2021 Systems and Information Engineering Design Symposium (SIEDS)最新文献

{"title":"A Comprehensive Guide to Sweet Briar College’s Greenhouse Hydroponics System","authors":"Angelika Lindberg, Rachel Logan, H. Marron, B. Brinkman, M. Gervasio, B. Kuhr","doi":"10.1109/SIEDS52267.2021.9483761","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483761","url":null,"abstract":"Completed in the summer of 2020, Sweet Briar College’s 26,000 square foot greenhouse is home to a variety of vegetables, providing fresh food for the campus dining hall as well as giving students the opportunity to learn about food sustainability. The current horticulture practices in the greenhouse are functional, but growth rates and processing could be improved with a hydroponic system. Hydroponics is a subset of horticulture in which plants are rooted in nutrient-rich water rather than soil. A hydroponics system would not only serve as an educational opportunity for Sweet Briar’s environmental science students but would also allow for the experimentation of a variety of new plants and growing methods. Some other benefits of a hydroponic system include a significant decrease in water waste, reduced need for pesticides and herbicides, and more efficient use of space.Through extensive research and compliance with customer specifications, we determined that using a Nutrient Film Technique with an A-frame design would be the best option for the Sweet Briar College greenhouse. The NFT water flow method is one of the most respected in the field of hydroponics, as it is typically extremely reliable and user-friendly. A thin film of nutrient-laden water gently passes over the roots of the system, allowing the plant to absorb as needed. Incorporating this technique with an A-frame design would allow for the best use of space while still allowing each plant to receive optimal sunlight as compared to other vertically designed hydroponic systems. By incorporating microcontrollers and sensors to monitor the water level, pH, and electrical conductivity in the reservoir, our system will be able to dispense nutrient solution and water as needed. We plan to measure and track plant growth and survivability based on both new plant growth as well as fruit/vegetable production with the goal of exceeding that of standard soil-grown plants of the same variety and anticipate having results by April 2021. We would also like to track system water loss, either from leaks, evaporation, or absorption, by monitoring the main water reservoirs with the goal of 75% efficiency over the course of a month. Finally, we would like to measure the amount of light the plants are getting throughout the day, using either a photoresistor and microcontroller or a lux meter, to determine if additional synthetic lighting options would be beneficial to the system. We plan to have the system fully functioning and operable by May 2021.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132019024","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":"Exploring Amateur Performance in Athletic Tests Using Wearable Sensors","authors":"Stephen Mitchell, Jason Forsyth, Michael S. Thompson","doi":"10.1109/SIEDS52267.2021.9483715","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483715","url":null,"abstract":"The growing market for sports analytics has spurred more interest than ever in quantifying athletic performance. This trend, alongside the proliferation of new wearable technologies, has expanded the possibilities for both professional and amateur athletes to instrument themselves and collect meaningful data. The reactive strength index (RSI) can be used to communicate this kind of data by presenting a person’s ability for rapid movement. A user study was conducted in which young adults of amateur athletic status performed a jumping exercise to assess the feasibility of using a commercial off-the-shelf inertial measurement unit (IMU) to measure this metric compared to the usual method of using a force plate. Results suggest that the measurement of meaningful RSI improvements is possible using inexpensive IMUs with comparable results to costly force plates.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132610080","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":"Improving the Efficiency and Effectiveness of Multilingual Classification Methods for Sentiment Analysis","authors":"Pantea Ferdosian, Sean Grace, Vasudha Manikandan, Lucas Moles, Debajyoti Datta, Donald E. Brown","doi":"10.1109/SIEDS52267.2021.9483767","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483767","url":null,"abstract":"The growing field of customer experience management relies heavily on natural language processing (NLP). An important current use of NLP in this industry is to efficiently build sentiment models in new languages. These new language models will allow access to a greater range of clients. In this work, we examine the practical effectiveness and training data requirements of transfer learning methods, specifically mBERT and XLM-RoBERTa, for developing sentiment analysis models in German. To provide a meaningful comparison that excludes transfer learning, we also utilize and train an LSTM classification model. The models are tested by studying the performance gains for different amounts of target language training data. The results enable efficient building of NLP models by allowing prediction of the data requirements for a desired accuracy.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117120746","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":"Extensions and Application of the Robust Shared Response Model to Electroencephalography Data for Enhancing Brain-Computer Interface Systems","authors":"Andrew J. Graves, Cory Clayton, Joon Yuhl Soh, Gabe Yohe, P. Sederberg","doi":"10.1109/SIEDS52267.2021.9483745","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483745","url":null,"abstract":"Brain Computer Interfaces (BCI) decode electroencephalography (EEG) data collected from the human brain to predict subsequent behavior. While this technology has promising applications, successfully implementing a model is challenging. The typical BCI control application requires many hours of training data from each individual to make predictions of intended activity specific to that individual. Moreover, there are individual differences in the organization of brain activity and low signal-to-noise