Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms

PubMed Central

Joshi, Alark; Scheinost, Dustin; Okuda, Hirohito; Belhachemi, Dominique; Murphy, Isabella; Staib, Lawrence H.; Papademetris, Xenophon

2011-01-01

Developing both graphical and command-line user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet their potential only if they can be easily and frequently used by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires consistency of user interface controls, consistent results across various platforms and thorough testing. We present the design and implementation of a novel object-oriented framework that allows for rapid development of complex image analysis algorithms with many reusable components and the ability to easily add graphical user interface controls. Our framework also allows for simplified yet robust nightly testing of the algorithms to ensure stability and cross platform interoperability. All of the functionality is encapsulated into a software object requiring no separate source code for user interfaces, testing or deployment. This formulation makes our framework ideal for developing novel, stable and easy-to-use algorithms for medical image analysis and computer assisted interventions. The framework has been both deployed at Yale and released for public use in the open source multi-platform image analysis software—BioImage Suite (bioimagesuite.org). PMID:21249532

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

PubMed Central

Svečko, Rajko

2014-01-01

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

A Framework for Information Theoretic Cooperative Sensing and Predictive Control

DTIC Science & Technology

2012-09-11

Miroslav Barić and Francesco Borelli , Decentralized Robust Control Invariance for a Network of Integrators, Proceeding of American Control...from http: //www.mpc.berkeley.edu. P4 Miroslav Barić and Francesco Borelli , Distributed Averaging with Flow Constraints, Proceeding of American Control

A robust model predictive control algorithm for uncertain nonlinear systems that guarantees resolvability

NASA Technical Reports Server (NTRS)

Acikmese, Ahmet Behcet; Carson, John M., III

2006-01-01

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees resolvability. With resolvability, initial feasibility of the finite-horizon optimal control problem implies future feasibility in a receding-horizon framework. The control consists of two components; (i) feed-forward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives and derivatives in polytopes. An illustrative numerical example is also provided.

Active Fault Tolerant Control for Ultrasonic Piezoelectric Motor

NASA Astrophysics Data System (ADS)

Boukhnifer, Moussa

2012-07-01

Ultrasonic piezoelectric motor technology is an important system component in integrated mechatronics devices working on extreme operating conditions. Due to these constraints, robustness and performance of the control interfaces should be taken into account in the motor design. In this paper, we apply a new architecture for a fault tolerant control using Youla parameterization for an ultrasonic piezoelectric motor. The distinguished feature of proposed controller architecture is that it shows structurally how the controller design for performance and robustness may be done separately which has the potential to overcome the conflict between performance and robustness in the traditional feedback framework. A fault tolerant control architecture includes two parts: one part for performance and the other part for robustness. The controller design works in such a way that the feedback control system will be solely controlled by the proportional plus double-integral PI2 performance controller for a nominal model without disturbances and H∞ robustification controller will only be activated in the presence of the uncertainties or an external disturbances. The simulation results demonstrate the effectiveness of the proposed fault tolerant control architecture.

Scalable large format 3D displays

NASA Astrophysics Data System (ADS)

Chang, Nelson L.; Damera-Venkata, Niranjan

2010-02-01

We present a general framework for the modeling and optimization of scalable large format 3-D displays using multiple projectors. Based on this framework, we derive algorithms that can robustly optimize the visual quality of an arbitrary combination of projectors (e.g. tiled, superimposed, combinations of the two) without manual adjustment. The framework creates for the first time a new unified paradigm that is agnostic to a particular configuration of projectors yet robustly optimizes for the brightness, contrast, and resolution of that configuration. In addition, we demonstrate that our algorithms support high resolution stereoscopic video at real-time interactive frame rates achieved on commodity graphics hardware. Through complementary polarization, the framework creates high quality multi-projector 3-D displays at low hardware and operational cost for a variety of applications including digital cinema, visualization, and command-and-control walls.

A Generally Robust Approach for Testing Hypotheses and Setting Confidence Intervals for Effect Sizes

ERIC Educational Resources Information Center

Keselman, H. J.; Algina, James; Lix, Lisa M.; Wilcox, Rand R.; Deering, Kathleen N.

2008-01-01

Standard least squares analysis of variance methods suffer from poor power under arbitrarily small departures from normality and fail to control the probability of a Type I error when standard assumptions are violated. This article describes a framework for robust estimation and testing that uses trimmed means with an approximate degrees of…

Analysis of airframe/engine interactions in integrated flight and propulsion control

NASA Technical Reports Server (NTRS)

Schierman, John D.; Schmidt, David K.

1991-01-01

An analysis framework for the assessment of dynamic cross-coupling between airframe and engine systems from the perspective of integrated flight/propulsion control is presented. This analysis involves to determining the significance of the interactions with respect to deterioration in stability robustness and performance, as well as critical frequency ranges where problems may occur due to these interactions. The analysis illustrated here investigates both the airframe's effects on the engine control loops and the engine's effects on the airframe control loops in two case studies. The second case study involves a multi-input/multi-output analysis of the airframe. Sensitivity studies are performed on critical interactions to examine the degradations in the system's stability robustness and performance. Magnitudes of the interactions required to cause instabilities, as well as the frequencies at which the instabilities occur are recorded. Finally, the analysis framework is expanded to include control laws which contain cross-feeds between the airframe and engine systems.

Robust stability bounds for multi-delay networked control systems

NASA Astrophysics Data System (ADS)

Seitz, Timothy; Yedavalli, Rama K.; Behbahani, Alireza

2018-04-01

In this paper, the robust stability of a perturbed linear continuous-time system is examined when controlled using a sampled-data networked control system (NCS) framework. Three new robust stability bounds on the time-invariant perturbations to the original continuous-time plant matrix are presented guaranteeing stability for the corresponding discrete closed-loop augmented delay-free system (ADFS) with multiple time-varying sensor and actuator delays. The bounds are differentiated from previous work by accounting for the sampled-data nature of the NCS and for separate communication delays for each sensor and actuator, not a single delay. Therefore, this paper expands the knowledge base in multiple inputs multiple outputs (MIMO) sampled-data time delay systems. Bounds are presented for unstructured, semi-structured, and structured perturbations.

Development of a robust framework for controlling high performance turbofan engines

NASA Astrophysics Data System (ADS)

Miklosovic, Robert

This research involves the development of a robust framework for controlling complex and uncertain multivariable systems. Where mathematical modeling is often tedious or inaccurate, the new method uses an extended state observer (ESO) to estimate and cancel dynamic information in real time and dynamically decouple the system. As a result, controller design and tuning become transparent as the number of required model parameters is reduced. Much research has been devoted towards the application of modern multivariable control techniques on aircraft engines. However, few, if any, have been implemented on an operational aircraft, partially due to the difficulty in tuning the controller for satisfactory performance. The new technique is applied to a modern two-spool, high-pressure ratio, low-bypass turbofan with mixed-flow afterburning. A realistic Modular Aero-Propulsion System Simulation (MAPSS) package, developed by NASA, is used to demonstrate the new design process and compare its performance with that of a supplied nominal controller. This approach is expected to reduce gain scheduling over the full operating envelope of the engine and allow a controller to be tuned for engine-to-engine variations.

Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles

NASA Astrophysics Data System (ADS)

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

A decentralized control methodology is designed for the control of ankle dorsiflexion and plantarflexion in paraplegic subjects with electrical stimulation of tibialis anterior and calf muscles. Each muscle joint is considered as a subsystem and individual controllers are designed for each subsystem. Each controller operates solely on its associated subsystem, with no exchange of information between the subsystems. The interactions between the subsystems are taken as external disturbances for each isolated subsystem. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed which is based on the synergistic combination of an adaptive nonlinear compensator with a sliding mode control and is referred to as an adaptive robust control. Extensive simulations and experiments on healthy and paraplegic subjects were performed to demonstrate the robustness against the time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, fast convergence, stability and tracking accuracy of the proposed method. The results indicate that the decentralized robust control provides excellent tracking control for different reference trajectories and can generate control signals to compensate the muscle fatigue and reject the external disturbance. Moreover, the controller is able to automatically regulate the interaction between agonist and antagonist muscles under different conditions of operating without any preprogrammed antagonist activities.

Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles.

PubMed

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

A decentralized control methodology is designed for the control of ankle dorsiflexion and plantarflexion in paraplegic subjects with electrical stimulation of tibialis anterior and calf muscles. Each muscle joint is considered as a subsystem and individual controllers are designed for each subsystem. Each controller operates solely on its associated subsystem, with no exchange of information between the subsystems. The interactions between the subsystems are taken as external disturbances for each isolated subsystem. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed which is based on the synergistic combination of an adaptive nonlinear compensator with a sliding mode control and is referred to as an adaptive robust control. Extensive simulations and experiments on healthy and paraplegic subjects were performed to demonstrate the robustness against the time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, fast convergence, stability and tracking accuracy of the proposed method. The results indicate that the decentralized robust control provides excellent tracking control for different reference trajectories and can generate control signals to compensate the muscle fatigue and reject the external disturbance. Moreover, the controller is able to automatically regulate the interaction between agonist and antagonist muscles under different conditions of operating without any preprogrammed antagonist activities.

Microgravity isolation system design: A modern control analysis framework

NASA Technical Reports Server (NTRS)

Hampton, R. D.; Knospe, C. R.; Allaire, P. E.; Grodsinsky, C. M.

1994-01-01

Many acceleration-sensitive, microgravity science experiments will require active vibration isolation from the manned orbiters on which they will be mounted. The isolation problem, especially in the case of a tethered payload, is a complex three-dimensional one that is best suited to modern-control design methods. These methods, although more powerful than their classical counterparts, can nonetheless go only so far in meeting the design requirements for practical systems. Once a tentative controller design is available, it must still be evaluated to determine whether or not it is fully acceptable, and to compare it with other possible design candidates. Realistically, such evaluation will be an inherent part of a necessary iterative design process. In this paper, an approach is presented for applying complex mu-analysis methods to a closed-loop vibration isolation system (experiment plus controller). An analysis framework is presented for evaluating nominal stability, nominal performance, robust stability, and robust performance of active microgravity isolation systems, with emphasis on the effective use of mu-analysis methods.

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

PubMed Central

He, Fei; Murabito, Ettore; Westerhoff, Hans V.

2016-01-01

Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. PMID:27075000

Singularity-robustness and task-prioritization in configuration control of redundant robots

NASA Technical Reports Server (NTRS)

Seraji, H.; Colbaugh, R.

1990-01-01

The authors present a singularity-robust task-prioritized reformulation of the configuration control for redundant robot manipulators. This reformation suppresses large joint velocities to induce minimal errors in the task performance by modifying the task trajectories. Furthermore, the same framework provides a means for assignment of priorities between the basic task of end-effector motion and the user-defined additional task for utilizing redundancy. This allows automatic relaxation of the additional task constraints in favor of the desired end-effector motion when both cannot be achieved exactly.

An LMI approach to design H(infinity) controllers for discrete-time nonlinear systems based on unified models.

PubMed

Liu, Meiqin; Zhang, Senlin

2008-10-01

A unified neural network model termed standard neural network model (SNNM) is advanced. Based on the robust L(2) gain (i.e. robust H(infinity) performance) analysis of the SNNM with external disturbances, a state-feedback control law is designed for the SNNM to stabilize the closed-loop system and eliminate the effect of external disturbances. The control design constraints are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms (e.g. interior-point algorithms) to determine the control law. Most discrete-time recurrent neural network (RNNs) and discrete-time nonlinear systems modelled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be robust H(infinity) performance analyzed or robust H(infinity) controller synthesized in a unified SNNM's framework. Finally, some examples are presented to illustrate the wide application of the SNNMs to the nonlinear systems, and the proposed approach is compared with related methods reported in the literature.

FPGA-Based Efficient Hardware/Software Co-Design for Industrial Systems with Consideration of Output Selection

NASA Astrophysics Data System (ADS)

Deliparaschos, Kyriakos M.; Michail, Konstantinos; Zolotas, Argyrios C.; Tzafestas, Spyros G.

2016-05-01

This work presents a field programmable gate array (FPGA)-based embedded software platform coupled with a software-based plant, forming a hardware-in-the-loop (HIL) that is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, linear-quadratic-Gaussian (LQG)-type control, and the nonlinear model of a maglev suspension. A robustness analysis of the closed-loop is followed (prior to implementation) supporting the appropriateness of the solution under parametric variation. The analysis also shows that quantization is robust under different controller gains. While the LQG controller is implemented on an FPGA, the physical process is realized in a high-level system modeling environment. FPGA technology enables rapid evaluation of the algorithms and test designs under realistic scenarios avoiding heavy time penalty associated with hardware description language (HDL) simulators. The HIL technique facilitates significant speed-up in the required execution time when compared to its software-based counterpart model.

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

PubMed

He, Fei; Murabito, Ettore; Westerhoff, Hans V

2016-04-01

Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways. © 2016 The Author(s).

FRIEDA: Flexible Robust Intelligent Elastic Data Management Framework

DOE PAGES

Ghoshal, Devarshi; Hendrix, Valerie; Fox, William; ...

2017-02-01

Scientific applications are increasingly using cloud resources for their data analysis workflows. However, managing data effectively and efficiently over these cloud resources is challenging due to the myriad storage choices with different performance, cost trade-offs, complex application choices and complexity associated with elasticity, failure rates in these environments. The different data access patterns for data-intensive scientific applications require a more flexible and robust data management solution than the ones currently in existence. FRIEDA is a Flexible Robust Intelligent Elastic Data Management framework that employs a range of data management strategies in cloud environments. FRIEDA can manage storage and data lifecyclemore » of applications in cloud environments. There are four different stages in the data management lifecycle of FRIEDA – (i) storage planning, (ii) provisioning and preparation, (iii) data placement, and (iv) execution. FRIEDA defines a data control plane and an execution plane. The data control plane defines the data partition and distribution strategy, whereas the execution plane manages the execution of the application using a master-worker paradigm. FRIEDA also provides different data management strategies, either to partition the data in real-time, or predetermine the data partitions prior to application execution.« less

FRIEDA: Flexible Robust Intelligent Elastic Data Management Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghoshal, Devarshi; Hendrix, Valerie; Fox, William

Scientific applications are increasingly using cloud resources for their data analysis workflows. However, managing data effectively and efficiently over these cloud resources is challenging due to the myriad storage choices with different performance, cost trade-offs, complex application choices and complexity associated with elasticity, failure rates in these environments. The different data access patterns for data-intensive scientific applications require a more flexible and robust data management solution than the ones currently in existence. FRIEDA is a Flexible Robust Intelligent Elastic Data Management framework that employs a range of data management strategies in cloud environments. FRIEDA can manage storage and data lifecyclemore » of applications in cloud environments. There are four different stages in the data management lifecycle of FRIEDA – (i) storage planning, (ii) provisioning and preparation, (iii) data placement, and (iv) execution. FRIEDA defines a data control plane and an execution plane. The data control plane defines the data partition and distribution strategy, whereas the execution plane manages the execution of the application using a master-worker paradigm. FRIEDA also provides different data management strategies, either to partition the data in real-time, or predetermine the data partitions prior to application execution.« less

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view. PMID:23515240

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology.

PubMed

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view.

VR Simulation Testbed: Improving Surface Telerobotics for the Deep Space Gateway

NASA Astrophysics Data System (ADS)

Walker, M. E.; Burns, J. O.; Szafir, D. J.

2018-02-01

Design of a virtual reality simulation testbed for prototyping surface telerobotics. The goal is to create a framework with robust physics and kinematics to allow simulated teleoperation and supervised control of lunar rovers and rapid UI prototyping.

Beyond optimality: Multistakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty

NASA Astrophysics Data System (ADS)

Herman, Jonathan D.; Zeff, Harrison B.; Reed, Patrick M.; Characklis, Gregory W.

2014-10-01

While optimality is a foundational mathematical concept in water resources planning and management, "optimal" solutions may be vulnerable to failure if deeply uncertain future conditions deviate from those assumed during optimization. These vulnerabilities may produce severely asymmetric impacts across a region, making it vital to evaluate the robustness of management strategies as well as their impacts for regional stakeholders. In this study, we contribute a multistakeholder many-objective robust decision making (MORDM) framework that blends many-objective search and uncertainty analysis tools to discover key tradeoffs between water supply alternatives and their robustness to deep uncertainties (e.g., population pressures, climate change, and financial risks). The proposed framework is demonstrated for four interconnected water utilities representing major stakeholders in the "Research Triangle" region of North Carolina, U.S. The utilities supply well over one million customers and have the ability to collectively manage drought via transfer agreements and shared infrastructure. We show that water portfolios for this region that compose optimal tradeoffs (i.e., Pareto-approximate solutions) under expected future conditions may suffer significantly degraded performance with only modest changes in deeply uncertain hydrologic and economic factors. We then use the Patient Rule Induction Method (PRIM) to identify which uncertain factors drive the individual and collective vulnerabilities for the four cooperating utilities. Our framework identifies key stakeholder dependencies and robustness tradeoffs associated with cooperative regional planning, which are critical to understanding the tensions between individual versus regional water supply goals. Cooperative demand management was found to be the key factor controlling the robustness of regional water supply planning, dominating other hydroclimatic and economic uncertainties through the 2025 planning horizon. Results suggest that a modest reduction in the projected rate of demand growth (from approximately 3% per year to 2.4%) will substantially improve the utilities' robustness to future uncertainty and reduce the potential for regional tensions. The proposed multistakeholder MORDM framework offers critical insights into the risks and challenges posed by rising water demands and hydrological uncertainties, providing a planning template for regions now forced to confront rapidly evolving water scarcity risks.

A μ analysis-based, controller-synthesis framework for robust bioinspired visual navigation in less-structured environments.

PubMed

Keshavan, J; Gremillion, G; Escobar-Alvarez, H; Humbert, J S

2014-06-01

Safe, autonomous navigation by aerial microsystems in less-structured environments is a difficult challenge to overcome with current technology. This paper presents a novel visual-navigation approach that combines bioinspired wide-field processing of optic flow information with control-theoretic tools for synthesis of closed loop systems, resulting in robustness and performance guarantees. Structured singular value analysis is used to synthesize a dynamic controller that provides good tracking performance in uncertain environments without resorting to explicit pose estimation or extraction of a detailed environmental depth map. Experimental results with a quadrotor demonstrate the vehicle's robust obstacle-avoidance behaviour in a straight line corridor, an S-shaped corridor and a corridor with obstacles distributed in the vehicle's path. The computational efficiency and simplicity of the current approach offers a promising alternative to satisfying the payload, power and bandwidth constraints imposed by aerial microsystems.

JACOB: an enterprise framework for computational chemistry.

PubMed

Waller, Mark P; Dresselhaus, Thomas; Yang, Jack

2013-06-15

Here, we present just a collection of beans (JACOB): an integrated batch-based framework designed for the rapid development of computational chemistry applications. The framework expedites developer productivity by handling the generic infrastructure tier, and can be easily extended by user-specific scientific code. Paradigms from enterprise software engineering were rigorously applied to create a scalable, testable, secure, and robust framework. A centralized web application is used to configure and control the operation of the framework. The application-programming interface provides a set of generic tools for processing large-scale noninteractive jobs (e.g., systematic studies), or for coordinating systems integration (e.g., complex workflows). The code for the JACOB framework is open sourced and is available at: www.wallerlab.org/jacob. Copyright © 2013 Wiley Periodicals, Inc.

Robust synergetic control design under inputs and states constraints

NASA Astrophysics Data System (ADS)

Rastegar, Saeid; Araújo, Rui; Sadati, Jalil

2018-03-01

In this paper, a novel robust-constrained control methodology for discrete-time linear parameter-varying (DT-LPV) systems is proposed based on a synergetic control theory (SCT) approach. It is shown that in DT-LPV systems without uncertainty, and for any unmeasured bounded additive disturbance, the proposed controller accomplishes the goal of stabilising the system by asymptotically driving the error of the controlled variable to a bounded set containing the origin and then maintaining it there. Moreover, given an uncertain DT-LPV system jointly subject to unmeasured and constrained additive disturbances, and constraints in states, input commands and reference signals (set points), then invariant set theory is used to find an appropriate polyhedral robust invariant region in which the proposed control framework is guaranteed to robustly stabilise the closed-loop system. Furthermore, this is achieved even for the case of varying non-zero control set points in such uncertain DT-LPV systems. The controller is characterised to have a simple structure leading to an easy implementation, and a non-complex design process. The effectiveness of the proposed method and the implications of the controller design on feasibility and closed-loop performance are demonstrated through application examples on the temperature control on a continuous-stirred tank reactor plant, on the control of a real-coupled DC motor plant, and on an open-loop unstable system example.

Robust all-source positioning of UAVs based on belief propagation

NASA Astrophysics Data System (ADS)

Chen, Xi; Gao, Wenyun; Wang, Jiabo

2013-12-01

For unmanned air vehicles (UAVs) to survive hostile operational environments, it is always preferable to utilize all wireless positioning sources available to fuse a robust position. While belief propagation is a well-established method for all source data fusion, it is not an easy job to handle all the mathematics therein. In this work, a comprehensive mathematical framework for belief propagation-based all-source positioning of UAVs is developed, taking wireless sources including Global Navigation Satellite Systems (GNSS) space vehicles, peer UAVs, ground control stations, and signal of opportunities. Based on the mathematical framework, a positioning algorithm named Belief propagation-based Opportunistic Positioning of UAVs (BOPU) is proposed, with an unscented particle filter for Bayesian approximation. The robustness of the proposed BOPU is evaluated by a fictitious scenario that a group of formation flying UAVs encounter GNSS countermeasures en route. Four different configurations of measurements availability are simulated. The results show that the performance of BOPU varies only slightly with different measurements availability.

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices.

PubMed

Keller, Brad M; Oustimov, Andrew; Wang, Yan; Chen, Jinbo; Acciavatti, Raymond J; Zheng, Yuanjie; Ray, Shonket; Gee, James C; Maidment, Andrew D A; Kontos, Despina

2015-04-01

An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., [Formula: see text]) and with a larger offset length (i.e., [Formula: see text]), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Robust Stability of Scaled-Four-Channel Teleoperation with Internet Time-Varying Delays

PubMed Central

Delgado, Emma; Barreiro, Antonio; Falcón, Pablo; Díaz-Cacho, Miguel

2016-01-01

We describe the application of a generic stability framework for a teleoperation system under time-varying delay conditions, as addressed in a previous work, to a scaled-four-channel (γ-4C) control scheme. Described is how varying delays are dealt with by means of dynamic encapsulation, giving rise to mu-test conditions for robust stability and offering an appealing frequency technique to deal with the stability robustness of the architecture. We discuss ideal transparency problems and we adapt classical solutions so that controllers are proper, without single or double differentiators, and thus avoid the negative effects of noise. The control scheme was fine-tuned and tested for complete stability to zero of the whole state, while seeking a practical solution to the trade-off between stability and transparency in the Internet-based teleoperation. These ideas were tested on an Internet-based application with two Omni devices at remote laboratory locations via simulations and real remote experiments that achieved robust stability, while performing well in terms of position synchronization and force transparency. PMID:27128914

ATLAS Metadata Infrastructure Evolution for Run 2 and Beyond

NASA Astrophysics Data System (ADS)

van Gemmeren, P.; Cranshaw, J.; Malon, D.; Vaniachine, A.

2015-12-01

ATLAS developed and employed for Run 1 of the Large Hadron Collider a sophisticated infrastructure for metadata handling in event processing jobs. This infrastructure profits from a rich feature set provided by the ATLAS execution control framework, including standardized interfaces and invocation mechanisms for tools and services, segregation of transient data stores with concomitant object lifetime management, and mechanisms for handling occurrences asynchronous to the control framework's state machine transitions. This metadata infrastructure is evolving and being extended for Run 2 to allow its use and reuse in downstream physics analyses, analyses that may or may not utilize the ATLAS control framework. At the same time, multiprocessing versions of the control framework and the requirements of future multithreaded frameworks are leading to redesign of components that use an incident-handling approach to asynchrony. The increased use of scatter-gather architectures, both local and distributed, requires further enhancement of metadata infrastructure in order to ensure semantic coherence and robust bookkeeping. This paper describes the evolution of ATLAS metadata infrastructure for Run 2 and beyond, including the transition to dual-use tools—tools that can operate inside or outside the ATLAS control framework—and the implications thereof. It further examines how the design of this infrastructure is changing to accommodate the requirements of future frameworks and emerging event processing architectures.

Lyapunov function-based control laws for revolute robot arms - Tracking control, robustness, and adaptive control

NASA Technical Reports Server (NTRS)

Wen, John T.; Kreutz-Delgado, Kenneth; Bayard, David S.

1992-01-01

A new class of joint level control laws for all-revolute robot arms is introduced. The analysis is similar to a recently proposed energy-like Liapunov function approach, except that the closed-loop potential function is shaped in accordance with the underlying joint space topology. This approach gives way to a much simpler analysis and leads to a new class of control designs which guarantee both global asymptotic stability and local exponential stability. When Coulomb and viscous friction and parameter uncertainty are present as model perturbations, a sliding mode-like modification of the control law results in a robustness-enhancing outer loop. Adaptive control is formulated within the same framework. A linear-in-the-parameters formulation is adopted and globally asymptotically stable adaptive control laws are derived by simply replacing unknown model parameters by their estimates (i.e., certainty equivalence adaptation).

A new class of energy based control laws for revolute robot arms - Tracking control, robustness enhancement and adaptive control

NASA Technical Reports Server (NTRS)

Wen, John T.; Kreutz, Kenneth; Bayard, David S.

1988-01-01

A class of joint-level control laws for all-revolute robot arms is introduced. The analysis is similar to the recently proposed energy Liapunov function approach except that the closed-loop potential function is shaped in accordance with the underlying joint space topology. By using energy Liapunov functions with the modified potential energy, a much simpler analysis can be used to show closed-loop global asymptotic stability and local exponential stability. When Coulomb and viscous friction and model parameter errors are present, a sliding-mode-like modification of the control law is proposed to add a robustness-enhancing outer loop. Adaptive control is also addressed within the same framework. A linear-in-the-parameters formulation is adopted, and globally asymptotically stable adaptive control laws are derived by replacing the model parameters in the nonadaptive control laws by their estimates.

Analysis and design of gain scheduled control systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Shamma, Jeff S.

1988-01-01

Gain scheduling, as an idea, is to construct a global feedback control system for a time varying and/or nonlinear plant from a collection of local time invariant designs. However in the absence of a sound analysis, these designs come with no guarantees on the robustness, performance, or even nominal stability of the overall gain schedule design. Such an analysis is presented for three types of gain scheduling situations: (1) a linear parameter varying plant scheduling on its exogenous parameters, (2) a nonlinear plant scheduling on a prescribed reference trajectory, and (3) a nonlinear plant scheduling on the current plant output. Conditions are given which guarantee that the stability, robustness, and performance properties of the fixed operating point designs carry over to the global gain scheduled designs, such as the scheduling variable should vary slowly and capture the plants nonlinearities. Finally, an alternate design framework is proposed which removes the slowing varying restriction or gain scheduled systems. This framework addresses some fundamental feedback issues previously ignored in standard gain.

Combining synthetic controls and interrupted time series analysis to improve causal inference in program evaluation.

PubMed

Linden, Ariel

2018-04-01

Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied over time and the intervention is expected to "interrupt" the level and/or trend of the outcome. The internal validity is strengthened considerably when the treated unit is contrasted with a comparable control group. In this paper, we introduce a robust evaluation framework that combines the synthetic controls method (SYNTH) to generate a comparable control group and ITSA regression to assess covariate balance and estimate treatment effects. We evaluate the effect of California's Proposition 99 for reducing cigarette sales, by comparing California to other states not exposed to smoking reduction initiatives. SYNTH is used to reweight nontreated units to make them comparable to the treated unit. These weights are then used in ITSA regression models to assess covariate balance and estimate treatment effects. Covariate balance was achieved for all but one covariate. While California experienced a significant decrease in the annual trend of cigarette sales after Proposition 99, there was no statistically significant treatment effect when compared to synthetic controls. The advantage of using this framework over regression alone is that it ensures that a comparable control group is generated. Additionally, it offers a common set of statistical measures familiar to investigators, the capability for assessing covariate balance, and enhancement of the evaluation with a comprehensive set of postestimation measures. Therefore, this robust framework should be considered as a primary approach for evaluating treatment effects in multiple group time series analysis. © 2018 John Wiley & Sons, Ltd.

Robust Stabilization of Uncertain Systems Based on Energy Dissipation Concepts

NASA Technical Reports Server (NTRS)

Gupta, Sandeep

1996-01-01

Robust stability conditions obtained through generalization of the notion of energy dissipation in physical systems are discussed in this report. Linear time-invariant (LTI) systems which dissipate energy corresponding to quadratic power functions are characterized in the time-domain and the frequency-domain, in terms of linear matrix inequalities (LMls) and algebraic Riccati equations (ARE's). A novel characterization of strictly dissipative LTI systems is introduced in this report. Sufficient conditions in terms of dissipativity and strict dissipativity are presented for (1) stability of the feedback interconnection of dissipative LTI systems, (2) stability of dissipative LTI systems with memoryless feedback nonlinearities, and (3) quadratic stability of uncertain linear systems. It is demonstrated that the framework of dissipative LTI systems investigated in this report unifies and extends small gain, passivity, and sector conditions for stability. Techniques for selecting power functions for characterization of uncertain plants and robust controller synthesis based on these stability results are introduced. A spring-mass-damper example is used to illustrate the application of these methods for robust controller synthesis.

Robust design optimization using the price of robustness, robust least squares and regularization methods

NASA Astrophysics Data System (ADS)

Bukhari, Hassan J.

2017-12-01

In this paper a framework for robust optimization of mechanical design problems and process systems that have parametric uncertainty is presented using three different approaches. Robust optimization problems are formulated so that the optimal solution is robust which means it is minimally sensitive to any perturbations in parameters. The first method uses the price of robustness approach which assumes the uncertain parameters to be symmetric and bounded. The robustness for the design can be controlled by limiting the parameters that can perturb.The second method uses the robust least squares method to determine the optimal parameters when data itself is subjected to perturbations instead of the parameters. The last method manages uncertainty by restricting the perturbation on parameters to improve sensitivity similar to Tikhonov regularization. The methods are implemented on two sets of problems; one linear and the other non-linear. This methodology will be compared with a prior method using multiple Monte Carlo simulation runs which shows that the approach being presented in this paper results in better performance.

The Diamond Beamline Controls and Data Acquisition Software Architecture

NASA Astrophysics Data System (ADS)

Rees, N.

2010-06-01

The software for the Diamond Light Source beamlines[1] is based on two complementary software frameworks: low level control is provided by the Experimental Physics and Industrial Control System (EPICS) framework[2][3] and the high level user interface is provided by the Java based Generic Data Acquisition or GDA[4][5]. EPICS provides a widely used, robust, generic interface across a wide range of hardware where the user interfaces are focused on serving the needs of engineers and beamline scientists to obtain detailed low level views of all aspects of the beamline control systems. The GDA system provides a high-level system that combines an understanding of scientific concepts, such as reciprocal lattice coordinates, a flexible python syntax scripting interface for the scientific user to control their data acquisition, and graphical user interfaces where necessary. This paper describes the beamline software architecture in more detail, highlighting how these complementary frameworks provide a flexible system that can accommodate a wide range of requirements.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example. PMID:23515190

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology.

PubMed

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example.

Development of a Framework for Model-Based Analysis, Uncertainty Quantification, and Robust Control Design of Nonlinear Smart Composite Systems

DTIC Science & Technology

2015-06-04

control, vibration and noise control, health monitoring, and energy harvesting . However, these advantages come at the cost of rate-dependent hysteresis...configuration used for energy harvesting . Uncertainty Quantification Uncertainty quantification is pursued in two steps: (i) determination of densities...Crews and R.C. Smith, “Quantification of parameter and model uncertainty for shape mem- ory alloy bending actuators,” Journal of Intelligent material

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Tradeoff on Phenotype Robustness in Biological Networks Part II: Ecological Networks

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

In ecological networks, network robustness should be large enough to confer intrinsic robustness for tolerating intrinsic parameter fluctuations, as well as environmental robustness for resisting environmental disturbances, so that the phenotype stability of ecological networks can be maintained, thus guaranteeing phenotype robustness. However, it is difficult to analyze the network robustness of ecological systems because they are complex nonlinear partial differential stochastic systems. This paper develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance sensitivity in ecological networks. We found that the phenotype robustness criterion for ecological networks is that if intrinsic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations and environmental disturbances. These results in robust ecological networks are similar to that in robust gene regulatory networks and evolutionary networks even they have different spatial-time scales. PMID:23515112

Robustness Metrics: How Are They Calculated, When Should They Be Used and Why Do They Give Different Results?

NASA Astrophysics Data System (ADS)

McPhail, C.; Maier, H. R.; Kwakkel, J. H.; Giuliani, M.; Castelletti, A.; Westra, S.

2018-02-01

Robustness is being used increasingly for decision analysis in relation to deep uncertainty and many metrics have been proposed for its quantification. Recent studies have shown that the application of different robustness metrics can result in different rankings of decision alternatives, but there has been little discussion of what potential causes for this might be. To shed some light on this issue, we present a unifying framework for the calculation of robustness metrics, which assists with understanding how robustness metrics work, when they should be used, and why they sometimes disagree. The framework categorizes the suitability of metrics to a decision-maker based on (1) the decision-context (i.e., the suitability of using absolute performance or regret), (2) the decision-maker's preferred level of risk aversion, and (3) the decision-maker's preference toward maximizing performance, minimizing variance, or some higher-order moment. This article also introduces a conceptual framework describing when relative robustness values of decision alternatives obtained using different metrics are likely to agree and disagree. This is used as a measure of how "stable" the ranking of decision alternatives is when determined using different robustness metrics. The framework is tested on three case studies, including water supply augmentation in Adelaide, Australia, the operation of a multipurpose regulated lake in Italy, and flood protection for a hypothetical river based on a reach of the river Rhine in the Netherlands. The proposed conceptual framework is confirmed by the case study results, providing insight into the reasons for disagreements between rankings obtained using different robustness metrics.

Design of Optimally Robust Control Systems.

DTIC Science & Technology

1980-01-01

approach is that the optimization framework is an artificial device. While some design constraints can easily be incorporated into a single cost function...indicating that that point was indeed the solution. Also, an intellegent initial guess for k was important in order to avoid being hung up at the double

Molecular Retrofitting Adapts a Metal–Organic Framework to Extreme Pressure

DOE PAGES

Kapustin, Eugene A.; Lee, Seungkyu; Alshammari, Ahmad S.; ...

2017-06-07

Despite numerous studies on chemical and thermal stability of metal-organic frameworks (MOFs), mechanical stability remains largely undeveloped. No strategy exists to control the mechanical deformation of MOFs under ultrahigh pressure, to date. We show that the mechanically unstable MOF-520 can be retrofitted by precise placement of a rigid 4,4'-biphenyldicarboxylate (BPDC) linker as a "girder" to afford a mechanically robust framework: MOF-520-BPDC. This retrofitting alters how the structure deforms under ultrahigh pressure and thus leads to a drastic enhancement of its mechanical robustness. While in the parent MOF-520 the pressure transmitting medium molecules diffuse into the pore and expand the structuremore » from the inside upon compression, the girder in the new retrofitted MOF-520-BPDC prevents the framework from expansion by linking two adjacent secondary building units together. As a result, the modified MOF is stable under hydrostatic compression in a diamond-anvil cell up to 5.5 gigapascal. The increased mechanical stability of MOF-520-BPDC prohibits the typical amorphization observed for MOFs in this pressure range. Direct correlation between the orientation of these girders within the framework and its linear strain was estimated, providing new insights for the design of MOFs with optimized mechanical properties.« less

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

PubMed Central

Carmena, Jose M.

2016-01-01

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics. PMID:27035820

Tobacco control efforts in the Gulf Cooperation Council countries: achievements and challenges.

PubMed

Hassounah, S; Rawaf, D; Khoja, T; Rawaf, S; Hussein, M S; Qidwai, W; Majeed, A

2014-08-19

This paper reports a review into the current state of tobacco use, governance and national commitment for control, and current intervention frameworks in place to reduce the use of tobacco among the populations of the Gulf Cooperation Council (GCC) member states and Yemen. It further reviews structured policy-oriented interventions (in line with the MPOWER package of 6 evidence-based tobacco control measures) that represent government actions to strengthen, implement and manage tobacco control programmes and to address the growing epidemic of tobacco use. Our findings show that tobacco control in the GCC countries has witnessed real progress over the past decades. These are still early days but they indicate steps in the right direction. Future investment in implementation and enforcement of the Framework Convention on Tobacco Control, production of robust tobacco control legislation and the establishment of universally available tobacco cessation services are essential to sustain and strengthen tobacco control in the GCC region.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kapustin, Eugene A.; Lee, Seungkyu; Alshammari, Ahmad S.

Despite numerous studies on chemical and thermal stability of metal-organic frameworks (MOFs), mechanical stability remains largely undeveloped. No strategy exists to control the mechanical deformation of MOFs under ultrahigh pressure, to date. We show that the mechanically unstable MOF-520 can be retrofitted by precise placement of a rigid 4,4'-biphenyldicarboxylate (BPDC) linker as a "girder" to afford a mechanically robust framework: MOF-520-BPDC. This retrofitting alters how the structure deforms under ultrahigh pressure and thus leads to a drastic enhancement of its mechanical robustness. While in the parent MOF-520 the pressure transmitting medium molecules diffuse into the pore and expand the structuremore » from the inside upon compression, the girder in the new retrofitted MOF-520-BPDC prevents the framework from expansion by linking two adjacent secondary building units together. As a result, the modified MOF is stable under hydrostatic compression in a diamond-anvil cell up to 5.5 gigapascal. The increased mechanical stability of MOF-520-BPDC prohibits the typical amorphization observed for MOFs in this pressure range. Direct correlation between the orientation of these girders within the framework and its linear strain was estimated, providing new insights for the design of MOFs with optimized mechanical properties.« less

Linear quadratic servo control of a reusable rocket engine

NASA Technical Reports Server (NTRS)

Musgrave, Jeffrey L.

1991-01-01

The paper deals with the development of a design method for a servo component in the frequency domain using singular values and its application to a reusable rocket engine. A general methodology used to design a class of linear multivariable controllers for intelligent control systems is presented. Focus is placed on performance and robustness characteristics, and an estimator design performed in the framework of the Kalman-filter formalism with emphasis on using a sensor set different from the commanded values is discussed. It is noted that loop transfer recovery modifies the nominal plant noise intensities in order to obtain the desired degree of robustness to uncertainty reflected at the plant input. Simulation results demonstrating the performance of the linear design on a nonlinear engine model over all power levels during mainstage operation are discussed.

Power system security enhancement through direct non-disruptive load control

NASA Astrophysics Data System (ADS)

Ramanathan, Badri Narayanan

The transition to a competitive market structure raises significant concerns regarding reliability of the power grid. A need to build tools for security assessment that produce operating limit boundaries for both static and dynamic contingencies is recognized. Besides, an increase in overall uncertainty in operating conditions makes corrective actions at times ineffective leaving the system vulnerable to instability. The tools that are in place for stability enhancement are mostly corrective and suffer from lack of robustness to operating condition changes. They often pose serious coordination challenges. With deregulation, there have also been ownership and responsibility issues associated with stability controls. However, the changing utility business model and the developments in enabling technologies such as two-way communication, metering, and control open up several new possibilities for power system security enhancement. This research proposes preventive modulation of selected loads through direct control for power system security enhancement. Two main contributions of this research are the following: development of an analysis framework and two conceptually different analysis approaches for load modulation to enhance oscillatory stability, and the development and study of algorithms for real-time modulation of thermostatic loads. The underlying analysis framework is based on the Structured Singular Value (SSV or mu) theory. Based on the above framework, two fundamentally different approaches towards analysis of the amount of load modulation for desired stability performance have been developed. Both the approaches have been tested on two different test systems: CIGRE Nordic test system and an equivalent of the Western Electric Coordinating Council test system. This research also develops algorithms for real-time modulation of thermostatic loads that use the results of the analysis. In line with some recent load management programs executed by utilities, two different algorithms based on dynamic programming are proposed for air-conditioner loads, while a decision-tree based algorithm is proposed for water-heater loads. An optimization framework has been developed employing the above algorithms. Monte Carlo simulations have been performed using this framework with the objective of studying the impact of different parameters and constraints on the effectiveness as well as the effect of control. The conclusions drawn from this research strongly advocate direct load control for stability enhancement from the perspectives of robustness and coordination, as well as economic viability and the developments towards availability of the institutional framework for load participation in providing system reliability services.

A Hierarchical Framework for Demand-Side Frequency Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moya, Christian; Zhang, Wei; Lian, Jianming

2014-06-02

With large-scale plans to integrate renewable generation, more resources will be needed to compensate for the uncertainty associated with intermittent generation resources. Under such conditions, performing frequency control using only supply-side resources become not only prohibitively expensive but also technically difficult. It is therefore important to explore how a sufficient proportion of the loads could assume a routine role in frequency control to maintain the stability of the system at an acceptable cost. In this paper, a novel hierarchical decentralized framework for frequency based load control is proposed. The framework involves two decision layers. The top decision layer determines themore » optimal droop gain required from the aggregated load response on each bus using a robust decentralized control approach. The second layer consists of a large number of devices, which switch probabilistically during contingencies so that the aggregated power change matches the desired droop amount according to the updated gains. The proposed framework is based on the classical nonlinear multi-machine power system model, and can deal with timevarying system operating conditions while respecting the physical constraints of individual devices. Realistic simulation results based on a 68-bus system are provided to demonstrate the effectiveness of the proposed strategy.« less

CAD-Based Aerodynamic Design of Complex Configurations using a Cartesian Method

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.; Pulliam, Thomas H.

2003-01-01

A modular framework for aerodynamic optimization of complex geometries is developed. By working directly with a parametric CAD system, complex-geometry models are modified nnd tessellated in an automatic fashion. The use of a component-based Cartesian method significantly reduces the demands on the CAD system, and also provides for robust and efficient flowfield analysis. The optimization is controlled using either a genetic or quasi-Newton algorithm. Parallel efficiency of the framework is maintained even when subject to limited CAD resources by dynamically re-allocating the processors of the flow solver. Overall, the resulting framework can explore designs incorporating large shape modifications and changes in topology.

Adaptive GSA-based optimal tuning of PI controlled servo systems with reduced process parametric sensitivity, robust stability and controller robustness.

PubMed

Precup, Radu-Emil; David, Radu-Codrut; Petriu, Emil M; Radac, Mircea-Bogdan; Preitl, Stefan

2014-11-01

This paper suggests a new generation of optimal PI controllers for a class of servo systems characterized by saturation and dead zone static nonlinearities and second-order models with an integral component. The objective functions are expressed as the integral of time multiplied by absolute error plus the weighted sum of the integrals of output sensitivity functions of the state sensitivity models with respect to two process parametric variations. The PI controller tuning conditions applied to a simplified linear process model involve a single design parameter specific to the extended symmetrical optimum (ESO) method which offers the desired tradeoff to several control system performance indices. An original back-calculation and tracking anti-windup scheme is proposed in order to prevent the integrator wind-up and to compensate for the dead zone nonlinearity of the process. The minimization of the objective functions is carried out in the framework of optimization problems with inequality constraints which guarantee the robust stability with respect to the process parametric variations and the controller robustness. An adaptive gravitational search algorithm (GSA) solves the optimization problems focused on the optimal tuning of the design parameter specific to the ESO method and of the anti-windup tracking gain. A tuning method for PI controllers is proposed as an efficient approach to the design of resilient control systems. The tuning method and the PI controllers are experimentally validated by the adaptive GSA-based tuning of PI controllers for the angular position control of a laboratory servo system.

Active stability augmentation of large space structures: A stochastic control problem

NASA Technical Reports Server (NTRS)

Balakrishnan, A. V.

1987-01-01

A problem in SCOLE is that of slewing an offset antenna on a long flexible beam-like truss attached to the space shuttle, with rather stringent pointing accuracy requirements. The relevant methodology aspects in robust feedback-control design for stability augmentation of the beam using on-board sensors is examined. It is framed as a stochastic control problem, boundary control of a distributed parameter system described by partial differential equations. While the framework is mathematical, the emphasis is still on an engineering solution. An abstract mathematical formulation is developed as a nonlinear wave equation in a Hilbert space. That the system is controllable is shown and a feedback control law that is robust in the sense that it does not require quantitative knowledge of system parameters is developed. The stochastic control problem that arises in instrumenting this law using appropriate sensors is treated. Using an engineering first approximation which is valid for small damping, formulas for optimal choice of the control gain are developed.

Robust control of integrated motor-transmission powertrain system over controller area network for automotive applications

NASA Astrophysics Data System (ADS)

Zhu, Xiaoyuan; Zhang, Hui; Cao, Dongpu; Fang, Zongde

2015-06-01

Integrated motor-transmission (IMT) powertrain system with directly coupled motor and gearbox is a good choice for electric commercial vehicles (e.g., pure electric buses) due to its potential in motor size reduction and energy efficiency improvement. However, the controller design for powertrain oscillation damping becomes challenging due to the elimination of damping components. On the other hand, as controller area network (CAN) is commonly adopted in modern vehicle system, the network-induced time-varying delays that caused by bandwidth limitation will further lead to powertrain vibration or even destabilize the powertrain control system. Therefore, in this paper, a robust energy-to-peak controller is proposed for the IMT powertrain system to address the oscillation damping problem and also attenuate the external disturbance. The control law adopted here is based on a multivariable PI control, which ensures the applicability and performance of the proposed controller in engineering practice. With the linearized delay uncertainties characterized by polytopic inclusions, a delay-free closed-loop augmented system is established for the IMT powertrain system under discrete-time framework. The proposed controller design problem is then converted to a static output feedback (SOF) controller design problem where the feedback control gains are obtained by solving a set of linear matrix inequalities (LMIs). The effectiveness as well as robustness of the proposed controller is demonstrated by comparing its performance against that of a conventional PI controller.

The Researching on Evaluation of Automatic Voltage Control Based on Improved Zoning Methodology

NASA Astrophysics Data System (ADS)

Xiao-jun, ZHU; Ang, FU; Guang-de, DONG; Rui-miao, WANG; De-fen, ZHU

2018-03-01

According to the present serious phenomenon of increasing size and structure of power system, hierarchically structured automatic voltage control(AVC) has been the researching spot. In the paper, the reduced control model is built and the adaptive reduced control model is researched to improve the voltage control effect. The theories of HCSD, HCVS, SKC and FCM are introduced and the effect on coordinated voltage regulation caused by different zoning methodologies is also researched. The generic framework for evaluating performance of coordinated voltage regulation is built. Finally, the IEEE-96 stsyem is used to divide the network. The 2383-bus Polish system is built to verify that the selection of a zoning methodology affects not only the coordinated voltage regulation operation, but also its robustness to erroneous data and proposes a comprehensive generic framework for evaluating its performance. The New England 39-bus network is used to verify the adaptive reduced control models’ performance.

Synchrony and entrainment properties of robust circadian oscillators

PubMed Central

Bagheri, Neda; Taylor, Stephanie R.; Meeker, Kirsten; Petzold, Linda R.; Doyle, Francis J.

2008-01-01

Systems theoretic tools (i.e. mathematical modelling, control, and feedback design) advance the understanding of robust performance in complex biological networks. We highlight phase entrainment as a key performance measure used to investigate dynamics of a single deterministic circadian oscillator for the purpose of generating insight into the behaviour of a population of (synchronized) oscillators. More specifically, the analysis of phase characteristics may facilitate the identification of appropriate coupling mechanisms for the ensemble of noisy (stochastic) circadian clocks. Phase also serves as a critical control objective to correct mismatch between the biological clock and its environment. Thus, we introduce methods of investigating synchrony and entrainment in both stochastic and deterministic frameworks, and as a property of a single oscillator or population of coupled oscillators. PMID:18426774

Robustness of movement models: can models bridge the gap between temporal scales of data sets and behavioural processes?

PubMed

Schlägel, Ulrike E; Lewis, Mark A

2016-12-01

Discrete-time random walks and their extensions are common tools for analyzing animal movement data. In these analyses, resolution of temporal discretization is a critical feature. Ideally, a model both mirrors the relevant temporal scale of the biological process of interest and matches the data sampling rate. Challenges arise when resolution of data is too coarse due to technological constraints, or when we wish to extrapolate results or compare results obtained from data with different resolutions. Drawing loosely on the concept of robustness in statistics, we propose a rigorous mathematical framework for studying movement models' robustness against changes in temporal resolution. In this framework, we define varying levels of robustness as formal model properties, focusing on random walk models with spatially-explicit component. With the new framework, we can investigate whether models can validly be applied to data across varying temporal resolutions and how we can account for these different resolutions in statistical inference results. We apply the new framework to movement-based resource selection models, demonstrating both analytical and numerical calculations, as well as a Monte Carlo simulation approach. While exact robustness is rare, the concept of approximate robustness provides a promising new direction for analyzing movement models.

Robust control of mitotic spindle orientation in the developing epidermis

PubMed Central

Poulson, Nicholas D.

2010-01-01

Progenitor cells must balance self-amplification and production of differentiated progeny during development and homeostasis. In the epidermis, progenitors divide symmetrically to increase surface area and asymmetrically to promote stratification. In this study, we show that individual epidermal cells can undergo both types of division, and therefore, the balance is provided by the sum of individual cells’ choices. In addition, we define two control points for determining a cell’s mode of division. First is the expression of the mouse Inscuteable gene, which is sufficient to drive asymmetric cell division (ACD). However, there is robust control of division orientation as excessive ACDs are prevented by a change in the localization of NuMA, an effector of spindle orientation. Finally, we show that p63, a transcriptional regulator of stratification, does not control either of these processes. These data have uncovered two important regulatory points controlling ACD in the epidermis and allow a framework for analysis of how external cues control this important choice. PMID:21098114

Using permutation tests to enhance causal inference in interrupted time series analysis.

PubMed

Linden, Ariel

2018-06-01

Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied serially over time and the intervention is expected to "interrupt" the level and/or trend of that outcome. The internal validity is strengthened considerably when the treated unit is contrasted with a comparable control group. In this paper, we introduce a robustness check based on permutation tests to further improve causal inference. We evaluate the effect of California's Proposition 99 for reducing cigarette sales by iteratively casting each nontreated state into the role of "treated," creating a comparable control group using the ITSAMATCH package in Stata, and then evaluating treatment effects using ITSA regression. If statistically significant "treatment effects" are estimated for pseudotreated states, then any significant changes in the outcome of the actual treatment unit (California) cannot be attributed to the intervention. We perform these analyses setting the cutpoint significance level to P > .40 for identifying balanced matches (the highest threshold possible for which controls could still be found for California) and use the difference in differences of trends as the treatment effect estimator. Only California attained a statistically significant treatment effect, strengthening confidence in the conclusion that Proposition 99 reduced cigarette sales. The proposed permutation testing framework provides an additional robustness check to either support or refute a treatment effect identified in for the true treated unit in ITSA. Given its value and ease of implementation, this framework should be considered as a standard robustness test in all multiple group interrupted time series analyses. © 2018 John Wiley & Sons, Ltd.

A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles.

PubMed

Wang, Ning; Sun, Jing-Chao; Er, Meng Joo; Liu, Yan-Cheng

2016-05-01

In this paper, an extreme learning control (ELC) framework using the single-hidden-layer feedforward network (SLFN) with random hidden nodes for tracking an unmanned surface vehicle suffering from unknown dynamics and external disturbances is proposed. By combining tracking errors with derivatives, an error surface and transformed states are defined to encapsulate unknown dynamics and disturbances into a lumped vector field of transformed states. The lumped nonlinearity is further identified accurately by an extreme-learning-machine-based SLFN approximator which does not require a priori system knowledge nor tuning input weights. Only output weights of the SLFN need to be updated by adaptive projection-based laws derived from the Lyapunov approach. Moreover, an error compensator is incorporated to suppress approximation residuals, and thereby contributing to the robustness and global asymptotic stability of the closed-loop ELC system. Simulation studies and comprehensive comparisons demonstrate that the ELC framework achieves high accuracy in both tracking and approximation.

Robust linear parameter-varying control of blood pressure using vasoactive drugs

NASA Astrophysics Data System (ADS)

Luspay, Tamas; Grigoriadis, Karolos

2015-10-01

Resuscitation of emergency care patients requires fast restoration of blood pressure to a target value to achieve hemodynamic stability and vital organ perfusion. A robust control design methodology is presented in this paper for regulating the blood pressure of hypotensive patients by means of the closed-loop administration of vasoactive drugs. To this end, a dynamic first-order delay model is utilised to describe the vasoactive drug response with varying parameters that represent intra-patient and inter-patient variability. The proposed framework consists of two components: first, an online model parameter estimation is carried out using a multiple-model extended Kalman-filter. Second, the estimated model parameters are used for continuously scheduling a robust linear parameter-varying (LPV) controller. The closed-loop behaviour is characterised by parameter-varying dynamic weights designed to regulate the mean arterial pressure to a target value. Experimental data of blood pressure response of anesthetised pigs to phenylephrine injection are used for validating the LPV blood pressure models. Simulation studies are provided to validate the online model estimation and the LPV blood pressure control using phenylephrine drug injection models representing patients showing sensitive, nominal and insensitive response to the drug.

Hypercrosslinked phenolic polymers with well developed mesoporous frameworks

DOE PAGES

Zhang, Jinshui; Qiao, Zhenan -An; Mahurin, Shannon Mark; ...

2015-02-12

A soft chemistry synthetic strategy based on a Friedel Crafts alkylation reaction is developed for the textural engineering of phenolic resin (PR) with a robust mesoporous framework to avoid serious framework shrinkage and maximize retention of organic functional moieties. By taking advantage of the structural benefits of molecular bridges, the resultant sample maintains a bimodal micro-mesoporous architecture with well-preserved organic functional groups, which is effective for carbon capture. Furthermore, this soft chemistry synthetic protocol can be further extended to nanotexture other aromatic-based polymers with robust frameworks.

A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI.

