Uncertainty

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Uncertainty is a term used in subtly different ways in a number of fields, including physics, philosophy, statistics, economics, finance, insurance, psychology, sociology, engineering, and information science. It applies to predictions of future events, to physical measurements already made, or to the unknown. (Wikipedia.org)






Conferences related to Uncertainty

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2013 IEEE Multi-Conference on Systems and Control (MSC)

MSC at brings together under a unique forum different groups of qualified scientists, engineers, researchers and practitioners from Academia and Industry, to discuss the state-of-the-art and future directions in advanced control technology and applications.

  • 2012 IEEE Multi-Conference on Systems and Control (MSC)

    MSC 2012 aims at bringing together different groups of qualified scientists, engineers, researchers and practitioners from Academia and Industry to discuss the state-of-the-art and future directions in advanced control technology and applications, intelligent systems, computational intelligence, and novel and cutting edge technologies as applied to complex systems.

  • 2011 IEEE Multi-Conference on Systems and Control (MSC)

    To bring together people from academia, industry, government and funding agencies. To discuss state-of-the-art in systems, control and their wide range of applications. To discuss future directions in research and development. To identify key areas of common interest.

  • 2010 IEEE Multi-Conference on Systems and Control (MSC)

    The 2010 IEEE Multi-conference on Systems and Control (MSC) gathers up three of the major international conferences promoted by the IEEE Control Systems Society: the International Conference on Control Applications (CCA), the International Symposium on Computer-Aided Control System Design (CACSD), the International Symposium on Intelligent Control (ISIC).

  • 2009 IEEE Multi-Conference on Systems and Control (MSC)

    MSC 2009 includes the 18th IEEE International Conference on Control Applications (CCA 2009) and the 24th IEEE International Symposium on Intelligent Control (ISIC 2009). The newest and more challenging control applications and recent advancements in intelligent control, including innovative control algorithms and computational intelligence methodologies that enable systems to to achieve and maintain high performance under uncertain conditions will be discussed.


2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

IFSA-NAFIPS 2013 aims to bring together researchers, engineers and practitioners to present the latest achievements and innovations in the area of fuzzy information processing, to discuss thought-provoking developments and challenges.

  • NAFIPS 2012 - 2012 Annual Meeting of the North American Fuzzy Information Processing Society

    NAFIPS 2012 aims to bring together researchers, engineers and practitioners to present the latest achievements and innovations in the area of fuzzy information processing, to discuss thought-provoking developments and challenges.

  • NAFIPS 2011 - 2011 Annual Meeting of the North American Fuzzy Information Processing Society

    Promote and encourage the development of fuzzy sets, fuzzy systems, and related technologies.


2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy theory and foundations; stability of fuzzy systems; fuzzy methods and algorithms; fuzzy image, speech and signal processing; multimedia; fuzzy hardware and architectures; data mining.

  • 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy theory and foundations; stability of fuzzy systems; fuzzy methods and algorithms; fuzzy image, speech and signal processing; multimedia; fuzzy hardware and architectures; data mining.

  • 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy theory and foundations; stability of fuzzy systems; fuzzy methods and algorithms; fuzzy image, speech and signal processing; multimedia; fuzzy hardware and architectures; data mining.


2012 9th International Conference on Service Systems and Service Management (ICSSSM 2012)

The scope of the conference includes topics on: Theory and Principle of Service Sciences;Service System Design, Operations, and Management, Supply Chain Management for Service, Service Marketing and Financial Management, Specific Industrial Service Management, Service Information Technology and Decision Making,Service Experiential Studies and Case Studies.

  • 2011 8th International Conference on Service Systems and Service Management (ICSSSM 2011)

    The main interests of the confernce includes the following areas: Theory and Principle of Service Sciences; Service System Design, Operations, and Management; Supply Chain Management for Service; Service Marketing and Financial Management; Specifically Industrial Service Management; Service Information Technology and Decision Making;Service Experiential Studies and Case Studies

  • 2010 7th International Conference on Service Systems and Service Management (ICSSSM 2010)

    The main interests of the confernce includes the following areas: Theory and Principle of Service Sciences; Service System Design, Operations, and Management; Supply Chain Management for Service; Service Marketing and Financial Management; Specifically Industrial Service Management; Service Information Technology and Decision Making;Service Experiential Studies and Case Studies


2012 IEEE 5th International Measurement University (IMU)

The IMU is intended to provide paticipants with a background of knowledge in the field of Instrumentation and Measurements, so that they have the know -how to perform different kind of measurements in any possible application field. Young engineers and scientists that want to increase their competence in this fascinating area of the human scientific knowledge should attend the IMU. Here they can meet the major worldwide experts in the different fields of instrumentation and measurement.

