Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799339
Qianhong Zhao, G. Tao
This paper studies the control problems of a vehicle passing an intersection: the designed controller can make the controlled vehicle pass the intersection quickly and avoid any collision. In this research, the state-space model of the vehicle dynamics, containing several uncertain parameters, is established. The adaptive control method is adopted to deal with the systems parameter uncertainties in such vehicle control problems. For this study, two adaptive control designs are developed to solve the problem: a baseline adaptive control design and an enhanced adaptive control design. Unlike the classic PI controller which can only make the vehicle track constant velocity trajectories, both two adaptive control designs can achieve asymptotic tracking of arbitrary vehicle velocity trajectories. The enhanced adaptive design can even further improve the system tracking performance.
{"title":"Autonomous Vehicle Tracking and Collision Avoidance Using Adaptive Control Algorithms","authors":"Qianhong Zhao, G. Tao","doi":"10.1109/sieds55548.2022.9799339","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799339","url":null,"abstract":"This paper studies the control problems of a vehicle passing an intersection: the designed controller can make the controlled vehicle pass the intersection quickly and avoid any collision. In this research, the state-space model of the vehicle dynamics, containing several uncertain parameters, is established. The adaptive control method is adopted to deal with the systems parameter uncertainties in such vehicle control problems. For this study, two adaptive control designs are developed to solve the problem: a baseline adaptive control design and an enhanced adaptive control design. Unlike the classic PI controller which can only make the vehicle track constant velocity trajectories, both two adaptive control designs can achieve asymptotic tracking of arbitrary vehicle velocity trajectories. The enhanced adaptive design can even further improve the system tracking performance.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124907525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799320
Luke Palmieri, Ekrem Kaya, Gowri Prathap, A. Korb, Saltuk Karahan, Hamdi Kavak
Mass media is a medium of communication with a significant impact on public opinion and perception of issues of global significance. This study is centered around developing a software system to detect and analyze disinformation efforts through mass media outlets and predict shifts in public opinion or reveal active campaigns. The developed system uses a multistep process to analyze and reveal anti-American sentiment in any country of interest, particularly US allies. We used Turkey as a use case to test our system. Turkey is an important country because it holds a critical role within NATO as a US ally and has recently had significant shifts in anti-American views. We collected mass media articles from various Turkish media outlets. The articles were translated to English and stored in the system database. Roughly 3,500 articles are being published and added to the database each month. Using this system, we were able to conduct both exploratory and targeted analyses. For instance, an Iranian disseminated Turkish language newspaper was found to have the most negative Anti-American sentiment. Additionally, we retrieved and analyzed news articles related to Turkey's S-400 missile purchase from Russia, proving our system has significant potential.
{"title":"Investigating Disinformation Through the Lens of Mass Media: A System Design","authors":"Luke Palmieri, Ekrem Kaya, Gowri Prathap, A. Korb, Saltuk Karahan, Hamdi Kavak","doi":"10.1109/sieds55548.2022.9799320","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799320","url":null,"abstract":"Mass media is a medium of communication with a significant impact on public opinion and perception of issues of global significance. This study is centered around developing a software system to detect and analyze disinformation efforts through mass media outlets and predict shifts in public opinion or reveal active campaigns. The developed system uses a multistep process to analyze and reveal anti-American sentiment in any country of interest, particularly US allies. We used Turkey as a use case to test our system. Turkey is an important country because it holds a critical role within NATO as a US ally and has recently had significant shifts in anti-American views. We collected mass media articles from various Turkish media outlets. The articles were translated to English and stored in the system database. Roughly 3,500 articles are being published and added to the database each month. Using this system, we were able to conduct both exploratory and targeted analyses. For instance, an Iranian disseminated Turkish language newspaper was found to have the most negative Anti-American sentiment. Additionally, we retrieved and analyzed news articles related to Turkey's S-400 missile purchase from Russia, proving our system has significant potential.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114257829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799418
Jiacheng Chen, G. Tao
This paper studies the multiple-model based adaptive control of a robot manipulator moving in varying environments. The research problem is divided into two parts: the modeling of the system consisting of the robot manipulator and the varying environment, and the multiple-model based adaptive control of the robot manipulator. This paper considers the added mass, added moment of inertia, drag, and buoyancy as the environmental factors. In these dynamic models, the environmental factors and the mass, moment of inertia, and gravity of the robot are unknown parameters. By the linearity in the parameters property, we can write these parameters independently of the robot joint variables and thus can be estimated using an adaptive control law. After obtaining the system model, we adopt a multiple-model based adaptive control scheme. When the model of the robot changes, the parameter estimates can rapidly convert to a relatively closer one for the new true values. With a multiple-model based adaptive controller, the asymptotic tracking of the robot and the parameter boundedness are achieved, and the tracking is not disturbed by the variance of the environment parameters in the multiple model control case, which has better performance than the single model case.
