A generic architecture for an adaptive, interoperable and intelligent type 2 diabetes mellitus care system.

PubMed

Uribe, Gustavo A; Blobel, Bernd; López, Diego M; Schulz, Stefan

2015-01-01

Chronic diseases such as Type 2 Diabetes Mellitus (T2DM) constitute a big burden to the global health economy. T2DM Care Management requires a multi-disciplinary and multi-organizational approach. Because of different languages and terminologies, education, experiences, skills, etc., such an approach establishes a special interoperability challenge. The solution is a flexible, scalable, business-controlled, adaptive, knowledge-based, intelligent system following a systems-oriented, architecture-centric, ontology-based and policy-driven approach. The architecture of real systems is described, using the basics and principles of the Generic Component Model (GCM). For representing the functional aspects of a system the Business Process Modeling Notation (BPMN) is used. The system architecture obtained is presented using a GCM graphical notation, class diagrams and BPMN diagrams. The architecture-centric approach considers the compositional nature of the real world system and its functionalities, guarantees coherence, and provides right inferences. The level of generality provided in this paper facilitates use case specific adaptations of the system. By that way, intelligent, adaptive and interoperable T2DM care systems can be derived from the presented model as presented in another publication.

An adaptive signal-processing approach to online adaptive tutoring.

PubMed

Bergeron, Bryan; Cline, Andrew

2011-01-01

Conventional intelligent or adaptive tutoring online systems rely on domain-specific models of learner behavior based on rules, deep domain knowledge, and other resource-intensive methods. We have developed and studied a domain-independent methodology of adaptive tutoring based on domain-independent signal-processing approaches that obviate the need for the construction of explicit expert and student models. A key advantage of our method over conventional approaches is a lower barrier to entry for educators who want to develop adaptive online learning materials.

Providing Adaptivity in Moodle LMS Courses

ERIC Educational Resources Information Center

Despotovic-Zrakic, Marijana; Markovic, Aleksandar; Bogdanovic, Zorica; Barac, Dusan; Krco, Srdjan

2012-01-01

In this paper, we describe an approach to providing adaptivity in e-education courses. The primary goal of the paper is to enhance an existing e-education system, namely Moodle LMS, by developing a method for creating adaptive courses, and to compare its effectiveness with non-adaptive education approach. First, we defined the basic requirements…

Improved methods in neural network-based adaptive output feedback control, with applications to flight control

NASA Astrophysics Data System (ADS)

Kim, Nakwan

Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

Adaptive Modulation for DFIG and STATCOM With High-Voltage Direct Current Transmission.

PubMed

Tang, Yufei; He, Haibo; Ni, Zhen; Wen, Jinyu; Huang, Tingwen

2016-08-01

This paper develops an adaptive modulation approach for power system control based on the approximate/adaptive dynamic programming method, namely, the goal representation heuristic dynamic programming (GrHDP). In particular, we focus on the fault recovery problem of a doubly fed induction generator (DFIG)-based wind farm and a static synchronous compensator (STATCOM) with high-voltage direct current (HVDC) transmission. In this design, the online GrHDP-based controller provides three adaptive supplementary control signals to the DFIG controller, STATCOM controller, and HVDC rectifier controller, respectively. The mechanism is to observe the system states and their derivatives and then provides supplementary control to the plant according to the utility function. With the GrHDP design, the controller can adaptively develop an internal goal representation signal according to the observed power system states, therefore, to achieve more effective learning and modulating. Our control approach is validated on a wind power integrated benchmark system with two areas connected by HVDC transmission lines. Compared with the classical direct HDP and proportional integral control, our GrHDP approach demonstrates the improved transient stability under system faults. Moreover, experiments under different system operating conditions with signal transmission delays are also carried out to further verify the effectiveness and robustness of the proposed approach.

Complex adaptive systems: A new approach for understanding health practices.

PubMed

Gomersall, Tim

2018-06-22

This article explores the potential of complex adaptive systems theory to inform behaviour change research. A complex adaptive system describes a collection of heterogeneous agents interacting within a particular context, adapting to each other's actions. In practical terms, this implies that behaviour change is 1) socially and culturally situated; 2) highly sensitive to small baseline differences in individuals, groups, and intervention components; and 3) determined by multiple components interacting "chaotically". Two approaches to studying complex adaptive systems are briefly reviewed. Agent-based modelling is a computer simulation technique that allows researchers to investigate "what if" questions in a virtual environment. Applied qualitative research techniques, on the other hand, offer a way to examine what happens when an intervention is pursued in real-time, and to identify the sorts of rules and assumptions governing social action. Although these represent very different approaches to complexity, there may be scope for mixing these methods - for example, by grounding models in insights derived from qualitative fieldwork. Finally, I will argue that the concept of complex adaptive systems offers one opportunity to gain a deepened understanding of health-related practices, and to examine the social psychological processes that produce health-promoting or damaging actions.

Social determinants of health inequalities: towards a theoretical perspective using systems science.

PubMed

Jayasinghe, Saroj

2015-08-25

A systems approach offers a novel conceptualization to natural and social systems. In recent years, this has led to perceiving population health outcomes as an emergent property of a dynamic and open, complex adaptive system. The current paper explores these themes further and applies the principles of systems approach and complexity science (i.e. systems science) to conceptualize social determinants of health inequalities. The conceptualization can be done in two steps: viewing health inequalities from a systems approach and extending it to include complexity science. Systems approach views health inequalities as patterns within the larger rubric of other facets of the human condition, such as educational outcomes and economic development. This anlysis requires more sophisticated models such as systems dynamic models. An extension of the approach is to view systems as complex adaptive systems, i.e. systems that are 'open' and adapt to the environment. They consist of dynamic adapting subsystems that exhibit non-linear interactions, while being 'open' to a similarly dynamic environment of interconnected systems. They exhibit emergent properties that cannot be estimated with precision by using the known interactions among its components (such as economic development, political freedom, health system, culture etc.). Different combinations of the same bundle of factors or determinants give rise to similar patterns or outcomes (i.e. property of convergence), and minor variations in the initial condition could give rise to widely divergent outcomes. Novel approaches using computer simulation models (e.g. agent-based models) would shed light on possible mechanisms as to how factors or determinants interact and lead to emergent patterns of health inequalities of populations.

L1 Adaptive Control Augmentation System with Application to the X-29 Lateral/Directional Dynamics: A Multi-Input Multi-Output Approach

NASA Technical Reports Server (NTRS)

Griffin, Brian Joseph; Burken, John J.; Xargay, Enric

2010-01-01

This paper presents an L(sub 1) adaptive control augmentation system design for multi-input multi-output nonlinear systems in the presence of unmatched uncertainties which may exhibit significant cross-coupling effects. A piecewise continuous adaptive law is adopted and extended for applicability to multi-input multi-output systems that explicitly compensates for dynamic cross-coupling. In addition, explicit use of high-fidelity actuator models are added to the L1 architecture to reduce uncertainties in the system. The L(sub 1) multi-input multi-output adaptive control architecture is applied to the X-29 lateral/directional dynamics and results are evaluated against a similar single-input single-output design approach.

Complexity Thinking in PE: Game-Centred Approaches, Games as Complex Adaptive Systems, and Ecological Values

ERIC Educational Resources Information Center

Storey, Brian; Butler, Joy

2013-01-01

Background: This article draws on the literature relating to game-centred approaches (GCAs), such as Teaching Games for Understanding, and dynamical systems views of motor learning to demonstrate a convergence of ideas around games as complex adaptive learning systems. This convergence is organized under the title "complexity thinking"…

Advances in adaptive control theory: Gradient- and derivative-free approaches

NASA Astrophysics Data System (ADS)

Yucelen, Tansel

In this dissertation, we present new approaches to improve standard designs in adaptive control theory, and novel adaptive control architectures. We first present a novel Kalman filter based approach for approximately enforcing a linear constraint in standard adaptive control design. One application is that this leads to alternative forms for well known modification terms such as e-modification. In addition, it leads to smaller tracking errors without incurring significant oscillations in the system response and without requiring high modification gain. We derive alternative forms of e- and adaptive loop recovery (ALR-) modifications. Next, we show how to use Kalman filter optimization to derive a novel adaptation law. This results in an optimization-based time-varying adaptation gain that reduces the need for adaptation gain tuning. A second major contribution of this dissertation is the development of a novel derivative-free, delayed weight update law for adaptive control. The assumption of constant unknown ideal weights is relaxed to the existence of time-varying weights, such that fast and possibly discontinuous variation in weights are allowed. This approach is particulary advantageous for applications to systems that can undergo a sudden change in dynamics, such as might be due to reconfiguration, deployment of a payload, docking, or structural damage, and for rejection of external disturbance processes. As a third and final contribution, we develop a novel approach for extending all the methods developed in this dissertation to the case of output feedback. The approach is developed only for the case of derivative-free adaptive control, and the extension of the other approaches developed previously for the state feedback case to output feedback is left as a future research topic. The proposed approaches of this dissertation are illustrated in both simulation and flight test.

Adaptive Systems in Education: A Review and Conceptual Unification

ERIC Educational Resources Information Center

Wilson, Chunyu; Scott, Bernard

2017-01-01

Purpose: The purpose of this paper is to review the use of adaptive systems in education. It is intended to be a useful introduction for the non-specialist reader. Design/methodology/approach: A distinction is made between intelligent tutoring systems (ITSs) and adaptive hypermedia systems (AHSs). The two kinds of system are defined, compared and…

Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft.

PubMed

Maity, Arnab; Hocht, Leonhard; Heise, Christian; Holzapfel, Florian

2018-01-01

A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

The New Trends in Adaptive Educational Hypermedia Systems

ERIC Educational Resources Information Center

Somyürek, Sibel

2015-01-01

This paper aims to give a general review of existing literature on adaptive educational hypermedia systems and to reveal technological trends and approaches within these studies. Fifty-six studies conducted between 2002 and 2012 were examined, to identify prominent themes and approaches. According to the content analysis, the new technological…

The role of adaptive management as an operational approach for resource management agencies

USGS Publications Warehouse

Johnson, B.L.

1999-01-01

In making resource management decisions, agencies use a variety of approaches that involve different levels of political concern, historical precedence, data analyses, and evaluation. Traditional decision-making approaches have often failed to achieve objectives for complex problems in large systems, such as the Everglades or the Colorado River. I contend that adaptive management is the best approach available to agencies for addressing this type of complex problem, although its success has been limited thus far. Traditional decision-making approaches have been fairly successful at addressing relatively straightforward problems in small, replicated systems, such as management of trout in small streams or pulp production in forests. However, this success may be jeopardized as more users place increasing demands on these systems. Adaptive management has received little attention from agencies for addressing problems in small-scale systems, but I suggest that it may be a useful approach for creating a holistic view of common problems and developing guidelines that can then be used in simpler, more traditional approaches to management. Although adaptive management may be more expensive to initiate than traditional approaches, it may be less expensive in the long run if it leads to more effective management. The overall goal of adaptive management is not to maintain an optimal condition of the resource, but to develop an optimal management capacity. This is accomplished by maintaining ecological resilience that allows the system to react to inevitable stresses, and generating flexibility in institutions and stakeholders that allows managers to react when conditions change. The result is that, rather than managing for a single, optimal state, we manage within a range of acceptable outcomes while avoiding catastrophes and irreversible negative effects. Copyright ?? 1999 by The Resilience Alliance.

Application of Complex Adaptive Systems in Portfolio Management

ERIC Educational Resources Information Center

Su, Zheyuan

2017-01-01

Simulation-based methods are becoming a promising research tool in financial markets. A general Complex Adaptive System can be tailored to different application scenarios. Based on the current research, we built two models that would benefit portfolio management by utilizing Complex Adaptive Systems (CAS) in Agent-based Modeling (ABM) approach.…

Output Feedback Adaptive Control of Non-Minimum Phase Systems Using Optimal Control Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

2018-01-01

This paper describes output feedback adaptive control approaches for non-minimum phase SISO systems with relative degree 1 and non-strictly positive real (SPR) MIMO systems with uniform relative degree 1 using the optimal control modification method. It is well-known that the standard model-reference adaptive control (MRAC) cannot be used to control non-SPR plants to track an ideal SPR reference model. Due to the ideal property of asymptotic tracking, MRAC attempts an unstable pole-zero cancellation which results in unbounded signals for non-minimum phase SISO systems. The optimal control modification can be used to prevent the unstable pole-zero cancellation which results in a stable adaptation of non-minimum phase SISO systems. However, the tracking performance using this approach could suffer if the unstable zero is located far away from the imaginary axis. The tracking performance can be recovered by using an observer-based output feedback adaptive control approach which uses a Luenberger observer design to estimate the state information of the plant. Instead of explicitly specifying an ideal SPR reference model, the reference model is established from the linear quadratic optimal control to account for the non-minimum phase behavior of the plant. With this non-minimum phase reference model, the observer-based output feedback adaptive control can maintain stability as well as tracking performance. However, in the presence of the mismatch between the SPR reference model and the non-minimum phase plant, the standard MRAC results in unbounded signals, whereas a stable adaptation can be achieved with the optimal control modification. An application of output feedback adaptive control for a flexible wing aircraft illustrates the approaches.

Multidimensional Adaptation in MAS Organizations.

PubMed

Alberola, Juan M; Julian, Vicente; Garcia-Fornes, Ana

2013-04-01

Organization adaptation requires determining the consequences of applying changes not only in terms of the benefits provided but also measuring the adaptation costs as well as the impact that these changes have on all of the components of the organization. In this paper, we provide an approach for adaptation in multiagent systems based on a multidimensional transition deliberation mechanism (MTDM). This approach considers transitions in multiple dimensions and is aimed at obtaining the adaptation with the highest potential for improvement in utility based on the costs of adaptation. The approach provides an accurate measurement of the impact of the adaptation since it determines the organization that is to be transitioned to as well as the changes required to carry out this transition. We show an example of adaptation in a service provider network environment in order to demonstrate that the measurement of the adaptation consequences taken by the MTDM improves the organization performance more than the other approaches.

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

NASA Astrophysics Data System (ADS)

Mishkov, Rumen; Darmonski, Stanislav

2018-01-01

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

An Approach to V&V of Embedded Adaptive Systems

NASA Technical Reports Server (NTRS)

Liu, Yan; Yerramalla, Sampath; Fuller, Edgar; Cukic, Bojan; Gururajan, Srikaruth

2004-01-01

Rigorous Verification and Validation (V&V) techniques are essential for high assurance systems. Lately, the performance of some of these systems is enhanced by embedded adaptive components in order to cope with environmental changes. Although the ability of adapting is appealing, it actually poses a problem in terms of V&V. Since uncertainties induced by environmental changes have a significant impact on system behavior, the applicability of conventional V&V techniques is limited. In safety-critical applications such as flight control system, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. In this paper, we propose a non-conventional V&V approach suitable for online adaptive systems. We apply our approach to an intelligent flight control system that employs a particular type of Neural Networks (NN) as the adaptive learning paradigm. Presented methodology consists of a novelty detection technique and online stability monitoring tools. The novelty detection technique is based on Support Vector Data Description that detects novel (abnormal) data patterns. The Online Stability Monitoring tools based on Lyapunov's Stability Theory detect unstable learning behavior in neural networks. Cases studies based on a high fidelity simulator of NASA's Intelligent Flight Control System demonstrate a successful application of the presented V&V methodology. ,

Proposed Multiconjugate Adaptive Optics Experiment at Lick Observatory

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

Bauman, B J; Gavel, D T; Flath, L M

2001-08-15

While the theory behind design of multiconjugate adaptive optics (MCAO) systems is growing, there is still a paucity of experience building and testing such instruments. We propose using the Lick adaptive optics (AO) system as a basis for demonstrating the feasibility/workability of MCAO systems, testing underlying assumptions, and experimenting with different approaches to solving MCAO system issues.

Visual Cues for an Adaptive Expert System.

ERIC Educational Resources Information Center

Miller, Helen B.

NCR (National Cash Register) Corporation is pursuing opportunities to make their point of sale (POS) terminals easy to use and easy to learn. To approach the goal of making the technology invisible to the user, NCR has developed an adaptive expert prototype system for a department store POS operation. The structure for the adaptive system, the…

People-centred health systems, a bottom-up approach: where theory meets empery.

PubMed

Sturmberg, Joachim P; Njoroge, Alice

2017-04-01

Health systems are complex and constantly adapt to changing demands. These complex-adaptive characteristics are rarely considered in the current bureaucratic top-down approaches to health system reforms aimed to constrain demand and expenditure growth. The economic focus fails to address the needs of patients, providers and communities, and ultimately results in declining effectiveness and efficiency of the health care system as well as the health of the wider community. A needs-focused complex-adaptive health system can be represented by the 'healthcare vortex' model; how to build a needs-focused complex-adaptive health system is illustrated by Eastern Deanery AIDS Relief Program approaches in the poor neighbourhoods of Nairobi, Kenya. A small group of nurses and community health workers focused on the care of terminally ill HIV/AIDS patients. This work identified additional problems: tuberculosis (TB) was underdiagnosed and undertreated, a local TB-technician was trained to run a local lab, a courier services helped to reach all at need, collaboration with the Ministry of Health established local TB and HIV treatment programmes and philanthropists helped to supplement treatment with nutrition support. Maternal-to-child HIV-prevention and adolescent counselling services addressed additional needs. The 'theory of the healthcare vortex' indeed matches the 'empery of the real world experiences'. Locally developed and delivered adaptive, people-centred health systems, a bottom-up community and provider initiated approach, deliver highly effective and sustainable health care despite significant resource constraints. © 2016 John Wiley & Sons, Ltd.

Design and Integration for High Performance Robotic Systems Based on Decomposition and Hybridization Approaches

PubMed Central

Zhang, Dan; Wei, Bin

2017-01-01

Currently, the uses of robotics are limited with respect to performance capabilities. Improving the performance of robotic mechanisms is and still will be the main research topic in the next decade. In this paper, design and integration for improving performance of robotic systems are achieved through three different approaches, i.e., structure synthesis design approach, dynamic balancing approach, and adaptive control approach. The purpose of robotic mechanism structure synthesis design is to propose certain mechanism that has better kinematic and dynamic performance as compared to the old ones. For the dynamic balancing design approach, it is normally accomplished based on employing counterweights or counter-rotations. The potential issue is that more weight and inertia will be included in the system. Here, reactionless based on the reconfiguration concept is put forward, which can address the mentioned problem. With the mechanism reconfiguration, the control system needs to be adapted thereafter. One way to address control system adaptation is by applying the “divide and conquer” methodology. It entails modularizing the functionalities: breaking up the control functions into small functional modules, and from those modules assembling the control system according to the changing needs of the mechanism. PMID:28075360

Introduction to Fuzzy Set Theory

NASA Technical Reports Server (NTRS)

Kosko, Bart

1990-01-01

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Is it possible to implement a complex adaptive systems approach for marine systems? The experience of Italy and the Adriatic Sea.

PubMed

Bigagli, Emanuele

2017-11-15

•This paper evaluates the implementation of the MSFD in the Adriatic Sea.•The MSFD is the first policy for marine complex adaptive systems in the EU.•Ecological and jurisdictional boundaries overlap and cross-border cooperation is low.•Integrative assessments of marine systems may be impossible to achieve.•Relative isolation of theoretical approaches and management practices.

Adaptive approaches to licensing, health technology assessment, and introduction of drugs and devices.

PubMed

Husereau, Don; Henshall, Chris; Jivraj, Jamil

2014-07-01

Adaptive approaches to the introduction of drugs and medical devices involve the use of an evolving evidence base rather than conventional single-point-in-time evaluations as a proposed means to promote patient access to innovation, reduce clinical uncertainty, ensure effectiveness, and improve the health technology development process. This report summarizes a Health Technology Assessment International (HTAi) Policy Forum discussion, drawing on presentations from invited experts, discussions among attendees about real-world case examples, and background paper. For adaptive approaches to be understood, accepted, and implemented, the Forum identified several key issues that must be addressed. These include the need to define the goals of and to set priorities for adaptive approaches; to examine evidence collection approaches; to clarify the roles and responsibilities of stakeholders; to understand the implications of adaptive approaches on current legal and ethical standards; to determine costs of such approaches and how they will be met; and to identify differences in applying adaptive approaches to drugs versus medical devices. The Forum also explored the different implications of adaptive approaches for various stakeholders, including patients, regulators, HTA/coverage bodies, health systems, clinicians, and industry. A key outcome of the meeting was a clearer understanding of the opportunities and challenges adaptive approaches present. Furthermore, the Forum brought to light the critical importance of recognizing and including a full range of stakeholders as contributors to a shared decision-making model implicit in adaptive pathways in future discussions on, and implementation of, adaptive approaches.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

PubMed

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-07-01

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

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

Luo, Shaohua; Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021; Wu, Songli

2015-07-15

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in themore » closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.« less

Quantifying the adaptive cycle

USGS Publications Warehouse

Angeler, David G.; Allen, Craig R.; Garmestani, Ahjond S.; Gunderson, Lance H.; Hjerne, Olle; Winder, Monika

2015-01-01

The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation) in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurring spring and summer blooms, a well-established paradigm in planktology and succession theory, with characteristic temporal trajectories during blooms that may be consistent with adaptive cycle phases. We used long-term (1994–2011) data and multivariate analysis of community structure to assess key components of the adaptive cycle. Specifically, we tested predictions about: reorganisation: spring and summer blooms comprise distinct community states; conservatism: community trajectories during individual adaptive cycles are conservative; and adaptation: phytoplankton species during blooms change in the long term. All predictions were supported by our analyses. Results suggest that traditional ecological paradigms such as phytoplankton successional models have potential for moving the adaptive cycle from a metaphor to a framework that can improve our understanding how complex systems organize and reorganize following collapse. Quantifying reorganization, conservatism and adaptation provides opportunities to cope with the intricacies and uncertainties associated with fast ecological change, driven by shifting system controls. Ultimately, combining traditional ecological paradigms with heuristics of complex system dynamics using quantitative approaches may help refine ecological theory and improve our understanding of the resilience of ecosystems.

Quantifying the Adaptive Cycle.

PubMed

Angeler, David G; Allen, Craig R; Garmestani, Ahjond S; Gunderson, Lance H; Hjerne, Olle; Winder, Monika

2015-01-01

The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation) in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurring spring and summer blooms, a well-established paradigm in planktology and succession theory, with characteristic temporal trajectories during blooms that may be consistent with adaptive cycle phases. We used long-term (1994-2011) data and multivariate analysis of community structure to assess key components of the adaptive cycle. Specifically, we tested predictions about: reorganisation: spring and summer blooms comprise distinct community states; conservatism: community trajectories during individual adaptive cycles are conservative; and adaptation: phytoplankton species during blooms change in the long term. All predictions were supported by our analyses. Results suggest that traditional ecological paradigms such as phytoplankton successional models have potential for moving the adaptive cycle from a metaphor to a framework that can improve our understanding how complex systems organize and reorganize following collapse. Quantifying reorganization, conservatism and adaptation provides opportunities to cope with the intricacies and uncertainties associated with fast ecological change, driven by shifting system controls. Ultimately, combining traditional ecological paradigms with heuristics of complex system dynamics using quantitative approaches may help refine ecological theory and improve our understanding of the resilience of ecosystems.

A Flight Control System for Small Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Tunik, A. A.; Nadsadnaya, O. I.

2018-03-01

The program adaptation of the controller for the flight control system (FCS) of an unmanned aerial vehicle (UAV) is considered. Linearized flight dynamic models depend mainly on the true airspeed of the UAV, which is measured by the onboard air data system. This enables its use for program adaptation of the FCS over the full range of altitudes and velocities, which define the flight operating range. FCS with program adaptation, based on static feedback (SF), is selected. The SF parameters for every sub-range of the true airspeed are determined using the linear matrix inequality approach in the case of discrete systems for synthesis of a suboptimal robust H ∞-controller. The use of the Lagrange interpolation between true airspeed sub-ranges provides continuous adaptation. The efficiency of the proposed approach is shown against an example of the heading stabilization system.

Handwritten word preprocessing for database adaptation

NASA Astrophysics Data System (ADS)

Oprean, Cristina; Likforman-Sulem, Laurence; Mokbel, Chafic

2013-01-01

Handwriting recognition systems are typically trained using publicly available databases, where data have been collected in controlled conditions (image resolution, paper background, noise level,...). Since this is not often the case in real-world scenarios, classification performance can be affected when novel data is presented to the word recognition system. To overcome this problem, we present in this paper a new approach called database adaptation. It consists of processing one set (training or test) in order to adapt it to the other set (test or training, respectively). Specifically, two kinds of preprocessing, namely stroke thickness normalization and pixel intensity normalization are considered. The advantage of such approach is that we can re-use the existing recognition system trained on controlled data. We conduct several experiments with the Rimes 2011 word database and with a real-world database. We adapt either the test set or the training set. Results show that training set adaptation achieves better results than test set adaptation, at the cost of a second training stage on the adapted data. Accuracy of data set adaptation is increased by 2% to 3% in absolute value over no adaptation.

Implementation of Environmental Flows for Intermittent River Systems: Adaptive Management and Stakeholder Participation Facilitate Implementation

NASA Astrophysics Data System (ADS)

Conallin, John; Wilson, Emma; Campbell, Josh

2018-03-01

Anthropogenic pressure on freshwater ecosystems is increasing, and often leading to unacceptable social-ecological outcomes. This is even more prevalent in intermittent river systems where many are already heavily modified, or human encroachment is increasing. Although adaptive management approaches have the potential to aid in providing the framework to consider the complexities of intermittent river systems and improve utility within the management of these systems, success has been variable. This paper looks at the application of an adaptive management pilot project within an environmental flows program in an intermittent stream (Tuppal Creek) in the Murray Darling Basin, Australia. The program focused on stakeholder involvement, participatory decision-making, and simple monitoring as the basis of an adaptive management approach. The approach found that by building trust and ownership through concentrating on inclusiveness and transparency, partnerships between government agencies and landholders were developed. This facilitated a willingness to accept greater risks and unintended consequences allowing implementation to occur.

Adaptive Fuzzy Tracking Control for a Class of MIMO Nonlinear Systems in Nonstrict-Feedback Form.

PubMed

Chen, Bing; Lin, Chong; Liu, Xiaoping; Liu, Kefu

2015-12-01

This paper focuses on the problem of fuzzy adaptive control for a class of multiinput and multioutput (MIMO) nonlinear systems in nonstrict-feedback form, which contains the strict-feedback form as a special case. By the condition of variable partition, a new fuzzy adaptive backstepping is proposed for such a class of nonlinear MIMO systems. The suggested fuzzy adaptive controller guarantees that the proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors eventually converge to a small neighborhood around the origin. The main advantage of this paper is that a control approach is systematically derived for nonlinear systems with strong interconnected terms which are the functions of all states of the whole system. Simulation results further illustrate the effectiveness of the suggested approach.

Climate change adaptation and Integrated Water Resource Management in the water sector

NASA Astrophysics Data System (ADS)

Ludwig, Fulco; van Slobbe, Erik; Cofino, Wim

2014-10-01

Integrated Water Resources Management (IWRM) was introduced in 1980s to better optimise water uses between different water demanding sectors. However, since it was introduced water systems have become more complicated due to changes in the global water cycle as a result of climate change. The realization that climate change will have a significant impact on water availability and flood risks has driven research and policy making on adaptation. This paper discusses the main similarities and differences between climate change adaptation and IWRM. The main difference between the two is the focus on current and historic issues of IWRM compared to the (long-term) future focus of adaptation. One of the main problems of implementing climate change adaptation is the large uncertainties in future projections. Two completely different approaches to adaptation have been developed in response to these large uncertainties. A top-down approach based on large scale biophysical impacts analyses focussing on quantifying and minimizing uncertainty by using a large range of scenarios and different climate and impact models. The main problem with this approach is the propagation of uncertainties within the modelling chain. The opposite is the bottom up approach which basically ignores uncertainty. It focusses on reducing vulnerabilities, often at local scale, by developing resilient water systems. Both these approaches however are unsuitable for integrating into water management. The bottom up approach focuses too much on socio-economic vulnerability and too little on developing (technical) solutions. The top-down approach often results in an “explosion” of uncertainty and therefore complicates decision making. A more promising direction of adaptation would be a risk based approach. Future research should further develop and test an approach which starts with developing adaptation strategies based on current and future risks. These strategies should then be evaluated using a range of future scenarios in order to develop robust adaptation measures and strategies.