  • 2011 IEEE 4th International Measurement University (IMU)

    The IMU is intended to provide paticipants with a background of knowledge in the field of Instrumentation and Measurements, so that they have the know -how to perform different kind of measurements in any possible application field. Young engineers and scientists that want to increase their competence in this fascinating area of the human scientific knowledge should attend the IMU. Here they can meet the major worldwide experts in the different fields of instrumentation and measurement.

  • 2010 IEEE International Measurement University (IMU)

    The IMU is intended to provide paticipants with a background of knowledge in the field of Instrumentation and Measurements, so that they have the know -how to perform different kind of measurements in any possible application field. Young engineers and scientists that want to increase their competence in this fascinating area of the human scientific knowledge should attend the IMU. Here they can meet the major worldwide experts in the different fields of instrumentation and measurement.

  • 2009 IEEE 2nd Annual International Measurement University (IMU)

    The IMU is intended to provide paticipants with a background of knowledge in the field of Instrumentation and Measurements, so that they have the know -how to perform different kind of measurements in any possible application field. Young engineers and scientists that want to increase their competence in this fascinating area of the human scientific knowledge should attend the IMU. Here they can meet the major worldwide experts in the different fields of instrumentation and measurement.

  • 2008 IEEE 1st Annual International Measurement University (IMU)

    The IMU is intended to provide paticipants with a background of knowledge in the field of Instrumentation and Measurements, so that they have the know-how to perform different kind of measurements in any possible application field. Young engineers and scientists that want to increase their competence in this fascinating area of the human scientific knowledge should attend the IMU. Here they can meet the major worldwide experts in the different fields of instrumentation and measurement.


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Periodicals related to Uncertainty

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Control Systems Technology, IEEE Transactions on

Serves as a compendium for papers on the technological advances in control engineering and as an archival publication which will bridge the gap between theory and practice. Papers will highlight the latest knowledge, exploratory developments, and practical applications in all aspects of the technology needed to implement control systems from analysis and design through simulation and hardware.


Electronics Packaging Manufacturing, IEEE Transactions on

Design for manufacturability, cost and process modeling, process control and automation, factory analysis and improvement, information systems, statistical methods, environmentally-friendly processing, and computer-integrated manufacturing for the production of electronic assemblies, products, and systems.


Instrumentation and Measurement, IEEE Transactions on

Measurements and instrumentation utilizing electrical and electronic techniques.


Visualization and Computer Graphics, IEEE Transactions on

Specific topics include, but are not limited to: a) visualization techniques and methodologies; b) visualization systems and software; c) volume visulaization; d) flow visualization; e) information visualization; f) multivariate visualization; g) modeling and surfaces; h) rendering techniques and methodologies; i) graphics systems and software; j) animation and simulation; k) user interfaces; l) virtual reality; m) visual programming and program visualization; ...



Most published Xplore authors for Uncertainty

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Xplore Articles related to Uncertainty

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Stress multipliers for the NSTX upgrade digital coil protection system

Peter H. Titus; R. Woolley; R. Hatcher 2011 IEEE/NPSS 24th Symposium on Fusion Engineering, 2011

Conceptual design of the upgrade to NSTX, explored designs sized to accept the worst loads that power supplies could produce. This produced excessive structures that would have been difficult to install and were much more costly than needed to meet the scenarios required for the upgrade mission. Instead, the project decided to rely on a digital coil protection system (DCPS). ...


Using a Multi-Agent Evidential Reasoning Network as the Objective Function for an Evolutionary Algorithm

Robert Woodley; Eric Lindahl; Joseph Barker 2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems, 2007

A culturally diverse group of people are now participating in military multinational coalition operations (e.g., combined air operations center, training exercises such as Red Flag at Nellis AFB, NATO AWACS), as well as in extreme environments. Human biases and routines, capabilities, and limitations strongly influence overall system performance; whether during operations or simulations using models of humans. Many missions and ...


