An LMI Approach to Discrete-Time Observer Design with Stochastic Resilience

Much of the recent work on robust control or observer design has focused on preservation of stability of the controlled system or the convergence of the observer in the presence of parameter perturbations in the plant or the measurement model. The present work addresses the important problem of stochastic resilience or non-fragility of a discrete-time Luenberger observer which is the maintenance of convergence and/or performance when the observer is erroneously implemented possibly due to computational errors i.e. round off errors in digital implementation or sensor errors, etc. A common linear matrix inequality framework is presented to address the stochastic resilient design problem for various performance criteria in the implementation based on the knowledge of an upper bound on the variance of the random error in the observer gain. Present results are compared to earlier designs for stochastic robustness. Illustrative examples are given to complement the theoretical results

Stochastically Resilient Observer Design for a Class of Continuous-Time Nonlinear Systems

This work addresses the design of stochastically resilient or non-fragile continuous-time Luenberger observers for systems with incrementally conic nonlinearities. Such designs maintain the convergence and/or performance when the observer gain is erroneously implemented due possibly to computational errors i.e. round off errors in computing the observer gain or changes in the observer parameters during operation. The error in the observer gain is modeled as a random process and a common linear matrix inequality formulation is presented to address the stochastically resilient observer design problem for a variety of performance criteria. Numerical examples are given to illustrate the theoretical results

Resilient Observer Design for Discrete-Time Nonlinear Systems with General Criteria

A class of discrete-time nonlinear system and measurement equations having incrementally conic nonlinearities and finite energy disturbances is considered. A linear matrix inequality based resilient observer design approach is presented to guarantee the satisfaction of a variety of performance criteria ranging from simple estimation error boundedness to dissipativity in the presence of bounded perturbations on the gain. Some simulation examples are included to illustrate the proposed design methodology

Logistic regression models to predict solvent accessible residues using sequence- and homology-based qualitative and quantitative descriptors applied to a domain-complete X-ray structure learning set

A working example of relative solvent accessibility (RSA) prediction for proteins is presented. Novel logistic regression models with various qualitative descriptors that include amino acid type and quantitative descriptors that include 20- and six-term sequence entropy have been built and validated. A domain-complete learning set of over 1300 proteins is used to fit initial models with various sequence homology descriptors as well as query residue qualitative descriptors. Homology descriptors are derived from BLASTp sequence alignments, whereas the RSA values are determined directly from the crystal structure. The logistic regression models are fitted using dichotomous responses indicating buried or accessible solvent, with binary classifications obtained from the RSA values. The fitted models determine binary predictions of residue solvent accessibility with accuracies comparable to other less computationally intensive methods using the standard RSA threshold criteria 20 and 25% as solvent accessible. When an additional non-homology descriptor describing Lobanov–Galzitskaya residue disorder propensity is included, incremental improvements in accuracy are achieved with 25% threshold accuracies of 76.12 and 74.45% for the Manesh-215 and CASP(8+9) test sets, respectively. Moreover, the described software and the accompanying learning and validation sets allow students and researchers to explore the utility of RSA prediction with simple, physically intuitive models in any number of related applications

Incorporating Soil Surface Erosion Prediction into Forest Road Alignment Optimization

A previous study introduced a forest road design model developed to simultaneously optimize horizontal and vertical alignments of forest roads using a Tabu Search optimization technique and a high-resolution Digital Elevation Model (DEM). In this study, surface erosion prediction was incorporated into the road design model, so that users can optimize horizontal and vertical alignments of forest roads while constrained by maximum allowable sediment delivery from roads to streams. The road alignment optimization model was applied to a part of the Capitol State Forest in western Washington state. The application confirms the potential of the model to determine forest road alignments in a way to reduce total road costs as well as sediment delivery to streams. This paper also discusses the effects of DEM resolution on forest road alignment optimization. The accuracy of generating ground profile and forest road alignments depends on the resolution and accuracy of the DEM. The study results suggest that a 10-m grid DEM might be inappropriate to use for the purpose of road design and alignment optimization due to the lower accuracy in its elevation representation