Performance Monitoring and Assessment of Neuro-Adaptive Controllers for Aerospace Applications Using a Bayesian Approach

NASA Technical Reports Server (NTRS)

Gupta, Pramod; Guenther, Kurt; Hodgkinson, John; Jacklin, Stephen; Richard, Michael; Schumann, Johann; Soares, Fola

2005-01-01

Modern exploration missions require modern control systems-control systems that can handle catastrophic changes in the system's behavior, compensate for slow deterioration in sustained operations, and support fast system ID. Adaptive controllers, based upon Neural Networks have these capabilities, but they can only be used safely if proper verification & validation (V&V) can be done. In this paper we present our V & V approach and simulation result within NASA's Intelligent Flight Control Systems (IFCS).

An Agent-Based Optimization Framework for Engineered Complex Adaptive Systems with Application to Demand Response in Electricity Markets

NASA Astrophysics Data System (ADS)

Haghnevis, Moeed

The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.

An improved adaptive weighting function method for State Estimation in Power Systems with VSC-MTDC

NASA Astrophysics Data System (ADS)

Zhao, Kun; Yang, Xiaonan; Lang, Yansheng; Song, Xuri; Wang, Minkun; Luo, Yadi; Wu, Lingyun; Liu, Peng

2017-04-01

This paper presents an effective approach for state estimation in power systems that include multi-terminal voltage source converter based high voltage direct current (VSC-MTDC), called improved adaptive weighting function method. The proposed approach is simplified in which the VSC-MTDC system is solved followed by the AC system. Because the new state estimation method only changes the weight and keeps the matrix dimension unchanged. Accurate and fast convergence of AC/DC system can be realized by adaptive weight function method. This method also provides the technical support for the simulation analysis and accurate regulation of AC/DC system. Both the oretical analysis and numerical tests verify practicability, validity and convergence of new method.

Global References, Local Translation: Adaptation of the Bologna Process Degree Structure and Credit System at Universities in Cameroon

ERIC Educational Resources Information Center

Eta, Elizabeth Agbor; Vubo, Emmanuel Yenshu

2016-01-01

This article uses temporal comparison and thematic analytical approaches to analyse text documents and interviews, examining the adaptation of the Bologna Process degree structure and credit system in two sub-systems of education in Cameroon: the Anglo-Saxon and the French systems. The central aim is to verify whether such adaptation has replaced,…

Robust Adaptive Control Using a Filtering Action

DTIC Science & Technology

2009-09-01

research performed on this class of control systems , sensitivity to external disturbances and modeling errors together with poor transient response...dissertation, we address the problems of designing a class of Adaptive Control systems which yield fast adaptation, thus good transient response, and...unable to stabilize the system . Although this approach requires more knowledge about the system in order to control it, it is still attractive in cases

Predictive optimized adaptive PSS in a single machine infinite bus.

PubMed

Milla, Freddy; Duarte-Mermoud, Manuel A

2016-07-01

Power System Stabilizer (PSS) devices are responsible for providing a damping torque component to generators for reducing fluctuations in the system caused by small perturbations. A Predictive Optimized Adaptive PSS (POA-PSS) to improve the oscillations in a Single Machine Infinite Bus (SMIB) power system is discussed in this paper. POA-PSS provides the optimal design parameters for the classic PSS using an optimization predictive algorithm, which adapts to changes in the inputs of the system. This approach is part of small signal stability analysis, which uses equations in an incremental form around an operating point. Simulation studies on the SMIB power system illustrate that the proposed POA-PSS approach has better performance than the classical PSS. In addition, the effort in the control action of the POA-PSS is much less than that of other approaches considered for comparison. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A systems approach to the physiology of weightlessness

NASA Technical Reports Server (NTRS)

White, Ronald J.; Leonard, Joel I.; Rummel, John A.; Leach, Carolyn S.

1991-01-01

A general systems approach to conducting and analyzing research on the human adaptation to weightlessness is presented. The research is aimed at clarifying the role that each of the major components of the human system plays following the transition to and from space. The approach utilizes a variety of mathematical models in order to pose and test alternative hypotheses concerned with the adaptation process. Certain aspects of the problem of fluid and electrolyte shifts in weightlessnes are considered, and an integrated hypothesis based on numerical simulation studies and experimental data is presented.

Drivers' safety needs, behavioural adaptations and acceptance of new driving support systems.

PubMed

Saad, Farida; Van Elslande, Pierre

2012-01-01

The aim of this paper is to discuss the contribution of two complementary approaches for designing and evaluating new driver support systems likely to improve the operation and safety of the road traffic system. The first approach is based on detailed analyses of traffic crashes so as to estimate drivers' needs for assistance and the situational constraints that safety functions should address to be efficient. The second approach is based on in depth-analyses of behavioral adaptations induced by the usage of new driver support systems in regular driving situations and on drivers' acceptance of the assistance provided by the systems.

Simple robust control laws for robot manipulators. Part 2: Adaptive case

NASA Technical Reports Server (NTRS)

Bayard, D. S.; Wen, J. T.

1987-01-01

A new class of asymptotically stable adaptive control laws is introduced for application to the robotic manipulator. Unlike most applications of adaptive control theory to robotic manipulators, this analysis addresses the nonlinear dynamics directly without approximation, linearization, or ad hoc assumptions, and utilizes a parameterization based on physical (time-invariant) quantities. This approach is made possible by using energy-like Lyapunov functions which retain the nonlinear character and structure of the dynamics, rather than simple quadratic forms which are ubiquitous to the adaptive control literature, and which have bound the theory tightly to linear systems with unknown parameters. It is a unique feature of these results that the adaptive forms arise by straightforward certainty equivalence adaptation of their nonadaptive counterparts found in the companion to this paper (i.e., by replacing unknown quantities by their estimates) and that this simple approach leads to asymptotically stable closed-loop adaptive systems. Furthermore, it is emphasized that this approach does not require convergence of the parameter estimates (i.e., via persistent excitation), invertibility of the mass matrix estimate, or measurement of the joint accelerations.

Game-theoretic approach to joint transmitter adaptation and power control in wireless systems.

PubMed

Popescu, Dimitrie C; Rawat, Danda B; Popescu, Otilia; Saquib, Mohamad

2010-06-01

Game theory has emerged as a new mathematical tool in the analysis and design of wireless communication systems, being particularly useful in studying the interactions among adaptive transmitters that attempt to achieve specific objectives without cooperation. In this paper, we present a game-theoretic approach to the problem of joint transmitter adaptation and power control in wireless systems, where users' transmissions are subject to quality-of-service requirements specified in terms of target signal-to-interference-plus-noise ratios (SINRs) and nonideal vector channels between transmitters and receivers are explicitly considered. Our approach is based on application of separable games, which are a specific class of noncooperative games where the players' cost is a separable function of their strategic choices. We formally state a joint codeword and power adaptation game, which is separable, and we study its properties in terms of its subgames, namely, the codeword adaptation subgame and the power adaptation subgame. We investigate the necessary conditions for an optimal Nash equilibrium and show that this corresponds to an ensemble of user codewords and powers, which maximizes the sum capacity of the corresponding multiaccess vector channel model, and for which the specified target SINRs are achieved with minimum transmitted power.

Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics.

PubMed

Tong, Shaocheng; Li, Yongming

2017-02-01

This paper investigates a robust adaptive fuzzy control stabilization problem for a class of uncertain nonlinear systems with arbitrary switching signals that use an observer-based output feedback scheme. The considered switched nonlinear systems possess the unstructured uncertainties, unmodeled dynamics, and without requiring the states being available for measurement. A state observer which is independent of switching signals is designed to solve the problem of unmeasured states. Fuzzy logic systems are used to identify unknown lumped nonlinear functions so that the problem of unstructured uncertainties can be solved. By combining adaptive backstepping design principle and small-gain approach, a novel robust adaptive fuzzy output feedback stabilization control approach is developed. The stability of the closed-loop system is proved via the common Lyapunov function theory and small-gain theorem. Finally, the simulation results are given to demonstrate the validity and performance of the proposed control strategy.

Adaptive structures for precision controlled large space systems

NASA Technical Reports Server (NTRS)

Garba, John A.; Wada, Ben K.; Fanson, James L.

1991-01-01

The stringent accuracy and ground test validation requirements of some of the future space missions will require new approaches in structural design. Adaptive structures, structural systems that can vary their geometric congiguration as well as their physical properties, are primary candidates for meeting the functional requirements for such missions. Research performed in the development of such adaptive structural systems is described.

Systems and Methods for Parameter Dependent Riccati Equation Approaches to Adaptive Control

NASA Technical Reports Server (NTRS)

Kim, Kilsoo (Inventor); Yucelen, Tansel (Inventor); Calise, Anthony J. (Inventor)

2015-01-01

Systems and methods for adaptive control are disclosed. The systems and methods can control uncertain dynamic systems. The control system can comprise a controller that employs a parameter dependent Riccati equation. The controller can produce a response that causes the state of the system to remain bounded. The control system can control both minimum phase and non-minimum phase systems. The control system can augment an existing, non-adaptive control design without modifying the gains employed in that design. The control system can also avoid the use of high gains in both the observer design and the adaptive control law.

A new approach for designing self-organizing systems and application to adaptive control

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.; Zhang, Shi; Lin, Yueqing; Huang, Song

1993-01-01

There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed.

A Hybrid Approach for Supporting Adaptivity in E-Learning Environments

ERIC Educational Resources Information Center

Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco

2016-01-01

Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity…

A New Trans-Disciplinary Approach to Regional Integrated Assessment of Climate Impact and Adaptation in Agricultural Systems (Invited)

NASA Astrophysics Data System (ADS)

Antle, J. M.; Valdivia, R. O.; Jones, J.; Rosenzweig, C.; Ruane, A. C.

2013-12-01

This presentation provides an overview of the new methods developed by researchers in the Agricultural Model Inter-comparison and Improvement Project (AgMIP) for regional climate impact assessment and analysis of adaptation in agricultural systems. This approach represents a departure from approaches in the literature in several dimensions. First, the approach is based on the analysis of agricultural systems (not individual crops) and is inherently trans-disciplinary: it is based on a deep collaboration among a team of climate scientists, agricultural scientists and economists to design and implement regional integrated assessments of agricultural systems. Second, in contrast to previous approaches that have imposed future climate on models based on current socio-economic conditions, this approach combines bio-physical and economic models with a new type of pathway analysis (Representative Agricultural Pathways) to parameterize models consistent with a plausible future world in which climate change would be occurring. Third, adaptation packages for the agricultural systems in a region are designed by the research team with a level of detail that is useful to decision makers, such as research administrators and donors, who are making agricultural R&D investment decisions. The approach is illustrated with examples from AgMIP's projects currently being carried out in Africa and South Asia.

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.

QPSO-Based Adaptive DNA Computing Algorithm

PubMed Central

Karakose, Mehmet; Cigdem, Ugur

2013-01-01

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. PMID:23935409

Adaptive identification and control of structural dynamics systems using recursive lattice filters

NASA Technical Reports Server (NTRS)

Sundararajan, N.; Montgomery, R. C.; Williams, J. P.

1985-01-01

A new approach for adaptive identification and control of structural dynamic systems by using least squares lattice filters thar are widely used in the signal processing area is presented. Testing procedures for interfacing the lattice filter identification methods and modal control method for stable closed loop adaptive control are presented. The methods are illustrated for a free-free beam and for a complex flexible grid, with the basic control objective being vibration suppression. The approach is validated by using both simulations and experimental facilities available at the Langley Research Center.

Testing the adaptive value of circadian systems.

PubMed

Johnson, Carl Hirschie

2005-01-01

Circadian clocks are thought to enhance reproductive fitness. However, most of the evidence that supports the adaptiveness of clocks is not rigorous and falls into the category of "adaptive storytelling." Approaches that an evolutionary biologist would consider appropriate to address this issue are described along with an analysis of the evidence-past and present-that has been evoked to demonstrate the adaptive value of circadian systems.

Making adaptable systems work for mission operations: A case study

NASA Technical Reports Server (NTRS)

Holder, Barbara E.; Levesque, Michael E.

1993-01-01

The Advanced Multimission Operations System (AMMOS) at NASA's Jet Propulsion Laboratory is based on a highly adaptable multimission ground data system (MGDS) for mission operations. The goal for MGDS is to support current flight project science and engineering personnel and to meet the demands of future missions while reducing associated operations and software development costs. MGDS has become a powerful and flexible mission operations system by using a network of heterogeneous workstations, emerging open system standards, and selecting an adaptable tools-based architecture. Challenges in developing adaptable systems for mission operations and the benefits of this approach are described.

The Optimization by Using the Learning Styles in the Adaptive Hypermedia Applications

ERIC Educational Resources Information Center

Hamza, Lamia; Tlili, Guiassa Yamina

2018-01-01

This article addresses the learning style as a criterion for optimization of adaptive content in hypermedia applications. First, the authors present the different optimization approaches proposed in the area of adaptive hypermedia systems whose goal is to define the optimization problem in this type of system. Then, they present the architecture…

Adaptive Control of Non-Minimum Phase Modal Systems Using Residual Mode Filters2. Parts 1 and 2

NASA Technical Reports Server (NTRS)

Balas, Mark J.; Frost, Susan

2011-01-01

Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. This paper will be divided into two parts. Here in Part I we will review the basic adaptive control approach and introduce the primary ideas. In Part II, we will present the RMF methodology and complete the proofs of all our results. Also, we will apply the above theoretical results to a simple flexible structure example to illustrate the behavior with and without the residual mode filter.

Adaptive and Adaptable Automation Design: A Critical Review of the Literature and Recommendations for Future Research

NASA Technical Reports Server (NTRS)

Prinzel, Lawrence J., III; Kaber, David B.

2006-01-01

This report presents a review of literature on approaches to adaptive and adaptable task/function allocation and adaptive interface technologies for effective human management of complex systems that are likely to be issues for the Next Generation Air Transportation System, and a focus of research under the Aviation Safety Program, Integrated Intelligent Flight Deck Project. Contemporary literature retrieved from an online database search is summarized and integrated. The major topics include the effects of delegation-type, adaptable automation on human performance, workload and situation awareness, the effectiveness of various automation invocation philosophies and strategies to function allocation in adaptive systems, and the role of user modeling in adaptive interface design and the performance implications of adaptive interface technology.

The Role of Bridging Organizations in Enhancing Ecosystem Services and Facilitating Adaptive Management of Social-Ecological Systems

EPA Science Inventory

Adaptive management is an approach for monitoring the response of ecological systems to different policies and practices and attempts to reduce the inherent uncertainty in ecological systems via system monitoring and iterative decision making and experimentation (Holling 1978). M...

Systems integration of innate and adaptive immunity.

PubMed

Zak, Daniel E; Aderem, Alan

2015-09-29

The pathogens causing AIDS, malaria, and tuberculosis have proven too complex to be overcome by classical approaches to vaccination. The complexities of human immunology and pathogen-induced modulation of the immune system mandate new approaches to vaccine discovery and design. A new field, systems vaccinology, weds holistic analysis of innate and adaptive immunity within a quantitative framework to enable rational design of new vaccines that elicit tailored protective immune responses. A key step in the approach is to discover relationships between the earliest innate inflammatory responses to vaccination and the subsequent vaccine-induced adaptive immune responses and efficacy. Analysis of these responses in clinical studies is complicated by the inaccessibility of relevant tissue compartments (such as the lymph node), necessitating reliance upon peripheral blood responses as surrogates. Blood transcriptomes, although indirect to vaccine mechanisms, have proven very informative in systems vaccinology studies. The approach is most powerful when innate and adaptive immune responses are integrated with vaccine efficacy, which is possible for malaria with the advent of a robust human challenge model. This is more difficult for AIDS and tuberculosis, given that human challenge models are lacking and efficacy observed in clinical trials has been low or highly variable. This challenge can be met by appropriate clinical trial design for partially efficacious vaccines and by analysis of natural infection cohorts. Ultimately, systems vaccinology is an iterative approach in which mechanistic hypotheses-derived from analysis of clinical studies-are evaluated in model systems, and then used to guide the development of new vaccine strategies. In this review, we will illustrate the above facets of the systems vaccinology approach with case studies. Copyright © 2015. Published by Elsevier Ltd.

An adaptive brain actuated system for augmenting rehabilitation

PubMed Central

Roset, Scott A.; Gant, Katie; Prasad, Abhishek; Sanchez, Justin C.

2014-01-01

For people living with paralysis, restoration of hand function remains the top priority because it leads to independence and improvement in quality of life. In approaches to restore hand and arm function, a goal is to better engage voluntary control and counteract maladaptive brain reorganization that results from non-use. Standard rehabilitation augmented with developments from the study of brain-computer interfaces could provide a combined therapy approach for motor cortex rehabilitation and to alleviate motor impairments. In this paper, an adaptive brain-computer interface system intended for application to control a functional electrical stimulation (FES) device is developed as an experimental test bed for augmenting rehabilitation with a brain-computer interface. The system's performance is improved throughout rehabilitation by passive user feedback and reinforcement learning. By continuously adapting to the user's brain activity, similar adaptive systems could be used to support clinical brain-computer interface neurorehabilitation over multiple days. PMID:25565945

Adaptive Management of Computing and Network Resources for Spacecraft Systems

NASA Technical Reports Server (NTRS)

Pfarr, Barbara; Welch, Lonnie R.; Detter, Ryan; Tjaden, Brett; Huh, Eui-Nam; Szczur, Martha R. (Technical Monitor)

2000-01-01

It is likely that NASA's future spacecraft systems will consist of distributed processes which will handle dynamically varying workloads in response to perceived scientific events, the spacecraft environment, spacecraft anomalies and user commands. Since all situations and possible uses of sensors cannot be anticipated during pre-deployment phases, an approach for dynamically adapting the allocation of distributed computational and communication resources is needed. To address this, we are evolving the DeSiDeRaTa adaptive resource management approach to enable reconfigurable ground and space information systems. The DeSiDeRaTa approach embodies a set of middleware mechanisms for adapting resource allocations, and a framework for reasoning about the real-time performance of distributed application systems. The framework and middleware will be extended to accommodate (1) the dynamic aspects of intra-constellation network topologies, and (2) the complete real-time path from the instrument to the user. We are developing a ground-based testbed that will enable NASA to perform early evaluation of adaptive resource management techniques without the expense of first deploying them in space. The benefits of the proposed effort are numerous, including the ability to use sensors in new ways not anticipated at design time; the production of information technology that ties the sensor web together; the accommodation of greater numbers of missions with fewer resources; and the opportunity to leverage the DeSiDeRaTa project's expertise, infrastructure and models for adaptive resource management for distributed real-time systems.

Quality based approach for adaptive face recognition

NASA Astrophysics Data System (ADS)

Abboud, Ali J.; Sellahewa, Harin; Jassim, Sabah A.

2009-05-01

Recent advances in biometric technology have pushed towards more robust and reliable systems. We aim to build systems that have low recognition errors and are less affected by variation in recording conditions. Recognition errors are often attributed to the usage of low quality biometric samples. Hence, there is a need to develop new intelligent techniques and strategies to automatically measure/quantify the quality of biometric image samples and if necessary restore image quality according to the need of the intended application. In this paper, we present no-reference image quality measures in the spatial domain that have impact on face recognition. The first is called symmetrical adaptive local quality index (SALQI) and the second is called middle halve (MH). Also, an adaptive strategy has been developed to select the best way to restore the image quality, called symmetrical adaptive histogram equalization (SAHE). The main benefits of using quality measures for adaptive strategy are: (1) avoidance of excessive unnecessary enhancement procedures that may cause undesired artifacts, and (2) reduced computational complexity which is essential for real time applications. We test the success of the proposed measures and adaptive approach for a wavelet-based face recognition system that uses the nearest neighborhood classifier. We shall demonstrate noticeable improvements in the performance of adaptive face recognition system over the corresponding non-adaptive scheme.

Adaptive Modeling of the International Space Station Electrical Power System

NASA Technical Reports Server (NTRS)

Thomas, Justin Ray

2007-01-01

Software simulations provide NASA engineers the ability to experiment with spacecraft systems in a computer-imitated environment. Engineers currently develop software models that encapsulate spacecraft system behavior. These models can be inaccurate due to invalid assumptions, erroneous operation, or system evolution. Increasing accuracy requires manual calibration and domain-specific knowledge. This thesis presents a method for automatically learning system models without any assumptions regarding system behavior. Data stream mining techniques are applied to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). We also explore a knowledge fusion approach that uses traditional engineered EPS models to supplement the learned models. We observed that these engineered EPS models provide useful background knowledge to reduce predictive error spikes when confronted with making predictions in situations that are quite different from the training scenarios used when learning the model. Evaluations using ISS sensor data and existing EPS models demonstrate the success of the adaptive approach. Our experimental results show that adaptive modeling provides reductions in model error anywhere from 80% to 96% over these existing models. Final discussions include impending use of adaptive modeling technology for ISS mission operations and the need for adaptive modeling in future NASA lunar and Martian exploration.

Variable Neural Adaptive Robust Control: A Switched System Approach

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

Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

2015-05-01

Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewisemore » quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.« less

Development of fault tolerant adaptive control laws for aerospace systems

NASA Astrophysics Data System (ADS)

Perez Rocha, Andres E.

The main topic of this dissertation is the design, development and implementation of intelligent adaptive control techniques designed to maintain healthy performance of aerospace systems subjected to malfunctions, external parameter changes and/or unmodeled dynamics. The dissertation is focused on the development of novel adaptive control configurations that rely on non-linear functions that appear in the immune system of living organisms as main source of adaptation. One of the main goals of this dissertation is to demonstrate that these novel adaptive control architectures are able to improve overall performance and protect the system while reducing control effort and maintaining adequate operation outside bounds of nominal design. This research effort explores several phases, ranging from theoretical stability analysis, simulation and hardware implementation on different types of aerospace systems including spacecraft, aircraft and quadrotor vehicles. The results presented in this dissertation are focused on two main adaptivity approaches, the first one is intended for aerospace systems that do not attain large angles and use exact feedback linearization of Euler angle kinematics. A proof of stability is presented by means of the circle Criterion and Lyapunov's direct method. The second approach is intended for aerospace systems that can attain large attitude angles (e.g. space systems in gravity-less environments), the adaptation is incorporated on a baseline architecture that uses partial feedback linearization of quaternions kinematics. In this case, the closed loop stability was analyzed using Lyapunov's direct method and Barbalat's Lemma. It is expected that some results presented in this dissertation can contribute towards the validation and certification of direct adaptive controllers.

A User-Centered Approach to Adaptive Hypertext Based on an Information Relevance Model

NASA Technical Reports Server (NTRS)

Mathe, Nathalie; Chen, James

1994-01-01

Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.

A bottom-up, vulnerability-based framework for identifying the adaptive capacity of water resources systems in a changing climate

NASA Astrophysics Data System (ADS)

Culley, Sam; Noble, Stephanie; Timbs, Michael; Yates, Adam; Giuliani, Matteo; Castelletti, Andrea; Maier, Holger; Westra, Seth

2015-04-01

Water resource system infrastructure and operating policies are commonly designed on the assumption that the statistics of future rainfall, temperature and other hydrometeorological variables are equal to those of the historical record. There is now substantial evidence demonstrating that this assumption is no longer valid, and that climate change will significantly impact water resources systems worldwide. Under different climatic inputs, the performance of these systems may degrade to a point where they become unable to meet the primary objectives for which they were built. In such a changing context, using existing infrastructure more efficiently - rather than planning additional infrastructure - becomes key to restore the system's performance at acceptable levels and minimize financial investments and associated risk. The traditional top-down approach for assessing climate change impacts relies on the use of a cascade of models from the global to the local scale. However, it is often difficult to utilize this top-down approach in a decision-making procedure, as there is disparity amongst various climate projections, arising from incomplete scientific understanding of the complicated processes and feedbacks within the climate system, and model limitations in reproducing those relationships. In contrast with this top-down approach, this study contributes a framework to identify the adaptive capacity of water resource systems under changing climatic conditions adopting a bottom-up, vulnerability-based approach. The performance of the current system management is first assessed for a comprehensive range of climatic conditions, which are independent of climate model forecasts. The adaptive capacity of the system is then estimated by re-evaluating the performance of a set of adaptive operating policies, which are optimized for each climatic condition under which the system is simulated. The proposed framework reverses the perspective by identifying water system vulnerability drivers and by enhancing the adaptive capacity of the system to respond to unforeseen events, in order to design robust and resilient adaptation measures. The approach is demonstrated on the multipurpose operation of the Lake Como system, located in Northern Italy, accounting for flood protection and irrigation supply. Numerical results show that our framework successfully identified the failure boundary based on current system management policies, which is demonstrated as being particularly sensitive to decreases in both precipitation and temperature. To estimate the likelihood of the climate being in states causing system failures and to provide a time frame for reaching such states, we consider 22 climate model projections; these projections suggest that the current management policies will lead to a high chance of failure over the next 40 years. The adaptive capacity of the re-optimized operating policies exhibits the potential for partially mitigating adverse climate change impacts and for extending the life of the system.

A Model of Internal Communication in Adaptive Communication Systems.

ERIC Educational Resources Information Center

Williams, M. Lee

A study identified and categorized different types of internal communication systems and developed an applied model of internal communication in adaptive organizational systems. Twenty-one large organizations were selected for their varied missions and diverse approaches to managing internal communication. Individual face-to-face or telephone…

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.

The Limits to Adaptation: A Systems Approach

EPA Science Inventory

The ability to adapt to climate change is delineated by capacity thresholds, after which climate damages begin to overwhelm the adaptation response. Such thresholds depend upon physical properties (natural processes and engineering parameters), resource constraints (expressed th...

Systematic, Multimethod Assessment of Adaptations Across Four Diverse Health Systems Interventions.

PubMed

Rabin, Borsika A; McCreight, Marina; Battaglia, Catherine; Ayele, Roman; Burke, Robert E; Hess, Paul L; Frank, Joseph W; Glasgow, Russell E

2018-01-01

Many health outcomes and implementation science studies have demonstrated the importance of tailoring evidence-based care interventions to local context to improve fit. By adapting to local culture, history, resources, characteristics, and priorities, interventions are more likely to lead to improved outcomes. However, it is unclear how best to adapt evidence-based programs and promising innovations. There are few guides or examples of how to best categorize or assess health-care adaptations, and even fewer that are brief and practical for use by non-researchers. This study describes the importance and potential of assessing adaptations before, during, and after the implementation of health systems interventions. We present a promising multilevel and multimethod approach developed and being applied across four different health systems interventions. Finally, we discuss implications and opportunities for future research. The four case studies are diverse in the conditions addressed, interventions, and implementation strategies. They include two nurse coordinator-based transition of care interventions, a data and training-driven multimodal pain management project, and a cardiovascular patient-reported outcomes project, all of which are using audit and feedback. We used the same modified adaptation framework to document changes made to the interventions and implementation strategies. To create the modified framework, we started with the adaptation and modification model developed by Stirman and colleagues and expanded it by adding concepts from the RE-AIM framework. Our assessments address the intuitive domains of Who, How, When, What, and Why to classify and organize adaptations. For each case study, we discuss how the modified framework was operationalized, the multiple methods used to collect data, results to date and approaches utilized for data analysis. These methods include a real-time tracking system and structured interviews at key times during the intervention. We provide descriptive data on the types and categories of adaptations made and discuss lessons learned. The multimethod approaches demonstrate utility across diverse health systems interventions. The modified adaptations model adequately captures adaptations across the various projects and content areas. We recommend systematic documentation of adaptations in future clinical and public health research and have made our assessment materials publicly available.

Towards Self-adaptation for Dependable Service-Oriented Systems

NASA Astrophysics Data System (ADS)

Cardellini, Valeria; Casalicchio, Emiliano; Grassi, Vincenzo; Lo Presti, Francesco; Mirandola, Raffaela

Increasingly complex information systems operating in dynamic environments ask for management policies able to deal intelligently and autonomously with problems and tasks. An attempt to deal with these aspects can be found in the Service-Oriented Architecture (SOA) paradigm that foresees the creation of business applications from independently developed services, where services and applications build up complex dependencies. Therefore the dependability of SOA systems strongly depends on their ability to self-manage and adapt themselves to cope with changes in the operating conditions and to meet the required dependability with a minimum of resources. In this paper we propose a model-based approach to the realization of self-adaptable SOA systems, aimed at the fulfillment of dependability requirements. Specifically, we provide a methodology driving the system adaptation and we discuss the architectural issues related to its implementation. To bring this approach to fruition, we developed a prototype tool and we show the results that can be achieved with a simple example.