Robust High Bandwidth Discrete-Time Predictive Current Control with Predictive Internal Model—A Unified Approach for Voltage-Source PWM Converters

Yasser Abdel-Rady Ibrahim Mohamed; Ehab F. El-Saadany IEEE Transactions on Power Electronics, 2008

This paper presents a robust high bandwidth discrete-time predictive current control scheme for voltage-source pulsewidth-modulated (VS-PWM) converters. First, to achieve high bandwidth current control characteristics, a digital predictive current controller with delay compensation is adopted. The compensation method utilizes a current observer with an adaptive internal model for system uncertainties. The predictive nature of both the current observer and the ...


Frequency Adaptive CDSC-PLL Using Axis Drift Control Under Adverse Grid Condition

Hany A. Hamed; Ahmed F. Abdou; Ehab H. E. Bayoumi; E. E. EL-Kholy IEEE Transactions on Industrial Electronics, 2017

This paper presents a new technique to adapt the cascaded delayed signal cancellation phase-locked loop (CDSC-PLL) using a proposed axis drift control (ADC). When grid frequency changes, the estimated grid angle by PLL drifts with a residual error proportional to the frequency change value thus, the accuracy and performance of the conventional CDSC-PLL is deteriorated which can lead to malfunction ...


Exact Nyquist-like stability results for ellipsoidal uncertainties

H. A. Latchman; O. D. Crisalle Conference Record Southcon, 1994

In this paper we develop a stability criterion for systems with uncertainties which are manifested in the frequency domain by simply-connected and closed, arbitrary uncertainty regions which satisfy a mild convexity constraint. In particular, well-known stability results for the case of disk-bounded frequency domain uncertainties are recovered as a special case of the proposed approach. The main results hinge on ...


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Educational Resources on Uncertainty

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eLearning

Stress multipliers for the NSTX upgrade digital coil protection system

Peter H. Titus; R. Woolley; R. Hatcher 2011 IEEE/NPSS 24th Symposium on Fusion Engineering, 2011

Conceptual design of the upgrade to NSTX, explored designs sized to accept the worst loads that power supplies could produce. This produced excessive structures that would have been difficult to install and were much more costly than needed to meet the scenarios required for the upgrade mission. Instead, the project decided to rely on a digital coil protection system (DCPS). ...


Using a Multi-Agent Evidential Reasoning Network as the Objective Function for an Evolutionary Algorithm

Robert Woodley; Eric Lindahl; Joseph Barker 2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems, 2007

A culturally diverse group of people are now participating in military multinational coalition operations (e.g., combined air operations center, training exercises such as Red Flag at Nellis AFB, NATO AWACS), as well as in extreme environments. Human biases and routines, capabilities, and limitations strongly influence overall system performance; whether during operations or simulations using models of humans. Many missions and ...


Robust High Bandwidth Discrete-Time Predictive Current Control with Predictive Internal Model—A Unified Approach for Voltage-Source PWM Converters

Yasser Abdel-Rady Ibrahim Mohamed; Ehab F. El-Saadany IEEE Transactions on Power Electronics, 2008

This paper presents a robust high bandwidth discrete-time predictive current control scheme for voltage-source pulsewidth-modulated (VS-PWM) converters. First, to achieve high bandwidth current control characteristics, a digital predictive current controller with delay compensation is adopted. The compensation method utilizes a current observer with an adaptive internal model for system uncertainties. The predictive nature of both the current observer and the ...


Frequency Adaptive CDSC-PLL Using Axis Drift Control Under Adverse Grid Condition

Hany A. Hamed; Ahmed F. Abdou; Ehab H. E. Bayoumi; E. E. EL-Kholy IEEE Transactions on Industrial Electronics, 2017

This paper presents a new technique to adapt the cascaded delayed signal cancellation phase-locked loop (CDSC-PLL) using a proposed axis drift control (ADC). When grid frequency changes, the estimated grid angle by PLL drifts with a residual error proportional to the frequency change value thus, the accuracy and performance of the conventional CDSC-PLL is deteriorated which can lead to malfunction ...