PubMed

Dillon, Keith; Calhoun, Vince; Wang, Yu-Ping

2017-01-30

Our goal is to identify the brain regions most relevant to mental illness using neuroimaging. State of the art machine learning methods commonly suffer from repeatability difficulties in this application, particularly when using large and heterogeneous populations for samples. We revisit both dimensionality reduction and sparse modeling, and recast them in a common optimization-based framework. This allows us to combine the benefits of both types of methods in an approach which we call unambiguous components. We use this to estimate the image component with a constrained variability, which is best correlated with the unknown disease mechanism. We apply the method to the estimation of neuroimaging biomarkers for schizophrenia, using task fMRI data from a large multi-site study. The proposed approach yields an improvement in both robustness of the estimate and classification accuracy. We find that unambiguous components incorporate roughly two thirds of the same brain regions as sparsity-based methods LASSO and elastic net, while roughly one third of the selected regions differ. Further, unambiguous components achieve superior classification accuracy in differentiating cases from controls. Unambiguous components provide a robust way to estimate important regions of imaging data. Copyright © 2016 Elsevier B.V. All rights reserved.

Verification and Tuning of an Adaptive Controller for an Unmanned Air Vehicle

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Matsutani, Megumi; Annaswamy, Anuradha M.

2010-01-01

This paper focuses on the analysis and tuning of a controller based on the Adaptive Control Technology for Safe Flight (ACTS) architecture. The ACTS architecture consists of a nominal, non-adaptive controller that provides satisfactory performance under nominal flying conditions, and an adaptive controller that provides robustness under off-nominal ones. A framework unifying control verification and gain tuning is used to make the controller s ability to satisfy the closed-loop requirements more robust to uncertainty. In this paper we tune the gains of both controllers using this approach. Some advantages and drawbacks of adaptation are identified by performing a global robustness assessment of both the adaptive controller and its non-adaptive counterpart. The analyses used to determine these characteristics are based on evaluating the degradation in closed-loop performance resulting from uncertainties having increasing levels of severity. The specific adverse conditions considered can be grouped into three categories: aerodynamic uncertainties, structural damage, and actuator failures. These failures include partial and total loss of control effectiveness, locked-in-place control surface deflections, and engine out conditions. The requirements considered are the peak structural loading, the ability of the controller to track pilot commands, the ability of the controller to keep the aircraft s state within the reliable flight envelope, and the handling/riding qualities of the aircraft. The nominal controller resulting from these tuning strategies was successfully validated using the NASA GTM Flight Test Vehicle.

Design and synthesis of polyoxometalate-framework materials from cluster precursors

NASA Astrophysics Data System (ADS)

Vilà-Nadal, Laia; Cronin, Leroy

2017-10-01

Inorganic oxide materials are used in semiconductor electronics, ion exchange, catalysis, coatings, gas sensors and as separation materials. Although their synthesis is well understood, the scope for new materials is reduced because of the stability limits imposed by high-temperature processing and top-down synthetic approaches. In this Review, we describe the derivatization of polyoxometalate (POM) clusters, which enables their assembly into a range of frameworks by use of organic or inorganic linkers. Additionally, bottom-up synthetic approaches can be used to make metal oxide framework materials, and the features of the molecular POM precursors are retained in these structures. Highly robust all-inorganic frameworks can be made using metal-ion linkers, which combine molecular synthetic control without the need for organic components. The resulting frameworks have high stability, and high catalytic, photochemical and electrochemical activity. Conceptually, these inorganic oxide materials bridge the gap between zeolites and metal-organic frameworks (MOFs) and establish a new class of all-inorganic POM frameworks that can be designed using topological and reactivity principles similar to MOFs.

Statistics based sampling for controller and estimator design

NASA Astrophysics Data System (ADS)

Tenne, Dirk

The purpose of this research is the development of statistical design tools for robust feed-forward/feedback controllers and nonlinear estimators. This dissertation is threefold and addresses the aforementioned topics nonlinear estimation, target tracking and robust control. To develop statistically robust controllers and nonlinear estimation algorithms, research has been performed to extend existing techniques, which propagate the statistics of the state, to achieve higher order accuracy. The so-called unscented transformation has been extended to capture higher order moments. Furthermore, higher order moment update algorithms based on a truncated power series have been developed. The proposed techniques are tested on various benchmark examples. Furthermore, the unscented transformation has been utilized to develop a three dimensional geometrically constrained target tracker. The proposed planar circular prediction algorithm has been developed in a local coordinate framework, which is amenable to extension of the tracking algorithm to three dimensional space. This tracker combines the predictions of a circular prediction algorithm and a constant velocity filter by utilizing the Covariance Intersection. This combined prediction can be updated with the subsequent measurement using a linear estimator. The proposed technique is illustrated on a 3D benchmark trajectory, which includes coordinated turns and straight line maneuvers. The third part of this dissertation addresses the design of controller which include knowledge of parametric uncertainties and their distributions. The parameter distributions are approximated by a finite set of points which are calculated by the unscented transformation. This set of points is used to design robust controllers which minimize a statistical performance of the plant over the domain of uncertainty consisting of a combination of the mean and variance. The proposed technique is illustrated on three benchmark problems. The first relates to the design of prefilters for a linear and nonlinear spring-mass-dashpot system and the second applies a feedback controller to a hovering helicopter. Lastly, the statistical robust controller design is devoted to a concurrent feed-forward/feedback controller structure for a high-speed low tension tape drive.

What Is Robustness?: Problem Framing Challenges for Water Systems Planning Under Change

NASA Astrophysics Data System (ADS)

Herman, J. D.; Reed, P. M.; Zeff, H. B.; Characklis, G. W.

2014-12-01

Water systems planners have long recognized the need for robust solutions capable of withstanding deviations from the conditions for which they were designed. Faced with a set of alternatives to choose from—for example, resulting from a multi-objective optimization—existing analysis frameworks offer competing definitions of robustness under change. Robustness analyses have moved from expected utility to exploratory "bottom-up" approaches in which vulnerable scenarios are identified prior to assigning likelihoods; examples include Robust Decision Making (RDM), Decision Scaling, Info-Gap, and Many-Objective Robust Decision Making (MORDM). We propose a taxonomy of robustness frameworks to compare and contrast these approaches, based on their methods of (1) alternative selection, (2) sampling of states of the world, (3) quantification of robustness measures, and (4) identification of key uncertainties using sensitivity analysis. Using model simulations from recent work in multi-objective urban water supply portfolio planning, we illustrate the decision-relevant consequences that emerge from each of these choices. Results indicate that the methodological choices in the taxonomy lead to substantially different planning alternatives, underscoring the importance of an informed definition of robustness. We conclude with a set of recommendations for problem framing: that alternatives should be searched rather than prespecified; dominant uncertainties should be discovered rather than assumed; and that a multivariate satisficing measure of robustness allows stakeholders to achieve their problem-specific performance requirements. This work highlights the importance of careful problem formulation, and provides a common vocabulary to link the robustness frameworks widely used in the field of water systems planning.

Distributed attitude synchronization of formation flying via consensus-based virtual structure

NASA Astrophysics Data System (ADS)

Cong, Bing-Long; Liu, Xiang-Dong; Chen, Zhen

2011-06-01

This paper presents a general framework for synchronized multiple spacecraft rotations via consensus-based virtual structure. In this framework, attitude control systems for formation spacecrafts and virtual structure are designed separately. Both parametric uncertainty and external disturbance are taken into account. A time-varying sliding mode control (TVSMC) algorithm is designed to improve the robustness of the actual attitude control system. As for the virtual attitude control system, a behavioral consensus algorithm is presented to accomplish the attitude maneuver of the entire formation and guarantee a consistent attitude among the local virtual structure counterparts during the attitude maneuver. A multiple virtual sub-structures (MVSSs) system is introduced to enhance current virtual structure scheme when large amounts of spacecrafts are involved in the formation. The attitude of spacecraft is represented by modified Rodrigues parameter (MRP) for its non-redundancy. Finally, a numerical simulation with three synchronization situations is employed to illustrate the effectiveness of the proposed strategy.

A vehicle health monitoring system for the Space Shuttle Reaction Control System during reentry. M.S. Thesis - Massachusetts Inst. of Technology

NASA Technical Reports Server (NTRS)

Rosello, Anthony David

1995-01-01

A general two tier framework for vehicle health monitoring of Guidance Navigation and Control (GN&C) system actuators, effectors, and propulsion devices is presented. In this context, a top level monitor that estimates jet thrust is designed for the Space Shuttle Reaction Control System (RCS) during the reentry phase of flight. Issues of importance for the use of estimation technologies in vehicle health monitoring are investigated and quantified for the Shuttle RCS demonstration application. These issues include rate of convergence, robustness to unmodeled dynamics, sensor quality, sensor data rates, and information recording objectives. Closed loop simulations indicate that a Kalman filter design is sensitive to modeling error and robust estimators may reduce this sensitivity. Jet plume interaction with the aerodynamic flowfield is shown to be a significant effect adversely impacting the ability to accurately estimate thrust.

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

DOE PAGES

Zhao, Junbo; Wang, Shaobu; Mili, Lamine; ...

2018-01-08

Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Junbo; Wang, Shaobu; Mili, Lamine

Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

Many-objective robust decision making for water allocation under climate change.

PubMed

Yan, Dan; Ludwig, Fulco; Huang, He Qing; Werners, Saskia E

2017-12-31

Water allocation is facing profound challenges due to climate change uncertainties. To identify adaptive water allocation strategies that are robust to climate change uncertainties, a model framework combining many-objective robust decision making and biophysical modeling is developed for large rivers. The framework was applied to the Pearl River basin (PRB), China where sufficient flow to the delta is required to reduce saltwater intrusion in the dry season. Before identifying and assessing robust water allocation plans for the future, the performance of ten state-of-the-art MOEAs (multi-objective evolutionary algorithms) is evaluated for the water allocation problem in the PRB. The Borg multi-objective evolutionary algorithm (Borg MOEA), which is a self-adaptive optimization algorithm, has the best performance during the historical periods. Therefore it is selected to generate new water allocation plans for the future (2079-2099). This study shows that robust decision making using carefully selected MOEAs can help limit saltwater intrusion in the Pearl River Delta. However, the framework could perform poorly due to larger than expected climate change impacts on water availability. Results also show that subjective design choices from the researchers and/or water managers could potentially affect the ability of the model framework, and cause the most robust water allocation plans to fail under future climate change. Developing robust allocation plans in a river basin suffering from increasing water shortage requires the researchers and water managers to well characterize future climate change of the study regions and vulnerabilities of their tools. Copyright © 2017 Elsevier B.V. All rights reserved.

Robustness Analysis of Integrated LPV-FDI Filters and LTI-FTC System for a Transport Aircraft

NASA Technical Reports Server (NTRS)

Khong, Thuan H.; Shin, Jong-Yeob

2007-01-01

This paper proposes an analysis framework for robustness analysis of a nonlinear dynamics system that can be represented by a polynomial linear parameter varying (PLPV) system with constant bounded uncertainty. The proposed analysis framework contains three key tools: 1) a function substitution method which can convert a nonlinear system in polynomial form into a PLPV system, 2) a matrix-based linear fractional transformation (LFT) modeling approach, which can convert a PLPV system into an LFT system with the delta block that includes key uncertainty and scheduling parameters, 3) micro-analysis, which is a well known robust analysis tool for linear systems. The proposed analysis framework is applied to evaluating the performance of the LPV-fault detection and isolation (FDI) filters of the closed-loop system of a transport aircraft in the presence of unmodeled actuator dynamics and sensor gain uncertainty. The robustness analysis results are compared with nonlinear time simulations.

Robustness and Reliability of Synergy-Based Myocontrol of a Multiple Degree of Freedom Robotic Arm.

PubMed

Lunardini, Francesca; Casellato, Claudia; d'Avella, Andrea; Sanger, Terence D; Pedrocchi, Alessandra

2016-09-01

In this study, we test the feasibility of the synergy- based approach for application in the realistic and clinically oriented framework of multi-degree of freedom (DOF) robotic control. We developed and tested online ten able-bodied subjects in a semi-supervised method to achieve simultaneous, continuous control of two DOFs of a robotic arm, using muscle synergies extracted from upper limb muscles while performing flexion-extension movements of the elbow and shoulder joints in the horizontal plane. To validate the efficacy of the synergy-based approach in extracting reliable control signals, compared to the simple muscle-pair method typically used in commercial applications, we evaluated the repeatability of the algorithm over days, the effect of the arm dynamics on the control performance, and the robustness of the control scheme to the presence of co-contraction between pairs of antagonist muscles. Results showed that, without the need for a daily calibration, all subjects were able to intuitively and easily control the synergy-based myoelectric interface in different scenarios, using both dynamic and isometric muscle contractions. The proposed control scheme was shown to be robust to co-contraction between antagonist muscles, providing better performance compared to the traditional muscle-pair approach. The current study is a first step toward user-friendly application of synergy-based myocontrol of assistive robotic devices.

A reliable algorithm for optimal control synthesis

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1992-01-01

In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jinshui; Qiao, Zhenan -An; Mahurin, Shannon Mark

A soft chemistry synthetic strategy based on a Friedel Crafts alkylation reaction is developed for the textural engineering of phenolic resin (PR) with a robust mesoporous framework to avoid serious framework shrinkage and maximize retention of organic functional moieties. By taking advantage of the structural benefits of molecular bridges, the resultant sample maintains a bimodal micro-mesoporous architecture with well-preserved organic functional groups, which is effective for carbon capture. Furthermore, this soft chemistry synthetic protocol can be further extended to nanotexture other aromatic-based polymers with robust frameworks.

An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder.

PubMed

Werling, Donna M; Brand, Harrison; An, Joon-Yong; Stone, Matthew R; Zhu, Lingxue; Glessner, Joseph T; Collins, Ryan L; Dong, Shan; Layer, Ryan M; Markenscoff-Papadimitriou, Eirene; Farrell, Andrew; Schwartz, Grace B; Wang, Harold Z; Currall, Benjamin B; Zhao, Xuefang; Dea, Jeanselle; Duhn, Clif; Erdman, Carolyn A; Gilson, Michael C; Yadav, Rachita; Handsaker, Robert E; Kashin, Seva; Klei, Lambertus; Mandell, Jeffrey D; Nowakowski, Tomasz J; Liu, Yuwen; Pochareddy, Sirisha; Smith, Louw; Walker, Michael F; Waterman, Matthew J; He, Xin; Kriegstein, Arnold R; Rubenstein, John L; Sestan, Nenad; McCarroll, Steven A; Neale, Benjamin M; Coon, Hilary; Willsey, A Jeremy; Buxbaum, Joseph D; Daly, Mark J; State, Matthew W; Quinlan, Aaron R; Marth, Gabor T; Roeder, Kathryn; Devlin, Bernie; Talkowski, Michael E; Sanders, Stephan J

2018-05-01

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

PubMed

He, Fei; Fromion, Vincent; Westerhoff, Hans V

2013-11-21

Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. This study integrates control engineering and classical MCA augmented with supply-demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the 'integral control' (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of 'integral control' should rarely be expected to lead to the 'perfect adaptation': although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the 'perfect' regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering.

NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.

PubMed

Vandewalle, Joos; De Moor, Bart L.R.; Suykens, Johan A.K.

1997-06-01

In this paper a framework for model-based neural control design is presented, consisting of nonlinear state space models and controllers, parametrized by multilayer feedforward neural networks. The models and closed-loop systems are transformed into so-called NL(q) system form. NL(q) systems represent a large class of nonlinear dynamical systems consisting of q layers with alternating linear and static nonlinear operators that satisfy a sector condition. For such NL(q)s sufficient conditions for global asymptotic stability, input/output stability (dissipativity with finite L(2)-gain) and robust stability and performance are presented. The stability criteria are expressed as linear matrix inequalities. In the analysis problem it is shown how stability of a given controller can be checked. In the synthesis problem two methods for neural control design are discussed. In the first method Narendra's dynamic backpropagation for tracking on a set of specific reference inputs is modified with an NL(q) stability constraint in order to ensure, e.g., closed-loop stability. In a second method control design is done without tracking on specific reference inputs, but based on the input/output stability criteria itself, within a standard plant framework as this is done, for example, in H( infinity ) control theory and &mgr; theory. Copyright 1997 Elsevier Science Ltd.

Adaptive nonlinear robust relative pose control of spacecraft autonomous rendezvous and proximity operations.

PubMed

Sun, Liang; Huo, Wei; Jiao, Zongxia

2017-03-01

This paper studies relative pose control for a rigid spacecraft with parametric uncertainties approaching to an unknown tumbling target in disturbed space environment. State feedback controllers for relative translation and relative rotation are designed in an adaptive nonlinear robust control framework. The element-wise and norm-wise adaptive laws are utilized to compensate the parametric uncertainties of chaser and target spacecraft, respectively. External disturbances acting on two spacecraft are treated as a lumped and bounded perturbation input for system. To achieve the prescribed disturbance attenuation performance index, feedback gains of controllers are designed by solving linear matrix inequality problems so that lumped disturbance attenuation with respect to the controlled output is ensured in the L 2 -gain sense. Moreover, in the absence of lumped disturbance input, asymptotical convergence of relative pose are proved by using the Lyapunov method. Numerical simulations are performed to show that position tracking and attitude synchronization are accomplished in spite of the presence of couplings and uncertainties. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

How Robust is Your System Resilience?

NASA Astrophysics Data System (ADS)

Homayounfar, M.; Muneepeerakul, R.

2017-12-01

Robustness and resilience are concepts in system thinking that have grown in importance and popularity. For many complex social-ecological systems, however, robustness and resilience are difficult to quantify and the connections and trade-offs between them difficult to study. Most studies have either focused on qualitative approaches to discuss their connections or considered only one of them under particular classes of disturbances. In this study, we present an analytical framework to address the linkage between robustness and resilience more systematically. Our analysis is based on a stylized dynamical model that operationalizes a widely used concept framework for social-ecological systems. The model enables us to rigorously define robustness and resilience and consequently investigate their connections. The results reveal the tradeoffs among performance, robustness, and resilience. They also show how the nature of the such tradeoffs varies with the choices of certain policies (e.g., taxation and investment in public infrastructure), internal stresses and external disturbances.

Robust algebraic image enhancement for intelligent control systems

NASA Technical Reports Server (NTRS)

Lerner, Bao-Ting; Morrelli, Michael

1993-01-01

Robust vision capability for intelligent control systems has been an elusive goal in image processing. The computationally intensive techniques a necessary for conventional image processing make real-time applications, such as object tracking and collision avoidance difficult. In order to endow an intelligent control system with the needed vision robustness, an adequate image enhancement subsystem capable of compensating for the wide variety of real-world degradations, must exist between the image capturing and the object recognition subsystems. This enhancement stage must be adaptive and must operate with consistency in the presence of both statistical and shape-based noise. To deal with this problem, we have developed an innovative algebraic approach which provides a sound mathematical framework for image representation and manipulation. Our image model provides a natural platform from which to pursue dynamic scene analysis, and its incorporation into a vision system would serve as the front-end to an intelligent control system. We have developed a unique polynomial representation of gray level imagery and applied this representation to develop polynomial operators on complex gray level scenes. This approach is highly advantageous since polynomials can be manipulated very easily, and are readily understood, thus providing a very convenient environment for image processing. Our model presents a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets.

Covalent Organic Frameworks as a Platform for Multidimensional Polymerization.

PubMed

Bisbey, Ryan P; Dichtel, William R

2017-06-28

The simultaneous polymerization and crystallization of monomers featuring directional bonding designs provides covalent organic frameworks (COFs), which are periodic polymer networks with robust covalent bonds arranged in two- or three-dimensional topologies. The range of properties characterized in COFs has rapidly expanded to include those of interest for heterogeneous catalysis, energy storage and photovoltaic devices, and proton-conducting membranes. Yet many of these applications will require materials quality, morphological control, and synthetic efficiency exceeding the capabilities of contemporary synthetic methods. This level of control will emerge from an improved fundamental understanding of COF nucleation and growth processes. More powerful characterization of structure and defects, improved syntheses guided by mechanistic understanding, and accessing diverse isolated forms, ranging from single crystals to thin films to colloidal suspensions, remain important frontier problems.

Chemical Engineering of Photoactivity in Heterometallic Titanium-Organic Frameworks by Metal Doping.

PubMed

Castells-Gil, Javier; Padial, Natalia M; Almora-Barrios, Neyvis; Albero, Josep; Ruiz-Salvador, A Rabdel; González-Platas, Javier; García, Hermenegildo; Martí-Gastaldo, Carlos

2018-06-06

We report a new family of titanium-organic frameworks that enlarges the limited number of crystalline, porous materials available for this metal. They are chemically robust and can be prepared as single crystals at multi-gram scale from multiple precursors. Their heterometallic structure enables engineering of their photoactivity by metal doping rather than by linker functionalization. Compared to other methodologies based on the post-synthetic metallation of MOFs, our approach is well-fitted for controlling the positioning of dopants at an atomic level to gain more precise control over the band-gap and electronic properties of the porous solid. Changes in the band-gap are also rationalized with computational modelling and experimentally confirmed by photocatalytic H 2 production. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Covalent Organic Frameworks as a Platform for Multidimensional Polymerization

PubMed Central

2017-01-01

The simultaneous polymerization and crystallization of monomers featuring directional bonding designs provides covalent organic frameworks (COFs), which are periodic polymer networks with robust covalent bonds arranged in two- or three-dimensional topologies. The range of properties characterized in COFs has rapidly expanded to include those of interest for heterogeneous catalysis, energy storage and photovoltaic devices, and proton-conducting membranes. Yet many of these applications will require materials quality, morphological control, and synthetic efficiency exceeding the capabilities of contemporary synthetic methods. This level of control will emerge from an improved fundamental understanding of COF nucleation and growth processes. More powerful characterization of structure and defects, improved syntheses guided by mechanistic understanding, and accessing diverse isolated forms, ranging from single crystals to thin films to colloidal suspensions, remain important frontier problems. PMID:28691064

(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

PubMed Central

2013-01-01

Background Metabolic control analysis (MCA) and supply–demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply–demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. Results This study integrates control engineering and classical MCA augmented with supply–demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the ‘integral control’ (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of ‘integral control’ should rarely be expected to lead to the ‘perfect adaptation’: although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. Conclusions A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the ‘perfect’ regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering. PMID:24261908

Programmable multi-node quantum network design and simulation

NASA Astrophysics Data System (ADS)

Dasari, Venkat R.; Sadlier, Ronald J.; Prout, Ryan; Williams, Brian P.; Humble, Travis S.

2016-05-01

Software-defined networking offers a device-agnostic programmable framework to encode new network functions. Externally centralized control plane intelligence allows programmers to write network applications and to build functional network designs. OpenFlow is a key protocol widely adopted to build programmable networks because of its programmability, flexibility and ability to interconnect heterogeneous network devices. We simulate the functional topology of a multi-node quantum network that uses programmable network principles to manage quantum metadata for protocols such as teleportation, superdense coding, and quantum key distribution. We first show how the OpenFlow protocol can manage the quantum metadata needed to control the quantum channel. We then use numerical simulation to demonstrate robust programmability of a quantum switch via the OpenFlow network controller while executing an application of superdense coding. We describe the software framework implemented to carry out these simulations and we discuss near-term efforts to realize these applications.

Control volume based hydrocephalus research; analysis of human data

NASA Astrophysics Data System (ADS)

Cohen, Benjamin; Wei, Timothy; Voorhees, Abram; Madsen, Joseph; Anor, Tomer

2010-11-01

Hydrocephalus is a neuropathophysiological disorder primarily diagnosed by increased cerebrospinal fluid volume and pressure within the brain. To date, utilization of clinical measurements have been limited to understanding of the relative amplitude and timing of flow, volume and pressure waveforms; qualitative approaches without a clear framework for meaningful quantitative comparison. Pressure volume models and electric circuit analogs enforce volume conservation principles in terms of pressure. Control volume analysis, through the integral mass and momentum conservation equations, ensures that pressure and volume are accounted for using first principles fluid physics. This approach is able to directly incorporate the diverse measurements obtained by clinicians into a simple, direct and robust mechanics based framework. Clinical data obtained for analysis are discussed along with data processing techniques used to extract terms in the conservation equation. Control volume analysis provides a non-invasive, physics-based approach to extracting pressure information from magnetic resonance velocity data that cannot be measured directly by pressure instrumentation.

Quantitative impurity analysis of monoclonal antibody size heterogeneity by CE-LIF: example of development and validation through a quality-by-design framework.

PubMed

Michels, David A; Parker, Monica; Salas-Solano, Oscar

2012-03-01

This paper describes the framework of quality by design applied to the development, optimization and validation of a sensitive capillary electrophoresis-sodium dodecyl sulfate (CE-SDS) assay for monitoring impurities that potentially impact drug efficacy or patient safety produced in the manufacture of therapeutic MAb products. Drug substance or drug product samples are derivatized with fluorogenic 3-(2-furoyl)quinoline-2-carboxaldehyde and nucleophilic cyanide before separation by CE-SDS coupled to LIF detection. Three design-of-experiments enabled critical labeling parameters to meet method requirements for detecting minor impurities while building precision and robustness into the assay during development. The screening design predicted optimal conditions to control labeling artifacts while two full factorial designs demonstrated method robustness through control of temperature and cyanide parameters within the normal operating range. Subsequent validation according to the guidelines of the International Committee of Harmonization showed the CE-SDS/LIF assay was specific, accurate, and precise (RSD ≤ 0.8%) for relative peak distribution and linear (R > 0.997) between the range of 0.5-1.5 mg/mL with LOD and LOQ of 10 ng/mL and 35 ng/mL, respectively. Validation confirmed the system suitability criteria used as a level of control to ensure reliable method performance. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Design of Distributed Engine Control Systems with Uncertain Delay.

PubMed

Liu, Xiaofeng; Li, Yanxi; Sun, Xu

Future gas turbine engine control systems will be based on distributed architecture, in which, the sensors and actuators will be connected to the controllers via a communication network. The performance of the distributed engine control (DEC) is dependent on the network performance. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS). Typical turboshaft engine-distributed controllers are designed based on the NCCS framework with a H∞ output feedback under network-induced time delays and uncertain disturbances. The sufficient conditions for robust stability are derived via the Lyapunov stability theory and linear matrix inequality approach. Both numerical and hardware-in-loop simulations illustrate the effectiveness of the presented method.

Design of Distributed Engine Control Systems with Uncertain Delay

PubMed Central

Li, Yanxi; Sun, Xu

2016-01-01

Future gas turbine engine control systems will be based on distributed architecture, in which, the sensors and actuators will be connected to the controllers via a communication network. The performance of the distributed engine control (DEC) is dependent on the network performance. This study introduces a distributed control system architecture based on a networked cascade control system (NCCS). Typical turboshaft engine-distributed controllers are designed based on the NCCS framework with a H∞ output feedback under network-induced time delays and uncertain disturbances. The sufficient conditions for robust stability are derived via the Lyapunov stability theory and linear matrix inequality approach. Both numerical and hardware-in-loop simulations illustrate the effectiveness of the presented method. PMID:27669005

A metamorphic inorganic framework that can be switched between eight single-crystalline states

NASA Astrophysics Data System (ADS)

Zhan, Caihong; Cameron, Jamie M.; Gabb, David; Boyd, Thomas; Winter, Ross S.; Vilà-Nadal, Laia; Mitchell, Scott G.; Glatzel, Stefan; Breternitz, Joachim; Gregory, Duncan H.; Long, De-Liang; MacDonell, Andrew; Cronin, Leroy

2017-02-01

The design of highly flexible framework materials requires organic linkers, whereas inorganic materials are more robust but inflexible. Here, by using linkable inorganic rings made up of tungsten oxide (P8W48O184) building blocks, we synthesized an inorganic single crystal material that can undergo at least eight different crystal-to-crystal transformations, with gigantic crystal volume contraction and expansion changes ranging from -2,170 to +1,720 Å3 with no reduction in crystallinity. Not only does this material undergo the largest single crystal-to-single crystal volume transformation thus far reported (to the best of our knowledge), the system also shows conformational flexibility while maintaining robustness over several cycles in the reversible uptake and release of guest molecules switching the crystal between different metamorphic states. This material combines the robustness of inorganic materials with the flexibility of organic frameworks, thereby challenging the notion that flexible materials with robustness are mutually exclusive.

Robust, Efficient Depth Reconstruction With Hierarchical Confidence-Based Matching.

PubMed

Sun, Li; Chen, Ke; Song, Mingli; Tao, Dacheng; Chen, Gang; Chen, Chun

2017-07-01

In recent years, taking photos and capturing videos with mobile devices have become increasingly popular. Emerging applications based on the depth reconstruction technique have been developed, such as Google lens blur. However, depth reconstruction is difficult due to occlusions, non-diffuse surfaces, repetitive patterns, and textureless surfaces, and it has become more difficult due to the unstable image quality and uncontrolled scene condition in the mobile setting. In this paper, we present a novel hierarchical framework with multi-view confidence-based matching for robust, efficient depth reconstruction in uncontrolled scenes. Particularly, the proposed framework combines local cost aggregation with global cost optimization in a complementary manner that increases efficiency and accuracy. A depth map is efficiently obtained in a coarse-to-fine manner by using an image pyramid. Moreover, confidence maps are computed to robustly fuse multi-view matching cues, and to constrain the stereo matching on a finer scale. The proposed framework has been evaluated with challenging indoor and outdoor scenes, and has achieved robust and efficient depth reconstruction.

Robust PBPK/PD-Based Model Predictive Control of Blood Glucose.

PubMed

Schaller, Stephan; Lippert, Jorg; Schaupp, Lukas; Pieber, Thomas R; Schuppert, Andreas; Eissing, Thomas

2016-07-01

Automated glucose control (AGC) has not yet reached the point where it can be applied clinically [3]. Challenges are accuracy of subcutaneous (SC) glucose sensors, physiological lag times, and both inter- and intraindividual variability. To address above issues, we developed a novel scheme for MPC that can be applied to AGC. An individualizable generic whole-body physiology-based pharmacokinetic and dynamics (PBPK/PD) model of the glucose, insulin, and glucagon metabolism has been used as the predictive kernel. The high level of mechanistic detail represented by the model takes full advantage of the potential of MPC and may make long-term prediction possible as it captures at least some relevant sources of variability [4]. Robustness against uncertainties was increased by a control cascade relying on proportional-integrative derivative-based offset control. The performance of this AGC scheme was evaluated in silico and retrospectively using data from clinical trials. This analysis revealed that our approach handles sensor noise with a MARD of 10%-14%, and model uncertainties and disturbances. The results suggest that PBPK/PD models are well suited for MPC in a glucose control setting, and that their predictive power in combination with the integrated database-driven (a priori individualizable) model framework will help overcome current challenges in the development of AGC systems. This study provides a new, generic, and robust mechanistic approach to AGC using a PBPK platform with extensive a priori (database) knowledge for individualization.

Application of a disturbance-rejection controller for robotic-enhanced limb rehabilitation trainings.

PubMed

Madoński, R; Kordasz, M; Sauer, P

2014-07-01

The paper presents an application of a special case of an Active Disturbance Rejection Controller (ADRC) in governing a proper realization of basic limb rehabilitation trainings. The experimental study is performed on a model of a flexible joint manipulator, whose behavior resembles a real robotic rehabilitation device. The multidimensional character of the considered assisting mechanism makes it a nontrivial modeling and control problem. However, by the use of the ADRC approach, the modeling uncertainty in the plant is partially decoupled from the system, which increases the robustness of the whole control framework against both internal and external disturbances. © 2013 ISA. Published by ISA. All rights reserved.

A Public Health Grid (PHGrid): Architecture and value proposition for 21st century public health.

PubMed

Savel, T; Hall, K; Lee, B; McMullin, V; Miles, M; Stinn, J; White, P; Washington, D; Boyd, T; Lenert, L

2010-07-01

This manuscript describes the value of and proposal for a high-level architectural framework for a Public Health Grid (PHGrid), which the authors feel has the capability to afford the public health community a robust technology infrastructure for secure and timely data, information, and knowledge exchange, not only within the public health domain, but between public health and the overall health care system. The CDC facilitated multiple Proof-of-Concept (PoC) projects, leveraging an open-source-based software development methodology, to test four hypotheses with regard to this high-level framework. The outcomes of the four PoCs in combination with the use of the Federal Enterprise Architecture Framework (FEAF) and the newly emerging Federal Segment Architecture Methodology (FSAM) was used to develop and refine a high-level architectural framework for a Public Health Grid infrastructure. The authors were successful in documenting a robust high-level architectural framework for a PHGrid. The documentation generated provided a level of granularity needed to validate the proposal, and included examples of both information standards and services to be implemented. Both the results of the PoCs as well as feedback from selected public health partners were used to develop the granular documentation. A robust high-level cohesive architectural framework for a Public Health Grid (PHGrid) has been successfully articulated, with its feasibility demonstrated via multiple PoCs. In order to successfully implement this framework for a Public Health Grid, the authors recommend moving forward with a three-pronged approach focusing on interoperability and standards, streamlining the PHGrid infrastructure, and developing robust and high-impact public health services. Published by Elsevier Ireland Ltd.

Transition metal complexes supported on metal-organic frameworks for heterogeneous catalysts

DOEpatents

Farha, Omar K.; Hupp, Joseph T.; Delferro, Massimiliano; Klet, Rachel C.

2017-02-07

A robust mesoporous metal-organic framework comprising a hafnium-based metal-organic framework and a single-site zirconium-benzyl species is provided. The hafnium, zirconium-benzyl metal-organic framework is useful as a catalyst for the polymerization of an alkene.

Compromise-based Robust Prioritization of Climate Change Adaptation Strategies for Watershed Management

NASA Astrophysics Data System (ADS)

Kim, Y.; Chung, E. S.

2014-12-01

This study suggests a robust prioritization framework for climate change adaptation strategies under multiple climate change scenarios with a case study of selecting sites for reusing treated wastewater (TWW) in a Korean urban watershed. The framework utilizes various multi-criteria decision making techniques, including the VIKOR method and the Shannon entropy-based weights. In this case study, the sustainability of TWW use is quantified with indicator-based approaches with the DPSIR framework, which considers both hydro-environmental and socio-economic aspects of the watershed management. Under the various climate change scenarios, the hydro-environmental responses to reusing TWW in potential alternative sub-watersheds are determined using the Hydrologic Simulation Program in Fortran (HSPF). The socio-economic indicators are obtained from the statistical databases. Sustainability scores for multiple scenarios are estimated individually and then integrated with the proposed approach. At last, the suggested framework allows us to prioritize adaptation strategies in a robust manner with varying levels of compromise between utility-based and regret-based strategies.

Robust detection of multiple sclerosis lesions from intensity-normalized multi-channel MRI

NASA Astrophysics Data System (ADS)

Karpate, Yogesh; Commowick, Olivier; Barillot, Christian

2015-03-01

Multiple sclerosis (MS) is a disease with heterogeneous evolution among the patients. Quantitative analysis of longitudinal Magnetic Resonance Images (MRI) provides a spatial analysis of the brain tissues which may lead to the discovery of biomarkers of disease evolution. Better understanding of the disease will lead to a better discovery of pathogenic mechanisms, allowing for patient-adapted therapeutic strategies. To characterize MS lesions, we propose a novel paradigm to detect white matter lesions based on a statistical framework. It aims at studying the benefits of using multi-channel MRI to detect statistically significant differences between each individual MS patient and a database of control subjects. This framework consists in two components. First, intensity standardization is conducted to minimize the inter-subject intensity difference arising from variability of the acquisition process and different scanners. The intensity normalization maps parameters obtained using a robust Gaussian Mixture Model (GMM) estimation not affected by the presence of MS lesions. The second part studies the comparison of multi-channel MRI of MS patients with respect to an atlas built from the control subjects, thereby allowing us to look for differences in normal appearing white matter, in and around the lesions of each patient. Experimental results demonstrate that our technique accurately detects significant differences in lesions consequently improving the results of MS lesion detection.

Persistent model order reduction for complex dynamical systems using smooth orthogonal decomposition

NASA Astrophysics Data System (ADS)

Ilbeigi, Shahab; Chelidze, David

2017-11-01

Full-scale complex dynamic models are not effective for parametric studies due to the inherent constraints on available computational power and storage resources. A persistent reduced order model (ROM) that is robust, stable, and provides high-fidelity simulations for a relatively wide range of parameters and operating conditions can provide a solution to this problem. The fidelity of a new framework for persistent model order reduction of large and complex dynamical systems is investigated. The framework is validated using several numerical examples including a large linear system and two complex nonlinear systems with material and geometrical nonlinearities. While the framework is used for identifying the robust subspaces obtained from both proper and smooth orthogonal decompositions (POD and SOD, respectively), the results show that SOD outperforms POD in terms of stability, accuracy, and robustness.

Closed-loop neuromodulation of spinal sensorimotor circuits controls refined locomotion after complete spinal cord injury.

PubMed

Wenger, Nikolaus; Moraud, Eduardo Martin; Raspopovic, Stanisa; Bonizzato, Marco; DiGiovanna, Jack; Musienko, Pavel; Morari, Manfred; Micera, Silvestro; Courtine, Grégoire

2014-09-24

Neuromodulation of spinal sensorimotor circuits improves motor control in animal models and humans with spinal cord injury. With common neuromodulation devices, electrical stimulation parameters are tuned manually and remain constant during movement. We developed a mechanistic framework to optimize neuromodulation in real time to achieve high-fidelity control of leg kinematics during locomotion in rats. We first uncovered relationships between neuromodulation parameters and recruitment of distinct sensorimotor circuits, resulting in predictive adjustments of leg kinematics. Second, we established a technological platform with embedded control policies that integrated robust movement feedback and feed-forward control loops in real time. These developments allowed us to conceive a neuroprosthetic system that controlled a broad range of foot trajectories during continuous locomotion in paralyzed rats. Animals with complete spinal cord injury performed more than 1000 successive steps without failure, and were able to climb staircases of various heights and lengths with precision and fluidity. Beyond therapeutic potential, these findings provide a conceptual and technical framework to personalize neuromodulation treatments for other neurological disorders. Copyright © 2014, American Association for the Advancement of Science.

Camera Control and Geo-Registration for Video Sensor Networks

NASA Astrophysics Data System (ADS)

Davis, James W.

With the use of large video networks, there is a need to coordinate and interpret the video imagery for decision support systems with the goal of reducing the cognitive and perceptual overload of human operators. We present computer vision strategies that enable efficient control and management of cameras to effectively monitor wide-coverage areas, and examine the framework within an actual multi-camera outdoor urban video surveillance network. First, we construct a robust and precise camera control model for commercial pan-tilt-zoom (PTZ) video cameras. In addition to providing a complete functional control mapping for PTZ repositioning, the model can be used to generate wide-view spherical panoramic viewspaces for the cameras. Using the individual camera control models, we next individually map the spherical panoramic viewspace of each camera to a large aerial orthophotograph of the scene. The result provides a unified geo-referenced map representation to permit automatic (and manual) video control and exploitation of cameras in a coordinated manner. The combined framework provides new capabilities for video sensor networks that are of significance and benefit to the broad surveillance/security community.

Connecting Core Percolation and Controllability of Complex Networks

PubMed Central

Jia, Tao; Pósfai, Márton

2014-01-01

Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks. PMID:24946797

High precision tracking of a piezoelectric nano-manipulator with parameterized hysteresis compensation

NASA Astrophysics Data System (ADS)

Yan, Peng; Zhang, Yangming

2018-06-01

High performance scanning of nano-manipulators is widely deployed in various precision engineering applications such as SPM (scanning probe microscope), where trajectory tracking of sophisticated reference signals is an challenging control problem. The situation is further complicated when rate dependent hysteresis of the piezoelectric actuators and the stress-stiffening induced nonlinear stiffness of the flexure mechanism are considered. In this paper, a novel control framework is proposed to achieve high precision tracking of a piezoelectric nano-manipulator subjected to hysteresis and stiffness nonlinearities. An adaptive parameterized rate-dependent Prandtl-Ishlinskii model is constructed and the corresponding adaptive inverse model based online compensation is derived. Meanwhile a robust adaptive control architecture is further introduced to improve the tracking accuracy and robustness of the compensated system, where the parametric uncertainties of the nonlinear dynamics can be well eliminated by on-line estimations. Comparative experimental studies of the proposed control algorithm are conducted on a PZT actuated nano-manipulating stage, where hysteresis modeling accuracy and excellent tracking performance are demonstrated in real-time implementations, with significant improvement over existing results.

Monitoring the solid-state electrochemistry of Cu(2,7-AQDC) (AQDC = anthraquinone dicarboxylate) in a lithium battery: coexistence of metal and ligand redox activities in a metal-organic framework.

PubMed

Zhang, Zhongyue; Yoshikawa, Hirofumi; Awaga, Kunio

2014-11-19

By adopting a facile synthetic strategy, we obtained a microporous redox-active metal-organic framework (MOF), namely, Cu(2,7-AQDC) (2,7-H2AQDC = 2,7-anthraquinonedicarboxylic acid) (1), and utilized it as a cathode active material in lithium batteries. With a voltage window of 4.0-1.7 V, both metal clusters and anthraquinone groups in the ligands exhibited reversible redox activity. The valence change of copper cations was clearly evidenced by in situ XANES analysis. By controlling the voltage window of operation, extremely high recyclability of batteries was achieved, suggesting the framework was robust. This MOF is the first example of a porous material showing independent redox activity on both metal cluster nodes and ligand sites.

Unified framework for automated iris segmentation using distantly acquired face images.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2012-09-01

Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.

Cooperative fault-tolerant distributed computing U.S. Department of Energy Grant DE-FG02-02ER25537 Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sunderam, Vaidy S.

2007-01-09

The Harness project has developed novel software frameworks for the execution of high-end simulations in a fault-tolerant manner on distributed resources. The H2O subsystem comprises the kernel of the Harness framework, and controls the key functions of resource management across multiple administrative domains, especially issues of access and allocation. It is based on a “pluggable” architecture that enables the aggregated use of distributed heterogeneous resources for high performance computing. The major contributions of the Harness II project result in significantly enhancing the overall computational productivity of high-end scientific applications by enabling robust, failure-resilient computations on cooperatively pooled resource collections.

Hypersonic vehicle control law development using H(infinity) and micron-synthesis

NASA Technical Reports Server (NTRS)

Gregory, Irene M.; Mcminn, John D.; Shaughnessy, John D.; Chowdhry, Rajiv S.

1993-01-01

Hypersonic vehicle control law development using H(infinity) and mu-synthesis is discussed. Airbreathing SSTO vehicles has a mutli-faceted mission that includes orbital operations, as well as re-entry and descent culminating in horizontal landing. However, the most challenging part of the operations is the ascent to orbit. The airbreathing propulsion requires lengthy atmospheric flight that may last as long as 30 minutes and take the vehicle half way around the globe. The vehicles's ascent is characterized by tight payload to orbit margins which translate into minimum fuel orbit as the performance criteria. Issues discussed include: SSTO airbreathing vehicle issues; control system performance requirements; robust control law framework; H(infinity) controller frequency analysis; and mu controller frequency analysis.

Robust Decision-making Applied to Model Selection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hemez, Francois M.

2012-08-06

The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define eachmore » of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.« less

Precise control of molecular dynamics with a femtosecond frequency comb.

PubMed

Pe'er, Avi; Shapiro, Evgeny A; Stowe, Matthew C; Shapiro, Moshe; Ye, Jun

2007-03-16

We present a general and highly efficient scheme for performing narrow-band Raman transitions between molecular vibrational levels using a coherent train of weak pump-dump pairs of shaped ultrashort pulses. The use of weak pulses permits an analytic description within the framework of coherent control in the perturbative regime, while coherent accumulation of many pulse pairs enables near unity transfer efficiency with a high spectral selectivity, thus forming a powerful combination of pump-dump control schemes and the precision of the frequency comb. Simulations verify the feasibility and robustness of this concept, with the aim to form deeply bound, ultracold molecules.

Multidisciplinary Design Optimization of A Highly Flexible Aeroservoelastic Wing

NASA Astrophysics Data System (ADS)

Haghighat, Sohrab

A multidisciplinary design optimization framework is developed that integrates control system design with aerostructural design for a highly-deformable wing. The objective of this framework is to surpass the existing aircraft endurance limits through the use of an active load alleviation system designed concurrently with the rest of the aircraft. The novelty of this work is two fold. First, a unified dynamics framework is developed to represent the full six-degree-of-freedom rigid-body along with the structural dynamics. It allows for an integrated control design to account for both manoeuvrability (flying quality) and aeroelasticity criteria simultaneously. Secondly, by synthesizing the aircraft control system along with the structural sizing and aerodynamic shape design, the final design has the potential to exploit synergies among the three disciplines and yield higher performing aircraft. A co-rotational structural framework featuring Euler--Bernoulli beam elements is developed to capture the wing's nonlinear deformations under the effect of aerodynamic and inertial loadings. In this work, a three-dimensional aerodynamic panel code, capable of calculating both steady and unsteady loadings is used. Two different control methods, a model predictive controller (MPC) and a 2-DOF mixed-norm robust controller, are considered in this work to control a highly flexible aircraft. Both control techniques offer unique advantages that make them promising for controlling a highly flexible aircraft. The control system works towards executing time-dependent manoeuvres along with performing gust/manoeuvre load alleviation. The developed framework is investigated for demonstration in two design cases: one in which the control system simply worked towards achieving or maintaining a target altitude, and another where the control system is also performing load alleviation. The use of the active load alleviation system results in a significant improvement in the aircraft performance relative to the optimum result without load alleviation. The results show that the inclusion of control system discipline along with other disciplines at early stages of aircraft design improves aircraft performance. It is also shown that structural stresses due to gust excitations can be better controlled by the use of active structural control systems which can improve the fatigue life of the structure.

Behavioral and Neurodevelopmental Precursors to Binge-Type Eating Disorders: Support for the Role of Negative Valence Systems

PubMed Central

Vannucci, Anna; Nelson, Eric E.; Bongiorno, Diana M.; Pine, Daniel S.; Yanovski, Jack A.; Tanofsky-Kraff, Marian

2015-01-01

Background Pediatric loss-of-control eating is a robust behavioral precursor to binge-type eating disorders. Elucidating precursors to loss-of-control eating and binge-type eating disorders may refine developmental risk models of eating disorders and inform interventions. Method We review evidence within constructs of the Negative Valence Systems (NVS)-domain, as specified by the Research Domain Criteria framework. Based on published studies, we propose an integrated NVS model of binge-type eating disorder risk. Results Data implicate altered corticolimbic functioning, neuroendocrine dysregulation, and self-reported negative affect as possible risk-factors. However, neuroimaging and physiological data in children and adolescents are sparse, and most prospective studies are limited to self-report measures. Conclusions We discuss a broad NVS framework for conceptualizing early risk for binge-type eating disorders. Future neural and behavioral research on the developmental trajectory of loss-of-control and binge-type eating disorders is required. PMID:26040923

A modular robust control framework for control of movement elicited by multi-electrode intraspinal microstimulation

NASA Astrophysics Data System (ADS)

Roshani, Amir; Erfanian, Abbas

2016-08-01

Objective. An important issue in restoring motor function through intraspinal microstimulation (ISMS) is the motor control. To provide a physiologically plausible motor control using ISMS, it should be able to control the individual motor unit which is the lowest functional unit of motor control. By focal stimulation only a small group of motor neurons (MNs) within a motor pool can be activated. Different groups of MNs within a motor pool can potentially be activated without involving adjacent motor pools by local stimulation of different parts of a motor pool via microelectrode array implanted into a motor pool. However, since the system has multiple inputs with single output during multi-electrode ISMS, it poses a challenge to movement control. In this paper, we proposed a modular robust control strategy for movement control, whereas multi-electrode array is implanted into each motor activation pool of a muscle. Approach. The controller was based on the combination of proportional-integral-derivative and adaptive fuzzy sliding mode control. The global stability of the controller was guaranteed. Main results. The results of the experiments on rat models showed that the multi-electrode control can provide a more robust control and accurate tracking performance than a single-electrode control. The control output can be pulse amplitude (pulse amplitude modulation, PAM) or pulse width (pulse width modulation, PWM) of the stimulation signal. The results demonstrated that the controller with PAM provided faster convergence rate and better tracking performance than the controller with PWM. Significance. This work represents a promising control approach to the restoring motor functions using ISMS. The proposed controller requires no prior knowledge about the dynamics of the system to be controlled and no offline learning phase. The proposed control design is modular in the sense that each motor pool has an independent controller and each controller is able to control ISMS through an array of microelectrodes.

Heat-Passing Framework for Robust Interpretation of Data in Networks

PubMed Central

Fang, Yi; Sun, Mengtian; Ramani, Karthik

2015-01-01

Researchers are regularly interested in interpreting the multipartite structure of data entities according to their functional relationships. Data is often heterogeneous with intricately hidden inner structure. With limited prior knowledge, researchers are likely to confront the problem of transforming this data into knowledge. We develop a new framework, called heat-passing, which exploits intrinsic similarity relationships within noisy and incomplete raw data, and constructs a meaningful map of the data. The proposed framework is able to rank, cluster, and visualize the data all at once. The novelty of this framework is derived from an analogy between the process of data interpretation and that of heat transfer, in which all data points contribute simultaneously and globally to reveal intrinsic similarities between regions of data, meaningful coordinates for embedding the data, and exemplar data points that lie at optimal positions for heat transfer. We demonstrate the effectiveness of the heat-passing framework for robustly partitioning the complex networks, analyzing the globin family of proteins and determining conformational states of macromolecules in the presence of high levels of noise. The results indicate that the methodology is able to reveal functionally consistent relationships in a robust fashion with no reference to prior knowledge. The heat-passing framework is very general and has the potential for applications to a broad range of research fields, for example, biological networks, social networks and semantic analysis of documents. PMID:25668316

Cardea: Providing Support for Dynamic Resource Access in a Distributed Computing Environment

NASA Technical Reports Server (NTRS)

Lepro, Rebekah

2003-01-01

The environment framing the modem authorization process span domains of administration, relies on many different authentication sources, and manages complex attributes as part of the authorization process. Cardea facilitates dynamic access control within this environment as a central function of an inter-operable authorization framework. The system departs from the traditional authorization model by separating the authentication and authorization processes, distributing the responsibility for authorization data and allowing collaborating domains to retain control over their implementation mechanisms. Critical features of the system architecture and its handling of the authorization process differentiate the system from existing authorization components by addressing common needs not adequately addressed by existing systems. Continuing system research seeks to enhance the implementation of the current authorization model employed in Cardea, increase the robustness of current features, further the framework for establishing trust and promote interoperability with existing security mechanisms.

A Decentralized Framework for Multi-Agent Robotic Systems

PubMed Central

2018-01-01

Over the past few years, decentralization of multi-agent robotic systems has become an important research area. These systems do not depend on a central control unit, which enables the control and assignment of distributed, asynchronous and robust tasks. However, in some cases, the network communication process between robotic agents is overlooked, and this creates a dependency for each agent to maintain a permanent link with nearby units to be able to fulfill its goals. This article describes a communication framework, where each agent in the system can leave the network or accept new connections, sending its information based on the transfer history of all nodes in the network. To this end, each agent needs to comply with four processes to participate in the system, plus a fifth process for data transfer to the nearest nodes that is based on Received Signal Strength Indicator (RSSI) and data history. To validate this framework, we use differential robotic agents and a monitoring agent to generate a topological map of an environment with the presence of obstacles. PMID:29389849

Network robustness assessed within a dual connectivity framework: joint dynamics of the Active and Idle Networks.

PubMed

Tejedor, Alejandro; Longjas, Anthony; Zaliapin, Ilya; Ambroj, Samuel; Foufoula-Georgiou, Efi

2017-08-17

Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. But current definition of robustness is only accounting for half of the story: the connectivity of the nodes unaffected by the attack. Here we propose a new framework to assess network robustness, wherein the connectivity of the affected nodes is also taken into consideration, acknowledging that it plays a crucial role in properly evaluating the overall network robustness in terms of its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and that of building-up the IN. We show, via analysis of well-known prototype networks and real world data, that trade-offs between the efficiency of Active and Idle Network dynamics give rise to surprising robustness crossovers and re-rankings, which can have significant implications for decision making.

Simultaneous fault detection and control design for switched systems with two quantized signals.

PubMed

Li, Jian; Park, Ju H; Ye, Dan

2017-01-01

The problem of simultaneous fault detection and control design for switched systems with two quantized signals is presented in this paper. Dynamic quantizers are employed, respectively, before the output is passed to fault detector, and before the control input is transmitted to the switched system. Taking the quantized errors into account, the robust performance for this kind of system is given. Furthermore, sufficient conditions for the existence of fault detector/controller are presented in the framework of linear matrix inequalities, and fault detector/controller gains and the supremum of quantizer range are derived by a convex optimized method. Finally, two illustrative examples demonstrate the effectiveness of the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A comparison of a novel robust decentralised control strategy and MPC for industrial high purity, high recovery, multicomponent distillation.

PubMed

Udugama, Isuru A; Wolfenstetter, Florian; Kirkpatrick, Robert; Yu, Wei; Young, Brent R

2017-07-01

In this work we have developed a novel, robust practical control structure to regulate an industrial methanol distillation column. This proposed control scheme is based on a override control framework and can manage a non-key trace ethanol product impurity specification while maintaining high product recovery. For comparison purposes, a MPC with a discrete process model (based on step tests) was also developed and tested. The results from process disturbance testing shows that, both the MPC and the proposed controller were capable of maintaining both the trace level ethanol specification in the distillate (X D ) and high product recovery (β). Closer analysis revealed that the MPC controller has a tighter X D control, while the proposed controller was tighter in β control. The tight X D control allowed the MPC to operate at a higher X D set point (closer to the 10ppm AA grade methanol standard), allowing for savings in energy usage. Despite the energy savings of the MPC, the proposed control scheme has lower installation and running costs. An economic analysis revealed a multitude of other external economic and plant design factors, that should be considered when making a decision between the two controllers. In general, we found relatively high energy costs favour MPC. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Control strategies for robots in contact

NASA Astrophysics Data System (ADS)

Park, Jaeheung

In the field of robotics, there is a growing need to provide robots with the ability to interact with complex and unstructured environments. Operations in such environments pose significant challenges in terms of sensing, planning, and control. In particular, it is critical to design control algorithms that account for the dynamics of the robot and environment at multiple contacts. The work in this thesis focuses on the development of a control framework that addresses these issues. The approaches are based on the operational space control framework and estimation methods. By accounting for the dynamics of the robot and environment, modular and systematic methods are developed for robots interacting with the environment at multiple locations. The proposed force control approach demonstrates high performance in the presence of uncertainties. Building on this basic capability, new control algorithms have been developed for haptic teleoperation, multi-contact interaction with the environment, and whole body motion of non-fixed based robots. These control strategies have been experimentally validated through simulations and implementations on physical robots. The results demonstrate the effectiveness of the new control structure and its robustness to uncertainties. The contact control strategies presented in this thesis are expected to contribute to the needs in advanced controller design for humanoid and other complex robots interacting with their environments.

