BMEBT Master of Science Thesis Defense by Wen Mao Hsu Date: Thursday, May 6, 2021 Time: 9:00 AM Topic: Application of Portable 3D Ultrasound Scanning based on LiDAR Assisted Positioning: Preliminary Implantation and Study Zoom Conference Link: https://umassd.zoom.us/j/94839576629?pwd=Y1lzaVhpVUpycHI0UFBrWXRtSXBLdz09 Abstract: Medical Ultrasound scanning has been used in clinical diagnostics for years and has shown incremental improvement over the years. Currently, there is a series of low-priced, battery-operated products that connect to mobile devices. These devices are known as Point-of-Care Ultrasound (POCUS). POCUS is the new generation tool that can potentially be equipped by every doctor in the field. While POCUS is cost-effective, it is currently lacking advanced functions found in the high-end equipment. However, advancing mobile device capabilities could provide a solution. With an iPhone 12 pro that is equipped with light detection and ranging (LiDAR), The accuracy helps create a coordinating system for the POCUS device. The coordinating system gives XYZ data for every pixel, distribute its value to near voxel using weighting computation. The result show that smart phone is at the stage of building 3D model for medical scanning, even though the hardware is not specifically design for the purpose, the result still shows feasible outcome. With current universal use of smart phone and more companies' collaboration in the future, it will not be long for POCUS to become universal devise for doctors. ADVISOR(S): Dr. Xiaoqin Zhang, Department of Computer Science (email@example.com, 508.999.8560) COMMITTEE MEMBERS: Dr. Tracie Ferreira, Department of Bioengineering Dr. Wei-Ren Yao, Department of Nuclear Medicine, National Cheng Kung University NOTE: All BMEBT Graduate Students are ENCOURAGED to attend.
Topic: Hyperparameter Optimization in Convolutional Neural Network Using Genetic Algorithm with Stopping Criterion ZOOM Teleconference: https://umassd.zoom.us/j/98584029023 Abstract: Hyperparameter optimization is the maximization of a neural network's accuracy using a set of input hyperparameters. With much research going into neural networks, in this case convolutional neural networks (CNNs), most research uses manually chosen hyperparameters instead of choosing optimal hyperparameters. Much work has gone into optimizing hyperparameters as they greatly affect the accuracy of CNNs and enable automated machine learning. Choosing the optimal hyperparameters requires applying an algorithm, in this case the genetic algorithm, to search for them. Each search operation requires a full training using a set of hyperparameters which is computationally expensive. New research has shown advantages to applying a stopping criterion to improve the neural network accuracy while reducing the computational cost. The goal of this research project is to use the genetic algorithm with adapted stopping criterion to reduce the computational cost of optimizing hyperparameters in CNNs. NOTE: All ECE Graduate Students are ENCOURAGED to join the zoom teleconference. All interested parties are invited to join. Advisor: Dr. Ruolin Zhou Committee Members: Dr. Honggang Wang, and Dr. Liudong Xing, Department of Electrical & Computer Engineering, University of Massachusetts Dartmouth *For further information, please contact Dr. Ruolin Zhou via email at rzhou1@umassd
UMass Dartmouth 2021 MFA Thesis Exhibition Exhibition dates: March 30-May 7, 2021 Exhibiting Artists: Ryan Cooley Paulina Fuenzalida-Guzman Taylor Hickey Shabnam Jannesari Sung Ji Lee Madison Moreno Danielle O'Malley Valleri Rami Cynthia Bryndis Schilling Tran Duc Binh Vu Emma Young The UMass Dartmouth 2021 MFA Thesis Exhibition is a much anticipated and celebrated annual event showcasing the artwork of graduating students from the College of Visual and Performing Arts. The creative work of graduating students ranges from traditional media such as painting, sculpture, ceramics, textile, to digital media or site-specific installation. Free timed ticket to see the exhibition for the visitors outside of the UMassD community will be available on the website https://umassdartmouthgalleries.eventbrite.com UMassD students and faculty can visit the exhibition without a ticket. Gallery Hours: Monday - Friday 9 am to 6 pm Exhibition also viewable during CVPA Star Series Finale, Thursdays April 29th from 6 to 8 pm Contact: Viera Levitt, Gallery Director and exhibition curator, firstname.lastname@example.org www.umassd.edu/cvpa/galleries
EAS Doctoral Dissertation Defense by Md Fazlay Rabbi Date: Tuesday May 10, 2021 Time: 10:00am Topic: Mechanics of Additively Manufactured Polymer Composites Zoom Teleconference: https://umassd.zoom.us/j/96640466766?pwd=S1A4TXM4VEVySmtkRlJNWGdDQWFwdz09 Meeting ID: 966 4046 6766 Abstract: The aim of this dissertation is to investigate the mechanics of additively manufactured polymers under various loading conditions. First, an experimental study is performed to investigate the dynamic fracture properties of additive manufactured Acrylonitrile Butadiene Styrene(ABS). The effect of novel toughening mechanism through surface topology and printing orientation on dynamic fracture toughness and crack dynamics is explored. Fracture initiation toughness is increased by 138% for a vertical printing orientation compared to horizontal orientation.Introducing a surface pattern to the specimen increases the fracture toughness by 58% as compared to specimens without a surface pattern. Additionally, higher fracture initiation toughness is achieved with the increase in the size of the pattern and the change of the pattern shape. Later, an experimental investigation is performed to observe the electro-mechanical response of CB/ABS additive manufactured composite under quasi-static and dynamic loading conditions for the potential damage sensing applications. In the case of tensile loading, +45o/-45o printed specimens show a nonlinear change of electrical response due to a nonlinear failure mode. Filaments debonding is the major failure mode for 0o printed specimens under both tensile and shear loading. For mode-I fracture under both static and dynamic fracture loading, a minimal change of electrical response is observed before crack initiation due to the cancellation effect of the tension and compression on both sides of the neutral surface. Finally, a comprehensive experimental investigation is performed to observe the interfacial fracture toughness of bi-material additively manufactured composites. It is observed that process parameters have a significant impact on the bi-material interfacial fracture toughness. Improved molecular diffusion enhances the fracture toughness by 95% with the increase of the printing temperature. Although printing speed has not any significant impact on fracture toughness, thinner layers provide a better bond strength and polymer wetting, resulting in a higher fracture initiation toughness compared to thicker layers. To improve the interfacial fracture toughness a post-processing such as isothermal annealing is performed at a wide range of temperatures for different durations. It is observed that fracture toughness improves significantly when specimens are annealed at the melting temperature of the polymers. ADVISOR(S): Dr. Vijay Chalivendra. Department of Mechanical Engineering (email@example.com, 508.910.6572) COMMITTEE MEMBERS: Dr. Jianyi Wang, Department of Physics Dr. Jun Li, Department of Mechanical Engineering Dr. Helio Matos, Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island NOTE: All EAS Students are ENCOURAGED to attend.
