dr. Leonardo Martucci and Florian Volk General Information What? Read
More informationSenior Research Fellow, Intelligent Mobility Design Centre
ROYAL COLLEGE OF ART JOB DESCRIPTION Post: Department: Post-doctoral Research Associate Intelligent Mobility Design Centre Grade: 7 Responsible to: Senior Research Fellow, Intelligent Mobility Design Centre
More informationUSER ADAPTATION IN E-LEARNING ENVIRONMENTS
USER ADAPTATION IN E-LEARNING ENVIRONMENTS Paraskevi Tzouveli Image, Video and Multimedia Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens tpar@image.
More informationSSE - Supervision of Electrical Systems
Coordinating unit: 205 - ESEIAAT - Terrassa School of Industrial, Aerospace and Audiovisual Engineering Teaching unit: 709 - EE - Department of Electrical Engineering Academic year: Degree: 2017 BACHELOR'S
More informationA Pipelined Approach for Iterative Software Process Model
A Pipelined Approach for Iterative Software Process Model Ms.Prasanthi E R, Ms.Aparna Rathi, Ms.Vardhani J P, Mr.Vivek Krishna Electronics and Radar Development Establishment C V Raman Nagar, Bangalore-560093,
More informationLaboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning
More informationRendezvous with Comet Halley Next Generation of Science Standards
Next Generation of Science Standards 5th Grade 6 th Grade 7 th Grade 8 th Grade 5-PS1-3 Make observations and measurements to identify materials based on their properties. MS-PS1-4 Develop a model that
More informationLEGO MINDSTORMS Education EV3 Coding Activities
LEGO MINDSTORMS Education EV3 Coding Activities s t e e h s k r o W t n e d Stu LEGOeducation.com/MINDSTORMS Contents ACTIVITY 1 Performing a Three Point Turn 3-6 ACTIVITY 2 Written Instructions for a
More informationIntegrating simulation into the engineering curriculum: a case study
Integrating simulation into the engineering curriculum: a case study Baidurja Ray and Rajesh Bhaskaran Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USA E-mail:
More informationJournal title ISSN Full text from
Title listings ejournals Management ejournals Database and Specialist ejournals Collections Emerald Insight Management ejournals Database Journal title ISSN Full text from Accounting, Finance & Economics
More informationScenario Design for Training Systems in Crisis Management: Training Resilience Capabilities
Scenario Design for Training Systems in Crisis Management: Training Resilience Capabilities Amy Rankin 1, Joris Field 2, William Wong 3, Henrik Eriksson 4, Jonas Lundberg 5 Chris Rooney 6 1, 4, 5 Department
More informationLearning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for
Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email Marilyn A. Walker Jeanne C. Fromer Shrikanth Narayanan walker@research.att.com jeannie@ai.mit.edu shri@research.att.com
More informationUCEAS: User-centred Evaluations of Adaptive Systems
UCEAS: User-centred Evaluations of Adaptive Systems Catherine Mulwa, Séamus Lawless, Mary Sharp, Vincent Wade Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College,
More informationExperiences Using Defect Checklists in Software Engineering Education
Experiences Using Defect Checklists in Software Engineering Education Kendra Cooper 1, Sheila Liddle 1, Sergiu Dascalu 2 1 Department of Computer Science The University of Texas at Dallas Richardson, TX,
More informationFragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing
Fragment Analysis and Test Case Generation using F- Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing D. Indhumathi Research Scholar Department of Information Technology
More informationInnovating Toward a Vibrant Learning Ecosystem:
KnowledgeWorks Forecast 3.0 Innovating Toward a Vibrant Learning Ecosystem: Ten Pathways for Transforming Learning Katherine Prince Senior Director, Strategic Foresight, KnowledgeWorks KnowledgeWorks Forecast
More informationCertified Six Sigma Professionals International Certification Courses in Six Sigma Green Belt
Certification Singapore Institute Certified Six Sigma Professionals Certification Courses in Six Sigma Green Belt ly Licensed Course for Process Improvement/ Assurance Managers and Engineers Leading the
More informationDirector, Intelligent Mobility Design Centre