Effective real-time vehicle tracking using discriminative sparse coding on local patches

NASA Astrophysics Data System (ADS)

Chen, XiangJun; Ye, Feiyue; Ruan, Yaduan; Chen, Qimei

2016-01-01

A visual tracking framework that provides an object detector and tracker, which focuses on effective and efficient visual tracking in surveillance of real-world intelligent transport system applications, is proposed. The framework casts the tracking task as problems of object detection, feature representation, and classification, which is different from appearance model-matching approaches. Through a feature representation of discriminative sparse coding on local patches called DSCLP, which trains a dictionary on local clustered patches sampled from both positive and negative datasets, the discriminative power and robustness has been improved remarkably, which makes our method more robust to a complex realistic setting with all kinds of degraded image quality. Moreover, by catching objects through one-time background subtraction, along with offline dictionary training, computation time is dramatically reduced, which enables our framework to achieve real-time tracking performance even in a high-definition sequence with heavy traffic. Experiment results show that our work outperforms some state-of-the-art methods in terms of speed, accuracy, and robustness and exhibits increased robustness in a complex real-world scenario with degraded image quality caused by vehicle occlusion, image blur of rain or fog, and change in viewpoint or scale.

A fast, robust and tunable synthetic gene oscillator.

PubMed

Stricker, Jesse; Cookson, Scott; Bennett, Matthew R; Mather, William H; Tsimring, Lev S; Hasty, Jeff

2008-11-27

One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to 'design specifications' generated from computational modelling. This approach provides a systematic framework for exploring how a given regulatory network generates a particular phenotypic behaviour. Several fundamental gene circuits have been developed using this approach, including toggle switches and oscillators, and these have been applied in new contexts such as triggered biofilm development and cellular population control. Here we describe an engineered genetic oscillator in Escherichia coli that is fast, robust and persistent, with tunable oscillatory periods as fast as 13 min. The oscillator was designed using a previously modelled network architecture comprising linked positive and negative feedback loops. Using a microfluidic platform tailored for single-cell microscopy, we precisely control environmental conditions and monitor oscillations in individual cells through multiple cycles. Experiments reveal remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude fluorescence oscillations throughout observation runs. The oscillatory period can be tuned by altering inducer levels, temperature and the media source. Computational modelling demonstrates that the key design principle for constructing a robust oscillator is a time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. The positive feedback loop increases the robustness of the oscillations and allows for greater tunability. Examination of our refined model suggested the existence of a simplified oscillator design without positive feedback, and we construct an oscillator strain confirming this computational prediction.

A Fine-Tuned Metal-Organic Framework for Autonomous Indoor Moisture Control.

PubMed

AbdulHalim, Rasha G; Bhatt, Prashant M; Belmabkhout, Youssef; Shkurenko, Aleksander; Adil, Karim; Barbour, Leonard J; Eddaoudi, Mohamed

2017-08-09

Conventional adsorbents, namely zeolites and silica gel, are often used to control humidity by adsorbing water; however, adsorbents capable of the dual functionality of humidification and dehumidification, offering the desired control of the moisture level at room temperature, have yet to be explored. Here we report Y-shp-MOF-5, a hybrid microporous highly connected rare-earth-based metal-organic framework (MOF), with dual functionality for moisture control within the recommended range of relative humidity (45%-65% RH) set by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). Y-shp-MOF-5 exhibits exceptional structural integrity, robustness, and unique humidity-control performance, as confirmed by the large number (thousand) of conducted water vapor adsorption-desorption cycles. The retained structural integrity and the mechanism of water sorption were corroborated using in situ single-crystal X-ray diffraction (SCXRD) studies. The resultant working water uptake of 0.45 g·g -1 is solely regulated by a simple adjustment of the relative humidity, positioning this hydrolytically stable MOF as a prospective adsorbent for humidity control in confined spaces, such as space shuttles, aircraft cabins, and air-conditioned buildings.

Control of Multilayer Networks

PubMed Central

Menichetti, Giulia; Dall’Asta, Luca; Bianconi, Ginestra

2016-01-01

The controllability of a network is a theoretical problem of relevance in a variety of contexts ranging from financial markets to the brain. Until now, network controllability has been characterized only on isolated networks, while the vast majority of complex systems are formed by multilayer networks. Here we build a theoretical framework for the linear controllability of multilayer networks by mapping the problem into a combinatorial matching problem. We found that correlating the external signals in the different layers can significantly reduce the multiplex network robustness to node removal, as it can be seen in conjunction with a hybrid phase transition occurring in interacting Poisson networks. Moreover we observe that multilayer networks can stabilize the fully controllable multiplex network configuration that can be stable also when the full controllability of the single network is not stable. PMID:26869210

Model-based framework for multi-axial real-time hybrid simulation testing

NASA Astrophysics Data System (ADS)

Fermandois, Gaston A.; Spencer, Billie F.

2017-10-01

Real-time hybrid simulation is an efficient and cost-effective dynamic testing technique for performance evaluation of structural systems subjected to earthquake loading with rate-dependent behavior. A loading assembly with multiple actuators is required to impose realistic boundary conditions on physical specimens. However, such a testing system is expected to exhibit significant dynamic coupling of the actuators and suffer from time lags that are associated with the dynamics of the servo-hydraulic system, as well as control-structure interaction (CSI). One approach to reducing experimental errors considers a multi-input, multi-output (MIMO) controller design, yielding accurate reference tracking and noise rejection. In this paper, a framework for multi-axial real-time hybrid simulation (maRTHS) testing is presented. The methodology employs a real-time feedback-feedforward controller for multiple actuators commanded in Cartesian coordinates. Kinematic transformations between actuator space and Cartesian space are derived for all six-degrees-offreedom of the moving platform. Then, a frequency domain identification technique is used to develop an accurate MIMO transfer function of the system. Further, a Cartesian-domain model-based feedforward-feedback controller is implemented for time lag compensation and to increase the robustness of the reference tracking for given model uncertainty. The framework is implemented using the 1/5th-scale Load and Boundary Condition Box (LBCB) located at the University of Illinois at Urbana- Champaign. To demonstrate the efficacy of the proposed methodology, a single-story frame subjected to earthquake loading is tested. One of the columns in the frame is represented physically in the laboratory as a cantilevered steel column. For realtime execution, the numerical substructure, kinematic transformations, and controllers are implemented on a digital signal processor. Results show excellent performance of the maRTHS framework when six-degrees-of-freedom are controlled at the interface between substructures.

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

NASA Technical Reports Server (NTRS)

Bregon, Anibal; Daigle, Matthew J.; Roychoudhury, Indranil

2012-01-01

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

Robust adaptive control modeling of human arm movements subject to altered gravity and mechanical loads

NASA Astrophysics Data System (ADS)

Tryfonidis, Michail

It has been observed that during orbital spaceflight the absence of gravitation related sensory inputs causes incongruence between the expected and the actual sensory feedback resulting from voluntary movements. This incongruence results in a reinterpretation or neglect of gravity-induced sensory input signals. Over time, new internal models develop, gradually compensating for the loss of spatial reference. The study of adaptation of goal-directed movements is the main focus of this thesis. The hypothesis is that during the adaptive learning process the neural connections behave in ways that can be described by an adaptive control method. The investigation presented in this thesis includes two different sets of experiments. A series of dart throwing experiments took place onboard the space station Mir. Experiments also took place at the Biomechanics lab at MIT, where the subjects performed a series of continuous trajectory tracking movements while a planar robotic manipulandum exerted external torques on the subjects' moving arms. The experimental hypothesis for both experiments is that during the first few trials the subjects will perform poorly trying to follow a prescribed trajectory, or trying to hit a target. A theoretical framework is developed that is a modification of the sliding control method used in robotics. The new control framework is an attempt to explain the adaptive behavior of the subjects. Numerical simulations of the proposed framework are compared with experimental results and predictions from competitive models. The proposed control methodology extends the results of the sliding mode theory to human motor control. The resulting adaptive control model of the motor system is robust to external dynamics, even those of negative gain, uses only position and velocity feedback, and achieves bounded steady-state error without explicit knowledge of the system's nonlinearities. In addition, the experimental and modeling results demonstrate that visuomotor learning is important not only for error correction through internal model adaptation on ground or in microgravity, but also for the minimization of the total mean-square error in the presence of random variability. Thus human intelligent decision displays certain attributes that seem to conform to Bayesian statistical games. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

Adaptive PID formation control of nonholonomic robots without leader's velocity information.

PubMed

Shen, Dongbin; Sun, Weijie; Sun, Zhendong

2014-03-01

This paper proposes an adaptive proportional integral derivative (PID) algorithm to solve a formation control problem in the leader-follower framework where the leader robot's velocities are unknown for the follower robots. The main idea is first to design some proper ideal control law for the formation system to obtain a required performance, and then to propose the adaptive PID methodology to approach the ideal controller. As a result, the formation is achieved with much more enhanced robust formation performance. The stability of the closed-loop system is theoretically proved by Lyapunov method. Both numerical simulations and physical vehicle experiments are presented to verify the effectiveness of the proposed adaptive PID algorithm. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images

PubMed Central

Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi

2016-01-01

Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. PMID:27399704

LAMOST CCD camera-control system based on RTS2

NASA Astrophysics Data System (ADS)

Tian, Yuan; Wang, Zheng; Li, Jian; Cao, Zi-Huang; Dai, Wei; Wei, Shou-Lin; Zhao, Yong-Heng

2018-05-01

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is the largest existing spectroscopic survey telescope, having 32 scientific charge-coupled-device (CCD) cameras for acquiring spectra. Stability and automation of the camera-control software are essential, but cannot be provided by the existing system. The Remote Telescope System 2nd Version (RTS2) is an open-source and automatic observatory-control system. However, all previous RTS2 applications were developed for small telescopes. This paper focuses on implementation of an RTS2-based camera-control system for the 32 CCDs of LAMOST. A virtual camera module inherited from the RTS2 camera module is built as a device component working on the RTS2 framework. To improve the controllability and robustness, a virtualized layer is designed using the master-slave software paradigm, and the virtual camera module is mapped to the 32 real cameras of LAMOST. The new system is deployed in the actual environment and experimentally tested. Finally, multiple observations are conducted using this new RTS2-framework-based control system. The new camera-control system is found to satisfy the requirements for automatic camera control in LAMOST. This is the first time that RTS2 has been applied to a large telescope, and provides a referential solution for full RTS2 introduction to the LAMOST observatory control system.

Network control principles predict neuron function in the Caenorhabditis elegans connectome

PubMed Central

Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-01-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social and technological networks1–3. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode C. elegans4–6, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires twelve neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation7–13, as well as one previously uncharacterised neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed, with single-cell ablations of DD04 or DD05, but not DD02 or DD03, specifically affecting posterior body movements. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterised connectomes. PMID:29045391

Network control principles predict neuron function in the Caenorhabditis elegans connectome

NASA Astrophysics Data System (ADS)

Yan, Gang; Vértes, Petra E.; Towlson, Emma K.; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-10-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Network control principles predict neuron function in the Caenorhabditis elegans connectome.

PubMed

Yan, Gang; Vértes, Petra E; Towlson, Emma K; Chew, Yee Lian; Walker, Denise S; Schafer, William R; Barabási, Albert-László

2017-10-26

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Human Guidance Behavior Decomposition and Modeling

NASA Astrophysics Data System (ADS)

Feit, Andrew James

Trained humans are capable of high performance, adaptable, and robust first-person dynamic motion guidance behavior. This behavior is exhibited in a wide variety of activities such as driving, piloting aircraft, skiing, biking, and many others. Human performance in such activities far exceeds the current capability of autonomous systems in terms of adaptability to new tasks, real-time motion planning, robustness, and trading safety for performance. The present work investigates the structure of human dynamic motion guidance that enables these performance qualities. This work uses a first-person experimental framework that presents a driving task to the subject, measuring control inputs, vehicle motion, and operator visual gaze movement. The resulting data is decomposed into subspace segment clusters that form primitive elements of action-perception interactive behavior. Subspace clusters are defined by both agent-environment system dynamic constraints and operator control strategies. A key contribution of this work is to define transitions between subspace cluster segments, or subgoals, as points where the set of active constraints, either system or operator defined, changes. This definition provides necessary conditions to determine transition points for a given task-environment scenario that allow a solution trajectory to be planned from known behavior elements. In addition, human gaze behavior during this task contains predictive behavior elements, indicating that the identified control modes are internally modeled. Based on these ideas, a generative, autonomous guidance framework is introduced that efficiently generates optimal dynamic motion behavior in new tasks. The new subgoal planning algorithm is shown to generate solutions to certain tasks more quickly than existing approaches currently used in robotics.

Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2012-01-01

This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

A Control Framework for Anthropomorphic Biped Walking Based on Stabilizing Feedforward Trajectories.

PubMed

Rezazadeh, Siavash; Gregg, Robert D

2016-10-01

Although dynamic walking methods have had notable successes in control of bipedal robots in the recent years, still most of the humanoid robots rely on quasi-static Zero Moment Point controllers. This work is an attempt to design a highly stable controller for dynamic walking of a human-like model which can be used both for control of humanoid robots and prosthetic legs. The method is based on using time-based trajectories that can induce a highly stable limit cycle to the bipedal robot. The time-based nature of the controller motivates its use to entrain a model of an amputee walking, which can potentially lead to a better coordination of the interaction between the prosthesis and the human. The simulations demonstrate the stability of the controller and its robustness against external perturbations.

Network planning under uncertainties

NASA Astrophysics Data System (ADS)

Ho, Kwok Shing; Cheung, Kwok Wai

2008-11-01

One of the main focuses for network planning is on the optimization of network resources required to build a network under certain traffic demand projection. Traditionally, the inputs to this type of network planning problems are treated as deterministic. In reality, the varying traffic requirements and fluctuations in network resources can cause uncertainties in the decision models. The failure to include the uncertainties in the network design process can severely affect the feasibility and economics of the network. Therefore, it is essential to find a solution that can be insensitive to the uncertain conditions during the network planning process. As early as in the 1960's, a network planning problem with varying traffic requirements over time had been studied. Up to now, this kind of network planning problems is still being active researched, especially for the VPN network design. Another kind of network planning problems under uncertainties that has been studied actively in the past decade addresses the fluctuations in network resources. One such hotly pursued research topic is survivable network planning. It considers the design of a network under uncertainties brought by the fluctuations in topology to meet the requirement that the network remains intact up to a certain number of faults occurring anywhere in the network. Recently, the authors proposed a new planning methodology called Generalized Survivable Network that tackles the network design problem under both varying traffic requirements and fluctuations of topology. Although all the above network planning problems handle various kinds of uncertainties, it is hard to find a generic framework under more general uncertainty conditions that allows a more systematic way to solve the problems. With a unified framework, the seemingly diverse models and algorithms can be intimately related and possibly more insights and improvements can be brought out for solving the problem. This motivates us to seek a generic framework for solving the network planning problem under uncertainties. In addition to reviewing the various network planning problems involving uncertainties, we also propose that a unified framework based on robust optimization can be used to solve a rather large segment of network planning problem under uncertainties. Robust optimization is first introduced in the operations research literature and is a framework that incorporates information about the uncertainty sets for the parameters in the optimization model. Even though robust optimization is originated from tackling the uncertainty in the optimization process, it can serve as a comprehensive and suitable framework for tackling generic network planning problems under uncertainties. In this paper, we begin by explaining the main ideas behind the robust optimization approach. Then we demonstrate the capabilities of the proposed framework by giving out some examples of how the robust optimization framework can be applied to the current common network planning problems under uncertain environments. Next, we list some practical considerations for solving the network planning problem under uncertainties with the proposed framework. Finally, we conclude this article with some thoughts on the future directions for applying this framework to solve other network planning problems.

Development of fuzzy multi-criteria approach to prioritize locations of treated wastewater use considering climate change scenarios.

PubMed

Chung, Eun-Sung; Kim, Yeonjoo

2014-12-15

This study proposed a robust prioritization framework to identify the priorities of treated wastewater (TWW) use locations with consideration of various uncertainties inherent in the climate change scenarios and the decision-making process. First, a fuzzy concept was applied because future forecast precipitation and their hydrological impact analysis results displayed significant variances when considering various climate change scenarios and long periods (e.g., 2010-2099). Second, various multi-criteria decision making (MCDM) techniques including weighted sum method (WSM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy TOPSIS were introduced to robust prioritization because different MCDM methods use different decision philosophies. Third, decision making method under complete uncertainty (DMCU) including maximin, maximax, minimax regret, Hurwicz, and equal likelihood were used to find robust final rankings. This framework is then applied to a Korean urban watershed. As a result, different rankings were obviously appeared between fuzzy TOPSIS and non-fuzzy MCDMs (e.g., WSM and TOPSIS) because the inter-annual variability in effectiveness was considered only with fuzzy TOPSIS. Then, robust prioritizations were derived based on 18 rankings from nine decadal periods of RCP4.5 and RCP8.5. For more robust rankings, five DMCU approaches using the rankings from fuzzy TOPSIS were derived. This framework combining fuzzy TOPSIS with DMCU approaches can be rendered less controversial among stakeholders under complete uncertainty of changing environments. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sparse alignment for robust tensor learning.

PubMed

Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming

2014-10-01

Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

A new decentralised controller design method for a class of strongly interconnected systems

NASA Astrophysics Data System (ADS)

Duan, Zhisheng; Jiang, Zhong-Ping; Huang, Lin

2017-02-01

In this paper, two interconnected structures are first discussed, under which some closed-loop subsystems must be unstable to make the whole interconnected system stable, which can be viewed as a kind of strongly interconnected systems. Then, comparisons with small gain theorem are discussed and large gain interconnected characteristics are shown. A new approach for the design of decentralised controllers is presented by determining the Lyapunov function structure previously, which allows the existence of unstable subsystems. By fully utilising the orthogonal space information of input matrix, some new understandings are presented for the construction of Lyapunov matrix. This new method can deal with decentralised state feedback, static output feedback and dynamic output feedback controllers in a unified framework. Furthermore, in order to reduce the design conservativeness and deal with robustness, a new robust decentralised controller design method is given by combining with the parameter-dependent Lyapunov function method. Some basic rules are provided for the choice of initial variables in Lyapunov matrix or new introduced slack matrices. As byproducts, some linear matrix inequality based sufficient conditions are established for centralised static output feedback stabilisation. Effects of unstable subsystems in nonlinear Lur'e systems are further discussed. The corresponding decentralised controller design method is presented for absolute stability. The examples illustrate that the new method is significantly effective.

Design of an embedded inverse-feedforward biomolecular tracking controller for enzymatic reaction processes.

PubMed

Foo, Mathias; Kim, Jongrae; Sawlekar, Rucha; Bates, Declan G

2017-04-06

Feedback control is widely used in chemical engineering to improve the performance and robustness of chemical processes. Feedback controllers require a 'subtractor' that is able to compute the error between the process output and the reference signal. In the case of embedded biomolecular control circuits, subtractors designed using standard chemical reaction network theory can only realise one-sided subtraction, rendering standard controller design approaches inadequate. Here, we show how a biomolecular controller that allows tracking of required changes in the outputs of enzymatic reaction processes can be designed and implemented within the framework of chemical reaction network theory. The controller architecture employs an inversion-based feedforward controller that compensates for the limitations of the one-sided subtractor that generates the error signals for a feedback controller. The proposed approach requires significantly fewer chemical reactions to implement than alternative designs, and should have wide applicability throughout the fields of synthetic biology and biological engineering.

Comprehensive security framework for the communication and storage of medical images

NASA Astrophysics Data System (ADS)

Slik, David; Montour, Mike; Altman, Tym

2003-05-01

Confidentiality, integrity verification and access control of medical imagery and associated metadata is critical for the successful deployment of integrated healthcare networks that extend beyond the department level. As medical imagery continues to become widely accessed across multiple administrative domains and geographically distributed locations, image data should be able to travel and be stored on untrusted infrastructure, including public networks and server equipment operated by external entities. Given these challenges associated with protecting large-scale distributed networks, measures must be taken to protect patient identifiable information while guarding against tampering, denial of service attacks, and providing robust audit mechanisms. The proposed framework outlines a series of security practices for the protection of medical images, incorporating Transport Layer Security (TLS), public and secret key cryptography, certificate management and a token based trusted computing base. It outlines measures that can be utilized to protect information stored within databases, online and nearline storage, and during transport over trusted and untrusted networks. In addition, it provides a framework for ensuring end-to-end integrity of image data from acquisition to viewing, and presents a potential solution to the challenges associated with access control across multiple administrative domains and institution user bases.

What are the assets and weaknesses of HFO detectors? A benchmark framework based on realistic simulations

PubMed Central

Pizzo, Francesca; Bartolomei, Fabrice; Wendling, Fabrice; Bénar, Christian-George

2017-01-01

High-frequency oscillations (HFO) have been suggested as biomarkers of epileptic tissues. While visual marking of these short and small oscillations is tedious and time-consuming, automatic HFO detectors have not yet met a large consensus. Even though detectors have been shown to perform well when validated against visual marking, the large number of false detections due to their lack of robustness hinder their clinical application. In this study, we developed a validation framework based on realistic and controlled simulations to quantify precisely the assets and weaknesses of current detectors. We constructed a dictionary of synthesized elements—HFOs and epileptic spikes—from different patients and brain areas by extracting these elements from the original data using discrete wavelet transform coefficients. These elements were then added to their corresponding simulated background activity (preserving patient- and region- specific spectra). We tested five existing detectors against this benchmark. Compared to other studies confronting detectors, we did not only ranked them according their performance but we investigated the reasons leading to these results. Our simulations, thanks to their realism and their variability, enabled us to highlight unreported issues of current detectors: (1) the lack of robust estimation of the background activity, (2) the underestimated impact of the 1/f spectrum, and (3) the inadequate criteria defining an HFO. We believe that our benchmark framework could be a valuable tool to translate HFOs into a clinical environment. PMID:28406919

A framework for streamlining research workflow in neuroscience and psychology

PubMed Central

Kubilius, Jonas

2014-01-01

Successful accumulation of knowledge is critically dependent on the ability to verify and replicate every part of scientific conduct. However, such principles are difficult to enact when researchers continue to resort on ad-hoc workflows and with poorly maintained code base. In this paper I examine the needs of neuroscience and psychology community, and introduce psychopy_ext, a unifying framework that seamlessly integrates popular experiment building, analysis and manuscript preparation tools by choosing reasonable defaults and implementing relatively rigid patterns of workflow. This structure allows for automation of multiple tasks, such as generated user interfaces, unit testing, control analyses of stimuli, single-command access to descriptive statistics, and publication quality plotting. Taken together, psychopy_ext opens an exciting possibility for a faster, more robust code development and collaboration for researchers. PMID:24478691

Moving Beyond Boron: The Emergence of New Linkage Chemistries in Covalent Organic Frameworks

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeBlase, Catherine R.; Dichtel, William R.

Since their discovery in 2005, covalent organic frameworks (COFs) have attracted interest as potential materials for gas storage, catalysis, energy storage, and other applications because of their ability to periodically and reliably organize designed functionality into high surface area materials. Most of the first examples relied on boron-containing linkages, which suffer from hydrolytic and oxidative instability that limit their utility. In this Perspective, we describe the trend toward more robust linkages by highlighting the design, synthesis, and properties of several recent examples. Finally, the continued development of new COF chemistries, along with improved understanding of their formation and control ofmore » their final form, will provide a means to harness their molecularly precise solidstate structures for useful purposes.« less

Learning consensus in adversarial environments

NASA Astrophysics Data System (ADS)

Vamvoudakis, Kyriakos G.; García Carrillo, Luis R.; Hespanha, João. P.

2013-05-01

This work presents a game theory-based consensus problem for leaderless multi-agent systems in the presence of adversarial inputs that are introducing disturbance to the dynamics. Given the presence of enemy components and the possibility of malicious cyber attacks compromising the security of networked teams, a position agreement must be reached by the networked mobile team based on environmental changes. The problem is addressed under a distributed decision making framework that is robust to possible cyber attacks, which has an advantage over centralized decision making in the sense that a decision maker is not required to access information from all the other decision makers. The proposed framework derives three tuning laws for every agent; one associated with the cost, one associated with the controller, and one with the adversarial input.

Moving Beyond Boron: The Emergence of New Linkage Chemistries in Covalent Organic Frameworks

DOE PAGES

DeBlase, Catherine R.; Dichtel, William R.

2016-06-21

Since their discovery in 2005, covalent organic frameworks (COFs) have attracted interest as potential materials for gas storage, catalysis, energy storage, and other applications because of their ability to periodically and reliably organize designed functionality into high surface area materials. Most of the first examples relied on boron-containing linkages, which suffer from hydrolytic and oxidative instability that limit their utility. In this Perspective, we describe the trend toward more robust linkages by highlighting the design, synthesis, and properties of several recent examples. Finally, the continued development of new COF chemistries, along with improved understanding of their formation and control ofmore » their final form, will provide a means to harness their molecularly precise solidstate structures for useful purposes.« less

Fault-tolerant Control of a Cyber-physical System

NASA Astrophysics Data System (ADS)

Roxana, Rusu-Both; Eva-Henrietta, Dulf

2017-10-01

Cyber-physical systems represent a new emerging field in automatic control. The fault system is a key component, because modern, large scale processes must meet high standards of performance, reliability and safety. Fault propagation in large scale chemical processes can lead to loss of production, energy, raw materials and even environmental hazard. The present paper develops a multi-agent fault-tolerant control architecture using robust fractional order controllers for a (13C) cryogenic separation column cascade. The JADE (Java Agent DEvelopment Framework) platform was used to implement the multi-agent fault tolerant control system while the operational model of the process was implemented in Matlab/SIMULINK environment. MACSimJX (Multiagent Control Using Simulink with Jade Extension) toolbox was used to link the control system and the process model. In order to verify the performance and to prove the feasibility of the proposed control architecture several fault simulation scenarios were performed.

Rigorous control conditions diminish treatment effects in weight loss randomized controlled trials

PubMed Central

Dawson, John A.; Kaiser, Kathryn A.; Affuso, Olivia; Cutter, Gary R.; Allison, David B.

2015-01-01

Background It has not been established whether control conditions with large weight losses (WLs) diminish expected treatment effects in WL or prevention of weight gain (PWG) randomized controlled trials (RCTs). Subjects/Methods We performed a meta-analysis of 239 WL/PWG RCTs that include a control group and at least one treatment group. A maximum likelihood meta-analysis framework is used in order to model and understand the relationship between treatment effects and control group outcomes. Results Under the informed model, an increase in control group WL of one kilogram corresponds with an expected shrinkage of the treatment effect by 0.309 kg [95% CI (−0.480, −0.138), p = 0.00081]; this result is robust against violations of the model assumptions. Conclusions We find that control conditions with large weight losses diminish expected treatment effects. Our investigation may be helpful to clinicians as they design future WL/PWG studies. PMID:26449419

Vehicle active steering control research based on two-DOF robust internal model control

NASA Astrophysics Data System (ADS)

Wu, Jian; Liu, Yahui; Wang, Fengbo; Bao, Chunjiang; Sun, Qun; Zhao, Youqun

2016-07-01

Because of vehicle's external disturbances and model uncertainties, robust control algorithms have obtained popularity in vehicle stability control. The robust control usually gives up performance in order to guarantee the robustness of the control algorithm, therefore an improved robust internal model control(IMC) algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness. The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties. In order to separate the design process of model tracking from the robustness design process, the improved 2 degree of freedom(DOF) robust internal model controller structure is given from the standard Youla parameterization. Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm, on the basis of a nonlinear vehicle simulation model with a magic tyre model. Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance, which can enhance the vehicle stability and handling, regardless of variations of the vehicle model parameters and the external crosswind interferences. Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Zeolitic imidazolate framework-coated acoustic sensors for room temperature detection of carbon dioxide and methane

DOE PAGES

Devkota, Jagannath; Kim, Ki-Joong; Ohodnicki, Paul R.; ...

2018-01-01

The integration of nanoporous materials such as metal organic frameworks (MOFs) with sensitive transducers can result in robust sensing platforms for monitoring gases and chemical vapors for a range of applications.

Deep Neural Networks for Speech Separation With Application to Robust Speech Recognition

DTIC Science & Technology

acoustic -phonetic features. The second objective is integration of spectrotemporal context for improved separation performance. Conditional random fields...will be used to encode contextual constraints. The third objective is to achieve robust ASR in the DNN framework through integrated acoustic modeling

Robust infrared targets tracking with covariance matrix representation

NASA Astrophysics Data System (ADS)

Cheng, Jian

2009-07-01

Robust infrared target tracking is an important and challenging research topic in many military and security applications, such as infrared imaging guidance, infrared reconnaissance, scene surveillance, etc. To effectively tackle the nonlinear and non-Gaussian state estimation problems, particle filtering is introduced to construct the theory framework of infrared target tracking. Under this framework, the observation probabilistic model is one of main factors for infrared targets tracking performance. In order to improve the tracking performance, covariance matrices are introduced to represent infrared targets with the multi-features. The observation probabilistic model can be constructed by computing the distance between the reference target's and the target samples' covariance matrix. Because the covariance matrix provides a natural tool for integrating multiple features, and is scale and illumination independent, target representation with covariance matrices can hold strong discriminating ability and robustness. Two experimental results demonstrate the proposed method is effective and robust for different infrared target tracking, such as the sensor ego-motion scene, and the sea-clutter scene.

Improved configuration control for redundant robots

NASA Technical Reports Server (NTRS)

Seraji, H.; Colbaugh, R.

1990-01-01

This article presents a singularity-robust task-prioritized reformulation of the configuration control scheme for redundant robot manipulators. This reformulation suppresses large joint velocities near singularities, at the expense of small task trajectory errors. This is achieved by optimally reducing the joint velocities to induce minimal errors in the task performance by modifying the task trajectories. Furthermore, the same framework provides a means for assignment of priorities between the basic task of end-effector motion and the user-defined additional task for utilizing redundancy. This allows automatic relaxation of the additional task constraints in favor of the desired end-effector motion, when both cannot be achieved exactly. The improved configuration control scheme is illustrated for a variety of additional tasks, and extensive simulation results are presented.

Improving Multi-Objective Management of Water Quality Tipping Points: Revisiting the Classical Shallow Lake Problem

NASA Astrophysics Data System (ADS)

Quinn, J. D.; Reed, P. M.; Keller, K.

2015-12-01

Recent multi-objective extensions of the classical shallow lake problem are useful for exploring the conceptual and computational challenges that emerge when managing irreversible water quality tipping points. Building on this work, we explore a four objective version of the lake problem where a hypothetical town derives economic benefits from polluting a nearby lake, but at the risk of irreversibly tipping the lake into a permanently polluted state. The trophic state of the lake exhibits non-linear threshold dynamics; below some critical phosphorus (P) threshold it is healthy and oligotrophic, but above this threshold it is irreversibly eutrophic. The town must decide how much P to discharge each year, a decision complicated by uncertainty in the natural P inflow to the lake. The shallow lake problem provides a conceptually rich set of dynamics, low computational demands, and a high level of mathematical difficulty. These properties maximize its value for benchmarking the relative merits and limitations of emerging decision support frameworks, such as Direct Policy Search (DPS). Here, we explore the use of DPS as a formal means of developing robust environmental pollution control rules that effectively account for deeply uncertain system states and conflicting objectives. The DPS reformulation of the shallow lake problem shows promise in formalizing pollution control triggers and signposts, while dramatically reducing the computational complexity of the multi-objective pollution control problem. More broadly, the insights from the DPS variant of the shallow lake problem formulated in this study bridge emerging work related to socio-ecological systems management, tipping points, robust decision making, and robust control.

A robust nonparametric framework for reconstruction of stochastic differential equation models

NASA Astrophysics Data System (ADS)

Rajabzadeh, Yalda; Rezaie, Amir Hossein; Amindavar, Hamidreza

2016-05-01

In this paper, we employ a nonparametric framework to robustly estimate the functional forms of drift and diffusion terms from discrete stationary time series. The proposed method significantly improves the accuracy of the parameter estimation. In this framework, drift and diffusion coefficients are modeled through orthogonal Legendre polynomials. We employ the least squares regression approach along with the Euler-Maruyama approximation method to learn coefficients of stochastic model. Next, a numerical discrete construction of mean squared prediction error (MSPE) is established to calculate the order of Legendre polynomials in drift and diffusion terms. We show numerically that the new method is robust against the variation in sample size and sampling rate. The performance of our method in comparison with the kernel-based regression (KBR) method is demonstrated through simulation and real data. In case of real dataset, we test our method for discriminating healthy electroencephalogram (EEG) signals from epilepsy ones. We also demonstrate the efficiency of the method through prediction in the financial data. In both simulation and real data, our algorithm outperforms the KBR method.

Multi-Agent Many-Objective Robust Decision Making: Supporting Cooperative Regional Water Portfolio Planning in the Eastern United States

NASA Astrophysics Data System (ADS)

Herman, J. D.; Zeff, H. B.; Reed, P. M.; Characklis, G. W.

2013-12-01

In the Eastern United States, water infrastructure and institutional frameworks have evolved in a historically water-rich environment. However, large regional droughts over the past decade combined with continuing population growth have marked a transition to a state of water scarcity, for which current planning paradigms are ill-suited. Significant opportunities exist to improve the efficiency of water infrastructure via regional coordination, namely, regional 'portfolios' of water-related assets such as reservoirs, conveyance, conservation measures, and transfer agreements. Regional coordination offers the potential to improve reliability, cost, and environmental impact in the expected future state of the world, and, with informed planning, to improve robustness to future uncertainty. In support of this challenge, this study advances a multi-agent many-objective robust decision making (multi-agent MORDM) framework that blends novel computational search and uncertainty analysis tools to discover flexible, robust regional portfolios. Our multi-agent MORDM framework is demonstrated for four water utilities in the Research Triangle region of North Carolina, USA. The utilities supply nearly two million customers and have the ability to interact with one another via transfer agreements and shared infrastructure. We show that strategies for this region which are Pareto-optimal in the expected future state of the world remain vulnerable to performance degradation under alternative scenarios of deeply uncertain hydrologic and economic factors. We then apply the Patient Rule Induction Method (PRIM) to identify which of these uncertain factors drives the individual and collective vulnerabilities for the four cooperating utilities. Our results indicate that clear multi-agent tradeoffs emerge for attaining robustness across the utilities. Furthermore, the key factor identified for improving the robustness of the region's water supply is cooperative demand reduction. This type of approach is critically important given the risks and challenges posed by rising supply development costs, limits on new infrastructure, growing water demands and the underlying uncertainties associated with climate change. The proposed framework serves as a planning template for other historically water-rich regions which must now confront the reality of impending water scarcity.

A modal H∞-norm-based performance requirement for damage-tolerant active controller design

NASA Astrophysics Data System (ADS)

Genari, Helói F. G.; Mechbal, Nazih; Coffignal, Gérard; Nóbrega, Eurípedes G. O.

2017-04-01

Damage-tolerant active control (DTAC) is a recent research area that encompasses control design methodologies resulting from the application of fault-tolerant control methods to vibration control of structures subject to damage. The possibility of damage occurrence is not usually considered in the active vibration control design requirements. Damage changes the structure dynamics, which may produce unexpected modal behavior of the closed-loop system, usually not anticipated by the controller design approaches. A modal H∞ norm and a respective robust controller design framework were recently introduced, and this method is here extended to face a new DTAC strategy implementation. Considering that damage affects each vibration mode differently, this paper adopts the modal H∞ norm to include damage as a design requirement. The basic idea is to create an appropriate energy distribution over the frequency range of interest and respective vibration modes, guaranteeing robustness, damage tolerance, and adequate overall performance, taking into account that it is common to have previous knowledge of the structure regions where damage may occur during its operational life. For this purpose, a structural health monitoring technique is applied to evaluate modal modifications caused by damage. This information is used to create modal weighing matrices, conducting to the modal H∞ controller design. Finite element models are adopted for a case study structure, including different damage severities, in order to validate the proposed control strategy. Results show the effectiveness of the proposed methodology with respect to damage tolerance.

Software Framework for Controlling Unsupervised Scientific Instruments.

PubMed

Schmid, Benjamin; Jahr, Wiebke; Weber, Michael; Huisken, Jan

2016-01-01

Science outreach and communication are gaining more and more importance for conveying the meaning of today's research to the general public. Public exhibitions of scientific instruments can provide hands-on experience with technical advances and their applications in the life sciences. The software of such devices, however, is oftentimes not appropriate for this purpose. In this study, we describe a software framework and the necessary computer configuration that is well suited for exposing a complex self-built and software-controlled instrument such as a microscope to laymen under limited supervision, e.g. in museums or schools. We identify several aspects that must be met by such software, and we describe a design that can simultaneously be used to control either (i) a fully functional instrument in a robust and fail-safe manner, (ii) an instrument that has low-cost or only partially working hardware attached for illustration purposes or (iii) a completely virtual instrument without hardware attached. We describe how to assess the educational success of such a device, how to monitor its operation and how to facilitate its maintenance. The introduced concepts are illustrated using our software to control eduSPIM, a fluorescent light sheet microscope that we are currently exhibiting in a technical museum.

Fly-by-feel aeroservoelasticity

NASA Astrophysics Data System (ADS)

Suryakumar, Vishvas Samuel

Recent experiments have suggested a strong correlation between local flow features on the airfoil surface such as the leading edge stagnation point (LESP), transition or the flow separation point with global integrated quantities such as aerodynamic lift. "Fly-By-Feel" refers to a physics-based sensing and control framework where local flow features are tracked in real-time to determine aerodynamic loads. This formulation offers possibilities for the development of robust, low-order flight control architectures. An essential contribution towards this objective is the theoretical development showing the direct relationship of the LESP with circulation for small-amplitude, unsteady, airfoil maneuvers. The theory is validated through numerical simulations and wind tunnel tests. With the availability of an aerodynamic observable, a low-order, energy-based control formulation is derived for aeroelastic stabilization and gust load alleviation. The sensing and control framework is implemented on the Nonlinear Aeroelastic Test Apparatus at Texas A&M University. The LESP is located using hot-film sensors distributed around the wing leading edge. Stabilization of limit cycle oscillations exhibited by a nonlinear wing section is demonstrated in the presence of gusts. Aeroelastic stabilization is also demonstrated on a flying wing configuration exhibiting body freedom flutter through numerical simulations.

Nonlinear gearshifts control of dual-clutch transmissions during inertia phase.

PubMed

Hu, Yunfeng; Tian, Lu; Gao, Bingzhao; Chen, Hong

2014-07-01

In this paper, a model-based nonlinear gearshift controller is designed by the backstepping method to improve the shift quality of vehicles with a dual-clutch transmission (DCT). Considering easy-implementation, the controller is rearranged into a concise structure which contains a feedforward control and a feedback control. Then, robustness of the closed-loop error system is discussed in the framework of the input to state stability (ISS) theory, where model uncertainties are considered as the additive disturbance inputs. Furthermore, due to the application of the backstepping method, the closed-loop error system is ordered as a linear system. Using the linear system theory, a guideline for selecting the controller parameters is deduced which could reduce the workload of parameters tuning. Finally, simulation results and Hardware in the Loop (HiL) simulation are presented to validate the effectiveness of the designed controller. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Stochastic Robust Mathematical Programming Model for Power System Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Cong; Changhyeok, Lee; Haoyong, Chen

2016-01-01

This paper presents a stochastic robust framework for two-stage power system optimization problems with uncertainty. The model optimizes the probabilistic expectation of different worst-case scenarios with ifferent uncertainty sets. A case study of unit commitment shows the effectiveness of the proposed model and algorithms.

Robust boundary treatment for open-channel flows in divergence-free incompressible SPH

NASA Astrophysics Data System (ADS)

Pahar, Gourabananda; Dhar, Anirban

2017-03-01

A robust Incompressible Smoothed Particle Hydrodynamics (ISPH) framework is developed to simulate specified inflow and outflow boundary conditions for open-channel flow. Being purely divergence-free, the framework offers smoothed and structured pressure distribution. An implicit treatment of Pressure Poison Equation and Dirichlet boundary condition is applied on free-surface to minimize error in velocity-divergence. Beyond inflow and outflow threshold, multiple layers of dummy particles are created according to specified boundary condition. Inflow boundary acts as a soluble wave-maker. Fluid particles beyond outflow threshold are removed and replaced with dummy particles with specified boundary velocity. The framework is validated against different cases of open channel flow with different boundary conditions. The model can efficiently capture flow evolution and vortex generation for random geometry and variable boundary conditions.

Pandemic influenza preparedness: an ethical framework to guide decision-making.

PubMed

Thompson, Alison K; Faith, Karen; Gibson, Jennifer L; Upshur, Ross E G

2006-12-04

Planning for the next pandemic influenza outbreak is underway in hospitals across the world. The global SARS experience has taught us that ethical frameworks to guide decision-making may help to reduce collateral damage and increase trust and solidarity within and between health care organisations. Good pandemic planning requires reflection on values because science alone cannot tell us how to prepare for a public health crisis. In this paper, we present an ethical framework for pandemic influenza planning. The ethical framework was developed with expertise from clinical, organisational and public health ethics and validated through a stakeholder engagement process. The ethical framework includes both substantive and procedural elements for ethical pandemic influenza planning. The incorporation of ethics into pandemic planning can be helped by senior hospital administrators sponsoring its use, by having stakeholders vet the framework, and by designing or identifying decision review processes. We discuss the merits and limits of an applied ethical framework for hospital decision-making, as well as the robustness of the framework. The need for reflection on the ethical issues raised by the spectre of a pandemic influenza outbreak is great. Our efforts to address the normative aspects of pandemic planning in hospitals have generated interest from other hospitals and from the governmental sector. The framework will require re-evaluation and refinement and we hope that this paper will generate feedback on how to make it even more robust.

PELEC

DOE Office of Scientific and Technical Information (OSTI.GOV)

2017-05-17

PeleC is an adaptive-mesh compressible hydrodynamics code for reacting flows. It solves the compressible Navier-Stokes with multispecies transport in a block structured framework. The resulting algorithm is well suited for flows with localized resolution requirements and robust to discontinuities. User controllable refinement crieteria has the potential to result in extremely small numerical dissipation and dispersion, making this code appropriate for both research and applied usage. The code is built on the AMReX library which facilitates hierarchical parallelism and manages distributed memory parallism. PeleC algorithms are implemented to express shared memory parallelism.

Flow chemistry meets advanced functional materials.

PubMed

Myers, Rebecca M; Fitzpatrick, Daniel E; Turner, Richard M; Ley, Steven V

2014-09-22

Flow chemistry and continuous processing techniques are beginning to have a profound impact on the production of functional materials ranging from quantum dots, nanoparticles and metal organic frameworks to polymers and dyes. These techniques provide robust procedures which not only enable accurate control of the product material's properties but they are also ideally suited to conducting experiments on scale. The modular nature of flow and continuous processing equipment rapidly facilitates reaction optimisation and variation in function of the products. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Integrated health management and control of complex dynamical systems

NASA Astrophysics Data System (ADS)

Tolani, Devendra K.

2005-11-01

A comprehensive control and health management strategy for human-engineered complex dynamical systems is formulated for achieving high performance and reliability over a wide range of operation. Results from diverse research areas such as Probabilistic Robust Control (PRC), Damage Mitigating/Life Extending Control (DMC), Discrete Event Supervisory (DES) Control, Symbolic Time Series Analysis (STSA) and Health and Usage Monitoring System (HUMS) have been employed to achieve this goal. Continuous-domain control modules at the lower level are synthesized by PRC and DMC theories, whereas the upper-level supervision is based on DES control theory. In the PRC approach, by allowing different levels of risk under different flight conditions, the control system can achieve the desired trade off between stability robustness and nominal performance. In the DMC approach, component damage is incorporated in the control law to reduce the damage rate for enhanced structural durability. The DES controller monitors the system performance and, based on the mission requirements (e.g., performance metrics and level of damage mitigation), switches among various lower-level controllers. The core idea is to design a framework where the DES controller at the upper-level, mimics human intelligence and makes appropriate decisions to satisfy mission requirements, enhance system performance and structural durability. Recently developed tools in STSA have been used for anomaly detection and failure prognosis. The DMC deals with the usage monitoring or operational control part of health management, where as the issue of health monitoring is addressed by the anomaly detection tools. The proposed decision and control architecture has been validated on two test-beds, simulating the operations of rotorcraft dynamics and aircraft propulsion.

Vision and Operational Concept for Enabling Advanced Traveler Information Services : Operational Concept

DOT National Transportation Integrated Search

2012-05-01

EnableATIS is looking ahead to a future operational environment that will support and enable an advanced, transformational traveler information services framework. This future framework is envisioned to be enabled with a much more robust pool of real...

Robust group-wise rigid registration of point sets using t-mixture model

NASA Astrophysics Data System (ADS)

Ravikumar, Nishant; Gooya, Ali; Frangi, Alejandro F.; Taylor, Zeike A.

2016-03-01

A probabilistic framework for robust, group-wise rigid alignment of point-sets using a mixture of Students t-distribution especially when the point sets are of varying lengths, are corrupted by an unknown degree of outliers or in the presence of missing data. Medical images (in particular magnetic resonance (MR) images), their segmentations and consequently point-sets generated from these are highly susceptible to corruption by outliers. This poses a problem for robust correspondence estimation and accurate alignment of shapes, necessary for training statistical shape models (SSMs). To address these issues, this study proposes to use a t-mixture model (TMM), to approximate the underlying joint probability density of a group of similar shapes and align them to a common reference frame. The heavy-tailed nature of t-distributions provides a more robust registration framework in comparison to state of the art algorithms. Significant reduction in alignment errors is achieved in the presence of outliers, using the proposed TMM-based group-wise rigid registration method, in comparison to its Gaussian mixture model (GMM) counterparts. The proposed TMM-framework is compared with a group-wise variant of the well-known Coherent Point Drift (CPD) algorithm and two other group-wise methods using GMMs, using both synthetic and real data sets. Rigid alignment errors for groups of shapes are quantified using the Hausdorff distance (HD) and quadratic surface distance (QSD) metrics.

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion.

PubMed

Luo, Xiongbiao; Wan, Ying; He, Xiangjian

2015-04-01

Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods.

A distributed model predictive control scheme for leader-follower multi-agent systems

NASA Astrophysics Data System (ADS)

Franzè, Giuseppe; Lucia, Walter; Tedesco, Francesco

2018-02-01

In this paper, we present a novel receding horizon control scheme for solving the formation problem of leader-follower configurations. The algorithm is based on set-theoretic ideas and is tuned for agents described by linear time-invariant (LTI) systems subject to input and state constraints. The novelty of the proposed framework relies on the capability to jointly use sequences of one-step controllable sets and polyhedral piecewise state-space partitions in order to online apply the 'better' control action in a distributed receding horizon fashion. Moreover, we prove that the design of both robust positively invariant sets and one-step-ahead controllable regions is achieved in a distributed sense. Simulations and numerical comparisons with respect to centralised and local-based strategies are finally performed on a group of mobile robots to demonstrate the effectiveness of the proposed control strategy.

Distributed Control of Inverter-Based Lossy Microgrids for Power Sharing and Frequency Regulation Under Voltage Constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Chin-Yao; Zhang, Wei

This paper presents a new distributed control framework to coordinate inverter-interfaced distributed energy resources (DERs) in island microgrids. We show that under bounded load uncertainties, the proposed control method can steer the microgrid to a desired steady state with synchronized inverter frequency across the network and proportional sharing of both active and reactive powers among the inverters. We also show that such convergence can be achieved while respecting constraints on voltage magnitude and branch angle differences. The controller is robust under various contingency scenarios, including loss of communication links and failures of DERs. The proposed controller is applicable to lossymore » mesh microgrids with heterogeneous R/X distribution lines and reasonable parameter variations. Simulations based on various microgrid operation scenarios are also provided to show the effectiveness of the proposed control method.« less

A model predictive speed tracking control approach for autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Zhu, Min; Chen, Huiyan; Xiong, Guangming

2017-03-01

This paper presents a novel speed tracking control approach based on a model predictive control (MPC) framework for autonomous ground vehicles. A switching algorithm without calibration is proposed to determine the drive or brake control. Combined with a simple inverse longitudinal vehicle model and adaptive regulation of MPC, this algorithm can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. A simplified quadratic program (QP) solving algorithm is used to reduce the computational time, and the approach has been applied in a 16-bit microcontroller. The performance of the proposed approach is evaluated via simulations and vehicle tests, which were carried out in a range of speed-profile tracking tasks. With a well-designed system structure, high-precision speed control is achieved. The system can robustly model uncertainty and external disturbances, and yields a faster response with less overshoot than a PI controller.

Switching LPV Control with Double-Layer LPV Model for Aero-Engines

NASA Astrophysics Data System (ADS)

Tang, Lili; Huang, Jinquan; Pan, Muxuan

2017-11-01

To cover the whole range of operating conditions of aero-engine, a double-layer LPV model is built so as to take into account of the variability due to the flight altitude, Mach number and the rotational speed. With this framework, the problem of designing LPV state-feedback robust controller that guarantees desired bounds on both H_∞ and H_2 performances is considered. Besides this, to reduce the conservativeness caused by a single LPV controller of the whole flight envelope and the common Lyapunov function method, a new method is proposed to design a family of LPV switching controllers. The switching LPV controllers can ensure that the closed-loop system remains stable in the sense of Lyapunov under arbitrary switching logic. Meanwhile, the switching LPV controllers can ensure the parameters change smoothly. The validity and performance of the theoretical results are demonstrated through a numerical example.

A new scheme for biomonitoring heavy metal concentrations in semi-natural wetlands.

PubMed

Batzias, A F; Siontorou, C G

2008-10-30

This work introduces a semi-natural wetland biomonitoring framework for heavy metal concentrations based on a robust dynamic integration between biological assemblages and relevant biosensors. The cooperative/synergistic scheme developed minimizes uncertainty and monitoring costs and increases reliability of pollution control and abatement. Attention is given to establishing a fully functioning and reliable network approach for monitoring inflows and achieving dose-response relations and calibration of biomonitoring species. The biomonitoring network initially consists of both, biosensors and species, as a validation phase in each wetland of the surveillance area; once the species monitoring efficiency is verified by the biosensors, the biosensor network moves to the next wetland and so on, following a circular pattern until all area wetlands have a fully functional natural monitoring scheme. By means of species recalibration with periodic revisiting of the biosensors, the scheme progressively reaches a quasi steady-state (including seasonality), thus ensuring reliability and robustness. This framework, currently pilot-tested in Voiotia, Greece, for assessing chromium levels, has been built to cover short-, medium- and long-term monitoring requirements. The results gathered so far, support the employment of the proposed scheme in heavy metal monitoring, and, further, arise the need for volunteer involvement to achieve long-term viability.

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

DOE PAGES

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.; ...

2017-09-22

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Bivalent rLP2086 (Trumenba®): Development of a well-characterized vaccine through commercialization.

PubMed

Sunasara, Khurram; Cundy, John; Srinivasan, Sriram; Evans, Brad; Sun, Weiqiang; Cook, Scott; Bortell, Eric; Farley, John; Griffin, Daniel; Bailey Piatchek, Michele; Arch-Douglas, Katherine

2018-05-24

The phrase "Process is the Product" is often applied to biologics, including multicomponent vaccines composed of complex components that evade complete characterization. Vaccine production processes must be defined and locked early in the development cycle to ensure consistent quality of the vaccine throughout scale-up, clinical studies, and commercialization. This approach of front-loading the development work helped facilitate the accelerated approval of the Biologic License Application for the well-characterized vaccine bivalent rLP2086 (Trumenba®, Pfizer Inc) in 2014 under Breakthrough Therapy Designation. Bivalent rLP2086 contains two rLP2086 antigens and is licensed for the prevention of meningococcal meningitis disease caused by Neisseria meningitidis serogroup B in individuals 10-25years of age in the United States. This paper discusses the development of the manufacturing process of the two antigens for the purpose of making it amenable to any manufacturing facility. For the journey to commercialization, the operating model used to manage this highly accelerated program led to a framework that ensured "right the first time" execution, robust process characterization, and proactive process monitoring. This framework enabled quick problem identification and proactive resolutions, resulting in a robust control strategy for the commercial process. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modelling and Simulation of the Dynamics of the Antigen-Specific T Cell Response Using Variable Structure Control Theory.

PubMed

Anelone, Anet J N; Spurgeon, Sarah K

2016-01-01

Experimental and mathematical studies in immunology have revealed that the dynamics of the programmed T cell response to vigorous infection can be conveniently modelled using a sigmoidal or a discontinuous immune response function. This paper hypothesizes strong synergies between this existing work and the dynamical behaviour of engineering systems with a variable structure control (VSC) law. These findings motivate the interpretation of the immune system as a variable structure control system. It is shown that dynamical properties as well as conditions to analytically assess the transition from health to disease can be developed for the specific T cell response from the theory of variable structure control. In particular, it is shown that the robustness properties of the specific T cell response as observed in experiments can be explained analytically using a VSC perspective. Further, the predictive capacity of the VSC framework to determine the T cell help required to overcome chronic Lymphocytic Choriomeningitis Virus (LCMV) infection is demonstrated. The findings demonstrate that studying the immune system using variable structure control theory provides a new framework for evaluating immunological dynamics and experimental observations. A modelling and simulation tool results with predictive capacity to determine how to modify the immune response to achieve healthy outcomes which may have application in drug development and vaccine design.

An index-based robust decision making framework for watershed management in a changing climate.

PubMed

Kim, Yeonjoo; Chung, Eun-Sung

2014-03-01

This study developed an index-based robust decision making framework for watershed management dealing with water quantity and quality issues in a changing climate. It consists of two parts of management alternative development and analysis. The first part for alternative development consists of six steps: 1) to understand the watershed components and process using HSPF model, 2) to identify the spatial vulnerability ranking using two indices: potential streamflow depletion (PSD) and potential water quality deterioration (PWQD), 3) to quantify the residents' preferences on water management demands and calculate the watershed evaluation index which is the weighted combinations of PSD and PWQD, 4) to set the quantitative targets for water quantity and quality, 5) to develop a list of feasible alternatives and 6) to eliminate the unacceptable alternatives. The second part for alternative analysis has three steps: 7) to analyze all selected alternatives with a hydrologic simulation model considering various climate change scenarios, 8) to quantify the alternative evaluation index including social and hydrologic criteria with utilizing multi-criteria decision analysis methods and 9) to prioritize all options based on a minimax regret strategy for robust decision. This framework considers the uncertainty inherent in climate models and climate change scenarios with utilizing the minimax regret strategy, a decision making strategy under deep uncertainty and thus this procedure derives the robust prioritization based on the multiple utilities of alternatives from various scenarios. In this study, the proposed procedure was applied to the Korean urban watershed, which has suffered from streamflow depletion and water quality deterioration. Our application shows that the framework provides a useful watershed management tool for incorporating quantitative and qualitative information into the evaluation of various policies with regard to water resource planning and management. Copyright © 2013 Elsevier B.V. All rights reserved.

