Italian Studies Panel Discussion Date: Wednesday, November 20, 2019 Time: 3:00 PM Place: Library 206 Description: UMD faculty from various disciplines will present CONTROL (by individuals, by institutions, by the state) in relation to Italy. - Teaching Devotion, Teaching Peace: Confraternities in Early Modern Italy, Matthew Sneider (History) - King Francis I of France: Out of Control in Italy, Stephanie OHara (Foreign Literature and Languages) - Uncensoring Anglo-American Science Fiction in Italy, Rose Facchini (Foreign Literature and Languages) Contact Prof. Rose E. Facchini email@example.com
Topic: Improve Decision Support System Operations Through Evidence Based Knowledge Evolution Location: Lester W. Cory Conference Room, Science & Engineering Building (Seng), Room 213a Abstract: This dissertation describes a new and novel approach to machine learning design maximizing the balance between accuracy, efficiency, solution justification, and rule evolution. This dissertation improves four factors of machine learning within a single design. Different components of the decision support systems (DSS) design shall aim to improve one of the four factors compared to traditional designs. During this dissertation, traditional machine learning methodologies were altered to improve different aspects of machine learning, tested against a single application. The researched examples depict the tradeoff between system accuracy, time efficiency, and storage space efficiency. Typically, to increase the accuracy of a machine, the system requires more time for computation and more historical data points for comparison. Similarly, to improve run-time efficiency or storage efficiency, the user must trade off the solution accuracy. A key area investigated was how to build a DSS system capable of providing solution justification to the user. A DSS's purpose is to aid the user in making decisions. However, if a user does not understand why the DSS is providing a given recommended solution, the user is not able to make an informed decision. An outcome of this dissertation is an investigation of how a DSS can provide the probabilistic rationale behind the decisions the DSS presents to the operator. The second area of focus is the DSS operational efficiency, including both the time and storage requirements. As part of this dissertation, an investigation into methods to reduce the quantity of historical data required to provide consistent rule validation was conducted. New rule evolution and frequent pattern tree algorithms were tested in an attempt to reduce the time necessary to refactor the decision tree and provide results based on a new case. New algorithms from Amazon and Google were tested to see if advancements in the last ten years provide accurate results within the bounds of space and time requirements. Accompanying the operational efficiency is improving the accuracy of the knowledge base. Beyond testing new algorithms, data requirements for providing accurate results were investigated. When a knowledge base is given bad data to train, the knowledge base learns incorrect rules. For a knowledge base to learn and validate rules, the knowledge base needs to be trained using useful, valid data. During this dissertation, new and novel ways to both minimize the data required for a knowledge base, and ways to provide the right data were explored. The final area of investigation was an improvement in rule evolution within a knowledge base. A crucial piece of a knowledge base is the ruleset used for evaluating new cases. Rules are generated based on expert knowledge or by allowing a machine to learn autonomously through accessed data. An outcome for this research area is determining how a knowledge base can do rule validation after rules are added, removed, or altered. To validate new rule sets some traditional methods require rerunning all previous cases through the new rule set. Part of this dissertation was exploring ways to validate rule changes against a smaller historical set, without requiring a massive amount of computational resources necessary to process a full historical data set. At the end of this dissertation, a single design was delivered addressing each of the four factors in a single design. The final design depicts a single working decision support system that improves accuracy, efficiency, justification, and rule evolution. The implementation compares the improved system against a traditionally designed system. Each improvement is system-agnostic, allowing use in other systems without the other alterations. NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. Advisor: Paul J. Fortier Committee Members: Dr. Honggang Wang and Dr. Liudong Xing, Department of Electrical & Computer Engineering, UMass Dartmouth; Dr. John T. Hays, Senior Fellow, General Dynamics Mission Systems *For further information, please contact Dr. Paul J. Fortier at 508.999.8544, or via email at firstname.lastname@example.org.
