1-17 of 17
Probability
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Energy Transfer in a Quarter-Car Model With Inertially Nonlinear Inerter-Based Pendulum Vibration Absorber

Joel A. Cosner, Wei-Che Tai
Proc. ASME. SMASIS2023, ASME 2023 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, V001T07A002, September 11–13, 2023
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2023-110951
https://doi.org/10.1115/SMASIS2023-110951
View Paper titled, Energy Transfer in a Quarter-Car Model With Inertially Nonlinear Inerter-Based Pendulum Vibration Absorber
Open the PDF for Energy Transfer in a Quarter-Car Model With Inertially Nonlinear Inerter-Based Pendulum Vibration Absorber in another window
Topics: Energy transformation, Pendulums, Vibration absorbers, Bifurcation, Roads, Density, Energy harvesting, Engineering simulation, Flow (Dynamics), Linear systems
Proceedings Papers

Stochastic Signal Analysis and Processing of Non-Harmonic, Periodic Vibrational Energy Harvesters

Maxim Germer, Uwe Marschner, Andreas Richter
Proc. ASME. SMASIS2021, ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, V001T04A010, September 14–15, 2021
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2021-68310
https://doi.org/10.1115/SMASIS2021-68310
View Paper titled, Stochastic Signal Analysis and Processing of Non-Harmonic, Periodic Vibrational Energy Harvesters
Open the PDF for Stochastic Signal Analysis and Processing of Non-Harmonic, Periodic Vibrational Energy Harvesters in another window
Topics: Energy harvesting, Signals, Circuits, Transducers, Capacitors, Density, Energy storage, Probability, Storage, Waves
Proceedings Papers

Design of a Probabilistic Health Monitoring System Using Embedded Piezoelectric Patch Sensors

Amin Toghi Eshghi, Soobum Lee, HyunJun Jung, Pingfeng Wang
Proc. ASME. SMASIS2019, ASME 2019 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, V001T05A001, September 9–11, 2019
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2019-5506
https://doi.org/10.1115/SMASIS2019-5506
View Paper titled, Design of a Probabilistic Health Monitoring System Using Embedded Piezoelectric Patch Sensors
Open the PDF for Design of a Probabilistic Health Monitoring System Using Embedded Piezoelectric Patch Sensors in another window
Topics: Design, Monitoring systems, Sensors, Failure, Structural health monitoring, Vibration, Failure mechanisms, Finite element analysis, Modeling, Screws
Proceedings Papers

Composite Laminate Fatigue Damage Detection and Prognosis Using Embedded Fiber Bragg Gratings

Michael Todd, William Gregory, Christopher Key, Michael Yeager, Jordan Ye
Proc. ASME. SMASIS2018, Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies, V002T05A011, September 10–12, 2018
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2018-8182
https://doi.org/10.1115/SMASIS2018-8182
View Paper titled, Composite Laminate Fatigue Damage Detection and Prognosis Using Embedded Fiber Bragg Gratings
Open the PDF for Composite Laminate Fatigue Damage Detection and Prognosis Using Embedded Fiber Bragg Gratings in another window
Topics: Composite materials, Fatigue damage, Fiber Bragg gratings, Laminates, Failure, Structural health monitoring, Acoustical properties, Algorithms, Corrosion resistance, Cycles
Proceedings Papers

Predicting Non-Stationary and Stochastic Activation of Saddle-Node Bifurcation

Jinki Kim, R. L. Harne, K. W. Wang
Proc. ASME. SMASIS2016, Volume 2: Modeling, Simulation and Control; Bio-Inspired Smart Materials and Systems; Energy Harvesting, V002T03A003, September 28–30, 2016
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2016-9051
https://doi.org/10.1115/SMASIS2016-9051
View Paper titled, Predicting Non-Stationary and Stochastic Activation of Saddle-Node Bifurcation
Open the PDF for Predicting Non-Stationary and Stochastic Activation of Saddle-Node Bifurcation in another window
Topics: Bifurcation, Noise (Sound), Analog circuits, Approximation, Density, Energy harvesting, Parameter estimation, Probability, Signals, Smart materials
Proceedings Papers

Analysis and Optimization of a Shape Memory Alloy-Based Self-Folding Sheet Considering Material Uncertainties