Positronium emission spectra from self-assembled metal-organic frameworks

NASA Astrophysics Data System (ADS)

Crivelli, P.; Cooke, D.; Barbiellini, B.; Brown, B. L.; Feldblyum, J. I.; Guo, P.; Gidley, D. W.; Gerchow, L.; Matzger, A. J.

2014-06-01

Results of positronium (Ps) emission into vacuum from self-assembled metal-organic frameworks (MOFs) are presented and discussed in detail. Four different MOF crystals are considered, namely, MOF-5, IRMOF-8, ZnO4(FMA)3, and IRMOF-20. The measurements reveal that a fraction of the Ps is emitted into vacuum with a distinctly smaller energy than what one would expect for Ps localized in the MOFs' cells. Only calculations considering the Ps delocalized in a Bloch state can reproduce the measured Ps emission energy providing a robust demonstration of wave function delocalization in quantum mechanics. We show how the Bloch state population can be controlled by tuning the initial positron beam energy. Therefore, Ps in MOFs can be used both to simulate the dynamics of delocalized excitations in materials and to probe the MOFs for their advanced characterization.

Cloud computing strategic framework (FY13 - FY15).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arellano, Lawrence R.; Arroyo, Steven C.; Giese, Gerald J.

This document presents an architectural framework (plan) and roadmap for the implementation of a robust Cloud Computing capability at Sandia National Laboratories. It is intended to be a living document and serve as the basis for detailed implementation plans, project proposals and strategic investment requests.

Toward a bioethical framework for antibiotic use, antimicrobial resistance and for empirically designing ethically robust strategies to protect human health: a research protocol

PubMed Central

Martins Pereira, Sandra; de Sá Brandão, Patrícia Joana; Araújo, Joana; Carvalho, Ana Sofia

2017-01-01

Introduction Antimicrobial resistance (AMR) is a challenging global and public health issue, raising bioethical challenges, considerations and strategies. Objectives This research protocol presents a conceptual model leading to formulating an empirically based bioethics framework for antibiotic use, AMR and designing ethically robust strategies to protect human health. Methods Mixed methods research will be used and operationalized into five substudies. The bioethical framework will encompass and integrate two theoretical models: global bioethics and ethical decision-making. Results Being a study protocol, this article reports on planned and ongoing research. Conclusions Based on data collection, future findings and using a comprehensive, integrative, evidence-based approach, a step-by-step bioethical framework will be developed for (i) responsible use of antibiotics in healthcare and (ii) design of strategies to decrease AMR. This will entail the analysis and interpretation of approaches from several bioethical theories, including deontological and consequentialist approaches, and the implications of uncertainty to these approaches. PMID:28459355

Robust estimation approach for blind denoising.

PubMed

Rabie, Tamer

2005-11-01

This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.

Towards building a robust computational framework to simulate multi-physics problems - a solution technique for three-phase (gas-liquid-solid) interactions

NASA Astrophysics Data System (ADS)

Zhang, Lucy

In this talk, we show a robust numerical framework to model and simulate gas-liquid-solid three-phase flows. The overall algorithm adopts a non-boundary-fitted approach that avoids frequent mesh-updating procedures by defining independent meshes and explicit interfacial points to represent each phase. In this framework, we couple the immersed finite element method (IFEM) and the connectivity-free front tracking (CFFT) method that model fluid-solid and gas-liquid interactions, respectively, for the three-phase models. The CFFT is used here to simulate gas-liquid multi-fluid flows that uses explicit interfacial points to represent the gas-liquid interface and for its easy handling of interface topology changes. Instead of defining different levels simultaneously as used in level sets, an indicator function naturally couples the two methods together to represent and track each of the three phases. Several 2-D and 3-D testing cases are performed to demonstrate the robustness and capability of the coupled numerical framework in dealing with complex three-phase problems, in particular free surfaces interacting with deformable solids. The solution technique offers accuracy and stability, which provides a means to simulate various engineering applications. The author would like to acknowledge the supports from NIH/DHHS R01-2R01DC005642-10A1 and the National Natural Science Foundation of China (NSFC) 11550110185.

A new approach to mixed H2/H infinity controller synthesis using gradient-based parameter optimization methods

NASA Technical Reports Server (NTRS)

Ly, Uy-Loi; Schoemig, Ewald

1993-01-01

In the past few years, the mixed H(sub 2)/H-infinity control problem has been the object of much research interest since it allows the incorporation of robust stability into the LQG framework. The general mixed H(sub 2)/H-infinity design problem has yet to be solved analytically. Numerous schemes have considered upper bounds for the H(sub 2)-performance criterion and/or imposed restrictive constraints on the class of systems under investigation. Furthermore, many modern control applications rely on dynamic models obtained from finite-element analysis and thus involve high-order plant models. Hence the capability to design low-order (fixed-order) controllers is of great importance. In this research a new design method was developed that optimizes the exact H(sub 2)-norm of a certain subsystem subject to robust stability in terms of H-infinity constraints and a minimal number of system assumptions. The derived algorithm is based on a differentiable scalar time-domain penalty function to represent the H-infinity constraints in the overall optimization. The scheme is capable of handling multiple plant conditions and hence multiple performance criteria and H-infinity constraints and incorporates additional constraints such as fixed-order and/or fixed structure controllers. The defined penalty function is applicable to any constraint that is expressible in form of a real symmetric matrix-inequity.

Sparse coding for flexible, robust 3D facial-expression synthesis.

PubMed

Lin, Yuxu; Song, Mingli; Quynh, Dao Thi Phuong; He, Ying; Chen, Chun

2012-01-01

Computer animation researchers have been extensively investigating 3D facial-expression synthesis for decades. However, flexible, robust production of realistic 3D facial expressions is still technically challenging. A proposed modeling framework applies sparse coding to synthesize 3D expressive faces, using specified coefficients or expression examples. It also robustly recovers facial expressions from noisy and incomplete data. This approach can synthesize higher-quality expressions in less time than the state-of-the-art techniques.

Efficient Computation of Info-Gap Robustness for Finite Element Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stull, Christopher J.; Hemez, Francois M.; Williams, Brian J.

2012-07-05

A recent research effort at LANL proposed info-gap decision theory as a framework by which to measure the predictive maturity of numerical models. Info-gap theory explores the trade-offs between accuracy, that is, the extent to which predictions reproduce the physical measurements, and robustness, that is, the extent to which predictions are insensitive to modeling assumptions. Both accuracy and robustness are necessary to demonstrate predictive maturity. However, conducting an info-gap analysis can present a formidable challenge, from the standpoint of the required computational resources. This is because a robustness function requires the resolution of multiple optimization problems. This report offers anmore » alternative, adjoint methodology to assess the info-gap robustness of Ax = b-like numerical models solved for a solution x. Two situations that can arise in structural analysis and design are briefly described and contextualized within the info-gap decision theory framework. The treatments of the info-gap problems, using the adjoint methodology are outlined in detail, and the latter problem is solved for four separate finite element models. As compared to statistical sampling, the proposed methodology offers highly accurate approximations of info-gap robustness functions for the finite element models considered in the report, at a small fraction of the computational cost. It is noted that this report considers only linear systems; a natural follow-on study would extend the methodologies described herein to include nonlinear systems.« less

From Field Notes to Data Portal - A Scalable Data QA/QC Framework for Tower Networks: Progress and Preliminary Results

NASA Astrophysics Data System (ADS)

Sturtevant, C.; Hackley, S.; Lee, R.; Holling, G.; Bonarrigo, S.

2017-12-01

Quality assurance and control (QA/QC) is one of the most important yet challenging aspects of producing research-quality data. Data quality issues are multi-faceted, including sensor malfunctions, unmet theoretical assumptions, and measurement interference from humans or the natural environment. Tower networks such as Ameriflux, ICOS, and NEON continue to grow in size and sophistication, yet tools for robust, efficient, scalable QA/QC have lagged. Quality control remains a largely manual process heavily relying on visual inspection of data. In addition, notes of measurement interference are often recorded on paper without an explicit pathway to data flagging. As such, an increase in network size requires a near-proportional increase in personnel devoted to QA/QC, quickly stressing the human resources available. We present a scalable QA/QC framework in development for NEON that combines the efficiency and standardization of automated checks with the power and flexibility of human review. This framework includes fast-response monitoring of sensor health, a mobile application for electronically recording maintenance activities, traditional point-based automated quality flagging, and continuous monitoring of quality outcomes and longer-term holistic evaluations. This framework maintains the traceability of quality information along the entirety of the data generation pipeline, and explicitly links field reports of measurement interference to quality flagging. Preliminary results show that data quality can be effectively monitored and managed for a multitude of sites with a small group of QA/QC staff. Several components of this framework are open-source, including a R-Shiny application for efficiently monitoring, synthesizing, and investigating data quality issues.

Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis

ERIC Educational Resources Information Center

Young, Cristobal; Holsteen, Katherine

2017-01-01

Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all…

Robust and efficient anomaly detection using heterogeneous representations

NASA Astrophysics Data System (ADS)

Hu, Xing; Hu, Shiqiang; Xie, Jinhua; Zheng, Shiyou

2015-05-01

Various approaches have been proposed for video anomaly detection. Yet these approaches typically suffer from one or more limitations: they often characterize the pattern using its internal information, but ignore its external relationship which is important for local anomaly detection. Moreover, the high-dimensionality and the lack of robustness of pattern representation may lead to problems, including overfitting, increased computational cost and memory requirements, and high false alarm rate. We propose a video anomaly detection framework which relies on a heterogeneous representation to account for both the pattern's internal information and external relationship. The internal information is characterized by slow features learned by slow feature analysis from low-level representations, and the external relationship is characterized by the spatial contextual distances. The heterogeneous representation is compact, robust, efficient, and discriminative for anomaly detection. Moreover, both the pattern's internal information and external relationship can be taken into account in the proposed framework. Extensive experiments demonstrate the robustness and efficiency of our approach by comparison with the state-of-the-art approaches on the widely used benchmark datasets.

Fuzzy robust credibility-constrained programming for environmental management and planning.

PubMed

Zhang, Yimei; Hang, Guohe

2010-06-01

In this study, a fuzzy robust credibility-constrained programming (FRCCP) is developed and applied to the planning for waste management systems. It incorporates the concepts of credibility-based chance-constrained programming and robust programming within an optimization framework. The developed method can reflect uncertainties presented as possibility-density by fuzzy-membership functions. Fuzzy credibility constraints are transformed to the crisp equivalents with different credibility levels, and ordinary fuzzy inclusion constraints are determined by their robust deterministic constraints by setting a-cut levels. The FRCCP method can provide different system costs under different credibility levels (lambda). From the results of sensitivity analyses, the operation cost of the landfill is a critical parameter. For the management, any factors that would induce cost fluctuation during landfilling operation would deserve serious observation and analysis. By FRCCP, useful solutions can be obtained to provide decision-making support for long-term planning of solid waste management systems. It could be further enhanced through incorporating methods of inexact analysis into its framework. It can also be applied to other environmental management problems.

Image retrieval by information fusion based on scalable vocabulary tree and robust Hausdorff distance

NASA Astrophysics Data System (ADS)

Che, Chang; Yu, Xiaoyang; Sun, Xiaoming; Yu, Boyang

2017-12-01

In recent years, Scalable Vocabulary Tree (SVT) has been shown to be effective in image retrieval. However, for general images where the foreground is the object to be recognized while the background is cluttered, the performance of the current SVT framework is restricted. In this paper, a new image retrieval framework that incorporates a robust distance metric and information fusion is proposed, which improves the retrieval performance relative to the baseline SVT approach. First, the visual words that represent the background are diminished by using a robust Hausdorff distance between different images. Second, image matching results based on three image signature representations are fused, which enhances the retrieval precision. We conducted intensive experiments on small-scale to large-scale image datasets: Corel-9, Corel-48, and PKU-198, where the proposed Hausdorff metric and information fusion outperforms the state-of-the-art methods by about 13, 15, and 15%, respectively.

Designing robust control laws using genetic algorithms

NASA Technical Reports Server (NTRS)

Marrison, Chris

1994-01-01

The purpose of this research is to create a method of finding practical, robust control laws. The robustness of a controller is judged by Stochastic Robustness metrics and the level of robustness is optimized by searching for design parameters that minimize a robustness cost function.

Viewing relational aggression through multiple lenses: temperament, personality, and personality pathology.

PubMed

Tackett, Jennifer L; Kushner, Shauna C; Herzhoff, Kathrin; Smack, Avante J; Reardon, Kathleen W

2014-08-01

Dispositional trait frameworks offer great potential to elucidate the nature and development of psychopathology, including the construct of relational aggression. The present study sought to explore the dispositional context of relational aggression across three dispositional frameworks: temperament, personality, and personality pathology. Participants comprised a large community sample of youth, aged 6 to 18 years (N = 1,188; 51.2% female). Ratings of children's relational aggression, temperament, personality, and personality pathology traits were obtained through parent report (86.3% mothers). Results showed convergence and divergence across these three dispositional frameworks. Like other antisocial behavior subtypes, relational aggression generally showed connections with traits reflecting negative emotionality and poor self-regulation. Relational aggression showed stronger connections with temperament traits than with personality traits, suggesting that temperament frameworks may capture more relationally aggressive content. Findings at the lower order trait level help differentiate relational aggression from other externalizing problems by providing a more nuanced perspective (e.g., both sociability and shyness positively predicted relational aggression). In addition, there was little evidence of moderation of these associations by gender, age, or age2, and findings remained robust even after controlling for physical aggression. Results are discussed in the broader context of conceptualizing relational aggression in an overarching personality-psychopathology framework.

Biologically inspired design of feedback control systems implemented using DNA strand displacement reactions.

PubMed

Foo, Mathias; Sawlekar, Rucha; Kulkarni, Vishwesh V; Bates, Declan G

2016-08-01

The use of abstract chemical reaction networks (CRNs) as a modelling and design framework for the implementation of computing and control circuits using enzyme-free, entropy driven DNA strand displacement (DSD) reactions is starting to garner widespread attention in the area of synthetic biology. Previous work in this area has demonstrated the theoretical plausibility of using this approach to design biomolecular feedback control systems based on classical proportional-integral (PI) controllers, which may be constructed from CRNs implementing gain, summation and integrator operators. Here, we propose an alternative design approach that utilises the abstract chemical reactions involved in cellular signalling cycles to implement a biomolecular controller - termed a signalling-cycle (SC) controller. We compare the performance of the PI and SC controllers in closed-loop with a nonlinear second-order chemical process. Our results show that the SC controller outperforms the PI controller in terms of both performance and robustness, and also requires fewer abstract chemical reactions to implement, highlighting its potential usefulness in the construction of biomolecular control circuits.

Figures of Merit for Control Verification

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Goesu. Daniel P.

2008-01-01

This paper proposes a methodology for evaluating a controller's ability to satisfy a set of closed-loop specifications when the plant has an arbitrary functional dependency on uncertain parameters. Control verification metrics applicable to deterministic and probabilistic uncertainty models are proposed. These metrics, which result from sizing the largest uncertainty set of a given class for which the specifications are satisfied, enable systematic assessment of competing control alternatives regardless of the methods used to derive them. A particularly attractive feature of the tools derived is that their efficiency and accuracy do not depend on the robustness of the controller. This is in sharp contrast to Monte Carlo based methods where the number of simulations required to accurately approximate the failure probability grows exponentially with its closeness to zero. This framework allows for the integration of complex, high-fidelity simulations of the integrated system and only requires standard optimization algorithms for its implementation.

Experimental study of adaptive pointing and tracking for large flexible space structures

NASA Technical Reports Server (NTRS)

Boussalis, D.; Bayard, D. S.; Ih, C.; Wang, S. J.; Ahmed, A.

1991-01-01

This paper describes an experimental study of adaptive pointing and tracking control for flexible spacecraft conducted on a complex ground experiment facility. The algorithm used in this study is based on a multivariable direct model reference adaptive control law. Several experimental validation studies were performed earlier using this algorithm for vibration damping and robust regulation, with excellent results. The current work extends previous studies by addressing the pointing and tracking problem. As is consistent with an adaptive control framework, the plant is assumed to be poorly known to the extent that only system level knowledge of its dynamics is available. Explicit bounds on the steady-state pointing error are derived as functions of the adaptive controller design parameters. It is shown that good tracking performance can be achieved in an experimental setting by adjusting adaptive controller design weightings according to the guidelines indicated by the analytical expressions for the error.

Pandemic influenza preparedness: an ethical framework to guide decision-making

PubMed Central

Thompson, Alison K; Faith, Karen; Gibson, Jennifer L; Upshur, Ross EG

2006-01-01

Background Planning for the next pandemic influenza outbreak is underway in hospitals across the world. The global SARS experience has taught us that ethical frameworks to guide decision-making may help to reduce collateral damage and increase trust and solidarity within and between health care organisations. Good pandemic planning requires reflection on values because science alone cannot tell us how to prepare for a public health crisis. Discussion In this paper, we present an ethical framework for pandemic influenza planning. The ethical framework was developed with expertise from clinical, organisational and public health ethics and validated through a stakeholder engagement process. The ethical framework includes both substantive and procedural elements for ethical pandemic influenza planning. The incorporation of ethics into pandemic planning can be helped by senior hospital administrators sponsoring its use, by having stakeholders vet the framework, and by designing or identifying decision review processes. We discuss the merits and limits of an applied ethical framework for hospital decision-making, as well as the robustness of the framework. Summary The need for reflection on the ethical issues raised by the spectre of a pandemic influenza outbreak is great. Our efforts to address the normative aspects of pandemic planning in hospitals have generated interest from other hospitals and from the governmental sector. The framework will require re-evaluation and refinement and we hope that this paper will generate feedback on how to make it even more robust. PMID:17144926

Death of a Simulated Pediatric Patient: Toward a More Robust Theoretical Framework.

PubMed

McBride, Mary E; Schinasi, Dana Aronson; Moga, Michael Alice; Tripathy, Shreepada; Calhoun, Aaron

2017-12-01

A theoretical framework was recently proposed that encapsulates learner responses to simulated death due to action or inaction in the pediatric context. This framework, however, was developed at an institution that allows simulated death and thus does not address the experience of those centers at which this technique is not used. To address this, we performed a parallel qualitative study with the intent of augmenting the initial framework. We conducted focus groups, using a constructivist grounded theory approach, using physicians and nurses who have experienced a simulated cardiac arrest. The participants were recruited via e-mail. Transcripts were analyzed by coders blinded to the original framework to generate a list of provisional themes that were iteratively refined. These themes were then compared with the themes from the original article and used to derive a consensus model that incorporated the most relevant features of each. Focus group data yielded 7 themes. Six were similar to those developed in the original framework. One important exception was noted; however, those learners not exposed to patient death due to action or inaction often felt that the mannequin's survival was artificial. This additional theme was incorporated into a revised framework. The original framework addresses most aspects of learner reactions to simulated death. Our work suggests that adding the theme pertaining to the lack of realism that can be perceived when the mannequin is unexpectedly saved results in a more robust theoretical framework transferable to centers that do not allow mannequin death.

Collective learning for the emergence of social norms in networked multiagent systems.

PubMed

Yu, Chao; Zhang, Minjie; Ren, Fenghui

2014-12-01

Social norms such as social rules and conventions play a pivotal role in sustaining system order by regulating and controlling individual behaviors toward a global consensus in large-scale distributed systems. Systematic studies of efficient mechanisms that can facilitate the emergence of social norms enable us to build and design robust distributed systems, such as electronic institutions and norm-governed sensor networks. This paper studies the emergence of social norms via learning from repeated local interactions in networked multiagent systems. A collective learning framework, which imitates the opinion aggregation process in human decision making, is proposed to study the impact of agent local collective behaviors on the emergence of social norms in a number of different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action toward each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. Extensive experiments are carried out to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, influences of nonlearning agents, and so on. Experimental results reveal some significant insights into the manipulation and control of norm emergence in networked multiagent systems achieved through local collective behaviors.

A General Framework of Persistence Strategies for Biological Systems Helps Explain Domains of Life

PubMed Central

Yafremava, Liudmila S.; Wielgos, Monica; Thomas, Suravi; Nasir, Arshan; Wang, Minglei; Mittenthal, Jay E.; Caetano-Anollés, Gustavo

2012-01-01

The nature and cause of the division of organisms in superkingdoms is not fully understood. Assuming that environment shapes physiology, here we construct a novel theoretical framework that helps identify general patterns of organism persistence. This framework is based on Jacob von Uexküll’s organism-centric view of the environment and James G. Miller’s view of organisms as matter-energy-information processing molecular machines. Three concepts describe an organism’s environmental niche: scope, umwelt, and gap. Scope denotes the entirety of environmental events and conditions to which the organism is exposed during its lifetime. Umwelt encompasses an organism’s perception of these events. The gap is the organism’s blind spot, the scope that is not covered by umwelt. These concepts bring organisms of different complexity to a common ecological denominator. Ecological and physiological data suggest organisms persist using three strategies: flexibility, robustness, and economy. All organisms use umwelt information to flexibly adapt to environmental change. They implement robustness against environmental perturbations within the gap generally through redundancy and reliability of internal constituents. Both flexibility and robustness improve survival. However, they also incur metabolic matter-energy processing costs, which otherwise could have been used for growth and reproduction. Lineages evolve unique tradeoff solutions among strategies in the space of what we call “a persistence triangle.” Protein domain architecture and other evidence support the preferential use of flexibility and robustness properties. Archaea and Bacteria gravitate toward the triangle’s economy vertex, with Archaea biased toward robustness. Eukarya trade economy for survivability. Protista occupy a saddle manifold separating akaryotes from multicellular organisms. Plants and the more flexible Fungi share an economic stratum, and Metazoa are locked in a positive feedback loop toward flexibility. PMID:23443991

Selective adsorption of sulfur dioxide in a robust metal-organic framework material

DOE PAGES

Savage, Mathew; Cheng, Yongqiang; Easun, Timothy L.; ...

2016-08-16

Here, selective adsorption of SO 2 is realized in a porous metal–organic framework material, and in-depth structural and spectroscopic investigations using X-rays, infrared, and neutrons define the underlying interactions that cause SO 2 to bind more strongly than CO 2 and N 2.

A constrained robust least squares approach for contaminant release history identification

NASA Astrophysics Data System (ADS)

Sun, Alexander Y.; Painter, Scott L.; Wittmeyer, Gordon W.

2006-04-01

Contaminant source identification is an important type of inverse problem in groundwater modeling and is subject to both data and model uncertainty. Model uncertainty was rarely considered in the previous studies. In this work, a robust framework for solving contaminant source recovery problems is introduced. The contaminant source identification problem is first cast into one of solving uncertain linear equations, where the response matrix is constructed using a superposition technique. The formulation presented here is general and is applicable to any porous media flow and transport solvers. The robust least squares (RLS) estimator, which originated in the field of robust identification, directly accounts for errors arising from model uncertainty and has been shown to significantly reduce the sensitivity of the optimal solution to perturbations in model and data. In this work, a new variant of RLS, the constrained robust least squares (CRLS), is formulated for solving uncertain linear equations. CRLS allows for additional constraints, such as nonnegativity, to be imposed. The performance of CRLS is demonstrated through one- and two-dimensional test problems. When the system is ill-conditioned and uncertain, it is found that CRLS gave much better performance than its classical counterpart, the nonnegative least squares. The source identification framework developed in this work thus constitutes a reliable tool for recovering source release histories in real applications.

Instrument Remote Control via the Astronomical Instrument Markup Language

NASA Technical Reports Server (NTRS)

Sall, Ken; Ames, Troy; Warsaw, Craig; Koons, Lisa; Shafer, Richard

1998-01-01

The Instrument Remote Control (IRC) project ongoing at NASA's Goddard Space Flight Center's (GSFC) Information Systems Center (ISC) supports NASA's mission by defining an adaptive intranet-based framework that provides robust interactive and distributed control and monitoring of remote instruments. An astronomical IRC architecture that combines the platform-independent processing capabilities of Java with the power of Extensible Markup Language (XML) to express hierarchical data in an equally platform-independent, as well as human readable manner, has been developed. This architecture is implemented using a variety of XML support tools and Application Programming Interfaces (API) written in Java. IRC will enable trusted astronomers from around the world to easily access infrared instruments (e.g., telescopes, cameras, and spectrometers) located in remote, inhospitable environments, such as the South Pole, a high Chilean mountaintop, or an airborne observatory aboard a Boeing 747. Using IRC's frameworks, an astronomer or other scientist can easily define the type of onboard instrument, control the instrument remotely, and return monitoring data all through the intranet. The Astronomical Instrument Markup Language (AIML) is the first implementation of the more general Instrument Markup Language (IML). The key aspects of our approach to instrument description and control applies to many domains, from medical instruments to machine assembly lines. The concepts behind AIML apply equally well to the description and control of instruments in general. IRC enables us to apply our techniques to several instruments, preferably from different observatories.

Optimization of storage tank locations in an urban stormwater drainage system using a two-stage approach.

PubMed

Wang, Mingming; Sun, Yuanxiang; Sweetapple, Chris

2017-12-15

Storage is important for flood mitigation and non-point source pollution control. However, to seek a cost-effective design scheme for storage tanks is very complex. This paper presents a two-stage optimization framework to find an optimal scheme for storage tanks using storm water management model (SWMM). The objectives are to minimize flooding, total suspended solids (TSS) load and storage cost. The framework includes two modules: (i) the analytical module, which evaluates and ranks the flooding nodes with the analytic hierarchy process (AHP) using two indicators (flood depth and flood duration), and then obtains the preliminary scheme by calculating two efficiency indicators (flood reduction efficiency and TSS reduction efficiency); (ii) the iteration module, which obtains an optimal scheme using a generalized pattern search (GPS) method based on the preliminary scheme generated by the analytical module. The proposed approach was applied to a catchment in CZ city, China, to test its capability in choosing design alternatives. Different rainfall scenarios are considered to test its robustness. The results demonstrate that the optimal framework is feasible, and the optimization is fast based on the preliminary scheme. The optimized scheme is better than the preliminary scheme for reducing runoff and pollutant loads under a given storage cost. The multi-objective optimization framework presented in this paper may be useful in finding the best scheme of storage tanks or low impact development (LID) controls. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Robust Learning Model (RLM): A Comprehensive Approach to a New Online University

ERIC Educational Resources Information Center

Neumann, Yoram; Neumann, Edith F.

2010-01-01

This paper outlines the components of the Robust Learning Model (RLM) as a conceptual framework for creating a new online university offering numerous degree programs at all degree levels. The RLM is a multi-factorial model based on the basic belief that successful learning outcomes depend on multiple factors employed together in a holistic…

Robust and Fragile Mathematical Identities: A Framework for Exploring Racialized Experiences and High Achievement among Black College Students

ERIC Educational Resources Information Center

McGee, Ebony O.

2015-01-01

I introduce the construct of fragile and robust identities for the purpose of exploring the experiences that influenced the mathematical and racial identities of high-achieving Black college students in mathematics and engineering. These students maintained high levels of academic achievement in these fields while enduring marginalization,…

Using the 'Social Marketing Mix Framework' to explore recruitment barriers and facilitators in palliative care randomised controlled trials? A narrative synthesis review.

PubMed

Dunleavy, Lesley; Walshe, Catherine; Oriani, Anna; Preston, Nancy

2018-05-01

Effective recruitment to randomised controlled trials is critically important for a robust, trustworthy evidence base in palliative care. Many trials fail to achieve recruitment targets, but the reasons for this are poorly understood. Understanding barriers and facilitators is a critical step in designing optimal recruitment strategies. To identify, explore and synthesise knowledge about recruitment barriers and facilitators in palliative care trials using the '6 Ps' of the 'Social Marketing Mix Framework'. A systematic review with narrative synthesis. Medline, CINAHL, PsycINFO and Embase databases (from January 1990 to early October 2016) were searched. Papers included the following: interventional and qualitative studies addressing recruitment, palliative care randomised controlled trial papers or reports containing narrative observations about the barriers, facilitators or strategies to increase recruitment. A total of 48 papers met the inclusion criteria. Uninterested participants (Product), burden of illness (Price) and 'identifying eligible participants' were barriers. Careful messaging and the use of scripts/role play (Promotion) were recommended. The need for intensive resources and gatekeeping by professionals were barriers while having research staff on-site and lead clinician support (Working with Partners) was advocated. Most evidence is based on researchers' own reports of experiences of recruiting to trials rather than independent evaluation. The 'Social Marketing Mix Framework' can help guide researchers when planning and implementing their recruitment strategy but suggested strategies need to be tested within embedded clinical trials. The findings of this review are applicable to all palliative care research and not just randomised controlled trials.

Construction of hierarchically porous metal–organic frameworks through linker labilization

DOE PAGES

Yuan, Shuai; Zou, Lanfang; Qin, Jun-Sheng; ...

2017-05-25

One major goal of metal–organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. W present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragmentsmore » by acid treatment. We also demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis.« less

Construction of hierarchically porous metal-organic frameworks through linker labilization

NASA Astrophysics Data System (ADS)

Yuan, Shuai; Zou, Lanfang; Qin, Jun-Sheng; Li, Jialuo; Huang, Lan; Feng, Liang; Wang, Xuan; Bosch, Mathieu; Alsalme, Ali; Cagin, Tahir; Zhou, Hong-Cai

2017-05-01

A major goal of metal-organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. Herein, we present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragments by acid treatment. We demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis.

Construction of hierarchically porous metal–organic frameworks through linker labilization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yuan, Shuai; Zou, Lanfang; Qin, Jun-Sheng

One major goal of metal–organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. W present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragmentsmore » by acid treatment. We also demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis.« less

A Survey and Analysis of Frameworks and Framework Issues for Information Fusion Applications

NASA Astrophysics Data System (ADS)

Llinas, James

This paper was stimulated by the proposed project for the Santander Bank-sponsored "Chairs of Excellence" program in Spain, of which the author is a recipient. That project involves research on characterizing a robust, problem-domain-agnostic framework in which Information Fusion (IF) processes of all description, to include artificial intelligence processes and techniques could be developed. The paper describes the IF process and its requirements, a literature survey on IF frameworks, and a new proposed framework that will be implemented and evaluated at Universidad Carlos III de Madrid, Colmenarejo Campus.

Robust nano-fabrication of an integrated platform for spin control in a tunable microcavity

NASA Astrophysics Data System (ADS)

Bogdanović, Stefan; Liddy, Madelaine S. Z.; van Dam, Suzanne B.; Coenen, Lisanne C.; Fink, Thomas; Lončar, Marko; Hanson, Ronald

2017-12-01

Coupling nitrogen-vacancy (NV) centers in diamonds to optical cavities is a promising way to enhance the efficiency of diamond-based quantum networks. An essential aspect of the full toolbox required for the operation of these networks is the ability to achieve the microwave control of the electron spin associated with this defect within the cavity framework. Here, we report on the fabrication of an integrated platform for the microwave control of an NV center electron spin in an open, tunable Fabry-Pérot microcavity. A critical aspect of the measurements of the cavity's finesse reveals that the presented fabrication process does not compromise its optical properties. We provide a method to incorporate a thin diamond slab into the cavity architecture and demonstrate the control of the NV center spin. These results show the promise of this design for future cavity-enhanced NV center spin-photon entanglement experiments.

Molecular inspired models for prediction and control of directional FSO/RF wireless networks

NASA Astrophysics Data System (ADS)

Llorca, Jaime; Milner, Stuart D.; Davis, Christopher C.

2010-08-01

Directional wireless networks using FSO and RF transmissions provide wireless backbone support for mobile communications in dynamic environments. The heterogeneous and dynamic nature of such networks challenges their robustness and requires self-organization mechanisms to assure end-to-end broadband connectivity. We developed a framework based on the definition of a potential energy function to characterize robustness in communication networks and the study of first and second order variations of the potential energy to provide prediction and control strategies for network performance optimization. In this paper, we present non-convex molecular potentials such as the Morse Potential, used to describe the potential energy of bonds within molecules, for the characterization of communication links in the presence of physical constraints such as the power available at the network nodes. The inclusion of the Morse Potential translates into adaptive control strategies where forces on network nodes drive the release, retention or reconfiguration of communication links for network performance optimization. Simulation results show the effectiveness of our self-organized control mechanism, where the physical topology reorganizes to maximize the number of source to destination communicating pairs. Molecular Normal Mode Analysis (NMA) techniques for assessing network performance degradation in dynamic networks are also presented. Preliminary results show correlation between peaks in the eigenvalues of the Hessian of the network potential and network degradation.

Many-Objective Robust Decision Making: Managing Water in a Deeply Uncertain World of Change (Invited)

NASA Astrophysics Data System (ADS)

Reed, P. M.

2013-12-01

Water resources planning and management has always required the consideration of uncertainties and the associated system vulnerabilities that they may cause. Despite the long legacy of these issues, our decision support frameworks that have dominated the literature over the past 50 years have struggled with the strongly multiobjective and deeply uncertain nature of water resources systems. The term deep uncertainty (or Knightian uncertainty) refers to factors in planning that strongly shape system risks that maybe unknown and even if known there is a strong lack of consensus on their likelihoods over decadal planning horizons (population growth, financial stability, valuation of resources, ecosystem requirements, evolving water institutions, regulations, etc). In this presentation, I will propose and demonstrate the many-objective robust decision making (MORDM) framework for water resources management under deep uncertainty. The MORDM framework will be demonstrated using an urban water portfolio management test case. In the test case, a city in the Lower Rio Grande Valley managing population and drought pressures must cost effectively maintain the reliability of its water supply by blending permanent rights to reservoir inflows with alternative strategies for purchasing water within the region's water market. The case study illustrates the significant potential pitfalls in the classic Cost-Reliability conception of the problem. Moreover, the proposed MORDM framework exploits recent advances in multiobjective search, visualization, and sensitivity analysis to better expose these pitfalls en route to identifying highly robust water planning alternatives.

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach

PubMed Central

Girrbach, Fabian; Hol, Jeroen D.; Bellusci, Giovanni; Diehl, Moritz

2017-01-01

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem. PMID:28534857

Cognitive simulators for medical education and training.

PubMed

Kahol, Kanav; Vankipuram, Mithra; Smith, Marshall L

2009-08-01

Simulators for honing procedural skills (such as surgical skills and central venous catheter placement) have proven to be valuable tools for medical educators and students. While such simulations represent an effective paradigm in surgical education, there is an opportunity to add a layer of cognitive exercises to these basic simulations that can facilitate robust skill learning in residents. This paper describes a controlled methodology, inspired by neuropsychological assessment tasks and embodied cognition, to develop cognitive simulators for laparoscopic surgery. These simulators provide psychomotor skill training and offer the additional challenge of accomplishing cognitive tasks in realistic environments. A generic framework for design, development and evaluation of such simulators is described. The presented framework is generalizable and can be applied to different task domains. It is independent of the types of sensors, simulation environment and feedback mechanisms that the simulators use. A proof of concept of the framework is provided through developing a simulator that includes cognitive variations to a basic psychomotor task. The results of two pilot studies are presented that show the validity of the methodology in providing an effective evaluation and learning environments for surgeons.

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach.

PubMed

Girrbach, Fabian; Hol, Jeroen D; Bellusci, Giovanni; Diehl, Moritz

2017-05-19

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem.

Adaptive fixed-time trajectory tracking control of a stratospheric airship.

PubMed

Zheng, Zewei; Feroskhan, Mir; Sun, Liang

2018-05-01

This paper addresses the fixed-time trajectory tracking control problem of a stratospheric airship. By extending the method of adding a power integrator to a novel adaptive fixed-time control method, the convergence of a stratospheric airship to its reference trajectory is guaranteed to be achieved within a fixed time. The control algorithm is firstly formulated without the consideration of external disturbances to establish the stability of the closed-loop system in fixed-time and demonstrate that the convergence time of the airship is essentially independent of its initial conditions. Subsequently, a smooth adaptive law is incorporated into the proposed fixed-time control framework to provide the system with robustness to external disturbances. Theoretical analyses demonstrate that under the adaptive fixed-time controller, the tracking errors will converge towards a residual set in fixed-time. The results of a comparative simulation study with other recent methods illustrate the remarkable performance and superiority of the proposed control method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Developing a complex systems perspective for medical education to facilitate the integration of basic science and clinical medicine.

PubMed

Aron, David C

2017-04-01

The purpose of medical education is to produce competent and capable professional practitioners who can combine the art and science of medicine. Moreover, this process must prepare individuals to practise in a field in which knowledge is increasing and the contexts in which that knowledge is applied are changing in unpredictable ways. The 'basic sciences' are important in the training of a physician. The goal of basic science training is to learn it in a way that the material can be applied in practice. Much effort has been expended to integrate basic science and clinical training, while adding many other topics to the medical curriculum. This effort has been challenging. The aims of the paper are (1) to propose a unifying conceptual framework that facilitates knowledge integration among all levels of living systems from cell to society and (2) illustrate the organizing principles with two examples of the framework in action - cybernetic systems (with feedback) and distributed robustness. Literature related to hierarchical and holarchical frameworks was reviewed. An organizing framework derived from living systems theory and spanning the range from molecular biology to health systems management was developed. The application of cybernetic systems to three levels (regulation of pancreatic beta cell production of insulin, physician adjustment of medication for glycaemic control and development and action of performance measures for diabetes care) was illustrated. Similarly distributed robustness was illustrated by the DNA damage response system and principles underlying patient safety. Each of the illustrated organizing principles offers a means to facilitate the weaving of basic science and clinical medicine throughout the course of study. The use of such an approach may promote systems thinking, which is a core competency for effective and capable medical practice. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning

ERIC Educational Resources Information Center

Koedinger, Kenneth R.; Corbett, Albert T.; Perfetti, Charles

2012-01-01

Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of…

Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems

DOE PAGES

Rangarajan, Srinivas; Maravelias, Christos T.; Mavrikakis, Manos

2017-11-09

Here, we present a general optimization-based framework for (i) ab initio and experimental data driven mechanistic modeling and (ii) optimal catalyst design of heterogeneous catalytic systems. Both cases are formulated as a nonlinear optimization problem that is subject to a mean-field microkinetic model and thermodynamic consistency requirements as constraints, for which we seek sparse solutions through a ridge (L 2 regularization) penalty. The solution procedure involves an iterative sequence of forward simulation of the differential algebraic equations pertaining to the microkinetic model using a numerical tool capable of handling stiff systems, sensitivity calculations using linear algebra, and gradient-based nonlinear optimization.more » A multistart approach is used to explore the solution space, and a hierarchical clustering procedure is implemented for statistically classifying potentially competing solutions. An example of methanol synthesis through hydrogenation of CO and CO 2 on a Cu-based catalyst is used to illustrate the framework. The framework is fast, is robust, and can be used to comprehensively explore the model solution and design space of any heterogeneous catalytic system.« less

Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rangarajan, Srinivas; Maravelias, Christos T.; Mavrikakis, Manos

Here, we present a general optimization-based framework for (i) ab initio and experimental data driven mechanistic modeling and (ii) optimal catalyst design of heterogeneous catalytic systems. Both cases are formulated as a nonlinear optimization problem that is subject to a mean-field microkinetic model and thermodynamic consistency requirements as constraints, for which we seek sparse solutions through a ridge (L 2 regularization) penalty. The solution procedure involves an iterative sequence of forward simulation of the differential algebraic equations pertaining to the microkinetic model using a numerical tool capable of handling stiff systems, sensitivity calculations using linear algebra, and gradient-based nonlinear optimization.more » A multistart approach is used to explore the solution space, and a hierarchical clustering procedure is implemented for statistically classifying potentially competing solutions. An example of methanol synthesis through hydrogenation of CO and CO 2 on a Cu-based catalyst is used to illustrate the framework. The framework is fast, is robust, and can be used to comprehensively explore the model solution and design space of any heterogeneous catalytic system.« less

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Xiongbiao, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; Wan, Ying, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; He, Xiangjian

Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) asmore » a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods.« less

A new robust adaptive controller for vibration control of active engine mount subjected to large uncertainties

NASA Astrophysics Data System (ADS)

Fakhari, Vahid; Choi, Seung-Bok; Cho, Chang-Hyun

2015-04-01

This work presents a new robust model reference adaptive control (MRAC) for vibration control caused from vehicle engine using an electromagnetic type of active engine mount. Vibration isolation performances of the active mount associated with the robust controller are evaluated in the presence of large uncertainties. As a first step, an active mount with linear solenoid actuator is prepared and its dynamic model is identified via experimental test. Subsequently, a new robust MRAC based on the gradient method with σ-modification is designed by selecting a proper reference model. In designing the robust adaptive control, structured (parametric) uncertainties in the stiffness of the passive part of the mount and in damping ratio of the active part of the mount are considered to investigate the robustness of the proposed controller. Experimental and simulation results are presented to evaluate performance focusing on the robustness behavior of the controller in the face of large uncertainties. The obtained results show that the proposed controller can sufficiently provide the robust vibration control performance even in the presence of large uncertainties showing an effective vibration isolation.

Robust parameter design for automatically controlled systems and nanostructure synthesis

NASA Astrophysics Data System (ADS)

Dasgupta, Tirthankar

2007-12-01

This research focuses on developing comprehensive frameworks for developing robust parameter design methodology for dynamic systems with automatic control and for synthesis of nanostructures. In many automatically controlled dynamic processes, the optimal feedback control law depends on the parameter design solution and vice versa and therefore an integrated approach is necessary. A parameter design methodology in the presence of feedback control is developed for processes of long duration under the assumption that experimental noise factors are uncorrelated over time. Systems that follow a pure-gain dynamic model are considered and the best proportional-integral and minimum mean squared error control strategies are developed by using robust parameter design. The proposed method is illustrated using a simulated example and a case study in a urea packing plant. This idea is also extended to cases with on-line noise factors. The possibility of integrating feedforward control with a minimum mean squared error feedback control scheme is explored. To meet the needs of large scale synthesis of nanostructures, it is critical to systematically find experimental conditions under which the desired nanostructures are synthesized reproducibly, at large quantity and with controlled morphology. The first part of the research in this area focuses on modeling and optimization of existing experimental data. Through a rigorous statistical analysis of experimental data, models linking the probabilities of obtaining specific morphologies to the process variables are developed. A new iterative algorithm for fitting a Multinomial GLM is proposed and used. The optimum process conditions, which maximize the above probabilities and make the synthesis process less sensitive to variations of process variables around set values, are derived from the fitted models using Monte-Carlo simulations. The second part of the research deals with development of an experimental design methodology, tailor-made to address the unique phenomena associated with nanostructure synthesis. A sequential space filling design called Sequential Minimum Energy Design (SMED) for exploring best process conditions for synthesis of nanowires. The SMED is a novel approach to generate sequential designs that are model independent, can quickly "carve out" regions with no observable nanostructure morphology, and allow for the exploration of complex response surfaces.

A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

NASA Astrophysics Data System (ADS)

Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

Harnessing Sparse and Low-Dimensional Structures for Robust Clustering of Imagery Data

ERIC Educational Resources Information Center

Rao, Shankar Ramamohan

2009-01-01

We propose a robust framework for clustering data. In practice, data obtained from real measurement devices can be incomplete, corrupted by gross errors, or not correspond to any assumed model. We show that, by properly harnessing the intrinsic low-dimensional structure of the data, these kinds of practical problems can be dealt with in a uniform…

A model to assess the Mars Telecommunications Network relay robustness

NASA Technical Reports Server (NTRS)

Girerd, Andre R.; Meshkat, Leila; Edwards, Charles D., Jr.; Lee, Charles H.

2005-01-01

The relatively long mission durations and compatible radio protocols of current and projected Mars orbiters have enabled the gradual development of a heterogeneous constellation providing proximity communication services for surface assets. The current and forecasted capability of this evolving network has reached the point that designers of future surface missions consider complete dependence on it. Such designers, along with those architecting network requirements, have a need to understand the robustness of projected communication service. A model has been created to identify the robustness of the Mars Network as a function of surface location and time. Due to the decade-plus time horizon considered, the network will evolve, with emerging productive nodes and nodes that cease or fail to contribute. The model is a flexible framework to holistically process node information into measures of capability robustness that can be visualized for maximum understanding. Outputs from JPL's Telecom Orbit Analysis Simulation Tool (TOAST) provide global telecom performance parameters for current and projected orbiters. Probabilistic estimates of orbiter fuel life are derived from orbit keeping burn rates, forecasted maneuver tasking, and anomaly resolution budgets. Orbiter reliability is estimated probabilistically. A flexible scheduling framework accommodates the projected mission queue as well as potential alterations.

Robust output tracking control of a laboratory helicopter for automatic landing

NASA Astrophysics Data System (ADS)

Liu, Hao; Lu, Geng; Zhong, Yisheng

2014-11-01

In this paper, robust output tracking control problem of a laboratory helicopter for automatic landing in high seas is investigated. The motion of the helicopter is required to synchronise with that of an oscillating platform, e.g. the deck of a vessel subject to wave-induced motions. A robust linear time-invariant output feedback controller consisting of a nominal controller and a robust compensator is designed. The robust compensator is introduced to restrain the influences of parametric uncertainties, nonlinearities and external disturbances. It is shown that robust stability and robust tracking property can be achieved simultaneously. Experimental results on the laboratory helicopter for automatic landing demonstrate the effectiveness of the designed control approach.

A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

NASA Astrophysics Data System (ADS)

Anees, Asim; Aryal, Jagannath; O'Reilly, Małgorzata M.; Gale, Timothy J.; Wardlaw, Tim

2016-12-01

A robust non-parametric framework, based on multiple Radial Basic Function (RBF) kernels, is proposed in this study, for detecting land/forest cover changes using Landsat 7 ETM+ images. One of the widely used frameworks is to find change vectors (difference image) and use a supervised classifier to differentiate between change and no-change. The Bayesian Classifiers e.g. Maximum Likelihood Classifier (MLC), Naive Bayes (NB), are widely used probabilistic classifiers which assume parametric models, e.g. Gaussian function, for the class conditional distributions. However, their performance can be limited if the data set deviates from the assumed model. The proposed framework exploits the useful properties of Least Squares Probabilistic Classifier (LSPC) formulation i.e. non-parametric and probabilistic nature, to model class posterior probabilities of the difference image using a linear combination of a large number of Gaussian kernels. To this end, a simple technique, based on 10-fold cross-validation is also proposed for tuning model parameters automatically instead of selecting a (possibly) suboptimal combination from pre-specified lists of values. The proposed framework has been tested and compared with Support Vector Machine (SVM) and NB for detection of defoliation, caused by leaf beetles (Paropsisterna spp.) in Eucalyptus nitens and Eucalyptus globulus plantations of two test areas, in Tasmania, Australia, using raw bands and band combination indices of Landsat 7 ETM+. It was observed that due to multi-kernel non-parametric formulation and probabilistic nature, the LSPC outperforms parametric NB with Gaussian assumption in change detection framework, with Overall Accuracy (OA) ranging from 93.6% (κ = 0.87) to 97.4% (κ = 0.94) against 85.3% (κ = 0.69) to 93.4% (κ = 0.85), and is more robust to changing data distributions. Its performance was comparable to SVM, with added advantages of being probabilistic and capable of handling multi-class problems naturally with its original formulation.

Optimizing surveillance for livestock disease spreading through animal movements

PubMed Central

Bajardi, Paolo; Barrat, Alain; Savini, Lara; Colizza, Vittoria

2012-01-01

The spatial propagation of many livestock infectious diseases critically depends on the animal movements among premises; so the knowledge of movement data may help us to detect, manage and control an outbreak. The identification of robust spreading features of the system is however hampered by the temporal dimension characterizing population interactions through movements. Traditional centrality measures do not provide relevant information as results strongly fluctuate in time and outbreak properties heavily depend on geotemporal initial conditions. By focusing on the case study of cattle displacements in Italy, we aim at characterizing livestock epidemics in terms of robust features useful for planning and control, to deal with temporal fluctuations, sensitivity to initial conditions and missing information during an outbreak. Through spatial disease simulations, we detect spreading paths that are stable across different initial conditions, allowing the clustering of the seeds and reducing the epidemic variability. Paths also allow us to identify premises, called sentinels, having a large probability of being infected and providing critical information on the outbreak origin, as encoded in the clusters. This novel procedure provides a general framework that can be applied to specific diseases, for aiding risk assessment analysis and informing the design of optimal surveillance systems. PMID:22728387

Spontaneous neuronal activity as a self-organized critical phenomenon

NASA Astrophysics Data System (ADS)

de Arcangelis, L.; Herrmann, H. J.

2013-01-01

Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.

Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes.

PubMed

Cook, James P; Mahajan, Anubha; Morris, Andrew P

2017-02-01

Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population stratification and relatedness through inclusion of random effects for a genetic relationship matrix. However, the utility of linear (mixed) models in the context of meta-analysis of GWAS of binary phenotypes has not been previously explored. In this investigation, we present simulations to compare the performance of linear and logistic regression models under alternative weighting schemes in a fixed-effects meta-analysis framework, considering designs that incorporate variable case-control imbalance, confounding factors and population stratification. Our results demonstrate that linear models can be used for meta-analysis of GWAS of binary phenotypes, without loss of power, even in the presence of extreme case-control imbalance, provided that one of the following schemes is used: (i) effective sample size weighting of Z-scores or (ii) inverse-variance weighting of allelic effect sizes after conversion onto the log-odds scale. Our conclusions thus provide essential recommendations for the development of robust protocols for meta-analysis of binary phenotypes with linear models.

Robustness surfaces of complex networks

NASA Astrophysics Data System (ADS)

Manzano, Marc; Sahneh, Faryad; Scoglio, Caterina; Calle, Eusebi; Marzo, Jose Luis

2014-09-01

Despite the robustness of complex networks has been extensively studied in the last decade, there still lacks a unifying framework able to embrace all the proposed metrics. In the literature there are two open issues related to this gap: (a) how to dimension several metrics to allow their summation and (b) how to weight each of the metrics. In this work we propose a solution for the two aforementioned problems by defining the R*-value and introducing the concept of robustness surface (Ω). The rationale of our proposal is to make use of Principal Component Analysis (PCA). We firstly adjust to 1 the initial robustness of a network. Secondly, we find the most informative robustness metric under a specific failure scenario. Then, we repeat the process for several percentage of failures and different realizations of the failure process. Lastly, we join these values to form the robustness surface, which allows the visual assessment of network robustness variability. Results show that a network presents different robustness surfaces (i.e., dissimilar shapes) depending on the failure scenario and the set of metrics. In addition, the robustness surface allows the robustness of different networks to be compared.

Robustness surfaces of complex networks.

PubMed

Manzano, Marc; Sahneh, Faryad; Scoglio, Caterina; Calle, Eusebi; Marzo, Jose Luis

2014-09-02

Despite the robustness of complex networks has been extensively studied in the last decade, there still lacks a unifying framework able to embrace all the proposed metrics. In the literature there are two open issues related to this gap: (a) how to dimension several metrics to allow their summation and (b) how to weight each of the metrics. In this work we propose a solution for the two aforementioned problems by defining the R*-value and introducing the concept of robustness surface (Ω). The rationale of our proposal is to make use of Principal Component Analysis (PCA). We firstly adjust to 1 the initial robustness of a network. Secondly, we find the most informative robustness metric under a specific failure scenario. Then, we repeat the process for several percentage of failures and different realizations of the failure process. Lastly, we join these values to form the robustness surface, which allows the visual assessment of network robustness variability. Results show that a network presents different robustness surfaces (i.e., dissimilar shapes) depending on the failure scenario and the set of metrics. In addition, the robustness surface allows the robustness of different networks to be compared.

Dissipative rendering and neural network control system design

NASA Technical Reports Server (NTRS)

Gonzalez, Oscar R.

1995-01-01

Model-based control system designs are limited by the accuracy of the models of the plant, plant uncertainty, and exogenous signals. Although better models can be obtained with system identification, the models and control designs still have limitations. One approach to reduce the dependency on particular models is to design a set of compensators that will guarantee robust stability to a set of plants. Optimization over the compensator parameters can then be used to get the desired performance. Conservativeness of this approach can be reduced by integrating fundamental properties of the plant models. This is the approach of dissipative control design. Dissipative control designs are based on several variations of the Passivity Theorem, which have been proven for nonlinear/linear and continuous-time/discrete-time systems. These theorems depend not on a specific model of a plant, but on its general dissipative properties. Dissipative control design has found wide applicability in flexible space structures and robotic systems that can be configured to be dissipative. Currently, there is ongoing research to improve the performance of dissipative control designs. For aircraft systems that are not dissipative active control may be used to make them dissipative and then a dissipative control design technique can be used. It is also possible that rendering a system dissipative and dissipative control design may be combined into one step. Furthermore, the transformation of a non-dissipative system to dissipative can be done robustly. One sequential design procedure for finite dimensional linear time-invariant systems has been developed. For nonlinear plants that cannot be controlled adequately with a single linear controller, model-based techniques have additional problems. Nonlinear system identification is still a research topic. Lacking analytical models for model-based design, artificial neural network algorithms have recently received considerable attention. Using their universal approximation property, neural networks have been introduced into nonlinear control designs in several ways. Unfortunately, little work has appeared that analyzes neural network control systems and establishes margins for stability and performance. One approach for this analysis is to set up neural network control systems in the framework presented above. For example, one neural network could be used to render a system to be dissipative, a second strictly dissipative neural network controller could be used to guarantee robust stability.

Robust Fixed-Structure Controller Synthesis

NASA Technical Reports Server (NTRS)

Corrado, Joseph R.; Haddad, Wassim M.; Gupta, Kajal (Technical Monitor)

2000-01-01

The ability to develop an integrated control system design methodology for robust high performance controllers satisfying multiple design criteria and real world hardware constraints constitutes a challenging task. The increasingly stringent performance specifications required for controlling such systems necessitates a trade-off between controller complexity and robustness. The principle challenge of the minimal complexity robust control design is to arrive at a tractable control design formulation in spite of the extreme complexity of such systems. Hence, design of minimal complexitY robust controllers for systems in the face of modeling errors has been a major preoccupation of system and control theorists and practitioners for the past several decades.

