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@article{gyory2022areal-timeartificial,
abstract = {Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team to reap the most impact. In this work, an Artificial Intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored …},
author = {Joshua T Gyory and Nicolas F Soria Zurita and Jay Martin and Corey Balon and Christopher McComb and Kenneth Kotovsky and Jonathan Cagan},
citation = {ASME 2022 International Design Engineering Technical Conferences and …, 2022},
conference = {ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
pub_year = {2022},
title = {A Real-Time Artificial Intelligence Process Manager for Engineering Design}
}
| {
"ENTRYTYPE": "article",
"ID": "gyory2022areal-timeartificial",
"abstract": "Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team to reap the most impact. In this work, an Artificial Intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored …",
"author": "Joshua T Gyory and Nicolas F Soria Zurita and Jay Martin and Corey Balon and Christopher McComb and Kenneth Kotovsky and Jonathan Cagan",
"citation": "ASME 2022 International Design Engineering Technical Conferences and …, 2022",
"conference": "ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2022,
"publisher": null,
"title": "A Real-Time Artificial Intelligence Process Manager for Engineering Design",
"volume": null
} | 0P9w_S0AAAAJ:35r97b3x0nAC | 0 | null | null | https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-abstract/IDETC-CIE2022/86236/1150462 | /scholar?oi=bibs&hl=en&q=related:2UAl4m6_5hoJ:scholar.google.com/ | null | null | null | null | null | null | null | null | null | [
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@article{basu2022processmonitoring:distinguishing,
abstract = {In this research, a theoretic physics-based framework for identification of defects via analysis of strain fields is presented. This framework comprises identification of self-similarity of strain fields followed by their dimensionality reduction using kernel based principal component analysis. The efficacy of this framework is tested qualitatively, by visual analysis, and quantitatively, using numerical classification algorithms. We see high (> 95%) accuracy of classification via cross-validation studies using support vector machine algorithm. These results suggest that strain field can provide a viable approach for constructing highly robust in line defect detection system in modern manufacturing environments.© 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.},
author = {Saurabh Basu and Christopher McComb and Mustafa Rifat and Hongtao Sun and Soundar Kumara},
citation = {North American Manufacturing Research Conference, 2022},
conference = {North American Manufacturing Research Conference},
pub_year = {2022},
title = {Process Monitoring: Distinguishing Defect Shapes by Strain Field Signatures}
}
| {
"ENTRYTYPE": "article",
"ID": "basu2022processmonitoring:distinguishing",
"abstract": "In this research, a theoretic physics-based framework for identification of defects via analysis of strain fields is presented. This framework comprises identification of self-similarity of strain fields followed by their dimensionality reduction using kernel based principal component analysis. The efficacy of this framework is tested qualitatively, by visual analysis, and quantitatively, using numerical classification algorithms. We see high (> 95%) accuracy of classification via cross-validation studies using support vector machine algorithm. These results suggest that strain field can provide a viable approach for constructing highly robust in line defect detection system in modern manufacturing environments.© 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.",
"author": "Saurabh Basu and Christopher McComb and Mustafa Rifat and Hongtao Sun and Soundar Kumara",
"citation": "North American Manufacturing Research Conference, 2022",
"conference": "North American Manufacturing Research Conference",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2022,
"publisher": null,
"title": "Process Monitoring: Distinguishing Defect Shapes by Strain Field Signatures",
"volume": null
} | 0P9w_S0AAAAJ:evX43VCCuoAC | 0 | null | null | https://par.nsf.gov/servlets/purl/10483141 | /scholar?oi=bibs&hl=en&q=related:FM80SN4BNA0J:scholar.google.com/ | null | null | null | null | null | null | null | null | null | [
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@article{xiao2021infrastructureandcommunity,
