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John E. Gibson

Bio: John E. Gibson is an academic researcher from University of Virginia. The author has contributed to research in topics: Systems analysis & Project management. The author has an hindex of 5, co-authored 10 publications receiving 459 citations.

Papers
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Book
01 Jan 1963

359 citations

Book
18 May 2007
TL;DR: In this paper, the authors present a system analysis approach for large-scale systems, which they call System Analysis Equals Operations Research plus Policy Analysis. But they do not consider the role of the user in the analysis process.
Abstract: Preface. A Personal Note from William T. Scherer. A Personal Note from William F. Gibson. A Personal Note from Scott F. Ferber. Original Preface from Jack Gibson. Acknowledgments. 1. Introduction. 1.1 What is a System? 1.2 Terminology Confusion. 1.3 Systems Analysis Equals Operations Research Plus Policy Analysis. 1.4 Attributes of Large-Scale Systems. 1.5 Intelligent Transportation Systems (ITS): An Example of a Large-Scale System. 1.6 Systems Integration. 1.7 What Makes a "Systems Analysis" Different? 1.8 Distant Roots of Systems Analysis. 1.9 Immediate Precursors to Systems Analysis. 1.10 Development of Systems Analysis As a Distinct Discipline: The Influence of RAND. Historical Case Study: IIASA (A). Exercises. Case Study: Fun at Six Flags? Historical Case Study: IIASA (B). 2. Six Major Phases of Systems Analysis. 2.1 The System Analysis Method: Six Major Phases. 2.2 The Goal-Centered or Top-Down Approach. 2.3 The Index of Performance Concept. 2.4 Developing Alternative Scenarios. 2.5 Ranking Alternatives. 2.6 Iteration and the "Error-Embracing" Approach. 2.7 The Action Phase: The Life-Cycle of a System. Exercises. Case Study: Methodologies or Chaos? Part A. Case Study: Methodologies or Chaos? Part B. Case Study: Wal-Mart Crisis! 3. Goal Development. 3.1 Seven Steps in Goal Development. 3.2 On Generalizing the Question. 3.3 The Descriptive Scenario. 3.4 The Normative Scenario. 3.5 The Axiological Component. 3.6 Developing an Objectives Tree. 3.7 Fitch's Goals for Urbanizing America: An Example of Objectives Tree Construction. 3.8 Content Analysis of Fitch's Goals. 3.9 Validate. 3.10 Iterate. Case Study: Distance Learning in the Future? Historical Case Study: Goals of 4C, Inc. 4. The Index of Performance. 4.1 Introduction. 4.2 Desirable Characteristics for an Index of Performance. 4.3 Economic Criteria. 4.4 Compound Interest. 4.5 Four Common Criteria for Economic Efficiency. 4.6 Is there a Problem with Multiple Criteria? 4.7 What is Wrong with the B-C Ratio? 4.8 Can IRR be Fixed? 4.9 Expected Monetary Value. 4.10 Nonmonetary Performance Indices. Exercises. Case Study: Sky High Airlines Case Study: Bridges-Where to Spend the Security Dollars? Case Study: Measuring the Process and Outcomes of Regional Transportation Collaboration. Case Study: Baseball Free Agent Draft. 5. Develop Alternative Candidate Solutions. 5.1 Introduction. 5.2 The Classical Approach to Creativity. 5.3 Concepts in Creativity. 5.4 Brainstorming. 5.5 Brainwriting. 5.6 Dynamic Confrontation. 5.7 Zwicky's Morphological Box. 5.8 The Options Field/Options Profile Approach. 5.9 Computer Creativity. 5.10 Computer Simulation: a Tool in Option Development. 5.11 Why a Dynamic Simulation for Creating Options? 5.12 Context-Free Simulation Models? 5.13 Bottom-Up Simulation or Top-Down? 5.14 Lessons from the Susquehanna River Basin Model. 5.15 The Forrester Urban Model (FUM) and Societal Values. 5.16 Extensions and Variations. 5.17 Where to go from Here? Exercises. Case Study: Winnebago. Case Study: Distance Learning in the Future? Historical Case Study: Real-Time Television Link with Mars Orbiter. Historical Case Study: A Highway Vehicle Simulator. RFP from DOT. 6. Rank Alternative Candidates. 6.1 Introduction. 6.2 Rating and Ranking Methods. 6.3 Condorcet and Arrow Voting Paradoxes. 6.4 A MultiStage Rating Process. 6.5 Decision Analysis. 6.6 Basic Axioms of Decision Theory. 6.7 Properties of Utility Functions. 6.8 Constructing a Utility Curve. 6.9 Some Decision Analysis Classic Examples. 6.10 Estimation Theory in Decision Analysis. 6.11 Some Practical Problems with Decision Analysis. 6.12 Practical Trade Studies. Exercises. Case Study: Training Center Location. Case Study: Corporate Headquarters Location. Case Study: Business School Selection. 7. Iteration and Transition. 7.1 Iteration. 7.2 Segment and Focus. 7.3 The Transition Scenario. 7.4 The Gantt Chart. 7.5 Interaction Matrices. 7.6 The Delta Chart. 7.7 The Audit Trail. 7.8 Cost of Failure to Stay on Schedule. 7.9 Responsibilities of Major Actors. 7.10 Sign-Offs by Cooperating Groups. Exercises. 8. Management of the Systems Team. 8.1 Introduction. 8.2 Personal Style in an Interdisciplinary Team. 8.3."Out-Scoping" and "In-Scoping" in a System Study. 8.4 Building the Systems Team. 8.5 Tips on Managing the Team. 8.6 Functional or Project Management? 8.7 How to Make an Effective Oral Presentation. 8.8 How to Write a Report. 9. Project Management. 9.1 Introduction. 9.2 Project Management Versus Process Management. 9.3 The Hersey-Blanchard Four-Mode Theory. 9.4 Relation of Management Style to Project Management. 9.5 Preliminary Project Planning. 9.6 Dealing with Conflict in Project Management. 9.7 Life-Cycle Planning and Design. 9.8 PERT/CPM Program Planning Method: An Example. 9.9 Quality Control in Systems Projects. Case Study: Project Management. 10. The 10 Golden Rules of Systems Analysis. 10.1 Introduction. 10.2 Rule 1: There Always Is a Client. 10.3 Rule 2: Your Client Does Not Understand His Own Problem. 10.4 Rule 3: The Original Problem Statement is too Specific: You Must Generalize the Problem to Give it Contextual Integrity. 10.5 Rule 4: The Client Does Not Understand the Concept of the Index of Performance. 10.6 Rule 5: You are the Analyst, Not the Decision-Maker. 10.7 Rule 6: Meet the Time Deadline and the Cost Budget. 10.8 Rule 7: Take a Goal-Centered Approach to the Problem, Not a Technology-Centered or Chronological Approach . 10.9 Rule 8: Nonusers Must be Considered in the Analysis and in the Final Recommendations. 10.10 Rule 9: The Universal Computer Model is a Fantasy. 10.11 Rule 10: The Role of Decision-Maker in Public Systems is Often a Confused One. References. Index.