{"title":"Adaptive Control of Robot Manipulators in Varying Environments","authors":"Jiacheng Chen, G. Tao","doi":"10.1109/sieds55548.2022.9799418","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799418","url":null,"abstract":"This paper studies the multiple-model based adaptive control of a robot manipulator moving in varying environments. The research problem is divided into two parts: the modeling of the system consisting of the robot manipulator and the varying environment, and the multiple-model based adaptive control of the robot manipulator. This paper considers the added mass, added moment of inertia, drag, and buoyancy as the environmental factors. In these dynamic models, the environmental factors and the mass, moment of inertia, and gravity of the robot are unknown parameters. By the linearity in the parameters property, we can write these parameters independently of the robot joint variables and thus can be estimated using an adaptive control law. After obtaining the system model, we adopt a multiple-model based adaptive control scheme. When the model of the robot changes, the parameter estimates can rapidly convert to a relatively closer one for the new true values. With a multiple-model based adaptive controller, the asymptotic tracking of the robot and the parameter boundedness are achieved, and the tracking is not disturbed by the variance of the environment parameters in the multiple model control case, which has better performance than the single model case.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114547362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/SIEDS55548.2022.9799376
Carly E. Gray, A.F. Chesser, A. Atchley, R. C. Smitherman, N. Tenhundfeld
Human-machine interactions have become a staple of people's daily lives through the use of mobile devices, robotics, and a myriad of smart technologies. Previous research has established that anthropomorphism can significantly affect subjective perceptions of, and interactions with, machines. Furthermore, the ability to customize digital tools has been shown to affect user preferences, video game enjoyment, and the efficacy of digital mental health interventions. This study examined whether the customization of a machine teammate could influence the performance of the human-machine team and generate an affective response on the part of the human teammate. To evaluate this premise, we developed a bomb-defusing task simulation using the Unity game engine wherein participants were randomly assigned to one of two (humanlike or machinelike) robot avatars or were given the ability to customize one. The customizable robot avatar allows the participant to select either a humanlike or machinelike robot and customize the color of the wheels and casing. The customization is aesthetic in nature and has no effect on the functionality of the robot. The game design incorporates a high-risk environment and uncertainty with respect to the bomb-defusing distance and required button presses to encourage cautious guidance of the robot. We predicted that the ability to customize the robot will increase performance and subjective measures of trust, affect, attachment, identification, immersion, and control. We also predicted that the humanlikeness of the robot would increase performance and our subjective measures. Finally, we expected to see a significant effect of customization and humanlikeness such that the customization and humanlikeness have an additive effect on performance and our subjective measures. The results of all analyses were nonsignificant. These results may help inform the design of such systems and address fears that customization could lead to over-empathizing with a machine teammate in a way that would reduce use in high-risk environments.
