abdollah (ebbie) homaifar duke energy eminent professor and director of autonomous control and...

Abdollah (Ebbie) Homaifar Duke Energy Eminent Professor and Director of Autonomous Control and Information Technology (ACIT) Institute Testing, Evaluation, and Control of Heterogeneous Large-Scale Systems of Autonomous Vehicles (TECHLAV) Department of Electrical and Computer Engineering North Carolina A&T State University 1601 E. Market Street/ 517 McNair Hall Greensboro, NC 27411 Email: [email protected]@ncat.edu Office: 336-285-3709 Lab: 336-285-3271 Fax: 336-334-7716 http://acitcenter.ncat.edu/index.html Winning a DoD Center of Excellence: Process and Protocol Summary of Funded Research in Progress DoD 2015 Taking the Pentagon to the People HBCU/MI Technical Assistance Training Greensboro, NC 8 June 2015

Outline Tips for successfully winning a proposal Summary of Funded research in progress Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) Mining Climate and Ecosystem Data The Center for the Study of Evolution in Action (BEACON) The Crash Imminent Safety (Cris) University Transportation Center (UTC) Perception Inference Engine (PIE) for Test & Evaluation of autonomous systems Acknowledgment Questions 2

Tips for successfully winning a Proposal Start early and plan ahead Build life-long relationships with stakeholders Understand the requirements and needs of the stakeholders External Read and execute the RFP Have a team of internal and external reviewers Collaborating team Have diverse individual(s) in each subject matter Understand the expectations, strengths, and needs of each individual 3

Tips continued.. Be organized and delegate responsibilities Expect the unexpected (have a backup plan) Dont take things personal and do not internalize issues Be a good listener and treat everyone with respect Good ethics History Track record Be passionate about what you do and have fun Remember: Winning and sustaining is about team effort (Credit goes to all) 4

Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) 5

6 DoD Roadmap

About TECHLAV 7 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale Systems of Autonomous Vehicles (TECHLAV) Testing, Evaluation, and Control of Heterogeneous Large Scale Systems of Autonomous Vehicles (TECHLAV) is a multidisciplinary research Center on the cutting edge of Control, Communication, Computation and Human Cognition to address two main problems: 1.Teaming and Cooperative Control of Large Scale Autonomous Systems of Vehicles (LSASVs) integrated with human operators. 2.Testing, Evaluation, Validation, and Verification of LSASVs.

Systems of Vehicles Systems of Vehicles: a (large) team of heterogeneous (aerial, ground, sea surface, and underwater) vehicles that together create a new, more complex networked system that is more capable and robust. 8 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV)

Challenges A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) 9 TEACHLAV How to mathematically describe the collective behaviors of systems of vehicles? How to reliably design decentralized control and communication for systems of vehicles to achieve desired collective performance? How to test and evaluate the control and communication between systems of vehicles? TEACHLAV creates a comprehensive umbrella covering different aspects of systems of vehicles ranging from Modeling and Analysis to Control and Communication and Testing and Evaluation. Modeling and Analysis Reliable Control and Communication Test and Evaluation

About TECHLAV 10 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale Systems of Autonomous Vehicles (TECHLAV) Department of Defense (DoD) Office of the Secretary of Defense (OSD) Air Force Research Laboratory (AFRL) Remark: The views and conclusions being discussed here are those of the presenters and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory, OSD, or the U.S. Government. TECHLAV Center has been funded at $5 M by the Department of Defense (DoD) as a Center of Excellence in Autonomy in 2015. The grant is operated by the Air Force Research Laboratory (AFRL) and the Office of the Secretary of Defense (OSD).

TECHLAV Mission and Vision 11 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) 1.Address fundamental problems in modeling, analysis, control, coordination, test and evaluation of autonomous systems 2.Serve as a national resource in education and research in Autonomy 3.Provide outreach services in autonomy related area 4.Foster linkages among national institutions of higher education, government agencies and industries 5. Commercialize TECHLAV technologies for the benefit of the national economy. The TECHLAV as a Center of Excellence in Autonomy will

Collaborators 12 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) Southwestern Indian Polytechnic InstituteUniversity of Texas at San Antonio North Carolina A&T State University TECHLAV is led by North Carolina Agricultural and Technical State University (N.C. A&T) in collaborate with the University of Texas at San Antonio to conduct strong integrated multi-disciplinary research and education activities on Large Scale Autonomous Systems of Vehicles (LSASV). The Center also partners with Southwestern Indian Polytechnic Institute (SIPI) to provide and promote education and outreach activities and curriculum development to the larger Native American community.