68 citations

Book
01 Jan 1982
TL;DR: In this article, the authors present an overview of the first and second generations of planning information systems in the context of urban design and its application to the problem of energy efficiency in cities.
Abstract: 1.0. General Introduction.- 2.0. Part 1 - Systems and Methods.- 2.1. Systems: Introduction.- 2.2. Systems and Models in Urban Design - A Tutorial Overview.- 2.3. Systems Analysis of 'The First and Second Generations'.- 2.4. Structure and Usefulness of Planning Information Systems.- 2.5. Planning and the Systems Approach - Exploding some Myths, Creating a Reality.- 2.6. Methods: Introduction.- 2.7. Human Settlements as Self-Organizing Open Systems.- 2.8. Multi-Criteria Analysis and Fuzzy Set Theory Applications to Urban Design.- 2.9. The Systems Approach in Physical Planning - An Illustrated Consideration of its Possibilities and Limitations.- 3.0. Part 2 - Subsystems.- 3.1. Human Subsystems: Introduction.- 3.2. Urban Design and Human Systems - On Ways of Relating Buildings to Urban Fabric.- 3.3. Urban Design - Some Relevant Social Forces in Developing Societies.- 3.4. Urban Design and the Role of Traditional Urban Systems.- 3.5. Energy Subsystems: Introduction.- 3.6. Urbanization and the Global Energy Problem.- 3.7. Assessment of the Energy Consumption of Urban Forms.- 3.8. Well-Being in Cities - The Low-Energy City.- 3.9. The Energy Crisis and Urban Form - A Comment.- 3.10. Physical Layout and Energy Consumption - The Case of Louvain-la-Neuve, Belgium.- 3.11. The "Misperception" of Car Running Costs and its Impact on the Demand for Energy in the Transport Sector.- 3.12. Examining the Effectiveness of a Car Running Cost Meter.- 3.13. Energy and Territory - A Proposal for Research in an Area of Calabria.- 4.0. General Conclusion.- 5.0. About the Authors.