{"title":"Humanlikeness and Aesthetic Customization's Effect on Trust, Performance, and Affect","authors":"Carly E. Gray, A.F. Chesser, A. Atchley, R. C. Smitherman, N. Tenhundfeld","doi":"10.1109/SIEDS55548.2022.9799376","DOIUrl":"https://doi.org/10.1109/SIEDS55548.2022.9799376","url":null,"abstract":"Human-machine interactions have become a staple of people's daily lives through the use of mobile devices, robotics, and a myriad of smart technologies. Previous research has established that anthropomorphism can significantly affect subjective perceptions of, and interactions with, machines. Furthermore, the ability to customize digital tools has been shown to affect user preferences, video game enjoyment, and the efficacy of digital mental health interventions. This study examined whether the customization of a machine teammate could influence the performance of the human-machine team and generate an affective response on the part of the human teammate. To evaluate this premise, we developed a bomb-defusing task simulation using the Unity game engine wherein participants were randomly assigned to one of two (humanlike or machinelike) robot avatars or were given the ability to customize one. The customizable robot avatar allows the participant to select either a humanlike or machinelike robot and customize the color of the wheels and casing. The customization is aesthetic in nature and has no effect on the functionality of the robot. The game design incorporates a high-risk environment and uncertainty with respect to the bomb-defusing distance and required button presses to encourage cautious guidance of the robot. We predicted that the ability to customize the robot will increase performance and subjective measures of trust, affect, attachment, identification, immersion, and control. We also predicted that the humanlikeness of the robot would increase performance and our subjective measures. Finally, we expected to see a significant effect of customization and humanlikeness such that the customization and humanlikeness have an additive effect on performance and our subjective measures. The results of all analyses were nonsignificant. These results may help inform the design of such systems and address fears that customization could lead to over-empathizing with a machine teammate in a way that would reduce use in high-risk environments.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117282514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799329
Sheri Leder, Kristin Weger, Bryan L. Mesmer
Enhancing undergraduate programs with interdisciplinary collaboration is increasingly important to prepare students for the demands of the competitive multi-stakeholder market. In this study, the Interdisciplinary Undergraduate Experience (INCLUDE) program brings undergraduates from disciplines such as industrial and systems engineering, computer science, psychology, ethics, art, and marketing together and grants them the opportunity to network with practitioner mentors from large stakeholder organizations (e.g., NASA, Dynetics, AOA, the U.S. Army, and Navy) to solve a Grand Challenge. Although interdisciplinary teams are key to innovation, monodisciplinary programs tend to be divergent in nature and may not expose students to project teamwork. Problems arise when trying to foster effective outcomes for students who are unfamiliar with the skills necessary for self-managed teamwork. The purpose of this study is to share the results of exploratory qualitative research designed to better understand the challenges that senior undergraduates from two separate teams (Fall 2020 - Spring 2021 and Fall 2021 - Spring 2022) faced in the INCLUDE program at The University of Alabama in Huntsville (UAH). This analysis focuses on the individual, institutional, and gender factors that shaped student perceptions between the two focus groups who were tasked with different Grand Challenges and access to the same campus resources. Data sets from observations and interviews are used in this study. This approach, combined with extensive literature research, provides valuable insight into their perceptions and attitudes about interdisciplinary teamwork and possible conflict resolutions. The results are expected to reveal the complexity of interdisciplinary collaboration for educational researchers.
{"title":"An Analysis on the Factors Affecting Undergraduate Interdisciplinary Research Programs","authors":"Sheri Leder, Kristin Weger, Bryan L. Mesmer","doi":"10.1109/sieds55548.2022.9799329","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799329","url":null,"abstract":"Enhancing undergraduate programs with interdisciplinary collaboration is increasingly important to prepare students for the demands of the competitive multi-stakeholder market. In this study, the Interdisciplinary Undergraduate Experience (INCLUDE) program brings undergraduates from disciplines such as industrial and systems engineering, computer science, psychology, ethics, art, and marketing together and grants them the opportunity to network with practitioner mentors from large stakeholder organizations (e.g., NASA, Dynetics, AOA, the U.S. Army, and Navy) to solve a Grand Challenge. Although interdisciplinary teams are key to innovation, monodisciplinary programs tend to be divergent in nature and may not expose students to project teamwork. Problems arise when trying to foster effective outcomes for students who are unfamiliar with the skills necessary for self-managed teamwork. The purpose of this study is to share the results of exploratory qualitative research designed to better understand the challenges that senior undergraduates from two separate teams (Fall 2020 - Spring 2021 and Fall 2021 - Spring 2022) faced in the INCLUDE program at The University of Alabama in Huntsville (UAH). This analysis focuses on the individual, institutional, and gender factors that shaped student perceptions between the two focus groups who were tasked with different Grand Challenges and access to the same campus resources. Data sets from observations and interviews are used in this study. This approach, combined with extensive literature research, provides valuable insight into their perceptions and attitudes about interdisciplinary teamwork and possible conflict resolutions. The results are expected to reveal the complexity of interdisciplinary collaboration for educational researchers.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130507083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799410