abstract = {Permafrost thaw is one of the world’s most pressing climate problems, already disrupting lifestyles, livelihoods, economies, and ecosystems in the north, and threatening to spill beyond the boundaries of the Arctic as our planet continues to warm. To examine the effects of permafrost degradation, and increase our understanding of what this phenomenon means for the future of the region (and the world), The Arctic Institute’s new two-part permafrost series aims to analyze the topic from scientific, security, legal, and personal perspectives. The second installment of our series features eight new articles on permafrost degradation and its effects on Arctic life, research, and the world at large. But before, check out the seven articles from our first installment, starting with the. Intro},
author = {Ming Xiao and Lilian Alessa and Louise Farquharson and Dmitry Nicolsky and Benjamin M. Jones and Vladimir E. Romanovsky and Anne Jensen and Christopher McComb and Xiong Zhang and Min Liew and Xiaohang Ji},
citation = {https://www.thearcticinstitute.org/infrastructure-community-resilience …, 2021},
pub_year = {2021},
title = {Infrastructure and Community Resilience in the Changing Arctic: Status, Challenges, and Research Needs}
}
| {
"ENTRYTYPE": "article",
"ID": "xiao2021infrastructureandcommunity",
"abstract": "Permafrost thaw is one of the world’s most pressing climate problems, already disrupting lifestyles, livelihoods, economies, and ecosystems in the north, and threatening to spill beyond the boundaries of the Arctic as our planet continues to warm. To examine the effects of permafrost degradation, and increase our understanding of what this phenomenon means for the future of the region (and the world), The Arctic Institute’s new two-part permafrost series aims to analyze the topic from scientific, security, legal, and personal perspectives. The second installment of our series features eight new articles on permafrost degradation and its effects on Arctic life, research, and the world at large. But before, check out the seven articles from our first installment, starting with the. Intro",
"author": "Ming Xiao and Lilian Alessa and Louise Farquharson and Dmitry Nicolsky and Benjamin M. Jones and Vladimir E. Romanovsky and Anne Jensen and Christopher McComb and Xiong Zhang and Min Liew and Xiaohang Ji",
"citation": "https://www.thearcticinstitute.org/infrastructure-community-resilience …, 2021",
"conference": null,
"journal": null,
"number": null,
"pages": null,
"pub_year": 2021,
"publisher": null,
"title": "Infrastructure and Community Resilience in the Changing Arctic: Status, Challenges, and Research Needs",
"volume": null
} | 0P9w_S0AAAAJ:WqliGbK-hY8C | 0 | null | null | https://par.nsf.gov/servlets/purl/10289553 | /scholar?oi=bibs&hl=en&q=related:KhIRa5FTeX0J:scholar.google.com/ | null | null | null | null | null | null | null | null | null | [
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@article{holowsko2021multi-objectivemodel-basedoptimization,
abstract = {The emergence of an Urban Air Mobility (UAM) market utilizing fleets of vertical takeoff and land (VTOL) aircraft has the potential to shorten commutes, alleviate traffic congestion, and transform the way people interact with cities. Both industry and academic effort has been aimed towards predicting and simulating fleet activity for the purpose of informing vehicle design decisions and infrastructure planning activities. However, little effort has been targeted at analyzing aircraft fleet performance and sensitivity to pilot decision making, infrastructure availability, and changing vehicle characteristics. In this work, we utilize an existing proprietary, industry developed, discrete-event simulation tool that calculates air vehicle fleet performance with user specified vehicle and demand parameters. Building on this simulation, we then apply an optimization and sensitivity analysis, identifying the relative importance of different …},
author = {Nicholas Holowsko and Christopher McComb},
citation = {ASME 2021 International Mechanical Engineering Congress and Exposition, 2021},
conference = {ASME 2021 International Mechanical Engineering Congress and Exposition},
pub_year = {2021},
title = {Multi-Objective Model-Based Optimization of Pilot Decision Making for Urban Air Mobility}
}
| {
"ENTRYTYPE": "article",
"ID": "holowsko2021multi-objectivemodel-basedoptimization",