Kinetic Monte Carlo Simulations

Kinetic Monte Carlo (kMC) is a set of scientific libraries designed to deploy kMC simulations intended to simulate the time evolution of some processes occurring in nature. kMC is currently allows the user to intuitively generate single component crystal lattices to simulate, post process, and visualize the kinetic Monte Carlo-based atomistic evolution of materials. kMC provides an interface to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) [1] and is specifically designed to simulate individual atomic deposition (condensation) and dissolution (evaporation) events, while simultaneously tracking the surface and bulk crystallographic anisotropic diffusion. The main goal of this project is to create Graphical User Interfaces for WulffShape and Physical Vapor Deposition (PVD) examples. The Wulff shape is the shape that possesses the lowest surface energy for a fixed volume and Physical Vapor Deposition is a collective set of processes used to deposit thin layers of material. We are trying to offer the user an option to choose a material, specify the material and change environmental parameters. kMC could generate crystal lattices, simulate, and render images according to the user\u27s setting. Moreover, there is an option for users to see three-dimensional structured atoms created by visIt. In conclusion, this application is going to simulate the time evolution of Wulff Shape and PVD

Robust Nonlinear Feedback Control of Discrete-Time Nonlinear Systems with Mixed Performance Criteria

Abstract-A novel nonlinear state feedback control design is presented for discrete-time nonlinear systems and mixed performance criteria. The purpose behind this new approach is to convert a nonlinear system control design into a convex optimization problem involving state dependent linear matrix inequality solutions. By solving the inequalities at each time step, the optimal control solution is found to satisfy mixed performance criteria guaranteeing quadratic optimality with inherent stability property in combination wit

An Orion/Ares I Launch and Ascent Simulation: One Segment of the Distributed Space Exploration Simulation (DSES)

This paper describes the architecture and implementation of a distributed launch and ascent simulation of NASA's Orion spacecraft and Ares I launch vehicle. This simulation is one segment of the Distributed Space Exploration Simulation (DSES) Project. The DSES project is a research and development collaboration between NASA centers which investigates technologies and processes for distributed simulation of complex space systems in support of NASA's Exploration Initiative. DSES is developing an integrated end-to-end simulation capability to support NASA development and deployment of new exploration spacecraft and missions. This paper describes the first in a collection of simulation capabilities that DSES will support

Welfare State Regimes, Gender, and Depression : a Multilevel Analysis of Middle and High Income Countries

Using the 2002 World Health Survey, we examine the association between welfare state regimes, gender and mental health among 26 countries classified into seven distinct regimes: Conservative, Southeast Asian, Eastern European, Latin American, Liberal, Southern/Ex-dictatorship, and Social Democratic. A two-level hierarchical model found that the odds of experiencing a brief depressive episode in the last 12 months was significantly higher for Southern/Ex- dictatorship countries than for Southeast Asian (odds ratio (OR) = 0.12, 95% confidence interval (CI) 0.05-0.27) and Eastern European (OR = 0.36, 95% CI 0.22-0.58) regimes after controlling for gender, age, education, marital status, and economic development. In adjusted interaction models, compared to Southern/Ex-dictatorship males (reference category), the odds ratios of depression were significantly lower among Southeast Asian males (OR = 0.16, 95% CI 0.08-0.34) and females (OR = 0.23, 95% CI 0.10-0.53) and Eastern European males (OR = 0.41, 95% CI 0.26-0.63) and significantly higher among females in Liberal (OR = 2.00, 95% CI 1.14-3.49) and Southern (OR = 2.42, 95% CI 1.86-3.15) regimes. Our results highlight the importance of incorporating middle-income countries into comparative welfare regime research and testing for interactions between welfare regimes and gender on mental health