Spencer Bozsik, Xinlun Cheng, Malvika Kuncham, E. Mitchell
Charlottesville is an independent city in central Virginia currently undergoing a rezoning process in an attempt to address the local impact of the housing affordability crisis. Restrictive, low-density zoning and gentrification have led to the displacement of many income-constrained Charlottesville residents. Local journalists are the primary information sources for residents about the crisis, but public data quantifying crisis impacts are often inaccessible to the public and to local media due to the technical experience required for data access and analysis. As a result, residents seeking to understand the land-scape of housing in Charlottesville are not taking full advantage of the information contained within public datasets as rezoning discussions take place. The focus of this project is to create a public dashboard in cooperation with local journalists to display and contextualize Charlottesville housing affordability data. Figures included in the dashboard make use of publicly available data from the Census, Bureau of Labor Statistics, ALICE (Asset Limited, Income Constrained, Employed) Project, and Charlottesville Open Data Portal, which have been cleaned, joined, and aggregated for plotting. The dashboard displays maps that geographically visualize home rental and purchase prices and identify the locations of resources such as public transportation and grocery stores, animated bar charts representing historical demographic information from the Census, and line graphs of historical median home prices. Neighborhood development is a central focus. We hope this platform for contextualizing and communicating data through data journalism will support advocacy for affordable housing initiatives and encourage more data scientists to carry out similar projects highlighting trends in their own communities.
{"title":"Democratizing Housing Affordability Data: Open Data and Data Journalism in Charlottesville, VA","authors":"Spencer Bozsik, Xinlun Cheng, Malvika Kuncham, E. Mitchell","doi":"10.1109/sieds55548.2022.9799410","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799410","url":null,"abstract":"Charlottesville is an independent city in central Virginia currently undergoing a rezoning process in an attempt to address the local impact of the housing affordability crisis. Restrictive, low-density zoning and gentrification have led to the displacement of many income-constrained Charlottesville residents. Local journalists are the primary information sources for residents about the crisis, but public data quantifying crisis impacts are often inaccessible to the public and to local media due to the technical experience required for data access and analysis. As a result, residents seeking to understand the land-scape of housing in Charlottesville are not taking full advantage of the information contained within public datasets as rezoning discussions take place. The focus of this project is to create a public dashboard in cooperation with local journalists to display and contextualize Charlottesville housing affordability data. Figures included in the dashboard make use of publicly available data from the Census, Bureau of Labor Statistics, ALICE (Asset Limited, Income Constrained, Employed) Project, and Charlottesville Open Data Portal, which have been cleaned, joined, and aggregated for plotting. The dashboard displays maps that geographically visualize home rental and purchase prices and identify the locations of resources such as public transportation and grocery stores, animated bar charts representing historical demographic information from the Census, and line graphs of historical median home prices. Neighborhood development is a central focus. We hope this platform for contextualizing and communicating data through data journalism will support advocacy for affordable housing initiatives and encourage more data scientists to carry out similar projects highlighting trends in their own communities.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132491100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799332
R. Rice, K. North, G. Hansen, D. Pearson, Oliver Schaer, T. Sherman, Daniel Vassallo
Future predicted energy demand on the grid is a major factor that drives the prices of energy contracts on trading markets. Errors in forecasting are problematic for energy traders who buy and sell futures contracts on the expected price of energy: when decisions are made on inaccurate predictions, the market will be inefficient, leading to price volatility and investment losses. This paper proposes the use of an ensemble model of lasso and ridge regressions to predict energy loads. Specifically, the methodology is used to forecast hourly energy demand for up to forty-one hours in the future for the Electric Reliability Council of Texas (ERCOT). The features in the model include previous energy loads and time identifiers such as month, day, and hour of the prediction horizon. The methodology resulted in the creation of forty-one hourly models, each an ensemble of lasso and ridge regression models. The performance of the methodology is measured via out-of-sample data from ERCOT in 2020 against the ERCOT predictions for the same period.
