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Operations Research, Systems Engineering and Industrial Engineering Commons™
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- Economic and Social Benefits (2)
- Evaluation (2)
- Rural Broadband (2)
- Underserved Community (2)
- Wireless (2)
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- Aerial imagery (1)
- Approximate policy iteration (1)
- Climate change adaptation (1)
- Climate vulnerability (1)
- Community capitals (1)
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- Deep reinforcement learning (1)
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- Genome-wide association studies (1)
- Landmark detection (1)
- Machine learning (1)
- Meshed return plane (1)
- Model building (1)
- NOAA steps to resilience (1)
- Noise reduction (1)
- Parkonson's disease (1)
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- Rural resilience (1)
- S-parameter (1)
- Signal detection theory (1)
- Social bots (1)
Articles 1 - 10 of 10
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Machine Learning-Based Seismic Damage Assessment Of Residential Buildings Considering Multiple Earthquake And Structure Uncertainties, Xinzhe Yuan, Liujun Li, Haibin Zhang, Yanping Zhu, Genda Chen, Cihan H. Dagli
Machine Learning-Based Seismic Damage Assessment Of Residential Buildings Considering Multiple Earthquake And Structure Uncertainties, Xinzhe Yuan, Liujun Li, Haibin Zhang, Yanping Zhu, Genda Chen, Cihan H. Dagli
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Wood-frame structures are used in almost 90% of residential buildings in the United States. It is thus imperative to rapidly and accurately assess the damage of wood-frame structures in the wake of an earthquake event. This study aims to develop a machine-learning-based seismic classifier for a portfolio of 6,113 wood-frame structures near the New Madrid Seismic Zone (NMSZ) in which synthesized ground motions are adopted to characterize potential earthquakes. This seismic classifier, based on a multilayer perceptron (MLP), is compared with existing fragility curves developed for the same wood-frame buildings near the NMSZ. This comparative study indicates that the MLP …
Don’T Touch That Dial: Psychological Reactance, Transparency, And User Acceptance Of Smart Thermostat Setting Changes, Matthew Heatherly, Denise A. Baker, Casey I. Canfield
Don’T Touch That Dial: Psychological Reactance, Transparency, And User Acceptance Of Smart Thermostat Setting Changes, Matthew Heatherly, Denise A. Baker, Casey I. Canfield
Psychological Science Faculty Research & Creative Works
Automation inherently removes a certain amount of user control. If perceived as a loss of freedom, users may experience psychological reactance, which is a motivational state that can lead a person to engage in behaviors to reassert their freedom. In an online experiment, participants set up and communicated with a hypothetical smart thermostat. Participants read notifications about a change in the thermostat's setting. Phrasing of notifications was altered across three dimensions: strength of authoritative language, deviation of temperature change from preferences, and whether or not the reason for the change was transparent. Authoritative language, temperatures outside the user's preferences, and …
Evaluating The Impact Of Broadband Access And Internet Use In A Small Underserved Rural Community, Javier Valentín-Sívico, Casey I. Canfield, Sarah A. Low, Christel Gollnick
Evaluating The Impact Of Broadband Access And Internet Use In A Small Underserved Rural Community, Javier Valentín-Sívico, Casey I. Canfield, Sarah A. Low, Christel Gollnick
Engineering Management and Systems Engineering Faculty Research & Creative Works
Having adequate access to the internet at home enhances quality-of-life for households and facilitates economic and social opportunities. Despite increased investment in response to the COVID-19 pandemic, millions of households in the rural United States still lack adequate access to high-speed internet. In this study, we evaluate a wireless broadband network deployed in Turney, a small, underserved rural community in northwest Missouri. In addition to collecting survey data before and after this internet intervention, we collected pre-treatment and post-treatment survey data from comparison communities to serve as a control group. Due to technical constraints, some of Turney's interested participants could …
Evaluating The Impact Of Broadband Access And Internet Use In A Small Underserved Rural Community, Javier Valentín-Sívico, Casey I. Canfield, Sarah A. Low, Christel Gollnick
Evaluating The Impact Of Broadband Access And Internet Use In A Small Underserved Rural Community, Javier Valentín-Sívico, Casey I. Canfield, Sarah A. Low, Christel Gollnick
Engineering Management and Systems Engineering Faculty Research & Creative Works
Having adequate access to the internet at home enhances quality-of-life for households and facilitates economic and social opportunities. Despite increased investment in response to the COVID-19 pandemic, millions of households in the rural United States still lack adequate access to high-speed internet. In this study, we evaluate a wireless broadband network deployed in Turney, a small, underserved rural community in northwest Missouri. In addition to collecting survey data before and after this internet intervention, we collected pre-treatment and post-treatment survey data from comparison communities to serve as a control group. Due to technical constraints, some of Turney's interested participants could …
An Effective Transfer Learning Based Landmark Detection Framework For Uav-Based Aerial Imagery Of Urban Landscapes, Bishwas Praveen, Vineetha Menon, Tathagata Mukherjee, Bryan Mesmer, Sampson Gholston, Steven Corns