"abstract": "The emergence of an Urban Air Mobility (UAM) market utilizing fleets of vertical takeoff and land (VTOL) aircraft has the potential to shorten commutes, alleviate traffic congestion, and transform the way people interact with cities. Both industry and academic effort has been aimed towards predicting and simulating fleet activity for the purpose of informing vehicle design decisions and infrastructure planning activities. However, little effort has been targeted at analyzing aircraft fleet performance and sensitivity to pilot decision making, infrastructure availability, and changing vehicle characteristics. In this work, we utilize an existing proprietary, industry developed, discrete-event simulation tool that calculates air vehicle fleet performance with user specified vehicle and demand parameters. Building on this simulation, we then apply an optimization and sensitivity analysis, identifying the relative importance of different …",
"author": "Nicholas Holowsko and Christopher McComb",
"citation": "ASME 2021 International Mechanical Engineering Congress and Exposition, 2021",
"conference": "ASME 2021 International Mechanical Engineering Congress and Exposition",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2021,
"publisher": null,
"title": "Multi-Objective Model-Based Optimization of Pilot Decision Making for Urban Air Mobility",
"volume": null
} | 0P9w_S0AAAAJ:AXPGKjj_ei8C | 0 | null | null | https://asmedigitalcollection.asme.org/IMECE/proceedings-abstract/IMECE2021/85604/1132672 | /scholar?oi=bibs&hl=en&q=related:X2ROi4YRIEsJ:scholar.google.com/ | null | null | null | null | null | null | null | null | null | [
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@article{li2021usingphysics-informedgenerative,
author = {Matthew Li and Christopher McComb},
citation = {ASME 2021 International Design Engineering Technical Conferences and …, 2021},
conference = {ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
pub_year = {2021},
title = {Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations}
}
| {
"ENTRYTYPE": "article",
"ID": "li2021usingphysics-informedgenerative",
"abstract": null,
"author": "Matthew Li and Christopher McComb",
"citation": "ASME 2021 International Design Engineering Technical Conferences and …, 2021",
"conference": "ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2021,
"publisher": null,
"title": "Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations",
"volume": null
} | 0P9w_S0AAAAJ:1qzjygNMrQYC | 0 | null | null | null | null | null | null | null | null | null | null | null | null | null | [
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@article{raina2019learningtodesign,
author = {Ayush Raina and Christopher McComb and Jonathan Cagan},
citation = {International Design Engineering Technical Conferences and Computers and …, 2019},
conference = {International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
pub_year = {2019},
title = {Learning to Design From Humans: Imitating Human Designers Through Deep Learning},
volume = {2}
}
| {
"ENTRYTYPE": "article",
"ID": "raina2019learningtodesign",
"abstract": null,
"author": "Ayush Raina and Christopher McComb and Jonathan Cagan",
"citation": "International Design Engineering Technical Conferences and Computers and …, 2019",
"conference": "International Design Engineering Technical Conferences and Computers and Information in Engineering Conference",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2019,
"publisher": null,
"title": "Learning to Design From Humans: Imitating Human Designers Through Deep Learning",
"volume": "2"
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@article{li2019imagecollectionof,
abstract = {Image processing refers to the use of computer algorithms to manipulate and enhance digital images to improve their quality or to make them more suitable for tasks such as classification. Common benchmarking datasets in this field include the imagenet, CIFAR-100, and MNIST datasets. This dataset is a collection of images that are particularly relevant to engineering and design, consisting of two categories: 3D-printed prototypes, and non-3D-printed prototypes This data was collected through a hybrid approach that entailed both web scraping and direct collection from engineering labs and workspaces at Penn State University. The initial data was then augmented using several data augmentation techniques including rotation, noise, blur, and color shifting. This dataset is potentially useful to train image classification algorithms or attentional mapping approaches. This data can be used either by itself or used to …},
author = {Matthew Li and Ninad Mahajan and Jessica Menold and Christopher McComb},
citation = {Data in Brief, 2019},
journal = {Data in Brief},
pub_year = {2019},
title = {Image collection of 3D-printed prototypes and non-3D-printed prototypes}
}
| {
"ENTRYTYPE": "article",
"ID": "li2019imagecollectionof",