TECHLAV Core Research 13 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV)

Research Thrust 1 14 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) Thrust 1 (Modeling, Analysis and Control of Large-scale Autonomous Vehicles (MACLAV)) will develop scalable methodologies to improve modeling, analysis, localization, navigation, and control of LSASV. Managing modern large scale systems requires new scalable techniques to integrate communication, control and computation that can interact with humans through many new and emerging modalities. Lead: Dr. M. Jamshidi, UTSA. Members: Dr. J.J. Prevost and Mr. P. Benavidez, UTSA; Dr. A. Karimoddini, N.C. A&T

Research Thrust 2 15 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) Thrust 2 (Resilient Control and Communication of Large-scale Autonomous Vehicles (RC2LAV)) will develop systematic methods to enhance the reliability and efficacy of the control structure and the communication backbone for LSASV integrated with human operators in dynamic and uncertain environments such as a battlefield. Lead: Dr. A. Karimoddini, N.C. A&T Members: Drs. F. Afghah and S. Yi, N.C. A&T; Drs. M. Jamshidi and B. Kelley, UTSA

Research Thrust 3 16 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) Thrust 3 (Testing, Evaluation and Verification of Large-scale Autonomous Vehicles (TEVLAV)) will develop and provide technologies and tools for testing, evaluation, validation, and verification of heterogeneous LSASV. This will be done through a runtime formal verification approach which uses a divide-and-conquer technique to modularly check whether LSASV can satisfy the desired (complex) high level specifications. Lead: Dr. Y. Seong, N.C. A&T Members: Drs. A. Homaifar, A. Karimoddini and J. Stephens, N.C. A&T

TECHLAV Education and Outreach 17 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) Collaborative Curriculum Development: Developing or reorganizing autonomy related courses at NC A&T, UTSA, SIPI which will be shared with the collaborating institutions and 11 other Tribal Colleges and Universities Training Pathway to Next Generation of STEM Leaders TECHLAV effectively trains and educates students with backgrounds in robotics, and control systems Student mobility-doctoral students from N.C. A&T and UTSA will visit other institutions to share research results and exchange research ideas The graduate students from N.C. A&T and UTSA will visit SIPI to help students with their engineering education and to assist in the preparation of educational laboratories Outcome Graduating approximately 45 PhD and MS from N.C. A&T and UTSA Supporting 105 Undergraduate students from all three institutions Influence more than 300 Native American students in Albuquerque, NM, and 150 high school teachers and students in Greensboro, NC, via year-round courses offered through outreach activities

TECHLAV People 18 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale systems of Autonomous Vehicles (TECHLAV) Dr. A. Homaifar, Director NC A&T Dr. M. Jamshidi Lead for Thrust 1 UTSA Dr. A. Karimoddini Deputy Director and Lead for Thrust 2 NC A&T Dr. Y. Seong, Lead for Thrust 3 NC A&T Dr. F. Afghah Lead for DII NC A&T Dr. J. Kelly Lead for Education NC A&T Dr. N. Vadiee Lead for Outreach SIPI Dr. B. Kelley Member, UTSA Dr. S. Yi Member, NC A&T Dr. J. Stephens Member, NC A&T Mrs. Shar Seyedin Program manager NC A&T

TECHLAV Advisory Board 19 A. Homaifar Testing, Evaluation and Control of Heterogeneous Large-scale Systems of Autonomous Vehicles (TECHLAV) Dr. Barry L. Burks, chair, Strategic Planning and Policy Board (SPPB) Edward Tunstel, Jr., Senior Robotics at John Hopkins University Applied Physics Laboratory, Chair of Scientific and Industrial Advisory Board (SIAB) Garry Roedler, Engineering Outreach Program Manager at Lockheed Martin Corporate Engineering Richard A. Carreras, Senior Researcher at Laser Division at Air Force Research Lab/RDLE Paul C. Hershey, Senior Engineering Fellow at Raytheon Intelligence Information and Services Daniel DeLarentis, Professor, Department of Aerospace Engineering, Purdue Russell L. (Rusty) Roberts, Director of Aerospace Transportation & Advanced Systems Lab, Georgia Tech Research Institute Roberto Horowitz, Professor, James Fife Endowed Chair, Department of Mechanical Engineering at UC, Berkeley and Director of Partners for Advanced Transportation Technology (PATH) Dr. Julius Yellowhair, (Navajo), Senior Member of the Technical Staff at Sandia National Laboratories Ms. Sandra Begay-Campbell (Navajo)-[manages Sandia National Laboratories tribal energy technical assistance program] Wallace Alton S., Senior Research Consultant Dr. Mark Noakes, Senior Engineer, Robotics and Remote Systems, at ORNL