Topic: Photonics at MIT Lincoln Laboratory: From Physics to Fielded Systems Location: DION Building, Room 114 Abstract: Lincoln Laboratory is a Federally Funded Research and Development Center (FFRDC) managed by the Massachusetts Institute of Technology (MIT) for the Department of Defense. Since its establishment in 1951, the Laboratory has performed research and development in support of national security. Much of the work comprises analysis and prototyping of complex systems (e.g., radar, communications, signal collection, imaging, cyber security) that is augmented by the internal development of advanced electronic and photonic materials, devices and integrated subsystems. In this talk, we'll provide an overview of Lincoln Laboratory, focusing on some of the photonic systems that have been fielded and the components that have enabled these system demonstrations. We'll also describe the Laboratory's resources and activities to develop photonic integrated circuits (PICs) and their application to microwave systems, inertial sensing, lidar, optical atomic clocks, and quantum computing. Biography: Dr. Paul Juodawlkis is Assistant Leader of the Quantum Information and Integrated Nanosystems Group at MIT Lincoln Laboratory where he is working to develop photonic integrated circuit (PIC) technology for application to quantum information systems, optical communications, laser radar, inertial navigation, and microwave sensing. Over the past decade, he led the team that developed the semiconductor slab-coupled optical waveguide amplifier (SCOWA) and used it to realize Watt-class power amplifiers, mode-locked lasers, and low-noise single-frequency lasers having record performance. In earlier work, he made key contributions to the development of optical sampling techniques for microwave frequency translation and photonic analog-to-digital conversion. Dr. Juodawlkis has authored or coauthored over 150 peer-reviewed journal and conference publications, and has participated on a number of technical program committees, including serving as Program Co-Chair (2010) and General Co-Chair (2012) of the Conference on Lasers and Electro-Optics (CLEO). He was an elected member of the IEEE Photonics Society Board of Governors (2011-2013), served as Vice President of Membership for the Society (2014-2016), and is presently Secretary-Treasurer for the Society. Dr. Juodawlkis is a Fellow of both the IEEE and the Optical Society (OSA). He holds a BS degree from Michigan Technological University, a MS degree from Purdue University, and a PhD degree from the Georgia Institute of Technology, all in electrical engineering. The Seminars is open to the public free of charge. *For further information, please contact Dr. Honggang Wang at 508.999.8469, or by via email at email@example.com.
Joint Mechanical Engineering (MNE) and Engineering and Applied Sciences (EAS) SEMINAR DATE: November 22, 2019 TIME: 2:00 p.m. 3:00 p.m. LOCATION: Textile Building, Room 101E SPEAKER: R. Venkatesan, PhD, National Institute of Ocean Technology, Chennai, India Fulbright Senior Scholar with Dr. Amit Tandon Professor UMASSD TOPIC: Recent Advancements in Ocean Technology in India - Societal Benefits ABSTRACT: Oceans, with an estimated asset value of about US$ 24 trillion are a promising strategic frontier for economic growth, water and food security. At NIOT we have demonstrated multiple technological capabilities over the past 25 years. Significant achievements have been made in the ocean desalination domain with 3 plants of 100 m3/day capacity operating in the 3 Islands based on Low Temperature Thermal Desalination technology and will be installed in 6 more islands of India. The demonstration of electricity generation using marine current turbines and the offshore wind energy assessment work using platforms is in progress. India has launched Deep Ocean Mission (DOM) program which will significantly augment the blue resource potential exploration and exploitation in the Indian oceans. Development of technologies for the exploration and exploitation of deep ocean minerals and Deep water ROV ROSUB6000 and the Polar ROV in the Antarctic. The Institute is now developing a 6000m depth rated 3 crew scientific manned submersible for deep ocean mining technology by pumping polymetallic nodules from this depth. NIOT is the first point in the chain of ocean observations in India and has made remarkable imprints in the operational forecasting and collaboration with US on observations are remarkable contribution to the national and international levels. Recent advances in underwater technology have led to the advancement in the design of structures and selection of materials for marine applications. Bio adhesion of marine barnacles on substrate and Development of antifouling material using silicone-based material would be presented. BIO: R. Venkatesan is a Fulbright Awardee and visiting Scholar at the Department of Mechanical Engineering at the UMASSD and is working as a Senior Scientist and Program Director at the National Institute of Ocean Technology, India, and served under UNEP Regional Seas Programme. He received a Ph.D. from the Indian Institute of Science, India, and PG Diploma courses in