An Approach for Autonomy: A Collaborative Communication Framework for Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Dufrene, Warren Russell, Jr.

2005-01-01

Research done during the last three years has studied the emersion properties of Complex Adaptive Systems (CAS). The deployment of Artificial Intelligence (AI) techniques applied to remote Unmanned Aerial Vehicles has led the author to investigate applications of CAS within the field of Autonomous Multi-Agent Systems. The core objective of current research efforts is focused on the simplicity of Intelligent Agents (IA) and the modeling of these agents within complex systems. This research effort looks at the communication, interaction, and adaptability of multi-agents as applied to complex systems control. The embodiment concept applied to robotics has application possibilities within multi-agent frameworks. A new framework for agent awareness within a virtual 3D world concept is possible where the vehicle is composed of collaborative agents. This approach has many possibilities for applications to complex systems. This paper describes the development of an approach to apply this virtual framework to the NASA Goddard Space Flight Center (GSFC) tetrahedron structure developed under the Autonomous Nano Technology Swarm (ANTS) program and the Super Miniaturized Addressable Reconfigurable Technology (SMART) architecture program. These projects represent an innovative set of novel concepts deploying adaptable, self-organizing structures composed of many tetrahedrons. This technology is pushing current applied Agents Concepts to new levels of requirements and adaptability.

[Working with a family systems therapy approach as part of the routine treatment on acute psychiatric wards: sustained effects on team members' workload].

PubMed

Haun, Markus W; Kordy, Henrike; Ochs, Matthias; Schweitzer, Jochen; Zwack, Julika

2012-11-01

Assessing long-term effects of a family systems therapy approach (systems therapy methods in acute psychiatry, SYMPA) on occupational stress and interdisciplinary cooperation of team members in three German psychiatric hospitals. Pre-post-follow-up survey using the Maslach Burnout Inventory (MBI) and Team Climate Inventory (TCI) questionnaires complemented by semi-structured in-depth interviews (N = 56). Three years after implementing a family systems therapy approach, experienced work load and staff burnout remain significantly lower than before. Interdisciplinary cooperation was intensified and nursing staff status increased. Following systemic case conceptualisations and interventions the therapeutic alliance moved towards a need-adapted treatment approach. Seven years after implementation, the family systems therapy approach still included significantly lower workload burden, an intensified interdisciplinary cooperation, and a need-adapted treatment orientation that strengthens the alliance between staff and client system. © Georg Thieme Verlag KG Stuttgart · New York.

Exploring the bases for a mixed reality stroke rehabilitation system, Part I: A unified approach for representing action, quantitative evaluation, and interactive feedback

PubMed Central

2011-01-01

Background Although principles based in motor learning, rehabilitation, and human-computer interfaces can guide the design of effective interactive systems for rehabilitation, a unified approach that connects these key principles into an integrated design, and can form a methodology that can be generalized to interactive stroke rehabilitation, is presently unavailable. Results This paper integrates phenomenological approaches to interaction and embodied knowledge with rehabilitation practices and theories to achieve the basis for a methodology that can support effective adaptive, interactive rehabilitation. Our resulting methodology provides guidelines for the development of an action representation, quantification of action, and the design of interactive feedback. As Part I of a two-part series, this paper presents key principles of the unified approach. Part II then describes the application of this approach within the implementation of the Adaptive Mixed Reality Rehabilitation (AMRR) system for stroke rehabilitation. Conclusions The accompanying principles for composing novel mixed reality environments for stroke rehabilitation can advance the design and implementation of effective mixed reality systems for the clinical setting, and ultimately be adapted for home-based application. They furthermore can be applied to other rehabilitation needs beyond stroke. PMID:21875441

Encoder-Decoder Optimization for Brain-Computer Interfaces

PubMed Central

Merel, Josh; Pianto, Donald M.; Cunningham, John P.; Paninski, Liam

2015-01-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages. PMID:26029919

Encoder-decoder optimization for brain-computer interfaces.

PubMed

Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

2015-06-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

Adaptation in Living Systems

NASA Astrophysics Data System (ADS)

Tu, Yuhai; Rappel, Wouter-Jan

2018-03-01

Adaptation refers to the biological phenomenon where living systems change their internal states in response to changes in their environments in order to maintain certain key functions critical for their survival and fitness. Adaptation is one of the most ubiquitous and arguably one of the most fundamental properties of living systems. It occurs throughout all biological scales, from adaptation of populations of species over evolutionary time to adaptation of a single cell to different environmental stresses during its life span. In this article, we review some of the recent progress made in understanding molecular mechanisms of cellular-level adaptation. We take the minimalist (or the physicist) approach and study the simplest systems that exhibit generic adaptive behaviors, namely chemotaxis in bacterium cells (Escherichia coli) and eukaryotic cells (Dictyostelium). We focus on understanding the basic biochemical interaction networks that are responsible for adaptation dynamics. By combining theoretical modeling with quantitative experimentation, we demonstrate universal features in adaptation as well as important differences in different cellular systems. Future work in extending the modeling framework to study adaptation in more complex systems such as sensory neurons is also discussed.

Use of complex adaptive systems metaphor to achieve professional and organizational change.

PubMed

Rowe, Ann; Hogarth, Annette

2005-08-01

This paper uses the experiences of a programme designed to bring about change in performance of public health nurses (health visitors and school nurses) in an inner city primary care trust, to explore the issues of professional and organizational change in health care organizations. The United Kingdom government has given increasing emphasis to programmes of modernization within the National Health Service. A central facet of this policy shift has been an expectation of behaviour and practice change by health care professionals. Change was brought about through use of a Complex Adaptive Systems approach. This enabled change to be seen as an inclusive, evolving and unpredictable process rather one which is linear and mechanistic. The paper examines in detail how the use of concepts and metaphors associated with Complex Adaptive Systems influenced the development of the programme, its implementation and outcomes. The programme resulted in extensive change in professional behaviour, service delivery and transformational change in the organizational structures and processes of the employing organization. This gave greater opportunities for experimentation and innovation, leading to new developments in service delivery, but also meant higher levels of uncertainty, responsibility, decision-making and risk management for practitioners. Using a Complex Adaptive Systems approach was helpful for developing alternative views of change and for understanding why and how some aspects of change were more successful than others. Its use encouraged the confrontation of some long-standing assumptions about change and service delivery patterns in the National Health Service, and the process exposed challenging tensions within the Service. The consequent destabilising of organizational and professional norms resulted in considerable emotional impacts for practitioners, an area which was found to be underplayed within the Complex Adaptive Systems literature. A Complex Adaptive Systems approach can support change, in particular a recognition and understanding of the emergence of unexpected structures, patterns and processes. The approach can support nurses to change their behaviour and innovate, but requires high levels of accountability, individual and professional creativity.

The reconstruction of narrative identity during mental health recovery: a complex adaptive systems perspective.

PubMed

Kerr, Douglas J R; Crowe, Trevor P; Oades, Lindsay G

2013-06-01

1) to understand the reconstruction of narrative identity during mental health recovery using a complex adaptive systems perspective, 2) to address the need for alternative approaches that embrace the complexities of health care. A narrative review of published literature was conducted. A complex adaptive systems perspective offers a framework and language that can assist individuals to make sense of their experiences and reconstruct their narratives during an often erratic and uncertain life transition. It is a novel research direction focused on a critical area of recovery and addresses the need for alternative approaches that embrace the complexities of health care. A complexity research approach to narrative identity reconstruction is valuable. It is an accessible model for addressing the complexities of recovery and may underpin the development of simple, practical recovery coaching tools. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Navy Aegis Ballistic Missile Defense (BMD) Program: Background and Issues for Congress

DTIC Science & Technology

2016-10-25

for European BMD On September 17, 2009, the Obama Administration announced a new approach for regional BMD operations called the Phased Adaptive...December 2010, the U.S. missile defense approach in Europe commits MDA to delivering systems and associated capabilities on a schedule that requires...announcement of the European Phased Adaptive Approach on September 17, 2009, stated, “This approach is based on an assessment of the Iranian missile

Disturbance observer based active and adaptive synchronization of energy resource chaotic system.

PubMed

Wei, Wei; Wang, Meng; Li, Donghai; Zuo, Min; Wang, Xiaoyi

2016-11-01

In this paper, synchronization of a three-dimensional energy resource chaotic system is considered. For the sake of achieving the synchronization between the drive and response systems, two different nonlinear control approaches, i.e. active control with known parameters and adaptive control with unknown parameters, have been designed. In order to guarantee the transient performance, finite-time boundedness (FTB) and finite-time stability (FTS) are introduced in the design of active control and adaptive control, respectively. Simultaneously, in view of the existence of disturbances, a new disturbance observer is proposed to estimate the disturbance. The conditions of the asymptotic stability for the closed-loop system are obtained. Numerical simulations are provided to illustrate the proposed approaches. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A survey of adaptive control technology in robotics

NASA Technical Reports Server (NTRS)

Tosunoglu, S.; Tesar, D.

1987-01-01

Previous work on the adaptive control of robotic systems is reviewed. Although the field is relatively new and does not yet represent a mature discipline, considerable attention has been given to the design of sophisticated robot controllers. Here, adaptive control methods are divided into model reference adaptive systems and self-tuning regulators with further definition of various approaches given in each class. The similarity and distinct features of the designed controllers are delineated and tabulated to enhance comparative review.

Adaptive control in the presence of unmodeled dynamics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

Stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances were investigated. The class of adaptive algorithms considered are those commonly referred to as model reference adaptive control algorithms, self-tuning controllers, and dead beat adaptive controllers, developed for both continuous-time systems and discrete-time systems. A unified analytical approach was developed to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. It is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

NASA Astrophysics Data System (ADS)

Meng, Qinggang; Lee, M. H.

2007-03-01

Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.

Adaptive learning and control for MIMO system based on adaptive dynamic programming.

PubMed

Fu, Jian; He, Haibo; Zhou, Xinmin

2011-07-01

Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg-Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance.

An on-line equivalent system identification scheme for adaptive control. Ph.D. Thesis - Stanford Univ.

NASA Technical Reports Server (NTRS)

Sliwa, S. M.

1984-01-01

A prime obstacle to the widespread use of adaptive control is the degradation of performance and possible instability resulting from the presence of unmodeled dynamics. The approach taken is to explicitly include the unstructured model uncertainty in the output error identification algorithm. The order of the compensator is successively increased by including identified modes. During this model building stage, heuristic rules are used to test for convergence prior to designing compensators. Additionally, the recursive identification algorithm as extended to multi-input, multi-output systems. Enhancements were also made to reduce the computational burden of an algorithm for obtaining minimal state space realizations from the inexact, multivariate transfer functions which result from the identification process. A number of potential adaptive control applications for this approach are illustrated using computer simulations. Results indicated that when speed of adaptation and plant stability are not critical, the proposed schemes converge to enhance system performance.

Neuro-adaptive backstepping control of SISO non-affine systems with unknown gain sign.

PubMed

Ramezani, Zahra; Arefi, Mohammad Mehdi; Zargarzadeh, Hassan; Jahed-Motlagh, Mohammad Reza

2016-11-01

This paper presents two neuro-adaptive controllers for a class of uncertain single-input, single-output (SISO) nonlinear non-affine systems with unknown gain sign. The first approach is state feedback controller, so that a neuro-adaptive state-feedback controller is constructed based on the backstepping technique. The second approach is an observer-based controller and K-filters are designed to estimate the system states. The proposed method relaxes a priori knowledge of control gain sign and therefore by utilizing the Nussbaum-type functions this problem is addressed. In these methods, neural networks are employed to approximate the unknown nonlinear functions. The proposed adaptive control schemes guarantee that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Finally, the theoretical results are numerically verified through simulation examples. Simulation results show the effectiveness of the proposed methods. Copyright © 2016 ISA. All rights reserved.

Analytical approach to an integrate-and-fire model with spike-triggered adaptation

NASA Astrophysics Data System (ADS)

Schwalger, Tilo; Lindner, Benjamin

2015-12-01

The calculation of the steady-state probability density for multidimensional stochastic systems that do not obey detailed balance is a difficult problem. Here we present the analytical derivation of the stationary joint and various marginal probability densities for a stochastic neuron model with adaptation current. Our approach assumes weak noise but is valid for arbitrary adaptation strength and time scale. The theory predicts several effects of adaptation on the statistics of the membrane potential of a tonically firing neuron: (i) a membrane potential distribution with a convex shape, (ii) a strongly increased probability of hyperpolarized membrane potentials induced by strong and fast adaptation, and (iii) a maximized variability associated with the adaptation current at a finite adaptation time scale.

Adaptive Delta Management: cultural aspects of dealing with uncertainty

NASA Astrophysics Data System (ADS)

Timmermans, Jos; Haasnoot, Marjolijn; Hermans, Leon; Kwakkel, Jan

2016-04-01

Deltas are generally recognized as vulnerable to climate change and therefore a salient topic in adaptation science. Deltas are also highly dynamic systems viewed from physical (erosion, sedimentation, subsidence), social (demographic), economic (trade), infrastructures (transport, energy, metropolization) and cultural (multi-ethnic) perspectives. This multi-faceted dynamic character of delta areas warrants the emergence of a branch of applied adaptation science, Adaptive Delta Management, which explicitly focuses on climate adaptation of such highly dynamic and deeply uncertain systems. The application of Adaptive Delta Management in the Dutch Delta Program and its active international dissemination by Dutch professionals results in the rapid dissemination of Adaptive Delta Management to deltas worldwide. This global dissemination raises concerns among professionals in delta management on its applicability in deltas with cultural conditions and historical developments quite different from those found in the Netherlands and the United Kingdom where the practices now labelled as Adaptive Delta Management first emerged. This research develops an approach and gives a first analysis of the interaction between the characteristics of different approaches in Adaptive Delta Management and their alignment with the cultural conditions encountered in various delta's globally. In this analysis, first different management theories underlying approaches to Adaptive Delta Management as encountered in both scientific and professional publications are identified and characterized on three dimensions: The characteristics dimensions used are: orientation on today, orientation on the future, and decision making (Timmermans, 2015). The different underlying management theories encountered are policy analysis, strategic management, transition management, and adaptive management. These four management theories underlying different approaches in Adaptive Delta Management are connected to Hofstede's (1983) cultural dimensions, of which uncertainty avoidance and long-term orientation are of particular relevance for our analysis. Our conclusions comment on the suitability of approaches in Adaptive Delta Management rooted in different management theories are more suitable for specific delta countries than others. The most striking conclusion is the unsuitability of rational policy analytic approaches for The Netherlands. Although surprising this conclusion finds some support in the process dominated approach taken in the Dutch Delta Program. In addition, the divergence between Vietnam, Bangladesh and Myanmar, all located in South East Asia, is striking. References Hofstede, G. (1983). The cultural relativity of organizational practices and theories. Journal of international business studies, 75-89. Jos Timmermans, Marjolijn Haasnoot, Leon Hermans, Jan Kwakkel, Martine Rutten and Wil Thissen (2015). Adaptive Delta Management: Roots and Branches, IAHR The Hague 2015.

Prehistory and History of the El Dorado Lake Area, Kansas. Phase II.

DTIC Science & Technology

1981-01-01

environmental condi- tions to which those systems were adapted . These goals pertain to both prehistoric and historic periods with research objectives accomplished...by site reconnaissance, testing and extensive data recovery excavations, and an interdisciplinary analytical approach to understanding human adaptive ...among prehistoric settlement and subsistence systems and the environmental conditions and resources to which they were adapted . Chapter 6 is a

Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems.

PubMed

Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi

2015-05-01

In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures.

PubMed

Choi, Yun Ho; Yoo, Sung Jin

2018-06-01

This paper investigates the event-triggered decentralized adaptive tracking problem of a class of uncertain interconnected nonlinear systems with unexpected actuator failures. It is assumed that local control signals are transmitted to local actuators with time-varying faults whenever predefined conditions for triggering events are satisfied. Compared with the existing control-input-based event-triggering strategy for adaptive control of uncertain nonlinear systems, the aim of this paper is to propose a tracking-error-based event-triggering strategy in the decentralized adaptive fault-tolerant tracking framework. The proposed approach can relax drastic changes in control inputs caused by actuator faults in the existing triggering strategy. The stability of the proposed event-triggering control system is analyzed in the Lyapunov sense. Finally, simulation comparisons of the proposed and existing approaches are provided to show the effectiveness of the proposed theoretical result in the presence of actuator faults. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A fault-tolerant control architecture for unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Drozeski, Graham R.

Research has presented several approaches to achieve varying degrees of fault-tolerance in unmanned aircraft. Approaches in reconfigurable flight control are generally divided into two categories: those which incorporate multiple non-adaptive controllers and switch between them based on the output of a fault detection and identification element, and those that employ a single adaptive controller capable of compensating for a variety of fault modes. Regardless of the approach for reconfigurable flight control, certain fault modes dictate system restructuring in order to prevent a catastrophic failure. System restructuring enables active control of actuation not employed by the nominal system to recover controllability of the aircraft. After system restructuring, continued operation requires the generation of flight paths that adhere to an altered flight envelope. The control architecture developed in this research employs a multi-tiered hierarchy to allow unmanned aircraft to generate and track safe flight paths despite the occurrence of potentially catastrophic faults. The hierarchical architecture increases the level of autonomy of the system by integrating five functionalities with the baseline system: fault detection and identification, active system restructuring, reconfigurable flight control; reconfigurable path planning, and mission adaptation. Fault detection and identification algorithms continually monitor aircraft performance and issue fault declarations. When the severity of a fault exceeds the capability of the baseline flight controller, active system restructuring expands the controllability of the aircraft using unconventional control strategies not exploited by the baseline controller. Each of the reconfigurable flight controllers and the baseline controller employ a proven adaptive neural network control strategy. A reconfigurable path planner employs an adaptive model of the vehicle to re-shape the desired flight path. Generation of the revised flight path is posed as a linear program constrained by the response of the degraded system. Finally, a mission adaptation component estimates limitations on the closed-loop performance of the aircraft and adjusts the aircraft mission accordingly. A combination of simulation and flight test results using two unmanned helicopters validates the utility of the hierarchical architecture.

A Robust Approach For Acoustic Noise Suppression In Speech Using ANFIS

NASA Astrophysics Data System (ADS)

Martinek, Radek; Kelnar, Michal; Vanus, Jan; Bilik, Petr; Zidek, Jan

2015-11-01

The authors of this article deals with the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear noise and interference suppression. This structure used is called an Adaptive Neuro Fuzzy Inference System (ANFIS). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc). Experimental methods based on the two-input adaptive noise cancellation concept was clearly outlined. Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for adaptive suppression of unwanted background interference that occurs in audio communication and degrades the audio signal. The system designed has been tested on real voice signals. This article presents the investigation and comparison amongst three distinct approaches to noise cancellation in speech; they are LMS (least mean squares) and RLS (recursive least squares) adaptive filtering and ANFIS. A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation. It was concluded that the ANFIS approach had the overall best performance as it efficiently cancelled noise even in highly noise-degraded speech. Results were drawn from the successful experimentation, subjective-based tests were used to analyse their comparative performance while objective tests were used to validate them. Implementation of algorithms was experimentally carried out in Matlab to justify the claims and determine their relative performances.

Sustainability, Complexity and Learning: Insights from Complex Systems Approaches

ERIC Educational Resources Information Center

Espinosa, A.; Porter, T.

2011-01-01

Purpose: The purpose of this research is to explore core contributions from two different approaches to complexity management in organisations aiming to improve their sustainability,: the Viable Systems Model (VSM), and the Complex Adaptive Systems (CAS). It is proposed to perform this by summarising the main insights each approach offers to…

State of the art in adaptive control of robotic systems

NASA Technical Reports Server (NTRS)

Tosunoglu, Sabri; Tesar, Delbert

1988-01-01

An up-to-date assessment of adaptive control technology as applied to robotics is presented. Although the field is relatively new and does not yet represent a mature discipline, considerable attention for the design of sophisticated robot controllers has occured. In this presentation, adaptive control methods are divided into model reference adaptive systems and self-tuning regulators, with further definition of various approaches given in each class. The similarity and distinct features of the designed controllers are delineated and tabulated to enhance comparative review.

Method Engineering: A Service-Oriented Approach

NASA Astrophysics Data System (ADS)

Cauvet, Corine

In the past, a large variety of methods have been published ranging from very generic frameworks to methods for specific information systems. Method Engineering has emerged as a research discipline for designing, constructing and adapting methods for Information Systems development. Several approaches have been proposed as paradigms in method engineering. The meta modeling approach provides means for building methods by instantiation, the component-based approach aims at supporting the development of methods by using modularization constructs such as method fragments, method chunks and method components. This chapter presents an approach (SO2M) for method engineering based on the service paradigm. We consider services as autonomous computational entities that are self-describing, self-configuring and self-adapting. They can be described, published, discovered and dynamically composed for processing a consumer's demand (a developer's requirement). The method service concept is proposed to capture a development process fragment for achieving a goal. Goal orientation in service specification and the principle of service dynamic composition support method construction and method adaptation to different development contexts.

Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning.

PubMed

Petrovic, Sanja; Khussainova, Gulmira; Jagannathan, Rupa

2016-03-01

Radiotherapy treatment planning aims at delivering a sufficient radiation dose to cancerous tumour cells while sparing healthy organs in the tumour-surrounding area. It is a time-consuming trial-and-error process that requires the expertise of a group of medical experts including oncologists and medical physicists and can take from 2 to 3h to a few days. Our objective is to improve the performance of our previously built case-based reasoning (CBR) system for brain tumour radiotherapy treatment planning. In this system, a treatment plan for a new patient is retrieved from a case base containing patient cases treated in the past and their treatment plans. However, this system does not perform any adaptation, which is needed to account for any difference between the new and retrieved cases. Generally, the adaptation phase is considered to be intrinsically knowledge-intensive and domain-dependent. Therefore, an adaptation often requires a large amount of domain-specific knowledge, which can be difficult to acquire and often is not readily available. In this study, we investigate approaches to adaptation that do not require much domain knowledge, referred to as knowledge-light adaptation. We developed two adaptation approaches: adaptation based on machine-learning tools and adaptation-guided retrieval. They were used to adapt the beam number and beam angles suggested in the retrieved case. Two machine-learning tools, neural networks and naive Bayes classifier, were used in the adaptation to learn how the difference in attribute values between the retrieved and new cases affects the output of these two cases. The adaptation-guided retrieval takes into consideration not only the similarity between the new and retrieved cases, but also how to adapt the retrieved case. The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. All experiments were performed using real-world brain cancer patient cases treated with three-dimensional (3D)-conformal radiotherapy. Neural networks-based adaptation improved the success rate of the CBR system with no adaptation by 12%. However, naive Bayes classifier did not improve the current retrieval results as it did not consider the interplay among attributes. The adaptation-guided retrieval of the case for beam number improved the success rate of the CBR system by 29%. However, it did not demonstrate good performance for the beam angle adaptation. Its success rate was 29% versus 39% when no adaptation was performed. The obtained empirical results demonstrate that the proposed adaptation methods improve the performance of the existing CBR system in recommending the number of beams to use. However, we also conclude that to be effective, the proposed adaptation of beam angles requires a large number of relevant cases in the case base. Copyright © 2016 Elsevier B.V. All rights reserved.

Development of flank wear model of cutting tool by using adaptive feedback linear control system on machining AISI D2 steel and AISI 4340 steel

NASA Astrophysics Data System (ADS)

Orra, Kashfull; Choudhury, Sounak K.

2016-12-01

The purpose of this paper is to build an adaptive feedback linear control system to check the variation of cutting force signal to improve the tool life. The paper discusses the use of transfer function approach in improving the mathematical modelling and adaptively controlling the process dynamics of the turning operation. The experimental results shows to be in agreement with the simulation model and error obtained is less than 3%. The state space approach model used in this paper successfully check the adequacy of the control system through controllability and observability test matrix and can be transferred from one state to another by appropriate input control in a finite time. The proposed system can be implemented to other machining process under varying range of cutting conditions to improve the efficiency and observability of the system.

A hydrogeological conceptual approach to study urban groundwater flow in Bucharest city, Romania

NASA Astrophysics Data System (ADS)

Boukhemacha, Mohamed Amine; Gogu, Constantin Radu; Serpescu, Irina; Gaitanaru, Dragos; Bica, Ioan

2015-05-01

Management of groundwater systems in urban areas is necessary and can be reliably performed by means of mathematical modeling combined with geospatial analysis. A conceptual approach for the study of urban hydrogeological systems is presented. The proposed approach is based on the features of Bucharest city (Romania) and can be adapted to other urban areas showing similar characteristics. It takes into account the interaction between groundwater and significant urban infrastructure elements that can be encountered in modern cities such as subway tunnels and water-supply networks, and gives special attention to the sewer system. In this respect, an adaptation of the leakage factor approach is proposed, which uses a sewer-system zoning function related to the conduits' location in the aquifer system and a sewer-conduits classification function related to their structural and/or hydraulic properties. The approach was used to elaborate a single-layered steady state groundwater flow model for a pilot zone of Bucharest city.

A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system.

PubMed

Chrysafiadi, Konstantina; Virvou, Maria

2013-12-01

In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner's knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner's knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

Improved Modeling of Intelligent Tutoring Systems Using Ant Colony Optimization

ERIC Educational Resources Information Center

Rastegarmoghadam, Mahin; Ziarati, Koorush

2017-01-01

Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is…

Adaptation Provisioning with Respect to Learning Styles in a Web-Based Educational System: An Experimental Study

ERIC Educational Resources Information Center

Popescu, E.

2010-01-01

Personalized instruction is seen as a desideratum of today's e-learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style-based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates…

Examining fire-prone forest landscapes as coupled human and natural systems

Treesearch

Thomas A. Spies; Eric M. White; Jeffrey D. Kline; A. Paige Fisher; Alan Ager; John Bailey; John Bolte; Jennifer Koch; Emily Platt; Christine S. Olsen; Derric Jacobs; Bruce Shindler; Michelle M. Steen-Adams; Roger Hammer

2014-01-01

Fire-prone landscapes are not well studied as coupled human and natural systems (CHANS) and present many challenges for understanding and promoting adaptive behaviors and institutions. Here, we explore how heterogeneity, feedbacks, and external drivers in this type of natural hazard system can lead to complexity and can limit the development of more adaptive approaches...

Land-based approach to evaluate sustainable land management and adaptive capacity of ecosystems/lands

NASA Astrophysics Data System (ADS)

Kust, German; Andreeva, Olga

2015-04-01

A number of new concepts and paradigms appeared during last decades, such as sustainable land management (SLM), climate change (CC) adaptation, environmental services, ecosystem health, and others. All of these initiatives still not having the common scientific platform although some agreements in terminology were reached, schemes of links and feedback loops created, and some models developed. Nevertheless, in spite of all these scientific achievements, the land related issues are still not in the focus of CC adaptation and mitigation. The last did not grow much beyond the "greenhouse gases" (GHG) concept, which makes land degradation as the "forgotten side of climate change" The possible decision to integrate concepts of climate and desertification/land degradation could be consideration of the "GHG" approach providing global solution, and "land" approach providing local solution covering other "locally manifesting" issues of global importance (biodiversity conservation, food security, disasters and risks, etc.) to serve as a central concept among those. SLM concept is a land-based approach, which includes the concepts of both ecosystem-based approach (EbA) and community-based approach (CbA). SLM can serve as in integral CC adaptation strategy, being based on the statement "the more healthy and resilient the system is, the less vulnerable and more adaptive it will be to any external changes and forces, including climate" The biggest scientific issue is the methods to evaluate the SLM and results of the SLM investments. We suggest using the approach based on the understanding of the balance or equilibrium of the land and nature components as the major sign of the sustainable system. Prom this point of view it is easier to understand the state of the ecosystem stress, size of the "health", range of adaptive capacity, drivers of degradation and SLM nature, as well as the extended land use, and the concept of environmental land management as the improved SLM approach. A number of case studies justify the schemes developed to explain this approach.