Mining Climate and Ecosystem Data 20

Analysis of time series with time-varying parameters Many real systems are time-varying, i.e., the parameters of mathematical models of the system are changing through time. The time series generated by a time-varying system has some regimes, while the parameters of the model remains constant in each regime. Given the time series, the aim is to find the number and time of changes between the regimes and to detect the parameters in each regime. Solving this problem is generally difficult, since there are more unknown parameters than known parameters. It has many applications in climatology, economy, engineering, and etc. Dr. Abdollah HomaifarMohammad GorjiDr. Stefan LiessJaquana McNeill NSF-funded project on understanding climate changes from data, in collaboration with North Carolina State Univ., Univ. of Minnesota, and Northeastern Univ. Michael Lowe

Example: How US temperature trends have changed We found that US temperature time series can be modeled by a piecewise linear trend, with a change point in 1958. There is a warming trend in many areas of the US after 1958. Also, there is a change point in the climate forcings time series (including greenhouse gases, solar activity, etc) in 1960. Other studies suggest that the post- WWII economic expansion is the reason for increases in anthropogenic forcings. This fact led to the occurrence of the common change point in forcings and temperature series, marking the beginning of sustained global warming. Forcing (W/m2)

Example: Modeling of switched dynamical systems Switched dynamical systems are mixtures of continuous (transfer function) and discrete events (switching mechanism). Given the input/outputs of the system, find the transfer functions and switching times. Switched dynamical systems are mixtures of continuous (transfer function) and discrete events (switching mechanism). Given the input/outputs of the system, find the transfer functions and switching times. The position and angular velocity of the car on the road are controlled by the gear and throttle. The system has four different dynamical systems with unknown switching times. Given the position and velocity of the car, we can find when the gears have been changed. The position and angular velocity of the car on the road are controlled by the gear and throttle. The system has four different dynamical systems with unknown switching times. Given the position and velocity of the car, we can find when the gears have been changed. Modeling switched systems has many applications for understanding the behavior of complex and nonlinear systems.

Chaunt Lacewell 1, Dr. Abdollah Homaifar 1, Dr. Yuh-Lang Lin 2, and Dr. Kenneth Knapp 3 Autonomous Control and Information Technology (ACIT) Center Department of Electrical and Computer Engineering 1 Department of Energy and Environmental Systems 2 North Carolina A&T State University NOAA - National Climatic Data Center 3 Title III PhD. Fellow Graduate Research Assistant Duke Energy Eminent Professor This work is partially supported by the Expeditions in Computing by the National Science Foundation under Award Number: CCF-1029731 Professor Senior Scientist Products Branch Chief

Problem Definition Investigates predictive features, such as geometric center and eccentricity, that contribute to the development of cloud clusters (CC) into a tropical cyclone (TC) in the North Atlantic Ocean Forecasters need better data-mining and data-driven techniques to provide advanced warning of rare events such as TCs Only global gridded satellite data that are readily available are used Without numerical weather prediction models Challenges Imbalanced data # non-developing CCs >>> # developing CCs Our proposed Selective Clustering-based Oversampling Technique (SCOT) Outperformed state-of-the-art methods Classification of multivariate time series with varying lengths Evolution of a CC is complex CCs can change shape and size rapidly No ground truth data of identified and tracked CCs 25

Neural Network Results Comparison of the geometric means and the performance of developing (recall) and non-developing (specificity) cloud clusters using (a) the imbalanced dataset and (b) the balanced dataset for each forecast hour for the neural network simulation. (a) (b)

The Center for the Study of Evolution in Action (BEACON) 27

Mohammad Gorji Dr. Abdollah Homaifar Real-life t ime series prediction with soft computing approaches Mina Moradi Joy Edward Larvie Jaquana McNeill This work is partially supported by the BEACON by the National Science Foundation under Cooperative Agreement No. DBI-0939454 : the Study of Evolution in Action BEACON is an NSF Science and Technology Center, headquartered at Michigan State University, with partners at North Carolina A&T State University, University of Idaho, University of Texas at Austin, and University of Washington. Dr. Joseph L. Graves, Jr. Dr. Scott Harrison

29 Sunspot time series is a chaotic and nonlinear system in the fields of geophysics and climatology, that has attracted the interest of many researchers to model the behaviors of the system by a variety of methods. It contains the yearly number of dark spots on the sun from 1700 to 1987, consisting of 288 observations Real-life t ime series prediction with soft computing approaches A time series is a sequence of data points that are measured over a time interval. Time series forecasting is the use of a model to find the internal dynamics of a system and predict future values based on previously observed values. Artificial neural network (ANN) is a data-driven approach capable of transforming the inputs into meaningful outputs when dynamics of the system is unknown The performance of ANN improves if an efficient set of inputs and lag observations are chosen by the designer We proposed partially connected artificial neural network with evolvable topology (PANNET) for predicting time series with complex dynamics PANNET is able to predict observations that have never been observed in a training dataset

