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Full-Text Articles in Engineering

Buckling Behavior Of Thin Wall Stiffened Cylindrical Shells Through Ml Techniques, Fnu Tabish, Muhammad Hamza Karim, Rajesh Godasu, Iraj H.P Mamaghani Feb 2024

Buckling Behavior Of Thin Wall Stiffened Cylindrical Shells Through Ml Techniques, Fnu Tabish, Muhammad Hamza Karim, Rajesh Godasu, Iraj H.P Mamaghani

SDSU Data Science Symposium

Stiffened cylindrical shell buckling strength mainly depends on the geometric and stiffness properties. A detailed parametric study was conducted to investigate the influence of these properties on the stiffened aluminium cylindrical shell buckling strength. The proposed framework involves an integration of finite element method and various machine learning techniques. The dataset obtained from the eigenvalue buckling analysis of 350 numerical simulations using ANSYS workbench 2022; however, the FE simulation of ten ring-stiffened cylindrical specimens was initially substantiated by experimental work conducted in the literature. 350 sample specimens were categorized into seven groups based on the no. of stiffeners varying from …


Multimode Point Spectroscopy For Food Authentication, Sayed Asaduzzaman, Nicholas Mackinnon, Hossein Kashani Zadeh Feb 2024

Multimode Point Spectroscopy For Food Authentication, Sayed Asaduzzaman, Nicholas Mackinnon, Hossein Kashani Zadeh

SDSU Data Science Symposium

Enhancing food quality measurement is a necessity to guarantee food safety and adherence to health regulations. Current methods involve lab testing which are time-consuming, costly, destructive and require skilled workers. Spectroscopy has the potential to overcome these challenges. This study employs a multi-mode point spectroscopy method to distinguish food products according to their spectral characteristics,. The system records fluorescence, excited at 365 and 405 nm, visible-near infrared (Vis-NIR) and short-wave infrared (SWIR) spectra. The three main subjects of the study are olive oil, milk, and honey. Samples were kept in a transparent cell culture pot, and Gray and White Spectralon …


Session 12: Active Learning To Minimize The Possible Risk From Future Epidemics, Kc Santosh Feb 2023

Session 12: Active Learning To Minimize The Possible Risk From Future Epidemics, Kc Santosh

SDSU Data Science Symposium

In medical imaging informatics, for any future epidemics (e.g., Covid-19), deep learning (DL) models are of no use as they require a large dataset as they take months and even years to collect enough data (with annotations). In such a context, active learning (or human/expert-in-the-loop) is the must, where a machine can learn from the first day with minimum possible labeled data. In unsupervised learning, we propose to build pre-trained DL models that iteratively learn independently over time, where human/expert intervenes only when it makes mistakes and for only a limited data. In our work, deep features are used to …


Session 11: Can Machine Learning Predict Particle Deposition At Specific Intranasal Regions Based On Computational Fluid Dynamics Inputs/Outputs And Nasal Geometry Measurements?, Mohammad Mehedi Hasan Akash, Zachary Silfen, Diane Joseph-Mccarthy, Arijit Chakravarty, Saikat Basu Feb 2023

Session 11: Can Machine Learning Predict Particle Deposition At Specific Intranasal Regions Based On Computational Fluid Dynamics Inputs/Outputs And Nasal Geometry Measurements?, Mohammad Mehedi Hasan Akash, Zachary Silfen, Diane Joseph-Mccarthy, Arijit Chakravarty, Saikat Basu

SDSU Data Science Symposium

Along with machine learning modeling, numerical simulations of respiratory airflow and particle transport can be used to improve targeted deposition at the upper respiratory infection site of numerous airborne diseases. Given the need for more patient data from varied demographics, we propose a machine learning-enabled protocol for determining optimal formulation design parameters that may match nasal spray device settings for successful drug delivery. We measured 11 anatomical parameters (including nasopharyngeal volume, nostril heights, and mid-nasal cavity volume) for 10 CT-based nasal geometries representative of the population for this aim. We also ran 160 computational fluid dynamics simulations of drug delivery …


Session 12: Analysis Of State And Parameter Estimation Techniques Using Dynamic Perturbation Signals, Timothy M. Hansen Feb 2023

Session 12: Analysis Of State And Parameter Estimation Techniques Using Dynamic Perturbation Signals, Timothy M. Hansen

SDSU Data Science Symposium

The trend in electric power systems is the displacement of traditional synchronous generation (e.g., coal, natural gas) with renewable energy resources (e.g., wind, solar photovoltaic) and battery energy storage. These energy resources require power electronic converters (PECs) to interconnect to the grid and have different response characteristics and dynamic stability issues compared to conventional synchronous generators. As a result, there is a need for validated models to study and mitigate PEC-based stability issues, especially for converter dominated power systems (e.g., island power systems, remote microgrids).