ROYAL COLLEGE OF ART ROLE DESCRIPTION Post: Department: Senior Research Fellow Intelligent Mobility Design Centre Grade: 10 Responsible to: Director, Intelligent Mobility Design Centre Background The Royal
More informationInteraction Design Considerations for an Aircraft Carrier Deck Agent-based Simulation
Interaction Design Considerations for an Aircraft Carrier Deck Agent-based Simulation Miles Aubert (919) 619-5078 Miles.Aubert@duke. edu Weston Ross (505) 385-5867 Weston.Ross@duke. edu Steven Mazzari
More informationAPB Step 3 Test, Evaluation, and Analysis Process
MP00W0000124 MITRE PAPER TEASG Step 3 Report on APB Step 3 Test, Evaluation, and Analysis Process April 2000 Michael Beasley, Digital Systems Resources David Colella, The MITRE Corporation, Chair Ronald
More informationIntroduction to Modeling and Simulation. Conceptual Modeling. OSMAN BALCI Professor
Introduction to Modeling and Simulation Conceptual Modeling OSMAN BALCI Professor Department of Computer Science Virginia Polytechnic Institute and State University (Virginia Tech) Blacksburg, VA 24061,
More informationGiven a directed graph G =(N A), where N is a set of m nodes and A. destination node, implying a direction for ow to follow. Arcs have limitations
4 Interior point algorithms for network ow problems Mauricio G.C. Resende AT&T Bell Laboratories, Murray Hill, NJ 07974-2070 USA Panos M. Pardalos The University of Florida, Gainesville, FL 32611-6595
More informationLecture 10: Reinforcement Learning
Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation
More informationLaboratorio di Intelligenza Artificiale e Robotica
Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning
More informationChapter 2. Intelligent Agents. Outline. Agents and environments. Rationality. PEAS (Performance measure, Environment, Actuators, Sensors)
Intelligent Agents Chapter 2 1 Outline Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Agent types 2 Agents and environments sensors environment percepts
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
More informationFinding a Classroom Volunteer
Finding a Classroom Volunteer 1 Teacher Looking for Volunteer Support Page My Requirements as a Teacher...1 Classroom Instruction Monitoring Volunteers Flexibility of Visits Volunteer Updates Looking for
More informationIncluding the Microsoft Solution Framework as an agile method into the V-Modell XT
Including the Microsoft Solution Framework as an agile method into the V-Modell XT Marco Kuhrmann 1 and Thomas Ternité 2 1 Technische Universität München, Boltzmann-Str. 3, 85748 Garching, Germany kuhrmann@in.tum.de
More informationEvolution of Symbolisation in Chimpanzees and Neural Nets
Evolution of Symbolisation in Chimpanzees and Neural Nets Angelo Cangelosi Centre for Neural and Adaptive Systems University of Plymouth (UK) a.cangelosi@plymouth.ac.uk Introduction Animal communication
More informationRWTH Aachen University
RWTH Aachen University Engineering Winter Schools 2018 Studying at one of the best German Universities in Engineering! New Winter and Summer Schools Welcome Why choose us Contact Our new Winter Schools
More informationA Coding System for Dynamic Topic Analysis: A Computer-Mediated Discourse Analysis Technique
A Coding System for Dynamic Topic Analysis: A Computer-Mediated Discourse Analysis Technique Hiromi Ishizaki 1, Susan C. Herring 2, Yasuhiro Takishima 1 1 KDDI R&D Laboratories, Inc. 2 Indiana University
More informationBlended Learning Models and Lessons from the Field. Julia Freeland Fisher
Blended Learning Models and Lessons from the Field Julia Freeland Fisher jfreelanditute.org Twitter: What is disruptive innovation? Clayton christensen institute Disruption in computing DEC Apple Online
More informationCREATING SHARABLE LEARNING OBJECTS FROM EXISTING DIGITAL COURSE CONTENT
CREATING SHARABLE LEARNING OBJECTS FROM EXISTING DIGITAL COURSE CONTENT Rajendra G. Singh Margaret Bernard Ross Gardler rajsingh@tstt.net.tt mbernard@fsa.uwi.tt rgardler@saafe.org Department of Mathematics
More informationSoftware Development Plan
Version 2.0e Software Development Plan Tom Welch, CPC Copyright 1997-2001, Tom Welch, CPC Page 1 COVER Date Project Name Project Manager Contact Info Document # Revision Level Label Business Confidential