"abstract": "Image processing refers to the use of computer algorithms to manipulate and enhance digital images to improve their quality or to make them more suitable for tasks such as classification. Common benchmarking datasets in this field include the imagenet, CIFAR-100, and MNIST datasets. This dataset is a collection of images that are particularly relevant to engineering and design, consisting of two categories: 3D-printed prototypes, and non-3D-printed prototypes This data was collected through a hybrid approach that entailed both web scraping and direct collection from engineering labs and workspaces at Penn State University. The initial data was then augmented using several data augmentation techniques including rotation, noise, blur, and color shifting. This dataset is potentially useful to train image classification algorithms or attentional mapping approaches. This data can be used either by itself or used to …",
"author": "Matthew Li and Ninad Mahajan and Jessica Menold and Christopher McComb",
"citation": "Data in Brief, 2019",
"conference": null,
"journal": "Data in Brief",
"number": null,
"pages": null,
"pub_year": 2019,
"publisher": null,
"title": "Image collection of 3D-printed prototypes and non-3D-printed prototypes",
"volume": null
} | 0P9w_S0AAAAJ:cFHS6HbyZ2cC | 0 | null | null | https://www.sciencedirect.com/science/article/pii/S2352340919310467 | /scholar?oi=bibs&hl=en&q=related:6WYLHsRKCK8J:scholar.google.com/ | null | null | null | null | null | null | null | null | null | [
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@article{roy2018ethicaldesignof,
abstract = {Ethical Design of Gendered Products — Penn State Skip to main navigation Skip to search
Skip to main content Penn State Home Penn State Logo Help & FAQ Home Researchers
Research output Research units Equipment Grants & Projects Prizes Activities Search by
expertise, name or affiliation Ethical Design of Gendered Products Olivia Roy, Christopher
McComb School of Engineering Design and Innovation Research output: Chapter in Book/Report/Conference
proceeding › Conference contribution Overview Original language Undefined/Unknown Title
of host publication 13th Engineering, Social Justice and Peace Conference State Published
- 2018 Cite this APA Author BIBTEX Harvard Standard RIS Vancouver Powered by Pure,
Scopus & Elsevier Fingerprint Engine™ © 2023 Elsevier BV We use cookies to help
provide and enhance our service and tailor content. By continuing you agree to the use of …},
author = {Olivia Roy and Christopher McComb},
citation = {13th Engineering, Social Justice and Peace Conference, 2018},
pub_year = {2018},
title = {Ethical Design of Gendered Products}
}
| {
"ENTRYTYPE": "article",
"ID": "roy2018ethicaldesignof",
"abstract": "Ethical Design of Gendered Products — Penn State Skip to main navigation Skip to search \nSkip to main content Penn State Home Penn State Logo Help & FAQ Home Researchers \nResearch output Research units Equipment Grants & Projects Prizes Activities Search by \nexpertise, name or affiliation Ethical Design of Gendered Products Olivia Roy, Christopher \nMcComb School of Engineering Design and Innovation Research output: Chapter in Book/Report/Conference \nproceeding › Conference contribution Overview Original language Undefined/Unknown Title \nof host publication 13th Engineering, Social Justice and Peace Conference State Published \n- 2018 Cite this APA Author BIBTEX Harvard Standard RIS Vancouver Powered by Pure, \nScopus & Elsevier Fingerprint Engine™ © 2023 Elsevier BV We use cookies to help \nprovide and enhance our service and tailor content. By continuing you agree to the use of …",
"author": "Olivia Roy and Christopher McComb",
"citation": "13th Engineering, Social Justice and Peace Conference, 2018",
"conference": null,
"journal": null,
"number": null,
"pages": null,
"pub_year": 2018,
"publisher": null,
"title": "Ethical Design of Gendered Products",
"volume": null
} | 0P9w_S0AAAAJ:ZeXyd9-uunAC | 0 | null | null | https://pure.psu.edu/en/publications/ethical-design-of-gendered-products | /scholar?oi=bibs&hl=en&q=related:kd_J-tPSfbsJ:scholar.google.com/ | null | null | null | null | null | null | null | null | null | [
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@article{mccomb2012enhancementofconcrete,
author = {Christopher McComb},
citation = {California State University, Fresno, 2012},
pub_year = {2012},
title = {Enhancement of Concrete Composite Decks}
}
| {
"ENTRYTYPE": "article",
"ID": "mccomb2012enhancementofconcrete",
"abstract": null,
"author": "Christopher McComb",
"citation": "California State University, Fresno, 2012",
"conference": null,
"journal": null,
"number": null,
"pages": null,
"pub_year": 2012,
"publisher": null,
"title": "Enhancement of Concrete Composite Decks",