Adaptive twisting sliding mode algorithm for hypersonic reentry vehicle attitude control based on finite-time observer.

PubMed

Guo, Zongyi; Chang, Jing; Guo, Jianguo; Zhou, Jun

2018-06-01

This paper focuses on the adaptive twisting sliding mode control for the Hypersonic Reentry Vehicles (HRVs) attitude tracking issue. The HRV attitude tracking model is transformed into the error dynamics in matched structure, whereas an unmeasurable state is redefined by lumping the existing unmatched disturbance with the angular rate. Hence, an adaptive finite-time observer is used to estimate the unknown state. Then, an adaptive twisting algorithm is proposed for systems subject to disturbances with unknown bounds. The stability of the proposed observer-based adaptive twisting approach is guaranteed, and the case of noisy measurement is analyzed. Also, the developed control law avoids the aggressive chattering phenomenon of the existing adaptive twisting approaches because the adaptive gains decrease close to the disturbance once the trajectories reach the sliding surface. Finally, numerical simulations on the attitude control of the HRV are conducted to verify the effectiveness and benefit of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A Prototype Instrument for Adaptive SPECT Imaging

PubMed Central

Freed, Melanie; Kupinski, Matthew A.; Furenlid, Lars R.; Barrett, Harrison H.

2015-01-01

We have designed and constructed a small-animal adaptive SPECT imaging system as a prototype for quantifying the potential benefit of adaptive SPECT imaging over the traditional fixed geometry approach. The optical design of the system is based on filling the detector with the object for each viewing angle, maximizing the sensitivity, and optimizing the resolution in the projection images. Additional feedback rules for determining the optimal geometry of the system can be easily added to the existing control software. Preliminary data have been taken of a phantom with a small, hot, offset lesion in a flat background in both adaptive and fixed geometry modes. Comparison of the predicted system behavior with the actual system behavior is presented along with recommendations for system improvements. PMID:26346820

Adapting agriculture to climate change.

PubMed

Howden, S Mark; Soussana, Jean-François; Tubiello, Francesco N; Chhetri, Netra; Dunlop, Michael; Meinke, Holger

2007-12-11

The strong trends in climate change already evident, the likelihood of further changes occurring, and the increasing scale of potential climate impacts give urgency to addressing agricultural adaptation more coherently. There are many potential adaptation options available for marginal change of existing agricultural systems, often variations of existing climate risk management. We show that implementation of these options is likely to have substantial benefits under moderate climate change for some cropping systems. However, there are limits to their effectiveness under more severe climate changes. Hence, more systemic changes in resource allocation need to be considered, such as targeted diversification of production systems and livelihoods. We argue that achieving increased adaptation action will necessitate integration of climate change-related issues with other risk factors, such as climate variability and market risk, and with other policy domains, such as sustainable development. Dealing with the many barriers to effective adaptation will require a comprehensive and dynamic policy approach covering a range of scales and issues, for example, from the understanding by farmers of change in risk profiles to the establishment of efficient markets that facilitate response strategies. Science, too, has to adapt. Multidisciplinary problems require multidisciplinary solutions, i.e., a focus on integrated rather than disciplinary science and a strengthening of the interface with decision makers. A crucial component of this approach is the implementation of adaptation assessment frameworks that are relevant, robust, and easily operated by all stakeholders, practitioners, policymakers, and scientists.

Adaptive control applied to Space Station attitude control system

NASA Technical Reports Server (NTRS)

Lam, Quang M.; Chipman, Richard; Hu, Tsay-Hsin G.; Holmes, Eric B.; Sunkel, John

1992-01-01

This paper presents an adaptive control approach to enhance the performance of current attitude control system used by the Space Station Freedom. The proposed control law was developed based on the direct adaptive control or model reference adaptive control scheme. Performance comparisons, subject to inertia variation, of the adaptive controller and the fixed-gain linear quadratic regulator currently implemented for the Space Station are conducted. Both the fixed-gain and the adaptive gain controllers are able to maintain the Station stability for inertia variations of up to 35 percent. However, when a 50 percent inertia variation is applied to the Station, only the adaptive controller is able to maintain the Station attitude.

An Adaptive Supervisory Sliding Fuzzy Cerebellar Model Articulation Controller for Sensorless Vector-Controlled Induction Motor Drive Systems

PubMed Central

Wang, Shun-Yuan; Tseng, Chwan-Lu; Lin, Shou-Chuang; Chiu, Chun-Jung; Chou, Jen-Hsiang

2015-01-01

This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes. PMID:25815450

An adaptive supervisory sliding fuzzy cerebellar model articulation controller for sensorless vector-controlled induction motor drive systems.

PubMed

Wang, Shun-Yuan; Tseng, Chwan-Lu; Lin, Shou-Chuang; Chiu, Chun-Jung; Chou, Jen-Hsiang

2015-03-25

This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes--the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC--were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes.

Adaptive approaches to biosecurity governance.

PubMed

Cook, David C; Liu, Shuang; Murphy, Brendan; Lonsdale, W Mark

2010-09-01

This article discusses institutional changes that may facilitate an adaptive approach to biosecurity risk management where governance is viewed as a multidisciplinary, interactive experiment acknowledging uncertainty. Using the principles of adaptive governance, evolved from institutional theory, we explore how the concepts of lateral information flows, incentive alignment, and policy experimentation might shape Australia's invasive species defense mechanisms. We suggest design principles for biosecurity policies emphasizing overlapping complementary response capabilities and the sharing of invasive species risks via a polycentric system of governance. © 2010 Society for Risk Analysis

Blind information-theoretic multiuser detection algorithms for DS-CDMA and WCDMA downlink systems.

PubMed

Waheed, Khuram; Salem, Fathi M

2005-07-01

Code division multiple access (CDMA) is based on the spread-spectrum technology and is a dominant air interface for 2.5G, 3G, and future wireless networks. For the CDMA downlink, the transmitted CDMA signals from the base station (BS) propagate through a noisy multipath fading communication channel before arriving at the receiver of the user equipment/mobile station (UE/MS). Classical CDMA single-user detection (SUD) algorithms implemented in the UE/MS receiver do not provide the required performance for modern high data-rate applications. In contrast, multi-user detection (MUD) approaches require a lot of a priori information not available to the UE/MS. In this paper, three promising adaptive Riemannian contra-variant (or natural) gradient based user detection approaches, capable of handling the highly dynamic wireless environments, are proposed. The first approach, blind multiuser detection (BMUD), is the process of simultaneously estimating multiple symbol sequences associated with all the users in the downlink of a CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. This approach is applicable to CDMA systems with relatively short spreading codes but becomes impractical for systems using long spreading codes. We also propose two other adaptive approaches, namely, RAKE -blind source recovery (RAKE-BSR) and RAKE-principal component analysis (RAKE-PCA) that fuse an adaptive stage into a standard RAKE receiver. This adaptation results in robust user detection algorithms with performance exceeding the linear minimum mean squared error (LMMSE) detectors for both Direct Sequence CDMA (DS-CDMA) and wide-band CDMA (WCDMA) systems under conditions of congestion, imprecise channel estimation and unmodeled multiple access interference (MAI).

Adaptive Approximation-Based Regulation Control for a Class of Uncertain Nonlinear Systems Without Feedback Linearizability.

PubMed

Wang, Ning; Sun, Jing-Chao; Han, Min; Zheng, Zhongjiu; Er, Meng Joo

2017-09-06

In this paper, for a general class of uncertain nonlinear (cascade) systems, including unknown dynamics, which are not feedback linearizable and cannot be solved by existing approaches, an innovative adaptive approximation-based regulation control (AARC) scheme is developed. Within the framework of adding a power integrator (API), by deriving adaptive laws for output weights and prediction error compensation pertaining to single-hidden-layer feedforward network (SLFN) from the Lyapunov synthesis, a series of SLFN-based approximators are explicitly constructed to exactly dominate completely unknown dynamics. By the virtue of significant advancements on the API technique, an adaptive API methodology is eventually established in combination with SLFN-based adaptive approximators, and it contributes to a recursive mechanism for the AARC scheme. As a consequence, the output regulation error can asymptotically converge to the origin, and all other signals of the closed-loop system are uniformly ultimately bounded. Simulation studies and comprehensive comparisons with backstepping- and API-based approaches demonstrate that the proposed AARC scheme achieves remarkable performance and superiority in dealing with unknown dynamics.

Adaptive gamma correction-based expert system for nonuniform illumination face enhancement

NASA Astrophysics Data System (ADS)

Abdelhamid, Iratni; Mustapha, Aouache; Adel, Oulefki

2018-03-01

The image quality of a face recognition system suffers under severe lighting conditions. Thus, this study aims to develop an approach for nonuniform illumination adjustment based on an adaptive gamma correction (AdaptGC) filter that can solve the aforementioned issue. An approach for adaptive gain factor prediction was developed via neural network model-based cross-validation (NN-CV). To achieve this objective, a gamma correction function and its effects on the face image quality with different gain values were examined first. Second, an orientation histogram (OH) algorithm was assessed as a face's feature descriptor. Subsequently, a density histogram module was developed for face label generation. During the NN-CV construction, the model was assessed to recognize the OH descriptor and predict the face label. The performance of the NN-CV model was evaluated by examining the statistical measures of root mean square error and coefficient of efficiency. Third, to evaluate the AdaptGC enhancement approach, an image quality metric was adopted using enhancement by entropy, contrast per pixel, second-derivative-like measure of enhancement, and sharpness, then supported by visual inspection. The experiment results were examined using five face's databases, namely, extended Yale-B, Carnegie Mellon University-Pose, Illumination, and Expression, Mobio, FERET, and Oulu-CASIA-NIR-VIS. The final results prove that AdaptGC filter implementation compared with state-of-the-art methods is the best choice in terms of contrast and nonuniform illumination adjustment. In summary, the benefits attained prove that AdaptGC is driven by a profitable enhancement rate, which provides satisfying features for high rate face recognition systems.

Investigating Work and Learning through Complex Adaptive Organisations

ERIC Educational Resources Information Center

Lizier, Amanda Louise

2017-01-01

Purpose: The purpose of this paper is to outline an empirical study of how professionals experience work and learning in complex adaptive organisations. The study uses a complex adaptive systems approach, which forms the basis of a specifically developed conceptual framework for explaining professionals' experiences of work and learning.…

Development of an Adaptive Learning System with Two Sources of Personalization Information

ERIC Educational Resources Information Center

Tseng, J. C. R.; Chu, H. C.; Hwang, G. J.; Tsai, C. C.

2008-01-01

Previous research of adaptive learning mainly focused on improving student learning achievements based only on single-source of personalization information, such as learning style, cognitive style or learning achievement. In this paper, an innovative adaptive learning approach is proposed by basing upon two main sources of personalization…

Adaptation in CRISPR-Cas Systems.

PubMed

Sternberg, Samuel H; Richter, Hagen; Charpentier, Emmanuelle; Qimron, Udi

2016-03-17

Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute an adaptive immune system in prokaryotes. The system preserves memories of prior infections by integrating short segments of foreign DNA, termed spacers, into the CRISPR array in a process termed adaptation. During the past 3 years, significant progress has been made on the genetic requirements and molecular mechanisms of adaptation. Here we review these recent advances, with a focus on the experimental approaches that have been developed, the insights they generated, and a proposed mechanism for self- versus non-self-discrimination during the process of spacer selection. We further describe the regulation of adaptation and the protein players involved in this fascinating process that allows bacteria and archaea to harbor adaptive immunity. Copyright © 2016 Elsevier Inc. All rights reserved.

Policy Tree Optimization for Adaptive Management of Water Resources Systems

NASA Astrophysics Data System (ADS)

Herman, J. D.; Giuliani, M.

2016-12-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points", which are threshold values of indicator variables that signal a change in policy. However, there remains a need for a general method to optimize the choice of indicators and their threshold values in a way that is easily interpretable for decision makers. Here we propose a conceptual framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. We demonstrate the approach using Folsom Reservoir, California as a case study, in which operating policies must balance the risk of both floods and droughts. Given a set of feature variables, such as reservoir level, inflow observations and forecasts, and time of year, the resulting policy defines the conditions under which flood control and water supply hedging operations should be triggered. Importantly, the tree-based rule sets are easy to interpret for decision making, and can be compared to historical operating policies to understand the adaptations needed under possible climate change scenarios. Several remaining challenges are discussed, including the empirical convergence properties of the method, and extensions to irreversible decisions such as infrastructure. Policy tree optimization, and corresponding open-source software, provide a generalizable, interpretable approach to designing adaptive policies under uncertainty for water resources systems.

Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2011-01-01

This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

Climate change mitigation and adaptation in the land use sector: from complementarity to synergy.

PubMed

Duguma, Lalisa A; Minang, Peter A; van Noordwijk, Meine

2014-09-01

Currently, mitigation and adaptation measures are handled separately, due to differences in priorities for the measures and segregated planning and implementation policies at international and national levels. There is a growing argument that synergistic approaches to adaptation and mitigation could bring substantial benefits at multiple scales in the land use sector. Nonetheless, efforts to implement synergies between adaptation and mitigation measures are rare due to the weak conceptual framing of the approach and constraining policy issues. In this paper, we explore the attributes of synergy and the necessary enabling conditions and discuss, as an example, experience with the Ngitili system in Tanzania that serves both adaptation and mitigation functions. An in-depth look into the current practices suggests that more emphasis is laid on complementarity-i.e., mitigation projects providing adaptation co-benefits and vice versa rather than on synergy. Unlike complementarity, synergy should emphasize functionally sustainable landscape systems in which adaptation and mitigation are optimized as part of multiple functions. We argue that the current practice of seeking co-benefits (complementarity) is a necessary but insufficient step toward addressing synergy. Moving forward from complementarity will require a paradigm shift from current compartmentalization between mitigation and adaptation to systems thinking at landscape scale. However, enabling policy, institutional, and investment conditions need to be developed at global, national, and local levels to achieve synergistic goals.

Adaptive Fuzzy Output-Constrained Fault-Tolerant Control of Nonlinear Stochastic Large-Scale Systems With Actuator Faults.

PubMed

Li, Yongming; Ma, Zhiyao; Tong, Shaocheng

2017-09-01

The problem of adaptive fuzzy output-constrained tracking fault-tolerant control (FTC) is investigated for the large-scale stochastic nonlinear systems of pure-feedback form. The nonlinear systems considered in this paper possess the unstructured uncertainties, unknown interconnected terms and unknown nonaffine nonlinear faults. The fuzzy logic systems are employed to identify the unknown lumped nonlinear functions so that the problems of structured uncertainties can be solved. An adaptive fuzzy state observer is designed to solve the nonmeasurable state problem. By combining the barrier Lyapunov function theory, adaptive decentralized and stochastic control principles, a novel fuzzy adaptive output-constrained FTC approach is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs.

PubMed

Shi, Wuxi; Luo, Rui; Li, Baoquan

2017-01-01

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Resilience Engineering in Critical Long Term Aerospace Software Systems: A New Approach to Spacecraft Software Safety

NASA Astrophysics Data System (ADS)

Dulo, D. A.

Safety critical software systems permeate spacecraft, and in a long term venture like a starship would be pervasive in every system of the spacecraft. Yet software failure today continues to plague both the systems and the organizations that develop them resulting in the loss of life, time, money, and valuable system platforms. A starship cannot afford this type of software failure in long journeys away from home. A single software failure could have catastrophic results for the spaceship and the crew onboard. This paper will offer a new approach to developing safe reliable software systems through focusing not on the traditional safety/reliability engineering paradigms but rather by focusing on a new paradigm: Resilience and Failure Obviation Engineering. The foremost objective of this approach is the obviation of failure, coupled with the ability of a software system to prevent or adapt to complex changing conditions in real time as a safety valve should failure occur to ensure safe system continuity. Through this approach, safety is ensured through foresight to anticipate failure and to adapt to risk in real time before failure occurs. In a starship, this type of software engineering is vital. Through software developed in a resilient manner, a starship would have reduced or eliminated software failure, and would have the ability to rapidly adapt should a software system become unstable or unsafe. As a result, long term software safety, reliability, and resilience would be present for a successful long term starship mission.

Suppression of the noise-induced effects in an electrostatic micro-plate using an adaptive back-stepping sliding mode control.

PubMed

Nwagoum Tuwa, Peguy Roussel; Woafo, P

2018-01-01

In this work, an adaptive backstepping sliding mode control approach is applied through the piezoelectric layer in order to control and to stabilize an electrostatic micro-plate. The mathematical model of the system by taking into account the small fluctuations in the gap considered as bounded noise is carried out. The accuracy of the proposed modal equation is proven using the method of lines. By using both approaches, the effects of noise are presented. It is found that they lead to pull-in instability as well as to random chaos. A suitable backstepping approach to improve the tracking performance is integrated to the adaptive sliding mode control in order to eliminate chattering phenomena and reinforce the robustness of the system in presence of uncertainties and external random disturbances. It is proved that all the variables of the closed-loop system are bounded and the system can follow the given reference signals as close as possible. Numerical simulations are provided to show the effectiveness of proposed controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Design and implementation of adaptive PI control schemes for web tension control in roll-to-roll (R2R) manufacturing.

PubMed

Raul, Pramod R; Pagilla, Prabhakar R

2015-05-01

In this paper, two adaptive Proportional-Integral (PI) control schemes are designed and discussed for control of web tension in Roll-to-Roll (R2R) manufacturing systems. R2R systems are used to transport continuous materials (called webs) on rollers from the unwind roll to the rewind roll. Maintaining web tension at the desired value is critical to many R2R processes such as printing, coating, lamination, etc. Existing fixed gain PI tension control schemes currently used in industrial practice require extensive tuning and do not provide the desired performance for changing operating conditions and material properties. The first adaptive PI scheme utilizes the model reference approach where the controller gains are estimated based on matching of the actual closed-loop tension control systems with an appropriately chosen reference model. The second adaptive PI scheme utilizes the indirect adaptive control approach together with relay feedback technique to automatically initialize the adaptive PI gains. These adaptive tension control schemes can be implemented on any R2R manufacturing system. The key features of the two adaptive schemes is that their designs are simple for practicing engineers, easy to implement in real-time, and automate the tuning process. Extensive experiments are conducted on a large experimental R2R machine which mimics many features of an industrial R2R machine. These experiments include trials with two different polymer webs and a variety of operating conditions. Implementation guidelines are provided for both adaptive schemes. Experimental results comparing the two adaptive schemes and a fixed gain PI tension control scheme used in industrial practice are provided and discussed. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A new generation of real-time DOS technology for mission-oriented system integration and operation

NASA Technical Reports Server (NTRS)

Jensen, E. Douglas

1988-01-01

Information is given on system integration and operation (SIO) requirements and a new generation of technical approaches for SIO. Real-time, distribution, survivability, and adaptability requirements and technical approaches are covered. An Alpha operating system program management overview is outlined.

Global adaptive control for uncertain nonaffine nonlinear hysteretic systems.

PubMed

Liu, Yong-Hua; Huang, Liangpei; Xiao, Dongming; Guo, Yong

2015-09-01

In this paper, the global output tracking is investigated for a class of uncertain nonlinear hysteretic systems with nonaffine structures. By combining the solution properties of the hysteresis model with the novel backstepping approach, a robust adaptive control algorithm is developed without constructing a hysteresis inverse. The proposed control scheme is further modified to tackle the bounded disturbances by adaptively estimating their bounds. It is rigorously proven that the designed adaptive controllers can guarantee global stability of the closed-loop system. Two numerical examples are provided to show the effectiveness of the proposed control schemes. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Survey on the use of smart and adaptive engineering systems in medicine.

PubMed

Abbod, M F; Linkens, D A; Mahfouf, M; Dounias, G

2002-11-01

In this paper, the current published knowledge about smart and adaptive engineering systems in medicine is reviewed. The achievements of frontier research in this particular field within medical engineering are described. A multi-disciplinary approach to the applications of adaptive systems is observed from the literature surveyed. The three modalities of diagnosis, imaging and therapy are considered to be an appropriate classification method for the analysis of smart systems being applied to specified medical sub-disciplines. It is expected that future research in biomedicine should identify subject areas where more advanced intelligent systems could be applied than is currently evident. The literature provides evidence of hybridisation of different types of adaptive and smart systems with applications in different areas of medical specifications. Copyright 2002 Elsevier Science B.V.

Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems

NASA Astrophysics Data System (ADS)

Lin, Tsung-Chih

2010-12-01

In this paper, a novel direct adaptive interval type-2 fuzzy-neural tracking control equipped with sliding mode and Lyapunov synthesis approach is proposed to handle the training data corrupted by noise or rule uncertainties for nonlinear SISO nonlinear systems involving external disturbances. By employing adaptive fuzzy-neural control theory, the update laws will be derived for approximating the uncertain nonlinear dynamical system. In the meantime, the sliding mode control method and the Lyapunov stability criterion are incorporated into the adaptive fuzzy-neural control scheme such that the derived controller is robust with respect to unmodeled dynamics, external disturbance and approximation errors. In comparison with conventional methods, the advocated approach not only guarantees closed-loop stability but also the output tracking error of the overall system will converge to zero asymptotically without prior knowledge on the upper bound of the lumped uncertainty. Furthermore, chattering effect of the control input will be substantially reduced by the proposed technique. To illustrate the performance of the proposed method, finally simulation example will be given.

Asymptotically inspired moment-closure approximation for adaptive networks

NASA Astrophysics Data System (ADS)

Shkarayev, Maxim; Shaw, Leah

2012-02-01

Adaptive social networks, in which nodes and network structure co-evolve, are often described using a mean-field system of equations for the density of node and link types. These equations constitute an open system due to dependence on higher order topological structures. We propose a moment-closure approximation based on the analytical description of the system in an asymptotic regime. We apply the proposed approach to two examples of adaptive networks: recruitment to a cause model and epidemic spread model. We show a good agreement between the improved mean-field prediction and simulations of the full network system.

Industrial WSN Based on IR-UWB and a Low-Latency MAC Protocol

NASA Astrophysics Data System (ADS)

Reinhold, Rafael; Underberg, Lisa; Wulf, Armin; Kays, Ruediger

2016-07-01

Wireless sensor networks for industrial communication require high reliability and low latency. As current wireless sensor networks do not entirely meet these requirements, novel system approaches need to be developed. Since ultra wideband communication systems seem to be a promising approach, this paper evaluates the performance of the IEEE 802.15.4 impulse-radio ultra-wideband physical layer and the IEEE 802.15.4 Low Latency Deterministic Network (LLDN) MAC for industrial applications. Novel approaches and system adaptions are proposed to meet the application requirements. In this regard, a synchronization approach based on circular average magnitude difference functions (CAMDF) and on a clean template (CT) is presented for the correlation receiver. An adapted MAC protocol titled aggregated low latency (ALL) MAC is proposed to significantly reduce the resulting latency. Based on the system proposals, a hardware prototype has been developed, which proves the feasibility of the system and visualizes the real-time performance of the MAC protocol.

Adaptive hyperspectral imager: design, modeling, and control

NASA Astrophysics Data System (ADS)

McGregor, Scot; Lacroix, Simon; Monmayrant, Antoine

2015-08-01

An adaptive, hyperspectral imager is presented. We propose a system with easily adaptable spectral resolution, adjustable acquisition time, and high spatial resolution which is independent of spectral resolution. The system yields the possibility to define a variety of acquisition schemes, and in particular near snapshot acquisitions that may be used to measure the spectral content of given or automatically detected regions of interest. The proposed system is modelled and simulated, and tests on a first prototype validate the approach to achieve near snapshot spectral acquisitions without resorting to any computationally heavy post-processing, nor cumbersome calibration

Detection of network attacks based on adaptive resonance theory

NASA Astrophysics Data System (ADS)

Bukhanov, D. G.; Polyakov, V. M.

2018-05-01

The paper considers an approach to intrusion detection systems using a neural network of adaptive resonant theory. It suggests the structure of an intrusion detection system consisting of two types of program modules. The first module manages connections of user applications by preventing the undesirable ones. The second analyzes the incoming network traffic parameters to check potential network attacks. After attack detection, it notifies the required stations using a secure transmission channel. The paper describes the experiment on the detection and recognition of network attacks using the test selection. It also compares the obtained results with similar experiments carried out by other authors. It gives findings and conclusions on the sufficiency of the proposed approach. The obtained information confirms the sufficiency of applying the neural networks of adaptive resonant theory to analyze network traffic within the intrusion detection system.

Adaptive coded aperture imaging in the infrared: towards a practical implementation

NASA Astrophysics Data System (ADS)

Slinger, Chris W.; Gilholm, Kevin; Gordon, Neil; McNie, Mark; Payne, Doug; Ridley, Kevin; Strens, Malcolm; Todd, Mike; De Villiers, Geoff; Watson, Philip; Wilson, Rebecca; Dyer, Gavin; Eismann, Mike; Meola, Joe; Rogers, Stanley

2008-08-01

An earlier paper [1] discussed the merits of adaptive coded apertures for use as lensless imaging systems in the thermal infrared and visible. It was shown how diffractive (rather than the more conventional geometric) coding could be used, and that 2D intensity measurements from multiple mask patterns could be combined and decoded to yield enhanced imagery. Initial experimental results in the visible band were presented. Unfortunately, radiosity calculations, also presented in that paper, indicated that the signal to noise performance of systems using this approach was likely to be compromised, especially in the infrared. This paper will discuss how such limitations can be overcome, and some of the tradeoffs involved. Experimental results showing tracking and imaging performance of these modified, diffractive, adaptive coded aperture systems in the visible and infrared will be presented. The subpixel imaging and tracking performance is compared to that of conventional imaging systems and shown to be superior. System size, weight and cost calculations indicate that the coded aperture approach, employing novel photonic MOEMS micro-shutter architectures, has significant merits for a given level of performance in the MWIR when compared to more conventional imaging approaches.

Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

NASA Technical Reports Server (NTRS)

Bosworth, John

2006-01-01

A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

Convergence of fractional adaptive systems using gradient approach.

PubMed

Gallegos, Javier A; Duarte-Mermoud, Manuel A

2017-07-01

Conditions for boundedness and convergence of the output error and the parameter error for various Caputo's fractional order adaptive schemes based on the steepest descent method are derived in this paper. To this aim, the concept of sufficiently exciting signals is introduced, characterized and related to the concept of persistently exciting signals used in the integer order case. An application is designed in adaptive indirect control of integer order systems using fractional equations to adjust parameters. This application is illustrated for a pole placement adaptive problem. Advantages of using fractional adjustment in control adaptive schemes are experimentally obtained. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A New Approach for Constructing the Concept Map

ERIC Educational Resources Information Center

Tseng, Shian-Shyong; Sue, Pei-Chi; Su, Jun-Ming; Weng, Jui-Feng; Tsai, Wen-Nung

2007-01-01

In recent years, e-learning system has become more and more popular and many adaptive learning environments have been proposed to offer learners customized courses in accordance with their aptitudes and learning results. For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance…

Adaptive monitoring design for ecosystem management

Treesearch

Paul L. Ringold; Jim Alegria; Raymond L. Czaplewski; Barry S. Mulder; Tim Tolle; Kelly Burnett

1996-01-01

Adaptive management of ecosystems (e.g., Holling 1978, Walters 1986, Everett et al. 1994, Grumbine 1994, Yaffee 1994, Gunderson et al. 1995, Frentz et al. 1995, Montgomery et al. 1995) structures a system in which monitoring iteratively improves the knowledge base and helps refine management plans. This adaptive approach acknowledges that action is necessary or...

Flight Test Comparison of Different Adaptive Augmentations for Fault Tolerant Control Laws for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Hanson, Curtis E.; Lee, James A.; Kaneshige, John T.

2009-01-01

This report describes the improvements and enhancements to a neural network based approach for directly adapting to aerodynamic changes resulting from damage or failures. This research is a follow-on effort to flight tests performed on the NASA F-15 aircraft as part of the Intelligent Flight Control System research effort. Previous flight test results demonstrated the potential for performance improvement under destabilizing damage conditions. Little or no improvement was provided under simulated control surface failures, however, and the adaptive system was prone to pilot-induced oscillations. An improved controller was designed to reduce the occurrence of pilot-induced oscillations and increase robustness to failures in general. This report presents an analysis of the neural networks used in the previous flight test, the improved adaptive controller, and the baseline case with no adaptation. Flight test results demonstrate significant improvement in performance by using the new adaptive controller compared with the previous adaptive system and the baseline system for control surface failures.