More informationInstitutionen för datavetenskap. Hardware test equipment utilization measurement
Institutionen för datavetenskap Department of Computer and Information Science Final thesis Hardware test equipment utilization measurement by Denis Golubovic, Niklas Nieminen LIU-IDA/LITH-EX-A 15/030
More informationProject Management for Rapid e-learning Development Jennifer De Vries Blue Streak Learning
601 Project Management for Rapid e-learning Development Jennifer De Vries Blue Streak Learning Produced by Tips, Tricks, and Techniques for Rapid e-learning Development Project Management for Rapid elearning
More informationHow to Do Research. Jeff Chase Duke University
How to Do Research Jeff Chase Duke University Sadly... Nobody can tell you how to do research. It is difficult enough just to define what research is, or define how to separate the wheat from the chaff.
More informationSummary BEACON Project IST-FP
BEACON Brazilian European Consortium for DTT Services www.beacon-dtt.com Project reference: IST-045313 Contract type: Specific Targeted Research Project Start date: 1/1/2007 End date: 31/03/2010 Project
More informationPower Systems Engineering
The Field of Power Systems Engineering Power engineering, also called power systems engineering, is the study in engineering as it deals with the generation, transmission, distribution, and utilization
More informationSeminar - Organic Computing
Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts
More informationTowards sustainability audits in Finnish schools Development of criteria for social and cultural sustainability
Towards sustainability audits in Finnish schools Development of criteria for social and cultural sustainability Erkka Laininen Planning Manager The OKKA Foundation The OKKA Foundation Is a foundation for
More informationSEDETEP Transformation of the Spanish Operation Research Simulation Working Environment
SEDETEP Transformation of the Spanish Operation Research Simulation Working Environment Cdr. Nelson Ameyugo Catalán (ESP-NAVY) Spanish Navy Operations Research Laboratory (Gimo) Arturo Soria 287 28033
More informationAgent-Based Software Engineering
Agent-Based Software Engineering Learning Guide Information for Students 1. Description Grade Module Máster Universitario en Ingeniería de Software - European Master on Software Engineering Advanced Software
More informationProFusion2 Sensor Data Fusion for Multiple Active Safety Applications
ProFusion2 Sensor Data Fusion for Multiple Active Safety Applications S.-B. Park 1, F. Tango 2, O. Aycard 3, A. Polychronopoulos 4, U. Scheunert 5, T. Tatschke 6 1 DELPHI, Electronics & Safety, 42119 Wuppertal,
More informationStudy in Berlin at the HTW. Study in Berlin at the HTW
Study in Berlin at the HTW Study in Berlin at the HTW Study in Berlin Study in Berlin at the HTW There are many reasons why you should study in Berlin Because it is a multicultural city Because of tuition
More informationTransfer Learning Action Models by Measuring the Similarity of Different Domains
Transfer Learning Action Models by Measuring the Similarity of Different Domains Hankui Zhuo 1, Qiang Yang 2, and Lei Li 1 1 Software Research Institute, Sun Yat-sen University, Guangzhou, China. zhuohank@gmail.com,lnslilei@mail.sysu.edu.cn
More informationIntroduction. Background. Social Work in Europe. Volume 5 Number 3
12 The Development of the MACESS Post-graduate Programme for the Social Professions in Europe: The Hogeschool Maastricht/ University of North London Experience Sue Lawrence and Nol Reverda The authors
More informationInfrared Paper Dryer Control Scheme
Infrared Paper Dryer Control Scheme INITIAL PROJECT SUMMARY 10/03/2005 DISTRIBUTED MEGAWATTS Carl Lee Blake Peck Rob Schaerer Jay Hudkins 1. Project Overview 1.1 Stake Holders Potlatch Corporation, Idaho
More informationTriple P Ontario Network Peaks and Valleys of Implementation HFCC Feb. 4, 2016
Triple P Ontario Network Peaks and Valleys of Implementation HFCC Feb. 4, 2016 WHO WE ARE. Triple P Ontario Network - multi-sectoral - voluntary - 10 years + Halton Region - York Region and Simcoe County
More informationGroup A Lecture 1. Future suite of learning resources. How will these be created?