A virtual information session on the graduate business programs at UMass Dartmouth. This event will include information on the following programs: - MBA - MS Accounting - MS Finance - MS Healthcare Management - MS Technology Management This is a virtual zoom event designed to answer questions you may have about the various degree and certificate programs.
Senior Design Projects, or Capstones, represent the culmination of UMass Dartmouth's talented engineering students' academic journey. Small teams of faculty-supervised undergraduates team up with industry partners to apply their skills in real-world applications to address engineering issues. These partnerships offer organizations a preview of potential talent and help in solving engineering-related problems. The University is excited to offer the opportunity for the regional community to learn more about this program from the faculty sponsors in a variety of disciplines to see if it might be a fit for you and your organization. Free, registration required. http://bit.ly/mecapstones For questions, please contact firstname.lastname@example.org
Senior Design Projects, or Capstones, represent the culmination of UMass Dartmouth's talented engineering students' academic journey. Small teams of faculty-supervised undergraduates team up with industry partners to apply their skills in real-world applications to address engineering issues. These partnerships offer organizations a preview of potential talent and help in solving engineering-related problems. The University is excited to offer the opportunity for the regional community to learn more about this program from the faculty sponsors in a variety of disciplines to see if it might be a fit for you and your organization. Free. Registration required here - https://bit.ly/ececapstones For questions, please contact email@example.com
Senior Design Projects, or Capstones, represent the culmination of UMass Dartmouth's talented engineering students' academic journey. Small teams of faculty-supervised undergraduates team up with industry partners to apply their skills in real-world applications to address engineering issues. These partnerships offer organizations a preview of potential talent and help in solving engineering-related problems. The University is excited to offer the opportunity for the regional community to learn more about this program from the faculty sponsors in a variety of disciplines to see if it might be a fit for you and your organization. Free. Registration required here - https://bit.ly/becapstones For questions, contact firstname.lastname@example.org
EAS PhD Program (CSE Option/Mechanical Engineering) PhD PROPOSAL DEFENSE by Richard Bellizzi Date: May 20, 2021 Time: 2:00 pm Topic: Strategic Implementation of Computational Methodologies in Lubricant Testing Analysis Zoom Teleconference: https://umassd.zoom.us/j/5903522937?pwd=VnNoT2dUOHVBVHdJSTg1MGFHV3l5QT09 Abstract: The Lubricant industry is prosperous with historical data and methodologies that establish opportunities to leverage Machine Learning and Computer Vision methods to advance lubricant testing analysis procedures. An approach like this requires digitized data to allow for a computer to interpret the inputs and provide a satisfactory output. The bearing corrosion analysis procedure from the ASTM EMCOR method is a perfect proof of concept for this type of analysis approach. The standard specifically calls for the inspection method to use only the visual acuity of a human technician. Techniques like this leave a lot of room for interpretation and variability that this project solves through modern imaging and analysis techniques. While this may lead to results that differ from technicians, the overall system can repeatably provide a meaningful result that allows for improved collaborative testing on a global platform by removing the variability technician to technician. Mask R-CNN models are representative of some improvements that modern methods provide that expand on human capability. With models like this used in medical imaging analysis and autonomous driving, it intuitively makes sense that they start migrating into other industrial realms, like the lubricant realm. The first project explores the feasibility of Convolutional Neural Networks (CNN) and Transfer Learning (TL) methods applied to bearing lubricant defect detection. After demonstrating this initial methodology, the second project progresses into more advanced methods, like the Mask R-CNN framework, yielding a model with bearing lubricant defect recognition, classification, and segmentation features. Overall, improving approaches in the lubricants industry that rely on technicians' visual interpretation shows one adoption of Machine Learning methods that provides immediate value. This type of analysis allows for increasing the depth of evaluation performed on specimens providing new ways for quantifying products. Automated segmentation presents various advantages since corrosion and other surface defects contain differences leaving room for further categorization. Differentiating these defects promotes research into surface interactions yielding different defect instances on bearings, further aiding product development. In addition to the model, methods for digitizing the data and establishing automated analyses contribute to the overall transformation occurring through the lubricant industry, like most other industries. ADVISOR(S): Dr. Alfa Heryudono, Department of Mathematics (email@example.com, 508-999-8516) Dr. Yanlai Chen, Department of Mathematics (firstname.lastname@example.org, 508-999-8438) COMMITTEE MEMBERS: Dr. Wenzhen Huang, Department of Mechanical Engineering Dr. Scott Field, Department of Mathematics Dr. Jason Galary, Nye Lubricants Research & Development NOTE: All MNE and EAS Students are ENCOURAGED to attend.