Exact Nyquist-like stability results for ellipsoidal uncertainties

H. A. Latchman; O. D. Crisalle Conference Record Southcon, 1994

In this paper we develop a stability criterion for systems with uncertainties which are manifested in the frequency domain by simply-connected and closed, arbitrary uncertainty regions which satisfy a mild convexity constraint. In particular, well-known stability results for the case of disk-bounded frequency domain uncertainties are recovered as a special case of the proposed approach. The main results hinge on ...


More eLearning Resources

IEEE-USA E-Books

  • The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty

    We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a goal. In many motion planning applications ranging from maneuvering vehicles over unfamiliar terrain to steering flexible medical needles through human tissue, the response of a robot to commanded actions cannot be precisely predicted. We propose to build a roadmap by sampling collision-free states in the configuration space and then locally sampling motions at each state to estimate state transition probabilities for each possible action. Given a query specifying initial and goal configurations, we use the roadmap to formulate a Markov Decision Process (MDP), which we solve using Infinite Horizon Dynamic Programming in polynomial time to compute stochastically optimal plans. The Stochastic Motion Roadmap (SMR) thus combines a sampling-based roadmap representation of the configuration space, as in PRM's, with the well-established theory of MDP's. Generating both states and transition probabilities by sampling is far more flexible than previous Markov motion planning approaches based on problem-specific or grid-based discretizations. We demonstrate the SMR framework by applying it to nonholonomic steerable needles, a new class of medical needles that follow curved paths through soft tissue, and confirm that SMR's generate motion plans with significantly higher probabilities of success compared to traditional shortest-path plans.

  • A Fundamental Tradeoff between Performance and Sensitivity within Haptic Rendering

    In this paper we show that for haptic rendering using position feedback, the structure of the feedback loop imposes a fundamental tradeoff between accurate rendering of virtual environments and sensitivity of closed-loop responses to hardware variations and uncertainty. Due to this tradeoff, any feedback design that achieves high-fidelity rendering incurs a quantifiable cost in terms of sensitivity. Analysis of the tradeoff reveals certain combinations of virtual environment and haptic device dynamics for which performance is achieved only by accepting very poor sensitivity. This analysis may be used to show that certain design specifications are feasible and may guide the choice of hardware to mitigate the tradeoff severity. We illustrate the predicted consequences of the tradeoff with an experimental study.

  • Sequential Decision Making

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • Online Stochastic Scheduling

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • References

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • RF Measurement Uncertainties

    This chapter contains sections titled: Mismatch Uncertainties RF Power Meter Measurement Uncertainties Uncertainty of VNA Measurement of Absolute Power Uncertainty of Spectrum Analyzer Measurements Measurement Uncertainties of Ratioed Measurements with a VNA Noise Figure Measurement Uncertainty Annotated Bibliography

  • Online Stochastic Routing

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • Uncertainty Quantification

    This chapter contains sections titled: Introduction Parametric Uncertainty Plant-Specific Data Log-Normal Distribution Uncertainty Propagation Monte Carlo Propagation Analytical Moment Propagation Discrete Probability Algebra Summary This chapter contains sections titled: References Chapter Eleven Appendices Problems

  • Index

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • Planning and Control of Meso-scale Manipulation Tasks with Uncertainties

    We develop a systematic approach to incorporating uncertainty into planning manipulation tasks with frictional contacts. We consider the canonical problem of assembling a peg into a hole at the meso scale using probes with minimal actuation but with visual feedback from an optical microscope. We consider three sources of uncertainty. Because of errors in sensing position and orientation of the parts to be assembled, we must consider uncertainty in the sensed configuration of the system. Second, there is uncertainty because of errors in actuation. Third, there are geometric and physical parameters characterizing the environment that are unknown. We discuss the synthesis of robust planning primitives using a single degreeof- freedom probe and the automated generation of plans for meso-scale manipulation. We show simulation and experimental results in support of our work.



Standards related to Uncertainty

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IEEE Guide for the Measurement of Quasi-Static Magnetic and Electric Fields

This project describes measurement goals associated with characterizing quasi-static magnetic and electric fields, e.g. power frequency and other extremely low frequency fields, and available methods for accomplishing them. The guide should be used in conjunction with IEEE Std 1308-1994 (IEEE Recommended Practice for Instrumentation: Specifications for Magnetic Flux Density Meters - 10 Hz to 3 kHz), which defines terminology and ...



Jobs related to Uncertainty

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