Self-Adaptive System based on Field Programmable Gate Array for Extreme Temperature Electronics

NASA Technical Reports Server (NTRS)

Keymeulen, Didier; Zebulum, Ricardo; Rajeshuni, Ramesham; Stoica, Adrian; Katkoori, Srinivas; Graves, Sharon; Novak, Frank; Antill, Charles

2006-01-01

In this work, we report the implementation of a self-adaptive system using a field programmable gate array (FPGA) and data converters. The self-adaptive system can autonomously recover the lost functionality of a reconfigurable analog array (RAA) integrated circuit (IC) [3]. Both the RAA IC and the self-adaptive system are operating in extreme temperatures (from 120 C down to -180 C). The RAA IC consists of reconfigurable analog blocks interconnected by several switches and programmable by bias voltages. It implements filters/amplifiers with bandwidth up to 20 MHz. The self-adaptive system controls the RAA IC and is realized on Commercial-Off-The-Shelf (COTS) parts. It implements a basic compensation algorithm that corrects a RAA IC in less than a few milliseconds. Experimental results for the cold temperature environment (down to -180 C) demonstrate the feasibility of this approach.

Toward Hamiltonian Adaptive QM/MM: Accurate Solvent Structures Using Many-Body Potentials.

PubMed

Boereboom, Jelle M; Potestio, Raffaello; Donadio, Davide; Bulo, Rosa E

2016-08-09

Adaptive quantum mechanical (QM)/molecular mechanical (MM) methods enable efficient molecular simulations of chemistry in solution. Reactive subregions are modeled with an accurate QM potential energy expression while the rest of the system is described in a more approximate manner (MM). As solvent molecules diffuse in and out of the reactive region, they are gradually included into (and excluded from) the QM expression. It would be desirable to model such a system with a single adaptive Hamiltonian, but thus far this has resulted in distorted structures at the boundary between the two regions. Solving this long outstanding problem will allow microcanonical adaptive QM/MM simulations that can be used to obtain vibrational spectra and dynamical properties. The difficulty lies in the complex QM potential energy expression, with a many-body expansion that contains higher order terms. Here, we outline a Hamiltonian adaptive multiscale scheme within the framework of many-body potentials. The adaptive expressions are entirely general, and complementary to all standard (nonadaptive) QM/MM embedding schemes available. We demonstrate the merit of our approach on a molecular system defined by two different MM potentials (MM/MM'). For the long-range interactions a numerical scheme is used (particle mesh Ewald), which yields energy expressions that are many-body in nature. Our Hamiltonian approach is the first to provide both energy conservation and the correct solvent structure everywhere in this system.

Quantifying the Adaptive Cycle | Science Inventory | US EPA

EPA Pesticide Factsheets

The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation) in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurring spring and summer blooms, a well-established paradigm in planktology and succession theory, with characteristic temporal trajectories during blooms that may be consistent with adaptive cycle phases. We used long-term (1994–2011) data and multivariate analysis of community structure to assess key components of the adaptive cycle. Specifically, we tested predictions about: reorganisation: spring and summer blooms comprise distinct community states; conservatism: community trajectories during individual adaptive cycles are conservative; and adaptation: phytoplankton species during blooms change in the long term. All predictions were supported by our analyses. Results suggest that traditional ecological paradigms such as phytoplankton successional models have potential for moving the adaptive cycle from a metaphor to a framework that can improve our understanding how complex systems organize and reorganize following collapse. Quantifying reorganization, conservatism and

Adaptive optics for the ESO-VLT

NASA Astrophysics Data System (ADS)

Merkle, Fritz

1989-04-01

This paper discusses adaptive optics, its performance, and its requirements for applications in astronomy to overcome limitations due to atmospheric turbulence. Guidelines for the implementation of these devices in telescopes are given, in particular for the Very Large Telescope (VLT) at ESO. It is intended to equip each one of the four 8-m telescopes of the VLT, which are arranged in a linear array with an independent adaptive optical system. These systems will serve the individual and the combined coude foci. A small-scale prototype adaptive system is under development. It is equipped with a 19-piezoelectric-actuator deformable mirror, a Shack-Hartmann-type wavefront sensor, and a dedicated wavefront computer for closing the feedback loop. This system is based on a polychromatic approach; i.e., it senses the wavefront in the visible, but the adaptive correction loop works at 3-5 microns.

Observer-Based Adaptive NN Control for a Class of Uncertain Nonlinear Systems With Nonsymmetric Input Saturation.

PubMed

Yong-Feng Gao; Xi-Ming Sun; Changyun Wen; Wei Wang

2017-07-01

This paper is concerned with the problem of adaptive tracking control for a class of uncertain nonlinear systems with nonsymmetric input saturation and immeasurable states. The radial basis function of neural network (NN) is employed to approximate unknown functions, and an NN state observer is designed to estimate the immeasurable states. To analyze the effect of input saturation, an auxiliary system is employed. By the aid of adaptive backstepping technique, an adaptive tracking control approach is developed. Under the proposed adaptive tracking controller, the boundedness of all the signals in the closed-loop system is achieved. Moreover, distinct from most of the existing references, the tracking error can be bounded by an explicit function of design parameters and saturation input error. Finally, an example is given to show the effectiveness of the proposed method.

Adaptive fuzzy sliding control of single-phase PV grid-connected inverter.

PubMed

Fei, Juntao; Zhu, Yunkai

2017-01-01

In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance.

An information theoretic approach of designing sparse kernel adaptive filters.

PubMed

Liu, Weifeng; Park, Il; Principe, José C

2009-12-01

This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented.

A New Multi-Agent Approach to Adaptive E-Education

NASA Astrophysics Data System (ADS)

Chen, Jing; Cheng, Peng

Improving customer satisfaction degree is important in e-Education. This paper describes a new approach to adaptive e-Education taking into account the full spectrum of Web service techniques and activities. It presents a multi-agents architecture based on artificial psychology techniques, which makes the e-Education process both adaptable and dynamic, and hence up-to-date. Knowledge base techniques are used to support the e-Education process, and artificial psychology techniques to deal with user psychology, which makes the e-Education system more effective and satisfying.

Pricing and reimbursement experiences and insights in the European Union and the United States: Lessons learned to approach adaptive payer pathways.

PubMed

Faulkner, S D; Lee, M; Qin, D; Morrell, L; Xoxi, E; Sammarco, A; Cammarata, S; Russo, P; Pani, L; Barker, R

2016-12-01

Earlier patient access to beneficial therapeutics that addresses unmet need is one of the main requirements of innovation in global healthcare systems already burdened by unsustainable budgets. "Adaptive pathways" encompass earlier cross-stakeholder engagement, regulatory tools, and iterative evidence generation through the life cycle of the medicinal product. A key enabler of earlier patient access is through more flexible and adaptive payer approaches to pricing and reimbursement that reflect the emerging evidence generated. © 2016 American Society for Clinical Pharmacology and Therapeutics.

Non-Kolmogorovian Approach to the Context-Dependent Systems Breaking the Classical Probability Law

NASA Astrophysics Data System (ADS)

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Yamato, Ichiro

2013-07-01

There exist several phenomena breaking the classical probability laws. The systems related to such phenomena are context-dependent, so that they are adaptive to other systems. In this paper, we present a new mathematical formalism to compute the joint probability distribution for two event-systems by using concepts of the adaptive dynamics and quantum information theory, e.g., quantum channels and liftings. In physics the basic example of the context-dependent phenomena is the famous double-slit experiment. Recently similar examples have been found in biological and psychological sciences. Our approach is an extension of traditional quantum probability theory, and it is general enough to describe aforementioned contextual phenomena outside of quantum physics.

Control allocation-based adaptive control for greenhouse climate

NASA Astrophysics Data System (ADS)

Su, Yuanping; Xu, Lihong; Goodman, Erik D.

2018-04-01

This paper presents an adaptive approach to greenhouse climate control, as part of an integrated control and management system for greenhouse production. In this approach, an adaptive control algorithm is first derived to guarantee the asymptotic convergence of the closed system with uncertainty, then using that control algorithm, a controller is designed to satisfy the demands for heat and mass fluxes to maintain inside temperature, humidity and CO2 concentration at their desired values. Instead of applying the original adaptive control inputs directly, second, a control allocation technique is applied to distribute the demands of the heat and mass fluxes to the actuators by minimising tracking errors and energy consumption. To find an energy-saving solution, both single-objective optimisation (SOO) and multiobjective optimisation (MOO) in the control allocation structure are considered. The advantage of the proposed approach is that it does not require any a priori knowledge of the uncertainty bounds, and the simulation results illustrate the effectiveness of the proposed control scheme. It also indicates that MOO saves more energy in the control process.

An adaptive learning control system for large flexible structures

NASA Technical Reports Server (NTRS)

Thau, F. E.

1985-01-01

The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

Adaptive management of rangeland systems

USGS Publications Warehouse

Allen, Craig R.; Angeler, David G.; Fontaine, Joseph J.; Garmestani, Ahjond S.; Hart, Noelle M.; Pope, Kevin L.; Twidwell, Dirac

2017-01-01

Adaptive management is an approach to natural resource management that uses structured learning to reduce uncertainties for the improvement of management over time. The origins of adaptive management are linked to ideas of resilience theory and complex systems. Rangeland management is particularly well suited for the application of adaptive management, having sufficient controllability and reducible uncertainties. Adaptive management applies the tools of structured decision making and requires monitoring, evaluation, and adjustment of management. Adaptive governance, involving sharing of power and knowledge among relevant stakeholders, is often required to address conflict situations. Natural resource laws and regulations can present a barrier to adaptive management when requirements for legal certainty are met with environmental uncertainty. However, adaptive management is possible, as illustrated by two cases presented in this chapter. Despite challenges and limitations, when applied appropriately adaptive management leads to improved management through structured learning, and rangeland management is an area in which adaptive management shows promise and should be further explored.

Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach

Treesearch

Michael S. Balshi; A. David McGuire; Paul Duffy; Mike Flannigan; John Walsh; Jerry Melillo

2009-01-01

We developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5o (latitude x longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was...

Intelligent Instructional Systems in Military Training.

ERIC Educational Resources Information Center

Fletcher, J.D.; Zdybel, Frank

Intelligent instructional systems can be distinguished from more conventional approaches by the automation of instructional interaction and choice of strategy. This approach promises to reduce the costs of instructional materials preparation and to increase the adaptability and individualization of the instruction delivered. Tutorial simulation…

Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems.

PubMed

Zhao, Xudong; Wang, Xinyong; Zong, Guangdeng; Zheng, Xiaolong

2017-10-01

This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practical systems in the actual engineering. By adopting the approximation ability of neural networks, common stochastic Lyapunov function method together with adding an improved power integrator technique, an adaptive state feedback controller with multiple adaptive laws is systematically designed for the systems. Subsequently, a controller with only two adaptive laws is proposed to solve the problem of over parameterization. Under the designed controllers, all the signals in the closed-loop system are bounded-input bounded-output stable in probability, and the system output can almost surely track the target trajectory within a specified bounded error. Finally, simulation results are presented to show the effectiveness of the proposed approaches.

Neural network based adaptive output feedback control: Applications and improvements

NASA Astrophysics Data System (ADS)

Kutay, Ali Turker

Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in simulation, a fictitious actuator model is developed that fits experimentally observed characteristics of flow control actuators in static flight conditions as well as possible coupling effects between actuation, the dynamics of flow field, and the rigid body dynamics of the vehicle.

Visually guided gait modifications for stepping over an obstacle: a bio-inspired approach.

PubMed

Silva, Pedro; Matos, Vitor; Santos, Cristina P

2014-02-01

There is an increasing interest in conceiving robotic systems that are able to move and act in an unstructured and not predefined environment, for which autonomy and adaptability are crucial features. In nature, animals are autonomous biological systems, which often serve as bio-inspiration models, not only for their physical and mechanical properties, but also their control structures that enable adaptability and autonomy-for which learning is (at least) partially responsible. This work proposes a system which seeks to enable a quadruped robot to online learn to detect and to avoid stumbling on an obstacle in its path. The detection relies in a forward internal model that estimates the robot's perceptive information by exploring the locomotion repetitive nature. The system adapts the locomotion in order to place the robot optimally before attempting to step over the obstacle, avoiding any stumbling. Locomotion adaptation is achieved by changing control parameters of a central pattern generator (CPG)-based locomotion controller. The mechanism learns the necessary alterations to the stride length in order to adapt the locomotion by changing the required CPG parameter. Both learning tasks occur online and together define a sensorimotor map, which enables the robot to learn to step over the obstacle in its path. Simulation results show the feasibility of the proposed approach.

Do You Think You Can? The Influence of Student Self-Efficacy on the Effectiveness of Tutorial Dialogue for Computer Science

ERIC Educational Resources Information Center

Wiggins, Joseph B.; Grafsgaard, Joseph F.; Boyer, Kristy Elizabeth; Wiebe, Eric N.; Lester, James C.

2017-01-01

In recent years, significant advances have been made in intelligent tutoring systems, and these advances hold great promise for adaptively supporting computer science (CS) learning. In particular, tutorial dialogue systems that engage students in natural language dialogue can create rich, adaptive interactions. A promising approach to increasing…

Application of the global Land-Potential Knowledge System (LandPKS) mobile apps to land degradation, restoration and climate change adaptation

USDA-ARS?s Scientific Manuscript database

Combatting land degradation, promoting restoration and adapting to climate change all require an understanding of land potential. A global Land-Potential Knowledge System (LandPKS) is being developed that will address many of these limitations using an open source approach designed to allow anyone w...

Adaptive sampling strategies with high-throughput molecular dynamics

NASA Astrophysics Data System (ADS)

Clementi, Cecilia

Despite recent significant hardware and software developments, the complete thermodynamic and kinetic characterization of large macromolecular complexes by molecular simulations still presents significant challenges. The high dimensionality of these systems and the complexity of the associated potential energy surfaces (creating multiple metastable regions connected by high free energy barriers) does not usually allow to adequately sample the relevant regions of their configurational space by means of a single, long Molecular Dynamics (MD) trajectory. Several different approaches have been proposed to tackle this sampling problem. We focus on the development of ensemble simulation strategies, where data from a large number of weakly coupled simulations are integrated to explore the configurational landscape of a complex system more efficiently. Ensemble methods are of increasing interest as the hardware roadmap is now mostly based on increasing core counts, rather than clock speeds. The main challenge in the development of an ensemble approach for efficient sampling is in the design of strategies to adaptively distribute the trajectories over the relevant regions of the systems' configurational space, without using any a priori information on the system global properties. We will discuss the definition of smart adaptive sampling approaches that can redirect computational resources towards unexplored yet relevant regions. Our approaches are based on new developments in dimensionality reduction for high dimensional dynamical systems, and optimal redistribution of resources. NSF CHE-1152344, NSF CHE-1265929, Welch Foundation C-1570.

Adaptive Fuzzy Control for Nonstrict Feedback Systems With Unmodeled Dynamics and Fuzzy Dead Zone via Output Feedback.

PubMed

Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan

2017-09-01

This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.

Performance Optimizing Multi-Objective Adaptive Control with Time-Varying Model Reference Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

2017-01-01

This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.

Adaptive Leadership Framework for Chronic Illness

PubMed Central

Anderson, Ruth A.; Bailey, Donald E.; Wu, Bei; Corazzini, Kirsten; McConnell, Eleanor S.; Thygeson, N. Marcus; Docherty, Sharron L.

2015-01-01

We propose the Adaptive Leadership Framework for Chronic Illness as a novel framework for conceptualizing, studying, and providing care. This framework is an application of the Adaptive Leadership Framework developed by Heifetz and colleagues for business. Our framework views health care as a complex adaptive system and addresses the intersection at which people with chronic illness interface with the care system. We shift focus from symptoms to symptoms and the challenges they pose for patients/families. We describe how providers and patients/families might collaborate to create shared meaning of symptoms and challenges to coproduce appropriate approaches to care. PMID:25647829

On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach.

PubMed

Baigzadehnoe, Barmak; Rahmani, Zahra; Khosravi, Alireza; Rezaie, Behrooz

2017-09-01

In this paper, the position and force tracking control problem of cooperative robot manipulator system handling a common rigid object with unknown dynamical models and unknown external disturbances is investigated. The universal approximation properties of fuzzy logic systems are employed to estimate the unknown system dynamics. On the other hand, by defining new state variables based on the integral and differential of position and orientation errors of the grasped object, the error system of coordinated robot manipulators is constructed. Subsequently by defining the appropriate change of coordinates and using the backstepping design strategy, an adaptive fuzzy backstepping position tracking control scheme is proposed for multi-robot manipulator systems. By utilizing the properties of internal forces, extra terms are also added to the control signals to consider the force tracking problem. Moreover, it is shown that the proposed adaptive fuzzy backstepping position/force control approach ensures all the signals of the closed loop system uniformly ultimately bounded and tracking errors of both positions and forces can converge to small desired values by proper selection of the design parameters. Finally, the theoretic achievements are tested on the two three-link planar robot manipulators cooperatively handling a common object to illustrate the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Subframe Partitioning and Efficient Packet Scheduling in OFDMA Cellular System with Fixed Decode-and-Forward Relays

NASA Astrophysics Data System (ADS)

Wang, Liping; Ji, Yusheng; Liu, Fuqiang

The integration of multihop relays with orthogonal frequency-division multiple access (OFDMA) cellular infrastructures can meet the growing demands for better coverage and higher throughput. Resource allocation in the OFDMA two-hop relay system is more complex than that in the conventional single-hop OFDMA system. With time division between transmissions from the base station (BS) and those from relay stations (RSs), fixed partitioning of the BS subframe and RS subframes can not adapt to various traffic demands. Moreover, single-hop scheduling algorithms can not be used directly in the two-hop system. Therefore, we propose a semi-distributed algorithm called ASP to adjust the length of every subframe adaptively, and suggest two ways to extend single-hop scheduling algorithms into multihop scenarios: link-based and end-to-end approaches. Simulation results indicate that the ASP algorithm increases system utilization and fairness. The max carrier-to-interference ratio (Max C/I) and proportional fairness (PF) scheduling algorithms extended using the end-to-end approach obtain higher throughput than those using the link-based approach, but at the expense of more overhead for information exchange between the BS and RSs. The resource allocation scheme using ASP and end-to-end PF scheduling achieves a tradeoff between system throughput maximization and fairness.

Evaluating Adaptive Governance Approaches to Sustainable Water Management in North-West Thailand

NASA Astrophysics Data System (ADS)

Clark, Julian R. A.; Semmahasak, Chutiwalanch

2013-04-01

Adaptive governance is advanced as a potent means of addressing institutional fit of natural resource systems with prevailing modes of political-administrative management. Its advocates also argue that it enhances participatory and learning opportunities for stakeholders over time. Yet an increasing number of studies demonstrate real difficulties in implementing adaptive governance `solutions'. This paper builds on these debates by examining the introduction of adaptive governance to water management in Chiang Mai province, north-west Thailand. The paper considers, first, the limitations of current water governance modes at the provincial scale, and the rationale for implementation of an adaptive approach. The new approach is then critically examined, with its initial performance and likely future success evaluated by (i) analysis of water stakeholders' opinions of its first year of operation; and (ii) comparison of its governance attributes against recent empirical accounts of implementation difficulty and failure of adaptive governance of natural resource management more generally. The analysis confirms the potentially significant role that the new approach can play in brokering and resolving the underlying differences in stakeholder representation and knowledge construction at the heart of the prevailing water governance modes in north-west Thailand.

Modelling the dynamics of traits involved in fighting-predators-prey system.

PubMed

Kooi, B W

2015-12-01

We study the dynamics of a predator-prey system where predators fight for captured prey besides searching for and handling (and digestion) of the prey. Fighting for prey is modelled by a continuous time hawk-dove game dynamics where the gain depends on the amount of disputed prey while the costs for fighting is constant per fighting event. The strategy of the predator-population is quantified by a trait being the proportion of the number of predator-individuals playing hawk tactics. The dynamics of the trait is described by two models of adaptation: the replicator dynamics (RD) and the adaptive dynamics (AD). In the RD-approach a variant individual with an adapted trait value changes the population's strategy, and consequently its trait value, only when its payoff is larger than the population average. In the AD-approach successful replacement of the resident population after invasion of a rare variant population with an adapted trait value is a step in a sequence changing the population's strategy, and hence its trait value. The main aim is to compare the consequences of the two adaptation models. In an equilibrium predator-prey system this will lead to convergence to a neutral singular strategy, while in the oscillatory system to a continuous singular strategy where in this endpoint the resident population is not invasible by any variant population. In equilibrium (low prey carrying capacity) RD and AD-approach give the same results, however not always in a periodically oscillating system (high prey carrying-capacity) where the trait is density-dependent. For low costs the predator population is monomorphic (only hawks) while for high costs dimorphic (hawks and doves). These results illustrate that intra-specific trait dynamics matters in predator-prey dynamics.

RISKS, OPPORTUNITIES, AND ADAPTATION TO CLIMATE CHANGE

EPA Science Inventory

Adaptation is an important approach for protecting human health, ecosystems, and economic systems from the risks posed by climate variability and change, and to exploit beneficial opportunities provided by a changing climate. This paper presents nine fundamental principles that ...

An adaptive semantic based mediation system for data interoperability among Health Information Systems.

PubMed

Khan, Wajahat Ali; Khattak, Asad Masood; Hussain, Maqbool; Amin, Muhammad Bilal; Afzal, Muhammad; Nugent, Christopher; Lee, Sungyoung

2014-08-01

Heterogeneity in the management of the complex medical data, obstructs the attainment of data level interoperability among Health Information Systems (HIS). This diversity is dependent on the compliance of HISs with different healthcare standards. Its solution demands a mediation system for the accurate interpretation of data in different heterogeneous formats for achieving data interoperability. We propose an adaptive AdapteR Interoperability ENgine mediation system called ARIEN, that arbitrates between HISs compliant to different healthcare standards for accurate and seamless information exchange to achieve data interoperability. ARIEN stores the semantic mapping information between different standards in the Mediation Bridge Ontology (MBO) using ontology matching techniques. These mappings are provided by our System for Parallel Heterogeneity (SPHeRe) matching system and Personalized-Detailed Clinical Model (P-DCM) approach to guarantee accuracy of mappings. The realization of the effectiveness of the mappings stored in the MBO is evaluation of the accuracy in transformation process among different standard formats. We evaluated our proposed system with the transformation process of medical records between Clinical Document Architecture (CDA) and Virtual Medical Record (vMR) standards. The transformation process achieved over 90 % of accuracy level in conversion process between CDA and vMR standards using pattern oriented approach from the MBO. The proposed mediation system improves the overall communication process between HISs. It provides an accurate and seamless medical information exchange to ensure data interoperability and timely healthcare services to patients.

A qualitative study of implementation and adaptations to Progressive Tinnitus Management (PTM) delivery.

PubMed

Tuepker, Anaïs; Elnitsky, Christine; Newell, Summer; Zaugg, Tara; Henry, James A

2018-01-01

Tinnitus is a common condition, especially prevalent among military Veterans. Progressive Tinnitus Management (PTM) is an interdisciplinary, structured, stepped-care approach to providing clinical services, including teaching coping skills, to people bothered by tinnitus. PTM has been shown to be effective at reducing functional distress, but implementation of the intervention outside of a research setting has not been studied, even though dissemination is underway within the Veterans Health Administration (VHA) system in the United States. This study was designed to address a gap in knowledge of PTM clinical implementation to date, with a focus on factors facilitating or hindering implementation in VHA audiology and mental health clinic contexts, and whether implementing sites had developed intervention adaptations. Qualitative interviews were conducted with 21 audiology and mental health clinicians and service chiefs across a regional service network. Interviews were transcribed and coded using a hybrid inductive-deductive analytic approach guided by existing implementation research frameworks and then iteratively developed for emergent themes. PTM prioritization was rare overall, with providers across disciplines challenged by lack of capacity for implementation, but with differences by discipline in challenges to prioritization. Where PTM was prioritized and delivered, this was facilitated by perception of unique value, provider's own experience of tinnitus, observation/experience with PTM delivery, intervention fit with provider's skills, and an environment with supportive leadership and adaptive reserve. PTM was frequently adapted to local contexts to address delivery challenges and diversify patient options. Adaptations included shifting from group to individual formats, reducing or combining sessions, and employing novel therapeutic approaches. Existing adaptations highlight the need to better understand mechanisms underlying PTM's effectiveness, and research on the impact of adaptations on patient outcomes is an important next step. Prioritization of PTM is a key barrier to the scale up and spread of this evidence-based intervention. Developing clinician champions may facilitate dissemination, especially if accompanied by signals of systemic prioritization. Novel approaches exposing clinicians and administrators to PTM may identify and develop clinical champions. Acknowledging the potential for PTM adaptations may make delivery more feasible in the context of existing system constraints and priorities.

Dynamic adaptive learning for decision-making supporting systems

NASA Astrophysics Data System (ADS)

He, Haibo; Cao, Yuan; Chen, Sheng; Desai, Sachi; Hohil, Myron E.

2008-03-01

This paper proposes a novel adaptive learning method for data mining in support of decision-making systems. Due to the inherent characteristics of information ambiguity/uncertainty, high dimensionality and noisy in many homeland security and defense applications, such as surveillances, monitoring, net-centric battlefield, and others, it is critical to develop autonomous learning methods to efficiently learn useful information from raw data to help the decision making process. The proposed method is based on a dynamic learning principle in the feature spaces. Generally speaking, conventional approaches of learning from high dimensional data sets include various feature extraction (principal component analysis, wavelet transform, and others) and feature selection (embedded approach, wrapper approach, filter approach, and others) methods. However, very limited understandings of adaptive learning from different feature spaces have been achieved. We propose an integrative approach that takes advantages of feature selection and hypothesis ensemble techniques to achieve our goal. Based on the training data distributions, a feature score function is used to provide a measurement of the importance of different features for learning purpose. Then multiple hypotheses are iteratively developed in different feature spaces according to their learning capabilities. Unlike the pre-set iteration steps in many of the existing ensemble learning approaches, such as adaptive boosting (AdaBoost) method, the iterative learning process will automatically stop when the intelligent system can not provide a better understanding than a random guess in that particular subset of feature spaces. Finally, a voting algorithm is used to combine all the decisions from different hypotheses to provide the final prediction results. Simulation analyses of the proposed method on classification of different US military aircraft databases show the effectiveness of this method.

Adaptive Control Using Residual Mode Filters Applied to Wind Turbines

NASA Technical Reports Server (NTRS)

Frost, Susan A.; Balas, Mark J.

2011-01-01

Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros.

Automated adaptive inference of phenomenological dynamical models.

PubMed

Daniels, Bryan C; Nemenman, Ilya

2015-08-21

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved.

Automated adaptive inference of phenomenological dynamical models

PubMed Central

Daniels, Bryan C.; Nemenman, Ilya

2015-01-01

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved. PMID:26293508

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention.

PubMed

Deshpande, Sunil; Rivera, Daniel E; Younger, Jarred W; Nandola, Naresh N

2014-09-01

The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in particular) are demonstrated using informative simulations.

Fuzzy Adaptive Output Feedback Control of Uncertain Nonlinear Systems With Prescribed Performance.

PubMed

Zhang, Jin-Xi; Yang, Guang-Hong

2018-05-01

This paper investigates the tracking control problem for a family of strict-feedback systems in the presence of unknown nonlinearities and immeasurable system states. A low-complexity adaptive fuzzy output feedback control scheme is proposed, based on a backstepping method. In the control design, a fuzzy adaptive state observer is first employed to estimate the unmeasured states. Then, a novel error transformation approach together with a new modification mechanism is introduced to guarantee the finite-time convergence of the output error to a predefined region and ensure the closed-loop stability. Compared with the existing methods, the main advantages of our approach are that: 1) without using extra command filters or auxiliary dynamic surface control techniques, the problem of explosion of complexity can still be addressed and 2) the design procedures are independent of the initial conditions. Finally, two practical examples are performed to further illustrate the above theoretic findings.

Optimal spectral tracking--adapting to dynamic regime change.