30 Real-Life Time Series Prediction: Reconstruction of Gene Regulatory Network Reconstruction of Gene Regulatory Network (GRN) Constructed network for target gene lexA which identifies the previously reported regulations of gene lexA by uvrD, umuD, lexA Temporal gene expression of the E. coli SOS DNA Repair System This GRN is known for repairing the DNA after some damage GRN is an abstract mapping of gene regulations in living cells. DNA microarray technology simultaneously measures the expression levels of thousands of genes inside cells in response to specific environmental conditions. Identification of GRNs and prediction of gene expression can open up a window on the disease progression and lead to accurate diagnosis, drug development, and treatment. The proposed PANNET is able to use temporal genes expression values and model the GRN by considering different time delays in gene interactions.

The Crash Imminent Safety (Cris) University Transportation Center (UTC) 31

32 Dr. Abdollah Homaifar Duke Energy Eminent Professor Graduate Research Assistants: Undergraduate Research Assistants: Saina Ramyar PhD Student Allan Anzagira PhD Student Seifemicheal Amsalu PhD Student Developing an Autonomous Driving Assistance System Senior Design Team: Thy Le, Keyur Vaidya, Josh White, Clarence Houser Xuyang Yan This work is partially supported by US Department of Transportation under Award Number: 60040605 Collaborating institutions:- Ohio State University (OSU), North Carolina A&T State University (NCAT), University of Wisconsin (UW), University of Massachusetts (Umass) Amherst Autonomous Control and Information Technology (ACIT) Center Department of Electrical and Computer Engineering 1 North Carolina A&T State University

Problem Definition Development of Autonomous Driver Assistance System (ADAS) Modeling and predicting the behavior of the driver in pre-crash scenarios Detection, modeling, and mitigation of driver distraction Challenges The reaction time before a crash happens is very small, so the ADAS must predict the crash possibility and react quickly 33 Driver distraction detection There are no direct indicators of driver cognitive distraction The distraction detection equipment (e.g., eye sensor) may disturb and distract the driver Our Work: The Real-time Technologies Inc. Driving Simulator is used to mimic real life driving scenarios alongside the eye tracking system to detect driver distraction behavior. There are no direct indicators of driver cognitive distraction The distraction detection equipment (e.g., eye sensor) may disturb and distract the driver Our Work: The Real-time Technologies Inc. Driving Simulator is used to mimic real life driving scenarios alongside the eye tracking system to detect driver distraction behavior.

34 Estimation of state at each time step for left and right maneuvers with SVM(red) and HMM (blue) Driver behavior modeling The driver behavior model has temporal as well as spatial dynamics The model takes dynamic inputs about the changing environment, therefore finding the switching time is important The model must simulate perception, attention, cognition, and control behavior of the driver Our Work: Driver behavior models are being developed using machine learning techniques: Hidden Markov Model, Support Vector Machines and Fuzzy Logic The driver behavior model has temporal as well as spatial dynamics The model takes dynamic inputs about the changing environment, therefore finding the switching time is important The model must simulate perception, attention, cognition, and control behavior of the driver Our Work: Driver behavior models are being developed using machine learning techniques: Hidden Markov Model, Support Vector Machines and Fuzzy Logic

My students at the ACIT center particularly Chaunt Lacewell, Mina Moradi Kordmahalleh, Mohammad Gorji Sefidmazgi, Joy Edward Larvie, Norbert Agana, Saina Ramyar, Seifemichael Amsalu, and Allan Anzagira My colleagues, particularly Drs. Goodman and Adami (Michigan State University), Vipin Kumar (UM), Umit Ozguner (Ohio State), Guiseppi-Elie (Clemson University), Dozier, Karimoddini, Graves and Lin (NC A&T University), and Dr. Kenneth Knapp at NOAA - National Climatic Data Center Department of Defense (DoD) - the Office of the Secretary of Defense (OSD) NSF Science and Technology Center NSF Expedition in Computing U.S. DOT University Transportation Center Test Resources Management Center (TRMC) Scientific Research Corporation (SRC) Acknowledgment 28/29 35

Developing a Data-Driven Perception Inference Engine (PIE) for Test & Evaluation of autonomous systems 36

Work is still in progress......feedback welcome! 37

abdollah (ebbie) homaifar duke energy eminent professor and director of autonomous control and...

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