Author: eya14001

UTC-IASE Faculty Spotlight: Dr. Xu Chen

 

This week’s faculty spotlight is on Professor Xu Chen, who is an assistant professor in the Department of Mechanical Engineering. Dr. Chen received his M.S. and Ph.D. degrees in Mechanical Engineering from the University of California, Berkeley in 2010 and 2013, respectively, and his Bachelor’s degree from Tsinghua University, China in 2008.  He is a recipient of the National Science Foundation CAREER award, the Young Investigator Award and the Best Paper Award from ISCIE / ASME International Symposium on Flexible Automation, the 2017 Best Vibrations Paper Award from the ASME Dynamic Systems and Control Division, the 2017 UConn University Teaching Fellow Award Nominee, and the 2012 Chinese Government Award for Outstanding Students Abroad.

 

Professor Chen is the principle investigator for the Machine, Automation and Control Systems Laboratory (MACS) through the Department of Mechanical Engineering. The overall research goal of his lab is to seek better understanding and engineering of the systematic interplay between data, systems and controls in machines and automation processes. For instance, fast situational awareness and agile response is imperative to advancing system operation in this information age. To reach such capabilities, Prof. Chen’s team exploits approaches to reliably and quickly combine all data from heterogeneous sources in a feedback control system. An example of such is a project conducted from Dr. Chen’s smart manufacturing research called “Model-Based Sparse Information Recovery by Collaborative Sensor Management”. This project provides a novel approach to collect dense information from a group of collaborative sensors at a significantly reduced computation burden and in real time. The result is particularly impactful for applications such as imagining-based automation, where vision data take time to collect and complex elaborations must be performed to extract information from the raw data. More broadly, this work relates to the overarching challenge of making full use of data to infer and respond to fast evolving situations in decentralized environments, and provides a pathway to better integrate multiple data-intensive sources.

 

During the summer of 2018, Dr. Chen and his laboratory team traveled to three flagship conferences in the fields of controls, automation, and 3D printing: The American Control Conference at Milwaukee in June, the International Symposium on Flexible Automation at Kanazawa, Japan in July, and the Annual international Solid Freedom Fabrication at Austin, Texas in August. The three published papers from Prof. Chen’s team discussed smart controls approaches for critically needed quality assurance of additive manufacturing (AM), a nascent manufacturing technology that offers untapped potential in a wide range of products for the energy, aerospace, automotive, healthcare and biomedical industries. In particular, the focused powder bed fusion process is increasingly preferred in applications ranging from advanced jet-engine components to custom-designed medical implants. Prof. Chen’s research looks into the convoluted thermomechanical interactions in the multi-physics multi-scale manufacturing process, and has been generating award-winning, internationally recognized results to enable substantially higher accuracy and greater reproducibility in AM. For instance, the recent paper titled “Synthesis and Analysis of Multirate Repetitive Control for Fractional-Order Periodic Disturbance Rejection in Powder Bed Fusion” was featured in the proceedings of the 2018 International Symposium on Flexible Automation, where this paper written by PhD Student, Dan Wang, and Dr. Chen, won the “Best Paper Award In Theory”.

UTC-IASE Faculty Spotlight: Dr. Ashwin Dani

 

This week’s UTC-IASE faculty spotlight is on Dr. Ashwin Dani, who is an assistant professor in the Department of Electrical and Computer Engineering. He received his B.S in Mechanical Engineering from the University of Pune in India, and then went on to receive a Ph.D in Mechanical and Aerospace Engineering from the University of Florida. He began his career in academia as a post-doctoral research associate at the University of Illinois at Urbana-Champaign in 2011, and then moved to the University of Connecticut in 2013 and currently works as an assistant professor.

 

Dr. Dani is the PI for the Robotics and Controls Lab, which focuses on solving various estimation and control challenges in engineering domains such as robotics, automation, industrial and biomedical applications. His research falls into areas such as (1) Model building from data using machine learning, (2) Human-Robot collaboration and safety issues in manufacturing environments, (2) GPS-denied navigation of unmanned aerial systems and improved autonomy, (4) sensor data fusion, and (5) estimation and control for neuro-prosthesis. He has also done previous work in areas such as estimation and control theory, robotics, autonomous navigation, localization and mapping, and vision-based control.