PubMed

Brittain, John-Stuart; Halliday, David M

2011-01-30

Real world data do not always obey the statistical restraints imposed upon them by sophisticated analysis techniques. In spectral analysis for instance, an ergodic process--the interchangeability of temporal for spatial averaging--is assumed for a repeat-trial design. Many evolutionary scenarios, such as learning and motor consolidation, do not conform to such linear behaviour and should be approached from a more flexible perspective. To this end we previously introduced the method of optimal spectral tracking (OST) in the study of trial-varying parameters. In this extension to our work we modify the OST routines to provide an adaptive implementation capable of reacting to dynamic transitions in the underlying system state. In so doing, we generalise our approach to characterise both slow-varying and rapid fluctuations in time-series, simultaneously providing a metric of system stability. The approach is first applied to a surrogate dataset and compared to both our original non-adaptive solution and spectrogram approaches. The adaptive OST is seen to display fast convergence and desirable statistical properties. All three approaches are then applied to a neurophysiological recording obtained during a study on anaesthetic monitoring. Local field potentials acquired from the posterior hypothalamic region of a deep brain stimulation patient undergoing anaesthesia were analysed. The characterisation of features such as response delay, time-to-peak and modulation brevity are considered. Copyright © 2010 Elsevier B.V. All rights reserved.

Planning for climate change: the need for mechanistic systems-based approaches to study climate change impacts on diarrheal diseases

PubMed Central

Levy, Karen; Zimmerman, Julie; Elliott, Mark; Bartram, Jamie; Carlton, Elizabeth; Clasen, Thomas; Dillingham, Rebecca; Eisenberg, Joseph; Guerrant, Richard; Lantagne, Daniele; Mihelcic, James; Nelson, Kara

2016-01-01

Increased precipitation and temperature variability as well as extreme events related to climate change are predicted to affect the availability and quality of water globally. Already heavily burdened with diarrheal diseases due to poor access to water, sanitation and hygiene facilities, communities throughout the developing world lack the adaptive capacity to sufficiently respond to the additional adversity caused by climate change. Studies suggest that diarrhea rates are positively correlated with increased temperature, and show a complex relationship with precipitation. Although climate change will likely increase rates of diarrheal diseases on average, there is a poor mechanistic understanding of the underlying disease transmission processes and substantial uncertainty surrounding current estimates. This makes it difficult to recommend appropriate adaptation strategies. We review the relevant climate-related mechanisms behind transmission of diarrheal disease pathogens and argue that systems-based mechanistic approaches incorporating human, engineered and environmental components are urgently needed. We then review successful systems-based approaches used in other environmental health fields and detail one modeling framework to predict climate change impacts on diarrheal diseases and design adaptation strategies. PMID:26799810

Decentralized Adaptive Neural Output-Feedback DSC for Switched Large-Scale Nonlinear Systems.

PubMed

Lijun Long; Jun Zhao

2017-04-01

In this paper, for a class of switched large-scale uncertain nonlinear systems with unknown control coefficients and unmeasurable states, a switched-dynamic-surface-based decentralized adaptive neural output-feedback control approach is developed. The approach proposed extends the classical dynamic surface control (DSC) technique for nonswitched version to switched version by designing switched first-order filters, which overcomes the problem of multiple "explosion of complexity." Also, a dual common coordinates transformation of all subsystems is exploited to avoid individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Nussbaum-type functions are utilized to handle the unknown control coefficients, and a switched neural network observer is constructed to estimate the unmeasurable states. Combining with the average dwell time method and backstepping and the DSC technique, decentralized adaptive neural controllers of subsystems are explicitly designed. It is proved that the approach provided can guarantee the semiglobal uniformly ultimately boundedness for all the signals in the closed-loop system under a class of switching signals with average dwell time, and the tracking errors to a small neighborhood of the origin. A two inverted pendulums system is provided to demonstrate the effectiveness of the method proposed.

Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Ishihara, Abraham K.; Krishnakumar, Kalmanje Srinlvas; Bakhtiari-Nejad, Maryam

2009-01-01

This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation.

Adaptive fuzzy sliding control of single-phase PV grid-connected inverter

PubMed Central

Zhu, Yunkai

2017-01-01

In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance. PMID:28797060

Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

PubMed

Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

2018-06-01

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

An Approach to Automated Fusion System Design and Adaptation

PubMed Central

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-01-01

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum. PMID:28300762

An Approach to Automated Fusion System Design and Adaptation.

PubMed

Fritze, Alexander; Mönks, Uwe; Holst, Christoph-Alexander; Lohweg, Volker

2017-03-16

Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum.

Operations management approach to hospitals.

PubMed

Harvey, J; Duguay, C R

1988-06-01

An operations management systems approach can be a useful tool for coordinating and planning in a complex organization. The authors argue for adapting such an approach to health care from the manufacturing industries in order to facilitate strategy formulation, communication and implementation.

Opportunities to utilize traditional phenological knowledge to support adaptive management of social-ecological systems vulnerable to changes in climate and fire regimes

Treesearch

Christopher A. Armatas; Tyron J. Venn; Brooke B. McBride; Alan E. Watson; Steve J. Carver

2016-01-01

The field of adaptive management has been embraced by researchers and managers in the United States as an approach to improve natural resource stewardship in the face of uncertainty and complex environmental problems. Integrating multiple knowledge sources and feedback mechanisms is an important step in this approach. Our objective is to contribute to the...

Resilience through adaptation

PubMed Central

van Voorn, George A. K.; Ligtenberg, Arend; Molenaar, Jaap

2017-01-01

Adaptation of agents through learning or evolution is an important component of the resilience of Complex Adaptive Systems (CAS). Without adaptation, the flexibility of such systems to cope with outside pressures would be much lower. To study the capabilities of CAS to adapt, social simulations with agent-based models (ABMs) provide a helpful tool. However, the value of ABMs for studying adaptation depends on the availability of methodologies for sensitivity analysis that can quantify resilience and adaptation in ABMs. In this paper we propose a sensitivity analysis methodology that is based on comparing time-dependent probability density functions of output of ABMs with and without agent adaptation. The differences between the probability density functions are quantified by the so-called earth-mover’s distance. We use this sensitivity analysis methodology to quantify the probability of occurrence of critical transitions and other long-term effects of agent adaptation. To test the potential of this new approach, it is used to analyse the resilience of an ABM of adaptive agents competing for a common-pool resource. Adaptation is shown to contribute positively to the resilience of this ABM. If adaptation proceeds sufficiently fast, it may delay or avert the collapse of this system. PMID:28196372

Resilience through adaptation.

PubMed

Ten Broeke, Guus A; van Voorn, George A K; Ligtenberg, Arend; Molenaar, Jaap

2017-01-01

Adaptation of agents through learning or evolution is an important component of the resilience of Complex Adaptive Systems (CAS). Without adaptation, the flexibility of such systems to cope with outside pressures would be much lower. To study the capabilities of CAS to adapt, social simulations with agent-based models (ABMs) provide a helpful tool. However, the value of ABMs for studying adaptation depends on the availability of methodologies for sensitivity analysis that can quantify resilience and adaptation in ABMs. In this paper we propose a sensitivity analysis methodology that is based on comparing time-dependent probability density functions of output of ABMs with and without agent adaptation. The differences between the probability density functions are quantified by the so-called earth-mover's distance. We use this sensitivity analysis methodology to quantify the probability of occurrence of critical transitions and other long-term effects of agent adaptation. To test the potential of this new approach, it is used to analyse the resilience of an ABM of adaptive agents competing for a common-pool resource. Adaptation is shown to contribute positively to the resilience of this ABM. If adaptation proceeds sufficiently fast, it may delay or avert the collapse of this system.

Behavioral personal digital assistants: The seventh generation of computing

PubMed Central

Stephens, Kenneth R.; Hutchison, William R.

1992-01-01

Skinner (1985) described two divergent approaches to developing computer systems that would behave with some approximation to intelligence. The first approach, which corresponds to the mainstream of artificial intelligence and expert systems, models intelligence as a set of production rules that incorporate knowledge and a set of heuristics for inference and symbol manipulation. The alternative is a system that models the behavioral repertoire as a network of associations between antecedent stimuli and operants, and adapts when supplied with reinforcement. The latter approach is consistent with developments in the field of “neural networks.” The authors describe how an existing adaptive network software system, based on behavior analysis and developed since 1983, can be extended to provide a new generation of software systems capable of acquiring verbal behavior. This effort will require the collaboration of the academic and commercial sectors of the behavioral community, but the end result will enable a generational change in computer systems and support for behavior analytic concepts. PMID:22477053

Certification Considerations for Adaptive Systems

NASA Technical Reports Server (NTRS)

Bhattacharyya, Siddhartha; Cofer, Darren; Musliner, David J.; Mueller, Joseph; Engstrom, Eric

2015-01-01

Advanced capabilities planned for the next generation of aircraft, including those that will operate within the Next Generation Air Transportation System (NextGen), will necessarily include complex new algorithms and non-traditional software elements. These aircraft will likely incorporate adaptive control algorithms that will provide enhanced safety, autonomy, and robustness during adverse conditions. Unmanned aircraft will operate alongside manned aircraft in the National Airspace (NAS), with intelligent software performing the high-level decision-making functions normally performed by human pilots. Even human-piloted aircraft will necessarily include more autonomy. However, there are serious barriers to the deployment of new capabilities, especially for those based upon software including adaptive control (AC) and artificial intelligence (AI) algorithms. Current civil aviation certification processes are based on the idea that the correct behavior of a system must be completely specified and verified prior to operation. This report by Rockwell Collins and SIFT documents our comprehensive study of the state of the art in intelligent and adaptive algorithms for the civil aviation domain, categorizing the approaches used and identifying gaps and challenges associated with certification of each approach.

Doing Away with the "Native Speaker": A Complex Adaptive Systems Approach to L2 Phonological Attainment

ERIC Educational Resources Information Center

Aslan, Erhan

2017-01-01

Employing the complex adaptive systems (CAS) model, the present case study provides a self-report description of the attitudes, perceptions and experiences of an advanced adult L2 English learner with respect to his L2 phonological attainment. CAS is predicated on the notion that an individual's cognitive processes are intricately related to his…

The Declining Suitability of the Formula Approach to Funding Fublic Higher Education: Rationale and Alternatives.

ERIC Educational Resources Information Center

Temple, Charles M.; Riggs, Robert O.

1978-01-01

After discussing and analyzing three available alternative budgeting approaches, including incremental, program budgeting, and zero-based budgeting, this article suggests the criteria necessary for a newly adapted system and contends that a program budgeting system would serve most effectively. (DS)

The Feldenkrais Method: A Dynamic Approach to Changing Motor Behavior.

ERIC Educational Resources Information Center

Buchanan, Patricia A.; Ulrich, Beverly D.

2001-01-01

Describes the Feldenkrais Method of somatic education, noting parallels with a dynamic systems theory (DST) approach to motor behavior. Feldenkrais uses movement and perception to foster individualized improvement in function. DST explains that a human-environment system continually adapts to changing conditions and assembles behaviors…

Adapting environmental management to uncertain but inevitable change.

PubMed

Nicol, Sam; Fuller, Richard A; Iwamura, Takuya; Chadès, Iadine

2015-06-07

Implementation of adaptation actions to protect biodiversity is limited by uncertainty about the future. One reason for this is the fear of making the wrong decisions caused by the myriad future scenarios presented to decision-makers. We propose an adaptive management (AM) method for optimally managing a population under uncertain and changing habitat conditions. Our approach incorporates multiple future scenarios and continually learns the best management strategy from observations, even as conditions change. We demonstrate the performance of our AM approach by applying it to the spatial management of migratory shorebird habitats on the East Asian-Australasian flyway, predicted to be severely impacted by future sea-level rise. By accounting for non-stationary dynamics, our solution protects 25,000 more birds per year than the current best stationary approach. Our approach can be applied to many ecological systems that require efficient adaptation strategies for an uncertain future. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Road map to adaptive optimal control. [jet engine control

NASA Technical Reports Server (NTRS)

Boyer, R.

1980-01-01

A building block control structure leading toward adaptive, optimal control for jet engines is developed. This approach simplifies the addition of new features and allows for easier checkout of the control by providing a baseline system for comparison. Also, it is possible to eliminate certain features that do not have payoff by being selective in the addition of new building blocks to be added to the baseline system. The minimum risk approach specifically addresses the need for active identification of the plant to be controlled in real time and real time optimization of the control for the identified plant.

Stable Direct Adaptive Control of Linear Infinite-dimensional Systems Using a Command Generator Tracker Approach

NASA Technical Reports Server (NTRS)

Balas, M. J.; Kaufman, H.; Wen, J.

1985-01-01

A command generator tracker approach to model following contol of linear distributed parameter systems (DPS) whose dynamics are described on infinite dimensional Hilbert spaces is presented. This method generates finite dimensional controllers capable of exponentially stable tracking of the reference trajectories when certain ideal trajectories are known to exist for the open loop DPS; we present conditions for the existence of these ideal trajectories. An adaptive version of this type of controller is also presented and shown to achieve (in some cases, asymptotically) stable finite dimensional control of the infinite dimensional DPS.

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

USGS Publications Warehouse

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

2000-01-01

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

Robust model reference adaptive output feedback tracking for uncertain linear systems with actuator fault based on reinforced dead-zone modification.

PubMed

Bagherpoor, H M; Salmasi, Farzad R

2015-07-01

In this paper, robust model reference adaptive tracking controllers are considered for Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) linear systems containing modeling uncertainties, unknown additive disturbances and actuator fault. Two new lemmas are proposed for both SISO and MIMO, under which dead-zone modification rule is improved such that the tracking error for any reference signal tends to zero in such systems. In the conventional approach, adaption of the controller parameters is ceased inside the dead-zone region which results tracking error, while preserving the system stability. In the proposed scheme, control signal is reinforced with an additive term based on tracking error inside the dead-zone which results in full reference tracking. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed approach. Closed loop system stability and zero tracking error are proved by considering a suitable Lyapunov functions candidate. It is shown that the proposed control approach can assure that all the signals of the close loop system are bounded in faulty conditions. Finally, validity and performance of the new schemes have been illustrated through numerical simulations of SISO and MIMO systems in the presence of actuator faults, modeling uncertainty and output disturbance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

An Open IMS-Based User Modelling Approach for Developing Adaptive Learning Management Systems

ERIC Educational Resources Information Center

Boticario, Jesus G.; Santos, Olga C.

2007-01-01

Adaptive LMS have not yet reached the eLearning marketplace due to methodological, technological and management open issues. At aDeNu group, we have been working on two key challenges for the last five years in related research projects. Firstly, develop the general framework and a running architecture to support the adaptive life cycle (i.e.,…

Intelligent multi-sensor integrations

NASA Technical Reports Server (NTRS)

Volz, Richard A.; Jain, Ramesh; Weymouth, Terry

1989-01-01

Growth in the intelligence of space systems requires the use and integration of data from multiple sensors. Generic tools are being developed for extracting and integrating information obtained from multiple sources. The full spectrum is addressed for issues ranging from data acquisition, to characterization of sensor data, to adaptive systems for utilizing the data. In particular, there are three major aspects to the project, multisensor processing, an adaptive approach to object recognition, and distributed sensor system integration.

A Distributed Value of Information (VoI)-Based Approach for Mission-Adaptive Context-Aware Information Management and Presentation

DTIC Science & Technology

2016-05-16

metrics involve regulating automation of complex systems , such as aircraft .12 Additionally, adaptive management of content in user interfaces has also...both the user and environmental context would aid in deciding how to present the information to the Warfighter. The prototype system currently...positioning system , and rate sensors can provide user - specific context to disambiguate physiologic data. The consumer “quantified self” market has driven

Pilot Inventory Complex Adaptive System (PICAS): An Artificial Life Approach to Managing Pilot Retention.

DTIC Science & Technology

1999-03-01

mates) and base their behaviors on this interactive information. This alone defines the nature of a complex adaptive system and it is based on this...world policy initiatives. 2.3.4. User Interaction Building the model with extensive user interaction gives the entire system a more appealing feel...complex behavior that hopefully mimics trends observed in reality . User interaction also allows for easier justification of assumptions used within

Course Ontology-Based User's Knowledge Requirement Acquisition from Behaviors within E-Learning Systems

ERIC Educational Resources Information Center

Zeng, Qingtian; Zhao, Zhongying; Liang, Yongquan

2009-01-01

User's knowledge requirement acquisition and analysis are very important for a personalized or user-adaptive learning system. Two approaches to capture user's knowledge requirement about course content within an e-learning system are proposed and implemented in this paper. The first approach is based on the historical data accumulated by an…

Policy tree optimization for adaptive management of water resources systems

NASA Astrophysics Data System (ADS)

Herman, Jonathan; Giuliani, Matteo

2017-04-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points" that suggest the need of updating the policy. However, there remains a need for a general method to optimize the choice of the signposts to be used and their threshold values. This work contributes a general framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. Given a set of feature variables (e.g., reservoir level, inflow observations, inflow forecasts), the resulting policy defines both the optimal reservoir operations and the conditions under which such operations should be triggered. We demonstrate the approach using Folsom Reservoir (California) as a case study, in which operating policies must balance the risk of both floods and droughts. Numerical results show that the tree-based policies outperform the ones designed via Dynamic Programming. In addition, they display good adaptive capacity to the changing climate, successfully adapting the reservoir operations across a large set of uncertain climate scenarios.

Communal Sensor Network for Adaptive Noise Reduction in Aircraft Engine Nacelles

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Nark, Douglas M.; Jones, Michael G.

2011-01-01

Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.

Robot Competence Development by Constructive Learning

NASA Astrophysics Data System (ADS)

Meng, Q.; Lee, M. H.; Hinde, C. J.

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system's adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.

On-board multispectral classification study. Volume 2: Supplementary tasks. [adaptive control

NASA Technical Reports Server (NTRS)

Ewalt, D.

1979-01-01

The operational tasks of the onboard multispectral classification study were defined. These tasks include: sensing characteristics for future space applications; information adaptive systems architectural approaches; data set selection criteria; and onboard functional requirements for interfacing with global positioning satellites.

The role of U.S. states in facilitating effective water governance under stress and change

NASA Astrophysics Data System (ADS)

Kirchhoff, Christine J.; Dilling, Lisa

2016-04-01

Worldwide water governance failures undermine effective water management under uncertainty and change. Overcoming these failures requires employing more adaptive, resilient water management approaches; yet, while scholars have advance theory of what adaptive, resilient approaches should be, there is little empirical evidence to support those normative propositions. To fill this gap, we reviewed the literature to derive theorized characteristics of adaptive, resilient water governance including knowledge generation and use, participation, clear rules for water use, and incorporating nonstationarity. Then, using interviews and documentary analysis focused on five U.S. states' allocation and planning approaches, we examined empirically if embodying these characteristics made states more (or less) adaptive and resilient in practice. We found that adaptive, resilient water governance requires not just possessing these characteristics but combining and building on them. That is, adaptive, resilient water governance requires well-funded, transparent knowledge systems combined with broad, multilevel participatory processes that support learning, strong institutional arrangements that establish authorities and rules and that allow flexibility as conditions change, and resources for integrated planning and allocation. We also found that difficulty incorporating climate change or altering existing water governance paradigms and inadequate funding of water programs undermine adaptive, resilient governance.

A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition

NASA Astrophysics Data System (ADS)

Oh, Yoo Rhee; Kim, Hong Kook

In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.

Teaching Service Modelling to a Mixed Class: An Integrated Approach

ERIC Educational Resources Information Center

Deng, Jeremiah D.; Purvis, Martin K.

2015-01-01

Service modelling has become an increasingly important area in today's telecommunications and information systems practice. We have adapted a Network Design course in order to teach service modelling to a mixed class of both the telecommunication engineering and information systems backgrounds. An integrated approach engaging mathematics teaching…

Adaptive leadership framework for chronic illness: framing a research agenda for transforming care delivery.

PubMed

Anderson, Ruth A; Bailey, Donald E; Wu, Bei; Corazzini, Kirsten; McConnell, Eleanor S; Thygeson, N Marcus; Docherty, Sharron L

2015-01-01

We propose the Adaptive Leadership Framework for Chronic Illness as a novel framework for conceptualizing, studying, and providing care. This framework is an application of the Adaptive Leadership Framework developed by Heifetz and colleagues for business. Our framework views health care as a complex adaptive system and addresses the intersection at which people with chronic illness interface with the care system. We shift focus from symptoms to symptoms and the challenges they pose for patients/families. We describe how providers and patients/families might collaborate to create shared meaning of symptoms and challenges to coproduce appropriate approaches to care.

Adaptive neuro-heuristic hybrid model for fruit peel defects detection.

PubMed

Woźniak, Marcin; Połap, Dawid

2018-02-01

Fusion of machine learning methods benefits in decision support systems. A composition of approaches gives a possibility to use the most efficient features composed into one solution. In this article we would like to present an approach to the development of adaptive method based on fusion of proposed novel neural architecture and heuristic search into one co-working solution. We propose a developed neural network architecture that adapts to processed input co-working with heuristic method used to precisely detect areas of interest. Input images are first decomposed into segments. This is to make processing easier, since in smaller images (decomposed segments) developed Adaptive Artificial Neural Network (AANN) processes less information what makes numerical calculations more precise. For each segment a descriptor vector is composed to be presented to the proposed AANN architecture. Evaluation is run adaptively, where the developed AANN adapts to inputs and their features by composed architecture. After evaluation, selected segments are forwarded to heuristic search, which detects areas of interest. As a result the system returns the image with pixels located over peel damages. Presented experimental research results on the developed solution are discussed and compared with other commonly used methods to validate the efficacy and the impact of the proposed fusion in the system structure and training process on classification results. Copyright © 2017 Elsevier Ltd. All rights reserved.

When everything is not everywhere but species evolve: an alternative method to model adaptive properties of marine ecosystems

PubMed Central

Sauterey, Boris; Ward, Ben A.; Follows, Michael J.; Bowler, Chris; Claessen, David

2015-01-01

The functional and taxonomic biogeography of marine microbial systems reflects the current state of an evolving system. Current models of marine microbial systems and biogeochemical cycles do not reflect this fundamental organizing principle. Here, we investigate the evolutionary adaptive potential of marine microbial systems under environmental change and introduce explicit Darwinian adaptation into an ocean modelling framework, simulating evolving phytoplankton communities in space and time. To this end, we adopt tools from adaptive dynamics theory, evaluating the fitness of invading mutants over annual timescales, replacing the resident if a fitter mutant arises. Using the evolutionary framework, we examine how community assembly, specifically the emergence of phytoplankton cell size diversity, reflects the combined effects of bottom-up and top-down controls. When compared with a species-selection approach, based on the paradigm that “Everything is everywhere, but the environment selects”, we show that (i) the selected optimal trait values are similar; (ii) the patterns emerging from the adaptive model are more robust, but (iii) the two methods lead to different predictions in terms of emergent diversity. We demonstrate that explicitly evolutionary approaches to modelling marine microbial populations and functionality are feasible and practical in time-varying, space-resolving settings and provide a new tool for exploring evolutionary interactions on a range of timescales in the ocean. PMID:25852217

When everything is not everywhere but species evolve: an alternative method to model adaptive properties of marine ecosystems.

PubMed

Sauterey, Boris; Ward, Ben A; Follows, Michael J; Bowler, Chris; Claessen, David

2015-01-01

The functional and taxonomic biogeography of marine microbial systems reflects the current state of an evolving system. Current models of marine microbial systems and biogeochemical cycles do not reflect this fundamental organizing principle. Here, we investigate the evolutionary adaptive potential of marine microbial systems under environmental change and introduce explicit Darwinian adaptation into an ocean modelling framework, simulating evolving phytoplankton communities in space and time. To this end, we adopt tools from adaptive dynamics theory, evaluating the fitness of invading mutants over annual timescales, replacing the resident if a fitter mutant arises. Using the evolutionary framework, we examine how community assembly, specifically the emergence of phytoplankton cell size diversity, reflects the combined effects of bottom-up and top-down controls. When compared with a species-selection approach, based on the paradigm that "Everything is everywhere, but the environment selects", we show that (i) the selected optimal trait values are similar; (ii) the patterns emerging from the adaptive model are more robust, but (iii) the two methods lead to different predictions in terms of emergent diversity. We demonstrate that explicitly evolutionary approaches to modelling marine microbial populations and functionality are feasible and practical in time-varying, space-resolving settings and provide a new tool for exploring evolutionary interactions on a range of timescales in the ocean.

Reinforcement-learning-based dual-control methodology for complex nonlinear discrete-time systems with application to spark engine EGR operation.

PubMed

Shih, Peter; Kaul, Brian C; Jagannathan, S; Drallmeier, James A

2008-08-01

A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary one for the affine nonlinear discrete-time system but the controllers together offer the desired performance. The primary adaptive critic NN controller includes an NN observer for estimating the states and output, an NN critic, and two action NNs for generating virtual control and actual control inputs for the nonstrict feedback nonlinear discrete-time system, whereas an additional critic NN and an action NN are included for the affine nonlinear discrete-time system by assuming the state availability. All NN weights adapt online towards minimization of a certain performance index, utilizing gradient-descent-based rule. Using Lyapunov theory, the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates, and observer estimates are shown. The adaptive critic NN controller performance is evaluated on an SI engine operating with high EGR levels where the controller objective is to reduce cyclic dispersion in heat release while minimizing fuel intake. Simulation and experimental results indicate that engine out emissions drop significantly at 20% EGR due to reduction in dispersion in heat release thus verifying the dual-control approach.

Conflicts of thermal adaptation and fever--a cybernetic approach based on physiological experiments.

PubMed

Werner, J; Beckmann, U

1998-01-01

Cold adaptation aims primarily at a better economy, i.e., preservation of energy often at the cost of a lower mean body temperature during cold stress, whereas heat adaptation whether achieved by exposure to a hot environment or by endogenous heat produced by muscle exercise, often brings about a higher efficiency of control, i.e., a lower mean body temperature during heat stress, at the cost of a higher water loss. While cold adaptation is beneficial in a cold environment, it may constitute a detrimental factor for exposure to a hot environment, mainly because of morphological adaptation. Heat adaptation may be maladaptive for cold exposure, mainly because of functional adaptation. Heat adaptation clearly is best suited to avoid higher body temperatures in fever, no matter which environmental conditions prevail. On the other hand, cold adaptation is detrimental for coping with fever in hot environment. Yet, in the cold, preceding cold adaptation may, because of reduced metabolic heat production, result in lower febrile increase of body temperature. Apparently controversial effects and results may be analyzed in the framework of a cybernetic approach to the main mechanisms of thermal adaptation and fever. Morphological adaptations alter the properties of the heat transfer characteristics of the body ("passive system"), whereas functional adaptation and fever concern the subsystems of control, namely sensors, integrative centers and effectors. In a closed control-loop the two types of adaptation have totally different consequences. It is shown that the experimental results are consistent with the predictions of such an approach.

Modeling Stochastic Complexity in Complex Adaptive Systems: Non-Kolmogorov Probability and the Process Algebra Approach.

PubMed

Sulis, William H

2017-10-01

Walter Freeman III pioneered the application of nonlinear dynamical systems theories and methodologies in his work on mesoscopic brain dynamics.Sadly, mainstream psychology and psychiatry still cling to linear correlation based data analysis techniques, which threaten to subvert the process of experimentation and theory building. In order to progress, it is necessary to develop tools capable of managing the stochastic complexity of complex biopsychosocial systems, which includes multilevel feedback relationships, nonlinear interactions, chaotic dynamics and adaptability. In addition, however, these systems exhibit intrinsic randomness, non-Gaussian probability distributions, non-stationarity, contextuality, and non-Kolmogorov probabilities, as well as the absence of mean and/or variance and conditional probabilities. These properties and their implications for statistical analysis are discussed. An alternative approach, the Process Algebra approach, is described. It is a generative model, capable of generating non-Kolmogorov probabilities. It has proven useful in addressing fundamental problems in quantum mechanics and in the modeling of developing psychosocial systems.

Verification and Validation of Adaptive and Intelligent Systems with Flight Test Results

NASA Technical Reports Server (NTRS)

Burken, John J.; Larson, Richard R.

2009-01-01

F-15 IFCS project goals are: a) Demonstrate Control Approaches that can Efficiently Optimize Aircraft Performance in both Normal and Failure Conditions [A] & [B] failures. b) Advance Neural Network-Based Flight Control Technology for New Aerospace Systems Designs with a Pilot in the Loop. Gen II objectives include; a) Implement and Fly a Direct Adaptive Neural Network Based Flight Controller; b) Demonstrate the Ability of the System to Adapt to Simulated System Failures: 1) Suppress Transients Associated with Failure; 2) Re-Establish Sufficient Control and Handling of Vehicle for Safe Recovery. c) Provide Flight Experience for Development of Verification and Validation Processes for Flight Critical Neural Network Software.