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application

NASA Technical Reports Server (NTRS)

Schmidt, Phillip; Garg, Sanjay; Holowecky, Brian

1992-01-01

A parameter optimization framework is presented to solve the problem of partitioning a centralized controller into a decentralized hierarchical structure suitable for integrated flight/propulsion control implementation. The controller partitioning problem is briefly discussed and a cost function to be minimized is formulated, such that the resulting 'optimal' partitioned subsystem controllers will closely match the performance (including robustness) properties of the closed-loop system with the centralized controller while maintaining the desired controller partitioning structure. The cost function is written in terms of parameters in a state-space representation of the partitioned sub-controllers. Analytical expressions are obtained for the gradient of this cost function with respect to parameters, and an optimization algorithm is developed using modern computer-aided control design and analysis software. The capabilities of the algorithm are demonstrated by application to partitioned integrated flight/propulsion control design for a modern fighter aircraft in the short approach to landing task. The partitioning optimization is shown to lead to reduced-order subcontrollers that match the closed-loop command tracking and decoupling performance achieved by a high-order centralized controller.

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application

NASA Technical Reports Server (NTRS)

Schmidt, Phillip H.; Garg, Sanjay; Holowecky, Brian R.

1993-01-01

A parameter optimization framework is presented to solve the problem of partitioning a centralized controller into a decentralized hierarchical structure suitable for integrated flight/propulsion control implementation. The controller partitioning problem is briefly discussed and a cost function to be minimized is formulated, such that the resulting 'optimal' partitioned subsystem controllers will closely match the performance (including robustness) properties of the closed-loop system with the centralized controller while maintaining the desired controller partitioning structure. The cost function is written in terms of parameters in a state-space representation of the partitioned sub-controllers. Analytical expressions are obtained for the gradient of this cost function with respect to parameters, and an optimization algorithm is developed using modern computer-aided control design and analysis software. The capabilities of the algorithm are demonstrated by application to partitioned integrated flight/propulsion control design for a modern fighter aircraft in the short approach to landing task. The partitioning optimization is shown to lead to reduced-order subcontrollers that match the closed-loop command tracking and decoupling performance achieved by a high-order centralized controller.

Robust, Chiral, and Porous BINAP-Based Metal–Organic Frameworks for Highly Enantioselective Cyclization Reactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sawano, Takahiro; Thacker, Nathan C.; Lin, Zekai

2016-05-06

We report here the design of BINAP-based metal–organic frameworks and their postsynthetic metalation with Rh complexes to afford highly active and enantioselective single-site solid catalysts for the asymmetric cyclization reactions of 1,6-enynes. Robust, chiral, and porous Zr-MOFs of UiO topology, BINAP-MOF (I) or BINAP-dMOF (II), were prepared using purely BINAP-derived dicarboxylate linkers or by mixing BINAP-derived linkers with unfunctionalized dicarboxylate linkers, respectively. Upon metalation with Rh(nbd)2BF4 and [Rh(nbd)Cl]2/AgSbF6, the MOF precatalysts I·Rh(BF4) and I·Rh(SbF6) efficiently catalyzed highly enantioselective (up to 99% ee) reductive cyclization and Alder-ene cycloisomerization of 1,6-enynes, respectively. I·Rh catalysts afforded cyclization products at comparable enantiomeric excesses (ee’s)more » and 4–7 times higher catalytic activity than the homogeneous controls, likely a result of catalytic site isolation in the MOF which prevents bimolecular catalyst deactivation pathways. However, I·Rh is inactive in the more sterically encumbered Pauson–Khand reactions between 1,6-enynes and carbon monoxide. In contrast, with a more open structure, Rh-functionalized BINAP-dMOF, II·Rh, effectively catalyzed Pauson–Khand cyclization reactions between 1,6-enynes and carbon monoxide at 10 times higher activity than the homogeneous control. II·Rh was readily recovered and used three times in Pauson–Khand cyclization reactions without deterioration of yields or ee’s. Our work has expanded the scope of MOF-catalyzed asymmetric reactions and showed that the mixed linker strategy can effectively enlarge the open space around the catalytic active site to accommodate highly sterically demanding polycyclic metallocycle transition states/intermediates in asymmetric intramolecular cyclization reactions.« less

The constrained discrete-time state-dependent Riccati equation technique for uncertain nonlinear systems

NASA Astrophysics Data System (ADS)

Chang, Insu

The objective of the thesis is to introduce a relatively general nonlinear controller/estimator synthesis framework using a special type of the state-dependent Riccati equation technique. The continuous time state-dependent Riccati equation (SDRE) technique is extended to discrete-time under input and state constraints, yielding constrained (C) discrete-time (D) SDRE, referred to as CD-SDRE. For the latter, stability analysis and calculation of a region of attraction are carried out. The derivation of the D-SDRE under state-dependent weights is provided. Stability of the D-SDRE feedback system is established using Lyapunov stability approach. Receding horizon strategy is used to take into account the constraints on D-SDRE controller. Stability condition of the CD-SDRE controller is analyzed by using a switched system. The use of CD-SDRE scheme in the presence of constraints is then systematically demonstrated by applying this scheme to problems of spacecraft formation orbit reconfiguration under limited performance on thrusters. Simulation results demonstrate the efficacy and reliability of the proposed CD-SDRE. The CD-SDRE technique is further investigated in a case where there are uncertainties in nonlinear systems to be controlled. First, the system stability under each of the controllers in the robust CD-SDRE technique is separately established. The stability of the closed-loop system under the robust CD-SDRE controller is then proven based on the stability of each control system comprising switching configuration. A high fidelity dynamical model of spacecraft attitude motion in 3-dimensional space is derived with a partially filled fuel tank, assumed to have the first fuel slosh mode. The proposed robust CD-SDRE controller is then applied to the spacecraft attitude control system to stabilize its motion in the presence of uncertainties characterized by the first fuel slosh mode. The performance of the robust CD-SDRE technique is discussed. Subsequently, filtering techniques are investigated by using the D-SDRE technique. Detailed derivation of the D-SDRE-based filter (D-SDREF) is provided under the assumption of Gaussian noises and the stability condition of the error signal between the measured signal and the estimated signals is proven to be input-to-state stable. For the non-Gaussian distributed noises, we propose a filter by combining the D-SDREF and the particle filter (PF), named the combined D-SDRE/PF. Two algorithms for the filtering techniques are provided. Several filtering techniques are compared with challenging numerical examples to show the reliability and efficacy of the proposed D-SDREF and the combined D-SDRE/PF.

Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials.

PubMed

Yan, Zhennan; Zhang, Shaoting; Tan, Chaowei; Qin, Hongxing; Belaroussi, Boubakeur; Yu, Hui Jing; Miller, Colin; Metaxas, Dimitris N

2015-04-01

Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic fat-fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to obtain fine segmentation. Fat-fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with two other atlas-based methods. Experimental results demonstrate the promises of our assessment framework. Copyright © 2014 Elsevier Ltd. All rights reserved.

Methods for compressible multiphase flows and their applications

NASA Astrophysics Data System (ADS)

Kim, H.; Choe, Y.; Kim, H.; Min, D.; Kim, C.

2018-06-01

This paper presents an efficient and robust numerical framework to deal with multiphase real-fluid flows and their broad spectrum of engineering applications. A homogeneous mixture model incorporated with a real-fluid equation of state and a phase change model is considered to calculate complex multiphase problems. As robust and accurate numerical methods to handle multiphase shocks and phase interfaces over a wide range of flow speeds, the AUSMPW+_N and RoeM_N schemes with a system preconditioning method are presented. These methods are assessed by extensive validation problems with various types of equation of state and phase change models. Representative realistic multiphase phenomena, including the flow inside a thermal vapor compressor, pressurization in a cryogenic tank, and unsteady cavitating flow around a wedge, are then investigated as application problems. With appropriate physical modeling followed by robust and accurate numerical treatments, compressible multiphase flow physics such as phase changes, shock discontinuities, and their interactions are well captured, confirming the suitability of the proposed numerical framework to wide engineering applications.

Catchment Classification: Connecting Climate, Structure and Function

NASA Astrophysics Data System (ADS)

Sawicz, K. A.; Wagener, T.; Sivapalan, M.; Troch, P. A.; Carrillo, G. A.

2010-12-01

Hydrology does not yet possess a generally accepted catchment classification framework. Such a classification framework needs to: [1] give names to things, i.e. the main classification step, [2] permit transfer of information, i.e. regionalization of information, [3] permit development of generalizations, i.e. to develop new theory, and [4] provide a first order environmental change impact assessment, i.e., the hydrologic implications of climate, land use and land cover change. One strategy is to create a catchment classification framework based on the notion of catchment functions (partitioning, storage, and release). Results of an empirical study presented here connects climate and structure to catchment function (in the form of select hydrologic signatures), based on analyzing over 300 US catchments. Initial results indicate a wide assortment of signature relationships with properties of climate, geology, and vegetation. The uncertainty in the different regionalized signatures varies widely, and therefore there is variability in the robustness of classifying ungauged basins. This research provides insight into the controls of hydrologic behavior of a catchment, and enables a classification framework applicable to gauged and ungauged across the study domain. This study sheds light on what we can expect to achieve in mapping climate, structure and function in a top-down manner. Results of this study complement work done using a bottom-up physically-based modeling framework to generalize this approach (Carrillo et al., this session).

Improving benchmarking by using an explicit framework for the development of composite indicators: an example using pediatric quality of care

PubMed Central

2010-01-01

Background The measurement of healthcare provider performance is becoming more widespread. Physicians have been guarded about performance measurement, in part because the methodology for comparative measurement of care quality is underdeveloped. Comprehensive quality improvement will require comprehensive measurement, implying the aggregation of multiple quality metrics into composite indicators. Objective To present a conceptual framework to develop comprehensive, robust, and transparent composite indicators of pediatric care quality, and to highlight aspects specific to quality measurement in children. Methods We reviewed the scientific literature on composite indicator development, health systems, and quality measurement in the pediatric healthcare setting. Frameworks were selected for explicitness and applicability to a hospital-based measurement system. Results We synthesized various frameworks into a comprehensive model for the development of composite indicators of quality of care. Among its key premises, the model proposes identifying structural, process, and outcome metrics for each of the Institute of Medicine's six domains of quality (safety, effectiveness, efficiency, patient-centeredness, timeliness, and equity) and presents a step-by-step framework for embedding the quality of care measurement model into composite indicator development. Conclusions The framework presented offers researchers an explicit path to composite indicator development. Without a scientifically robust and comprehensive approach to measurement of the quality of healthcare, performance measurement will ultimately fail to achieve its quality improvement goals. PMID:20181129

Tuning-free controller to accurately regulate flow rates in a microfluidic network

NASA Astrophysics Data System (ADS)

Heo, Young Jin; Kang, Junsu; Kim, Min Jun; Chung, Wan Kyun

2016-03-01

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework.

Tuning-free controller to accurately regulate flow rates in a microfluidic network

PubMed Central

Heo, Young Jin; Kang, Junsu; Kim, Min Jun; Chung, Wan Kyun

2016-01-01

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework. PMID:26987587

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Devkota, Jagannath; Kim, Ki-Joong; Ohodnicki, Paul R.

The integration of nanoporous materials such as metal organic frameworks (MOFs) with sensitive transducers can result in robust sensing platforms for monitoring gases and chemical vapors for a range of applications.

A Mechanism for Graded, Dynamically Routable Current Propagation in Pulse-Gated Synfire Chains and Implications for Information Coding

PubMed Central

Sornborger, Andrew T.; Wang, Zhuo; Tao, Louis

2015-01-01

Neural oscillations can enhance feature recognition [1], modulate interactions between neurons [2], and improve learning and memory [3]. Numerical studies have shown that coherent spiking can give rise to windows in time during which information transfer can be enhanced in neuronal networks [4–6]. Unanswered questions are: 1) What is the transfer mechanism? And 2) how well can a transfer be executed? Here, we present a pulse-based mechanism by which a graded current amplitude may be exactly propagated from one neuronal population to another. The mechanism relies on the downstream gating of mean synaptic current amplitude from one population of neurons to another via a pulse. Because transfer is pulse-based, information may be dynamically routed through a neural circuit with fixed connectivity. We demonstrate the transfer mechanism in a realistic network of spiking neurons and show that it is robust to noise in the form of pulse timing inaccuracies, random synaptic strengths and finite size effects. We also show that the mechanism is structurally robust in that it may be implemented using biologically realistic pulses. The transfer mechanism may be used as a building block for fast, complex information processing in neural circuits. We show that the mechanism naturally leads to a framework wherein neural information coding and processing can be considered as a product of linear maps under the active control of a pulse generator. Distinct control and processing components combine to form the basis for the binding, propagation, and processing of dynamically routed information within neural pathways. Using our framework, we construct example neural circuits to 1) maintain a short-term memory, 2) compute time-windowed Fourier transforms, and 3) perform spatial rotations. We postulate that such circuits, with automatic and stereotyped control and processing of information, are the neural correlates of Crick and Koch’s zombie modes. PMID:26227067

GLOFRIM v1.0 - A globally applicable computational framework for integrated hydrological-hydrodynamic modelling

NASA Astrophysics Data System (ADS)

Hoch, Jannis M.; Neal, Jeffrey C.; Baart, Fedor; van Beek, Rens; Winsemius, Hessel C.; Bates, Paul D.; Bierkens, Marc F. P.

2017-10-01

We here present GLOFRIM, a globally applicable computational framework for integrated hydrological-hydrodynamic modelling. GLOFRIM facilitates spatially explicit coupling of hydrodynamic and hydrologic models and caters for an ensemble of models to be coupled. It currently encompasses the global hydrological model PCR-GLOBWB as well as the hydrodynamic models Delft3D Flexible Mesh (DFM; solving the full shallow-water equations and allowing for spatially flexible meshing) and LISFLOOD-FP (LFP; solving the local inertia equations and running on regular grids). The main advantages of the framework are its open and free access, its global applicability, its versatility, and its extensibility with other hydrological or hydrodynamic models. Before applying GLOFRIM to an actual test case, we benchmarked both DFM and LFP for a synthetic test case. Results show that for sub-critical flow conditions, discharge response to the same input signal is near-identical for both models, which agrees with previous studies. We subsequently applied the framework to the Amazon River basin to not only test the framework thoroughly, but also to perform a first-ever benchmark of flexible and regular grids on a large-scale. Both DFM and LFP produce comparable results in terms of simulated discharge with LFP exhibiting slightly higher accuracy as expressed by a Kling-Gupta efficiency of 0.82 compared to 0.76 for DFM. However, benchmarking inundation extent between DFM and LFP over the entire study area, a critical success index of 0.46 was obtained, indicating that the models disagree as often as they agree. Differences between models in both simulated discharge and inundation extent are to a large extent attributable to the gridding techniques employed. In fact, the results show that both the numerical scheme of the inundation model and the gridding technique can contribute to deviations in simulated inundation extent as we control for model forcing and boundary conditions. This study shows that the presented computational framework is robust and widely applicable. GLOFRIM is designed as open access and easily extendable, and thus we hope that other large-scale hydrological and hydrodynamic models will be added. Eventually, more locally relevant processes would be captured and more robust model inter-comparison, benchmarking, and ensemble simulations of flood hazard on a large scale would be allowed for.

Introducing a New Learning and Teaching Evaluation Planning Framework for Small Internally Funded Projects in Higher Education

ERIC Educational Resources Information Center

Huber, Elaine

2017-01-01

Scholarly evaluation practices in learning and teaching projects are under-reported in the literature. In order for robust evaluative measures to be implemented, a project requires a well-designed evaluation plan. This research study describes the development of a practical evaluation planning framework through an action research approach, using…

A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring

USGS Publications Warehouse

Metzger, Marc J.; Bunce, Robert G.H.; Jongman, Rob H.G.; Sayre, Roger G.; Trabucco, Antonio; Zomer, Robert

2013-01-01

Main conclusions: The GEnS provides a robust spatial analytical framework for the aggregation of local observations, identification of gaps in current monitoring efforts and systematic design of complementary and new monitoring and research. The dataset is available for non-commercial use through the GEO portal (http://www.geoportal.org).

Contextualization of Nature of Science within the Socioscientific Issues Framework: A Review of Research

ERIC Educational Resources Information Center

Karisan, Dilek; Zeidler, Dana L.

2017-01-01

The aim of this paper is to examine the importance of contextualization of Nature of Science (NOS) within the Socioscientific Issues (SSI) framework, because of the importance to science education. The emphasis on advancing scientific literacy is contingent upon a robust understanding and appreciation of NOS, as well as the acquisition of…

That Your Education May Be Complete: Implementing the Bishops' Curriculum Framework in Continuity with the Christian Teaching Tradition

ERIC Educational Resources Information Center

Manning, Patrick R.

2012-01-01

While the U.S. Bishops' Doctrinal Elements of a Curriculum Framework provides robust content guidelines for a national high school Religion curriculum, its successful implementation will depend largely on concurrent development of, and training in, pedagogy suited to Christian education. This paper directs educators to existing catechetical…

The Interplay between Feedback and Buffering in Cellular Homeostasis.

PubMed

Hancock, Edward J; Ang, Jordan; Papachristodoulou, Antonis; Stan, Guy-Bart

2017-11-22

Buffering, the use of reservoirs of molecules to maintain concentrations of key molecular species, and negative feedback are the primary known mechanisms for robust homeostatic regulation. To our knowledge, however, the fundamental principles behind their combined effect have not been elucidated. Here, we study the interplay between buffering and negative feedback in the context of cellular homeostasis. We show that negative feedback counteracts slow-changing disturbances, whereas buffering counteracts fast-changing disturbances. Furthermore, feedback and buffering have limitations that create trade-offs for regulation: instability in the case of feedback and molecular noise in the case of buffering. However, because buffering stabilizes feedback and feedback attenuates noise from slower-acting buffering, their combined effect on homeostasis can be synergistic. These effects can be explained within a traditional control theory framework and are consistent with experimental observations of both ATP homeostasis and pH regulation in vivo. These principles are critical for studying robustness and homeostasis in biology and biotechnology. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

On-board orbit determination for low thrust LEO-MEO transfer by Consider Kalman Filtering and multi-constellation GNSS

NASA Astrophysics Data System (ADS)

Menzione, Francesco; Renga, Alfredo; Grassi, Michele

2017-09-01

In the framework of the novel navigation scenario offered by the next generation satellite low thrust autonomous LEO-to-MEO orbit transfer, this study proposes and tests a GNSS based navigation system aimed at providing on-board precise and robust orbit determination strategy to override rising criticalities. The analysis introduces the challenging design issues to simultaneously deal with the variable orbit regime, the electric thrust control and the high orbit GNSS visibility conditions. The Consider Kalman Filtering approach is here proposed as the filtering scheme to process the GNSS raw data provided by a multi-antenna/multi-constellation receiver in presence of uncertain parameters affecting measurements, actuation and spacecraft physical properties. Filter robustness and achievable navigation accuracy are verified using a high fidelity simulation of the low-thrust rising scenario and performance are compared with the one of a standard Extended Kalman Filtering approach to highlight the advantages of the proposed solution. Performance assessment of the developed navigation solution is accomplished for different transfer phases.

Robust representation and recognition of facial emotions using extreme sparse learning.

PubMed

Shojaeilangari, Seyedehsamaneh; Yau, Wei-Yun; Nandakumar, Karthik; Li, Jun; Teoh, Eam Khwang

2015-07-01

Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.

Space-time light field rendering.

PubMed

Wang, Huamin; Sun, Mingxuan; Yang, Ruigang

2007-01-01

In this paper, we propose a novel framework called space-time light field rendering, which allows continuous exploration of a dynamic scene in both space and time. Compared to existing light field capture/rendering systems, it offers the capability of using unsynchronized video inputs and the added freedom of controlling the visualization in the temporal domain, such as smooth slow motion and temporal integration. In order to synthesize novel views from any viewpoint at any time instant, we develop a two-stage rendering algorithm. We first interpolate in the temporal domain to generate globally synchronized images using a robust spatial-temporal image registration algorithm followed by edge-preserving image morphing. We then interpolate these software-synchronized images in the spatial domain to synthesize the final view. In addition, we introduce a very accurate and robust algorithm to estimate subframe temporal offsets among input video sequences. Experimental results from unsynchronized videos with or without time stamps show that our approach is capable of maintaining photorealistic quality from a variety of real scenes.

Intelligent robust control for uncertain nonlinear time-varying systems and its application to robotic systems.

PubMed

Chang, Yeong-Chan

2005-12-01

This paper addresses the problem of designing adaptive fuzzy-based (or neural network-based) robust controls for a large class of uncertain nonlinear time-varying systems. This class of systems can be perturbed by plant uncertainties, unmodeled perturbations, and external disturbances. Nonlinear H(infinity) control technique incorporated with adaptive control technique and VSC technique is employed to construct the intelligent robust stabilization controller such that an H(infinity) control is achieved. The problem of the robust tracking control design for uncertain robotic systems is employed to demonstrate the effectiveness of the developed robust stabilization control scheme. Therefore, an intelligent robust tracking controller for uncertain robotic systems in the presence of high-degree uncertainties can easily be implemented. Its solution requires only to solve a linear algebraic matrix inequality and a satisfactorily transient and asymptotical tracking performance is guaranteed. A simulation example is made to confirm the performance of the developed control algorithms.

Intelligent microchip networks: an agent-on-chip synthesis framework for the design of smart and robust sensor networks

NASA Astrophysics Data System (ADS)

Bosse, Stefan

2013-05-01

Sensorial materials consisting of high-density, miniaturized, and embedded sensor networks require new robust and reliable data processing and communication approaches. Structural health monitoring is one major field of application for sensorial materials. Each sensor node provides some kind of sensor, electronics, data processing, and communication with a strong focus on microchip-level implementation to meet the goals of miniaturization and low-power energy environments, a prerequisite for autonomous behaviour and operation. Reliability requires robustness of the entire system in the presence of node, link, data processing, and communication failures. Interaction between nodes is required to manage and distribute information. One common interaction model is the mobile agent. An agent approach provides stronger autonomy than a traditional object or remote-procedure-call based approach. Agents can decide for themselves, which actions are performed, and they are capable of flexible behaviour, reacting on the environment and other agents, providing some degree of robustness. Traditionally multi-agent systems are abstract programming models which are implemented in software and executed on program controlled computer architectures. This approach does not well scale to micro-chip level and requires full equipped computers and communication structures, and the hardware architecture does not consider and reflect the requirements for agent processing and interaction. We propose and demonstrate a novel design paradigm for reliable distributed data processing systems and a synthesis methodology and framework for multi-agent systems implementable entirely on microchip-level with resource and power constrained digital logic supporting Agent-On-Chip architectures (AoC). The agent behaviour and mobility is fully integrated on the micro-chip using pipelined communicating processes implemented with finite-state machines and register-transfer logic. The agent behaviour, interaction (communication), and mobility features are modelled and specified on a machine-independent abstract programming level using a state-based agent behaviour language (APL). With this APL a high-level agent compiler is able to synthesize a hardware model (RTL, VHDL), a software model (C, ML), or a simulation model (XML) suitable to simulate a multi-agent system using the SeSAm simulator framework. Agent communication is provided by a simple tuple-space database implemented on node level providing fault tolerant access of global data. A novel synthesis development kit (SynDK) based on a graph-structured database approach is introduced to support the rapid development of compilers and synthesis tools, used for example for the design and implementation of the APL compiler.

Robust tracking control of a magnetically suspended rigid body

NASA Technical Reports Server (NTRS)

Lim, Kyong B.; Cox, David E.

1994-01-01

This study is an application of H-infinity and micro-synthesis for designing robust tracking controllers for the Large Angle Magnetic Suspension Test Facility. The modeling, design, analysis, simulation, and testing of a control law that guarantees tracking performance under external disturbances and model uncertainties is investigated. The type of uncertainties considered and the tracking performance metric used is discussed. This study demonstrates the tradeoff between tracking performance at low frequencies and robustness at high frequencies. Two sets of controllers were designed and tested. The first set emphasized performance over robustness, while the second set traded off performance for robustness. Comparisons of simulation and test results are also included. Current simulation and experimental results indicate that reasonably good robust tracking performance can be attained for this system using multivariable robust control approach.

Feedforward/feedback control synthesis for performance and robustness

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1990-01-01

Both feedforward and feedback control approaches for uncertain dynamical systems are investigated. The control design objective is to achieve a fast settling time (high performance) and robustness (insensitivity) to plant modeling uncertainty. Preshapong of an ideal, time-optimal control input using a 'tapped-delay' filter is shown to provide a rapid maneuver with robust performance. A robust, non-minimum-phase feedback controller is synthesized with particular emphasis on its proper implementation for a non-zero set-point control problem. The proposed feedforward/feedback control approach is robust for a certain class of uncertain dynamical systems, since the control input command computed for a given desired output does not depend on the plant parameters.

Fortuitous phenomena: on complexity, pragmatic randomised controlled trials, and knowledge for evidence-based practice.

PubMed

Thompson, Carl

2004-01-01

Many of the interventions that nurses develop and implement are in themselves complex and have to operate in situations of irreducible complexity and uncertainty. This article argues that the primary means of generating knowledge for the evidence-based deployment of complex interventions should be the pragmatic randomised controlled trial. Randomised controlled trials represent the only research design to adequately deal with that which we know and (far more importantly) that which we do not. Using the example of practice development as an exemplar for complexity, and drawing on the objections often voiced as a response to calls to make use of randomised controlled trials in nursing and nursing research, the article presents a developmental framework and some methodological solutions to problems often encountered. Randomised controlled trials, whilst undoubtedly methodologically and strategically challenging, offer the most robust basis for developing primary research knowledge on the effects of complex interventions in nursing and their active components.

The Applications of Model-Based Geostatistics in Helminth Epidemiology and Control

PubMed Central

Magalhães, Ricardo J. Soares; Clements, Archie C.A.; Patil, Anand P.; Gething, Peter W.; Brooker, Simon

2011-01-01

Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes. PMID:21295680

Localized modelling and feedback control of linear instabilities in 2-D wall bounded shear flows

NASA Astrophysics Data System (ADS)

Tol, Henry; Kotsonis, Marios; de Visser, Coen

2016-11-01

A new approach is presented for control of instabilities in 2-D wall bounded shear flows described by the linearized Navier-Stokes equations (LNSE). The control design accounts both for spatially localized actuators/sensors and the dominant perturbation dynamics in an optimal control framework. An inflow disturbance model is proposed for streamwise instabilities that drive laminar-turbulent transition. The perturbation modes that contribute to the transition process can be selected and are included in the control design. A reduced order model is derived from the LNSE that captures the input-output behavior and the dominant perturbation dynamics. This model is used to design an optimal controller for suppressing the instability growth. A 2-D channel flow and a 2-D boundary layer flow over a flat plate are considered as application cases. Disturbances are generated upstream of the control domain and the resulting flow perturbations are estimated/controlled using wall shear measurements and localized unsteady blowing and suction at the wall. It will be shown that the controller is able to cancel the perturbations and is robust to unmodelled disturbances.

Application of controller partitioning optimization procedure to integrated flight/propulsion control design for a STOVL aircraft

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Schmidt, Phillip H.

1993-01-01

A parameter optimization framework has earlier been developed to solve the problem of partitioning a centralized controller into a decentralized, hierarchical structure suitable for integrated flight/propulsion control implementation. This paper presents results from the application of the controller partitioning optimization procedure to IFPC design for a Short Take-Off and Vertical Landing (STOVL) aircraft in transition flight. The controller partitioning problem and the parameter optimization algorithm are briefly described. Insight is provided into choosing various 'user' selected parameters in the optimization cost function such that the resulting optimized subcontrollers will meet the characteristics of the centralized controller that are crucial to achieving the desired closed-loop performance and robustness, while maintaining the desired subcontroller structure constraints that are crucial for IFPC implementation. The optimization procedure is shown to improve upon the initial partitioned subcontrollers and lead to performance comparable to that achieved with the centralized controller. This application also provides insight into the issues that should be addressed at the centralized control design level in order to obtain implementable partitioned subcontrollers.

[Social medicine and healthcare economics. The framework for future forms of healthcare].

PubMed

Rebscher, Herbert

2008-05-01

In the political debate, even in academia, the concepts of "profitability" or "efficiency" are thrown around very robustly and freely with no regard for the players themselves. Economically speaking there can be no efficiency without a definition of targets in terms of outcomes and their level of quality. If even the government's Council of Economic Experts itself finds in its assessment of hospital funding that the "reform's target parameters - improving the profitability of service provision - have developed positively", but adds that "whether this also applies to the quality of services provided or to the realisation of healthcare outcomes remains to be seen due to the lack of evidence" [21], this indicates a one-sided and problematic curtailment of the concept even by highly competent bodies. Economic control of new forms of healthcare by means of prices and fees for clearly defined services is a complex problem that has not been dealt with adequately. All pricing is based on classification models aimed at ensuring cost and benefit clusters that are as homogeneous as possible. Classification models in healthcare as a basis for price control targets need constant adjusting to ensure accuracy of mapping and appropriateness to performance. A prerequisite for the methodology behind price control models of this kind is presupposing a responsible, rule-bound and criteria-based handling of "variance" and "coincidence" by means of risk-adjusted quality and price systems. They will define the character of a wide range of steering tools and have an effect that goes beyond the narrow formal confines of the sector. That is why the regulatory framework will need first and foremost to define a qualitative framework for the political "security infrastructure" by means of deregulated economic processes in which price control becomes accountable and is justified in terms of content.

Superpixel guided active contour segmentation of retinal layers in OCT volumes

NASA Astrophysics Data System (ADS)

Bai, Fangliang; Gibson, Stuart J.; Marques, Manuel J.; Podoleanu, Adrian

2018-03-01

Retinal OCT image segmentation is a precursor to subsequent medical diagnosis by a clinician or machine learning algorithm. In the last decade, many algorithms have been proposed to detect retinal layer boundaries and simplify the image representation. Inspired by the recent success of superpixel methods for pre-processing natural images, we present a novel framework for segmentation of retinal layers in OCT volume data. In our framework, the region of interest (e.g. the fovea) is located using an adaptive-curve method. The cell layer boundaries are then robustly detected firstly using 1D superpixels, applied to A-scans, and then fitting active contours in B-scan images. Thereafter the 3D cell layer surfaces are efficiently segmented from the volume data. The framework was tested on healthy eye data and we show that it is capable of segmenting up to 12 layers. The experimental results imply the effectiveness of proposed method and indicate its robustness to low image resolution and intrinsic speckle noise.

Photocatalytic CO2 Reduction to Formate Using a Mn(I) Molecular Catalyst in a Robust Metal-Organic Framework.

PubMed

Fei, Honghan; Sampson, Matthew D; Lee, Yeob; Kubiak, Clifford P; Cohen, Seth M

2015-07-20

A manganese bipyridine complex, Mn(bpydc)(CO)3Br (bpydc = 5,5'-dicarboxylate-2,2'-bipyridine), has been incorporated into a highly robust Zr(IV)-based metal-organic framework (MOF) for use as a CO2 reduction photocatalyst. In conjunction with [Ru(dmb)3](2+) (dmb = 4,4'-dimethyl-2,2'-bipyridine) as a photosensitizer and 1-benzyl-1,4-dihydronicotinamide (BNAH) as a sacrificial reductant, Mn-incorporated MOFs efficiently catalyze CO2 reduction to formate in DMF/triethanolamine under visible-light irradiation. The photochemical performance of the Mn-incorporated MOF reached a turnover number of approximately 110 in 18 h, exceeding that of the homogeneous reference systems. The increased activity of the MOF-incorporated Mn catalyst is ascribed to the struts of the framework providing isolated active sites, which stabilize the catalyst and inhibit dimerization of the singly reduced Mn complex. The MOF catalyst largely retained its crystallinity throughout prolonged catalysis and was successfully reused over several catalytic runs.

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gamble, John; Jacobson, Noah Tobias; Baczewski, Andrew

EMTpY is an implementation of effective mass theory in python. It is designed to simulate semiconductor qubits within a non-perturbative, multi-valley effective mass theory framework using robust Gaussian basis sets.

Phased charging and discharging in capacitive desalinatio

DOEpatents

Stadermann, Michael; Qu, Yatian; Santiago, Juan G.; Hemmatifar, Ali

2017-09-12

A system combines complete, ultra-thin cells into a monolithic and robust framework necessary for desalination applications which yields orders of magnitude faster desalination. The electrode pairs are located so that a flow of feed water flows through or around the electrode pairs with the flow perpendicular to sequentially applied electric potentials. The system is controlled to charge the series of electrode pairs sequentially or phased. That means the charging of the second electrode pair is delayed with regard to the charging of the first electrode pair and the charging of a third electrode pair is delayed with respect to the charging of the second electrode pair.

WFIRST: Coronagraph Systems Engineering and Performance Budgets

NASA Astrophysics Data System (ADS)

Poberezhskiy, Ilya; cady, eric; Frerking, Margaret A.; Kern, Brian; Nemati, Bijan; Noecker, Martin; Seo, Byoung-Joon; Zhao, Feng; Zhou, Hanying

2018-01-01

The WFIRST coronagraph instrument (CGI) will be the first in-space coronagraph using active wavefront control to directly image and characterize mature exoplanets and zodiacal disks in reflected starlight. For CGI systems engineering, including requirements development, CGI performance is predicted using a hierarchy of performance budgets to estimate various noise components — spatial and temporal flux variations — that obscure exoplanet signals in direct imaging and spectroscopy configurations. These performance budgets are validated through a robust integrated modeling and testbed model validation efforts.We present the performance budgeting framework used by WFIRST for the flow-down of coronagraph science requirements, mission constraints, and observatory interfaces to measurable instrument engineering parameters.

Bio-inspired sensing and control for disturbance rejection and stabilization

NASA Astrophysics Data System (ADS)

Gremillion, Gregory; Humbert, James S.

2015-05-01

The successful operation of small unmanned aircraft systems (sUAS) in dynamic environments demands robust stability in the presence of exogenous disturbances. Flying insects are sensor-rich platforms, with highly redundant arrays of sensors distributed across the insect body that are integrated to extract rich information with diminished noise. This work presents a novel sensing framework in which measurements from an array of accelerometers distributed across a simulated flight vehicle are linearly combined to directly estimate the applied forces and torques with improvements in SNR. In simulation, the estimation performance is quantified as a function of sensor noise level, position estimate error, and sensor quantity.

Unsupervised learning in general connectionist systems.

PubMed

Dente, J A; Mendes, R Vilela

1996-01-01

There is a common framework in which different connectionist systems may be treated in a unified way. The general system in which they may all be mapped is a network which, in addition to the connection strengths, has an adaptive node parameter controlling the output intensity. In this paper we generalize two neural network learning schemes to networks with node parameters. In generalized Hebbian learning we find improvements to the convergence rate for small eigenvalues in principal component analysis. For competitive learning the use of node parameters also seems useful in that, by emphasizing or de-emphasizing the dominance of winning neurons, either improved robustness or discrimination is obtained.

Geographically weighted regression and multicollinearity: dispelling the myth

NASA Astrophysics Data System (ADS)

Fotheringham, A. Stewart; Oshan, Taylor M.

2016-10-01

Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking.

Robustness of Oscillatory Behavior in Correlated Networks

PubMed Central

Sasai, Takeyuki; Morino, Kai; Tanaka, Gouhei; Almendral, Juan A.; Aihara, Kazuyuki

2015-01-01

Understanding network robustness against failures of network units is useful for preventing large-scale breakdowns and damages in real-world networked systems. The tolerance of networked systems whose functions are maintained by collective dynamical behavior of the network units has recently been analyzed in the framework called dynamical robustness of complex networks. The effect of network structure on the dynamical robustness has been examined with various types of network topology, but the role of network assortativity, or degree–degree correlations, is still unclear. Here we study the dynamical robustness of correlated (assortative and disassortative) networks consisting of diffusively coupled oscillators. Numerical analyses for the correlated networks with Poisson and power-law degree distributions show that network assortativity enhances the dynamical robustness of the oscillator networks but the impact of network disassortativity depends on the detailed network connectivity. Furthermore, we theoretically analyze the dynamical robustness of correlated bimodal networks with two-peak degree distributions and show the positive impact of the network assortativity. PMID:25894574

Teacher Pupil Control Ideology and Behavior as Predictors of Classroom Robustness.

ERIC Educational Resources Information Center

Estep, Linda E.; And Others

1980-01-01

It was hypothesized that confrontations between a strict teacher and misbehaving students would add drama and robustness to the classroom. In 88 secondary classrooms, robustness and teacher's control ideology and behavior were measured. The hypothesis was rejected; humanistic control behavior related to high robustness. A companion elementary…

Robust Control Design for Systems With Probabilistic Uncertainty

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

This paper presents a reliability- and robustness-based formulation for robust control synthesis for systems with probabilistic uncertainty. In a reliability-based formulation, the probability of violating design requirements prescribed by inequality constraints is minimized. In a robustness-based formulation, a metric which measures the tendency of a random variable/process to cluster close to a target scalar/function is minimized. A multi-objective optimization procedure, which combines stability and performance requirements in time and frequency domains, is used to search for robustly optimal compensators. Some of the fundamental differences between the proposed strategy and conventional robust control methods are: (i) unnecessary conservatism is eliminated since there is not need for convex supports, (ii) the most likely plants are favored during synthesis allowing for probabilistic robust optimality, (iii) the tradeoff between robust stability and robust performance can be explored numerically, (iv) the uncertainty set is closely related to parameters with clear physical meaning, and (v) compensators with improved robust characteristics for a given control structure can be synthesized.

6-D, A Process Framework for the Design and Development of Web-based Systems.

ERIC Educational Resources Information Center

Christian, Phillip

2001-01-01

Explores how the 6-D framework can form the core of a comprehensive systemic strategy and help provide a supporting structure for more robust design and development while allowing organizations to support whatever methods and models best suit their purpose. 6-D stands for the phases of Web design and development: Discovery, Definition, Design,…

A general framework for parametric survival analysis.

PubMed

Crowther, Michael J; Lambert, Paul C

2014-12-30

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley & Sons, Ltd.

Designing a robust activity recognition framework for health and exergaming using wearable sensors.

PubMed

Alshurafa, Nabil; Xu, Wenyao; Liu, Jason J; Huang, Ming-Chun; Mortazavi, Bobak; Roberts, Christian K; Sarrafzadeh, Majid

2014-09-01

Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates and extracting human context awareness. Many classifiers that train on an activity at a subset of intensity levels fail to recognize the same activity at other intensity levels. This demonstrates weakness in the underlying classification method. Training a classifier for an activity at every intensity level is also not practical. In this paper, we tackle a novel intensity-independent activity recognition problem where the class labels exhibit large variability, the data are of high dimensionality, and clustering algorithms are necessary. We propose a new robust stochastic approximation framework for enhanced classification of such data. Experiments are reported using two clustering techniques, K-Means and Gaussian Mixture Models. The stochastic approximation algorithm consistently outperforms other well-known classification schemes which validate the use of our proposed clustered data representation. We verify the motivation of our framework in two applications that benefit from intensity-independent activity recognition. The first application shows how our framework can be used to enhance energy expenditure calculations. The second application is a novel exergaming environment aimed at using games to reward physical activity performed throughout the day, to encourage a healthy lifestyle.

Methodological development of the interactive INTERLINKS Framework for Long-term Care

PubMed Central

Billings, Jenny; Leichsenring, Kai

2014-01-01

There is increasing international research into health and social care services for older people in need of long-term care (LTC), but problems remain with respect to acquiring robust comparative information to enable judgements to be made regarding the most beneficial and cost-effective approaches. The project ‘INTERLINKS’ (‘Health systems and LTC for older people in Europe’) funded by the EU 7th Framework programme was developed to address the challenges associated with the accumulation and comparison of evidence in LTC across Europe. It developed a concept and method to describe and analyse LTC and its links with the health and social care system through the accumulation of policy and practice examples on an interactive web-based framework for LTC. This paper provides a critical overview of the theoretical and methodological approaches used to develop and implement the INTERLINKS Framework for LTC, with the aim of providing some guidance to researchers in this area. INTERLINKS has made a significant contribution to knowledge but robust evidence and comparability across European countries remain problematic due to the current and growing complexity and diversity of integrated LTC implementation. PMID:25120413

Electrochemically addressable trisradical rotaxanes organized within a metal–organic framework

DOE PAGES

McGonigal, Paul R.; Deria, Pravas; Hod, Idan; ...

2015-08-17

The organization of trisradical rotaxanes within the channels of a Zr 6-based metal–organic framework (NU-1000) has been achieved postsynthetically by solvent-assisted ligand incorporation. Robust ZrIV–carboxylate bonds are forged between the Zr clusters of NU-1000 and carboxylic acid groups of rotaxane precursors (semirotaxanes) as part of this building block replacement strategy. Ultraviolet–visible–near-infrared (UV-Vis-NIR), electron paramagnetic resonance (EPR), and 1H nuclear magnetic resonance (NMR) spectroscopies all confirm the capture of redox-active rotaxanes within the mesoscale hexagonal channels of NU-1000. Cyclic voltammetry measurements performed on electroactive thin films of the resulting material indicate that redox-active viologen subunits located on the rotaxane components canmore » be accessed electrochemically in the solid state. In contradistinction to previous methods, this strategy for the incorporation of mechanically interlocked molecules within porous materials circumvents the need for de novo synthesis of a metal–organic framework, making it a particularly convenient approach for the design and creation of solid-state molecular switches and machines. In conclusion, the results presented here provide proof-of-concept for the application of postsynthetic transformations in the integration of dynamic molecular machines with robust porous frameworks.« less

Uncertainties propagation and global sensitivity analysis of the frequency response function of piezoelectric energy harvesters

NASA Astrophysics Data System (ADS)

Ruiz, Rafael O.; Meruane, Viviana

2017-06-01

The goal of this work is to describe a framework to propagate uncertainties in piezoelectric energy harvesters (PEHs). These uncertainties are related to the incomplete knowledge of the model parameters. The framework presented could be employed to conduct prior robust stochastic predictions. The prior analysis assumes a known probability density function for the uncertain variables and propagates the uncertainties to the output voltage. The framework is particularized to evaluate the behavior of the frequency response functions (FRFs) in PEHs, while its implementation is illustrated by the use of different unimorph and bimorph PEHs subjected to different scenarios: free of uncertainties, common uncertainties, and uncertainties as a product of imperfect clamping. The common variability associated with the PEH parameters are tabulated and reported. A global sensitivity analysis is conducted to identify the Sobol indices. Results indicate that the elastic modulus, density, and thickness of the piezoelectric layer are the most relevant parameters of the output variability. The importance of including the model parameter uncertainties in the estimation of the FRFs is revealed. In this sense, the present framework constitutes a powerful tool in the robust design and prediction of PEH performance.

A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin V.

2016-01-01

Computer simulation models are continually growing in complexity with increasingly more factors to be identified. Sensitivity Analysis (SA) provides an essential means for understanding the role and importance of these factors in producing model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to "variogram analysis," that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. Synthetic functions that resemble actual model response surfaces are used to illustrate the concepts, and show VARS to be as much as two orders of magnitude more computationally efficient than the state-of-the-art Sobol approach. In a companion paper, we propose a practical implementation strategy, and demonstrate the effectiveness, efficiency, and reliability (robustness) of the VARS framework on real-data case studies.

Robustness surfaces of complex networks

PubMed Central

Manzano, Marc; Sahneh, Faryad; Scoglio, Caterina; Calle, Eusebi; Marzo, Jose Luis

2014-01-01

Despite the robustness of complex networks has been extensively studied in the last decade, there still lacks a unifying framework able to embrace all the proposed metrics. In the literature there are two open issues related to this gap: (a) how to dimension several metrics to allow their summation and (b) how to weight each of the metrics. In this work we propose a solution for the two aforementioned problems by defining the R*-value and introducing the concept of robustness surface (Ω). The rationale of our proposal is to make use of Principal Component Analysis (PCA). We firstly adjust to 1 the initial robustness of a network. Secondly, we find the most informative robustness metric under a specific failure scenario. Then, we repeat the process for several percentage of failures and different realizations of the failure process. Lastly, we join these values to form the robustness surface, which allows the visual assessment of network robustness variability. Results show that a network presents different robustness surfaces (i.e., dissimilar shapes) depending on the failure scenario and the set of metrics. In addition, the robustness surface allows the robustness of different networks to be compared. PMID:25178402

The internal audit of clinical areas: a pilot of the internal audit methodology in a health service emergency department.

PubMed

Brown, Alison; Santilli, Mario; Scott, Belinda

2015-12-01

Governing bodies of health services need assurance that major risks to achieving the health service objectives are being controlled. Currently, the main assurance mechanisms generated within the organization are through the review of implementation of policies and procedures and review of clinical audits and quality data. The governing bodies of health services need more robust, objective data to inform their understanding of the control of clinical risks. Internal audit provides a methodological framework that provides independent and objective assurance to the governing body on the control of significant risks. The article describes the pilot of the internal audit methodology in an emergency unit in a health service. An internal auditor was partnered with a clinical expert to assess the application of clinical criteria based on best practice guidelines. The pilot of the internal audit of a clinical area was successful in identifying significant clinical risks that required further management. The application of an internal audit methodology to a clinical area is a promising mechanism to gain robust assurance at the governance level regarding the management of significant clinical risks. This approach needs further exploration and trial in a range of health care settings. © The Author 2015. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

H∞ robust fault-tolerant controller design for an autonomous underwater vehicle's navigation control system

NASA Astrophysics Data System (ADS)

Cheng, Xiang-Qin; Qu, Jing-Yuan; Yan, Zhe-Ping; Bian, Xin-Qian

2010-03-01

In order to improve the security and reliability for autonomous underwater vehicle (AUV) navigation, an H∞ robust fault-tolerant controller was designed after analyzing variations in state-feedback gain. Operating conditions and the design method were then analyzed so that the control problem could be expressed as a mathematical optimization problem. This permitted the use of linear matrix inequalities (LMI) to solve for the H∞ controller for the system. When considering different actuator failures, these conditions were then also mathematically expressed, allowing the H∞ robust controller to solve for these events and thus be fault-tolerant. Finally, simulation results showed that the H∞ robust fault-tolerant controller could provide precise AUV navigation control with strong robustness.

Enhanced robust fractional order proportional-plus-integral controller based on neural network for velocity control of permanent magnet synchronous motor.

PubMed

Zhang, Bitao; Pi, YouGuo

2013-07-01

The traditional integer order proportional-integral-differential (IO-PID) controller is sensitive to the parameter variation or/and external load disturbance of permanent magnet synchronous motor (PMSM). And the fractional order proportional-integral-differential (FO-PID) control scheme based on robustness tuning method is proposed to enhance the robustness. But the robustness focuses on the open-loop gain variation of controlled plant. In this paper, an enhanced robust fractional order proportional-plus-integral (ERFOPI) controller based on neural network is proposed. The control law of the ERFOPI controller is acted on a fractional order implement function (FOIF) of tracking error but not tracking error directly, which, according to theory analysis, can enhance the robust performance of system. Tuning rules and approaches, based on phase margin, crossover frequency specification and robustness rejecting gain variation, are introduced to obtain the parameters of ERFOPI controller. And the neural network algorithm is used to adjust the parameter of FOIF. Simulation and experimental results show that the method proposed in this paper not only achieve favorable tracking performance, but also is robust with regard to external load disturbance and parameter variation. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Automatic cardiac LV segmentation in MRI using modified graph cuts with smoothness and interslice constraints.

PubMed

Albà, Xènia; Figueras I Ventura, Rosa M; Lekadir, Karim; Tobon-Gomez, Catalina; Hoogendoorn, Corné; Frangi, Alejandro F

2014-12-01

Magnetic resonance imaging (MRI), specifically late-enhanced MRI, is the standard clinical imaging protocol to assess cardiac viability. Segmentation of myocardial walls is a prerequisite for this assessment. Automatic and robust multisequence segmentation is required to support processing massive quantities of data. A generic rule-based framework to automatically segment the left ventricle myocardium is presented here. We use intensity information, and include shape and interslice smoothness constraints, providing robustness to subject- and study-specific changes. Our automatic initialization considers the geometrical and appearance properties of the left ventricle, as well as interslice information. The segmentation algorithm uses a decoupled, modified graph cut approach with control points, providing a good balance between flexibility and robustness. The method was evaluated on late-enhanced MRI images from a 20-patient in-house database, and on cine-MRI images from a 15-patient open access database, both using as reference manually delineated contours. Segmentation agreement, measured using the Dice coefficient, was 0.81±0.05 and 0.92±0.04 for late-enhanced MRI and cine-MRI, respectively. The method was also compared favorably to a three-dimensional Active Shape Model approach. The experimental validation with two magnetic resonance sequences demonstrates increased accuracy and versatility. © 2013 Wiley Periodicals, Inc.

Controle du vol longitudinal d'un avion civil avec satisfaction de qualiies de manoeuvrabilite

NASA Astrophysics Data System (ADS)

Saussie, David Alexandre

2010-03-01

Fulfilling handling qualities still remains a challenging problem during flight control design. These criteria of different nature are derived from a wide experience based upon flight tests and data analysis, and they have to be considered if one expects a good behaviour of the aircraft. The goal of this thesis is to develop synthesis methods able to satisfy these criteria with fixed classical architectures imposed by the manufacturer or with a new flight control architecture. This is applied to the longitudinal flight model of a Bombardier Inc. business jet aircraft, namely the Challenger 604. A first step of our work consists in compiling the most commonly used handling qualities in order to compare them. A special attention is devoted to the dropback criterion for which theoretical analysis leads us to establish a practical formulation for synthesis purpose. Moreover, the comparison of the criteria through a reference model highlighted dominant criteria that, once satisfied, ensure that other ones are satisfied too. Consequently, we are able to consider the fulfillment of these criteria in the fixed control architecture framework. Guardian maps (Saydy et al., 1990) are then considered to handle the problem. Initially for robustness study, they are integrated in various algorithms for controller synthesis. Incidently, this fixed architecture problem is similar to the static output feedback stabilization problem and reduced-order controller synthesis. Algorithms performing stabilization and pole assignment in a specific region of the complex plane are then proposed. Afterwards, they are extended to handle the gain-scheduling problem. The controller is then scheduled through the entire flight envelope with respect to scheduling parameters. Thereafter, the fixed architecture is put aside while only conserving the same output signals. The main idea is to use Hinfinity synthesis to obtain an initial controller satisfying handling qualities thanks to reference model pairing and robust versus mass and center of gravity variations. Using robust modal control (Magni, 2002), we are able to reduce substantially the controller order and to structure it in order to come close to a classical architecture. An auto-scheduling method finally allows us to schedule the controller with respect to scheduling parameters. Two different paths are used to solve the same problem; each one exhibits its own advantages and disadvantages.

Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.

PubMed

Xi, Jianing; Wang, Minghui; Li, Ao

2018-06-05

Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.

A robust real-time abnormal region detection framework from capsule endoscopy images

NASA Astrophysics Data System (ADS)

Cheng, Yanfen; Liu, Xu; Li, Huiping

2009-02-01

In this paper we present a novel method to detect abnormal regions from capsule endoscopy images. Wireless Capsule Endoscopy (WCE) is a recent technology where a capsule with an embedded camera is swallowed by the patient to visualize the gastrointestinal tract. One challenge is one procedure of diagnosis will send out over 50,000 images, making physicians' reviewing process expensive. Physicians' reviewing process involves in identifying images containing abnormal regions (tumor, bleeding, etc) from this large number of image sequence. In this paper we construct a novel framework for robust and real-time abnormal region detection from large amount of capsule endoscopy images. The detected potential abnormal regions can be labeled out automatically to let physicians review further, therefore, reduce the overall reviewing process. In this paper we construct an abnormal region detection framework with the following advantages: 1) Trainable. Users can define and label any type of abnormal region they want to find; The abnormal regions, such as tumor, bleeding, etc., can be pre-defined and labeled using the graphical user interface tool we provided. 2) Efficient. Due to the large number of image data, the detection speed is very important. Our system can detect very efficiently at different scales due to the integral image features we used; 3) Robust. After feature selection we use a cascade of classifiers to further enforce the detection accuracy.

Development of a Comprehensive Digital Avionics Curriculum for the Aeronautical Engineer

DTIC Science & Technology

2006-03-01

able to analyze and design aircraft and missile guidance and control systems, including feedback stabilization schemes and stochastic processes, using ...Uncertainty modeling for robust control; Robust closed-loop stability and performance; Robust H- infinity control; Robustness check using mu-analysis...Controlled feedback (reduces noise) 3. Statistical group response (reduce pressure toward conformity) When used as a tool to study a complex problem

Robust control of flexible space vehicles with minimum structural excitation: On-off pulse control of flexible space vehicles

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1992-01-01

Both feedback and feedforward control approaches for uncertain dynamical systems (in particular, with uncertainty in structural mode frequency) are investigated. The control objective is to achieve a fast settling time (high performance) and robustness (insensitivity) to plant uncertainty. Preshaping of an ideal, time optimal control input using a tapped-delay filter is shown to provide a fast settling time with robust performance. A robust, non-minimum-phase feedback controller is synthesized with particular emphasis on its proper implementation for a non-zero set-point control problem. It is shown that a properly designed, feedback controller performs well, as compared with a time optimal open loop controller with special preshaping for performance robustness. Also included are two separate papers by the same authors on this subject.

Nonlinear robust control of hypersonic aircrafts with interactions between flight dynamics and propulsion systems.

PubMed

Li, Zhaoying; Zhou, Wenjie; Liu, Hao

2016-09-01

This paper addresses the nonlinear robust tracking controller design problem for hypersonic vehicles. This problem is challenging due to strong coupling between the aerodynamics and the propulsion system, and the uncertainties involved in the vehicle dynamics including parametric uncertainties, unmodeled model uncertainties, and external disturbances. By utilizing the feedback linearization technique, a linear tracking error system is established with prescribed references. For the linear model, a robust controller is proposed based on the signal compensation theory to guarantee that the tracking error dynamics is robustly stable. Numerical simulation results are given to show the advantages of the proposed nonlinear robust control method, compared to the robust loop-shaping control approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Marital Processes, Neuroticism, and Stress as Risk Factors for Internalizing Symptoms

PubMed Central

Brock, Rebecca L.; Lawrence, Erika

2013-01-01

Objective Marital discord has a robust association with depression, yet it is rarely considered within broader etiological frameworks of psychopathology. Further, little is known about the particular aspects of relationships that have the greatest impact on psychopathology. The purpose of the present study was to test a novel conceptual framework including neuroticism, specific relationship processes (conflict management, partner support, emotional intimacy, and distribution of power and control), and stress as predictors of internalizing symptoms (depression and anxiety). Method Questionnaire and interview data were collected from 103 husbands and wives 5 times over the first 7 years of marriage. Results Results suggest that neuroticism (an expression of the underlying vulnerability for internalizing disorders) contributes to symptoms primarily through high levels of non-marital stress, an imbalance of power/control in one’s marriage, and poor partner support for husbands, and through greater emotional disengagement for wives. Conclusions Marital processes, neuroticism, and stress work together to significantly predict internalizing symptoms, demonstrating the need to routinely consider dyadic processes in etiological models of individual psychopathology. Specific recommendations for adapting and implementing couple interventions to prevent and treat individual psychopathology are discussed. PMID:24818069

Music training and inhibitory control: a multidimensional model.