Edwin A. Peraza Hernandez, Darren J. Hartl, Richard J. Malak, Jr., Dimitris C. Lagoudas
Proc. ASME. SMASIS2015, Volume 1: Development and Characterization of Multifunctional Materials; Mechanics and Behavior of Active Materials; Modeling, Simulation and Control of Adaptive Systems, V001T01A013, September 21–23, 2015
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2015-9001
https://doi.org/10.1115/SMASIS2015-9001
View Paper titled, Analysis and Optimization of a Shape Memory Alloy-Based Self-Folding Sheet Considering Material Uncertainties
Open the PDF for Analysis and Optimization of a Shape Memory Alloy-Based Self-Folding Sheet Considering Material Uncertainties in another window
Topics: Alloys, Optimization, Shapes, Uncertainty, Wire, Design, Elastomers, Materials properties, Adaptive structures, Degrees of freedom
Proceedings Papers

Uncertainty Quantification of Energy Harvesting Systems Using Method of Quadratures and Maximum Entropy Principle

Aditya Nanda, M. Amin Karami, Puneet Singla
Proc. ASME. SMASIS2015, Volume 2: Integrated System Design and Implementation; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting, V002T07A016, September 21–23, 2015
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2015-9026
https://doi.org/10.1115/SMASIS2015-9026
View Paper titled, Uncertainty Quantification of Energy Harvesting Systems Using Method of Quadratures and Maximum Entropy Principle
Open the PDF for Uncertainty Quantification of Energy Harvesting Systems Using Method of Quadratures and Maximum Entropy Principle in another window
Topics: Energy harvesting, Entropy, Uncertainty quantification, Deflection, Density, Excitation, Probability, Uncertainty, Dynamic systems, Engineering simulation
Proceedings Papers

Operational Model Updating of Low-Order HAWT Models for Structural Health Monitoring Applications

Antonio Velazquez, R. Andrew Swartz
Proc. ASME. SMASIS2013, Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting, V002T05A010, September 16–18, 2013
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2013-3164
https://doi.org/10.1115/SMASIS2013-3164
View Paper titled, Operational Model Updating of Low-Order HAWT Models for Structural Health Monitoring Applications
Open the PDF for Operational Model Updating of Low-Order HAWT Models for Structural Health Monitoring Applications in another window
Topics: Horizontal axis wind turbines, Structural health monitoring, Computer-aided engineering, Wind turbines, Engines, Machinery, Modal assurance criterion, Optimization, Probability, Composite materials
Proceedings Papers

The Quantification of Uncertainty in SHM Features Derived From Transmissibility Estimation

Zhu Mao, Michael Todd
Proc. ASME. SMASIS2012, Volume 1: Development and Characterization of Multifunctional Materials; Modeling, Simulation and Control of Adaptive Systems; Structural Health Monitoring, 633-641, September 19–21, 2012
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2012-7935
https://doi.org/10.1115/SMASIS2012-7935
View Paper titled, The Quantification of Uncertainty in SHM Features Derived From Transmissibility Estimation
Open the PDF for The Quantification of Uncertainty in SHM Features Derived From Transmissibility Estimation in another window
Topics: Structural health monitoring, Uncertainty, Probability, Density, Linear systems, Noise (Sound)
Proceedings Papers

Density Function Optimization for the Homogenized Energy Model of Shape Memory Alloys

John H. Crews, Ralph C. Smith
Proc. ASME. SMASIS2011, ASME 2011 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Volume 1, 371-379, September 18–21, 2011
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2011-5036
https://doi.org/10.1115/SMASIS2011-5036
View Paper titled, Density Function Optimization for the Homogenized Energy Model of Shape Memory Alloys
Open the PDF for Density Function Optimization for the Homogenized Energy Model of Shape Memory Alloys in another window
Topics: Density, Optimization, Shape memory alloys, Stress, Probability, Accounting, Algorithms, Genetic algorithms, Optimization algorithms, Real-time control
Proceedings Papers

Structural Damage Identification With Performance-Based Uncertainty Quantification

Jiann-Shiun Lew
Proc. ASME. SMASIS2010, ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2, 573-579, September 28–October 1, 2010
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2010-3622
https://doi.org/10.1115/SMASIS2010-3622
View Paper titled, Structural Damage Identification With Performance-Based Uncertainty Quantification
Open the PDF for Structural Damage Identification With Performance-Based Uncertainty Quantification in another window
Topics: Damage, Uncertainty quantification, Uncertainty, Feedback, Control equipment, Probability, Simulation, Structural dynamics
Proceedings Papers