Feedback (F) Fueling Adaptation (A) Network Growth (N) and Self-Organization (S): A Complex Systems Design and Evaluation Approach to Professional Development

ERIC Educational Resources Information Center

Yoon, Susan A.; Klopfer, Eric

2006-01-01

This paper reports on the efficacy of a professional development framework premised on four complex systems design principles: Feedback, Adaptation, Network Growth and Self-organization (FANS). The framework is applied to the design and delivery of the first 2 years of a 3-year study aimed at improving teacher and student understanding of…

Social vulnerability to climate change in primary producers: A typology approach

USDA-ARS?s Scientific Manuscript database

Adaptation in agro-ecological systems will be important for moderating the impacts of climate change. Vulnerability assessments provide the basis for developing strategies to reduce social vulnerability and plan for climate adaptation. Primary industries have been identified as the most vulnerable i...

Topics in space gerontology: Effects of altered gravity and the problem of biological age

NASA Technical Reports Server (NTRS)

Economos, A. C.

1982-01-01

The use of altered gravity experimentation as a gerontological research tool is examined and a rationale for a systems approach to the adaptation to spaceflight is presented. The dependence of adaptation capacity on biological age is also discussed.

Interoperability in Personalized Adaptive Learning

ERIC Educational Resources Information Center

Aroyo, Lora; Dolog, Peter; Houben, Geert-Jan; Kravcik, Milos; Naeve, Ambjorn; Nilsson, Mikael; Wild, Fridolin

2006-01-01

Personalized adaptive learning requires semantic-based and context-aware systems to manage the Web knowledge efficiently as well as to achieve semantic interoperability between heterogeneous information resources and services. The technological and conceptual differences can be bridged either by means of standards or via approaches based on the…

Sink or Swim: Adapting to the Hydrologic Impacts of Climate Change

NASA Astrophysics Data System (ADS)

Gleick, P. H.

2014-12-01

Climate changes lead to a wide range of societal and environmental impacts; indeed, strong evidence has accrued that such impacts are already occurring, as summarized by the newest National Climate Assessment and other analyses. Among the most important will be alterations in the hydrologic cycle, changes in water supply and demand, and impacts on existing water-related infrastructure. Because of the complexity of our water systems, adaptation responses will be equally complex. This problem has made it difficult for water managers and planners to develop and implement adaptation strategies. This talk will address three ways to think about water-related adaptation approaches to climate change: (1) strategies that are already being implemented to address population and economic changes without climate change; (2) whether these first-line strategies are appropriate for additional impacts that might result from climatic changes; and (3) new approaches that might be necessary for new, non-linear, or threshold impacts. An effort will also be made to differentiate between adaptation strategies that influence the hydrologic cycle directly (e.g., cloud seeding), those that influence supply management (e.g., construction of additional reservoirs or water-distribution systems), and those that affect water demand (e.g., removal of outdoor landscaping, installation of efficient irrigation systems).

Modern control concepts in hydrology. [parameter identification in adaptive stochastic control approach

NASA Technical Reports Server (NTRS)

Duong, N.; Winn, C. B.; Johnson, G. R.

1975-01-01

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Mission-based guidance system design for autonomous UAVs

NASA Astrophysics Data System (ADS)

Moon, Jongki

The advantages of UAVs in the aviation arena have led to extensive research activities on autonomous technology of UAVs to achieve specific mission objectives. This thesis mainly focuses on the development of a mission-based guidance system. Among various missions expected for future needs, autonomous formation flight (AFF) and obstacle avoidance within safe operation limits are investigated. In the design of an adaptive guidance system for AFF, the leader information except position is assumed to be unknown to a follower. Thus, the only measured information related to the leader is the line-of-sight (LOS) range and angle. Adding an adaptive element with neural networks into the guidance system provides a capability to effectively handle leader's velocity changes. Therefore, this method can be applied to the AFF control systems that use a passive sensing method. In this thesis, an adaptive velocity command guidance system and an adaptive acceleration command guidance system are developed and presented. Since relative degrees of the LOS range and angle are different depending on the outputs from the guidance system, the architecture of the guidance system changes accordingly. Simulations and flight tests are performed using the Georgia Tech UAV helicopter, the GTMax, to evaluate the proposed guidance systems. The simulation results show that the neural network (NN) based adaptive element can improve the tracking performance by effectively compensating for the effect of unknown dynamics. It has also been shown that the combination of an adaptive velocity command guidance system and the existing GTMax autopilot controller performs better than the combination of an adaptive acceleration command guidance system and the GTMax autopilot controller. The successful flight evaluation using an adaptive velocity command guidance system clearly shows that the adaptive guidance control system is a promising solution for autonomous formation flight of UAVs. In addition, an integrated approach is proposed to resolve the conflict between aggressive maneuvering needed for obstacle avoidance and the constrained maneuvering needed for envelope protection. A time-optimal problem with obstacle and envelope constraints is used for an integrated approach for obstacle avoidance and envelope protection. The Nonlinear trajectory generator (NTG) is used as a real-time optimization solver. The computational complexity arising from the obstacle constraints is reduced by converting the obstacle constraints into a safe waypoint constraint along with an implicit requirement that the horizontal velocity during the avoidance maneuver must be nonnegative. The issue of when to initiate a time-optimal avoidance maneuver is addressed by including a requirement that the vehicle must maintain its original flight path to the maximum extent possible. The simulation evaluations are preformed for the nominal case, the unsafe avoidance solution case, the multiple safe waypoint case, and the unidentified obstacle size case. Artificial values for the load factor limit and the longitudinal flap angle limit are imposed as safe operational boundaries. Also, simulation results for different limit values and different initial flight speed are compared. Simulation results using a nonlinear model of a rotary wing UAV demonstrate the feasibility of the proposed approach for obstacle avoidance with envelope protection.

Distributed parameter system coupled ARMA expansion identification and adaptive parallel IIR filtering - A unified problem statement. [Auto Regressive Moving-Average

NASA Technical Reports Server (NTRS)

Johnson, C. R., Jr.; Balas, M. J.

1980-01-01

A novel interconnection of distributed parameter system (DPS) identification and adaptive filtering is presented, which culminates in a common statement of coupled autoregressive, moving-average expansion or parallel infinite impulse response configuration adaptive parameterization. The common restricted complexity filter objectives are seen as similar to the reduced-order requirements of the DPS expansion description. The interconnection presents the possibility of an exchange of problem formulations and solution approaches not yet easily addressed in the common finite dimensional lumped-parameter system context. It is concluded that the shared problems raised are nevertheless many and difficult.

An Adaptive Pheromone Updation of the Ant-System using LMS Technique

NASA Astrophysics Data System (ADS)

Paul, Abhishek; Mukhopadhyay, Sumitra

2010-10-01

We propose a modified model of pheromone updation for Ant-System, entitled as Adaptive Ant System (AAS), using the properties of basic Adaptive Filters. Here, we have exploited the properties of Least Mean Square (LMS) algorithm for the pheromone updation to find out the best minimum tour for the Travelling Salesman Problem (TSP). TSP library has been used for the selection of benchmark problem and the proposed AAS determines the minimum tour length for the problems containing large number of cities. Our algorithm shows effective results and gives least tour length in most of the cases as compared to other existing approaches.

High precision tracking control of a servo gantry with dynamic friction compensation.

PubMed

Zhang, Yangming; Yan, Peng; Zhang, Zhen

2016-05-01

This paper is concerned with the tracking control problem of a voice coil motor (VCM) actuated servo gantry system. By utilizing an adaptive control technique combined with a sliding mode approach, an adaptive sliding mode control (ASMC) law with friction compensation scheme is proposed in presence of both frictions and external disturbances. Based on the LuGre dynamic friction model, a dual-observer structure is used to estimate the unmeasurable friction state, and an adaptive control law is synthesized to effectively handle the unknown friction model parameters as well as the bound of the disturbances. Moreover, the proposed control law is also implemented on a VCM servo gantry system for motion tracking. Simulations and experimental results demonstrate good tracking performance, which outperform traditional control approaches. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Delinquent-Victim Youth-Adapting a Trauma-Informed Approach for the Juvenile Justice System.

PubMed

Rapp, Lisa

2016-01-01

The connection between victimization and later delinquency is well established and most youth involved with the juvenile justice system have at least one if not multiple victimizations in their history. Poly-victimized youth or those presenting with complex trauma require specialized assessment and services to prevent deleterious emotional, physical, and social life consequences. Empirical studies have provided information which can guide practitioners work with these youth and families, yet many of the policies and practices of the juvenile justice system are counter to this model. Many youth-serving organizations are beginning to review their operations to better match a trauma-informed approach and in this article the author will highlight how a trauma-informed care model could be utilized to adapt the juvenile justice system.

Information security governance: a risk assessment approach to health information systems protection.

PubMed

Williams, Patricia A H

2013-01-01

It is no small task to manage the protection of healthcare data and healthcare information systems. In an environment that is demanding adaptation to change for all information collection, storage and retrieval systems, including those for of e-health and information systems, it is imperative that good information security governance is in place. This includes understanding and meeting legislative and regulatory requirements. This chapter provides three models to educate and guide organisations in this complex area, and to simplify the process of information security governance and ensure appropriate and effective measures are put in place. The approach is risk based, adapted and contextualized for healthcare. In addition, specific considerations of the impact of cloud services, secondary use of data, big data and mobile health are discussed.

Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

NASA Astrophysics Data System (ADS)

Ibrahim, Wael Refaat Anis

The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an alarm is instigated predicting the advent of system abnormal operation. The incoming data could be compared to previous trends as well as matched to trends developed through computer simulations and stored using fuzzy learning.

An adaptive and generalizable closed-loop system for control of medically induced coma and other states of anesthesia

NASA Astrophysics Data System (ADS)

Yang, Yuxiao; Shanechi, Maryam M.

2016-12-01

Objective. Design of closed-loop anesthetic delivery (CLAD) systems is an important topic, particularly for medically induced coma, which needs to be maintained for long periods. Current CLADs for medically induced coma require a separate offline experiment for model parameter estimation, which causes interruption in treatment and is difficult to perform. Also, CLADs may exhibit bias due to inherent time-variation and non-stationarity, and may have large infusion rate variations at steady state. Finally, current CLADs lack theoretical performance guarantees. We develop the first adaptive CLAD for medically induced coma, which addresses these limitations. Further, we extend our adaptive system to be generalizable to other states of anesthesia. Approach. We designed general parametric pharmacodynamic, pharmacokinetic and neural observation models with associated guidelines, and derived a novel adaptive controller. We further penalized large steady-state drug infusion rate variations in the controller. We derived theoretical guarantees that the adaptive system has zero steady-state bias. Using simulations that resembled real time-varying and noisy environments, we tested the closed-loop system for control of two different anesthetic states, burst suppression in medically induced coma and unconsciousness in general anesthesia. Main results. In 1200 simulations, the adaptive system achieved precise control of both anesthetic states despite non-stationarity, time-variation, noise, and no initial parameter knowledge. In both cases, the adaptive system performed close to a baseline system that knew the parameters exactly. In contrast, a non-adaptive system resulted in large steady-state bias and error. The adaptive system also resulted in significantly smaller steady-state infusion rate variations compared to prior systems. Significance. These results have significant implications for clinically viable CLAD design for a wide range of anesthetic states, with potential cost-saving and therapeutic benefits.

Probabilistic co-adaptive brain-computer interfacing

NASA Astrophysics Data System (ADS)

Bryan, Matthew J.; Martin, Stefan A.; Cheung, Willy; Rao, Rajesh P. N.

2013-12-01

Objective. Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain-computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions (‘beliefs’) over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP’s reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed-accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user’s control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user’s brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user’s changing circumstances.

A New Approach to Interference Excision in Radio Astronomy: Real-Time Adaptive Cancellation

NASA Astrophysics Data System (ADS)

Barnbaum, Cecilia; Bradley, Richard F.

1998-11-01

Every year, an increasing amount of radio-frequency (RF) spectrum in the VHF, UHF, and microwave bands is being utilized to support new commercial and military ventures, and all have the potential to interfere with radio astronomy observations. Such services already cause problems for radio astronomy even in very remote observing sites, and the potential for this form of light pollution to grow is alarming. Preventive measures to eliminate interference through FCC legislation and ITU agreements can be effective; however, many times this approach is inadequate and interference excision at the receiver is necessary. Conventional techniques such as RF filters, RF shielding, and postprocessing of data have been only somewhat successful, but none has been sufficient. Adaptive interference cancellation is a real-time approach to interference excision that has not been used before in radio astronomy. We describe here, for the first time, adaptive interference cancellation in the context of radio astronomy instrumentation, and we present initial results for our prototype receiver. In the 1960s, analog adaptive interference cancelers were developed that obtain a high degree of cancellation in problems of radio communications and radar. However, analog systems lack the dynamic range, noised performance, and versatility required by radio astronomy. The concept of digital adaptive interference cancellation was introduced in the mid-1960s as a way to reduce unwanted noise in low-frequency (audio) systems. Examples of such systems include the canceling of maternal ECG in fetal electrocardiography and the reduction of engine noise in the passenger compartments of automobiles. These audio-frequency applications require bandwidths of only a few tens of kilohertz. Only recently has high-speed digital filter technology made high dynamic range adaptive canceling possible in a bandwidth as large as a few megahertz, finally opening the door to application in radio astronomy. We have built a prototype adaptive canceler that consists of two receivers: the primary channel (input from the main beam of the telescope) and a separate reference channel. The primary channel receives the desired astronomical signal corrupted by RFI (radio-frequency interference) coming in the sidelobes of the main beam. A separate reference antenna is designed to receive only the RFI. The reference channel input is processed using a digital adaptive filter and then subtracted from the primary channel input, producing the system output. The weighting coefficients of the digital filter are adjusted by way of an algorithm that minimizes, in a least-squares sense, the power output of the system. Through an adaptive-iterative process, the canceler locks onto the RFI, and the filter adjusts itself to minimize the effect of the RFI at the system output. We have designed the adaptive canceler with an intermediate frequency (IF) of 40 MHz. This prototype system will ultimately be functional with a variety of radio astronomy receivers in the microwave band. We have also built a prototype receiver centered at 100 MHz (in the FM broadcast band) to test the adaptive canceler with actual interferers, which are well characterized. The initial laboratory tests of the adaptive canceler are encouraging, with attenuation of strong frequency-modulated (FM) interference to 72 dB (a factor of more than 10 million), which is at the performance limit of our measurements. We also consider requirements of the system and the RFI environment for effective adaptive canceling.

Adaptive Fuzzy Output Constrained Control Design for Multi-Input Multioutput Stochastic Nonstrict-Feedback Nonlinear Systems.

PubMed

Li, Yongming; Tong, Shaocheng

2017-12-01

In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Adaptive System Modeling for Spacecraft Simulation

NASA Technical Reports Server (NTRS)

Thomas, Justin

2011-01-01

This invention introduces a methodology and associated software tools for automatically learning spacecraft system models without any assumptions regarding system behavior. Data stream mining techniques were used to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). Evaluation on historical ISS telemetry data shows that adaptive system modeling reduces simulation error anywhere from 50 to 90 percent over existing approaches. The purpose of the methodology is to outline how someone can create accurate system models from sensor (telemetry) data. The purpose of the software is to support the methodology. The software provides analysis tools to design the adaptive models. The software also provides the algorithms to initially build system models and continuously update them from the latest streaming sensor data. The main strengths are as follows: Creates accurate spacecraft system models without in-depth system knowledge or any assumptions about system behavior. Automatically updates/calibrates system models using the latest streaming sensor data. Creates device specific models that capture the exact behavior of devices of the same type. Adapts to evolving systems. Can reduce computational complexity (faster simulations).

A neural learning classifier system with self-adaptive constructivism for mobile robot control.

PubMed

Hurst, Jacob; Bull, Larry

2006-01-01

For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

Planning for climate change: The need for mechanistic systems-based approaches to study climate change impacts on diarrheal diseases.

PubMed

Mellor, Jonathan E; Levy, Karen; Zimmerman, Julie; Elliott, Mark; Bartram, Jamie; Carlton, Elizabeth; Clasen, Thomas; Dillingham, Rebecca; Eisenberg, Joseph; Guerrant, Richard; Lantagne, Daniele; Mihelcic, James; Nelson, Kara

2016-04-01

Increased precipitation and temperature variability as well as extreme events related to climate change are predicted to affect the availability and quality of water globally. Already heavily burdened with diarrheal diseases due to poor access to water, sanitation and hygiene facilities, communities throughout the developing world lack the adaptive capacity to sufficiently respond to the additional adversity caused by climate change. Studies suggest that diarrhea rates are positively correlated with increased temperature, and show a complex relationship with precipitation. Although climate change will likely increase rates of diarrheal diseases on average, there is a poor mechanistic understanding of the underlying disease transmission processes and substantial uncertainty surrounding current estimates. This makes it difficult to recommend appropriate adaptation strategies. We review the relevant climate-related mechanisms behind transmission of diarrheal disease pathogens and argue that systems-based mechanistic approaches incorporating human, engineered and environmental components are urgently needed. We then review successful systems-based approaches used in other environmental health fields and detail one modeling framework to predict climate change impacts on diarrheal diseases and design adaptation strategies. Copyright © 2016 Elsevier B.V. All rights reserved.

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

PubMed

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Adaptive lenses using transparent dielectric elastomer actuators

NASA Astrophysics Data System (ADS)

Shian, Samuel; Diebold, Roger; Clarke, David

2013-03-01

Variable focal lenses, used in a vast number of applications such as endoscope, digital camera, binoculars, information storage, communication, and machine vision, are traditionally constructed as a lens system consisting of solid lenses and actuating mechanisms. However, such lens system is complex, bulky, inefficient, and costly. Each of these shortcomings can be addressed using an adaptive lens that performs as a lens system. In this presentation, we will show how we push the boundary of adaptive lens technology through the use of a transparent electroactive polymer actuator that is integral to the optics. Detail of our concepts and lens construction will be described as well as electromechanical and optical performances. Preliminary data indicate that our adaptive lens prototype is capable of varying its focus by more than 100%, which is higher than that of human eyes. Furthermore, we will show how our approach can be used to achieve certain controls over the lens characteristics such as adaptive aberration and optical axis, which are difficult or impossible to achieve in other adaptive lens configurations.

Adaptive robust fault-tolerant control for linear MIMO systems with unmatched uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Kangkang; Jiang, Bin; Yan, Xing-Gang; Mao, Zehui

2017-10-01

In this paper, two novel fault-tolerant control design approaches are proposed for linear MIMO systems with actuator additive faults, multiplicative faults and unmatched uncertainties. For time-varying multiplicative and additive faults, new adaptive laws and additive compensation functions are proposed. A set of conditions is developed such that the unmatched uncertainties are compensated by actuators in control. On the other hand, for unmatched uncertainties with their projection in unmatched space being not zero, based on a (vector) relative degree condition, additive functions are designed to compensate for the uncertainties from output channels in the presence of actuator faults. The developed fault-tolerant control schemes are applied to two aircraft systems to demonstrate the efficiency of the proposed approaches.

Monitoring is not enough: on the need for a model-based approach to migratory bird management

USGS Publications Warehouse

Nichols, J.D.; Bonney, Rick; Pashley, David N.; Cooper, Robert; Niles, Larry

2000-01-01

Informed management requires information about system state and about effects of potential management actions on system state. Population monitoring can provide the needed information about system state, as well as information that can be used to investigate effects of management actions. Three methods for investigating effects of management on bird populations are (1) retrospective analysis, (2) formal experimentation and constrained-design studies, and (3) adaptive management. Retrospective analyses provide weak inferences, regardless of the quality of the monitoring data. The active use of monitoring data in experimental or constrained-design studies or in adaptive management is recommended. Under both approaches, learning occurs via the comparison of estimates from the monitoring program with predictions from competing management models.

Evolving RBF neural networks for adaptive soft-sensor design.

PubMed

Alexandridis, Alex

2013-12-01

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

Adaptive tight frame based medical image reconstruction: a proof-of-concept study for computed tomography

NASA Astrophysics Data System (ADS)

Zhou, Weifeng; Cai, Jian-Feng; Gao, Hao

2013-12-01

A popular approach for medical image reconstruction has been through the sparsity regularization, assuming the targeted image can be well approximated by sparse coefficients under some properly designed system. The wavelet tight frame is such a widely used system due to its capability for sparsely approximating piecewise-smooth functions, such as medical images. However, using a fixed system may not always be optimal for reconstructing a variety of diversified images. Recently, the method based on the adaptive over-complete dictionary that is specific to structures of the targeted images has demonstrated its superiority for image processing. This work is to develop the adaptive wavelet tight frame method image reconstruction. The proposed scheme first constructs the adaptive wavelet tight frame that is task specific, and then reconstructs the image of interest by solving an l1-regularized minimization problem using the constructed adaptive tight frame system. The proof-of-concept study is performed for computed tomography (CT), and the simulation results suggest that the adaptive tight frame method improves the reconstructed CT image quality from the traditional tight frame method.

In situ 3D nanoprinting of free-form coupling elements for hybrid photonic integration

NASA Astrophysics Data System (ADS)

Dietrich, P.-I.; Blaicher, M.; Reuter, I.; Billah, M.; Hoose, T.; Hofmann, A.; Caer, C.; Dangel, R.; Offrein, B.; Troppenz, U.; Moehrle, M.; Freude, W.; Koos, C.

2018-04-01

Hybrid photonic integration combines complementary advantages of different material platforms, offering superior performance and flexibility compared with monolithic approaches. This applies in particular to multi-chip concepts, where components can be individually optimized and tested. The assembly of such systems, however, requires expensive high-precision alignment and adaptation of optical mode profiles. We show that these challenges can be overcome by in situ printing of facet-attached beam-shaping elements. Our approach allows precise adaptation of vastly dissimilar mode profiles and permits alignment tolerances compatible with cost-efficient passive assembly techniques. We demonstrate a selection of beam-shaping elements at chip and fibre facets, achieving coupling efficiencies of up to 88% between edge-emitting lasers and single-mode fibres. We also realize printed free-form mirrors that simultaneously adapt beam shape and propagation direction, and we explore multi-lens systems for beam expansion. The concept paves the way to automated assembly of photonic multi-chip systems with unprecedented performance and versatility.

Learning without labeling: domain adaptation for ultrasound transducer localization.

PubMed

Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

2013-01-01

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.

Neural network-based model reference adaptive control system.

PubMed

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Design of Adaptive Policy Pathways under Deep Uncertainties

NASA Astrophysics Data System (ADS)

Babovic, Vladan

2013-04-01

The design of large-scale engineering and infrastructural systems today is growing in complexity. Designers need to consider sociotechnical uncertainties, intricacies, and processes in the long- term strategic deployment and operations of these systems. In this context, water and spatial management is increasingly challenged not only by climate-associated changes such as sea level rise and increased spatio-temporal variability of precipitation, but also by pressures due to population growth and particularly accelerating rate of urbanisation. Furthermore, high investment costs and long term-nature of water-related infrastructure projects requires long-term planning perspective, sometimes extending over many decades. Adaptation to such changes is not only determined by what is known or anticipated at present, but also by what will be experienced and learned as the future unfolds, as well as by policy responses to social and water events. As a result, a pathway emerges. Instead of responding to 'surprises' and making decisions on ad hoc basis, exploring adaptation pathways into the future provide indispensable support in water management decision-making. In this contribution, a structured approach for designing a dynamic adaptive policy based on the concepts of adaptive policy making and adaptation pathways is introduced. Such an approach provides flexibility which allows change over time in response to how the future unfolds, what is learned about the system, and changes in societal preferences. The introduced flexibility provides means for dealing with complexities of adaptation under deep uncertainties. It enables engineering systems to change in the face of uncertainty to reduce impacts from downside scenarios while capitalizing on upside opportunities. This contribution presents comprehensive framework for development and deployment of adaptive policy pathway framework, and demonstrates its performance under deep uncertainties on a case study related to urban water catchment in Singapore. Ingredients of this approach are: (a) transient scenarios (time series of various uncertain developments such as climate change, economic developments, societal changes), (b) a methodology for exploring many options and sequences of these options across different futures, and (c) a stepwise policy analysis. The strategy is applied on case of flexible deployment of novel, so-called Next Generation Infrastructure, and assessed in context of the proposed. Results of the study show that flexible design alternatives deliver much enhanced performance compared to systems optimized under deterministic forecasts of the future. The work also demonstrates that explicit incorporation of uncertainty and flexibility into decision-making process reduces capital expenditures while allowing decision makers to learn about system evolution throughout the lifetime of the project.

Immunity-Based Optimal Estimation Approach for a New Real Time Group Elevator Dynamic Control Application for Energy and Time Saving

PubMed Central

Baygin, Mehmet; Karakose, Mehmet

2013-01-01

Nowadays, the increasing use of group elevator control systems owing to increasing building heights makes the development of high-performance algorithms necessary in terms of time and energy saving. Although there are many studies in the literature about this topic, they are still not effective enough because they are not able to evaluate all features of system. In this paper, a new approach of immune system-based optimal estimate is studied for dynamic control of group elevator systems. The method is mainly based on estimation of optimal way by optimizing all calls with genetic, immune system and DNA computing algorithms, and it is evaluated with a fuzzy system. The system has a dynamic feature in terms of the situation of calls and the option of the most appropriate algorithm, and it also adaptively works in terms of parameters such as the number of floors and cabins. This new approach which provides both time and energy saving was carried out in real time. The experimental results comparatively demonstrate the effects of method. With dynamic and adaptive control approach in this study carried out, a significant progress on group elevator control systems has been achieved in terms of time and energy efficiency according to traditional methods. PMID:23935433

Robust adaptive sliding mode control for uncertain systems with unknown time-varying delay input.

PubMed

Benamor, Anouar; Messaoud, Hassani

2018-05-02

This article focuses on robust adaptive sliding mode control law for uncertain discrete systems with unknown time-varying delay input, where the uncertainty is assumed unknown. The main results of this paper are divided into three phases. In the first phase, we propose a new sliding surface is derived within the Linear Matrix Inequalities (LMIs). In the second phase, using the new sliding surface, the novel Robust Sliding Mode Control (RSMC) is proposed where the upper bound of uncertainty is supposed known. Finally, the novel approach of Robust Adaptive Sliding ModeControl (RASMC) has been defined for this type of systems, where the upper limit of uncertainty which is assumed unknown. In this new approach, we have estimate the upper limit of uncertainties and we have determined the control law based on a sliding surface that will converge to zero. This novel control laws are been validated in simulation on an uncertain numerical system with good results and comparative study. This efficiency is emphasized through the application of the new controls on the two physical systems which are the process trainer PT326 and hydraulic system two tanks. Published by Elsevier Ltd.

Methods and Apparatus for Reducing Multipath Signal Error Using Deconvolution

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor); Lau, Kenneth H. (Inventor)

1999-01-01

A deconvolution approach to adaptive signal processing has been applied to the elimination of signal multipath errors as embodied in one preferred embodiment in a global positioning system receiver. The method and receiver of the present invention estimates then compensates for multipath effects in a comprehensive manner. Application of deconvolution, along with other adaptive identification and estimation techniques, results in completely novel GPS (Global Positioning System) receiver architecture.

15 Gbit/s indoor optical wireless systems employing fast adaptation and imaging reception in a realistic environment

NASA Astrophysics Data System (ADS)

Alsaadi, Fuad E.

2016-03-01

Optical wireless systems are promising candidates for next-generation indoor communication networks. Optical wireless technology offers freedom from spectrum regulations and, compared to current radio-frequency networks, higher data rates and increased security. This paper presents a fast adaptation method for multibeam angle and delay adaptation systems and a new spot-diffusing geometry, and also considers restrictions needed for complying with eye safety regulations. The fast adaptation algorithm reduces the computational load required to reconfigure the transmitter in the case of transmitter and/or receiver mobility. The beam clustering approach enables the transmitter to assign power to spots within the pixel's field of view (FOV) and increases the number of such spots. Thus, if the power per spot is restricted to comply with eye safety standards, the new approach, in which more spots are visible within the FOV of the pixel, leads to enhanced signal-to-noise ratio (SNR). Simulation results demonstrate that the techniques proposed in this paper lead to SNR improvements that enable reliable operation at data rates as high as 15 Gbit/s. These results are based on simulation and not on actual measurements or experiments.