PubMed

Moreno, Sylvain; Farzan, Faranak

2015-03-01

Training programs aimed to improve cognitive skills have either yielded mixed results or remain to be validated. The limited benefits of such regimens are largely attributable to weak understanding of (1) how (and which) interventions provide the most cognitive improvements; and (2) how brain networks and neural mechanisms that underlie specific cognitive abilities can be modified selectively. Studies indicate that music training leads to robust and long-lasting benefits to behavior. Importantly, behavioral advantages conferred by music extend beyond perceptual abilities to even nonauditory functions, such as inhibitory control (IC) and its neural correlates. Alternative forms of arts engagement or brain training do not appear to yield such enhancements, which suggests that music uniquely taps into brain networks subserving a variety of auditory as well as domain-general mechanisms such as IC. To account for such widespread benefits of music training, we propose a framework of transfer effects characterized by three dimensions: level of processing, nature of the transfer, and involvement of executive functions. We suggest that transfer of skills is mediated through modulation of general cognitive processes, in particular IC. We believe that this model offers a viable framework to test the extent and limitations of music-related changes. © 2014 New York Academy of Sciences.

Robust H(∞) positional control of 2-DOF robotic arm driven by electro-hydraulic servo system.

PubMed

Guo, Qing; Yu, Tian; Jiang, Dan

2015-11-01

In this paper an H∞ positional feedback controller is developed to improve the robust performance under structural and parametric uncertainty disturbance in electro-hydraulic servo system (EHSS). The robust control model is described as the linear state-space equation by upper linear fractional transformation. According to the solution of H∞ sub-optimal control problem, the robust controller is designed and simplified to lower order linear model which is easily realized in EHSS. The simulation and experimental results can validate the robustness of this proposed method. The comparison result with PI control shows that the robust controller is suitable for this EHSS under the critical condition where the desired system bandwidth is higher and the external load of the hydraulic actuator is closed to its limited capability. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A robust fractional-order PID controller design based on active queue management for TCP network

NASA Astrophysics Data System (ADS)

Hamidian, Hamideh; Beheshti, Mohammad T. H.

2018-01-01

In this paper, a robust fractional-order controller is designed to control the congestion in transmission control protocol (TCP) networks with time-varying parameters. Fractional controllers can increase the stability and robustness. Regardless of advantages of fractional controllers, they are still not common in congestion control in TCP networks. The network parameters are time-varying, so the robust stability is important in congestion controller design. Therefore, we focused on the robust controller design. The fractional PID controller is developed based on active queue management (AQM). D-partition technique is used. The most important property of designed controller is the robustness to the time-varying parameters of the TCP network. The vertex quasi-polynomials of the closed-loop characteristic equation are obtained, and the stability boundaries are calculated for each vertex quasi-polynomial. The intersection of all stability regions is insensitive to network parameter variations, and results in robust stability of TCP/AQM system. NS-2 simulations show that the proposed algorithm provides a stable queue length. Moreover, simulations show smaller oscillations of the queue length and less packet drop probability for FPID compared to PI and PID controllers. We can conclude from NS-2 simulations that the average packet loss probability variations are negligible when the network parameters change.

SU-E-T-574: Novel Chance-Constrained Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

DOE Office of Scientific and Technical Information (OSTI.GOV)

An, Y; Liang, J; Liu, W

2015-06-15

Purpose: We propose to apply a probabilistic framework, namely chanceconstrained optimization, in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to hedge against the influence of uncertainties and improve robustness of treatment plans. Methods: IMPT plans were generated for a typical prostate patient. Nine dose distributions are computed — the nominal one and one each for ±5mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. These nine dose distributions are supplied to the solver CPLEX as chance constraints to explicitly control plan robustness under these representative uncertainty scenarios withmore » certain probability. This probability is determined by the tolerance level. We make the chance-constrained model tractable by converting it to a mixed integer optimization problem. The quality of plans derived from this method is evaluated using dose-volume histogram (DVH) indices such as tumor dose homogeneity (D5% – D95%) and coverage (D95%) and normal tissue sparing like V70 of rectum, V65, and V40 of bladder. We also compare the results from this novel method with the conventional PTV-based method to further demonstrate its effectiveness Results: Our model can yield clinically acceptable plans within 50 seconds. The chance-constrained optimization produces IMPT plans with comparable target coverage, better target dose homogeneity, and better normal tissue sparing compared to the PTV-based optimization [D95% CTV: 67.9 vs 68.7 (Gy), D5% – D95% CTV: 11.9 vs 18 (Gy), V70 rectum: 0.0 % vs 0.33%, V65 bladder: 2.17% vs 9.33%, V40 bladder: 8.83% vs 21.83%]. It also simultaneously makes the plan more robust [Width of DVH band at D50%: 2.0 vs 10.0 (Gy)]. The tolerance level may be varied to control the tradeoff between plan robustness and quality. Conclusion: The chance-constrained optimization generates superior IMPT plan compared to the PTV-based optimization with explicit control of plan robustness. NIH/NCI K25CA168984, Eagles Cancer Research Career Development, The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and The Kemper Marley Foundation.« less

A Survey of Recent MARTe Based Systems

NASA Astrophysics Data System (ADS)

Neto, André C.; Alves, Diogo; Boncagni, Luca; Carvalho, Pedro J.; Valcarcel, Daniel F.; Barbalace, Antonio; De Tommasi, Gianmaria; Fernandes, Horácio; Sartori, Filippo; Vitale, Enzo; Vitelli, Riccardo; Zabeo, Luca

2011-08-01

The Multithreaded Application Real-Time executor (MARTe) is a data driven framework environment for the development and deployment of real-time control algorithms. The main ideas which led to the present version of the framework were to standardize the development of real-time control systems, while providing a set of strictly bounded standard interfaces to the outside world and also accommodating a collection of facilities which promote the speed and ease of development, commissioning and deployment of such systems. At the core of every MARTe based application, is a set of independent inter-communicating software blocks, named Generic Application Modules (GAM), orchestrated by a real-time scheduler. The platform independence of its core library provides MARTe the necessary robustness and flexibility for conveniently testing applications in different environments including non-real-time operating systems. MARTe is already being used in several machines, each with its own peculiarities regarding hardware interfacing, supervisory control configuration, operating system and target control application. This paper presents and compares the most recent results of systems using MARTe: the JET Vertical Stabilization system, which uses the Real Time Application Interface (RTAI) operating system on Intel multi-core processors; the COMPASS plasma control system, driven by Linux RT also on Intel multi-core processors; ISTTOK real-time tomography equilibrium reconstruction which shares the same support configuration of COMPASS; JET error field correction coils based on VME, PowerPC and VxWorks; FTU LH reflected power system running on VME, Intel with RTAI.

Percolation of localized attack on complex networks

NASA Astrophysics Data System (ADS)

Shao, Shuai; Huang, Xuqing; Stanley, H. Eugene; Havlin, Shlomo

2015-02-01

The robustness of complex networks against node failure and malicious attack has been of interest for decades, while most of the research has focused on random attack or hub-targeted attack. In many real-world scenarios, however, attacks are neither random nor hub-targeted, but localized, where a group of neighboring nodes in a network are attacked and fail. In this paper we develop a percolation framework to analytically and numerically study the robustness of complex networks against such localized attack. In particular, we investigate this robustness in Erdős-Rényi networks, random-regular networks, and scale-free networks. Our results provide insight into how to better protect networks, enhance cybersecurity, and facilitate the design of more robust infrastructures.

Robust Flutter Margin Analysis that Incorporates Flight Data

NASA Technical Reports Server (NTRS)

Lind, Rick; Brenner, Martin J.

1998-01-01

An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, mu, computes a stability margin that directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The mu margins are robust margins that indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 Systems Research Aircraft using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.

Robust adaptive vibration control of a flexible structure.

PubMed

Khoshnood, A M; Moradi, H M

2014-07-01

Different types of L1 adaptive control systems show that using robust theories with adaptive control approaches has produced high performance controllers. In this study, a model reference adaptive control scheme considering robust theories is used to propose a practical control system for vibration suppression of a flexible launch vehicle (FLV). In this method, control input of the system is shaped from the dynamic model of the vehicle and components of the control input are adaptively constructed by estimating the undesirable vibration frequencies. Robust stability of the adaptive vibration control system is guaranteed by using the L1 small gain theorem. Simulation results of the robust adaptive vibration control strategy confirm that the effects of vibration on the vehicle performance considerably decrease without the loss of the phase margin of the system. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Identification and robust control of an experimental servo motor.

PubMed

Adam, E J; Guestrin, E D

2002-04-01

In this work, the design of a robust controller for an experimental laboratory-scale position control system based on a dc motor drive as well as the corresponding identification and robust stability analysis are presented. In order to carry out the robust design procedure, first, a classic closed-loop identification technique is applied and then, the parametrization by internal model control is used. The model uncertainty is evaluated under both parametric and global representation. For the latter case, an interesting discussion about the conservativeness of this description is presented by means of a comparison between the uncertainty disk and the critical perturbation radius approaches. Finally, conclusions about the performance of the experimental system with the robust controller are discussed using comparative graphics of the controlled variable and the Nyquist stability margin as a robustness measurement.

The effectiveness of robust RMCD control chart as outliers’ detector

NASA Astrophysics Data System (ADS)

Darmanto; Astutik, Suci

2017-12-01

A well-known control chart to monitor a multivariate process is Hotelling’s T 2 which its parameters are estimated classically, very sensitive and also marred by masking and swamping of outliers data effect. To overcome these situation, robust estimators are strongly recommended. One of robust estimators is re-weighted minimum covariance determinant (RMCD) which has robust characteristics as same as MCD. In this paper, the effectiveness term is accuracy of the RMCD control chart in detecting outliers as real outliers. In other word, how effectively this control chart can identify and remove masking and swamping effects of outliers. We assessed the effectiveness the robust control chart based on simulation by considering different scenarios: n sample sizes, proportion of outliers, number of p quality characteristics. We found that in some scenarios, this RMCD robust control chart works effectively.

Putting intelligent structured intermittent auscultation (ISIA) into practice.

PubMed

Maude, Robyn M; Skinner, Joan P; Foureur, Maralyn J

2016-06-01

Fetal monitoring guidelines recommend intermittent auscultation for the monitoring of fetal wellbeing during labour for low-risk women. However, these guidelines are not being translated into practice and low-risk women birthing in institutional maternity units are increasingly exposed to continuous cardiotocographic monitoring, both on admission to hospital and during labour. When continuous fetal monitoring becomes routinised, midwives and obstetricians lose practical skills around intermittent auscultation. To support clinical practice and decision-making around auscultation modality, the intelligent structured intermittent auscultation (ISIA) framework was developed. The purpose of this discussion paper is to describe the application of intelligent structured intermittent auscultation in practice. The intelligent structured intermittent auscultation decision-making framework is a knowledge translation tool that supports the implementation of evidence into practice around the use of intermittent auscultation for fetal heart monitoring for low-risk women during labour. An understanding of the physiology of the materno-utero-placental unit and control of the fetal heart underpin the development of the framework. Intelligent structured intermittent auscultation provides midwives with a robust means of demonstrating their critical thinking and clinical reasoning and supports their understanding of normal physiological birth. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach.

PubMed

Gul, Sehrish; Zou, Xiang; Hassan, Che Hashim; Azam, Muhammad; Zaman, Khalid

2015-12-01

This study investigates the relationship between energy consumption and carbon dioxide emission in the causal framework, as the direction of causality remains has a significant policy implication for developed and developing countries. The study employed maximum entropy bootstrap (Meboot) approach to examine the causal nexus between energy consumption and carbon dioxide emission using bivariate as well as multivariate framework for Malaysia, over a period of 1975-2013. This is a unified approach without requiring the use of conventional techniques based on asymptotical theory such as testing for possible unit root and cointegration. In addition, it can be applied in the presence of non-stationary of any type including structural breaks without any type of data transformation to achieve stationary. Thus, it provides more reliable and robust inferences which are insensitive to time span as well as lag length used. The empirical results show that there is a unidirectional causality running from energy consumption to carbon emission both in the bivariate model and multivariate framework, while controlling for broad money supply and population density. The results indicate that Malaysia is an energy-dependent country and hence energy is stimulus to carbon emissions.

Design and Experimental Evaluation of a Robust Position Controller for an Electrohydrostatic Actuator Using Adaptive Antiwindup Sliding Mode Scheme

PubMed Central

Lee, Ji Min; Park, Sung Hwan; Kim, Jong Shik

2013-01-01

A robust control scheme is proposed for the position control of the electrohydrostatic actuator (EHA) when considering hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. To reduce overshoot due to a saturation of electric motor and to realize robustness against load disturbance and lumped system uncertainties such as varying parameters and modeling error, this paper proposes an adaptive antiwindup PID sliding mode scheme as a robust position controller for the EHA system. An optimal PID controller and an optimal anti-windup PID controller are also designed to compare control performance. An EHA prototype is developed, carrying out system modeling and parameter identification in designing the position controller. The simply identified linear model serves as the basis for the design of the position controllers, while the robustness of the control systems is compared by experiments. The adaptive anti-windup PID sliding mode controller has been found to have the desired performance and become robust against hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. PMID:23983640

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-06-01

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, somore » it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.« less

Non-fragile observer-based output feedback control for polytopic uncertain system under distributed model predictive control approach

NASA Astrophysics Data System (ADS)

Zhu, Kaiqun; Song, Yan; Zhang, Sunjie; Zhong, Zhaozhun

2017-07-01

In this paper, a non-fragile observer-based output feedback control problem for the polytopic uncertain system under distributed model predictive control (MPC) approach is discussed. By decomposing the global system into some subsystems, the computation complexity is reduced, so it follows that the online designing time can be saved.Moreover, an observer-based output feedback control algorithm is proposed in the framework of distributed MPC to deal with the difficulties in obtaining the states measurements. In this way, the presented observer-based output-feedback MPC strategy is more flexible and applicable in practice than the traditional state-feedback one. What is more, the non-fragility of the controller has been taken into consideration in favour of increasing the robustness of the polytopic uncertain system. After that, a sufficient stability criterion is presented by using Lyapunov-like functional approach, meanwhile, the corresponding control law and the upper bound of the quadratic cost function are derived by solving an optimisation subject to convex constraints. Finally, some simulation examples are employed to show the effectiveness of the method.

A single sensor and single actuator approach to performance tailoring over a prescribed frequency band.

PubMed

Wang, Jiqiang

2016-03-01

Restricted sensing and actuation control represents an important area of research that has been overlooked in most of the design methodologies. In many practical control engineering problems, it is necessitated to implement the design through a single sensor and single actuator for multivariate performance variables. In this paper, a novel approach is proposed for the solution to the single sensor and single actuator control problem where performance over any prescribed frequency band can also be tailored. The results are obtained for the broad band control design based on the formulation for discrete frequency control. It is shown that the single sensor and single actuator control problem over a frequency band can be cast into a Nevanlinna-Pick interpolation problem. An optimal controller can then be obtained via the convex optimization over LMIs. Even remarkable is that robustness issues can also be tackled in this framework. A numerical example is provided for the broad band attenuation of rotor blade vibration to illustrate the proposed design procedures. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Integrative evaluation for sustainable decisions of urban wastewater system management under uncertainty

NASA Astrophysics Data System (ADS)

Hadjimichael, A.; Corominas, L.; Comas, J.

2017-12-01

With sustainable development as their overarching goal, urban wastewater system (UWS) managers need to take into account multiple social, economic, technical and environmental facets related to their decisions. In this complex decision-making environment, uncertainty can be formidable. It is present both in the ways the system is interpreted stochastically, but also in its natural ever-shifting behavior. This inherent uncertainty suggests that wiser decisions would be made under an adaptive and iterative decision-making regime. No decision-support framework has been presented in the literature to effectively addresses all these needs. The objective of this work is to describe such a conceptual framework to evaluate and compare alternative solutions for various UWS challenges within an adaptive management structure. Socio-economic aspects such as externalities are taken into account, along with other traditional criteria as necessary. Robustness, reliability and resilience analyses test the performance of the system against present and future variability. A valuation uncertainty analysis incorporates uncertain valuation assumptions in the decision-making process. The framework is demonstrated with an application to a case study presenting a typical problem often faced by managers: poor river water quality, increasing population, and more stringent water quality legislation. The application of the framework made use of: i) a cost-benefit analysis including monetized environmental benefits and damages; ii) a robustness analysis of system performance against future conditions; iii) reliability and resilience analyses of the system given contextual variability; and iv) a valuation uncertainty analysis of model parameters. The results suggest that the installation of bigger volumes would give rise to increased benefits despite larger capital costs, as well as increased robustness and resilience. Population numbers appear to affect the estimated benefits most, followed by electricity prices and climate change projections. The presented framework is expected to be a valuable tool for the next generation of UWS decision-making and the application demonstrates a novel and valuable integration of metrics and methods for UWS analysis.

A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals.

PubMed

Zhang, Qingxue; Zhou, Dian; Zeng, Xuan

2016-11-01

This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (ECG) signals continuously and heavily corrupted by random motion artifacts in wearable applications. The framework includes two stages, i.e. heartbeat identification and refinement, respectively. In the first stage, an adaptive threshold-based auto-segmentation approach is proposed to select out heartbeat candidates, including the real heartbeats and large amounts of motion-artifact-induced interferential spikes. Then twenty-six features are extracted for each candidate in time, spatial, frequency and statistical domains, and evaluated by a spare support vector machine (SVM) to select out ten critical features which can effectively reveal residual heartbeat information. Afterwards, an SVM model, created on the training data using the selected feature set, is applied to find high confident heartbeats from a large number of candidates in the testing data. In the second stage, the SVM classification results are further refined by two steps: (1) a rule-based classifier with two attributes named 'continuity check' and 'locality check' for outlier (false positives) removal, and (2) a heartbeat interpolation strategy for missing-heartbeat (false negatives) recovery. The framework is evaluated on a wrist-ECG dataset acquired by a semi-customized platform and also a public dataset. When the signal-to-noise ratio is as low as  -7 dB, the mean absolute error of the estimated IHR is 1.4 beats per minute (BPM) and the root mean square error is 6.5 BPM. The proposed framework greatly outperforms well-established approaches, demonstrating that it can effectively identify the heartbeats from ECG signals continuously corrupted by intense motion artifacts and robustly estimate the IHR. This study is expected to contribute to robust long-term wearable IHR monitoring for pervasive heart health and fitness management.

Small Worldness in Dense and Weighted Connectomes

NASA Astrophysics Data System (ADS)

Colon-Perez, Luis; Couret, Michelle; Triplett, William; Price, Catherine; Mareci, Thomas

2016-05-01

The human brain is a heterogeneous network of connected functional regions; however, most brain network studies assume that all brain connections can be described in a framework of binary connections. The brain is a complex structure of white matter tracts connected by a wide range of tract sizes, which suggests a broad range of connection strengths. Therefore, the assumption that the connections are binary yields an incomplete picture of the brain. Various thresholding methods have been used to remove spurious connections and reduce the graph density in binary networks. But these thresholds are arbitrary and make problematic the comparison of networks created at different thresholds. The heterogeneity of connection strengths can be represented in graph theory by applying weights to the network edges. Using our recently introduced edge weight parameter, we estimated the topological brain network organization using a complimentary weighted connectivity framework to the traditional framework of a binary network. To examine the reproducibility of brain networks in a controlled condition, we studied the topological network organization of a single healthy individual by acquiring 10 repeated diffusion-weighted magnetic resonance image datasets, over a one-month period on the same scanner, and analyzing these networks with deterministic tractography. We applied a threshold to both the binary and weighted networks and determined that the extra degree of freedom that comes with the framework of weighting network connectivity provides a robust result as any threshold level. The proposed weighted connectivity framework provides a stable result and is able to demonstrate the small world property of brain networks in situations where the binary framework is inadequate and unable to demonstrate this network property.

Towards a robust framework for catchment classification

NASA Astrophysics Data System (ADS)

Deshmukh, A.; Samal, A.; Singh, R.

2017-12-01

Classification of catchments based on various measures of similarity has emerged as an important technique to understand regional scale hydrologic behavior. Classification of catchment characteristics and/or streamflow response has been used reveal which characteristics are more likely to explain the observed variability of hydrologic response. However, numerous algorithms for supervised or unsupervised classification are available, making it hard to identify the algorithm most suitable for the dataset at hand. Consequently, existing catchment classification studies vary significantly in the classification algorithms employed with no previous attempt at understanding the degree of uncertainty in classification due to this algorithmic choice. This hinders the generalizability of interpretations related to hydrologic behavior. Our goal is to develop a protocol that can be followed while classifying hydrologic datasets. We focus on a classification framework for unsupervised classification and provide a step-by-step classification procedure. The steps include testing the clusterabiltiy of original dataset prior to classification, feature selection, validation of clustered data, and quantification of similarity of two clusterings. We test several commonly available methods within this framework to understand the level of similarity of classification results across algorithms. We apply the proposed framework on recently developed datasets for India to analyze to what extent catchment properties can explain observed catchment response. Our testing dataset includes watershed characteristics for over 200 watersheds which comprise of both natural (physio-climatic) characteristics and socio-economic characteristics. This framework allows us to understand the controls on observed hydrologic variability across India.

Robust PD Sway Control of a Lifted Load for a Crane Using a Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kawada, Kazuo; Sogo, Hiroyuki; Yamamoto, Toru; Mada, Yasuhiro

PID control schemes still continue to be widely used for most industrial control systems. This is mainly because PID controllers have simple control structures, and are simple to maintain and tune. However, it is difficult to find a set of suitable control parameters in the case of time-varying and/or nonlinear systems. For such a problem, the robust controller has been proposed.Although it is important to choose the suitable nominal model in designing the robust controller, it is not usually easy.In this paper, a new robust PD controller design scheme is proposed, which utilizes a genetic algorithm.

Surface code architecture for donors and dots in silicon with imprecise and nonuniform qubit couplings

DOE PAGES

Pica, G.; Lovett, B. W.; Bhatt, R. N.; ...

2016-01-14

A scaled quantum computer with donor spins in silicon would benefit from a viable semiconductor framework and a strong inherent decoupling of the qubits from the noisy environment. Coupling neighboring spins via the natural exchange interaction according to current designs requires gate control structures with extremely small length scales. In this work, we present a silicon architecture where bismuth donors with long coherence times are coupled to electrons that can shuttle between adjacent quantum dots, thus relaxing the pitch requirements and allowing space between donors for classical control devices. An adiabatic SWAP operation within each donor/dot pair solves the scalabilitymore » issues intrinsic to exchange-based two-qubit gates, as it does not rely on subnanometer precision in donor placement and is robust against noise in the control fields. In conclusion, we use this SWAP together with well established global microwave Rabi pulses and parallel electron shuttling to construct a surface code that needs minimal, feasible local control.« less

Active learning: a step towards automating medical concept extraction.

PubMed

Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

2016-03-01

This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab. The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random sampling baseline, the saving is at least doubled. Incremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Business recovery: an assessment framework.

PubMed

Stevenson, Joanne R; Brown, Charlotte; Seville, Erica; Vargo, John

2018-07-01

This paper presents a Business Recovery Assessment Framework (BRAF) to help researchers and practitioners design robust, repeatable, and comparable studies of business recovery in various post-disruption contexts. Studies assessing business recovery without adequately considering the research aims, recovery definitions, and indicators can produce misleading findings. The BRAF is composed of a series of steps that guide the decisions that researchers need to make to ensure: (i) that recovery is indeed being measured; (ii) that the indicators of recovery that are selected align with the objectives of the study and the definition of recovery; and, where necessary, (iii) that appropriate comparative control variables are in place. The paper draws on a large dataset of business surveys collected following the earthquakes in Canterbury, New Zealand, on 4 September 2010 and 22 February 2011 to demonstrate the varied conclusions that different recovery indicators can produce and to justify the need for a systematic approach to business recovery assessments. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

Online Meta-data Collection and Monitoring Framework for the STAR Experiment at RHIC

NASA Astrophysics Data System (ADS)

Arkhipkin, D.; Lauret, J.; Betts, W.; Van Buren, G.

2012-12-01

The STAR Experiment further exploits scalable message-oriented model principles to achieve a high level of control over online data streams. In this paper we present an AMQP-powered Message Interface and Reliable Architecture framework (MIRA), which allows STAR to orchestrate the activities of Meta-data Collection, Monitoring, Online QA and several Run-Time and Data Acquisition system components in a very efficient manner. The very nature of the reliable message bus suggests parallel usage of multiple independent storage mechanisms for our meta-data. We describe our experience with a robust data-taking setup employing MySQL- and HyperTable-based archivers for meta-data processing. In addition, MIRA has an AJAX-enabled web GUI, which allows real-time visualisation of online process flow and detector subsystem states, and doubles as a sophisticated alarm system when combined with complex event processing engines like Esper, Borealis or Cayuga. The performance data and our planned path forward are based on our experience during the 2011-2012 running of STAR.

Estimating the rate of biological introductions: Lessepsian fishes in the Mediterranean.

PubMed

Belmaker, Jonathan; Brokovich, Eran; China, Victor; Golani, Daniel; Kiflawi, Moshe

2009-04-01

Sampling issues preclude the direct use of the discovery rate of exotic species as a robust estimate of their rate of introduction. Recently, a method was advanced that allows maximum-likelihood estimation of both the observational probability and the introduction rate from the discovery record. Here, we propose an alternative approach that utilizes the discovery record of native species to control for sampling effort. Implemented in a Bayesian framework using Markov chain Monte Carlo simulations, the approach provides estimates of the rate of introduction of the exotic species, and of additional parameters such as the size of the species pool from which they are drawn. We illustrate the approach using Red Sea fishes recorded in the eastern Mediterranean, after crossing the Suez Canal, and show that the two approaches may lead to different conclusions. The analytical framework is highly flexible and could provide a basis for easy modification to other systems for which first-sighting data on native and introduced species are available.

Visual tracking strategies for intelligent vehicle highway systems

NASA Astrophysics Data System (ADS)

Smith, Christopher E.; Papanikolopoulos, Nikolaos P.; Brandt, Scott A.; Richards, Charles

1995-01-01

The complexity and congestion of current transportation systems often produce traffic situations that jeopardize the safety of the people involved. These situations vary from maintaining a safe distance behind a leading vehicle to safely allowing a pedestrian to cross a busy street. Environmental sensing plays a critical role in virtually all of these situations. Of the sensors available, vision sensors provide information that is richer and more complete than other sensors, making them a logical choice for a multisensor transportation system. In this paper we present robust techniques for intelligent vehicle-highway applications where computer vision plays a crucial role. In particular, we demonstrate that the controlled active vision framework can be utilized to provide a visual sensing modality to a traffic advisory system in order to increase the overall safety margin in a variety of common traffic situations. We have selected two application examples, vehicle tracking and pedestrian tracking, to demonstrate that the framework can provide precisely the type of information required to effectively manage the given situation.

Simulating human behavior for national security human interactions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernard, Michael Lewis; Hart, Dereck H.; Verzi, Stephen J.

2007-01-01

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 humansmore » 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.« less

Stabilization of Silicon Carbide (SiC) micro- and nanoparticle dispersions in the presence of concentrated electrolyte.

PubMed

Vilinska, Annamaria; Ponnurangam, Sathish; Chernyshova, Irina; Somasundaran, Ponisseril; Eroglu, Damla; Martinez, Jose; West, Alan C

2014-06-01

Achieving a stable and robust dispersion of ultrafine particles in concentrated electrolytes is challenging due to the shielding of electrostatic repulsion. Stable dispersion of ultrafine particles in concentrated electrolytes is critical for several applications, including electro-codeposition of ceramic particles in protective metal coatings. We achieved the steric stabilization of SiC micro- and nano-particles in highly concentrated electroplating Watts solutions using their controlled coating with linear and branched polyethyleneimines (PEI) as dispersants. Branched polyethyleneimine of 60,000 MW effectively disperses both microparticles and nanoparticles at a concentration of 1000 ppm. However, lower polymer dosages and smaller polymers fail to disperse, presumably due to insufficient coverage and bridging flocculation. Dispersion stability correlates well with the adsorption density of PEI on microparticles. We discuss the results in the framework of DLVO theory and suggest possible dispersion mechanisms. However, though the dispersion is enhanced with extended adsorption time, the residual PEI in solution adversely affects electroplating. We overcome this drawback by precoating the particles with the polymer and resuspending them in Watts solution. With this novel approach, we obtained robust dispersions. These results offer new possibilities to control dispersion at high electrolyte concentration, as well as bring new insights into the dispersion phenomenon. Copyright © 2014 Elsevier Inc. All rights reserved.

A conceptual evolutionary aseismic decision support framework for hospitals

NASA Astrophysics Data System (ADS)

Hu, Yufeng; Dargush, Gary F.; Shao, Xiaoyun

2012-12-01

In this paper, aconceptual evolutionary framework for aseismic decision support for hospitalsthat attempts to integrate a range of engineering and sociotechnical models is presented. Genetic algorithms are applied to find the optimal decision sets. A case study is completed to demonstrate how the frameworkmay applytoa specific hospital.The simulations show that the proposed evolutionary decision support framework is able to discover robust policy sets in either uncertain or fixed environments. The framework also qualitatively identifies some of the characteristicbehavior of the critical care organization. Thus, by utilizing the proposedframework, the decision makers are able to make more informed decisions, especially toenhance the seismic safety of the hospitals.

Muon g-2 Reconstruction and Analysis Framework for the Muon Anomalous Precession Frequency

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khaw, Kim Siang

The Muon g-2 experiment at Fermilab, with the aim to measure the muon anomalous magnetic moment to an unprecedented level of 140~ppb, has started beam and detector commissioning in Summer 2017. To deal with incoming data projected to be around tens of petabytes, a robust data reconstruction and analysis chain based on Fermilab's \\textit{art} event-processing framework is developed. Herein, I report the current status of the framework, together with its novel features such as multi-threaded algorithms for online data quality monitor (DQM) and fast-turnaround operation (nearline). Performance of the framework during the commissioning run is also discussed.

Image Hashes as Templates for Verification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Janik, Tadeusz; Jarman, Kenneth D.; Robinson, Sean M.

2012-07-17

Imaging systems can provide measurements that confidently assess characteristics of nuclear weapons and dismantled weapon components, and such assessment will be needed in future verification for arms control. Yet imaging is often viewed as too intrusive, raising concern about the ability to protect sensitive information. In particular, the prospect of using image-based templates for verifying the presence or absence of a warhead, or of the declared configuration of fissile material in storage, may be rejected out-of-hand as being too vulnerable to violation of information barrier (IB) principles. Development of a rigorous approach for generating and comparing reduced-information templates from images,more » and assessing the security, sensitivity, and robustness of verification using such templates, are needed to address these concerns. We discuss our efforts to develop such a rigorous approach based on a combination of image-feature extraction and encryption-utilizing hash functions to confirm proffered declarations, providing strong classified data security while maintaining high confidence for verification. The proposed work is focused on developing secure, robust, tamper-sensitive and automatic techniques that may enable the comparison of non-sensitive hashed image data outside an IB. It is rooted in research on so-called perceptual hash functions for image comparison, at the interface of signal/image processing, pattern recognition, cryptography, and information theory. Such perceptual or robust image hashing—which, strictly speaking, is not truly cryptographic hashing—has extensive application in content authentication and information retrieval, database search, and security assurance. Applying and extending the principles of perceptual hashing to imaging for arms control, we propose techniques that are sensitive to altering, forging and tampering of the imaged object yet robust and tolerant to content-preserving image distortions and noise. Ensuring that the information contained in the hashed image data (available out-of-IB) cannot be used to extract sensitive information about the imaged object is of primary concern. Thus the techniques are characterized by high unpredictability to guarantee security. We will present an assessment of the performance of our techniques with respect to security, sensitivity and robustness on the basis of a methodical and mathematically precise framework.« less

H∞ Robust Control of a Large-Piston MEMS Micromirror for Compact Fourier Transform Spectrometer Systems.

PubMed

Chen, Huipeng; Li, Mengyuan; Zhang, Yi; Xie, Huikai; Chen, Chang; Peng, Zhangming; Su, Shaohui

2018-02-08

Incorporating linear-scanning micro-electro-mechanical systems (MEMS) micromirrors into Fourier transform spectral acquisition systems can greatly reduce the size of the spectrometer equipment, making portable Fourier transform spectrometers (FTS) possible. How to minimize the tilting of the MEMS mirror plate during its large linear scan is a major problem in this application. In this work, an FTS system has been constructed based on a biaxial MEMS micromirror with a large-piston displacement of 180 μm, and a biaxial H∞ robust controller is designed. Compared with open-loop control and proportional-integral-derivative (PID) closed-loop control, H∞ robust control has good stability and robustness. The experimental results show that the stable scanning displacement reaches 110.9 μm under the H∞ robust control, and the tilting angle of the MEMS mirror plate in that full scanning range falls within ±0.0014°. Without control, the FTS system cannot generate meaningful spectra. In contrast, the FTS yields a clean spectrum with a full width at half maximum (FWHM) spectral linewidth of 96 cm -1 under the H∞ robust control. Moreover, the FTS system can maintain good stability and robustness under various driving conditions.

H∞ Robust Control of a Large-Piston MEMS Micromirror for Compact Fourier Transform Spectrometer Systems

PubMed Central

Li, Mengyuan; Zhang, Yi; Chen, Chang; Peng, Zhangming; Su, Shaohui

2018-01-01

Incorporating linear-scanning micro-electro-mechanical systems (MEMS) micromirrors into Fourier transform spectral acquisition systems can greatly reduce the size of the spectrometer equipment, making portable Fourier transform spectrometers (FTS) possible. How to minimize the tilting of the MEMS mirror plate during its large linear scan is a major problem in this application. In this work, an FTS system has been constructed based on a biaxial MEMS micromirror with a large-piston displacement of 180 μm, and a biaxial H∞ robust controller is designed. Compared with open-loop control and proportional-integral-derivative (PID) closed-loop control, H∞ robust control has good stability and robustness. The experimental results show that the stable scanning displacement reaches 110.9 μm under the H∞ robust control, and the tilting angle of the MEMS mirror plate in that full scanning range falls within ±0.0014°. Without control, the FTS system cannot generate meaningful spectra. In contrast, the FTS yields a clean spectrum with a full width at half maximum (FWHM) spectral linewidth of 96 cm−1 under the H∞ robust control. Moreover, the FTS system can maintain good stability and robustness under various driving conditions. PMID:29419765

A multi-scale residual-based anti-hourglass control for compatible staggered Lagrangian hydrodynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kucharik, M.; Scovazzi, Guglielmo; Shashkov, Mikhail Jurievich

Hourglassing is a well-known pathological numerical artifact affecting the robustness and accuracy of Lagrangian methods. There exist a large number of hourglass control/suppression strategies. In the community of the staggered compatible Lagrangian methods, the approach of sub-zonal pressure forces is among the most widely used. However, this approach is known to add numerical strength to the solution, which can cause potential problems in certain types of simulations, for instance in simulations of various instabilities. To avoid this complication, we have adapted the multi-scale residual-based stabilization typically used in the finite element approach for staggered compatible framework. In this study, wemore » describe two discretizations of the new approach and demonstrate their properties and compare with the method of sub-zonal pressure forces on selected numerical problems.« less

Synthesis and operation of an FFT-decoupled fixed-order reversed-field pinch plasma control system based on identification data

NASA Astrophysics Data System (ADS)

Olofsson, K. Erik J.; Brunsell, Per R.; Witrant, Emmanuel; Drake, James R.

2010-10-01

Recent developments and applications of system identification methods for the reversed-field pinch (RFP) machine EXTRAP T2R have yielded plasma response parameters for decoupled dynamics. These data sets are fundamental for a real-time implementable fast Fourier transform (FFT) decoupled discrete-time fixed-order strongly stabilizing synthesis as described in this work. Robustness is assessed over the data set by bootstrap calculation of the sensitivity transfer function worst-case H_{\\infty} -gain distribution. Output tracking and magnetohydrodynamic mode m = 1 tracking are considered in the same framework simply as two distinct weighted traces of a performance channel output-covariance matrix as derived from the closed-loop discrete-time Lyapunov equation. The behaviour of the resulting multivariable controller is investigated with dedicated T2R experiments.

A multi-scale residual-based anti-hourglass control for compatible staggered Lagrangian hydrodynamics

DOE PAGES

Kucharik, M.; Scovazzi, Guglielmo; Shashkov, Mikhail Jurievich; ...

2017-10-28

Hourglassing is a well-known pathological numerical artifact affecting the robustness and accuracy of Lagrangian methods. There exist a large number of hourglass control/suppression strategies. In the community of the staggered compatible Lagrangian methods, the approach of sub-zonal pressure forces is among the most widely used. However, this approach is known to add numerical strength to the solution, which can cause potential problems in certain types of simulations, for instance in simulations of various instabilities. To avoid this complication, we have adapted the multi-scale residual-based stabilization typically used in the finite element approach for staggered compatible framework. In this study, wemore » describe two discretizations of the new approach and demonstrate their properties and compare with the method of sub-zonal pressure forces on selected numerical problems.« less

Design and implementation of robust controllers for a gait trainer.

PubMed

Wang, F C; Yu, C H; Chou, T Y

2009-08-01

This paper applies robust algorithms to control an active gait trainer for children with walking disabilities. Compared with traditional rehabilitation procedures, in which two or three trainers are required to assist the patient, a motor-driven mechanism was constructed to improve the efficiency of the procedures. First, a six-bar mechanism was designed and constructed to mimic the trajectory of children's ankles in walking. Second, system identification techniques were applied to obtain system transfer functions at different operating points by experiments. Third, robust control algorithms were used to design Hinfinity robust controllers for the system. Finally, the designed controllers were implemented to verify experimentally the system performance. From the results, the proposed robust control strategies are shown to be effective.

Study of Electrocatalytic Properties of Metal–Organic Framework PCN-223 for the Oxygen Reduction Reaction

DOE PAGES

Usov, Pavel M.; Huffman, Brittany; Epley, Charity C.; ...

2017-03-27

Here, a highly robust metal–organic framework (MOF) constructed from Zr 6 oxo clusters and Fe(III) porphyrin linkers, PCN-223-Fe was investigated as a heterogeneous catalyst for oxygen reduction reaction (ORR). Films of the framework were grown on a conductive FTO substrate and showed a high catalytic current upon application of cathodic potentials and achieved high H 2O/H 2O 2 selectivity. In addition, the effect of the proton source on the catalytic performance was also investigated.

The RAVE/VERTIGO vertex reconstruction toolkit and framework

NASA Astrophysics Data System (ADS)

Waltenberger, W.; Mitaroff, W.; Moser, F.; Pflugfelder, B.; Riedel, H. V.

2008-07-01

A detector-independent toolkit for vertex reconstruction (RAVE1) is being developed, along with a standalone framework (VERTIGO2) for testing, analyzing and debugging. The core algorithms represent state-of-the-art for geometric vertex finding and fitting by both linear (Kalman filter) and robust estimation methods. Main design goals are ease of use, flexibility for embedding into existing software frameworks, extensibility, and openness. The implementation is based on modern object-oriented techniques, is coded in C++ with interfaces for Java and Python, and follows an open-source approach. A beta release is available.

MARTe: A Multiplatform Real-Time Framework

NASA Astrophysics Data System (ADS)

Neto, André C.; Sartori, Filippo; Piccolo, Fabio; Vitelli, Riccardo; De Tommasi, Gianmaria; Zabeo, Luca; Barbalace, Antonio; Fernandes, Horacio; Valcarcel, Daniel F.; Batista, Antonio J. N.

2010-04-01

Development of real-time applications is usually associated with nonportable code targeted at specific real-time operating systems. The boundary between hardware drivers, system services, and user code is commonly not well defined, making the development in the target host significantly difficult. The Multithreaded Application Real-Time executor (MARTe) is a framework built over a multiplatform library that allows the execution of the same code in different operating systems. The framework provides the high-level interfaces with hardware, external configuration programs, and user interfaces, assuring at the same time hard real-time performances. End-users of the framework are required to define and implement algorithms inside a well-defined block of software, named Generic Application Module (GAM), that is executed by the real-time scheduler. Each GAM is reconfigurable with a set of predefined configuration meta-parameters and interchanges information using a set of data pipes that are provided as inputs and required as output. Using these connections, different GAMs can be chained either in series or parallel. GAMs can be developed and debugged in a non-real-time system and, only once the robustness of the code and correctness of the algorithm are verified, deployed to the real-time system. The software also supplies a large set of utilities that greatly ease the interaction and debugging of a running system. Among the most useful are a highly efficient real-time logger, HTTP introspection of real-time objects, and HTTP remote configuration. MARTe is currently being used to successfully drive the plasma vertical stabilization controller on the largest magnetic confinement fusion device in the world, with a control loop cycle of 50 ?s and a jitter under 1 ?s. In this particular project, MARTe is used with the Real-Time Application Interface (RTAI)/Linux operating system exploiting the new ?86 multicore processors technology.

From field notes to data portal - An operational QA/QC framework for tower networks

NASA Astrophysics Data System (ADS)

Sturtevant, C.; Hackley, S.; Meehan, T.; Roberti, J. A.; Holling, G.; Bonarrigo, S.

2016-12-01

Quality assurance and control (QA/QC) is one of the most important yet challenging aspects of producing research-quality data. This is especially so for environmental sensor networks collecting numerous high-frequency measurement streams at distributed sites. Here, the quality issues are multi-faceted, including sensor malfunctions, unmet theoretical assumptions, and measurement interference from the natural environment. To complicate matters, there are often multiple personnel managing different sites or different steps in the data flow. For large, centrally managed sensor networks such as NEON, the separation of field and processing duties is in the extreme. Tower networks such as Ameriflux, ICOS, and NEON continue to grow in size and sophistication, yet tools for robust, efficient, scalable QA/QC have lagged. Quality control remains a largely manual process relying on visual inspection of the data. In addition, notes of observed measurement interference or visible problems are often recorded on paper without an explicit pathway to data flagging during processing. As such, an increase in network size requires a near-proportional increase in personnel devoted to QA/QC, quickly stressing the human resources available. There is a need for a scalable, operational QA/QC framework that combines the efficiency and standardization of automated tests with the power and flexibility of visual checks, and includes an efficient communication pathway from field personnel to data processors to end users. Here we propose such a framework and an accompanying set of tools in development, including a mobile application template for recording tower maintenance and an R/shiny application for efficiently monitoring and synthesizing data quality issues. This framework seeks to incorporate lessons learned from the Ameriflux community and provide tools to aid continued network advancements.

Robust control of combustion instabilities

NASA Astrophysics Data System (ADS)

Hong, Boe-Shong

Several interactive dynamical subsystems, each of which has its own time-scale and physical significance, are decomposed to build a feedback-controlled combustion- fluid robust dynamics. On the fast-time scale, the phenomenon of combustion instability is corresponding to the internal feedback of two subsystems: acoustic dynamics and flame dynamics, which are parametrically dependent on the slow-time-scale mean-flow dynamics controlled for global performance by a mean-flow controller. This dissertation constructs such a control system, through modeling, analysis and synthesis, to deal with model uncertainties, environmental noises and time- varying mean-flow operation. Conservation law is decomposed as fast-time acoustic dynamics and slow-time mean-flow dynamics, served for synthesizing LPV (linear parameter varying)- L2-gain robust control law, in which a robust observer is embedded for estimating and controlling the internal status, while achieving trade- offs among robustness, performances and operation. The robust controller is formulated as two LPV-type Linear Matrix Inequalities (LMIs), whose numerical solver is developed by finite-element method. Some important issues related to physical understanding and engineering application are discussed in simulated results of the control system.

Hypersonic vehicle control law development using H infinity and mu-synthesis

NASA Technical Reports Server (NTRS)

Gregory, Irene M.; Chowdhry, Rajiv S.; Mcminn, John D.; Shaughnessy, John D.

1992-01-01

Applicability and effectiveness of robust control techniques to a single-stage-to-orbit (SSTO) airbreathing hypersonic vehicle on an ascent accelerating path and their effectiveness are explored in this paper. An SSTO control system design problem, requiring high accuracy tracking of velocity and altitude commands while limiting angle of attack oscillations, minimizing control power usage and stabilizing the vehicle all in the presence of atmospheric turbulence and uncertainty in the system, was formulated to compare results of the control designs using H infinity and mu-synthesis procedures. The math model, an integrated flight/propulsion dynamic model of a conical accelerator class vehicle, was linearized as the vehicle accelerated through Mach 8. Controller analysis was conducted using the singular value technique and the mu-analysis approach. Analysis results were obtained in both the frequency and the time domains. The results clearly demonstrate the inherent advantages of the structured singular value framework for this class of problems. Since payload performance margins are so critical for the SSTO mission, it is crucial that adequate stability margins be provided without sacrificing any payload mass.

Adaptive Critic Nonlinear Robust Control: A Survey.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H ∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

Generalized internal model robust control for active front steering intervention

NASA Astrophysics Data System (ADS)

Wu, Jian; Zhao, Youqun; Ji, Xuewu; Liu, Yahui; Zhang, Lipeng

2015-03-01

Because of the tire nonlinearity and vehicle's parameters' uncertainties, robust control methods based on the worst cases, such as H ∞, µ synthesis, have been widely used in active front steering control, however, in order to guarantee the stability of active front steering system (AFS) controller, the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control. In this paper, a generalized internal model robust control (GIMC) that can overcome the contradiction between performance and stability is used in the AFS control. In GIMC, the Youla parameterization is used in an improved way. And GIMC controller includes two sections: a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters' uncertainties and some external disturbances. Simulations of double lane change (DLC) maneuver and that of braking on split- µ road are conducted to compare the performance and stability of the GIMC control, the nominal performance PID controller and the H ∞ controller. Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle's parameters variations, H ∞ controller is conservative so that the performance is a little low, and only the GIMC controller overcomes the contradiction between performance and robustness, which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller. Therefore, the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system, that is, can solve the instability of PID or LQP control methods and the low performance of the standard H ∞ controller.

Discovering System Health Anomalies Using Data Mining Techniques

NASA Technical Reports Server (NTRS)

Sriastava, Ashok, N.

2005-01-01

We present a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an Integrated System Health Monitoring system. We specifically treat the problem of discovering anomalous features in the time series that may be indicative of a system anomaly, or in the case of a manned system, an anomaly due to the human. Identification of these anomalies is crucial to building stable, reusable, and cost-efficient systems. The framework consists of an analysis platform and new algorithms that can scale to thousands of sensor streams to discovers temporal anomalies. We discuss the mathematical framework that underlies the system and also describe in detail how this framework is general enough to encompass both discrete and continuous sensor measurements. We also describe a new set of data mining algorithms based on kernel methods and hidden Markov models that allow for the rapid assimilation, analysis, and discovery of system anomalies. We then describe the performance of the system on a real-world problem in the aircraft domain where we analyze the cockpit data from aircraft as well as data from the aircraft propulsion, control, and guidance systems. These data are discrete and continuous sensor measurements and are dealt with seamlessly in order to discover anomalous flights. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

Evaluating impact of clinical guidelines using a realist evaluation framework.

PubMed

Reddy, Sandeep; Wakerman, John; Westhorp, Gill; Herring, Sally

2015-12-01

The Remote Primary Health Care Manuals (RPHCM) project team manages the development and publication of clinical protocols and procedures for primary care clinicians practicing in remote Australia. The Central Australian Rural Practitioners Association Standard Treatment Manual, the flagship manual of the RPHCM suite, has been evaluated for accessibility and acceptability in remote clinics three times in its 20-year history. These evaluations did not consider a theory-based framework or a programme theory, resulting in some limitations with the evaluation findings. With the RPHCM having an aim of enabling evidence-based practice in remote clinics and anecdotally reported to do so, testing this empirically for the full suite is vital for both stakeholders and future editions of the RPHCM. The project team utilized a realist evaluation framework to assess how, why and for what the RPHCM were being used by remote practitioners. A theory regarding the circumstances in which the manuals have and have not enabled evidence-based practice in the remote clinical context was tested. The project assessed this theory for all the manuals in the RPHCM suite, across government and aboriginal community-controlled clinics, in three regions of Australia. Implementing a realist evaluation framework to generate robust findings in this context has required innovation in the evaluation design and adaptation by researchers. This article captures the RPHCM team's experience in designing this evaluation. © 2015 John Wiley & Sons, Ltd.

Risk, Robustness and Water Resources Planning Under Uncertainty

NASA Astrophysics Data System (ADS)

Borgomeo, Edoardo; Mortazavi-Naeini, Mohammad; Hall, Jim W.; Guillod, Benoit P.

2018-03-01

Risk-based water resources planning is based on the premise that water managers should invest up to the point where the marginal benefit of risk reduction equals the marginal cost of achieving that benefit. However, this cost-benefit approach may not guarantee robustness under uncertain future conditions, for instance under climatic changes. In this paper, we expand risk-based decision analysis to explore possible ways of enhancing robustness in engineered water resources systems under different risk attitudes. Risk is measured as the expected annual cost of water use restrictions, while robustness is interpreted in the decision-theoretic sense as the ability of a water resource system to maintain performance—expressed as a tolerable risk of water use restrictions—under a wide range of possible future conditions. Linking risk attitudes with robustness allows stakeholders to explicitly trade-off incremental increases in robustness with investment costs for a given level of risk. We illustrate the framework through a case study of London's water supply system using state-of-the -art regional climate simulations to inform the estimation of risk and robustness.

Effect of intermittent feedback control on robustness of human-like postural control system

NASA Astrophysics Data System (ADS)

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-03-01

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies.

Effect of intermittent feedback control on robustness of human-like postural control system.

PubMed

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-03-02

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies.

Effect of intermittent feedback control on robustness of human-like postural control system

PubMed Central

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-01-01

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies. PMID:26931281

Built environment change: a framework to support health-enhancing behaviour through environmental policy and health research.

PubMed

Berke, Ethan M; Vernez-Moudon, Anne

2014-06-01

As research examining the effect of the built environment on health accelerates, it is critical for health and planning researchers to conduct studies and make recommendations in the context of a robust theoretical framework. We propose a framework for built environment change (BEC) related to improving health. BEC consists of elements of the built environment, how people are exposed to and interact with them perceptually and functionally, and how this exposure may affect health-related behaviours. Integrated into this framework are the legal and regulatory mechanisms and instruments that are commonly used to effect change in the built environment. This framework would be applicable to medical research as well as to issues of policy and community planning.

Real-time control systems: feedback, scheduling and robustness

NASA Astrophysics Data System (ADS)

Simon, Daniel; Seuret, Alexandre; Sename, Olivier

2017-08-01

The efficient control of real-time distributed systems, where continuous components are governed through digital devices and communication networks, needs a careful examination of the constraints arising from the different involved domains inside co-design approaches. Thanks to the robustness of feedback control, both new control methodologies and slackened real-time scheduling schemes are proposed beyond the frontiers between these traditionally separated fields. A methodology to design robust aperiodic controllers is provided, where the sampling interval is considered as a control variable of the system. Promising experimental results are provided to show the feasibility and robustness of the approach.

Topologically protected modes in non-equilibrium stochastic systems.

PubMed

Murugan, Arvind; Vaikuntanathan, Suriyanarayanan

2017-01-10

Non-equilibrium driving of biophysical processes is believed to enable their robust functioning despite the presence of thermal fluctuations and other sources of disorder. Such robust functions include sensory adaptation, enhanced enzymatic specificity and maintenance of coherent oscillations. Elucidating the relation between energy consumption and organization remains an important and open question in non-equilibrium statistical mechanics. Here we report that steady states of systems with non-equilibrium fluxes can support topologically protected boundary modes that resemble similar modes in electronic and mechanical systems. Akin to their electronic and mechanical counterparts, topological-protected boundary steady states in non-equilibrium systems are robust and are largely insensitive to local perturbations. We argue that our work provides a framework for how biophysical systems can use non-equilibrium driving to achieve robust function.

Adaptive and neuroadaptive control for nonnegative and compartmental dynamical systems

NASA Astrophysics Data System (ADS)

Volyanskyy, Kostyantyn Y.

Neural networks have been extensively used for adaptive system identification as well as adaptive and neuroadaptive control of highly uncertain systems. The goal of adaptive and neuroadaptive control is to achieve system performance without excessive reliance on system models. To improve robustness and the speed of adaptation of adaptive and neuroadaptive controllers several controller architectures have been proposed in the literature. In this dissertation, we develop a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture with additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress system uncertainty. Linear and nonlinear parameterizations of the system uncertainty are considered and state and output feedback neuroadaptive controllers are developed. Furthermore, we extend the developed framework to discrete-time dynamical systems. To illustrate the efficacy of the proposed approach we apply our results to an aircraft model with wing rock dynamics, a spacecraft model with unknown moment of inertia, and an unmanned combat aerial vehicle undergoing actuator failures, and compare our results with standard neuroadaptive control methods. Nonnegative systems are essential in capturing the behavior of a wide range of dynamical systems involving dynamic states whose values are nonnegative. A sub-class of nonnegative dynamical systems are compartmental systems. These systems are derived from mass and energy balance considerations and are comprised of homogeneous interconnected microscopic subsystems or compartments which exchange variable quantities of material via intercompartmental flow laws. In this dissertation, we develop direct adaptive and neuroadaptive control framework for stabilization, disturbance rejection and noise suppression for nonnegative and compartmental dynamical systems with noise and exogenous system disturbances. We then use the developed framework to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of continuing hemorrhage and hemodilution. Critical care patients, whether undergoing surgery or recovering in intensive care units, require drug administration to regulate physiological variables such as blood pressure, cardiac output, heart rate, and degree of consciousness. The rate of infusion of each administered drug is critical, requiring constant monitoring and frequent adjustments. In this dissertation, we develop a neuroadaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs and noisy measurements. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neuroadaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the developed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for surgery in the face of noisy electroencephalographic (EEG) measurements. Clinical trials demonstrate excellent regulation of unconsciousness allowing for a safe and effective administration of the anesthetic agent propofol. Furthermore, a neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude and integral constraints is developed. Specifically, the neuroadaptive controller guarantees that the imposed amplitude and integral input constraints are satisfied and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and a drug dosing constraint over a specified period. In addition, the aforementioned control architecture is used to control lung volume and minute ventilation with input pressure constraints that also accounts for spontaneous breathing by the patient. Specifically, we develop a pressure- and work-limited neuroadaptive controller for mechanical ventilation based on a nonlinear multi-compartmental lung model. The control framework does not rely on any averaged data and is designed to automatically adjust the input pressure to the patient's physiological characteristics capturing lung resistance and compliance modeling uncertainty. Moreover, the controller accounts for input pressure constraints as well as work of breathing constraints. The effect of spontaneous breathing is incorporated within the lung model and the control framework. Finally, a neural network hybrid adaptive control framework for nonlinear uncertain hybrid dynamical systems is developed. The proposed hybrid adaptive control framework is Lyapunov-based and guarantees partial asymptotic stability of the closed-loop hybrid system; that is, asymptotic stability with respect to part of the closed-loop system states associated with the hybrid plant states. A numerical example is provided to demonstrate the efficacy of the proposed hybrid adaptive stabilization approach.