marine environmental pollution & management and maritime Law and having 37 years of experience. He is working on ocean observations and is also teaching ocean policy and ocean observation tools, as an adjunct faculty. He is honoured to have received eighteen awards including the Marine Technology Society Fellow Award at Seattle - a week ago. He has to his credit 2 patents awarded and 4 patents filed, 120 papers and editor of 3 books and has a project assessment of marine plastics. He shows interest in supporting students and has delivered lectures in Schools and Colleges and organised Guinness World Record on Largest Biology, and conducted Student Underwater AUV competition and winners were deputed to RoboSub San Diego USA. All are welcome and light refreshments will be served. Students taking MNE-500 are REQUIRED to attend! All other MNE BS and MS students are encouraged to attend. EAS students are also encouraged to attend. For more information, please contact Dr. Caiwei Shen, MNE Seminar Coordinator (firstname.lastname@example.org, 508-999-8357). Thank you, Sue Cunha, Administrative Assistant UMass Dartmouth Mechanical Engineering Department email@example.com 508-999-8492
Topic: Designing And Prototyping of an Unmanned Underwater Vehicle Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Abstract: Unmanned underwater vehicles (UUVs) continue to serve diverse roles in ocean sensing, surveillance, and engineering as they see increasing use across scientific, commercial, and education sectors. Valuable and exacting tasks are presently being performed by UUVs and continuing growth of underwater missions is envisioned for the future as vehicles become more capable and less expensive. UUVs are an enabling technology that serve a wide range of functions from oceanographic studies, and underwater acoustic bottom imaging and telemetry to video monitoring and surveillance operations. In order to meet goals in education and basic research academic institutions have invested in open source UUV architectures (McCarter, et al., 2014) that allow for complete control of the vehicle's functionality thereby opening opportunities for students in the multifaceted aspects of vehicle mission planning as well as command and control. This thesis describes the design, development and in-house fabrication of a low cost, fully scalable, modular and open-source UUV. All propulsion, control, sensor suite, fail-safe and battery management subsystems are described and demonstrated. The open source features allow users and operators to tailor and configure the platform to the mission. This UUV is a modular platform that can serve research goals in numerous blue economy fields spanning diverse areas from underwater acoustic communication and surveillance to navigation and control. In particular this UUV provides an enabling technology for testing ecologically friendly underwater acoustic signaling schemes that can reduce anthropogenic interference with marine life. NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. Advisor: Dr. Pual J. Gendron Committee Members: Dr. David A. Brown, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Raymond Laoulache, College of Engineering, UMASS Dartmouth; and Mr. Edward Spring, Center for Innovation & Entrepreneurship, UMASS Dartmouth *For further information, please contact Dr. Paul J. Gendron at 508.999.8510, or via email at firstname.lastname@example.org.
Topic: A Family of Software Reliability Models with Bathtub-Shaped Fault Detection Rate Location: Lester W. Cory Conference Room, Science & Engineering Building (Group II), Room 213A Abstract: Software reliability growth models (SRGM) enable prediction of future fault detection trends and related measures. However many previously proposed SRGM do not assess goodness of fit in a statistically sound manner. Specifically, some authors only minimize the error between the observed data and fitted model, but disregard predictive accuracy, which is essential to real-world application. Relatively recent research has demonstrated that SRGM with a bathtub-shaped fault detection rate can (1) minimize error between the observed data and fitted model, (2) exhibit superior information-theoretic goodness of fit despite a modest increase in the number of parameters, and (3) accurately predict future failures. Solving a system of complex nonlinear equations poses a challenge to fitting models with a modest increase in the number of parameters. Recent advances in numerical methods such as the expectation conditional maximization (ECM) algorithm reduce this barrier to fitting SRGM with a bathtub-shaped fault detection rate. This talk will present the development of software reliability growth models possessing a bathtub-shaped fault detection rate as well as the derivation and application of ECM algorithms to assess the statistical performance of this family of models. NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. Advisor: Dr. Lance Fiondella Committee Members: Dr. Honggang Wang and Dr. Liudong Xing, Department of Electrical & Computer Engineering, UMASS Dartmouth *For further information, please contact Dr. Lance Fiondella at 508.999.8596, or via email at email@example.com.