ratios in noninvasive measurement techniques such as EEG. There is a fundamental bias-variance trade-off between developing a single model for all human brains vs. an individual model for each specific human brain. The Robust Shared Response Model (RSRM) attempts to resolve this tradeoff by leveraging both the homogeneity and heterogeneity of brain signals across people. RSRM extracts components that are common and shared across individual brains, while simultaneously learning unique representations between individual brains. By learning a latent shared space in conjunction with subject-specific representations, RSRM tends to result in better predictive performance on functional magnetic resonance imaging (fMRI) data relative to other common dimension reduction techniques. To our knowledge, we are the first research team attempting to expand the domain of RSRM by applying this technique to controlled experimental EEG data in a BCI setting. Using the openly available Motor Movement/ Imagery dataset, the decoding accuracy of RSRM exceeded models whose input was reduced by Principal Component Analysis (PCA), Independent Component Analysis (ICA), and subject-specific PCA. The results of our experiments suggest that RSRM can recover distributed latent brain signals and improve decoding accuracy of BCI tasks when dimension reduction is implemented as a feature engineering step. Future directions of this work include augmenting state-of-the art BCI with efficient reduced representations extracted by RSRM. This could enhance the utility of BCI technology in the real world. Furthermore, RSRM could have wide-ranging applications across other machine-learning applications that require classification of naturalistic data using reduced representations.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127222234","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":"Designing the UVA Open Data Initiative: Increasing Engagement for Students, Faculty, Staff Members, and Other Stakeholders","authors":"Ronith Ranjan, Kasra Lekan, Vinay Bhaip","doi":"10.1109/SIEDS52267.2021.9483750","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483750","url":null,"abstract":"Open data, the distribution of universally available datasets, fosters transparency and accountability to serve the stakeholders of a community. Universities act as hubs of innovation and discovery where any individual can collaborate and freely explore new ideas in their endeavors to better the world. At many universities, including the University of Virginia (UVA), the incongruous communication of data between all stakeholders, and most especially its lack of clarity to students, contributes to student disengagement that threatens to compromise the vision of a transparent and effective university. Open data initiatives at colleges remain an underused tool to target campus improvement and to empower the next generation of civic-minded student leaders. This paper seeks to explore the strengths and needed improvements in current open data principles and projects throughout cities and colleges in the United States. Herein the authors will develop a framework for building an open data initiative at the University of Virginia through evaluating the best practices from similar projects. This research will directly lead into a student-led initiative to develop the Open Data Platform at UVA and will form the foundation of the principles and lessons to be applied at UVA and other similar open data projects elsewhere.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125850365","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":"Using Graph Algorithms for Skills Gap Analysis","authors":"Jason Choi, Brian Foster-Pegg, Joel Hensel, Oliver Schaer","doi":"10.1109/SIEDS52267.2021.9483769","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483769","url":null,"abstract":"With the development of graph databases, organizations can utilize this technology to enhance human capital allocation by better understanding and connecting employee skillsets with the requirements of positions. Specifically, by storing data in the form of a knowledge graph, organizations are enabled to profile the competencies of their employees and optimize the deployment of human capital to the company’s objectives. This study explores data provided by a large engineering organization which merges employee data, including project assignment and skills, with a public library of competency profiles from O*NET. The objective is to explore employee skills profiling, optimize project staffing, and identify employees best suited for upskilling through the use of graph databases and machine learning algorithms. The findings show that knowledge graphs present an opportunity for organizations to better understand their workforces and more optimally allocate and strengthen their human capital.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128612206","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":"Analyzing Homeless Service Systems in Local Government Using a Systems Engineering Framework","authors":"G. Norris, A. Qureshi, Katelyn Russo, Mariana Santander Gomez","doi":"10.1109/SIEDS52267.2021.9483721","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483721","url":null,"abstract":"This paper investigates efficiency improvement opportunities within homeless service systems in the United States through modeling and simulation. Homeless service systems in the United States continue to evolve but are challenged by facility capacity and operational constraints. In this paper, a Maryland county homeless service system is selected as the case study for analysis. Data is collected through personnel interviews, Housing and Urban Development (HUD) data, and summarized annual reports from the client. Using a regression analysis model, key variables in flow-rates to stable housing solutions are determined in order to construct a system dynamics model of the homeless service system. This model is run for a period of 2 years, using the simulation software Vensim to identify bottlenecks as potential areas of improvement within the system. Model success is defined by HUD system performance measures, such as the length of time persons remain homeless and the rates at which persons placed in stable housing solutions return to homelessness. The model is further evaluated with the findings from a directed literature search of related case studies, semi-structured interviews with industry personnel, and a comparison to national best practices. The model will be generalized to simulate the HUD system performance measures of other homeless service systems in the United States. Additionally, the model will inform recommendations of identified improvements, such as altering ratios of case managers to facility occupants, modifying the intake assessment process, and optimizing facility programs for improved client flow. These recommendations will be applied to create a prototype dashboard for the client. This dashboard will be used as a forecasting tool to aid in decision-making affecting the operation of local homeless service systems.