2020 Systems and Information Engineering Design Symposium (SIEDS)最新文献

{"title":"Analysis of Policy Factors Impacting the Use of Low-Cost Air Monitoring Networks in Washington, D.C.","authors":"Simon Saliby, Alexander Tong, Selin Ciesielski, Patrick Lim, R. Francis","doi":"10.1109/SIEDS49339.2020.9106694","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106694","url":null,"abstract":"The Environmental Protection Agency (EPA) sets national ambient air quality standards (NAAQS) for six criteria pollutants and monitors attainment to these standards through a system of federally accepted analytical methods (FEMs or FRMs). Due to their cost and technical sophistication, FEM monitoring networks may be good for measuring air pollution at a regional spatial resolution but may not be appropriate for monitoring neighborhood-level resolution. While FEM networks are effective at monitoring regional scale pollution, literature indicates that pollution varies considerably at sub-regional and even neighborhood-or block-level spatial scales. Therefore, while a region may be in attainment with the NAAQS, local communities’ air quality may not be represented by the data collected through the FEM network. In fact, public health trends, uneven distribution of tree canopy, and socioeconomic data indicate that particular communities in Washington, DC may have a high risk for nonattainment despite the District being in compliance with federal measures of air quality. With the overarching goal of mitigating this potential environmental and public health issue, the George Washington University Sustainability living lab project, Fresh Air DC, is working to deploy low-cost high-density air monitoring systems with at least one sensor in each advisory neighborhood council (ANC) throughout the District. In this paper, we formulate the elements of a policy framework that can support the establishment of this system. Our research will involve a comparative analysis between two existing low-cost high-density systems, one deployed in California and brought forth by state legislation, and the Breathe London hyperlocal air quality monitoring network. This comparative analysis will demonstrate the role of effective policy systems in supporting the development and implementation of a low-cost high-density system in DC. Moreover, this analysis demonstrates the role of the proposed policy framework in supporting the new DC Sustainability Plan.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115916457","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}

{"title":"Simulating Combat to Explore Motivations Behind Why Military Members Make Costly Sacrifices","authors":"Bianca Donadio, Á. Gómez, S. Atran, Jonathon Novak, Marshall Wheeler, Colin Marquez, E. D. Visser, Chad C. Tossell","doi":"10.1109/SIEDS49339.2020.9106644","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106644","url":null,"abstract":"Why are soldiers, sailors, airmen, and marines willing to make costly sacrifices? Previous research suggests loyalty (e.g., duty) to teammates is important among other reasons. More recently, studies conducted overseas have identified sacred values (i.e., values held so deeply they are immune to material tradeoffs) and group identity fusion as primary factors. Importantly, however, these studies have been conducted using survey-based and other social science methods which assess attitudes and beliefs, but not behavior. For example, it is one thing for a respondent to say they would jump on a grenade to sacrifice for their group but another to actually jump on a grenade in real life. Thus, we have developed a simulation to help bridge the gap between what people say and do in life-or-death scenarios. This high-fidelity simulation was developed to provide a more immersive means of testing realistic, “shoot or no shoot” hostage scenarios. Using feedback from individuals with military experience, the scenarios were designed to elicit more real-life stress than attitude-based surveys. This paper describes the systems engineering process we used to design the simulation as well as the proof-of-concept study developed to explore reasons behind why people are willing to make costly sacrifices. Early pilot data have revealed that values and identities related to religion, risk to self, and the Air Force predicted engagement decisions of Air Force cadets, in a series of simulated hostage scenarios. Possibilities for future use of this simulation will also be discussed. For example, while this experimental setup lacks high stakes consequences, this simulation could be useful for selection and training in addition to a research tool for studying motivations in different simulated combat environments.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123291416","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}

{"title":"Low Power Wireless Networks in Vineyards","authors":"Allison Renehan, B. Rombach, Anna Haikl, Corey Nolan, W. Lupton, E. Timmons, R. Bailey","doi":"10.1109/SIEDS49339.2020.9106693","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106693","url":null,"abstract":"The focus of this work is developing an effective and cost-efficient monitoring system that collects spatially-granular data within a vineyard. Many vineyard managers currently rely on limited data paired with past experiences to make key decisions pertaining to frost prediction, pest and disease prediction, and irrigation optimization. Considering that soil conditions and microclimates vary significantly within a single vineyard, this lack of data prevents them from precisely managing their vines.By engaging stakeholders in iterative prototype development, we identified key design features of a low-cost, high-density sensor network for vineyards. Functionally, an ideal system 1) uses Long Range (LoRa) wireless communication technology; and 2) places temperature, humidity, soil moisture, and light intensity sensors in relevant areas throughout the vineyard. Additionally, by engaging with industry competitors, we learned that the market lacks low-cost, high-density sensor network implementations.Using LoRa allows for a high density of sensors to be placed in every microclimate throughout a vineyard without relying on cellular coverage. The focus on temperature, humidity, soil moisture, and light intensity targets a low cost, minimally-viable set of metrics that can provide the necessary information for key models and decisions.User input and site visits suggested that the system must endure harsh environmental conditions and relay timely, actionable data without disrupting fieldwork. To prevent damage and extend device lifetime, the sensor housing and connections need to be waterproof and durable. Further, vine growing methods are not standardized across the industry, meaning the product needs to be adaptable to different growing styles. Vineyard managers want a system that informs their decisions by providing data and the results of established prediction models. The research presented here shows that a system incorporating these features and minimizing costs will be valuable in vineyards while also being broadly applicable to a variety of other agricultural applications.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128635976","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}