 

This summer, Dr. Dani attending the American Control Conference in Milwaukee, where a paper titled “Learning Stable Nonlinear Dynamical Systems with External Inputs using Gaussian Mixture Models” was presented by the UTC-IASE Fellow Iman Salehi. Iman Salehi, a UTC-Fellowship graduate student, worked under the counsel of Dr. Dani this summer. Salehi’s project is titled “Physics Informed Machine Learning Project”, which is researching how to develop models for heat exchangers using machine learning that embeds dynamical system properties. The efforts in this project bring a paradigmatic change to how model building using machine learning is looked at to include physical properties of the system. This paper presented a data-driven modeling method with convergence guarantees embedded in machine learning. This paper is closely related to the “Physics-Informed Machine Learning Project”.

 

Professor Dani worked with UTC this summer on a project titled “Physics Informed Machine Learning” and “Wire Harness Assembly using Robots”, with a goal to develop methodologies and architectures for data-driven model learning, containing dynamical system properties coming from physics of the system embedded in machine learning. In terms of research publications, Dr. Dani and his lab worked on extending the conference manuscripts presented over the summer, to journal manuscripts, for projects  “Physics Informed Machine Learning”.

Dr. Abhishek Dutta’s Microcircuit Shows Promise to Improve Biobots

 

UTC IASE faculty member, Dr. Abhishek Dutta, was recently highlighted by the university, in recognition of his work on biological control systems and bio-robotics. Dr. Dutta, who is an assistant professor of Electrical and Computer Engineering, is using his specialization in control systems, design and optimization and cybernetics, to build a hardwired biological insect that can be used to precisely control the insect’s motion.  Since then, the “UConn neuro controller” has been trending in hundreds of news outlets worldwide engaging as many discussions on social media.

The microcircuit is strapped to the roach, and then interfaces with wired micro-electrodes that connect into the antenna lobes. The signal is transmitted through a wireless device in the circuit and a receiver allows for the motion to be controlled through a ground station. The microcircuit sends tiny electrical currents through the wires into the neural tissue in the antenna lobe. Once the brain receives these impulses, it makes the insect believe that there is an obstacle away. Therefore, if an appropriate signal is received by the left antenna lobe, the insect will move to the right and vice-versa.

Professor Dutta is using the Madagascar Hissing Cockroach as the organism under study, where he has built a microcircuit that wires directly into the brain of the roach and uses the circuit as a neuro controller. The circuit incorporates a 9-axis inertial measurement unit that can detect the roach’s six degrees of free motion, its linear and rotational acceleration, as well as its compass heading. Other features that help with the performance of the circuit is a sensor to detect the ambient surrounding temperature, which will help map the correlation between the environmental conditions and the motion of the roach.

Although a couple of similar systems have been developed previously, this microcircuit stands out because it offers a multi-faceted approach for stimulating the nerve tissue, real-time feedback of the insect’s response to the electrical stimuli, and a greater degree of control of the insect’s motion, resulting in a more informed and precise control system. As the microcontroller and built-in potentiometer work in tandem, the operator is able to vary the output voltage, frequency, and cycle of stimuli delivered, which helps to produce a more robust response, while not causing damage to the roach.

This work shows great promise, with the possibility of human applications in the future. Microcircuit and micro-control systems, such as these, are the first step in developing  larger-scale controllers and circuits that can be used in industries like healthcare, automotive, and artificial intelligence. One proposed use is to use this technology to help control motor functions in organisms with spinal cord injury or paraplegic organisms. “Our microcircuit provides a sophisticated system for acquiring real-time data on an insect’s heading and acceleration, which allows us to extrapolate its trajectory,” says Dr. Dutta. “We believe this advanced closed loop, model-based system provides better control for precision maneuvering and overcomes some of the technical limitations currently plaguing today’s micro robots”. Dr. Dutta states that much more research is needed, although the current technology has great promise to lead to a new generation of products with even larger scale applications.

UTC IASE Faculty Spotlight: Dr. Peter Luh

 

This week’s faculty spotlight is on Dr. Peter Luh, who is the SNET Endowed Professor in the Electrical and Computer Engineering Department. He is affiliated with UTC IASE and Booth Engineering Center for Technology (BECAT). Dr. Luh received a Bachelors of Science in Electrical Engineering from the National Taiwan University in 1973. He then went on to receive a Master degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology and a Ph.D in Applied Mathematics from Harvard University. Dr. Luh is a Board of Trustees Distinguished Professor, the SNET Professor of Communications and Information Technologies at the University of Connecticut, Storrs, and a Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE).