Optimal Sensor Placement for Damage Detection in Complex Structures

Sunilkumar Soni, Santanu Das, Aditi Chattopadhyay
Proc. ASME. SMASIS2009, Volume 2: Multifunctional Materials; Enabling Technologies and Integrated System Design; Structural Health Monitoring/NDE; Bio-Inspired Smart Materials and Structures, 565-571, September 21–23, 2009
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2009-1419
https://doi.org/10.1115/SMASIS2009-1419
View Paper titled, Optimal Sensor Placement for Damage Detection in Complex Structures
Open the PDF for Optimal Sensor Placement for Damage Detection in Complex Structures in another window
Topics: Damage, Sensor placement, Sensors, Failure, Structural elements (Construction), Structural health monitoring, Damping, Finite element analysis, Probability, Redundancy (Engineering)
Proceedings Papers

Integrated Structural Damage Assessment, Motion Planning, and Decision-Making for Distressed Aircraft Under Uncertainty

Israel Lopez, Nesrin Sarigul-Klijn
Proc. ASME. SMASIS2009, Volume 2: Multifunctional Materials; Enabling Technologies and Integrated System Design; Structural Health Monitoring/NDE; Bio-Inspired Smart Materials and Structures, 499-509, September 21–23, 2009
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2009-1315
https://doi.org/10.1115/SMASIS2009-1315
View Paper titled, Integrated Structural Damage Assessment, Motion Planning, and Decision-Making for Distressed Aircraft Under Uncertainty
Open the PDF for Integrated Structural Damage Assessment, Motion Planning, and Decision-Making for Distressed Aircraft Under Uncertainty in another window
Topics: Aircraft, Damage assessment, Decision making, Path planning, Uncertainty, Failure, Emergencies, Trajectories (Physics), Damage, Dynamics (Mechanics)
Proceedings Papers

On the Use of the Posterior Crame´r-Rao Lower Bound for Damage Estimation in Structural Health Management

Jun Jason Zhang, Wenfan Zhou, Narayan Kovvali, Antonia Papandreou-Suppappola, Aditi Chattopadhyay
Proc. ASME. SMASIS2009, Volume 2: Multifunctional Materials; Enabling Technologies and Integrated System Design; Structural Health Monitoring/NDE; Bio-Inspired Smart Materials and Structures, 589-595, September 21–23, 2009
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2009-1454
https://doi.org/10.1115/SMASIS2009-1454
View Paper titled, On the Use of the Posterior Crame´r-Rao Lower Bound for Damage Estimation in Structural Health Management
Open the PDF for On the Use of the Posterior Crame´r-Rao Lower Bound for Damage Estimation in Structural Health Management in another window
Topics: Damage, Density, Probability, Aluminum, Dynamic systems, Errors, Fatigue cracks, Measurement uncertainty, Particle filtering (numerical methods), Tension
Proceedings Papers

Using Multiscale Modeling to Predict Material Response of Polymeric Materials

Richard V. Beblo, Lisa Mauck Weiland
Proc. ASME. SMASIS2009, Volume 1: Active Materials, Mechanics and Behavior; Modeling, Simulation and Control, 189-196, September 21–23, 2009
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2009-1333
https://doi.org/10.1115/SMASIS2009-1333
View Paper titled, Using Multiscale Modeling to Predict Material Response of Polymeric Materials
Open the PDF for Using Multiscale Modeling to Predict Material Response of Polymeric Materials in another window
Topics: Multiscale modeling, Shape memory polymers, Stress, Chain, Density, Junctions, Phantoms, Polymer chains, Polymers, Probability
Proceedings Papers

Modeling Smart Materials Using Hysteretic Recurrent Neural Networks

Arun Veeramani, John Crews, Gregory D. Buckner
Proc. ASME. SMASIS2009, Volume 1: Active Materials, Mechanics and Behavior; Modeling, Simulation and Control, 679-687, September 21–23, 2009
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2009-1476
https://doi.org/10.1115/SMASIS2009-1476
View Paper titled, Modeling Smart Materials Using Hysteretic Recurrent Neural Networks
Open the PDF for Modeling Smart Materials Using Hysteretic Recurrent Neural Networks in another window
Topics: Artificial neural networks, Modeling, Smart materials, Shape memory alloys, Algorithms, Displacement, Probability, Real-time control, Stress, Thermomechanics
Proceedings Papers

Optimal Sensor Placement for Active Sensing

Eric Flynn, Michael Todd
Proc. ASME. SMASIS2008, Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2, 105-113, October 28–30, 2008
Publisher: American Society of Mechanical Engineers
Paper No: SMASIS2008-439
https://doi.org/10.1115/SMASIS2008-439
View Paper titled, Optimal Sensor Placement for Active Sensing
Open the PDF for Optimal Sensor Placement for Active Sensing in another window
Topics: Sensor placement, Actuators, Damage, Optimization, Sensors, Structural health monitoring, Algorithms, Echoes, Errors, Genetic algorithms