Robustness of a distributed neural network controller for locomotion in a hexapod robot

NASA Technical Reports Server (NTRS)

Chiel, Hillel J.; Beer, Randall D.; Quinn, Roger D.; Espenschied, Kenneth S.

1992-01-01

A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

Hypersonic vehicle model and control law development using H(infinity) and micron synthesis

NASA Astrophysics Data System (ADS)

Gregory, Irene M.; Chowdhry, Rajiv S.; McMinn, John D.; Shaughnessy, John D.

1994-10-01

The control system design for a Single Stage To Orbit (SSTO) air breathing vehicle will be central to a successful mission because a precise ascent trajectory will preserve narrow payload margins. The air breathing propulsion system requires the vehicle to fly roughly halfway around the Earth through atmospheric turbulence. The turbulence, the high sensitivity of the propulsion system to inlet flow conditions, the relatively large uncertainty of the parameters characterizing the vehicle, and continuous acceleration make the problem especially challenging. Adequate stability margins must be provided without sacrificing payload mass since payload margins are critical. Therefore, a multivariable control theory capable of explicitly including both uncertainty and performance is needed. The H(infinity) controller in general provides good robustness but can result in conservative solutions for practical problems involving structured uncertainty. Structured singular value mu framework for analysis and synthesis is potentially much less conservative and hence more appropriate for problems with tight margins. An SSTO control system requires: highly accurate tracking of velocity and altitude commands while limiting angle-of-attack oscillations, minimized control power usage, and a stabilized vehicle when atmospheric turbulence and system uncertainty are present. The controller designs using H(infinity) and mu-synthesis procedures were compared. An integrated flight/propulsion dynamic mathematical model of a conical accelerator vehicle was linearized as the vehicle accelerated through Mach 8. Vehicle acceleration through the selected flight condition gives rise to parametric variation that was modeled as a structured uncertainty. The mu-analysis approach was used in the frequency domain to conduct controller analysis and was confirmed by time history plots. Results demonstrate the inherent advantages of the mu framework for this class of problems.

Hypersonic vehicle model and control law development using H(infinity) and micron synthesis

NASA Technical Reports Server (NTRS)

Gregory, Irene M.; Chowdhry, Rajiv S.; Mcminn, John D.; Shaughnessy, John D.

1994-01-01

The control system design for a Single Stage To Orbit (SSTO) air breathing vehicle will be central to a successful mission because a precise ascent trajectory will preserve narrow payload margins. The air breathing propulsion system requires the vehicle to fly roughly halfway around the Earth through atmospheric turbulence. The turbulence, the high sensitivity of the propulsion system to inlet flow conditions, the relatively large uncertainty of the parameters characterizing the vehicle, and continuous acceleration make the problem especially challenging. Adequate stability margins must be provided without sacrificing payload mass since payload margins are critical. Therefore, a multivariable control theory capable of explicitly including both uncertainty and performance is needed. The H(infinity) controller in general provides good robustness but can result in conservative solutions for practical problems involving structured uncertainty. Structured singular value mu framework for analysis and synthesis is potentially much less conservative and hence more appropriate for problems with tight margins. An SSTO control system requires: highly accurate tracking of velocity and altitude commands while limiting angle-of-attack oscillations, minimized control power usage, and a stabilized vehicle when atmospheric turbulence and system uncertainty are present. The controller designs using H(infinity) and mu-synthesis procedures were compared. An integrated flight/propulsion dynamic mathematical model of a conical accelerator vehicle was linearized as the vehicle accelerated through Mach 8. Vehicle acceleration through the selected flight condition gives rise to parametric variation that was modeled as a structured uncertainty. The mu-analysis approach was used in the frequency domain to conduct controller analysis and was confirmed by time history plots. Results demonstrate the inherent advantages of the mu framework for this class of problems.

Evolutionary Determinants of Cancer

PubMed Central

Greaves, Mel

2015-01-01

‘Nothing in biology makes sense except in the light of evolution’ Th. Dobzhansky, 1973 Our understanding of cancer is being transformed by exploring clonal diversity, drug resistance and causation within an evolutionary framework. The therapeutic resilience of advanced cancer is a consequence of its character as complex, dynamic and adaptive ecosystem engendering robustness, underpinned by genetic diversity and epigenetic plasticity. The risk of mutation-driven escape by self-renewing cells is intrinsic to multicellularity but is countered by multiple restraints facilitating increasing complexity and longevity of species. But our own has disrupted this historical narrative by rapidly escalating intrinsic risk. Evolutionary principles illuminate these challenges and provide new avenues to explore for more effective control. PMID:26193902

Multiscale CNNs for Brain Tumor Segmentation and Diagnosis.

PubMed

Zhao, Liya; Jia, Kebin

2016-01-01

Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.

A Robust, Scalable Framework for Conducting Climate Change Susceptibility Analyses

DTIC Science & Technology

2014-05-01

for identifying areas of heightened risk from varying forms of climate forcings is needed. Based on global climate model projections, deviations from...framework provides an opportunity to easily combine multiple data sources — that are often freely available from many federal, state, and global ...Climate change and extreme weather events: implications for food production, plant diseases, and pests. Global Change and Human Health 2:90–104. ERDC/EL

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

DOE PAGES

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; ...

2013-01-01

Background . The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective . To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods . The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expertmore » knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results . The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions . Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.« less

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

PubMed Central

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Varnum, Susan M.; Brown, Joseph N.; Riensche, Roderick M.; Adkins, Joshua N.; Jacobs, Jon M.; Hoidal, John R.; Scholand, Mary Beth; Pounds, Joel G.; Blackburn, Michael R.; Rodland, Karin D.; McDermott, Jason E.

2013-01-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification. PMID:24223463

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.

2013-10-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integratedmore » into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.« less

Integrated modeling and robust control for full-envelope flight of robotic helicopters

NASA Astrophysics Data System (ADS)

La Civita, Marco

Robotic helicopters have attracted a great deal of interest from the university, the industry, and the military world. They are versatile machines and there is a large number of important missions that they could accomplish. Nonetheless, there are only a handful of documented examples of robotic-helicopter applications in real-world scenarios. This situation is mainly due to the poor flight performance that can be achieved and---more important---guaranteed under automatic control. Given the maturity of control theory, and given the large body of knowledge in helicopter dynamics, it seems that the lack of success in flying high-performance controllers for robotic helicopters, especially by academic groups and by small industries, has nothing to do with helicopters or control theory as such. The problem lies instead in the large amount of time and resources needed to synthesize, test, and implement new control systems with the approach normally followed in the aeronautical industry. This thesis attempts to provide a solution by presenting a modeling and control framework that minimizes the time, cost, and both human and physical resources necessary to design high-performance flight controllers. The work is divided in two main parts. The first consists of the development of a modeling technique that allows the designer to obtain a high-fidelity model adequate for both real-time simulation and controller design, with few flight, ground, and wind-tunnel tests and a modest level of complexity in the dynamic equations. The second consists of the exploitation of the predictive capabilities of the model and of the robust stability and performance guarantees of the Hinfinity loop-shaping control theory to reduce the number of iterations of the design/simulated-evaluation/flight-test-evaluation procedure. The effectiveness of this strategy is demonstrated by designing and flight testing a wide-envelope high-performance controller for the Carnegie Mellon University robotic helicopter.

Optimal robust control strategy of a solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Wu, Xiaojuan; Gao, Danhui

2018-01-01

Optimal control can ensure system safe operation with a high efficiency. However, only a few papers discuss optimal control strategies for solid oxide fuel cell (SOFC) systems. Moreover, the existed methods ignore the impact of parameter uncertainty on system instantaneous performance. In real SOFC systems, several parameters may vary with the variation of operation conditions and can not be identified exactly, such as load current. Therefore, a robust optimal control strategy is proposed, which involves three parts: a SOFC model with parameter uncertainty, a robust optimizer and robust controllers. During the model building process, boundaries of the uncertain parameter are extracted based on Monte Carlo algorithm. To achieve the maximum efficiency, a two-space particle swarm optimization approach is employed to obtain optimal operating points, which are used as the set points of the controllers. To ensure the SOFC safe operation, two feed-forward controllers and a higher-order robust sliding mode controller are presented to control fuel utilization ratio, air excess ratio and stack temperature afterwards. The results show the proposed optimal robust control method can maintain the SOFC system safe operation with a maximum efficiency under load and uncertainty variations.

Robust on-off pulse control of flexible space vehicles

NASA Technical Reports Server (NTRS)

Wie, Bong; Sinha, Ravi

1993-01-01

The on-off reaction jet control system is often used for attitude and orbital maneuvering of various spacecraft. Future space vehicles such as the orbital transfer vehicles, orbital maneuvering vehicles, and space station will extensively use reaction jets for orbital maneuvering and attitude stabilization. The proposed robust fuel- and time-optimal control algorithm is used for a three-mass spacing model of flexible spacecraft. A fuel-efficient on-off control logic is developed for robust rest-to-rest maneuver of a flexible vehicle with minimum excitation of structural modes. The first part of this report is concerned with the problem of selecting a proper pair of jets for practical trade-offs among the maneuvering time, fuel consumption, structural mode excitation, and performance robustness. A time-optimal control problem subject to parameter robustness constraints is formulated and solved. The second part of this report deals with obtaining parameter insensitive fuel- and time- optimal control inputs by solving a constrained optimization problem subject to robustness constraints. It is shown that sensitivity to modeling errors can be significantly reduced by the proposed, robustified open-loop control approach. The final part of this report deals with sliding mode control design for uncertain flexible structures. The benchmark problem of a flexible structure is used as an example for the feedback sliding mode controller design with bounded control inputs and robustness to parameter variations is investigated.

Realistic Simulations of Coronagraphic Observations with Future Space Telescopes

NASA Astrophysics Data System (ADS)

Rizzo, M. J.; Roberge, A.; Lincowski, A. P.; Zimmerman, N. T.; Juanola-Parramon, R.; Pueyo, L.; Hu, M.; Harness, A.

2017-11-01

We present a framework to simulate realistic observations of future space-based coronagraphic instruments. This gathers state-of-the-art scientific and instrumental expertise allowing robust characterization of future instrument concepts.

A sol-gel monolithic metal-organic framework with enhanced methane uptake.

PubMed

Tian, Tian; Zeng, Zhixin; Vulpe, Diana; Casco, Mirian E; Divitini, Giorgio; Midgley, Paul A; Silvestre-Albero, Joaquin; Tan, Jin-Chong; Moghadam, Peyman Z; Fairen-Jimenez, David

2018-02-01

A critical bottleneck for the use of natural gas as a transportation fuel has been the development of materials capable of storing it in a sufficiently compact form at ambient temperature. Here we report the synthesis of a porous monolithic metal-organic framework (MOF), which after successful packing and densification reaches 259 cm 3 (STP) cm -3 capacity. This is the highest value reported to date for conformed shape porous solids, and represents a greater than 50% improvement over any previously reported experimental value. Nanoindentation tests on the monolithic MOF showed robust mechanical properties, with hardness at least 130% greater than that previously measured in its conventional MOF counterparts. Our findings represent a substantial step in the application of mechanically robust conformed and densified MOFs for high volumetric energy storage and other industrial applications.

A sol-gel monolithic metal-organic framework with enhanced methane uptake

NASA Astrophysics Data System (ADS)

Tian, Tian; Zeng, Zhixin; Vulpe, Diana; Casco, Mirian E.; Divitini, Giorgio; Midgley, Paul A.; Silvestre-Albero, Joaquin; Tan, Jin-Chong; Moghadam, Peyman Z.; Fairen-Jimenez, David

2018-02-01

A critical bottleneck for the use of natural gas as a transportation fuel has been the development of materials capable of storing it in a sufficiently compact form at ambient temperature. Here we report the synthesis of a porous monolithic metal-organic framework (MOF), which after successful packing and densification reaches 259 cm3 (STP) cm-3 capacity. This is the highest value reported to date for conformed shape porous solids, and represents a greater than 50% improvement over any previously reported experimental value. Nanoindentation tests on the monolithic MOF showed robust mechanical properties, with hardness at least 130% greater than that previously measured in its conventional MOF counterparts. Our findings represent a substantial step in the application of mechanically robust conformed and densified MOFs for high volumetric energy storage and other industrial applications.

Water resources planning under climate change: Assessing the robustness of real options for the Blue Nile

NASA Astrophysics Data System (ADS)

Jeuland, Marc; Whittington, Dale

2014-03-01

This article presents a methodology for planning new water resources infrastructure investments and operating strategies in a world of climate change uncertainty. It combines a real options (e.g., options to defer, expand, contract, abandon, switch use, or otherwise alter a capital investment) approach with principles drawn from robust decision-making (RDM). RDM comprises a class of methods that are used to identify investment strategies that perform relatively well, compared to the alternatives, across a wide range of plausible future scenarios. Our proposed framework relies on a simulation model that includes linkages between climate change and system hydrology, combined with sensitivity analyses that explore how economic outcomes of investments in new dams vary with forecasts of changing runoff and other uncertainties. To demonstrate the framework, we consider the case of new multipurpose dams along the Blue Nile in Ethiopia. We model flexibility in design and operating decisions—the selection, sizing, and sequencing of new dams, and reservoir operating rules. Results show that there is no single investment plan that performs best across a range of plausible future runoff conditions. The decision-analytic framework is then used to identify dam configurations that are both robust to poor outcomes and sufficiently flexible to capture high upside benefits if favorable future climate and hydrological conditions should arise. The approach could be extended to explore design and operating features of development and adaptation projects other than dams.

Robust evaluation of time series classification algorithms for structural health monitoring

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Worden, Keith; Todd, Michael D.

2014-03-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

A simple theoretical framework for understanding heterogeneous differentiation of CD4+ T cells

PubMed Central

2012-01-01

Background CD4+ T cells have several subsets of functional phenotypes, which play critical yet diverse roles in the immune system. Pathogen-driven differentiation of these subsets of cells is often heterogeneous in terms of the induced phenotypic diversity. In vitro recapitulation of heterogeneous differentiation under homogeneous experimental conditions indicates some highly regulated mechanisms by which multiple phenotypes of CD4+ T cells can be generated from a single population of naïve CD4+ T cells. Therefore, conceptual understanding of induced heterogeneous differentiation will shed light on the mechanisms controlling the response of populations of CD4+ T cells under physiological conditions. Results We present a simple theoretical framework to show how heterogeneous differentiation in a two-master-regulator paradigm can be governed by a signaling network motif common to all subsets of CD4+ T cells. With this motif, a population of naïve CD4+ T cells can integrate the signals from their environment to generate a functionally diverse population with robust commitment of individual cells. Notably, two positive feedback loops in this network motif govern three bistable switches, which in turn, give rise to three types of heterogeneous differentiated states, depending upon particular combinations of input signals. We provide three prototype models illustrating how to use this framework to explain experimental observations and make specific testable predictions. Conclusions The process in which several types of T helper cells are generated simultaneously to mount complex immune responses upon pathogenic challenges can be highly regulated, and a simple signaling network motif can be responsible for generating all possible types of heterogeneous populations with respect to a pair of master regulators controlling CD4+ T cell differentiation. The framework provides a mathematical basis for understanding the decision-making mechanisms of CD4+ T cells, and it can be helpful for interpreting experimental results. Mathematical models based on the framework make specific testable predictions that may improve our understanding of this differentiation system. PMID:22697466

R-IDEAL: A Framework for Systematic Clinical Evaluation of Technical Innovations in Radiation Oncology.

PubMed

Verkooijen, Helena M; Kerkmeijer, Linda G W; Fuller, Clifton D; Huddart, Robbert; Faivre-Finn, Corinne; Verheij, Marcel; Mook, Stella; Sahgal, Arjun; Hall, Emma; Schultz, Chris

2017-01-01

The pace of innovation in radiation oncology is high and the window of opportunity for evaluation narrow. Financial incentives, industry pressure, and patients' demand for high-tech treatments have led to widespread implementation of innovations before, or even without, robust evidence of improved outcomes has been generated. The standard phase I-IV framework for drug evaluation is not the most efficient and desirable framework for assessment of technological innovations. In order to provide a standard assessment methodology for clinical evaluation of innovations in radiotherapy, we adapted the surgical IDEAL framework to fit the radiation oncology setting. Like surgery, clinical evaluation of innovations in radiation oncology is complicated by continuous technical development, team and operator dependence, and differences in quality control. Contrary to surgery, radiotherapy innovations may be used in various ways, e.g., at different tumor sites and with different aims, such as radiation volume reduction and dose escalation. Also, the effect of radiation treatment can be modeled, allowing better prediction of potential benefits and improved patient selection. Key distinctive features of R-IDEAL include the important role of predicate and modeling studies (Stage 0), randomization at an early stage in the development of the technology, and long-term follow-up for late toxicity. We implemented R-IDEAL for clinical evaluation of a recent innovation in radiation oncology, the MRI-guided linear accelerator (MR-Linac). MR-Linac combines a radiotherapy linear accelerator with a 1.5-T MRI, aiming for improved targeting, dose escalation, and margin reduction, and is expected to increase the use of hypofractionation, improve tumor control, leading to higher cure rates and less toxicity. An international consortium, with participants from seven large cancer institutes from Europe and North America, has adopted the R-IDEAL framework to work toward coordinated, evidence-based introduction of the MR-Linac. R-IDEAL holds the promise for timely, evidence-based introduction of radiotherapy innovations with proven superior effectiveness, while preventing unnecessary exposure of patients to potentially harmful interventions.

Hydration and chemical ingredients in sport drinks: food safety in the European context.

PubMed

Urdampilleta, Aritz; Gómez-Zorita, Saioa; Soriano, José M; Martínez-Sanz, José M; Medina, Sonia; Gil-Izquierdo, Angel

2015-05-01

Before, during and after physical activity, hydration is a limiting factor in athletic performance. Therefore, adequate hydration provides benefits for health and performance of athletes. Besides, hydration is associated to the intake of carbohydrates, protein, sodium, caffeine and other substances by different dietary aids, during the training and/or competition by athletes. These requirements have led to the development of different products by the food industry, to cover the nutritional needs of athletes. Currently in the European context, the legal framework for the development of products, substances and health claims concerning to sport products is incomplete and scarce. Under these conditions, there are many products with different ingredients out of European Food Safety Authority (EFSA) control where claims are wrong due to no robust scientific evidence and it can be dangerous for the health. Further scientific evidence should be constructed by new clinical trials in order to assist to the Experts Commitees at EFSA for obtaining robust scientific opinions concerning to the functional foods and the individual ingredients for sport population. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Theory on the Dynamics of Oscillatory Loops in the Transcription Factor Networks

PubMed Central

Murugan, Rajamanickam

2014-01-01

We develop a detailed theoretical framework for various types of transcription factor gene oscillators. We further demonstrate that one can build genetic-oscillators which are tunable and robust against perturbations in the critical control parameters by coupling two or more independent Goodwin-Griffith oscillators through either -OR- or -AND- type logic. Most of the coupled oscillators constructed in the literature so far seem to be of -OR- type. When there are transient perturbations in one of the -OR- type coupled-oscillators, then the overall period of the system remains constant (period-buffering) whereas in case of -AND- type coupling the overall period of the system moves towards the perturbed oscillator. Though there is a period-buffering, the amplitudes of oscillators coupled through -OR- type logic are more sensitive to perturbations in the parameters associated with the promoter state dynamics than -AND- type. Further analysis shows that the period of -AND- type coupled dual-feedback oscillators can be tuned without conceding on the amplitudes. Using these results we derive the basic design principles governing the robust and tunable synthetic gene oscillators without compromising on their amplitudes. PMID:25111803

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

PubMed

Cui, Yuwei; Ahmad, Subutai; Hawkins, Jeff

2017-01-01

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

Modification of computational auditory scene analysis (CASA) for noise-robust acoustic feature

NASA Astrophysics Data System (ADS)

Kwon, Minseok

While there have been many attempts to mitigate interferences of background noise, the performance of automatic speech recognition (ASR) still can be deteriorated by various factors with ease. However, normal hearing listeners can accurately perceive sounds of their interests, which is believed to be a result of Auditory Scene Analysis (ASA). As a first attempt, the simulation of the human auditory processing, called computational auditory scene analysis (CASA), was fulfilled through physiological and psychological investigations of ASA. CASA comprised of Zilany-Bruce auditory model, followed by tracking fundamental frequency for voice segmentation and detecting pairs of onset/offset at each characteristic frequency (CF) for unvoiced segmentation. The resulting Time-Frequency (T-F) representation of acoustic stimulation was converted into acoustic feature, gammachirp-tone frequency cepstral coefficients (GFCC). 11 keywords with various environmental conditions are used and the robustness of GFCC was evaluated by spectral distance (SD) and dynamic time warping distance (DTW). In "clean" and "noisy" conditions, the application of CASA generally improved noise robustness of the acoustic feature compared to a conventional method with or without noise suppression using MMSE estimator. The intial study, however, not only showed the noise-type dependency at low SNR, but also called the evaluation methods in question. Some modifications were made to capture better spectral continuity from an acoustic feature matrix, to obtain faster processing speed, and to describe the human auditory system more precisely. The proposed framework includes: 1) multi-scale integration to capture more accurate continuity in feature extraction, 2) contrast enhancement (CE) of each CF by competition with neighboring frequency bands, and 3) auditory model modifications. The model modifications contain the introduction of higher Q factor, middle ear filter more analogous to human auditory system, the regulation of time constant update for filters in signal/control path as well as level-independent frequency glides with fixed frequency modulation. First, we scrutinized performance development in keyword recognition using the proposed methods in quiet and noise-corrupted environments. The results argue that multi-scale integration should be used along with CE in order to avoid ambiguous continuity in unvoiced segments. Moreover, the inclusion of the all modifications was observed to guarantee the noise-type-independent robustness particularly with severe interference. Moreover, the CASA with the auditory model was implemented into a single/dual-channel ASR using reference TIMIT corpus so as to get more general result. Hidden Markov model (HTK) toolkit was used for phone recognition in various environmental conditions. In a single-channel ASR, the results argue that unmasked acoustic features (unmasked GFCC) should combine with target estimates from the mask to compensate for missing information. From the observation of a dual-channel ASR, the combined GFCC guarantees the highest performance regardless of interferences within speech. Moreover, consistent improvement of noise robustness by GFCC (unmasked or combined) shows the validity of our proposed CASA implementation in dual microphone system. In conclusion, the proposed framework proves the robustness of the acoustic features in various background interferences via both direct distance evaluation and statistical assessment. In addition, the introduction of dual microphone system using the framework in this study shows the potential of the effective implementation of the auditory model-based CASA in ASR.

Fuzzy decision-making framework for treatment selection based on the combined QUALIFLEX-TODIM method

NASA Astrophysics Data System (ADS)

Ji, Pu; Zhang, Hong-yu; Wang, Jian-qiang

2017-10-01

Treatment selection is a multi-criteria decision-making problem of significant concern in the medical field. In this study, a fuzzy decision-making framework is established for treatment selection. The framework mitigates information loss by introducing single-valued trapezoidal neutrosophic numbers to denote evaluation information. Treatment selection has multiple criteria that remarkably exceed the alternatives. In consideration of this characteristic, the framework utilises the idea of the qualitative flexible multiple criteria method. Furthermore, it considers the risk-averse behaviour of a decision maker by employing a concordance index based on TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method. A sensitivity analysis is performed to illustrate the robustness of the framework. Finally, a comparative analysis is conducted to compare the framework with several extant methods. Results indicate the advantages of the framework and its better performance compared with the extant methods.

Robust Control for Microgravity Vibration Isolation using Fixed Order, Mixed H2/Mu Design

NASA Technical Reports Server (NTRS)

Whorton, Mark

2003-01-01

Many space-science experiments need an active isolation system to provide a sufficiently quiescent microgravity environment. Modern control methods provide the potential for both high-performance and robust stability in the presence of parametric uncertainties that are characteristic of microgravity vibration isolation systems. While H2 and H(infinity) methods are well established, neither provides the levels of attenuation performance and robust stability in a compensator with low order. Mixed H2/H(infinity), controllers provide a means for maximizing robust stability for a given level of mean-square nominal performance while directly optimizing for controller order constraints. This paper demonstrates the benefit of mixed norm design from the perspective of robustness to parametric uncertainties and controller order for microgravity vibration isolation. A nominal performance metric analogous to the mu measure, for robust stability assessment is also introduced in order to define an acceptable trade space from which different control methodologies can be compared.

Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways

PubMed Central

Kawakami, Eiryo; Singh, Vivek K; Matsubara, Kazuko; Ishii, Takashi; Matsuoka, Yukiko; Hase, Takeshi; Kulkarni, Priya; Siddiqui, Kenaz; Kodilkar, Janhavi; Danve, Nitisha; Subramanian, Indhupriya; Katoh, Manami; Shimizu-Yoshida, Yuki; Ghosh, Samik; Jere, Abhay; Kitano, Hiroaki

2016-01-01

Cellular stress responses require exquisite coordination between intracellular signaling molecules to integrate multiple stimuli and actuate specific cellular behaviors. Deciphering the web of complex interactions underlying stress responses is a key challenge in understanding robust biological systems and has the potential to lead to the discovery of targeted therapeutics for diseases triggered by dysregulation of stress response pathways. We constructed large-scale molecular interaction maps of six major stress response pathways in Saccharomyces cerevisiae (baker’s or budding yeast). Biological findings from over 900 publications were converted into standardized graphical formats and integrated into a common framework. The maps are posted at http://www.yeast-maps.org/yeast-stress-response/ for browse and curation by the research community. On the basis of these maps, we undertook systematic analyses to unravel the underlying architecture of the networks. A series of network analyses revealed that yeast stress response pathways are organized in bow–tie structures, which have been proposed as universal sub-systems for robust biological regulation. Furthermore, we demonstrated a potential role for complexes in stabilizing the conserved core molecules of bow–tie structures. Specifically, complex-mediated reversible reactions, identified by network motif analyses, appeared to have an important role in buffering the concentration and activity of these core molecules. We propose complex-mediated reactions as a key mechanism mediating robust regulation of the yeast stress response. Thus, our comprehensive molecular interaction maps provide not only an integrated knowledge base, but also a platform for systematic network analyses to elucidate the underlying architecture in complex biological systems. PMID:28725465

A Robust H ∞ Controller for an UAV Flight Control System.

PubMed

López, J; Dormido, R; Dormido, S; Gómez, J P

2015-01-01

The objective of this paper is the implementation and validation of a robust H ∞ controller for an UAV to track all types of manoeuvres in the presence of noisy environment. A robust inner-outer loop strategy is implemented. To design the H ∞ robust controller in the inner loop, H ∞ control methodology is used. The two controllers that conform the outer loop are designed using the H ∞ Loop Shaping technique. The reference vector used in the control architecture formed by vertical velocity, true airspeed, and heading angle, suggests a nontraditional way to pilot the aircraft. The simulation results show that the proposed control scheme works well despite the presence of noise and uncertainties, so the control system satisfies the requirements.

Closed-loop and robust control of quantum systems.

PubMed

Chen, Chunlin; Wang, Lin-Cheng; Wang, Yuanlong

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H(∞) control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention.

The applications of model-based geostatistics in helminth epidemiology and control.

PubMed

Magalhães, Ricardo J Soares; Clements, Archie C A; Patil, Anand P; Gething, Peter W; Brooker, Simon

2011-01-01

Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes. Copyright © 2011 Elsevier Ltd. All rights reserved.

Robust Control for The G-Limit Microgravity Vibration Isolation System

NASA Technical Reports Server (NTRS)

Whorton, Mark S.

2004-01-01

Many microgravity science experiments need an active isolation system to provide a sufficiently quiescent acceleration environment. The g-LIMIT vibration isolation system will provide isolation for Microgravity Science Glovebox experiments in the International Space Station. While standard control system technologies have been demonstrated for these applications, modern control methods have the potential for meeting performance requirements while providing robust stability in the presence of parametric uncertainties that are characteristic of microgravity vibration isolation systems. While H2 and H infinity methods are well established, neither provides the levels of attenuation performance and robust stability in a compensator with low order. Mixed H2/mu controllers provide a means for maximizing robust stability for a given level of mean-square nominal performance while directly optimizing for controller order constraints. This paper demonstrates the benefit of mixed norm design from the perspective of robustness to parametric uncertainties and controller order for microgravity vibration isolation. A nominal performance metric analogous to the mu measure for robust stability assessment is also introduced in order to define an acceptable trade space from which different control methodologies can be compared.

Modern CACSD using the Robust-Control Toolbox

NASA Technical Reports Server (NTRS)

Chiang, Richard Y.; Safonov, Michael G.

1989-01-01

The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction.

Worry is associated with robust reductions in heart rate variability: a transdiagnostic study of anxiety psychopathology.

PubMed

Chalmers, John A; Heathers, James A J; Abbott, Maree J; Kemp, Andrew H; Quintana, Daniel S

2016-06-03

Individuals with anxiety disorders display reduced resting-state heart rate variability (HRV), although findings have been contradictory and the role of specific symptoms has been less clear. It is possible that HRV reductions may transcend diagnostic categories, consistent with dimensional-trait models of psychopathology. Here we investigated whether anxiety disorders or symptoms of anxiety, stress, worry and depression are more strongly associated with resting-state HRV. Resting-state HRV was calculated in participants with clinical anxiety (n = 25) and healthy controls (n = 58). Symptom severity measures of worry, anxiety, stress, and depression were also collected from participants, regardless of diagnosis. Participants who fulfilled DSM-IV criteria for an anxiety disorder displayed diminished HRV, a difference at trend level significance (p = .1, Hedges' g = -.37, BF10 = .84). High worriers (Total n = 41; n = 22 diagnosed with an anxiety disorder and n = 19 not meeting criteria for any psychopathology) displayed a robust reduction in resting state HRV relative to low worriers (p = .001, Hedges' g = -.75, BF10 = 28.16). The specific symptom of worry - not the diagnosis of an anxiety disorder - was associated with the most robust reductions in HRV, indicating that HRV may provide a transdiagnostic biomarker of worry. These results enhance understanding of the relationship between the cardiac autonomic nervous system and anxiety psychopathology, providing support for dimensional-trait models consistent with the Research Domain Criteria framework.

A H∞/μ solution for microvibration mitigation in satellites: A case study

NASA Astrophysics Data System (ADS)

Preda, Valentin; Cieslak, Jérôme; Henry, David; Bennani, Samir; Falcoz, Alexandre

2017-07-01

The research work presented in this paper focuses on the development of a mixed active-passive microvibration mitigation solution capable of attenuating the transmitted vibrations generated by reaction wheels to a satellite structure. A representative benchmark provided by the European Space Agency (ESA) and Airbus Defence and Space, serves as a support for testing the proposed solution. The paper also covers modeling and design issues as well as a deep analysis of the solution within the H∞ / μ setting. Especially, an uncertainty modeling strategy is proposed to extract a Linear Fractional Transformation (LFT) model. Insight is naturally provided into various dynamical interactions between the plant elements such as bearing and isolator flexibility, gyroscopic effects, actuator dynamics and feedback-loop delays. The design of the mitigation solution is formulated into the H∞ / μ framework leading to a robust H∞ control strategy capable of achieving exemplary active attenuation performance across a wide range of reaction wheel speeds. A systematic analysis procedure based on the structured singular value μ is used to assess and demonstrate the robust stability and robust performance of the microvibration mitigation strategy. The proposed analysis method is also shown to be a powerful and reliable solution to identify worst-case scenarios without relying on traditional Monte Carlo campaigns. Time domain simulations based on a nonlinear high-fidelity industrial simulator are included as a validation step.

Robust control for uncertain structures

NASA Technical Reports Server (NTRS)

Douglas, Joel; Athans, Michael

1991-01-01

Viewgraphs on robust control for uncertain structures are presented. Topics covered include: robust linear quadratic regulator (RLQR) formulas; mismatched LQR design; RLQR design; interpretations of RLQR design; disturbance rejection; and performance comparisons: RLQR vs. mismatched LQR.

Evaluation of Ares-I Control System Robustness to Uncertain Aerodynamics and Flex Dynamics

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; VanTassel, Chris; Bedrossian, Nazareth; Hall, Charles; Spanos, Pol

2008-01-01

This paper discusses the application of robust control theory to evaluate robustness of the Ares-I control systems. Three techniques for estimating upper and lower bounds of uncertain parameters which yield stable closed-loop response are used here: (1) Monte Carlo analysis, (2) mu analysis, and (3) characteristic frequency response analysis. All three methods are used to evaluate stability envelopes of the Ares-I control systems with uncertain aerodynamics and flex dynamics. The results show that characteristic frequency response analysis is the most effective of these methods for assessing robustness.

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Langwen; Xie, Wei; Wang, Jingcheng

2017-11-01

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.

Robust control of accelerators

NASA Astrophysics Data System (ADS)

Joel, W.; Johnson, D.; Chaouki, Abdallah T.

1991-07-01

The problem of controlling the variations in the rf power system can be effectively cast as an application of modern control theory. Two components of this theory are obtaining a model and a feedback structure. The model inaccuracies influence the choice of a particular controller structure. Because of the modelling uncertainty, one has to design either a variable, adaptive controller or a fixed, robust controller to achieve the desired objective. The adaptive control scheme usually results in very complex hardware; and, therefore, shall not be pursued in this research. In contrast, the robust control method leads to simpler hardware. However, robust control requires a more accurate mathematical model of the physical process than is required by adaptive control. Our research at the Los Alamos National Laboratory (LANL) and the University of New Mexico (UNM) has led to the development and implementation of a new robust rf power feedback system. In this article, we report on our research progress. In section 1, the robust control problem for the rf power system and the philosophy adopted for the beginning phase of our research is presented. In section 2, the results of our proof-of-principle experiments are presented. In section 3, we describe the actual controller configuration that is used in LANL FEL physics experiments. The novelty of our approach is that the control hardware is implemented directly in rf. without demodulating, compensating, and then remodulating.

Dynamical Systems Approach to Endothelial Heterogeneity

PubMed Central

Regan, Erzsébet Ravasz; Aird, William C.

2012-01-01

Rationale Objective Here we reexamine our current understanding of the molecular basis of endothelial heterogeneity. We introduce multistability as a new explanatory framework in vascular biology. Methods We draw on the field of non-linear dynamics to propose a dynamical systems framework for modeling multistability and its derivative properties, including robustness, memory, and plasticity. Conclusions Our perspective allows for both a conceptual and quantitative description of system-level features of endothelial regulation. PMID:22723222

A Robust H ∞ Controller for an UAV Flight Control System

PubMed Central

López, J.

2015-01-01

The objective of this paper is the implementation and validation of a robust H ∞ controller for an UAV to track all types of manoeuvres in the presence of noisy environment. A robust inner-outer loop strategy is implemented. To design the H ∞ robust controller in the inner loop, H ∞ control methodology is used. The two controllers that conform the outer loop are designed using the H ∞ Loop Shaping technique. The reference vector used in the control architecture formed by vertical velocity, true airspeed, and heading angle, suggests a nontraditional way to pilot the aircraft. The simulation results show that the proposed control scheme works well despite the presence of noise and uncertainties, so the control system satisfies the requirements. PMID:26221622

Model robustness as a confirmatory virtue: The case of climate science.

PubMed

Lloyd, Elisabeth A

2015-02-01

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking

PubMed Central

Qu, Shiru

2016-01-01

Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness. PMID:27630710

Addressing uncertainty in adaptation planning for agriculture.

PubMed

Vermeulen, Sonja J; Challinor, Andrew J; Thornton, Philip K; Campbell, Bruce M; Eriyagama, Nishadi; Vervoort, Joost M; Kinyangi, James; Jarvis, Andy; Läderach, Peter; Ramirez-Villegas, Julian; Nicklin, Kathryn J; Hawkins, Ed; Smith, Daniel R

2013-05-21

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop-climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.

Addressing uncertainty in adaptation planning for agriculture

PubMed Central

Vermeulen, Sonja J.; Challinor, Andrew J.; Thornton, Philip K.; Campbell, Bruce M.; Eriyagama, Nishadi; Vervoort, Joost M.; Kinyangi, James; Jarvis, Andy; Läderach, Peter; Ramirez-Villegas, Julian; Nicklin, Kathryn J.; Hawkins, Ed; Smith, Daniel R.

2013-01-01

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop–climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty. PMID:23674681

Robust fractional order sliding mode control of doubly-fed induction generator (DFIG)-based wind turbines.

PubMed

Ebrahimkhani, Sadegh

2016-07-01

Wind power plants have nonlinear dynamics and contain many uncertainties such as unknown nonlinear disturbances and parameter uncertainties. Thus, it is a difficult task to design a robust reliable controller for this system. This paper proposes a novel robust fractional-order sliding mode (FOSM) controller for maximum power point tracking (MPPT) control of doubly fed induction generator (DFIG)-based wind energy conversion system. In order to enhance the robustness of the control system, uncertainties and disturbances are estimated using a fractional order uncertainty estimator. In the proposed method a continuous control strategy is developed to achieve the chattering free fractional order sliding-mode control, and also no knowledge of the uncertainties and disturbances or their bound is assumed. The boundedness and convergence properties of the closed-loop signals are proven using Lyapunov׳s stability theory. Simulation results in the presence of various uncertainties were carried out to evaluate the effectiveness and robustness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Model and controller reduction of large-scale structures based on projection methods

NASA Astrophysics Data System (ADS)

Gildin, Eduardo

The design of low-order controllers for high-order plants is a challenging problem theoretically as well as from a computational point of view. Frequently, robust controller design techniques result in high-order controllers. It is then interesting to achieve reduced-order models and controllers while maintaining robustness properties. Controller designed for large structures based on models obtained by finite element techniques yield large state-space dimensions. In this case, problems related to storage, accuracy and computational speed may arise. Thus, model reduction methods capable of addressing controller reduction problems are of primary importance to allow the practical applicability of advanced controller design methods for high-order systems. A challenging large-scale control problem that has emerged recently is the protection of civil structures, such as high-rise buildings and long-span bridges, from dynamic loadings such as earthquakes, high wind, heavy traffic, and deliberate attacks. Even though significant effort has been spent in the application of control theory to the design of civil structures in order increase their safety and reliability, several challenging issues are open problems for real-time implementation. This dissertation addresses with the development of methodologies for controller reduction for real-time implementation in seismic protection of civil structures using projection methods. Three classes of schemes are analyzed for model and controller reduction: nodal truncation, singular value decomposition methods and Krylov-based methods. A family of benchmark problems for structural control are used as a framework for a comparative study of model and controller reduction techniques. It is shown that classical model and controller reduction techniques, such as balanced truncation, modal truncation and moment matching by Krylov techniques, yield reduced-order controllers that do not guarantee stability of the closed-loop system, that is, the reduced-order controller implemented with the full-order plant. A controller reduction approach is proposed such that to guarantee closed-loop stability. It is based on the concept of dissipativity (or positivity) of linear dynamical systems. Utilizing passivity preserving model reduction together with dissipative-LQG controllers, effective low-order optimal controllers are obtained. Results are shown through simulations.

Costs and financial feasibility of malaria elimination

PubMed Central

Sabot, Oliver; Cohen, Justin M; Hsiang, Michelle S; Kahn, James G; Basu, Suprotik; Tang, Linhua; Zheng, Bin; Gao, Qi; Zou, Linda; Tatarsky, Allison; Aboobakar, Shahina; Usas, Jennifer; Barrett, Scott; Cohen, Jessica L; Jamison, Dean T; Feachem, Richard GA

2010-01-01

Summary The marginal costs and benefits of converting malaria programmes from a control to an elimination goal are central to strategic decisions, but empirical evidence is scarce. We present a conceptual framework to assess the economics of elimination and analyse a central component of that framework—potential short-term to medium-term financial savings. After a review that showed a dearth of existing evidence, the net present value of elimination in five sites was calculated and compared with effective control. The probability that elimination would be cost-saving over 50 years ranged from 0% to 42%, with only one site achieving cost-savings in the base case. These findings show that financial savings should not be a primary rationale for elimination, but that elimination might still be a worthy investment if total benefits are sufficient to outweigh marginal costs. Robust research into these elimination benefits is urgently needed. PMID:21035839

Intelligent Rover Execution for Detecting Life in the Atacama Desert

NASA Technical Reports Server (NTRS)

Baskaran, Vijayakumar; Muscettola, Nicola; Rijsman, David; Plaunt, Chris; Fry, Chuck

2006-01-01

On-board supervisory execution is crucial for the deployment of more capable and autonomous remote explorers. Planetary science is considering robotic explorers operating for long periods of time without ground supervision while interacting with a changing and often hostile environment. Effective and robust operations require on-board supervisory control with a high level of awareness of the principles of functioning of the environment and of the numerous internal subsystems that need to be coordinated. We describe an on-board rover executive that was deployed on a rover as past of the "Limits of Life in the Atacama Desert (LITA)" field campaign sponsored by the NASA ASTEP program. The executive was built using the Intelligent Distributed Execution Architecture (IDEA), an execution framework that uses model-based and plan-based supervisory control of its fundamental computational paradigm. We present the results of the third field experiment conducted in the Atacama desert (Chile) in August - October 2005.

Programmable disorder in random DNA tilings

NASA Astrophysics Data System (ADS)

Tikhomirov, Grigory; Petersen, Philip; Qian, Lulu

2017-03-01

Scaling up the complexity and diversity of synthetic molecular structures will require strategies that exploit the inherent stochasticity of molecular systems in a controlled fashion. Here we demonstrate a framework for programming random DNA tilings and show how to control the properties of global patterns through simple, local rules. We constructed three general forms of planar network—random loops, mazes and trees—on the surface of self-assembled DNA origami arrays on the micrometre scale with nanometre resolution. Using simple molecular building blocks and robust experimental conditions, we demonstrate control of a wide range of properties of the random networks, including the branching rules, the growth directions, the proximity between adjacent networks and the size distribution. Much as combinatorial approaches for generating random one-dimensional chains of polymers have been used to revolutionize chemical synthesis and the selection of functional nucleic acids, our strategy extends these principles to random two-dimensional networks of molecules and creates new opportunities for fabricating more complex molecular devices that are organized by DNA nanostructures.

Optimization and real-time control for laser treatment of heterogeneous soft tissues.

PubMed

Feng, Yusheng; Fuentes, David; Hawkins, Andrea; Bass, Jon M; Rylander, Marissa Nichole

2009-01-01

Predicting the outcome of thermotherapies in cancer treatment requires an accurate characterization of the bioheat transfer processes in soft tissues. Due to the biological and structural complexity of tumor (soft tissue) composition and vasculature, it is often very difficult to obtain reliable tissue properties that is one of the key factors for the accurate treatment outcome prediction. Efficient algorithms employing in vivo thermal measurements to determine heterogeneous thermal tissues properties in conjunction with a detailed sensitivity analysis can produce essential information for model development and optimal control. The goals of this paper are to present a general formulation of the bioheat transfer equation for heterogeneous soft tissues, review models and algorithms developed for cell damage, heat shock proteins, and soft tissues with nanoparticle inclusion, and demonstrate an overall computational strategy for developing a laser treatment framework with the ability to perform real-time robust calibrations and optimal control. This computational strategy can be applied to other thermotherapies using the heat source such as radio frequency or high intensity focused ultrasound.

Distributed robust finite-time nonlinear consensus protocols for multi-agent systems

NASA Astrophysics Data System (ADS)

Zuo, Zongyu; Tie, Lin

2016-04-01

This paper investigates the robust finite-time consensus problem of multi-agent systems in networks with undirected topology. Global nonlinear consensus protocols augmented with a variable structure are constructed with the aid of Lyapunov functions for each single-integrator agent dynamics in the presence of external disturbances. In particular, it is shown that the finite settling time of the proposed general framework for robust consensus design is upper bounded for any initial condition. This makes it possible for network consensus problems to design and estimate the convergence time offline for a multi-agent team with a given undirected information flow. Finally, simulation results are presented to demonstrate the performance and effectiveness of our finite-time protocols.

A Robust Decision-Making Technique for Water Management under Decadal Scale Climate Variability

NASA Astrophysics Data System (ADS)

Callihan, L.; Zagona, E. A.; Rajagopalan, B.

2013-12-01

Robust decision making, a flexible and dynamic approach to managing water resources in light of deep uncertainties associated with climate variability at inter-annual to decadal time scales, is an analytical framework that detects when a system is in or approaching a vulnerable state. It provides decision makers the opportunity to implement strategies that both address the vulnerabilities and perform well over a wide range of plausible future scenarios. A strategy that performs acceptably over a wide range of possible future states is not likely to be optimal with respect to the actual future state. The degree of success--the ability to avoid vulnerable states and operate efficiently--thus depends on the skill in projecting future states and the ability to select the most efficient strategies to address vulnerabilities. This research develops a robust decision making framework that incorporates new methods of decadal scale projections with selection of efficient strategies. Previous approaches to water resources planning under inter-annual climate variability combining skillful seasonal flow forecasts with climatology for subsequent years are not skillful for medium term (i.e. decadal scale) projections as decision makers are not able to plan adequately to avoid vulnerabilities. We address this need by integrating skillful decadal scale streamflow projections into the robust decision making framework and making the probability distribution of this projection available to the decision making logic. The range of possible future hydrologic scenarios can be defined using a variety of nonparametric methods. Once defined, an ensemble projection of decadal flow scenarios are generated from a wavelet-based spectral K-nearest-neighbor resampling approach using historical and paleo-reconstructed data. This method has been shown to generate skillful medium term projections with a rich variety of natural variability. The current state of the system in combination with the probability distribution of the projected flow ensembles enables the selection of appropriate decision options. This process is repeated for each year of the planning horizon--resulting in system outcomes that can be evaluated on their performance and resiliency. The research utilizes the RiverSMART suite of software modeling and analysis tools developed under the Bureau of Reclamation's WaterSMART initiative and built around the RiverWare modeling environment. A case study is developed for the Gunnison and Upper Colorado River Basins. The ability to mitigate vulnerability using the framework is gauged by system performance indicators that measure the ability of the system to meet various water demands (i.e. agriculture, environmental flows, hydropower etc.). Options and strategies for addressing vulnerabilities include measures such as conservation, reallocation and adjustments to operational policy. In addition to being able to mitigate vulnerabilities, options and strategies are evaluated based on benefits, costs and reliability. Flow ensembles are also simulated to incorporate mean and variance from climate change projections for the planning horizon and the above robust decision-making framework is applied to evaluate its performance under changing climate.

Automatic and accurate reconstruction of distal humerus contours through B-Spline fitting based on control polygon deformation.

PubMed

Mostafavi, Kamal; Tutunea-Fatan, O Remus; Bordatchev, Evgueni V; Johnson, James A

2014-12-01

The strong advent of computer-assisted technologies experienced by the modern orthopedic surgery prompts for the expansion of computationally efficient techniques to be built on the broad base of computer-aided engineering tools that are readily available. However, one of the common challenges faced during the current developmental phase continues to remain the lack of reliable frameworks to allow a fast and precise conversion of the anatomical information acquired through computer tomography to a format that is acceptable to computer-aided engineering software. To address this, this study proposes an integrated and automatic framework capable to extract and then postprocess the original imaging data to a common planar and closed B-Spline representation. The core of the developed platform relies on the approximation of the discrete computer tomography data by means of an original two-step B-Spline fitting technique based on successive deformations of the control polygon. In addition to its rapidity and robustness, the developed fitting technique was validated to produce accurate representations that do not deviate by more than 0.2 mm with respect to alternate representations of the bone geometry that were obtained through different-contact-based-data acquisition or data processing methods. © IMechE 2014.

Social Email: A Framework and Application for More Socially-Aware Communications

NASA Astrophysics Data System (ADS)

Tran, Thomas; Rowe, Jeff; Wu, S. Felix

As online social networks (OSN) attempt to mimic real life social networks, we have made progress towards using OSNs to provide us with data to allow for richer and more robust online communications. In this paper, we present a novel approach towards socially-aware email. Currently, email provides too little control to the recipient. Our approach, dubbed SoEmail, provides social context to messages using an OSN's underlying social graph. This not only gives the recipient control over who can message her, but it also provides her with an understanding of where the message originated from, socially. Furthermore, users who do not wish to use the built-in social aspect of SoEmail, can send and receive emails without any changes to their behavior. The modifications we made to the email application to provide this social context are not invasive and can be easily ignored by any existing email client. We built SoEmail on top of an existing framework, known as Davis Social Links, which allows SoEmail to be completely agnostic about the underlying OSN. We created a web-based, standards-based web client for SoEmail using Facebook and Gmail as the underlying systems which has been released for public use and has had a good adoption rate.

Real-time sensor validation and fusion for distributed autonomous sensors

NASA Astrophysics Data System (ADS)

Yuan, Xiaojing; Li, Xiangshang; Buckles, Bill P.

2004-04-01

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

Robust Stability and Control of Multi-Body Ground Vehicles with Uncertain Dynamics and Failures

DTIC Science & Technology

2010-01-01

and N. Zhang, 2008. “Robust stability control of vehicle rollover subject to actuator time delay”. Proc. IMechE Part I: J. of systems and control ...Dynamic Systems and Control Conference, Boston, MA, Sept 2010 R.K. Yedavalli,”Robust Stability of Linear Interval Parameter Matrix Family Problem...for control coupled output regulation for a class of systems is presented. In section 2.1.7, the control design algorithm developed in section

A robust H∞ control-based hierarchical mode transition control system for plug-in hybrid electric vehicle

NASA Astrophysics Data System (ADS)

Yang, Chao; Jiao, Xiaohong; Li, Liang; Zhang, Yuanbo; Chen, Zheng

2018-01-01

To realize a fast and smooth operating mode transition process from electric driving mode to engine-on driving mode, this paper presents a novel robust hierarchical mode transition control method for a plug-in hybrid electric bus (PHEB) with pre-transmission parallel hybrid powertrain. Firstly, the mode transition process is divided into five stages to clearly describe the powertrain dynamics. Based on the dynamics models of powertrain and clutch actuating mechanism, a hierarchical control structure including two robust H∞ controllers in both upper layer and lower layer is proposed. In upper layer, the demand clutch torque can be calculated by a robust H∞controller considering the clutch engaging time and the vehicle jerk. While in lower layer a robust tracking controller with L2-gain is designed to perform the accurate position tracking control, especially when the parameters uncertainties and external disturbance occur in the clutch actuating mechanism. Simulation and hardware-in-the-loop (HIL) test are carried out in a traditional driving condition of PHEB. Results show that the proposed hierarchical control approach can obtain the good control performance: mode transition time is greatly reduced with the acceptable jerk. Meanwhile, the designed control system shows the obvious robustness with the uncertain parameters and disturbance. Therefore, the proposed approach may offer a theoretical reference for the actual vehicle controller.

Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation

NASA Astrophysics Data System (ADS)

Otake, Yoshito; Wang, Adam S.; Webster Stayman, J.; Uneri, Ali; Kleinszig, Gerhard; Vogt, Sebastian; Khanna, A. Jay; Gokaslan, Ziya L.; Siewerdsen, Jeffrey H.

2013-12-01

We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with ‘success’ defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.

Aircraft ride quality controller design using new robust root clustering theory for linear uncertain systems

NASA Technical Reports Server (NTRS)

Yedavalli, R. K.

1992-01-01

The aspect of controller design for improving the ride quality of aircraft in terms of damping ratio and natural frequency specifications on the short period dynamics is addressed. The controller is designed to be robust with respect to uncertainties in the real parameters of the control design model such as uncertainties in the dimensional stability derivatives, imperfections in actuator/sensor locations and possibly variations in flight conditions, etc. The design is based on a new robust root clustering theory developed by the author by extending the nominal root clustering theory of Gutman and Jury to perturbed matrices. The proposed methodology allows to get an explicit relationship between the parameters of the root clustering region and the uncertainty radius of the parameter space. The current literature available for robust stability becomes a special case of this unified theory. The bounds derived on the parameter perturbation for robust root clustering are then used in selecting the robust controller.

MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis.

PubMed

Kim, SungHwan; Lin, Chien-Wei; Tseng, George C

2016-07-01

Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies. We proposed two frameworks, by averaging TSP scores or by combining P-values from individual studies, to select the top gene pairs for model construction. We applied the proposed methods in simulated data sets and three large-scale real applications in breast cancer, idiopathic pulmonary fibrosis and pan-cancer methylation. The result showed superior performance of cross-study validation accuracy and biomarker selection for the new meta-analytic framework. In conclusion, combining multiple omics data sets in the public domain increases robustness and accuracy of the classification model that will ultimately improve disease understanding and clinical treatment decisions to benefit patients. An R package MetaKTSP is available online. (http://tsenglab.biostat.pitt.edu/software.htm). ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Closed-Loop and Robust Control of Quantum Systems

PubMed Central

Wang, Lin-Cheng

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H ∞ control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention. PMID:23997680

Doubly robust nonparametric inference on the average treatment effect.

PubMed

Benkeser, D; Carone, M; Laan, M J Van Der; Gilbert, P B

2017-12-01

Doubly robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double robustness does not readily extend to inference. We present a general theoretical study of the behaviour of doubly robust estimators of an average treatment effect when one of the nuisance parameters is inconsistently estimated. We contrast different methods for constructing such estimators and investigate the extent to which they may be modified to also allow doubly robust inference. We find that while targeted minimum loss-based estimation can be used to solve this problem very naturally, common alternative frameworks appear to be inappropriate for this purpose. We provide a theoretical study and a numerical evaluation of the alternatives considered. Our simulations highlight the need for and usefulness of these approaches in practice, while our theoretical developments have broad implications for the construction of estimators that permit doubly robust inference in other problems.

Robust Fuzzy Logic Stabilization with Disturbance Elimination

PubMed Central

Danapalasingam, Kumeresan A.

2014-01-01

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design. PMID:25177713

A Robust Design Methodology for Optimal Microscale Secondary Flow Control in Compact Inlet Diffusers

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Keller, Dennis J.