 

Dr Luh is the Director of the Manufacturing Systems Laboratory in the Department of Electrical and Computer Engineering, which focuses on advancing information technologies and mathematical optimization techniques that are of strategic importance to society, including Intelligent Manufacturing Systems, Smart and Safe Buildings, Smart Grid, and decision-making under uncertain, distributed, or antagonistic environments. The research conducted in his lab has been supported by the NSF and other industry partners, such as UTC, Southern California Edison, ISO-New England, MISO, Alstom, Northeast Utilities, GE, and Toshiba, over the past 30+ years. His research has led to over 150 journal papers published, over 320 conference presentations, and 35 Ph.D students graduated.

 

Research-wise, he has made pioneering contributions to 1). Intelligent manufacturing for efficient production and on-time product delivery; 2). Smart grid for efficient generator coordination and renewable energy integration; and 3). Smart building for energy efficiency and safety. The overall goals of his research focus on efficiency, greenness and sustainability. Behind these three areas of focus/goals is a revolutionary approach to overcome the notorious combinatorial complexity of mixed-integer optimization problems (involving both discrete and continuous decision variables). The keys include an innovative way to formulate problems in a “separable” and “tightened” form, and an advanced price-based decomposition and coordination methodology. They represent two major breakthroughs and new directions in formulating and solving such important but difficult optimization problems. These exciting results were developed together with his graduate students and colleagues, in particular, Assistant Research Professors Mikhail Bragin and Bing Yan, and Associate Professor Peng Zhang.

 

Dr. Luh received the IEEE Robotics and Automation Society (RAS) Pioneering Award in 2013 for his contributions to the development of near-optimal and efficient planning, scheduling, and coordination methodologies for manufacturing and power systems. Beyond journal papers and conference presentations, Dr. Luh has delivered 140 invited talks around the world. His current research is supported by grants and contracts from the NSF, United Technologies Corporation, ISO-New England, MISO, ABB, Brookhaven National Laboratory, and UConn Provost Academic Plan.

 

In terms of his teaching career, Dr. Luh is a distinguished and accomplished professor who regularly teaches systems-related courses. Among his 35 Ph.D. students graduated, three received the School of Engineering’s Distinguished Engineering Alumni Award, one is a member of Chinese Academy of Sciences, and one served as a National Communication Commissioner in Taiwan. Professor Luh also has participated many times in the School of Engineering’s week-long daVinci Projects, which are designed for high school mathematics, science teachers, and counselors. During his time here at UConn, he has served as the director of BECAT (1997-2004), and the Head of the Electrical and Computer Engineering Department (2006-2009). He has served on various committees within the university, such as the Faculty Review Board (1997-1999), the Research Advisory Council (1998-1999), the University Academic Vision Committee (2013-2014), and the Center and Institute Review Committee (2017-2019).

 

Professor Luh has also played a vital role in IEEE. He served as the Editor-in-Chief of IEEE Transactions on Robotics and Automation (1999-2003), the founding EiC of IEEE Transaction on Automation Science and Engineering (2003-2007), RAS Vice President for Publications (2008-2011), and received the IEEE RAS George Saridis Leadership Awards for his exceptional vision and leadership in strengthening and advancing automation. He is currently the Chair of IEEE Technical Activities Board Periodicals Committee for the 2018-2019 term, and oversees 190 IEEE journals and magazines.

Dr. Xu Chen, PhD student Dan Wang awarded the ISFA Best Paper Award

 

UTC-IASE Professor Xu Chen and student Dan Wang received the Best Paper Award at the 2018 International Symposium on Flexible Automation (ISFA). The ISFA was initiated in 1986 under the co-sponsorship of the American Society of Mechanical Engineering (ASME) and the Institute of Systems, Control, and Information Engineers (ISCIE) in Japan. The symposium focuses on automation technologies that are essential to meet the increasing requirements of modern manufacturing and other related fields, such as dynamical systems, robotics, logistics, medical systems, and healthcare systems. The 2018 symposium was held in Kanazawa japan from July 15th, to July 19th. Each year, the symposium recognizes the two best papers appearing in the proceedings and presented at the symposium. One award emphasizes the paper’s contribution to theory, and the other award emphasized significant or innovative applications/practices. Criteria for selection include the quality of the written and oral presentation, the technical contribution, timeliness, and practicality. Each awardee is given a certificate and an honorarium of $1,000.