{"title":"An Interdisciplinary Approach to Sports Analytics in a University Setting","authors":"Jacqueline Hoege, Maryanna C. Lansing, Sarah Nelson, Daniel Ungerleider, Rishab Iyer, C. Rhodes, Ben Metzger, Peter Worcester, Aniket Chandra, Jacob Leonard, Rachel Kreitzer, W. Scherer","doi":"10.1109/SIEDS49339.2020.9106647","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106647","url":null,"abstract":"As of 2019, sports analytics has grown to be a $780 million industry [1]. Many organizations and institutions contribute to the field through research in exercise science, optimization of in-game decision making, sports marketing, business performance, and sports compliance fields. We propose an open, interdisciplinary approach to sports analytics within institutes of higher education to work across many fields and provide opportunities to diverse members within the community, enable research and communication across fields, serve the surrounding community, and ethically use data.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114644909","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}

{"title":"SIEDS 2020 Cover Page","authors":"","doi":"10.1109/sieds49339.2020.9106684","DOIUrl":"https://doi.org/10.1109/sieds49339.2020.9106684","url":null,"abstract":"","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132240881","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}

{"title":"Lessons Learned: A Case Study in Creating a Data Pipeline using Twitter’s API","authors":"Jason Tiezzi, Rice Tyler, Suchetha Sharma","doi":"10.1109/SIEDS49339.2020.9106584","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106584","url":null,"abstract":"With over 300 million users, including frequent postings by elites across the political and entertainment fields, Twitter has become a rich field for mining and analyzing data. Despite its prominence within social science research, relatively little attention has been paid to the process of data acquisition. To that end, our research uses a case study to illustrate the process of acquiring and storing tweets. To construct our data pipeline, we first applied for and created Twitter developer accounts and used the Tweepy app in Python to interact with Twitter’s API. We created a program that uses a producer-consumer multithreading model to request tweets from the API, then cleans the data and pushes it to a MySQL database with four tables: one for tweets, one for user information, one for retweets, and one for special entities (e.g., hashtags).With our pipeline operational, we explore how candidate gender affects Twitter discourse in the 2020 Democratic presidential primary. Specifically, we use unsupervised text analysis methods to examine differences in word frequencies, sentiment, and emotional dimensions. We find that gender is central to the discourse surrounding female candidates, but peripheral for male candidates. The discourse surrounding female candidates in our dataset is also more joyful and positive. Finally, with our case study concluded, we offer lessons to future researchers who wish to acquire and utilize Twitter data for social science research.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127182674","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}

{"title":"A New Dashboard Tool to Enhance Data Processing and Energy Efficiency Analysis in Modern Buildings","authors":"Abigail Sharp, D. Ojeda, Victoria Nilsen","doi":"10.1109/SIEDS49339.2020.9106664","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106664","url":null,"abstract":"In many buildings, energy tracking methods provide inadequate information regarding energy consumption, impeding the identification of economic and environmental waste in building operations and maintenance. M.C. Dean, an electrical design-build firm, recognized the importance of effective energy tracking methods in the large, complex buildings that it manages. Energy guidelines, such as Leadership in Energy and Environmental Design (LEED), are implemented to increase energy efficiency. Acquiring a LEED certification provides building owners with incentives, but it requires an in-depth documentation and understanding of energy usage prior to certification. Previously, M.C. Dean manually calculated average energy usage and created control charts that summarized annual statistics of their buildings. This method is sufficient for a single site but is an inefficient practice when applied manually across multiple sites. This project performed a requirements elicitation to determine the critical criteria for analyzing energy usage for M.C. Dean’s buildings. The results were used to develop a standardized Excel-based dashboard tool that instructs the user on importing and modifying raw energy data. Once the data is imported, the dashboard tool automatically tests for normality via probability plots and generates 3-sigma control charts. By automating this process, the dashboard tool enabled the user to gain detailed understanding of the energy usage of their site. Areas of potential improvement were identified through the implementation of three additional methods: analytic hierarchy process, cost simulation, and cost-benefit analysis. The manager’s preferences and energy guidelines generated a ranking of building usage factors, which can guide decisions on repurposing certain building elements. A Monte Carlo simulation was performed using parametric distribution analysis to predict future costs. Additionally, the dashboard increases awareness of energy usage by linking energy performance to the LEED Operations and Maintenance version 4.1 guidelines to estimate the current certification level and highlight areas for improvement. This research produced an energy performance tool that can be standardized to other complex buildings. It provides efficient energy tracking using standardized methods, allowing building owners to objectively assess the potential adoption of economical and sustainable practices.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126757760","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}

