This 3-year research and development effort focused on what we believe is a significant technical gap in existing modeling and simulation capabilities: the representation of plausible human cognition and behaviors within a dynamic, simulated environment. Specifically, the intent of the ''Simulating Human Behavior for National Security Human Interactions'' project was to demonstrate initial simulated human modeling capability that realistically represents intra- and inter-group interaction behaviors between simulated humans and human-controlled avatars as they respond to their environment. Significant process was made towards simulating human behaviors through the development of a framework that produces realistic characteristics and movement. The simulated humans were created from models designed to be psychologically plausible by being based on robust psychological research and theory. Progress was also made towards enhancing Sandia National Laboratories existing cognitive models to support culturally plausible behaviors that are important in representing group interactions. These models were implemented in the modular, interoperable, and commercially supported Umbra{reg_sign} simulation framework.
The development of tools for complex dynamic security systems is not a straight forward engineering task but, rather, a scientific task where discovery of new scientific principles and math is necessary. For years, scientists have observed complex behavior but have had difficulty understanding it. Prominent examples include: insect colony organization, the stock market, molecular interactions, fractals, and emergent behavior. Engineering such systems will be an even greater challenge. This report explores four tools for engineered complex dynamic security systems: Partially Observable Markov Decision Process, Percolation Theory, Graph Theory, and Exergy/Entropy Theory. Additionally, enabling hardware technology for next generation security systems are described: a 100 node wireless sensor network, unmanned ground vehicle and unmanned aerial vehicle.
Visual simultaneous localization and mapping (VSLAM) is the problem of using video input to reconstruct the 3D world and the path of the camera in an 'on-line' manner. Since the data is processed in real time, one does not have access to all of the data at once. (Contrast this with structure from motion (SFM), which is usually formulated as an 'off-line' process on all the data seen, and is not time dependent.) A VSLAM solution is useful for mobile robot navigation or as an assistant for humans exploring an unknown environment. This report documents the design and implementation of a VSLAM system that consists of a small inertial measurement unit (IMU) and camera. The approach is based on a modified Extended Kalman Filter. This research was performed under a Laboratory Directed Research and Development (LDRD) effort.
Gaussian processes are used as emulators for expensive computer simulations. Recently, Gaussian processes have also been used to model the "error field" or "code discrepancy" between a computer simulation code and experimental data, and the delta term between two levels of computer simulation (multi-fidelity codes). This work presents the use of Gaussian process models to approximate error or delta fields, and examines how one calculates the parameters governing the process. In multi-fidelity modeling, the delta term is used to correct a lower fidelity model to match or approximate a higher fidelity model. The terms governing the Gaussian process (e.g., the parameters of the covariance matrix) are updated using a Bayesian approach. We have found that use of Gaussian process models requires a good understanding of the method itself and an understanding of the problem in enough detail to identify reasonable covariance parameters. The methods are not "black-box" methods that can be used without some statistical understanding. However, Gaussian processes offer the ability to account for uncertainties in prediction. This approach can help reduce the number of high-fidelity function evaluations necessary in multi-fidelity optimization.
We define a new diagnostic method where computationally-intensive numerical solutions are used as an integral part of making difficult, non-contact, nanometer-scale measurements. The limited scope of this report comprises most of a due diligence investigation into implementing the new diagnostic for measuring dynamic operation of Sandia's RF Ohmic Switch. Our results are all positive, providing insight into how this switch deforms during normal operation. Future work should contribute important measurements on a variety of operating MEMS devices, with insights that are complimentary to those from measurements made using interferometry and laser Doppler methods. More generally, the work opens up a broad front of possibility where exploiting massive high-performance computers enable new measurements.