 

The paper, titled “Synthesis and Analysis of Multirate Repetitive Control for Fractional-order Periodic Disturbance Rejection in Powder Bed Fusion”, discusses control approaches to advance the quality of repetitive energy deposition in powder bed fusion (PBF) additive manufacturing. More specifically, it pertains to the repetitive deposition of the laser or electron beam energy. The paper addresses an intrinsic limitation in control schemes that can be used to leverage the periodicity of task patterns to significantly improve system performance. Some of the long-term impacts anticipated for this work include greater quality assurance of the manufactured parts, new capabilities for large-scale 3D printing of extreme materials, and smarter machines and automation in additive manufacturing processes.

UConn Receives National Award to Improve Smart Manufacturing

 

The Clean Energy Smart Manufacturing Innovation Institute (CESMII) recently announced its’ selections for its first public-private partnership projects to advance Smart Manufacturing technologies in the United States. Out of the 41 proposals submitted from 62 different organizations, The UConn project titled “Energy Management Systems for Subtractive and Additive Precision Manufacturing” spearheaded by UTC-IASE’s director Dr. George Bollas, was selected as one of only 10 projects from across the country. The ten nationally-selected projects, ranging from 12 to 24 months in duration, will be receiving approximately $10 million in funding, with an additional $6 million in cost share to advance Smart Manufacturing technologies.

 

The UConn project aims to develop and demonstrate benefits of using Smart Manufacturing (SM) approaches that are applicable to subtractive and additive precision manufacturing. The main objective of the project is to mitigate energy waste in manufacturing facilities, specifically additive and subtractive manufacturing, using model-based systems engineering principles. One of these principles is a process called Coordinated Utilization, which encompasses the fields of systems engineering, advanced controls, data analytics and secure communication protocols. This specific procedure enables efficiency improvements in the precision machining and hybrid manufacturing of metals/alloys to support cross-industry platforms, such as aerospace and orthopedics. This approach will be used to solve the problem of manufacturing waste in manufacturing facilities through five phases. To start, the requirements will be modeled and analyzed in terms of their energy saving capabilities. Sensor network architectures that identify the potential for energy savings will be implemented and optimized. Then, supervisory control structures will be designed for the realization of energy savings. Manufacturing big data will be reduced and securely communicated, and finally, validation of the resulting energy management systems in the manufacturing setting will be accomplished through a model in-the-loop environment.

 

Modules will also be used to help integrate the proposed approach into the Smart Manufacturing platform.  Some of these modules include a platform-based system, a multi-level, heterogeneous and hybrid model of the manufacturing and ancillary equipment, predictive analytics, context-driven supervisory control architecture, scheduling of manufacturing operation, and big data analytics. Using these modules will help to facilitate a variety of advantages and objectives, such as enabling formalization and re usability, anomaly detection, enabling model and control interoperability, securing IoT communication protocols, and to maximize energy savings.

 

 

Focusing on the overarching goal to establish high impact and cost savings through advanced manufacturing technologies, UConn will partner with the Connecticut Center for Advanced Technologies (CCAT), United Technologies Research Center (UTRC), Pratt and Whitney, DePuySynthes, and Johnson & Johnson to implement their design, and to study its efficiency and effectiveness in the field. The first trial run will be conducted at manufacturing facilities of CCAT, which will be used as test beds for the implementation and validation of the research tasks and the methods developed in this experiment. After the initial testing is done at CCAT, the maturation and application of the methodologies and technologies developed in this project will be implemented in four stages. The first stage is the deployment of methods developed in manufacturing facilities in UTRC and Pratt and Whitney for energy and yield efficient production of components for the aerospace industry. The second phase is the deployment of the methods developed in the manufacturing facilities of DePuySynthes and the Orthopedics division of Johnson & Johnson, for the energy and yield efficient production of components for the orthopedics industry. The third stage will be to fill in the gaps and enhance the value of the SM platform by demonstrating the core capabilities developed on the SM platform, using tools available in the SM platform and by building new capabilities to be shared through the platform. The final stage is the development of training programs targeted to challenges in smart manufacturing deployment and talent gaps.