{"title":"Site Selection Decision Support Tool Using Geographic Information Systems and Multi-Expert Analytic Hierarchy Process","authors":"Aditya Singh, Justin W. Williams, J. Barba","doi":"10.1109/SIEDS49339.2020.9106582","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106582","url":null,"abstract":"Site selection, the process of locating alternatives for new facilities, is a complex and crucial decision faced by growing companies. Organizations often employ time consuming and informal market research techniques, which may fail to capture institutional knowledge or consider all feasible alternatives. Advancements in geographic information systems (GIS) have allowed for analytical methods to be adopted, but current GIS- based methodologies may only be able to study a small area using expensive software, hardware, or data. The goal of this project is to create a decision support tool that can study a large area using open source GIS software and publicly available data, without the use of high-performance computing. The project client is a business that combines an urban winery, a multipurpose venue, and a restaurant into one facility. The company’s site selection problem focuses on finding locations where there is a high demand for their products and services. Requirements elicitation was performed on several experts, and group aggregation techniques were applied to the traditional analytic hierarchy process (AHP) to generate weights for various decision criteria. Data for each criterion was standardized into a consistent scale and then loaded into GIS map layers. A weighted overlay technique was implemented to rank feasible alternatives in map form. Inter- market analysis was conducted using variables that capture an area’s demand for weddings and corporate events, which are the company’s key sources of revenue. Variables that capture demand for the organization’s services include labor availability, existing event infrastructure, and wine consumption in the target region. Intra-market analysis is performed to provide granular recommendations by capturing factors such as crime statistics, accessibility, and proximity to complementary businesses. Recommendations were provided at a “census block group” level of granularity. Sensitivity analysis was performed to test model robustness, and model accuracy was validated through ex post analysis of the firm’s existing locations. Opportunities exist to apply the underlying methodology presented in this project for other companies in various industries to address site selection problems.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125921725","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}

{"title":"Developing State-Based Recommendation Systems for Golf Training","authors":"Kelly Rohrer, Jacob Ziller, Alanna Flores, W. Scherer, Christopher Kaylor, Orlando Jimenez, Stephen Adams","doi":"10.1109/SIEDS49339.2020.9106646","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106646","url":null,"abstract":"The NBA, MLB, NFL and other professional leagues utilize sports analytics, but the potential of professional golf analytics is largely untapped. Instead of using data-driven methods connecting practice to tournament performance, training regimens are often based on conventional wisdom. How can data be used to recommend training regimens for golfers to improve performance? We partnered with golf analytics company, GameForge, to develop tools and methods for golf analytics to capture these markets, including the development of a state-based training recommendation system. We used Gameforge, PGA, and LPGA data to build markov models using k-means clustering, and linear models. These two model types form the basis of our recommendation system. In the future, these methods can be used to inform training decisions, particularly as more data is collected.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128850009","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}

{"title":"Analysis of real-time particulate matter (PM2.5) concentrations in Washington, DC, using generalized additive models (GAMs)","authors":"Jordan Frengut, Anwesha Tomar, Andrew Burwell, R. Francis","doi":"10.1109/SIEDS49339.2020.9106580","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106580","url":null,"abstract":"The objective of this paper is to report the results of a generalized additive model used to predict local particulate matter concentrations at a Washington, DC Department of Energy and Environment (DOEE) federal regulatory monitoring station. While the DOEE uses state-of-the-art federal equivalent method (FEM) equipment to demonstrate compliance with the clean air act for regulatory purposes, these measurements reflect regional, not neighborhood air quality. A GW student-led living lab project—Fresh Air DC—has been testing uRAD INDUSTRIAL low-cost air quality sensors that can be used to collect air quality data at the neighborhood level using LoRaWAN based smart city technology. Because low-cost sensors often lack the accuracy and sensitivity of FEM equipment, research indicates that low-cost sensor (LCS) monitoring networks require post- processing and data modelling in order to apply findings to educational and policy goals. Although LCS data processing has been conducted using linear and nonlinear models, nonlinear models tend to have a greater ability to capture the nuanced relationships between air pollutants and meteorological influences. In this paper, we post-process uRAD PM 2.5 sensor data using DOEE FEM equipment as a reference instrument in the development of three models to adjust uRAD data to the DOEE FEM data—ordinary least squares linear regression, generalized linear models (GLMs), and generalized additive models (GAMs). Our model includes meteorological variables such as temperature, humidity, and wind speed. Our statistical models for post-processing are evaluated on the basis of deviance and Akaike Information Criterion (AIC). We expect that the GLM and GAM will be useful for capturing nonlinear relationships between the PM2.5 measurements and meteorological variables.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124900028","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}