9 citations

Book
01 Aug 2016
TL;DR: The How to Do Systems Analysis: Primer and Casebook as discussed by the authors is a reference for professionals in all fields that need systems analysis, such as telecommunications, transportation, business consulting, financial services, and healthcare.
Abstract: Presents the foundational systemic thinking needed to conceive systems that address complex socio-technical problems This book emphasizes the underlying systems analysis components and associated thought processes. The authors describe an approach that is appropriate for complex systems in diverse disciplines complemented by a case-based pedagogy for teaching systems analysis that includes numerous cases that can be used to teach both the art and methods of systems analysis. Covers the six major phases of systems analysis, as well as goal development, the index of performance, evaluating candidate solutions, managing systems teams, project management, and more Presents the core concepts of a general systems analysis methodology Introduces, motivates, and illustrates the case pedagogy as a means of teaching and practicing systems analysis concepts Provides numerous cases that challenge readers to practice systems thinking and the systems methodology How to Do Systems Analysis: Primer and Casebook is a reference for professionals in all fields that need systems analysis, such as telecommunications, transportation, business consulting, financial services, and healthcare. This book also serves as a textbook for undergraduate and graduate students in systems analysis courses in business schools, engineering schools, policy programs, and any course that promotes systems thinking.

7 citations


Cited by
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Journal ArticleDOI
TL;DR: The theory applies to movements across different distances, with different inertial loads, toward targets of different widths over a wide range of experimentally manipulated velocities and reconciles many apparent conflicts in the motor control literature.
Abstract: A theory is presented to explain how accurate, single-joint movements are controlled. The theory applies to movements across different distances, with different inertial loads, toward targets of different widths over a wide range of experimentally manipulated velocities. The theory is based on three propositions. (1) Movements are planned according to “strategies” of which there are at least two: a speed-insensitive (SI) and a speed-sensitive (SS) one. (2) These strategies can be equated with sets of rules for performing diverse movement tasks. The choice between SI and SS depends on whether movement speed and/or movement time (and hence appropriate muscle forces) must be constrained to meet task requirements. (3) The electromyogram can be interpreted as a low-pass filtered version of the controlling signal to the motoneuron pools. This controlling signal can be modelled as a rectangular excitation pulse in which modulation occurs in either pulse amplitude or pulse width. Movements to different distances and with loads are controlled by the SI strategy, which modulates pulse width. Movements in which speed must be explicitly regulated are controlled by the SS strategy, which modulates pulse amplitude. The distinction between the two movement strategies reconciles many apparent conflicts in the motor control literature.

531 citations

Journal ArticleDOI
01 Oct 1971-Nature
TL;DR: An explanation for spontaneous and broadly repetitive fluctuations in Mean arterial blood pressure is presented which may also be applicable to the vasomotor activity associated with thermoregulation and therefore widely relevant.
Abstract: MEAN arterial blood pressure in man undergoes spontaneous and broadly repetitive fluctuations with a typical period of about 10 s. We present an explanation for these fluctuations which we believe may also be applicable to the vasomotor activity associated with thermoregulation and therefore widely relevant.

375 citations

Journal ArticleDOI
TL;DR: Fuzzy set theory is a relatively new concept which allows this qualitativeness to be expressed rigorously and its usefulness for control is assessed and a surprising number of practical successes are revealed.

329 citations

Book
22 Nov 2014
TL;DR: This book treats the determination of dynamic models based on measurements taken at the process, known as system identification or process identification, and covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation and subspace methods.
Abstract: Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.

326 citations

Journal ArticleDOI
TL;DR: A shape memory alloy actuator consisting of a number of thin NiTi fibers woven in a counter rotating helical pattern around supporting disks accomplishes a highly efficient transformation between force and displacement overcoming the main mechanical drawback of shape memory alloys, that being limited strain.
Abstract: A shape memory alloy (SMA) actuator consisting of a number of thin NiTi fibers woven in a counter rotating helical pattern around supporting disks is first described. This structure accomplishes a highly efficient transformation between force and displacement overcoming the main mechanical drawback of shape memory alloys, that being limited strain. Time domain open loop experiments were then conducted to determine the intrinsic properties of the actuator. From these experiments and from the knowledge of the underlying physics of SMAs, a multiterm model, including linear and nonlinear elements, was proposed. After further investigation and simulation, it was found that most of these complexities did not need to be considered in order to explain the reported results, and that the model could be reduced to that of a single integrator. A variable structure controller was then applied to a pair of antagonist actuators. The feedback switches between the two actuators according to the sign of the displacement error. A further improvement was added to compensate for known gross nonlinearities by modulating the current magnitude in a discrete manner as a function of the state space position. It was therefore possible to realize smooth and robust control with very little cost in complexity.

188 citations