Group A Lecture 1 Future suite of learning resources Portable electronically based. User-friendly interface no steep learning curve. Adaptive to & Customizable by learner & teacher. Layered guide indexed
More informationEmma Kushtina ODL organisation system analysis. Szczecin University of Technology
Emma Kushtina ODL organisation system analysis Szczecin University of Technology 1 European Higher Education Area Ongoing Bologna Process (1999 2010, ) European Framework of Qualifications Open and Distance
More informationAxiom 2013 Team Description Paper
Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association
More informationSmart Grids Simulation with MECSYCO
Smart Grids Simulation with MECSYCO Julien Vaubourg, Yannick Presse, Benjamin Camus, Christine Bourjot, Laurent Ciarletta, Vincent Chevrier, Jean-Philippe Tavella, Hugo Morais, Boris Deneuville, Olivier
More informationAll Systems Go! Using a Systems Approach in Elementary Science
All Systems Go! CAST November Tracey Ramirez Professional Learning Facilitator The Charles A. Dana Center What we do and how we do it The Dana Center collaborates with others locally and nationally to
More informationCS Machine Learning
CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
More informationLearning Methods for Fuzzy Systems
Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8
More informationVirtual Teams: The Design of Architecture and Coordination for Realistic Performance and Shared Awareness
Virtual Teams: The Design of Architecture and Coordination for Realistic Performance and Shared Awareness Bryan Moser, Global Project Design John Halpin, Champlain College St. Lawrence Introduction Global
More informationCarter M. Mast. Participants: Peter Mackenzie-Helnwein, Pedro Arduino, and Greg Miller. 6 th MPM Workshop Albuquerque, New Mexico August 9-10, 2010
Representing Arbitrary Bounding Surfaces in the Material Point Method Carter M. Mast 6 th MPM Workshop Albuquerque, New Mexico August 9-10, 2010 Participants: Peter Mackenzie-Helnwein, Pedro Arduino, and
More informationReinforcement Learning by Comparing Immediate Reward
Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate
More informationMaster s Programme in Computer, Communication and Information Sciences, Study guide , ELEC Majors
Master s Programme in Computer, Communication and Information Sciences, Study guide 2015-2016, ELEC Majors Sisällysluettelo PS=pääsivu, AS=alasivu PS: 1 Acoustics and Audio Technology... 4 Objectives...
More informationHARPER ADAMS UNIVERSITY Programme Specification
HARPER ADAMS UNIVERSITY Programme Specification 1 Awarding Institution: Harper Adams University 2 Teaching Institution: Askham Bryan College 3 Course Accredited by: Not Applicable 4 Final Award and Level:
More informationThe Strong Minimalist Thesis and Bounded Optimality
The Strong Minimalist Thesis and Bounded Optimality DRAFT-IN-PROGRESS; SEND COMMENTS TO RICKL@UMICH.EDU Richard L. Lewis Department of Psychology University of Michigan 27 March 2010 1 Purpose of this
More informationACADEMIC AFFAIRS GUIDELINES
ACADEMIC AFFAIRS GUIDELINES Section 5: Course Instruction and Delivery Title: Instructional Methods: Schematic and Definitions Number (Current Format) Number (Prior Format) Date Last Revised 5.4 VI 08/2017
More informationEmergency Management Games and Test Case Utility:
IST Project N 027568 IRRIIS Project Rome Workshop, 18-19 October 2006 Emergency Management Games and Test Case Utility: a Synthetic Methodological Socio-Cognitive Perspective Adam Maria Gadomski, ENEA
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More information