{"title":"Time-Series Forecasting Energy Loads: A Case Study in Texas","authors":"R. Rice, K. North, G. Hansen, D. Pearson, Oliver Schaer, T. Sherman, Daniel Vassallo","doi":"10.1109/sieds55548.2022.9799332","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799332","url":null,"abstract":"Future predicted energy demand on the grid is a major factor that drives the prices of energy contracts on trading markets. Errors in forecasting are problematic for energy traders who buy and sell futures contracts on the expected price of energy: when decisions are made on inaccurate predictions, the market will be inefficient, leading to price volatility and investment losses. This paper proposes the use of an ensemble model of lasso and ridge regressions to predict energy loads. Specifically, the methodology is used to forecast hourly energy demand for up to forty-one hours in the future for the Electric Reliability Council of Texas (ERCOT). The features in the model include previous energy loads and time identifiers such as month, day, and hour of the prediction horizon. The methodology resulted in the creation of forty-one hourly models, each an ensemble of lasso and ridge regression models. The performance of the methodology is measured via out-of-sample data from ERCOT in 2020 against the ERCOT predictions for the same period.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131635468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799372
Aditi Jain, Amelia Norman, L. Alonzi, Michael C. Smith, Neal Goodloe, K. P. White
Officials in the United States correctional system have long been aware of the significant role that serious mental illness (SMI) plays in recidivism. In a 2011 study, Bronson reported that 68% of prison inmates with diagnosed SMI returned to custody at least once within 4 years, 8% higher than those without SMI [1]. This issue is especially prevalent in regional jails, where 63% of male inmates and 75% of female inmates in regional jails suffer from symptoms of serious mental illness every year, making immediate assistance to these individuals crucial [2]. In response, a team of University of Virginia (UVA) Systems Engineering students work in collaboration with an array of organizations in the Charlottesville-Albemarle region to identify and provide local jail inmates with the mental health services they need, and produce policy recommendations to improve conditions for individuals with SMI who are prone to exposure to the criminal justice system [3]. The current Capstone team consists of undergraduate UVA students who perform analysis using the data provided by the organizations, enabling the community to make informed decisions. However, these decisions are hindered because, since the data sets from different organizations are not linked with a unique identifier for individuals across the agencies that are responsible for the care and supervision of individuals suffering from SMI. This makes the matching of individuals between data sets difficult. This issue is exacerbated by recidivism, which results in multiple occurrences of similar (or identical) values, complicating typical record matching methods, which often rely on one-to-one matching methods. Moreover, the data include protected personal identifiers (PPI) and HIPPA protected data, which also restricts data sharing among the agencies. Thus, any effort to merge the data must adhere to applicable data security rules and non-disclosure agreements. To resolve these matching issues, we first condensed the reiterations of data within each dataset into one line per individual and included an internal consistency metric that reflects possible changes (i.e. preferred name, address, etc.) that could affect data matching. Then, we developed a matching algorithm using the Record Linkage package on Python that compares two data sets consisting of resident information from Region Ten Community Services (R10) and the Jail Management System (JMS) at the Albemarle-Charlottesville Regional Jail (ACRJ) [4]. As a result of this process, we identified over 95 additional matches and another 50 uncertain matches that required human spot-checking, which is an improvement of 10% to previous methods of record matching applied to the data set. Such results could have significant results to the Capstone team as well as to other fields of research, especially regarding medical, financial, or other forms of data that deal with changing data over time.