"volume": null
} | 0P9w_S0AAAAJ:7PzlFSSx8tAC | 0 | null | null | null | /scholar?oi=bibs&hl=en&q=related:sYaY5p8MGOwJ:scholar.google.com/ | null | null | null | null | null | null | null | null | null | [
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@article{xu0taylorserieserror,
abstract = {High-fidelity engineering simulations can impose an enormous computational burden, hindering their application in design processes or other scenarios where time or computational resources can be limited. An effective up-sampling method for generating high-resolution data can help reduce the computational resources and time required for these simulations. However, conventional up-sampling methods encounter challenges when estimating results based on low-resolution meshes due to the often non-linear behavior of discretization error induced by the coarse mesh. In this study, we present the Taylor Expansion Error Correction Network (TEECNet), a neural network designed to efficiently super-resolve partial differential equations (PDEs) solutions via graph representations. We use a neural network to learn high-dimensional non-linear mappings between low-and high-fidelity solution spaces to approximate the effects of discretization error. The learned mapping is then applied to the low-fidelity solution to obtain an error correction model. Building upon the notion that discretization error can be expressed as a Taylor series expansion based on the mesh size, we directly encode approximations of the numerical error in the network design. This novel approach is capable of correcting point-wise evaluations and emulating physical laws in infinite-dimensional solution spaces. Additionally, results from computational experiments verify that the proposed model exhibits the ability to generalize across diverse physics problems, including heat transfer, Burgers' equation, and cylinder wake flow, achieving over 96% accuracy by mean squared error …},
author = {Wenzhuo Xu and Christopher McComb and Noelia Grande Gutierrez},
citation = {Available at SSRN 4731728, 0},
journal = {Available at SSRN 4731728},
title = {Taylor Series Error Correction Network for Super-Resolution of Discretized Partial Differential Equation Solutions}
}
| {
"ENTRYTYPE": "article",
"ID": "xu0taylorserieserror",
"abstract": "High-fidelity engineering simulations can impose an enormous computational burden, hindering their application in design processes or other scenarios where time or computational resources can be limited. An effective up-sampling method for generating high-resolution data can help reduce the computational resources and time required for these simulations. However, conventional up-sampling methods encounter challenges when estimating results based on low-resolution meshes due to the often non-linear behavior of discretization error induced by the coarse mesh. In this study, we present the Taylor Expansion Error Correction Network (TEECNet), a neural network designed to efficiently super-resolve partial differential equations (PDEs) solutions via graph representations. We use a neural network to learn high-dimensional non-linear mappings between low-and high-fidelity solution spaces to approximate the effects of discretization error. The learned mapping is then applied to the low-fidelity solution to obtain an error correction model. Building upon the notion that discretization error can be expressed as a Taylor series expansion based on the mesh size, we directly encode approximations of the numerical error in the network design. This novel approach is capable of correcting point-wise evaluations and emulating physical laws in infinite-dimensional solution spaces. Additionally, results from computational experiments verify that the proposed model exhibits the ability to generalize across diverse physics problems, including heat transfer, Burgers' equation, and cylinder wake flow, achieving over 96% accuracy by mean squared error …",
"author": "Wenzhuo Xu and Christopher McComb and Noelia Grande Gutierrez",
"citation": "Available at SSRN 4731728, 0",
"conference": null,
"journal": "Available at SSRN 4731728",
"number": null,
"pages": null,
"pub_year": null,
"publisher": null,
"title": "Taylor Series Error Correction Network for Super-Resolution of Discretized Partial Differential Equation Solutions",
"volume": null
} | 0P9w_S0AAAAJ:XoXfffV-tXoC | 0 | null | null | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4731728 | /scholar?oi=bibs&hl=en&q=related:U_urtRBBZ6MJ:scholar.google.com/ | null | null | null | null | null | null | null | null | null | [
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