 

After the project completion in May of 2020, the team anticipates having achieved all the goals set forth for this project, and to help facilitate significant industrial impacts. The first of these goals is to lead a national effort to develop, research, test and widely validate SM technologies and practices in a continuously evolving manner. To test and validate SM technologies, the project will also need to develop a protocol for how these technologies should be employed and used effectively. To help with this, another goal for this project is to develop a road map for SM technologies, practices, services, and training and update the road map periodically as needed, to keep up with the changes and demand of the industry. The team will also work to support SM Research and development to provide capabilities for and collaboration in open, pre-competitive among multiple partners, parties, and companies. This work will help establish a technical education and workforce development program that leverages regional networks and will also stimulate growth of a SM domestic supply chain. Finally, at the end of the project, the team hopes to see that the research and work conducted will demonstrate participation of underrepresented groups in CESMII and that the work will be financially self-sustaining after the five-year period of federal funding. These goals and objectives set forth for this specific project are driven by the overall performance metrics for CESMII. Through all CESMII projects, the organization hopes to see energy productivity gains in US manufacturing doubled in 10 years, a 15% improvement in energy efficiency in industrial test beds within 5 years, costs of SM technologies reduced by 50% in 5 years, installed and operating costs of SM recovered through energy savings in 10 years, the SM workforce capacity in the US increased two-fold by 2020, and five-fold by 2030, and finally, the SM supply chain increasing value and participation 40% by 2030.

 

CESMII and all its partners aim to become financially self-sustaining after the end of the five-year federal funding period. This sustainability will be accomplished through the comprehensive membership model that CESMII is applying to industry members, as well as accomplishing both the project goals and the overall CESMII goals. As well as anticipating significant industrial impacts and an increase in sustainability, the CT project also projects to have significant reductions in manufacturing costs, energy savings, and a great increase in production efficiency.

UTC-IASE wins “Industry University Cooperative Research Center” grant

 

The UTC Institute for Advanced Systems Engineering received a small award from the National Science Foundation for the planning of an Industry University Cooperative Research Center. Led by Dr. George Bollas and with participation of researchers from the UTC-IASE and the Eversource Energy Center, UTC-IASE will organize workshops and info sessions with the goal to attract industry interest for participation in a Center researching Networked Embedded, Smart & Trusted Things. Modern Cyber-Physical Systems (CPS) enable integrated sensing, computing, actuation and communication functions in everyday customer devices (home appliances, buildings, roads, jet engines, automobiles, etc.). Their integration in the “Internet of Things” (IoT) provides an unprecedented opportunity to collectively access and process enormous amounts of data. The vision of the Center for Networked Embedded, Smart and Trusted Things (NESTT) is to contribute to the development of an equitable, safe and secure connected world. NESTT will achieve this vision by focusing on creating holistic IoT solutions, integrating technology disciplines with expertise in law, business, and humanities.

 

The University of Connecticut will work together with the Arizona State University, University of Southern California, University of Arizona, and Southern Illinois University to form a new NSF Industry University Cooperative Research Center (IUCRC) named NESTT. The objective of the UConn’s site planning project is to hold workshops with industry partners and NESTT partner universities. The goal of these workshops is to outline a research agenda and identify industry support for an IUCRC focused on providing IoT solutions specific to cyber-physical systems, IOT requirements and architecture, cybersecurity, industrial IOT for smart manufacturing, smart buildings, grid modernization, and transportation. These application areas will be supported by UConn’s expertise in systems engineering, distributed edge and cloud computing, formal methods, machine learning, data analytics, cyber-physical security, prognostics and diagnostics.

 

The multidisciplinary research at NESTT aims to integrate societal and technological aspects of IoT as a systems engineering and design problem. New scientific discoveries in NESTT and their integration with industrial practice could enable economic growth and provide societal and environmental benefits. Access and opportunities to students who are underrepresented in STEM disciplines and relevant technology areas will be a priority.

 

The agenda, list of participants and outcomes of the UConn’s NESTT IUCRC planning workshops will be posted online at UConn under the auspices of the UTC Institute for Advanced Systems Engineering (http://utc-iase.uconn.edu) and promoted through its social media venues. For more information about NESTT visit this link: https://fulton.sp10.asu.edu/cidse/CES/SitePages/NESTT.aspx

 

UTC-IASE Faculty Spotlight: Dr. Shalabh Gupta

 

This week’s faculty spotlight is on Dr. Shalabh Gupta, who is an associate professor in the Electrical and Computer Engineering Department at UConn. Dr. Gupta received a Bachelors of Technology in Mechanical Engineering from the Indian Institute of Technology-Roorkee. He then joined the  Pennsylvania State University where he received a Masters of Science in Mechanical Engineering, a Masters of Science in Electrical Engineering, and a PhD in Mechanical Engineering with focus on Systems Engineering. Before he joined UConn in 2011, he was a Research Associate in the Department of Mechanical and Nuclear Engineering at Pennsylvania State University from 2008-2011 and a Post-Doctoral Research Scholar for the department from 2006-2008. Currently, he is leading the Laboratory of Intelligent Networks and Knowledge-Perception Systems (LINKS) at UConn. Dr. Gupta holds a joint appointment with Management and Engineering for Manufacturing program.