{"title":"Linking Inmates to Mental Health Services by Matching Records Between Independent Data Sets","authors":"Aditi Jain, Amelia Norman, L. Alonzi, Michael C. Smith, Neal Goodloe, K. P. White","doi":"10.1109/sieds55548.2022.9799372","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799372","url":null,"abstract":"Officials in the United States correctional system have long been aware of the significant role that serious mental illness (SMI) plays in recidivism. In a 2011 study, Bronson reported that 68% of prison inmates with diagnosed SMI returned to custody at least once within 4 years, 8% higher than those without SMI [1]. This issue is especially prevalent in regional jails, where 63% of male inmates and 75% of female inmates in regional jails suffer from symptoms of serious mental illness every year, making immediate assistance to these individuals crucial [2]. In response, a team of University of Virginia (UVA) Systems Engineering students work in collaboration with an array of organizations in the Charlottesville-Albemarle region to identify and provide local jail inmates with the mental health services they need, and produce policy recommendations to improve conditions for individuals with SMI who are prone to exposure to the criminal justice system [3]. The current Capstone team consists of undergraduate UVA students who perform analysis using the data provided by the organizations, enabling the community to make informed decisions. However, these decisions are hindered because, since the data sets from different organizations are not linked with a unique identifier for individuals across the agencies that are responsible for the care and supervision of individuals suffering from SMI. This makes the matching of individuals between data sets difficult. This issue is exacerbated by recidivism, which results in multiple occurrences of similar (or identical) values, complicating typical record matching methods, which often rely on one-to-one matching methods. Moreover, the data include protected personal identifiers (PPI) and HIPPA protected data, which also restricts data sharing among the agencies. Thus, any effort to merge the data must adhere to applicable data security rules and non-disclosure agreements. To resolve these matching issues, we first condensed the reiterations of data within each dataset into one line per individual and included an internal consistency metric that reflects possible changes (i.e. preferred name, address, etc.) that could affect data matching. Then, we developed a matching algorithm using the Record Linkage package on Python that compares two data sets consisting of resident information from Region Ten Community Services (R10) and the Jail Management System (JMS) at the Albemarle-Charlottesville Regional Jail (ACRJ) [4]. As a result of this process, we identified over 95 additional matches and another 50 uncertain matches that required human spot-checking, which is an improvement of 10% to previous methods of record matching applied to the data set. Such results could have significant results to the Capstone team as well as to other fields of research, especially regarding medical, financial, or other forms of data that deal with changing data over time.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127580100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799422
K. Cox, Drew Hamrock, Sydney Lawrence, Sean Lynch, Jane Romness, Jonathan Saksvig, Alice Warner, Robert Gutierrez, Joe M. Hart, M. Boukhechba
Anterior Cruciate Ligament (ACL) reconstructions are among the most common sports medicine procedures performed in the world. Over 100,000 patients in the United States annually elect to have ACL reconstruction (ACLR) in hopes of returning to pre-injury level of activity. In the first two years following an ACLR, patients are at their highest risk for re-injury to both the repaired and contralateral knee. The overall incidence rate of an ACLR patient having to go through a second repair in 24 months is six times greater than someone who has never had an ACL tear. Early detection of functional deficits is vital to optimize post-operative rehabilitation and to restore normal movement patterns in patients, especially in those who are young with continued risk exposure from competitive sports. The decision about when to return to unrestricted physical activity or competitive sports has come under much scrutiny due to the lack of evidence-based criteria that have sufficient predictive value. Current methods of detection require unconventional movements which cannot be done in the early stages of recovery in fear of damaging the newly repaired ligament. The need for a precise, objective, and whole-body approach to movement evaluation is essential for the health and safety of patients recovering from ACLR. The objective of our research is to leverage sensing technologies to monitor patients post ACLR and investigate how body sensors can be used to aid medical decision-making regarding rehabilitation progressions. In our study, patient data, extracted from wearable sensors during several functional assessments, was used for multi-level analysis to extract features indicative of mobility and muscle activation. In conclusion of our pilot, we have identified key features effective in determining patient health post-ACLR and implemented these into a machine learning model to estimate the efficacy of lower-body wearable sensors as a means of assessing patient recovery.