2001-01-01

It is the purpose of this study to develop an economical Robust design methodology for microscale secondary flow control in compact inlet diffusers. To illustrate the potential of economical Robust Design methodology, two different mission strategies were considered for the subject inlet, namely Maximum Performance and Maximum HCF Life Expectancy. The Maximum Performance mission maximized total pressure recovery while the Maximum HCF Life Expectancy mission minimized the mean of the first five Fourier harmonic amplitudes, i.e., 'collectively' reduced all the harmonic 1/2 amplitudes of engine face distortion. Each of the mission strategies was subject to a low engine face distortion constraint, i.e., DC60<0.10, which is a level acceptable for commercial engines. For each of these missions strategies, an 'Optimal Robust' (open loop control) and an 'Optimal Adaptive' (closed loop control) installation was designed over a twenty degree angle-of-incidence range. The Optimal Robust installation used economical Robust Design methodology to arrive at a single design which operated over the entire angle-of-incident range (open loop control). The Optimal Adaptive installation optimized all the design parameters at each angle-of-incidence. Thus, the Optimal Adaptive installation would require a closed loop control system to sense a proper signal for each effector and modify that effector device, whether mechanical or fluidic, for optimal inlet performance. In general, the performance differences between the Optimal Adaptive and Optimal Robust installation designs were found to be marginal. This suggests, however, that Optimal Robust open loop installation designs can be very competitive with Optimal Adaptive close loop designs. Secondary flow control in inlets is inherently robust, provided it is optimally designed. Therefore, the new methodology presented in this paper, combined array 'Lower Order' approach to Robust DOE, offers the aerodynamicist a very viable and economical way of exploring the concept of Robust inlet design, where the mission variables are brought directly into the inlet design process and insensitivity or robustness to the mission variables becomes a design objective.

Messenger RNA (mRNA) nanoparticle tumour vaccination

NASA Astrophysics Data System (ADS)

Phua, Kyle K. L.; Nair, Smita K.; Leong, Kam W.

2014-06-01

Use of mRNA-based vaccines for tumour immunotherapy has gained increasing attention in recent years. A growing number of studies applying nanomedicine concepts to mRNA tumour vaccination show that the mRNA delivered in nanoparticle format can generate a more robust immune response. Advances in the past decade have deepened our understanding of gene delivery barriers, mRNA's biological stability and immunological properties, and support the notion for engineering innovations tailored towards a more efficient mRNA nanoparticle vaccine delivery system. In this review we will first examine the suitability of mRNA for engineering manipulations, followed by discussion of a model framework that highlights the barriers to a robust anti-tumour immunity mediated by mRNA encapsulated in nanoparticles. Finally, by consolidating existing literature on mRNA nanoparticle tumour vaccination within the context of this framework, we aim to identify bottlenecks that can be addressed by future nanoengineering research.

A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin V.

2016-01-01

Based on the theoretical framework for sensitivity analysis called "Variogram Analysis of Response Surfaces" (VARS), developed in the companion paper, we develop and implement a practical "star-based" sampling strategy (called STAR-VARS), for the application of VARS to real-world problems. We also develop a bootstrap approach to provide confidence level estimates for the VARS sensitivity metrics and to evaluate the reliability of inferred factor rankings. The effectiveness, efficiency, and robustness of STAR-VARS are demonstrated via two real-data hydrological case studies (a 5-parameter conceptual rainfall-runoff model and a 45-parameter land surface scheme hydrology model), and a comparison with the "derivative-based" Morris and "variance-based" Sobol approaches are provided. Our results show that STAR-VARS provides reliable and stable assessments of "global" sensitivity across the full range of scales in the factor space, while being 1-2 orders of magnitude more efficient than the Morris or Sobol approaches.

Speed-constrained three-axes attitude control using kinematic steering

NASA Astrophysics Data System (ADS)

Schaub, Hanspeter; Piggott, Scott

2018-06-01

Spacecraft attitude control solutions typically are torque-level algorithms that simultaneously control both the attitude and angular velocity tracking errors. In contrast, robotic control solutions are kinematic steering commands where rates are treated as the control variable, and a servo-tracking control subsystem is present to achieve the desired control rates. In this paper kinematic attitude steering controls are developed where an outer control loop establishes a desired angular response history to a tracking error, and an inner control loop tracks the commanded body angular rates. The overall stability relies on the separation principle of the inner and outer control loops which must have sufficiently different response time scales. The benefit is that the outer steering law response can be readily shaped to a desired behavior, such as limiting the approach angular velocity when a large tracking error is corrected. A Modified Rodrigues Parameters implementation is presented that smoothly saturates the speed response. A robust nonlinear body rate servo loop is developed which includes integral feedback. This approach provides a convenient modular framework that makes it simple to interchange outer and inner control loops to readily setup new control implementations. Numerical simulations illustrate the expected performance for an aggressive reorientation maneuver subject to an unknown external torque.

Robust Learning Control Design for Quantum Unitary Transformations.

PubMed

Wu, Chengzhi; Qi, Bo; Chen, Chunlin; Dong, Daoyi

2017-12-01

Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.

Robust Control Systems.

DTIC Science & Technology

1981-12-01

time control system algorithms that will perform adequately (i.e., at least maintain closed-loop system stability) when ucertain parameters in the...system design models vary significantly. Such a control algorithm is said to have stability robustness-or more simply is said to be "robust". This...cas6s above, the performance is analyzed using a covariance analysis. The development of all the controllers and the performance analysis algorithms is

Advanced Techniques for Deploying Reliable and Efficient Access Control: Application to E-healthcare.

PubMed

Jaïdi, Faouzi; Labbene-Ayachi, Faten; Bouhoula, Adel

2016-12-01

Nowadays, e-healthcare is a main advancement and upcoming technology in healthcare industry that contributes to setting up automated and efficient healthcare infrastructures. Unfortunately, several security aspects remain as main challenges towards secure and privacy-preserving e-healthcare systems. From the access control perspective, e-healthcare systems face several issues due to the necessity of defining (at the same time) rigorous and flexible access control solutions. This delicate and irregular balance between flexibility and robustness has an immediate impact on the compliance of the deployed access control policy. To address this issue, the paper defines a general framework to organize thinking about verifying, validating and monitoring the compliance of access control policies in the context of e-healthcare databases. We study the problem of the conformity of low level policies within relational databases and we particularly focus on the case of a medical-records management database defined in the context of a Medical Information System. We propose an advanced solution for deploying reliable and efficient access control policies. Our solution extends the traditional lifecycle of an access control policy and allows mainly managing the compliance of the policy. We refer to an example to illustrate the relevance of our proposal.

Monitoring and accountability for the Pacific response to the non-communicable diseases crisis.

PubMed

Tolley, Hilary; Snowdon, Wendy; Wate, Jillian; Durand, A Mark; Vivili, Paula; McCool, Judith; Novotny, Rachel; Dewes, Ofa; Hoy, Damian; Bell, Colin; Richards, Nicola; Swinburn, Boyd

2016-09-10

Non-communicable diseases (NCD) are the leading cause of premature death and disability in the Pacific. In 2011, Pacific Forum Leaders declared "a human, social and economic crisis" due to the significant and growing burden of NCDs in the region. In 2013, Pacific Health Ministers' commitment to 'whole of government' strategy prompted calls for the development of a robust, sustainable, collaborative NCD monitoring and accountability system to track, review and propose remedial action to ensure progress towards the NCD goals and targets. The purpose of this paper is to describe a regional, collaborative framework for coordination, innovation and application of NCD monitoring activities at scale, and to show how they can strengthen accountability for action on NCDs in the Pacific. A key component is the Dashboard for NCD Action which aims to strengthen mutual accountability by demonstrating national and regional progress towards agreed NCD policies and actions. The framework for the Pacific Monitoring Alliance for NCD Action (MANA) draws together core country-level components of NCD monitoring data (mortality, morbidity, risk factors, health system responses, environments, and policies) and identifies key cross-cutting issues for strengthening national and regional monitoring systems. These include: capacity building; a regional knowledge exchange hub; innovations (monitoring childhood obesity and food environments); and a robust regional accountability system. The MANA framework is governed by the Heads of Health and operationalised by a multi-agency technical Coordination Team. Alliance membership is voluntary and non-conditional, and aims to support the 22 Pacific Island countries and territories to improve the quality of NCD monitoring data across the region. In establishing a common vision for NCD monitoring, the framework combines data collected under the WHO Global Framework for NCDs with a set of action-orientated indicators captured in a NCD Dashboard for Action. Viewing NCD monitoring as a multi-component system and providing a robust, transparent mutual accountability mechanism helps align agendas, roles and responsibilities of countries and support organisations. The dashboard provides a succinct communication tool for reporting progress on implementation of agreed policies and actions and its flexible methodology can be easily expanded, or adapted for other regions.

A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme

NASA Astrophysics Data System (ADS)

Ghoman, Satyajit S.

The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M 3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M 3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M 3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a nonconventional supersonic tailless air vehicle wing shape optimization problem.

Model reference tracking control of an aircraft: a robust adaptive approach

NASA Astrophysics Data System (ADS)

Tanyer, Ilker; Tatlicioglu, Enver; Zergeroglu, Erkan

2017-05-01

This work presents the design and the corresponding analysis of a nonlinear robust adaptive controller for model reference tracking of an aircraft that has parametric uncertainties in its system matrices and additive state- and/or time-dependent nonlinear disturbance-like terms in its dynamics. Specifically, robust integral of the sign of the error feedback term and an adaptive term is fused with a proportional integral controller. Lyapunov-based stability analysis techniques are utilised to prove global asymptotic convergence of the output tracking error. Extensive numerical simulations are presented to illustrate the performance of the proposed robust adaptive controller.

Uncertainty analysis and robust trajectory linearization control of a flexible air-breathing hypersonic vehicle

NASA Astrophysics Data System (ADS)

Pu, Zhiqiang; Tan, Xiangmin; Fan, Guoliang; Yi, Jianqiang

2014-08-01

Flexible air-breathing hypersonic vehicles feature significant uncertainties which pose huge challenges to robust controller designs. In this paper, four major categories of uncertainties are analyzed, that is, uncertainties associated with flexible effects, aerodynamic parameter variations, external environmental disturbances, and control-oriented modeling errors. A uniform nonlinear uncertainty model is explored for the first three uncertainties which lumps all uncertainties together and consequently is beneficial for controller synthesis. The fourth uncertainty is additionally considered in stability analysis. Based on these analyses, the starting point of the control design is to decompose the vehicle dynamics into five functional subsystems. Then a robust trajectory linearization control (TLC) scheme consisting of five robust subsystem controllers is proposed. In each subsystem controller, TLC is combined with the extended state observer (ESO) technique for uncertainty compensation. The stability of the overall closed-loop system with the four aforementioned uncertainties and additional singular perturbations is analyzed. Particularly, the stability of nonlinear ESO is also discussed from a Liénard system perspective. At last, simulations demonstrate the great control performance and the uncertainty rejection ability of the robust scheme.

An eco-physiological model of the impact of temperature on Aedes aegypti life history traits.

PubMed

Padmanabha, Harish; Correa, Fabio; Legros, Mathieu; Nijhout, H Fredrick; Lord, Cynthia; Lounibos, L Philip

2012-12-01

Physiological processes mediate the impact of ecological conditions on the life histories of insect vectors. For the dengue/chikungunya mosquito, Aedes aegypti, three life history traits that are critical to urban population dynamics and control are: size, development rate and starvation mortality. In this paper we make use of prior laboratory experiments on each of these traits at 2°C intervals between 20 and 30°C, in conjunction with eco-evolutionary theory and studies on A.aegypti physiology, in order to develop a conceptual and mathematical framework that can predict their thermal sensitivity. Our model of reserve dependent growth (RDG), which considers a potential tradeoff between the accumulation of reserves and structural biomass, was able to robustly predict laboratory observations, providing a qualitative improvement over the approach most commonly used in other A.aegypti models. RDG predictions of reduced size at higher temperatures, but increased reserves relative to size, are supported by the available evidence in Aedes spp. We offer the potentially general hypothesis that temperature-size patterns in mosquitoes are driven by a net benefit of finishing the growing stage with proportionally greater reserves relative to structure at warmer temperatures. By relating basic energy flows to three fundamental life history traits, we provide a mechanistic framework for A.aegypti development to which ecological complexity can be added. Ultimately, this could provide a framework for developing and field testing hypotheses on how processes such as climate variation, density dependent regulation, human behavior or control strategies may influence A.aegypti population dynamics and disease risk. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimization of Systems with Uncertainty: Initial Developments for Performance, Robustness and Reliability Based Designs

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

This paper presents a study on the optimization of systems with structured uncertainties, whose inputs and outputs can be exhaustively described in the probabilistic sense. By propagating the uncertainty from the input to the output in the space of the probability density functions and the moments, optimization problems that pursue performance, robustness and reliability based designs are studied. Be specifying the desired outputs in terms of desired probability density functions and then in terms of meaningful probabilistic indices, we settle a computationally viable framework for solving practical optimization problems. Applications to static optimization and stability control are used to illustrate the relevance of incorporating uncertainty in the early stages of the design. Several examples that admit a full probabilistic description of the output in terms of the design variables and the uncertain inputs are used to elucidate the main features of the generic problem and its solution. Extensions to problems that do not admit closed form solutions are also evaluated. Concrete evidence of the importance of using a consistent probabilistic formulation of the optimization problem and a meaningful probabilistic description of its solution is provided in the examples. In the stability control problem the analysis shows that standard deterministic approaches lead to designs with high probability of running into instability. The implementation of such designs can indeed have catastrophic consequences.

Robust Low-dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

PubMed Central

Zhang, Shaoting; Chen, Tsuhan; Sanelli, Pina C.

2016-01-01

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain’ is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in-vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions. PMID:25706579

Detection of an explosive simulant via electrical impedance spectroscopy utilizing the UiO-66-NH2 metal-organic framework.

PubMed

Peterson, G W; McEntee, M; Harris, C R; Klevitch, A D; Fountain, A W; Soliz, J R; Balboa, A; Hauser, A J

2016-11-01

Electrical impedance spectroscopy, in conjunction with the metal-organic framework (MOF) UiO-66-NH 2 , is used to detect trace levels of the explosive simulant 2,6-dinitrotoluene. The combination of porosity and functionality of the MOF provides an effective dielectric structure, resulting in changes of impedance magnitude and phase angle. The promising data indicate that MOFs may be used in low-cost, robust explosive detection devices.

Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly screen thermomechanical properties of solids

NASA Astrophysics Data System (ADS)

Toher, Cormac; Oses, Corey; Plata, Jose J.; Hicks, David; Rose, Frisco; Levy, Ohad; de Jong, Maarten; Asta, Mark; Fornari, Marco; Buongiorno Nardelli, Marco; Curtarolo, Stefano

2017-06-01

Thorough characterization of the thermomechanical properties of materials requires difficult and time-consuming experiments. This severely limits the availability of data and is one of the main obstacles for the development of effective accelerated materials design strategies. The rapid screening of new potential materials requires highly integrated, sophisticated, and robust computational approaches. We tackled the challenge by developing an automated, integrated workflow with robust error-correction within the AFLOW framework which combines the newly developed "Automatic Elasticity Library" with the previously implemented GIBBS method. The first extracts the mechanical properties from automatic self-consistent stress-strain calculations, while the latter employs those mechanical properties to evaluate the thermodynamics within the Debye model. This new thermoelastic workflow is benchmarked against a set of 74 experimentally characterized systems to pinpoint a robust computational methodology for the evaluation of bulk and shear moduli, Poisson ratios, Debye temperatures, Grüneisen parameters, and thermal conductivities of a wide variety of materials. The effect of different choices of equations of state and exchange-correlation functionals is examined and the optimum combination of properties for the Leibfried-Schlömann prediction of thermal conductivity is identified, leading to improved agreement with experimental results than the GIBBS-only approach. The framework has been applied to the AFLOW.org data repositories to compute the thermoelastic properties of over 3500 unique materials. The results are now available online by using an expanded version of the REST-API described in the Appendix.

Economic Justification Of Robust Or Adaptive Planning And Design Of Resilient Water Resources Systems Under Deep Uncertainty: A Case Study In The Iolanda Water Treatment Plant Of Lusaka, Zambia

NASA Astrophysics Data System (ADS)

Mendoza, G.; Tkach, M.; Kucharski, J.; Chaudhry, R.

2017-12-01

This discussion is focused around the application of a bottom-up vulnerability assessment procedure for planning of climate resilience to a water treament plant for teh city of Iolanda, Zambia. This project is a Millennium Challenge Corporation (MCC) innitiaive with technical support by the UNESCO category II, International Center for Integrated Water Resources Management (ICIWaRM) with secretariat at the US Army Corps of Engineers Institute for Water Resources. The MCC is an innovative and independent U.S. foreign aid agency that is helping lead the fight against global poverty. The bottom-up vulnerability assessmentt framework examines critical performance thresholds, examines the external drivers that would lead to failure, establishes plausibility and analytical uncertainty that would lead to failure, and provides the economic justification for robustness or adaptability. This presentation will showcase the experiences in the application of the bottom-up framework to a region that is very vulnerable to climate variability, has poor instituional capacities, and has very limited data. It will illustrate the technical analysis and a decision process that led to a non-obvious climate robust solution. Most importantly it will highlight the challenges of utilizing discounted cash flow analysis (DCFA), such as net present value, in justifying robust or adaptive solutions, i.e. comparing solution under different future risks. We highlight a solution to manage the potential biases these DCFA procedures can incur.

Noise in Neuronal and Electronic Circuits: A General Modeling Framework and Non-Monte Carlo Simulation Techniques.

PubMed

Kilinc, Deniz; Demir, Alper

2017-08-01

The brain is extremely energy efficient and remarkably robust in what it does despite the considerable variability and noise caused by the stochastic mechanisms in neurons and synapses. Computational modeling is a powerful tool that can help us gain insight into this important aspect of brain mechanism. A deep understanding and computational design tools can help develop robust neuromorphic electronic circuits and hybrid neuroelectronic systems. In this paper, we present a general modeling framework for biological neuronal circuits that systematically captures the nonstationary stochastic behavior of ion channels and synaptic processes. In this framework, fine-grained, discrete-state, continuous-time Markov chain models of both ion channels and synaptic processes are treated in a unified manner. Our modeling framework features a mechanism for the automatic generation of the corresponding coarse-grained, continuous-state, continuous-time stochastic differential equation models for neuronal variability and noise. Furthermore, we repurpose non-Monte Carlo noise analysis techniques, which were previously developed for analog electronic circuits, for the stochastic characterization of neuronal circuits both in time and frequency domain. We verify that the fast non-Monte Carlo analysis methods produce results with the same accuracy as computationally expensive Monte Carlo simulations. We have implemented the proposed techniques in a prototype simulator, where both biological neuronal and analog electronic circuits can be simulated together in a coupled manner.

Robust dynamic inversion controller design and analysis (using the X-38 vehicle as a case study)

NASA Astrophysics Data System (ADS)

Ito, Daigoro

A new way to approach robust Dynamic Inversion controller synthesis is addressed in this paper. A Linear Quadratic Gaussian outer-loop controller improves the robustness of a Dynamic Inversion inner-loop controller in the presence of uncertainties. Desired dynamics are given by the dynamic compensator, which shapes the loop. The selected dynamics are based on both performance and stability robustness requirements. These requirements are straightforwardly formulated as frequency-dependent singular value bounds during synthesis of the controller. Performance and robustness of the designed controller is tested using a worst case time domain quadratic index, which is a simple but effective way to measure robustness due to parameter variation. Using this approach, a lateral-directional controller for the X-38 vehicle is designed and its robustness to parameter variations and disturbances is analyzed. It is found that if full state measurements are available, the performance of the designed lateral-directional control system, measured by the chosen cost function, improves by approximately a factor of four. Also, it is found that the designed system is stable up to a parametric variation of 1.65 standard deviation with the set of uncertainty considered. The system robustness is determined to be highly sensitive to the dihedral derivative and the roll damping coefficients. The controller analysis is extended to the nonlinear system where both control input displacements and rates are bounded. In this case, the considered nonlinear system is stable up to 48.1° in bank angle and 1.59° in sideslip angle variations, indicating it is more sensitive to variations in sideslip angle than in bank angle. This nonlinear approach is further extended for the actuator failure mode analysis. The results suggest that the designed system maintains a high level of stability in the event of aileron failure. However, only 35% or less of the original stability range is maintained for the rudder failure case. Overall, this combination of controller synthesis and robustness criteria compares well with the mu-synthesis technique. It also is readily accessible to the practicing engineer, in terms of understanding and use.

A geostatistical extreme-value framework for fast simulation of natural hazard events

PubMed Central

Stephenson, David B.

2016-01-01

We develop a statistical framework for simulating natural hazard events that combines extreme value theory and geostatistics. Robust generalized additive model forms represent generalized Pareto marginal distribution parameters while a Student’s t-process captures spatial dependence and gives a continuous-space framework for natural hazard event simulations. Efficiency of the simulation method allows many years of data (typically over 10 000) to be obtained at relatively little computational cost. This makes the model viable for forming the hazard module of a catastrophe model. We illustrate the framework by simulating maximum wind gusts for European windstorms, which are found to have realistic marginal and spatial properties, and validate well against wind gust measurements. PMID:27279768

A novel robust speed controller scheme for PMBLDC motor.

PubMed

Thirusakthimurugan, P; Dananjayan, P

2007-10-01

The design of speed and position controllers for permanent magnet brushless DC motor (PMBLDC) drive remains as an open problem in the field of motor drives. A precise speed control of PMBLDC motor is complex due to nonlinear coupling between winding currents and rotor speed. In addition, the nonlinearity present in the developed torque due to magnetic saturation of the rotor further complicates this issue. This paper presents a novel control scheme to the conventional PMBLDC motor drive, which aims at improving the robustness by complete decoupling of the design besides minimizing the mutual influence among the speed and current control loops. The interesting feature of this robust control scheme is its suitability for both static and dynamic aspects. The effectiveness of the proposed robust speed control scheme is verified through simulations.

Energetics of an rf SQUID Coupled to Two Thermal Reservoirs

DOE PAGES

Gardas, B.; Łuczka, J.; Ptok, A.; ...

2015-12-07

We study energetics of a Josephson tunnel junction connecting a superconducting loop pierced by an external magnetic flux (an rf SQUID) and coupled to two independent thermal reservoirs of different temperature. In the framework of the theory of quantum dissipative systems, we analyze energy currents in stationary states. The stationary energy flow can be periodically modulated by the external magnetic flux exemplifying the rf SQUID as a quantum heat interferometer. Additionally, we consider the transient regime and identify three distinct regimes: monotonic decay, damped oscillations and pulse-type behavior of energy currents. Furthermore, the first two regimes can be controlled bymore » the external magnetic flux while the last regime is robust against its variation.« less

Hidden Markov model analysis of force/torque information in telemanipulation

NASA Technical Reports Server (NTRS)

Hannaford, Blake; Lee, Paul

1991-01-01

A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.

Application of the double paddle oscillator for quantifying environmental, surface mass variation

NASA Astrophysics Data System (ADS)

Wei, Haoyan; Pomeroy, Joshua

2016-04-01

Sub-monolayer sensitivity to controlled gas adsorption and desorption is demonstrated using a double paddle oscillator (DPO) installed within an ultra-high vacuum (UHV) environmental chamber equipped with in situ film deposition, (multi)gas admission and temperature control. This effort is intended to establish a robust framework for quantitatively comparing mass changes due to gas loading and unloading on different materials systems selected or considered for use as mass artefacts. Our apparatus is composed of a UHV chamber with gas introduction and temperature control and in situ materials deposition for future materials testing enabling in situ preparation of virgin surfaces that can be monitored during initial exposure to gasses of interest. These tools are designed to allow us to comparatively evaluate how different materials gain or lose mass due to precisely controlled environmental excursions, with a long term goal of measuring changes in absolute mass. Herein, we provide a detailed experimental description of the apparatus, an evaluation of the initial performance, and demonstration measurements using nitrogen adsorption and desorption directly on the DPO.

Application of the double paddle oscillator for quantifying environmental, surface mass variation

PubMed Central

Wei, Haoyan; Pomeroy, Joshua

2016-01-01

Sub-monolayer sensitivity to controlled gas adsorption and desorption is demonstrated using a double paddle oscillator (DPO) installed within an UHV (ultra-high vacuum) environmental chamber equipped with in situ film deposition, (multi)gas admission and temperature control. This effort is intended to establish a robust framework for quantitatively comparing mass changes due to gas loading and unloading on different materials systems selected or considered for use as mass artifacts. Our apparatus is composed of a UHV chamber with gas introduction and temperature control and in-situ materials deposition for future materials testing enabling in situ preparation of virgin surfaces that can be monitored during initial exposure to gasses of interest. These tools are designed to allow us to comparatively evaluate how different materials gain or lose mass due to precisely controlled environmental excursions, with a long term goal of measuring changes in absolute mass. Herein, we provide a detailed experimental description of the apparatus, an evaluation of the initial performance, and demonstration measurements using nitrogen adsorption and desorption directly on the DPO. PMID:27212736

Developments for the Automation and Remote Control of the Radio Telescopes of the Geodetic Observatory Wettzell

NASA Astrophysics Data System (ADS)

Neidhardt, Alexander; Schönberger, Matthias; Plötz, Christian; Kronschnabl, Gerhard

2014-12-01

VGOS is a challenge for all fields of a new radio telescope. For the future software and hardware control mechanisms, it also requires new developments and solutions. More experiments, more data, high-speed data transfers through the Internet, and a real-time monitoring of current system status information must be handled. Additionally, an optimization of the observation shifts is required to reduce work load and costs. Within the framework of the development of the new 13.2-m Twin radio Telescopes Wettzell (TTW) and in combination with upgrades of the 20-m Radio Telescope Wettzell (RTW), some new technical realizations are under development and testing. Besides the activities for the realization of remote control, mainly supported during the project ``Novel EXploration Pushing Robust e-VLBI Services (NEXPReS)'' of the European VLBI Network (EVN), autonomous, automated, and unattended observations are also planned. A basic infrastructure should enable these, e.g., independent monitoring and security systems or additional, local high-speed transfer networks to ship data directly from a telescope to the main control room.

iNJclust: Iterative Neighbor-Joining Tree Clustering Framework for Inferring Population Structure.

PubMed

Limpiti, Tulaya; Amornbunchornvej, Chainarong; Intarapanich, Apichart; Assawamakin, Anunchai; Tongsima, Sissades

2014-01-01

Understanding genetic differences among populations is one of the most important issues in population genetics. Genetic variations, e.g., single nucleotide polymorphisms, are used to characterize commonality and difference of individuals from various populations. This paper presents an efficient graph-based clustering framework which operates iteratively on the Neighbor-Joining (NJ) tree called the iNJclust algorithm. The framework uses well-known genetic measurements, namely the allele-sharing distance, the neighbor-joining tree, and the fixation index. The behavior of the fixation index is utilized in the algorithm's stopping criterion. The algorithm provides an estimated number of populations, individual assignments, and relationships between populations as outputs. The clustering result is reported in the form of a binary tree, whose terminal nodes represent the final inferred populations and the tree structure preserves the genetic relationships among them. The clustering performance and the robustness of the proposed algorithm are tested extensively using simulated and real data sets from bovine, sheep, and human populations. The result indicates that the number of populations within each data set is reasonably estimated, the individual assignment is robust, and the structure of the inferred population tree corresponds to the intrinsic relationships among populations within the data.

A Robust Open Framework Formed by Decavanadate Clusters and Copper(II) Complexes of Macrocyclic Polyamines: Permanent Microporosity and Catalytic Oxidation of Cycloalkanes.

PubMed

Martín-Caballero, Jagoba; San José Wéry, Ana; Reinoso, Santiago; Artetxe, Beñat; San Felices, Leire; El Bakkali, Bouchra; Trautwein, Guido; Alcañiz-Monge, Juan; Vilas, José Luis; Gutiérrez-Zorrilla, Juan M

2016-05-16

The first decavanadate-based microporous hybrid, namely, [Cu(cyclam)][{Cu(cyclam)}2(V10O28)]·10H2O (1, cyclam = 1,4,8,11-tetraazacyclotetradecane) was prepared by reaction of (VO3)(-) anions and {Cu(cyclam)}(2+) complexes in NaCl (aq) at pH 4.6-4.7 and characterized by elemental analyses, thermogravimetry, and X-ray diffraction (powder, single-crystal) techniques. Compound 1 exhibits a POMOF-like supramolecular open-framework built of covalent decavanadate/metalorganic layers with square-like voids, the stacking of which is aided by interlamellar cementing complexes and generates water-filled channels with approximate cross sections of 10.4 × 8.8 Å(2). The framework is robust enough to remain virtually unaltered upon thermal evacuation of all water molecules of hydration, as demonstrated through single-crystal X-ray diffraction studies on the anhydrous phase 1a. This permanent microporosity renders interesting functionality to 1, such as selective adsorption of CO2 over N2 and remarkable activity as heterogeneous catalyst toward the H2O2-based oxidation of the highly-stable, tricyclic alkane adamantane.

An embedded system for face classification in infrared video using sparse representation

NASA Astrophysics Data System (ADS)

Saavedra M., Antonio; Pezoa, Jorge E.; Zarkesh-Ha, Payman; Figueroa, Miguel

2017-09-01

We propose a platform for robust face recognition in Infrared (IR) images using Compressive Sensing (CS). In line with CS theory, the classification problem is solved using a sparse representation framework, where test images are modeled by means of a linear combination of the training set. Because the training set constitutes an over-complete dictionary, we identify new images by finding their sparsest representation based on the training set, using standard l1-minimization algorithms. Unlike conventional face-recognition algorithms, we feature extraction is performed using random projections with a precomputed binary matrix, as proposed in the CS literature. This random sampling reduces the effects of noise and occlusions such as facial hair, eyeglasses, and disguises, which are notoriously challenging in IR images. Thus, the performance of our framework is robust to these noise and occlusion factors, achieving an average accuracy of approximately 90% when the UCHThermalFace database is used for training and testing purposes. We implemented our framework on a high-performance embedded digital system, where the computation of the sparse representation of IR images was performed by a dedicated hardware using a deeply pipelined architecture on an Field-Programmable Gate Array (FPGA).

Robust model predictive control for constrained continuous-time nonlinear systems

NASA Astrophysics Data System (ADS)

Sun, Tairen; Pan, Yongping; Zhang, Jun; Yu, Haoyong

2018-02-01

In this paper, a robust model predictive control (MPC) is designed for a class of constrained continuous-time nonlinear systems with bounded additive disturbances. The robust MPC consists of a nonlinear feedback control and a continuous-time model-based dual-mode MPC. The nonlinear feedback control guarantees the actual trajectory being contained in a tube centred at the nominal trajectory. The dual-mode MPC is designed to ensure asymptotic convergence of the nominal trajectory to zero. This paper extends current results on discrete-time model-based tube MPC and linear system model-based tube MPC to continuous-time nonlinear model-based tube MPC. The feasibility and robustness of the proposed robust MPC have been demonstrated by theoretical analysis and applications to a cart-damper springer system and a one-link robot manipulator.

Robust approximation-free prescribed performance control for nonlinear systems and its application

NASA Astrophysics Data System (ADS)

Sun, Ruisheng; Na, Jing; Zhu, Bin

2018-02-01

This paper presents a robust prescribed performance control approach and its application to nonlinear tail-controlled missile systems with unknown dynamics and uncertainties. The idea of prescribed performance function (PPF) is incorporated into the control design, such that both the steady-state and transient control performance can be strictly guaranteed. Unlike conventional PPF-based control methods, we further tailor a recently proposed systematic control design procedure (i.e. approximation-free control) using the transformed tracking error dynamics, which provides a proportional-like control action. Hence, the function approximators (e.g. neural networks, fuzzy systems) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. The proposed control design leads to a robust yet simplified function approximation-free control for nonlinear systems. The closed-loop system stability and the control error convergence are all rigorously proved. Finally, comparative simulations are conducted based on nonlinear missile systems to validate the improved response and the robustness of the proposed control method.

CFA-7: an interpenetrated metal-organic framework of the MFU-4 family.

PubMed

Schmieder, Phillip; Grzywa, Maciej; Denysenko, Dmytro; Hambach, Manuel; Volkmer, Dirk

2015-08-07

The novel interpenetrated metal-organic framework CFA-7 (Coordination Framework Augsburg University-7), [Zn5Cl4(tqpt)3], has been synthesized containing the organic linker {H2-tqpt = 6,6,14,14-tetramethyl-6,14-dihydroquinoxalino[2,3-b]phenazinebistriazole}. Reaction of H2-tqpt and anhydrous ZnCl2 in N,N-dimethylformamide (DMF) yields CFA-7 as pseudo-cubic crystals. CFA-7 serves as precursor for the synthesis of isostructural frameworks with redox-active metal centers, which is demonstrated by postsynthetic metal exchange of Zn(2+) by different M(2+) (M = Co, Ni, Cu) ions. The novel framework is robust upon solvent removal and has been structurally characterized by single-crystal X-ray diffraction, TGA and IR spectroscopy, as well as gas sorption (Ar, CO2 and H2).

NHDPlusHR: A national geospatial framework for surface-water information

USGS Publications Warehouse

Viger, Roland; Rea, Alan H.; Simley, Jeffrey D.; Hanson, Karen M.

2016-01-01

The U.S. Geological Survey is developing a new geospatial hydrographic framework for the United States, called the National Hydrography Dataset Plus High Resolution (NHDPlusHR), that integrates a diversity of the best-available information, robustly supports ongoing dataset improvements, enables hydrographic generalization to derive alternate representations of the network while maintaining feature identity, and supports modern scientific computing and Internet accessibility needs. This framework is based on the High Resolution National Hydrography Dataset, the Watershed Boundaries Dataset, and elevation from the 3-D Elevation Program, and will provide an authoritative, high precision, and attribute-rich geospatial framework for surface-water information for the United States. Using this common geospatial framework will provide a consistent basis for indexing water information in the United States, eliminate redundancy, and harmonize access to, and exchange of water information.

Gap-metric-based robustness analysis of nonlinear systems with full and partial feedback linearisation

NASA Astrophysics Data System (ADS)

Al-Gburi, A.; Freeman, C. T.; French, M. C.

2018-06-01

This paper uses gap metric analysis to derive robustness and performance margins for feedback linearising controllers. Distinct from previous robustness analysis, it incorporates the case of output unstructured uncertainties, and is shown to yield general stability conditions which can be applied to both stable and unstable plants. It then expands on existing feedback linearising control schemes by introducing a more general robust feedback linearising control design which classifies the system nonlinearity into stable and unstable components and cancels only the unstable plant nonlinearities. This is done in order to preserve the stabilising action of the inherently stabilising nonlinearities. Robustness and performance margins are derived for this control scheme, and are expressed in terms of bounds on the plant nonlinearities and the accuracy of the cancellation of the unstable plant nonlinearity by the controller. Case studies then confirm reduced conservatism compared with standard methods.

Determining the transport mechanism of an enzyme-catalytic complex metabolic network based on biological robustness.

PubMed

Wang, Lei

2013-04-01

Understanding the transport mechanism of 1,3-propanediol (1,3-PD) is of critical importance to do further research on gene regulation. Due to the lack of intracellular information, on the basis of enzyme-catalytic system, using biological robustness as performance index, we present a system identification model to infer the most possible transport mechanism of 1,3-PD, in which the performance index consists of the relative error of the extracellular substance concentrations and biological robustness of the intracellular substance concentrations. We will not use a Boolean framework but prefer a model description based on ordinary differential equations. Among other advantages, this also facilitates the robustness analysis, which is the main goal of this paper. An algorithm is constructed to seek the solution of the identification model. Numerical results show that the most possible transport way is active transport coupled with passive diffusion.

Rank-preserving regression: a more robust rank regression model against outliers.

PubMed

Chen, Tian; Kowalski, Jeanne; Chen, Rui; Wu, Pan; Zhang, Hui; Feng, Changyong; Tu, Xin M

2016-08-30

Mean-based semi-parametric regression models such as the popular generalized estimating equations are widely used to improve robustness of inference over parametric models. Unfortunately, such models are quite sensitive to outlying observations. The Wilcoxon-score-based rank regression (RR) provides more robust estimates over generalized estimating equations against outliers. However, the RR and its extensions do not sufficiently address missing data arising in longitudinal studies. In this paper, we propose a new approach to address outliers under a different framework based on the functional response models. This functional-response-model-based alternative not only addresses limitations of the RR and its extensions for longitudinal data, but, with its rank-preserving property, even provides more robust estimates than these alternatives. The proposed approach is illustrated with both real and simulated data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

PubMed

Wang, Ding; Liu, Derong

2018-06-01

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

Modeling the Arrest of Tissue Growth in Epithelia

NASA Astrophysics Data System (ADS)

Golden, Alexander; Lubensky, David

The mechanisms of control and eventual arrest of growth of tissues is an area that has received considerable attention, both experimentally and in the development of quantitative models. In particular, the Drosophila wing disc epithelium appears to robustly arrive at a unique final size. One mechanism that has the potential to play a role in the eventual cessation of growth is mechanical feedback from stresses induced by nonuniform growth. There is experimental support for an effect on the tissue growth rate by such mechanical stresses, and a number of numerical or cell-based models have been proposed that show that the arrest of growth can be achieved by mechanical feedback. We introduce an analytic framework that allows us to understand different coarse-grained feedback mechanisms on the same terms. We use the framework to distinguish between families of models that do not have a unique final size and those that do and give rough estimates for how much variability in the eventual organ size can be expected in models that do not have a unique final size. NSF Grant DMR-1056456.

Trust Management Considerations For the Cooperative Infrastructure Defense Framework: Trust Relationships, Evidence, and Decisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maiden, Wendy M.

Cooperative Infrastructure Defense (CID) is a hierarchical, agent-based, adaptive, cyber-security framework designed to collaboratively protect multiple enclaves or organizations participating in a complex infrastructure. CID employs a swarm of lightweight, mobile agents called Sensors designed to roam hosts throughout a security enclave to find indications of anomalies and report them to host-based Sentinels. The Sensors’ findings become pieces of a larger puzzle, which the Sentinel puts together to determine the problem and respond per policy as given by the enclave-level Sergeant agent. Horizontally across multiple enclaves and vertically within each enclave, authentication and access control technologies are necessary but insufficientmore » authorization mechanisms to ensure that CID agents continue to fulfill their roles in a trustworthy manner. Trust management fills the gap, providing mechanisms to detect malicious agents and offering more robust mechanisms for authorization. This paper identifies the trust relationships throughout the CID hierarchy, the types of trust evidence that could be gathered, and the actions that the CID system could take if an entity is determined to be untrustworthy.« less

Optimization of robustness of interdependent network controllability by redundant design

PubMed Central

2018-01-01

Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy) or DBS (degree based strategy) for node backup and HDF(high degree first) for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability. PMID:29438426

Robust control of electrostatic torsional micromirrors using adaptive sliding-mode control

NASA Astrophysics Data System (ADS)

Sane, Harshad S.; Yazdi, Navid; Mastrangelo, Carlos H.

2005-01-01

This paper presents high-resolution control of torsional electrostatic micromirrors beyond their inherent pull-in instability using robust sliding-mode control (SMC). The objectives of this paper are two-fold - firstly, to demonstrate the applicability of SMC for MEMS devices; secondly - to present a modified SMC algorithm that yields improved control accuracy. SMC enables compact realization of a robust controller tolerant of device characteristic variations and nonlinearities. Robustness of the control loop is demonstrated through extensive simulations and measurements on MEMS with a wide range in their characteristics. Control of two-axis gimbaled micromirrors beyond their pull-in instability with overall 10-bit pointing accuracy is confirmed experimentally. In addition, this paper presents an analysis of the sources of errors in discrete-time implementation of the control algorithm. To minimize these errors, we present an adaptive version of the SMC algorithm that yields substantial performance improvement without considerably increasing implementation complexity.

Concepts for VLBI Station Control as Part of NEXPReS

NASA Astrophysics Data System (ADS)

Ettl, M.; Neidhardt, A.; Schönberger, M.; Alef, W.; Himwich, E.; Beaudoin, C.; Plötz, C.; Lovell, J.; Hase, H.

2012-12-01

In the Novel EXploration Pushing Robust e-VLBI Services-project (NEXPReS) the Technische Universität München (TUM) realizes concepts for continuous quality monitoring and station remote control in cooperation with the Max-Planck-Institute for Radio Astronomy, Bonn. NEXPReS is a three-year project, funded within the European Seventh Framework program. It is aimed to develop e-VLBI services for the European VLBI Network (EVN), which can also support the IVS observations (VLBI2010). Within this project, the TUM focuses on developments of an operational remote control system (e-RemoteCtrl) with authentication and authorization. It includes an appropriate role management with different remote access states for future observation strategies. To allow a flexible control of different systems in parallel, sophisticated graphical user interfaces are designed and realized. The software is currently under test in the new AuScope network, Australia/New Zealand. Additional system parameters and information are collected with a new system monitoring (SysMon) for a higher degree of automation, which is currently under preparation for standardization within the IVS Monitoring and Control Infrastructure (MCI) Collaboration Group. The whole system for monitoring and control is fully compatible with the NASA Field System and extends it.

Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy.

PubMed

Yin, Xiuxing; Pan, Li

2018-01-01

A direct adaptive robust tracking control is proposed for trajectory tracking of 6 DOF industrial robot in the presence of parametric uncertainties, external disturbances and uncertain nonlinearities. The controller is designed based on the dynamic characteristics in the working space of the end-effector of the 6 DOF robot. The controller includes robust control term and model compensation term that is developed directly based on the input reference or desired motion trajectory. A projection-type parametric adaptation law is also designed to compensate for parametric estimation errors for the adaptive robust control. The feasibility and effectiveness of the proposed direct adaptive robust control law and the associated projection-type parametric adaptation law have been comparatively evaluated based on two 6 DOF industrial robots. The test results demonstrate that the proposed control can be employed to better maintain the desired trajectory tracking even in the presence of large parametric uncertainties and external disturbances as compared with PD controller and nonlinear controller. The parametric estimates also eventually converge to the real values along with the convergence of tracking errors, which further validate the effectiveness of the proposed parametric adaption law. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Advanced Plasma Shape Control to Enable High-Performance Divertor Operation on NSTX-U

NASA Astrophysics Data System (ADS)

Vail, Patrick; Kolemen, Egemen; Boyer, Mark; Welander, Anders

2017-10-01

This work presents the development of an advanced framework for control of the global plasma shape and its application to a variety of shape control challenges on NSTX-U. Operations in high-performance plasma scenarios will require highly-accurate and robust control of the plasma poloidal shape to accomplish such tasks as obtaining the strong-shaping required for the avoidance of MHD instabilities and mitigating heat flux through regulation of the divertor magnetic geometry. The new control system employs a high-fidelity model of the toroidal current dynamics in NSTX-U poloidal field coils and conducting structures as well as a first-principles driven calculation of the axisymmetric plasma response. The model-based nature of the control system enables real-time optimization of controller parameters in response to time-varying plasma conditions and control objectives. The new control scheme is shown to enable stable and on-demand plasma operations in complicated magnetic geometries such as the snowflake divertor. A recently-developed code that simulates the nonlinear evolution of the plasma equilibrium is used to demonstrate the capabilities of the designed shape controllers. Plans for future real-time implementations on NSTX-U and elsewhere are also presented. Supported by the US DOE under DE-AC02-09CH11466.

Robust control algorithms for Mars aerobraking

NASA Technical Reports Server (NTRS)

Shipley, Buford W., Jr.; Ward, Donald T.

1992-01-01

Four atmospheric guidance concepts have been adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. The first two offer improvements to the Analytic Predictor Corrector (APC) to increase its robustness to density variations. The second two are variations of a new Liapunov tracking exit phase algorithm, developed to guide the vehicle along a reference trajectory. These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. MARSGRAM is used to develop realistic atmospheres for the study. When square wave density pulses perturb the atmosphere all four controllers are successful. The algorithms are tested against atmospheres where the inbound and outbound density functions are different. Square wave density pulses are again used, but only for the outbound leg of the trajectory. Additionally, sine waves are used to perturb the density function. The new algorithms are found to be more robust than any previously tested and a Liapunov controller is selected as the most robust control algorithm overall examined.

A frequency-domain estimator for use in adaptive control systems

NASA Technical Reports Server (NTRS)

Lamaire, Richard O.; Valavani, Lena; Athans, Michael; Stein, Gunter

1991-01-01

This paper presents a frequency-domain estimator that can identify both a parametrized nominal model of a plant as well as a frequency-domain bounding function on the modeling error associated with this nominal model. This estimator, which we call a robust estimator, can be used in conjunction with a robust control-law redesign algorithm to form a robust adaptive controller.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two-input/two-output drone flight control system.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two output drone flight control system.

Robust stabilization of the Space Station in the presence of inertia matrix uncertainty

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang; Sunkel, John

1993-01-01

This paper presents a robust H-infinity full-state feedback control synthesis method for uncertain systems with D11 not equal to 0. The method is applied to the robust stabilization problem of the Space Station in the face of inertia matrix uncertainty. The control design objective is to find a robust controller that yields the largest stable hypercube in uncertain parameter space, while satisfying the nominal performance requirements. The significance of employing an uncertain plant model with D11 not equal 0 is demonstrated.

Design strategies for dynamic closed-loop optogenetic neurocontrol in vivo

NASA Astrophysics Data System (ADS)

Bolus, M. F.; Willats, A. A.; Whitmire, C. J.; Rozell, C. J.; Stanley, G. B.

2018-04-01

Objective. Controlling neural activity enables the possibility of manipulating sensory perception, cognitive processes, and body movement, in addition to providing a powerful framework for functionally disentangling the neural circuits that underlie these complex phenomena. Over the last decade, optogenetic stimulation has become an increasingly important and powerful tool for understanding neural circuit function, owing to the ability to target specific cell types and bidirectionally modulate neural activity. To date, most stimulation has been provided in open-loop or in an on/off closed-loop fashion, where previously-determined stimulation is triggered by an event. Here, we describe and demonstrate a design approach for precise optogenetic control of neuronal firing rate modulation using feedback to guide stimulation continuously. Approach. Using the rodent somatosensory thalamus as an experimental testbed for realizing desired time-varying patterns of firing rate modulation, we utilized a moving average exponential filter to estimate firing rate online from single-unit spiking measured extracellularly. This estimate of instantaneous rate served as feedback for a proportional integral (PI) controller, which was designed during the experiment based on a linear-nonlinear Poisson (LNP) model of the neuronal response to light. Main results. The LNP model fit during the experiment enabled robust closed-loop control, resulting in good tracking of sinusoidal and non-sinusoidal targets, and rejection of unmeasured disturbances. Closed-loop control also enabled manipulation of trial-to-trial variability. Significance. Because neuroscientists are faced with the challenge of dissecting the functions of circuit components, the ability to maintain control of a region of interest in spite of changes in ongoing neural activity will be important for disambiguating function within networks. Closed-loop stimulation strategies are ideal for control that is robust to such changes, and the employment of continuous feedback to adjust stimulation in real-time can improve the quality of data collected using optogenetic manipulation.

Synthetic environments

NASA Astrophysics Data System (ADS)

Lukes, George E.; Cain, Joel M.

1996-02-01

The Advanced Distributed Simulation (ADS) Synthetic Environments Program seeks to create robust virtual worlds from operational terrain and environmental data sources of sufficient fidelity and currency to interact with the real world. While some applications can be met by direct exploitation of standard digital terrain data, more demanding applications -- particularly those support operations 'close to the ground' -- are well-served by emerging capabilities for 'value-adding' by the user working with controlled imagery. For users to rigorously refine and exploit controlled imagery within functionally different workstations they must have a shared framework to allow interoperability within and between these environments in terms of passing image and object coordinates and other information using a variety of validated sensor models. The Synthetic Environments Program is now being expanded to address rapid construction of virtual worlds with research initiatives in digital mapping, softcopy workstations, and cartographic image understanding. The Synthetic Environments Program is also participating in a joint initiative for a sensor model applications programer's interface (API) to ensure that a common controlled imagery exploitation framework is available to all researchers, developers and users. This presentation provides an introduction to ADS and the associated requirements for synthetic environments to support synthetic theaters of war. It provides a technical rationale for exploring applications of image understanding technology to automated cartography in support of ADS and related programs benefitting from automated analysis of mapping, earth resources and reconnaissance imagery. And it provides an overview and status of the joint initiative for a sensor model API.

Genetic Algorithm-Based Model Order Reduction of Aeroservoelastic Systems with Consistant States

NASA Technical Reports Server (NTRS)

Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter M.; Brenner, Martin J.

2017-01-01

This paper presents a model order reduction framework to construct linear parameter-varying reduced-order models of flexible aircraft for aeroservoelasticity analysis and control synthesis in broad two-dimensional flight parameter space. Genetic algorithms are used to automatically determine physical states for reduction and to generate reduced-order models at grid points within parameter space while minimizing the trial-and-error process. In addition, balanced truncation for unstable systems is used in conjunction with the congruence transformation technique to achieve locally optimal realization and weak fulfillment of state consistency across the entire parameter space. Therefore, aeroservoelasticity reduced-order models at any flight condition can be obtained simply through model interpolation. The methodology is applied to the pitch-plant model of the X-56A Multi-Use Technology Testbed currently being tested at NASA Armstrong Flight Research Center for flutter suppression and gust load alleviation. The present studies indicate that the reduced-order model with more than 12× reduction in the number of states relative to the original model is able to accurately predict system response among all input-output channels. The genetic-algorithm-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The interpolated aeroservoelasticity reduced order models exhibit smooth pole transition and continuously varying gains along a set of prescribed flight conditions, which verifies consistent state representation obtained by congruence transformation. The present model order reduction framework can be used by control engineers for robust aeroservoelasticity controller synthesis and novel vehicle design.

Share Repository Framework: Component Specification and Otology

DTIC Science & Technology

2008-04-23

Palantir Technologies has created one such software application to support the DoD intelligence community by providing robust capabilities for...managing data from various sources. The Palantir tool is based on user-defined ontologies and supports multiple representation and analysis tools

Vehicle lateral motion regulation under unreliable communication links based on robust H∞ output-feedback control schema

NASA Astrophysics Data System (ADS)

Li, Cong; Jing, Hui; Wang, Rongrong; Chen, Nan

2018-05-01

This paper presents a robust control schema for vehicle lateral motion regulation under unreliable communication links via controller area network (CAN). The communication links between the system plant and the controller are assumed to be imperfect and therefore the data packet dropouts occur frequently. The paper takes the form of parallel distributed compensation and treats the dropouts as random binary numbers that form Bernoulli distribution. Both of the tire cornering stiffness uncertainty and external disturbances are considered to enhance the robustness of the controller. In addition, a robust H∞ static output-feedback control approach is proposed to realize the lateral motion control with relative low cost sensors. The stochastic stability of the closed-loop system and conservation of the guaranteed H∞ performance are investigated. Simulation results based on CarSim platform using a high-fidelity and full-car model verify the effectiveness of the proposed control approach.

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

Evaluating a robust contour tracker on echocardiographic sequences.

PubMed

Jacob, G; Noble, J A; Mulet-Parada, M; Blake, A

1999-03-01

In this paper we present an evaluation of a robust visual image tracker on echocardiographic image sequences. We show how the tracking framework can be customized to define an appropriate shape space that describes heart shape deformations that can be learnt from a training data set. We also investigate energy-based temporal boundary enhancement methods to improve image feature measurement. Results are presented demonstrating real-time tracking on real normal heart motion data sequences and abnormal synthesized and real heart motion data sequences. We conclude by discussing some of our current research efforts.

Robust H∞ output-feedback control for path following of autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Hu, Chuan; Jing, Hui; Wang, Rongrong; Yan, Fengjun; Chadli, Mohammed

2016-03-01

This paper presents a robust H∞ output-feedback control strategy for the path following of autonomous ground vehicles (AGVs). Considering the vehicle lateral velocity is usually hard to measure with low cost sensor, a robust H∞ static output-feedback controller based on the mixed genetic algorithms (GA)/linear matrix inequality (LMI) approach is proposed to realize the path following without the information of the lateral velocity. The proposed controller is robust to the parametric uncertainties and external disturbances, with the parameters including the tire cornering stiffness, vehicle longitudinal velocity, yaw rate and road curvature. Simulation results based on CarSim-Simulink joint platform using a high-fidelity and full-car model have verified the effectiveness of the proposed control approach.

Metal-Organic-Framework-Derived Dual Metal- and Nitrogen-Doped Carbon as Efficient and Robust Oxygen Reduction Reaction Catalysts for Microbial Fuel Cells.

PubMed

Tang, Haolin; Cai, Shichang; Xie, Shilei; Wang, Zhengbang; Tong, Yexiang; Pan, Mu; Lu, Xihong

2016-02-01

A new class of dual metal and N doped carbon catalysts with well-defined porous structure derived from metal-organic frameworks (MOFs) has been developed as a high-performance electrocatalyst for oxygen reduction reaction (ORR). Furthermore, the microbial fuel cell (MFC) device based on the as-prepared Ni/Co and N codoped carbon as air cathode catalyst achieves a maximum power density of 4335.6 mW m -2 and excellent durability.

Leverage effect, economic policy uncertainty and realized volatility with regime switching

NASA Astrophysics Data System (ADS)

Duan, Yinying; Chen, Wang; Zeng, Qing; Liu, Zhicao

2018-03-01

In this study, we first investigate the impacts of leverage effect and economic policy uncertainty (EPU) on future volatility in the framework of regime switching. Out-of-sample results show that the HAR-RV including the leverage effect and economic policy uncertainty with regimes can achieve higher forecast accuracy than RV-type and GARCH-class models. Our robustness results further imply that these factors in the framework of regime switching can substantially improve the HAR-RV's forecast performance.

A new look at the robust control of discrete-time Markov jump linear systems

NASA Astrophysics Data System (ADS)

Todorov, M. G.; Fragoso, M. D.

2016-03-01

In this paper, we make a foray in the role played by a set of four operators on the study of robust H2 and mixed H2/H∞ control problems for discrete-time Markov jump linear systems. These operators appear in the study of mean square stability for this class of systems. By means of new linear matrix inequality (LMI) characterisations of controllers, which include slack variables that, to some extent, separate the robustness and performance objectives, we introduce four alternative approaches to the design of controllers which are robustly stabilising and at the same time provide a guaranteed level of H2 performance. Since each operator provides a different degree of conservatism, the results are unified in the form of an iterative LMI technique for designing robust H2 controllers, whose convergence is attained in a finite number of steps. The method yields a new way of computing mixed H2/H∞ controllers, whose conservatism decreases with iteration. Two numerical examples illustrate the applicability of the proposed results for the control of a small unmanned aerial vehicle, and for an underactuated robotic arm.