In his professional career, Dr. Gupta has written over 100 research articles including book chapters, journal papers, patents, and conference papers. He is the Specialty Chief Editor of the journal “Frontiers in Robotics and AI” (Specialty: Sensor Fusion and Machine Perception)” since 2017. He has also served as an Associate Editor for “Structural health Monitoring- An International Journal” since 2010.  He served as the Program chair of the International Conference on Complex Systems Engineering organized by the UTC-Institute for Advanced Systems Engineering in 2015. He has supervised 15 senior design team, 3 of which received the top three positions in the ECE department in consecutive years.

 Dr. Gupta’s research is focused on the Science of Autonomy with emphasis on two key areas: Data Analytics and Networked-Intelligent systems. In essence, his research is centered around the essential characteristic of cyber-physical systems that links the domain of system dynamics with the domain of information & control. Some specific research areas include data selection, data reduction, and data interpretation; information fusion from heterogeneous sources for improved classification; 3C network autonomy via distributed classification, clustering and control in stochastic environment; path planning for autonomous vehicles- coverage path planning, time-optimal path planning, and safe path planning;  cooperative autonomy of unmanned vehicles; resilient control of complex systems in presence of failures; and fault diagnosis & prognosis in networked-control systems. Some examples of the application areas of his research include distributed sensor networks for Intelligence, Surveillance & Reconnaissance (ISR) operations, autonomous vehicles, smart buildings, smart grids, smart manufacturing, resilient infrastructures, and aerospace systems.

Recently, he published three articles which received good positive feedback. The first article “ε*: An Online Coverage Path Planning Algorithm” appeared in IEEE Transactions on Robotics (IEEE-TRO) in 2018.  Since publication, it has consistently achieved a rank in the top 15 of the most popular articles from all articles ever published in TRO.  The paper developed a novel algorithm for complete coverage path planning of unknown environment with theoretical guarantees. The applications include house cleaning robots, autonomous lawn mowers, underwater mine hunting, etc.

The second article “POSE: Prediction based Opportunistic Sensing for Energy-efficient Sensor Networks using Distributed Supervisors” appeared in IEEE Transactions on Cybernetics (IEEE-TOC) in 2018. The paper received significant media attention and was highlighted in “UConn Today” and “The Day”. The paper focuses on distributed probabilistic control of multi-modal sensor networks for energy-efficient target tracking. The applications include border surveillance, urban sensor networks, smart cities, etc.

 

The third article “Topological Characterization and Early Detection of Bifurcations and Chaos in Complex Systems using Persistent Homology” appeared in Chaos: An Interdisciplinary Journal of Nonlinear Science in 2017. The paper uses the concepts from algebraic topology for deeper insights into changes in topological  features in data as anomalies happen. The article was picked by the editor for Front Page Display on the Journal’s Website.

 

Dr. Gupta is currently working on the following research projects:

  1. Reconfigurable control of chiller plants via joint optimization of reliability and performance.  
  2. Real-time path planning of UUVs in complex environment.
  3. Data Selection and data reduction from big data for efficient analysis.
  4. Distributed control of sensor networks for 3C Network autonomy.

 

Dr. Gupta is also advising student research projects this summer. Khushboo Mittal, a student in the Electrical and Computer Engineering Department, is the recipient of a UTC fellowship. She is working on developing supervisory control concepts for complex systems with a focus on resilience to failures, reliability, and performance optimization. James Wilson, a student in the Electrical and Computer Engineering Department, is also the recipient of a UTC fellowship. He is working on developing novel data analytics tools for complex data.