{"title":"How wearable sensing can be used to monitor patient recovery following ACL reconstruction","authors":"K. Cox, Drew Hamrock, Sydney Lawrence, Sean Lynch, Jane Romness, Jonathan Saksvig, Alice Warner, Robert Gutierrez, Joe M. Hart, M. Boukhechba","doi":"10.1109/sieds55548.2022.9799422","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799422","url":null,"abstract":"Anterior Cruciate Ligament (ACL) reconstructions are among the most common sports medicine procedures performed in the world. Over 100,000 patients in the United States annually elect to have ACL reconstruction (ACLR) in hopes of returning to pre-injury level of activity. In the first two years following an ACLR, patients are at their highest risk for re-injury to both the repaired and contralateral knee. The overall incidence rate of an ACLR patient having to go through a second repair in 24 months is six times greater than someone who has never had an ACL tear. Early detection of functional deficits is vital to optimize post-operative rehabilitation and to restore normal movement patterns in patients, especially in those who are young with continued risk exposure from competitive sports. The decision about when to return to unrestricted physical activity or competitive sports has come under much scrutiny due to the lack of evidence-based criteria that have sufficient predictive value. Current methods of detection require unconventional movements which cannot be done in the early stages of recovery in fear of damaging the newly repaired ligament. The need for a precise, objective, and whole-body approach to movement evaluation is essential for the health and safety of patients recovering from ACLR. The objective of our research is to leverage sensing technologies to monitor patients post ACLR and investigate how body sensors can be used to aid medical decision-making regarding rehabilitation progressions. In our study, patient data, extracted from wearable sensors during several functional assessments, was used for multi-level analysis to extract features indicative of mobility and muscle activation. In conclusion of our pilot, we have identified key features effective in determining patient health post-ACLR and implemented these into a machine learning model to estimate the efficacy of lower-body wearable sensors as a means of assessing patient recovery.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131203405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-28DOI: 10.1109/sieds55548.2022.9799392
P. Corbett, N. Keeley, Gabriella Belmarez, F. W. Blickle, Oliver Schaer
Companies with wide product portfolios and multiple retail channels often have difficulty quantifying the sales impact of marketing programs due to the large number of factors that potentially influence sales. The amount of data and complex modeling necessary to get such a promotional model off the ground creates a challenge, especially for firms executing a wide variety of promotions across many retailers. This challenge can stunt any efforts to make data-driven decisions regarding marketing spending. Our work explores marketing program data from a national consumer packaged goods (CPG) manufacturer and related product sales data from one of its retail partners. We build two separate models that provide a measure of incremental sales attributable to marketing programs at the brand level. The findings show that under certain conditions, organizations can achieve a useful promotional sales model with modest data inputs. Applying this approach, organizations can gain insights into the sales impact of their marketing spending, especially if they incorporate partner data, limit data streams and features, and incorporate program tactics. Our models can be used for descriptive as well as predictive analysis, thus allowing a CPG company to improve decision making that relies on forecasts of future sales.
{"title":"Building a Better Benchmark: Predicting Effects of Shopper Marketing on Sales","authors":"P. Corbett, N. Keeley, Gabriella Belmarez, F. W. Blickle, Oliver Schaer","doi":"10.1109/sieds55548.2022.9799392","DOIUrl":"https://doi.org/10.1109/sieds55548.2022.9799392","url":null,"abstract":"Companies with wide product portfolios and multiple retail channels often have difficulty quantifying the sales impact of marketing programs due to the large number of factors that potentially influence sales. The amount of data and complex modeling necessary to get such a promotional model off the ground creates a challenge, especially for firms executing a wide variety of promotions across many retailers. This challenge can stunt any efforts to make data-driven decisions regarding marketing spending. Our work explores marketing program data from a national consumer packaged goods (CPG) manufacturer and related product sales data from one of its retail partners. We build two separate models that provide a measure of incremental sales attributable to marketing programs at the brand level. The findings show that under certain conditions, organizations can achieve a useful promotional sales model with modest data inputs. Applying this approach, organizations can gain insights into the sales impact of their marketing spending, especially if they incorporate partner data, limit data streams and features, and incorporate program tactics. Our models can be used for descriptive as well as predictive analysis, thus allowing a CPG company to improve decision making that relies on forecasts of future sales.","PeriodicalId":286724,"journal":{"name":"2022 Systems and Information Engineering Design Symposium (SIEDS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129377794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}