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128817138","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":"Detecting Research from an Uncurated HTML Archive Using Semi-Supervised Machine Learning","authors":"John McNulty, Sarai Alvarez, Michael Langmayr","doi":"10.1109/SIEDS52267.2021.9483725","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483725","url":null,"abstract":"The Internet Archive seeks to provide \"universal access to all knowledge\" through their digital library, which includes a digital repository of over 475 billion crawled web documents in addition to other content. Of particular interest, to those who use their platform, is the preservation and access to research due to its inherent value. Research or scholarly work outside of mainstream institutions, publishers, topics, or languages is at particular risk of not being properly archived. The Internet Archive preserves these documents in its attempts to archive all content, however, these documents of interest are still at risk of not being discoverable due to lack of proper indexing within this uncurated archive. We provide a preliminary classifier to identify and prioritize research, to include long tail research, which circumvents this issue and enhances their overall approach. Classification is complicated by the fact that documents are in many different formats, there are no clear boundaries between official and unofficial research, and documents are not labeled. To address this problem, we focus on HTML documents and develop a semi-supervised approach that identifies documents by their provenance, structure, content, and linguistic formality heuristics. We describe a semi-supervised machine learning classifier to filter crawled HTML documents as research, both mainstream and obscure, or non-research. Because the HTML datasets were not labelled, a provenanced approach was used where provenance was substituted for label. A data pipeline was built to deconstruct HTML website content into raw text. We targeted structural features, content features, and stylistic features which were extracted from the text and metadata. This methodology provides the ability to leverage the similarities found across differing subjects and languages in scholarly work. The optimal classifier explored, XGBoost, predicts whether a crawled HTML document is research or non-research with 98% accuracy. This project lays the foundation for future work to further distinguish between mainstream and long tail research, both English and non-English.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128562923","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":"Validation of ADS-B Aircraft Flight Path Data Using Onboard Digital Avionics Information","authors":"Luigi Raphael I. Dy, Kristoffer B. Borgen, John H. Mott, Chunkit Sharma, Zachary A. Marshall, Michael S. Kusz","doi":"10.1109/SIEDS52267.2021.9483712","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483712","url":null,"abstract":"The adoption of Automatic Dependent Surveillance-Broadcast (ADS-B) transponders has given researchers the ability to capture and record aircraft position data. However, due to the ADS-B system's characteristics, missing data may occur due to propagation anomalies and suboptimal aircraft orientation with respect to the ground-based receiver. The nature of general aviation operations exacerbates this problem. As a result, it may be difficult to accurately review a general aviation aircraft’s flight path with an adequate level of precision. To mitigate this, a five-dimensional modified Unscented Kalman Filter (UKF) was developed to produce statistically optimal aircraft position approximations during all flight phases. The researchers validated the UKF algorithm by comparing estimated flight paths to flight data logs from the Garmin G1000 flight instrument systems of Piper Archer aircraft used in flight training operations on February 23, 2021 at the Purdue University Airport (KLAF). Root mean square error (RMSE) was used to measure the filter’s accuracy. The filter was found to accurately compensate for missing data. This research details the formulation, implementation, and validation of the filtering algorithm.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121293599","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":"Evaluating Educational Intervention Fidelity with TranscriptSim, A Replicable NLP Technique","authors":"L. Hasan, Kip McCharen, Ashley Scurlock, Cong Xu, Briana K. Wright","doi":"10.1109/SIEDS52267.2021.9483724","DOIUrl":"https://doi.org/10.1109/SIEDS52267.2021.9483724","url":null,"abstract":"Pedagogical implementation research at large identifies and addresses factors affecting adoption and sustainability of evidence-based practices. Part of this work is elaborating and testing implementation strategies which prescribe educator intervention techniques and processes to adopt and integrate into educational settings. However, in practice, implementation research is slowed and constrained by the temporal and monetary costs of conducting manual evaluations. In this study, we use an automated, low-cost, and scalable natural language processing (NLP) approach we call TranscriptSim to assess intervention fidelity in a teacher coaching study (TeachSIM). TranscriptSim quantifies similarity between the intervention protocol and intervention transcripts as an approximation of coaches’ fidelity to the intervention protocol.","PeriodicalId":426747,"journal":{"name":"2021 Systems and Information Engineering Design Symposium (SIEDS)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132545054","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}