This presentation will introduce methods related to dynamic state and parameter estimation via the design …


2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh Feb 2023

2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh

SDSU Data Science Symposium

Abstract. In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients …


Application Of Gaussian Mixture Models To Simulated Additive Manufacturing, Jason Hasse, Semhar Michael, Anamika Prasad Feb 2023

Application Of Gaussian Mixture Models To Simulated Additive Manufacturing, Jason Hasse, Semhar Michael, Anamika Prasad

SDSU Data Science Symposium

Additive manufacturing (AM) is the process of building components through an iterative process of adding material in specific designs. AM has a wide range of process parameters that influence the quality of the component. This work applies Gaussian mixture models to detect clusters of similar stress values within and across components manufactured with varying process parameters. Further, a mixture of regression models is considered to simultaneously find groups and also fit regression within each group. The results are compared with a previous naive approach.


Spatial Data Analysis For The Development Of Expected Adverse Weather Charts For Transportation Construction Projects, S M Rahat Rashedi, Akosua Ofosua Okyere-Addo Feb 2023

Spatial Data Analysis For The Development Of Expected Adverse Weather Charts For Transportation Construction Projects, S M Rahat Rashedi, Akosua Ofosua Okyere-Addo

SDSU Data Science Symposium

Problem - Seasonal and daily weather events impact construction projects across the various climate regions of South Dakota in differing fashions. Additionally, the impacts for similar weather events can impact grading, surfacing, and structural construction activities in various ways. Adverse weather conditions can cause major delays which may lead to time extensions and increase project cost.

Purpose – To address these issues, South Dakota Department of Transportation (SDDOT) developed Working Day Weather Charts in 1998. However, advances in construction practices and weather prediction as well as climatic changes have occurred over the interim 25 years. This study is focused on …


Spatial Data Analysis For Traffic Safety Network Screening, Akosua Okyere-Addo, S. M. Rahat Rashedi Feb 2023

Spatial Data Analysis For Traffic Safety Network Screening, Akosua Okyere-Addo, S. M. Rahat Rashedi

SDSU Data Science Symposium

Problem - The roadway system represents a major investment, both public and private, and a valuable resource that enables mobility and accessibility to users. Due to degradation of aging infrastructure and increasing traffic, transportation agencies are seeking to effectively update or improve the system. With rising costs, tight budgets, and limited land resources, agencies are seeking effective techniques for identifying critical mobility and safety concerns. Historically, assignment of crashes to portions of the network, whether segments or intersections, has been the primary manner to link crash and road elements.

Purpose – The primary goal is to explore a potentially more …


Session 2: The Effect Of Boom Leveling On Spray Dispersion, Travis A. Burgers, Miguel Bustamante, Juan F. Vivanco Feb 2023

Session 2: The Effect Of Boom Leveling On Spray Dispersion, Travis A. Burgers, Miguel Bustamante, Juan F. Vivanco

SDSU Data Science Symposium

Self-propelled sprayers are commonly used in agriculture to disperse agrichemicals. These sprayers commonly have two boom wings with dozens of nozzles that disperse the chemicals. Automatic boom height systems reduce the variability of agricultural sprayer boom height, which is important to reduce uneven spray dispersion if the boom is not at the target height.

A computational model was created to simulate the spray dispersion under the following conditions: a) one stationary nozzle based on the measured spray pattern from one nozzle, b) one stationary model due to an angled boom, c) superposition of multiple stationary nozzles due an angled boom, …


Fico® Scores Through The Economic Cycle And Understanding Consumer Sensitivities To Economic Fluctuations, Gerald Fahner Feb 2019

Fico® Scores Through The Economic Cycle And Understanding Consumer Sensitivities To Economic Fluctuations, Gerald Fahner

SDSU Data Science Symposium

FICO® Scores assess default likelihood under “normal” conditions. Additional risk emanates from economic variability and manifests itself in changes to the score distribution as well as changes to the Odds-to-Score relationship. In this session we will examine credit bureau data from the Great Recession in the United States as compared to a stable economic period. We’ll share a framework that provides new and actionable insights into consumers’ sensitivities to such economic fluctuations such as:

  • Application of counterfactual analysis, machine learning, and scorecards, to rank-order consumers’ sensitivities

  • Consumer segmentation by economic sensitivities

  • Sensitivity profiling reveals interesting differences between the most and …