UTC-IASE Faculty Spotlight: Amy Thompson

 

May the Cooling Season Begin…. Amy Thompson, Associate Professor-In-Residence at the UConn UTC Institute for Advanced Systems Engineering is studying the impact in the field of retrofitting rooftop HVAC systems with market-ready fault detection and diagnosis (FDD) equipment. https://www.unewhaven-doe-fdd.com/

 

 

Through a multi-partner, $1.2 million, 3-year DOE EERE grant, the University of Connecticut, the University of New Haven, United Illuminating, Eversource, and United Technologies Research Center are studying the effectiveness and market barriers of fault detection and diagnosis (FDD) equipment for rooftop HVAC systems in the field in Connecticut. FDD technology has been studied in the laboratory, but no large-scale study of the technology in the field has been conducted in the U.S. Common HVAC faults that can be detected with FDD technologies include:

 

– Restricted indoor and outdoor airflow

– Incorrect refrigerant charge

– Refrigerant line blockage

– Malfunctioning expansion device

– Compressor valve leakage

– Non-condensable gases

– Short cycling

– Economizer Faults

 

Applications are now open to any Connecticut commercial or industrial organization that would like to become a study participant and study site. Benefits for sites include no-charge FDD retrofits for 1-2 rooftop units (RTUs), training on how to interpret FDD alarms and results, and a full report and analysis of the ability of the technology to lower energy usage and costs. Applications are open until June 30, 2018 here: https://www.unewhaven-doe-fdd.com/site-mou-and-application or contact Amy Thompson at the UTC Institute for Advanced Systems Engineering at amy.2.thompson@uconn.edu with questions.

 

UTC-IASE Faculty Spotlight: Dr Fei Miao

 

This week’s spotlight is on Professor Fei Miao, who is an assistant professor in the department of Computer Science and Engineering. Fei received a Bachelors of Science in Automation with a minor in finance from Shanghai Jiao Tong University. She then went on to receive a Ph.D. degree, a dual-Masters degree in Statistics, as well as the “Charles Hallac and Sarah Keil Wolf Award for Best Doctoral Dissertation” in Electrical and Systems Engineering from the University of Pennsylvania. Before joining UConn in 2017, she was a postdoc researcher at the GRASP lab and the PRECISE lab at the University of Pennsylvania. Some of her current key active research projects focus on (1) data-driven optimization and control and their application to transportation resource allocation, (2) cyber-physical systems security, (3) safety of autonomous transportation systems, and (4) coordination of autonomous vehicles. In terms of future directions for her research, she hopes to work on (1) the planning, reasoning and learning of autonomous systems, (2) safety assurance of autonomous vehicles, (3) resilient and secure autonomous systems, and (4) heterogenous transportation networks.

 

The broad agenda of her work is to develop the foundations for the science of data-driven cyber physical systems and autonomous transportation systems. More specifically, she focuses on the safety, efficiency, and security of these systems. Her background spans several technical fields that are all relevant to cyber-physical systems and autonomous transportation systems. This includes optimization, control theory, machine learning, game theory, and formal methods.

 

Thus far, her research has focused on data-driven dynamic robust resource allocation for system-level efficiency with learning, control, and optimization approaches. She has also worked on the optimal control and attack detection approaches for cyber-physical systems. In addition to system modeling, theoretical analysis, and algorithmic design, her work has also involved experimental validation in urban transportation data, simulators and small scale autonomous vehicles.

 

She recently finished two publications, new in 2018. The first is titled “A hybrid stochastic game for secure control of cyber-physical systems”, which was published in the journal Automatica. This paper establishes a zero-sum, hybrid state stochastic game model, which can be used to design defense policies for cyber-physical systems against different types of attacks. This model that was proposed in this paper consists of (1) physical states that are described by a set of system dynamics, and (2) a cyber state that represents the detection mode of the entire system, which is composed of a set of subsystems. Finally, the team applies a moving-horizon approach which provides scalable and real-time computations of switching strategies.

 

Her second publication is titled “Data-Driven Robust Taxi Dispatch Under Demand Uncertainties”, which was published in IEEE Transactions on Control Systems Technology. The paper sets out to address the problem on how to deal with uncertainties in terms of predicted customer demand, while also simultaneously fulfilling the system’s performance requirements. The solution was a data-driven robust taxi dispatch system that takes into account the spatial-temporally correlated demand uncertainties.

 

Dr. Miao is also current a Co-PI for two different projects. The first is titled “Modeling, Analysis, and Anomaly Detection for Cyber Secure Power Distribution Networks”, which is funded by Eversource Energy. The second is titled “Energy Management Systems for Subtractive and Additive Precision Manufacturing”, funded by CESMII.