INFORMS 2015 DDDAS Session Schedule

Session Title: DDDAS for Industrial and System Engineering Applications I
Chair: Shiyu Zhou,Professor, University of Wisconsin-Madison, Department of Industrial and Systems Eng, 1513 University Avenue, Madison WI 53706, United States of America, shiyuzhou@wisc.edu
Co-Chair: Yu Ding,Professor, Texas A&M University, ETB 4016, MS 3131, College Station, United States of America, yuding@iemail.tamu.edu

  • Dynamic Data Driven Applications Systems DDDAS): New Capabilities in Data Analytics
    • Frederica Darema
    • This talk provides an overview of future directions enabling in new methodologies for analytics through the DDDAS (Dynamic Data Driven Applications Systems) paradigm. We will discuss how DDDAS allows new capabilities in data analytics to enable optimized and fault tolerant systems management, improved analysis and prediction of system conditions, in a diverse set of application areas ranging from aerospace applications to smart cities, to manufacturing planning and control, and cybersecurity.
  • Offline Learning for Dynamic Data-driven Capability Estimation for Self-aware Aerospace Vehicles
    • Douglas Allaire, Benson Isaac
    • A self-aware aerospace vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings and responding intelligently. We present an information-theoretic approach to offline learning via the optimization of libraries of strain, capability, and maneuver loading using physics-based computational models. Online capability estimation is then achieved using by a Bayesian classification process that fuses dynamic, sensed data.
  • Multi-stage Nanocrystal Growth Identifying and Modeling via in-situ Tem Video
    • Yanjun Qian, Yu Ding, Jianhua Huang
    • While in-situ transmission electron microscopy technique has caught a lot of recent attention, one of the bottlenecks appears to be the lack of automated and quantitative analytic tools. We introduce an automated tool suitable for analyzing the in-situ TEM videos. It learns and tracks the normalized particle size distribution and identifies the phase change points delineating the stages in nanocrystal growth. We furthermore produce a quantitative physical-based model.
  • Cooperative Unmanned Vehicles for Vision-based Detection and Real-world Localization of Human Crowds
    • Sara Minaeian, Jian Liu, Young-jun Son
    • In crowd control using unmanned vehicles (UVs), the crowd detection and real-world localization are required to perform key functions such as tracking and motion planning. In this work, a team of UVs cooperates under a DDDAMS framework to detect the moving crowds by applying computer-vision techniques and to localize them using a new perspective transformation. A simulation model is also developed for validation, and the experimental results reveal the effectiveness of the proposed approach.
  • Fault Identifiability Analysis of Beam Structures using Dynamic Data-driven Approaches
    • Yuhang Liu, Shiyu Zhou, Jiong Tang
    • In this research, we study the parameterization and localization identifiability of beam structures based on the dynamic response information. We show that the stiffness parameters can be locally identifiable in general cases for the collocated single input and single output system. The unique relationship between the damage location and the dynamic response are also investigated. The identifiable sensitivity is studied for practical damage identification.
  • The Predict Project: Enhancing DDDAS/Infosymbiotics with Privacy and Security
    • Vaidy Sunderam, Li Xiong
    • The ubiquitousness of mobile devices will greatly expand the applicability of DDDAS, provided privacy and security issues are addressed. The PREDICT project is developing: (1) approaches to assign data-targets to participants with privacy protection; (2) methods for aggregating and fusing data that quantify veracity of the data sources and maintain high fidelity; and (3) secure distributed computation for field- and region-level deployment of the DDDAS paradigm with adaptation and feedback.
  • Securing Industrial Control Systems with Software-defined Networking
    • Dong Jin
    • Modern industrial control systems (ICSes) are increasingly adopting Internet technology to boost control efficiency, which unfortunately opens up a new frontier for cyber-security. With the goal of safely incorporating existing networking technologies in ICSes, we design a novel software-defined networking (SDN) architecture for ICSes, with innovative security applications (e.g., network verification and intrusion detection) and rigorous evaluation using IIT’s campus microgrid.
  • A DDDAS Approach to Distributed Control in Computationally Constrained Environments (UAV Swarms)
    • Vijay Gupta, Wann-jiun Ma, Greg Madey, Daniel Quevedo
    • In modern applications of distributed control, the traditional assumption of ample processing power at every time step at each agent can be challenged by use of processor intensive sensors such as cameras. Inspired by the Dynamic Data Driven Application System approach, we present an algorithm that shifts computational loads among the agents to guarantee performance in spite of reduced average processor availability. Analytical results and numerical simulations illustrate the approach.