An Effective Transfer Learning Based Landmark Detection Framework For Uav-Based Aerial Imagery Of Urban Landscapes, Bishwas Praveen, Vineetha Menon, Tathagata Mukherjee, Bryan Mesmer, Sampson Gholston, Steven Corns
Engineering Management and Systems Engineering Faculty Research & Creative Works
Aerial imagery captured through airborne sensors mounted on Unmanned Aerial Vehicles (UAVs), aircrafts, satellites, etc. in the form of RGB, LiDAR, multispectral or hyperspectral images provide a unique perspective for a variety of applications. These sensors capture high-resolution images that can be used for applications related to mapping, surveying, and monitoring of crops, infrastructure, and natural resources. Deep learning based algorithms are often the forerunners in facilitating practical solutions for such data-centric applications. Deep learning-based landmark detection is one such application which involves the use of deep learning algorithms to accurately identify and locate landmarks of interest in images captured …
A Genome-Wide Association Study Coupled With Machine Learning Approaches To Identify Influential Demographic And Genomic Factors Underlying Parkinson’S Disease, Md Asad Rahman, Jinling Liu
A Genome-Wide Association Study Coupled With Machine Learning Approaches To Identify Influential Demographic And Genomic Factors Underlying Parkinson’S Disease, Md Asad Rahman, Jinling Liu
Engineering Management and Systems Engineering Faculty Research & Creative Works
Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson's disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson's disease. Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. …
Improving Social Bot Detection Through Aid And Training, Ryan Kenny, Baruch Fischhoff, Alex Davis, Casey I. Canfield
Improving Social Bot Detection Through Aid And Training, Ryan Kenny, Baruch Fischhoff, Alex Davis, Casey I. Canfield
Engineering Management and Systems Engineering Faculty Research & Creative Works
Objective: We test the effects of three aids on individuals' ability to detect social bots among Twitter personas: a bot indicator score, a training video, and a warning. Background: Detecting social bots can prevent online deception. We use a simulated social media task to evaluate three aids. Method: Lay participants judged whether each of 60 Twitter personas was a human or social bot in a simulated online environment, using agreement between three machine learning algorithms to estimate the probability of each persona being a bot. Experiment 1 compared a control group and two intervention groups, one provided a bot indicator …
Show-Me Resilience: Assessing And Reconciling Rural Leaders’ Perceptions Of Climate Resilience In Missouri, Zachary J. Miller, Caleb O'Brien, Casey I. Canfield, Lauren Sullivan
Show-Me Resilience: Assessing And Reconciling Rural Leaders’ Perceptions Of Climate Resilience In Missouri, Zachary J. Miller, Caleb O'Brien, Casey I. Canfield, Lauren Sullivan
Engineering Management and Systems Engineering Faculty Research & Creative Works
Rural areas of the United States play a vital role in coping with, adapting to and mitigating climate change, yet they often lag urban areas in climate planning and action. Rural leaders—e.g., policymakers, state/federal agency professionals, non-profit organization leadership, and scholars – are pivotal for driving the programs and policies that support resilient practices, but our understanding of their perspectives on climate resilience writ large is limited. We conducted semi-structured interviews with 23 rural leaders in Missouri to elucidate their conceptualizations of climate resilience and identify catalysts and constraints for climate adaptation planning and action across rural landscapes. We investigated …
Deep Reinforcement Learning For Approximate Policy Iteration: Convergence Analysis And A Post-Earthquake Disaster Response Case Study, Abhijit Gosavi, L. (Lesley) H. Sneed, L. A. Spearing
Deep Reinforcement Learning For Approximate Policy Iteration: Convergence Analysis And A Post-Earthquake Disaster Response Case Study, Abhijit Gosavi, L. (Lesley) H. Sneed, L. A. Spearing
Engineering Management and Systems Engineering Faculty Research & Creative Works
Approximate Policy Iteration (API) is a Class of Reinforcement Learning (RL) Algorithms that Seek to Solve the Long-Run Discounted Reward Markov Decision Process (MDP), Via the Policy Iteration Paradigm, Without Learning the Transition Model in the Underlying Bellman Equation. Unfortunately, These Algorithms Suffer from a Defect Known as Chattering in Which the Solution (Policy) Delivered in Each Iteration of the Algorithm Oscillates between Improved and Worsened Policies, Leading to Sub-Optimal Behavior. Two Causes for This that Have Been Traced to the Crucial Policy Improvement Step Are: (I) the Inaccuracies in the Policy Improvement Function and (Ii) the Exploration/exploitation Tradeoff Integral …
Modeling Of A Microstrip Line Referenced To A Meshed Return Plane, Zeyi Sun, Jian Liu, Xiaoyan Xiong, Yuan Liu, Victor Khilkevich, Donghyun Kim, Daryl G. Beetner
Modeling Of A Microstrip Line Referenced To A Meshed Return Plane, Zeyi Sun, Jian Liu, Xiaoyan Xiong, Yuan Liu, Victor Khilkevich, Donghyun Kim, Daryl G. Beetner
Engineering Management and Systems Engineering Faculty Research & Creative Works
Transmission Lines Referenced to Meshed Return Planes Are Widely Used Because of the Physical Flexibility Imparted by the Meshed Plane. Poor Accounting for the Meshed Ground, However, Can Lead to Severe Signal Integrity and Radio Frequency Interference Issues. Full-Wave Simulation Can Characterize the Electrical Performance at an Early Design Stage, But It is Both Time and Computational Resource Consuming. to Make the Simulation More Efficient, a Method is Proposed in This Study to Model Transmission Lines with a Meshed Reference Ground using 2D Analysis. the 2D Analysis is Performed at Several Locations Along the Length of the Trace above the …