Failing Tests: Commentary on "Adapting Educational Measurement to the Demands of Test-Based Accountability"

ERIC Educational Resources Information Center

Thissen, David

2015-01-01

In "Adapting Educational Measurement to the Demands of Test-Based Accountability" Koretz takes the time-honored engineering approach to educational measurement, identifying specific problems with current practice and proposing minimal modifications of the system to alleviate those problems. In response to that article, David Thissen…

Ontology-Based Multimedia Authoring Tool for Adaptive E-Learning

ERIC Educational Resources Information Center

Deng, Lawrence Y.; Keh, Huan-Chao; Liu, Yi-Jen

2010-01-01

More video streaming technologies supporting distance learning systems are becoming popular among distributed network environments. In this paper, the authors develop a multimedia authoring tool for adaptive e-learning by using characterization of extended media streaming technologies. The distributed approach is based on an ontology-based model.…

Adaptation of neuronal signaling and cell stress response pathways in a multigenic response of Drosophila melanogaster to DDT selection

USDA-ARS?s Scientific Manuscript database

The adaptation of insect populations to insecticidal control is a continual threat human health and sustainable agriculture practices, but many complex genomic mechanisms involved remain poorly understood. A systems approach was applied to investigate the interconnections between structural and func...

An integrated approach to climate adaptation at the Chicago Transit Authority.

DOT National Transportation Integrated Search

2013-08-01

CTA was selected as one of seven pilots funded by FTA to advance the state of practice for adapting transit systems to the impacts of : climate change. This effort is in keeping with broader long-term goals to address state-of-good-repair needs and t...

Towards a Framework for Modeling Space Systems Architectures

NASA Technical Reports Server (NTRS)

Shames, Peter; Skipper, Joseph

2006-01-01

Topics covered include: 1) Statement of the problem: a) Space system architecture is complex; b) Existing terrestrial approaches must be adapted for space; c) Need a common architecture methodology and information model; d) Need appropriate set of viewpoints. 2) Requirements on a space systems model. 3) Model Based Engineering and Design (MBED) project: a) Evaluated different methods; b) Adapted and utilized RASDS & RM-ODP; c) Identified useful set of viewpoints; d) Did actual model exchanges among selected subset of tools. 4) Lessons learned & future vision.

System integration of pattern recognition, adaptive aided, upper limb prostheses

NASA Technical Reports Server (NTRS)

Lyman, J.; Freedy, A.; Solomonow, M.

1975-01-01

The requirements for successful integration of a computer aided control system for multi degree of freedom artificial arms are discussed. Specifications are established for a system which shares control between a human amputee and an automatic control subsystem. The approach integrates the following subsystems: (1) myoelectric pattern recognition, (2) adaptive computer aiding; (3) local reflex control; (4) prosthetic sensory feedback; and (5) externally energized arm with the functions of prehension, wrist rotation, elbow extension and flexion and humeral rotation.

Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems.

PubMed

Del Prado, A; Crosson, P; Olesen, J E; Rotz, C A

2013-06-01

The farm level is the most appropriate scale for evaluating options for mitigating greenhouse gas (GHG) emissions, because the farm represents the unit at which management decisions in livestock production are made. To date, a number of whole farm modelling approaches have been developed to quantify GHG emissions and explore climate change mitigation strategies for livestock systems. This paper analyses the limitations and strengths of the different existing approaches for modelling GHG mitigation by considering basic model structures, approaches for simulating GHG emissions from various farm components and the sensitivity of GHG outputs and mitigation measures to different approaches. Potential challenges for linking existing models with the simulation of impacts and adaptation measures under climate change are explored along with a brief discussion of the effects on other ecosystem services.

A tale of three bio-inspired computational approaches

NASA Astrophysics Data System (ADS)

Schaffer, J. David

2014-05-01

I will provide a high level walk-through for three computational approaches derived from Nature. First, evolutionary computation implements what we may call the "mother of all adaptive processes." Some variants on the basic algorithms will be sketched and some lessons I have gleaned from three decades of working with EC will be covered. Then neural networks, computational approaches that have long been studied as possible ways to make "thinking machines", an old dream of man's, and based upon the only known existing example of intelligence. Then, a little overview of attempts to combine these two approaches that some hope will allow us to evolve machines we could never hand-craft. Finally, I will touch on artificial immune systems, Nature's highly sophisticated defense mechanism, that has emerged in two major stages, the innate and the adaptive immune systems. This technology is finding applications in the cyber security world.

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

NASA Technical Reports Server (NTRS)

Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong; Farfan-Ramos, Luis; Simon, Donald L.

2010-01-01

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

Adaptive Policies for Reducing Inequalities in the Social Determinants of Health.

PubMed

Carey, Gemma; Crammond, Brad; Malbon, Eleanor; Carey, Nic

2015-09-18

Inequalities in the social determinants of health (SDH), which drive avoidable health disparities between different individuals or groups, is a major concern for a number of international organisations, including the World Health Organization (WHO). Despite this, the pathways to changing inequalities in the SDH remain elusive. The methodologies and concepts within system science are now viewed as important domains of knowledge, ideas and skills for tackling issues of inequality, which are increasingly understood as emergent properties of complex systems. In this paper, we introduce and expand the concept of adaptive policies to reduce inequalities in the distribution of the SDH. The concept of adaptive policy for health equity was developed through reviewing the literature on learning and adaptive policies. Using a series of illustrative examples from education and poverty alleviation, which have their basis in real world policies, we demonstrate how an adaptive policy approach is more suited to the management of the emergent properties of inequalities in the SDH than traditional policy approaches. This is because they are better placed to handle future uncertainties. Our intention is that these examples are illustrative, rather than prescriptive, and serve to create a conversation regarding appropriate adaptive policies for progressing policy action on the SDH. © 2015 by Kerman University of Medical Sciences.

[Health: an adaptive complex system].

PubMed

Toro-Palacio, Luis Fernando; Ochoa-Jaramillo, Francisco Luis

2012-02-01

This article points out the enormous gap that exists between complex thinking of an intellectual nature currently present in our environment, and complex experimental thinking that has facilitated the scientific and technological advances that have radically changed the world. The article suggests that life, human beings, global society, and all that constitutes health be considered as adaptive complex systems. This idea, in turn, prioritizes the adoption of a different approach that seeks to expand understanding. When this rationale is recognized, the principal characteristics and emerging properties of health as an adaptive complex system are sustained, following a care and services delivery model. Finally, some pertinent questions from this perspective are put forward in terms of research, and a series of appraisals are expressed that will hopefully serve to help us understand all that we have become as individuals and as a species. The article proposes that the delivery of health care services be regarded as an adaptive complex system.

Using evaluation to adapt health information outreach to the complex environments of community-based organizations.

PubMed

Olney, Cynthia A

2005-10-01

After arguing that most community-based organizations (CBOs) function as complex adaptive systems, this white paper describes the evaluation goals, questions, indicators, and methods most important at different stages of community-based health information outreach. This paper presents the basic characteristics of complex adaptive systems and argues that the typical CBO can be considered this type of system. It then presents evaluation as a tool for helping outreach teams adapt their outreach efforts to the CBO environment and thus maximize success. Finally, it describes the goals, questions, indicators, and methods most important or helpful at each stage of evaluation (community assessment, needs assessment and planning, process evaluation, and outcomes assessment). Literature from complex adaptive systems as applied to health care, business, and evaluation settings is presented. Evaluation models and applications, particularly those based on participatory approaches, are presented as methods for maximizing the effectiveness of evaluation in dynamic CBO environments. If one accepts that CBOs function as complex adaptive systems-characterized by dynamic relationships among many agents, influences, and forces-then effective evaluation at the stages of community assessment, needs assessment and planning, process evaluation, and outcomes assessment is critical to outreach success.

A Holistic Management Architecture for Large-Scale Adaptive Networks

DTIC Science & Technology

2007-09-01

transmission and processing overhead required for management. The challenges of building models to describe dynamic systems are well-known to the field of...increases the challenge of finding a simple approach to assessing the state of the network. Moreover, the performance state of one network link may be... challenging . These obstacles indicate the need for a less comprehensive-analytical, more systemic-holistic approach to managing networks. This approach might

Intelligent neural network and fuzzy logic control of industrial and power systems

NASA Astrophysics Data System (ADS)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.

Synergy of adaptive thresholds and multiple transmitters in free-space optical communication.

PubMed

Louthain, James A; Schmidt, Jason D

2010-04-26

Laser propagation through extended turbulence causes severe beam spread and scintillation. Airborne laser communication systems require special considerations in size, complexity, power, and weight. Rather than using bulky, costly, adaptive optics systems, we reduce the variability of the received signal by integrating a two-transmitter system with an adaptive threshold receiver to average out the deleterious effects of turbulence. In contrast to adaptive optics approaches, systems employing multiple transmitters and adaptive thresholds exhibit performance improvements that are unaffected by turbulence strength. Simulations of this system with on-off-keying (OOK) showed that reducing the scintillation variations with multiple transmitters improves the performance of low-frequency adaptive threshold estimators by 1-3 dB. The combination of multiple transmitters and adaptive thresholding provided at least a 10 dB gain over implementing only transmitter pointing and receiver tilt correction for all three high-Rytov number scenarios. The scenario with a spherical-wave Rytov number R=0.20 enjoyed a 13 dB reduction in the required SNR for BER's between 10(-5) to 10(-3), consistent with the code gain metric. All five scenarios between 0.06 and 0.20 Rytov number improved to within 3 dB of the SNR of the lowest Rytov number scenario.

ASICs Approach for the Implementation of a Symmetric Triangular Fuzzy Coprocessor and Its Application to Adaptive Filtering

NASA Technical Reports Server (NTRS)

Starks, Scott; Abdel-Hafeez, Saleh; Usevitch, Bryan

1997-01-01

This paper discusses the implementation of a fuzzy logic system using an ASICs design approach. The approach is based upon combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets to obtain a novel structure for fuzzy logic system application development. The resulting structure utilizes a fuzzy static RAM to store the rule-base and the end-points of the triangular membership functions. This provides advantages over other approaches in which all sampled values of membership functions for all universes must be stored. The fuzzy coprocessor structure implements the fuzzification and defuzzification processes through a two-stage parallel pipeline architecture which is capable of executing complex fuzzy computations in less than 0.55us with an accuracy of more than 95%, thus making it suitable for a wide range of applications. Using the approach presented in this paper, a fuzzy logic rule-base can be directly downloaded via a host processor to an onchip rule-base memory with a size of 64 words. The fuzzy coprocessor's design supports up to 49 rules for seven fuzzy membership functions associated with each of the chip's two input variables. This feature allows designers to create fuzzy logic systems without the need for additional on-board memory. Finally, the paper reports on simulation studies that were conducted for several adaptive filter applications using the least mean squared adaptive algorithm for adjusting the knowledge rule-base.

Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

PubMed

Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

2018-01-01

In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

NASA Astrophysics Data System (ADS)

Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

Completing and Adapting Models of Biological Processes

NASA Technical Reports Server (NTRS)

Margaria, Tiziana; Hinchey, Michael G.; Raffelt, Harald; Rash, James L.; Rouff, Christopher A.; Steffen, Bernhard

2006-01-01

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.

A problem-oriented approach to understanding adaptation: lessons learnt from Alpine Shire, Victoria Australia.

NASA Astrophysics Data System (ADS)

Roman, Carolina

2010-05-01

Climate change is gaining attention as a significant strategic issue for localities that rely on their business sectors for economic viability. For businesses in the tourism sector, considerable research effort has sought to characterise the vulnerability to the likely impacts of future climate change through scenarios or ‘end-point' approaches (Kelly & Adger, 2000). Whilst useful, there are few demonstrable case studies that complement such work with a ‘start-point' approach that seeks to explore contextual vulnerability (O'Brien et al., 2007). This broader approach is inclusive of climate change as a process operating within a biophysical system and allows recognition of the complex interactions that occur in the coupled human-environmental system. A problem-oriented and interdisciplinary approach was employed at Alpine Shire, in northeast Victoria Australia, to explore the concept of contextual vulnerability and adaptability to stressors that include, but are not limited to climatic change. Using a policy sciences approach, the objective was to identify factors that influence existing vulnerabilities and that might consequently act as barriers to effective adaptation for the Shire's business community involved in the tourism sector. Analyses of results suggest that many threats, including the effects climate change, compete for the resources, strategy and direction of local tourism management bodies. Further analysis of conditioning factors revealed that many complex and interacting factors define the vulnerability and adaptive capacity of the Shire's tourism sector to the challenges of global change, which collectively have more immediate implications for policy and planning than long-term future climate change scenarios. An approximation of the common interest, i.e. enhancing capacity in business acumen amongst tourism operators, would facilitate adaptability and sustainability through the enhancement of social capital in this business community. Kelly, P. M., & Adger, W. N. (2000). Theory and practice in assessing vulnerability to climatic change and facilitating adaptation. Climatic Change, 47, 325-352. O'Brien, K., Eriksen, S., Nygaard, L. P., & Schjolden, A. (2007). Why different interpretations of vulnerability matter in climate change discourses. Climate Policy, 7, 73-88.

Opportunistic management of estuaries under climate change: A new adaptive decision-making framework and its practical application.

PubMed

Peirson, William; Davey, Erica; Jones, Alan; Hadwen, Wade; Bishop, Keith; Beger, Maria; Capon, Samantha; Fairweather, Peter; Creese, Bob; Smith, Timothy F; Gray, Leigh; Tomlinson, Rodger

2015-11-01

Ongoing coastal development and the prospect of severe climate change impacts present pressing estuary management and governance challenges. Robust approaches must recognise the intertwined social and ecological vulnerabilities of estuaries. Here, a new governance and management framework is proposed that recognises the integrated social-ecological systems of estuaries so as to permit transformative adaptation to climate change within these systems. The framework lists stakeholders and identifies estuarine uses and values. Goals are categorised that are specific to ecosystems, private property, public infrastructure, and human communities. Systematic adaptation management strategies are proposed with conceptual examples and associated governance approaches. Contrasting case studies are used to illustrate the practical application of these ideas. The framework will assist estuary managers worldwide to achieve their goals, minimise maladaptative responses, better identify competing interests, reduce stakeholder conflict and exploit opportunities for appropriate ecosystem restoration and sustainable development. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effectiveness of Culturally Appropriate Adaptations to Juvenile Justice Services

PubMed Central

Vergara, Andrew T.; Kathuria, Parul; Woodmass, Kyler; Janke, Robert; Wells, Susan J.

2017-01-01

Despite efforts to increase cultural competence of services within juvenile justice systems, disproportional minority contact (DMC) persists throughout Canada and the United States. Commonly cited approaches to decreasing DMC include large-scale systemic changes as well as enhancement of the cultural relevance and responsiveness of services delivered. Cultural adaptations to service delivery focus on prevention, decision-making, and treatment services to reduce initial contact, minimize unnecessary restraint, and reduce recidivism. Though locating rigorous testing of these approaches compared to standard interventions is difficult, this paper identifies and reports on such research. The Cochrane guidelines for systematic literature reviews and meta-analyses served as a foundation for study methodology. Databases such as Legal Periodicals and Books were searched through June 2015. Three studies were sufficiently rigorous to identify the effect of the cultural adaptations, and three studies that are making potentially important contributions to the field were also reviewed. PMID:29468092

Particle systems for adaptive, isotropic meshing of CAD models

PubMed Central

Levine, Joshua A.; Whitaker, Ross T.

2012-01-01

We present a particle-based approach for generating adaptive triangular surface and tetrahedral volume meshes from computer-aided design models. Input shapes are treated as a collection of smooth, parametric surface patches that can meet non-smoothly on boundaries. Our approach uses a hierarchical sampling scheme that places particles on features in order of increasing dimensionality. These particles reach a good distribution by minimizing an energy computed in 3D world space, with movements occurring in the parametric space of each surface patch. Rather than using a pre-computed measure of feature size, our system automatically adapts to both curvature as well as a notion of topological separation. It also enforces a measure of smoothness on these constraints to construct a sizing field that acts as a proxy to piecewise-smooth feature size. We evaluate our technique with comparisons against other popular triangular meshing techniques for this domain. PMID:23162181

An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors

PubMed Central

2014-01-01

Background Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. Methods We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Results Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min −1 (0.3 min −1) and -0.7 bpm (1.7 bpm) (compared to -0.2 min −1 (0.4 min −1) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time. Conclusions It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals. PMID:24886253

An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors.

PubMed

Foussier, Jerome; Teichmann, Daniel; Jia, Jing; Misgeld, Berno; Leonhardt, Steffen

2014-05-09

Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time. It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals.

A Non–Gas-Based Cryotherapy System for the Treatment of Cervical Intraepithelial Neoplasia: A Mixed-Methods Approach for Initial Development and Testing

PubMed Central

Cremer, Miriam; Paul, Proma; Bergman, Katie; Haas, Michael; Maza, Mauricio; Zevallos, Albert; Ossandon, Miguel; Garai, Jillian D; Winkler, Jennifer L

2017-01-01

ABSTRACT Background: Gas-based cryotherapy is the most widely used treatment strategy for cervical intraepithelial neoplasia (CIN) in low-resource settings, but reliance on gas presents challenges in low- and middle-income countries (LMICs). Our team adapted the original CryoPen Cryosurgical System, a cryotherapy device that does not require compressed gas and is powered by electricity, for use in LMICs. Methods: A mixed-methods approach was used involving both qualitative and quantitative methods. First, we used a user-centered design approach to identify priority features of the adapted device. U.S.-based and global potential users of the adapted CryoPen participated in discussion groups and a card sorting activity to rank 7 features of the adapted CryoPen: cost, durability, efficacy and safety, maintenance, no need for electricity, patient throughput, and portability. Mean and median rankings, overall rankings, and summary rankings by discussion group were generated. In addition, results of several quantitative tests were analyzed including bench testing to determine tip temperature and heat extraction capabilities; a pathology review of CIN grade 3 cases (N=107) to determine target depth of necrosis needed to achieve high efficacy; and a pilot study (N=5) investigating depth of necrosis achieved with the adapted device to assess efficacy. Results: Discussion groups revealed 4 priority themes for device development in addition to the need to ensure high efficacy and safety and low cost: improved portability, durability, ease of use, and potential for cure. Adaptions to the original CryoPen system included a single-core, single-tip model; rugged carrying case; custom circuit to allow car battery charging; and sterilization by high-level disinfection. In bench testing, there were no significant differences in tip temperature or heat extraction capability between the adapted CryoPen and the standard cryotherapy device. In 80% of the cases in the pilot study, the adapted CryoPen achieved the target depth of necrosis 3.5 mm established in the pathology review. Conclusion: The LMIC-adapted CryoPen overcomes barriers to standard gas-based cryotherapy by eliminating dependency on gas, increasing portability, and ensuring consistent freeze temperatures. Further testing and evaluation of the adapted CryoPen will be pursued to assess scalability and potential impact of this device in decreasing the cervical cancer burden in LMICs. PMID:28351879

A Non-Gas-Based Cryotherapy System for the Treatment of Cervical Intraepithelial Neoplasia: A Mixed-Methods Approach for Initial Development and Testing.

PubMed

Cremer, Miriam; Paul, Proma; Bergman, Katie; Haas, Michael; Maza, Mauricio; Zevallos, Albert; Ossandon, Miguel; Garai, Jillian D; Winkler, Jennifer L

2017-03-24

Gas-based cryotherapy is the most widely used treatment strategy for cervical intraepithelial neoplasia (CIN) in low-resource settings, but reliance on gas presents challenges in low- and middle-income countries (LMICs). Our team adapted the original CryoPen Cryosurgical System, a cryotherapy device that does not require compressed gas and is powered by electricity, for use in LMICs. A mixed-methods approach was used involving both qualitative and quantitative methods. First, we used a user-centered design approach to identify priority features of the adapted device. U.S.-based and global potential users of the adapted CryoPen participated in discussion groups and a card sorting activity to rank 7 features of the adapted CryoPen: cost, durability, efficacy and safety, maintenance, no need for electricity, patient throughput, and portability. Mean and median rankings, overall rankings, and summary rankings by discussion group were generated. In addition, results of several quantitative tests were analyzed including bench testing to determine tip temperature and heat extraction capabilities; a pathology review of CIN grade 3 cases (N=107) to determine target depth of necrosis needed to achieve high efficacy; and a pilot study (N=5) investigating depth of necrosis achieved with the adapted device to assess efficacy. Discussion groups revealed 4 priority themes for device development in addition to the need to ensure high efficacy and safety and low cost: improved portability, durability, ease of use, and potential for cure. Adaptions to the original CryoPen system included a single-core, single-tip model; rugged carrying case; custom circuit to allow car battery charging; and sterilization by high-level disinfection. In bench testing, there were no significant differences in tip temperature or heat extraction capability between the adapted CryoPen and the standard cryotherapy device. In 80% of the cases in the pilot study, the adapted CryoPen achieved the target depth of necrosis 3.5 mm established in the pathology review. The LMIC-adapted CryoPen overcomes barriers to standard gas-based cryotherapy by eliminating dependency on gas, increasing portability, and ensuring consistent freeze temperatures. Further testing and evaluation of the adapted CryoPen will be pursued to assess scalability and potential impact of this device in decreasing the cervical cancer burden in LMICs. © Cremer et al.

Industry Cluster's Adaptive Co-competition Behavior Modeling Inspired by Swarm Intelligence

NASA Astrophysics Data System (ADS)

Xiang, Wei; Ye, Feifan

Adaptation helps the individual enterprise to adjust its behavior to uncertainties in environment and hence determines a healthy growth of both the individuals and the whole industry cluster as well. This paper is focused on the study on co-competition adaptation behavior of industry cluster, which is inspired by swarm intelligence mechanisms. By referencing to ant cooperative transportation and ant foraging behavior and their related swarm intelligence approaches, the cooperative adaptation and competitive adaptation behavior are studied and relevant models are proposed. Those adaptive co-competition behaviors model can be integrated to the multi-agent system of industry cluster to make the industry cluster model more realistic.

Adaptive/learning control of large space structures - System identification techniques. [for multi-configuration flexible spacecraft

NASA Technical Reports Server (NTRS)

Thau, F. E.; Montgomery, R. C.

1980-01-01

Techniques developed for the control of aircraft under changing operating conditions are used to develop a learning control system structure for a multi-configuration, flexible space vehicle. A configuration identification subsystem that is to be used with a learning algorithm and a memory and control process subsystem is developed. Adaptive gain adjustments can be achieved by this learning approach without prestoring of large blocks of parameter data and without dither signal inputs which will be suppressed during operations for which they are not compatible. The Space Shuttle Solar Electric Propulsion (SEP) experiment is used as a sample problem for the testing of adaptive/learning control system algorithms.

A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

PubMed Central

Griol, David

2016-01-01

Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users. PMID:26819592

Climate change and viticulture in Mediterranean climates: the complex response of socio-ecosystems. A comparative case study from France and Australia (1955-2040)

NASA Astrophysics Data System (ADS)

Lereboullet, A.-L.; Beltrando, G.; Bardsley, D. K.

2012-04-01

The wine industry is very sensitive to extreme weather events, especially to temperatures above 35°C and drought. In a context of global climate change, Mediterranean climate regions are predicted to experience higher variability in rainfall and temperatures and an increased occurrence of extreme weather events. Some viticultural systems could be particularly at risk in those regions, considering their marginal position in the growth climatic range of Vitis vinifera, the long commercial lifespan of a vineyard, the high added-value of wine and the volatile nature of global markets. The wine industry, like other agricultural systems, is inserted in complex networks of climatic and non-climatic (other physical, economical, social and legislative) components, with constant feedbacks. We use a socio-ecosystem approach to analyse the adaptation of two Mediterranean viticultural systems to recent and future increase of extreme weather events. The present analysis focuses on two wine regions with a hot-summer Mediterranean climate (CSb type in the Köppen classification): Côtes-du-Roussillon in southern France and McLaren Vale in southern Australia. Using climate data from two synoptic weather stations, Perpignan (France) and Adelaide (Australia), with time series running from 1955 to 2010, we highlight changes in rainfall patterns and an increase in the number of days with Tx >35°c since the last three decades in both regions. Climate models (DRIAS project data for France and CSIRO Mk3.5 for Australia) project similar trends in the future. To date, very few projects have focused on an international comparison of the adaptive capacity of viticultural systems to climate change with a holistic approach. Here, the analysis of climate data was complemented by twenty in-depth semi-structured interviews with key actors of the two regional wine industries, in order to analyse adaptation strategies put in place regarding recent climate evolution. This mixed-methods approach allows for a comprehensive assessment of adaptation capacity of the two viticultural systems to future climate change. The strategies of grape growers and wine producers focus on maintaining optimal yields and a constant wine style adapted to markets in a variable and uncertain climate. Their implementation and efficiency depend strongly on non-climatic factors. Thus, adaptation capacity to recent and future climate change depends strongly on adaptation to other non-climatic changes.

Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (University Level English Preparatory School Example)

ERIC Educational Resources Information Center

Aslan, Burak Galip; Öztürk, Özlem; Inceoglu, Mustafa Murat

2014-01-01

Considering the increasing importance of adaptive approaches in CALL systems, this study implemented a machine learning based student modeling middleware with Bayesian networks. The profiling approach of the student modeling system is based on Felder and Silverman's Learning Styles Model and Felder and Soloman's Index of Learning Styles…

Integrating adaptive management and ecosystem services concepts to improve natural resource management: Challenges and opportunities

USGS Publications Warehouse

Epanchin-Niell, Rebecca S.; Boyd, James W.; Macauley, Molly K.; Scarlett, Lynn; Shapiro, Carl D.; Williams, Byron K.

2018-05-07

Executive Summary—OverviewNatural resource managers must make decisions that affect broad-scale ecosystem processes involving large spatial areas, complex biophysical interactions, numerous competing stakeholder interests, and highly uncertain outcomes. Natural and social science information and analyses are widely recognized as important for informing effective management. Chief among the systematic approaches for improving the integration of science into natural resource management are two emergent science concepts, adaptive management and ecosystem services. Adaptive management (also referred to as “adaptive decision making”) is a deliberate process of learning by doing that focuses on reducing uncertainties about management outcomes and system responses to improve management over time. Ecosystem services is a conceptual framework that refers to the attributes and outputs of ecosystems (and their components and functions) that have value for humans.This report explores how ecosystem services can be moved from concept into practice through connection to a decision framework—adaptive management—that accounts for inherent uncertainties. Simultaneously, the report examines the value of incorporating ecosystem services framing and concepts into adaptive management efforts.Adaptive management and ecosystem services analyses have not typically been used jointly in decision making. However, as frameworks, they have a natural—but to date underexplored—affinity. Both are policy and decision oriented in that they attempt to represent the consequences of resource management choices on outcomes of interest to stakeholders. Both adaptive management and ecosystem services analysis take an empirical approach to the analysis of ecological systems. This systems orientation is a byproduct of the fact that natural resource actions affect ecosystems—and corresponding societal outcomes—often across large geographic scales. Moreover, because both frameworks focus on resource systems, both must confront the analytical challenges of systems modeling—in terms of complexity, dynamics, and uncertainty.Given this affinity, the integration of ecosystem services analysis and adaptive management poses few conceptual hurdles. In this report, we synthesize discussions from two workshops that considered ways in which adaptive management approaches and ecosystem service concepts may be complementary, such that integrating them into a common framework may lead to improved natural resource management outcomes. Although the literature on adaptive management and ecosystem services is vast and growing, the report focuses specifically on the integration of these two concepts rather than aiming to provide new definitions or an indepth review or primer of the concepts individually.Key issues considered include the bidirectional links between adaptive decision making and ecosystem services, as well as the potential benefits and inevitable challenges arising in the development and use of an integrated framework. Specifically, the workshops addressed the following questions:How can application of ecosystem service analysis within an adaptive decision process improve the outcomes of management and advance understanding of ecosystem service identification, production, and valuation?How can these concepts be integrated in concept and practice?What are the constraints and challenges to integrating adaptive management and ecosystem services?And, should the integration of these concepts be moved forward to wider application—and if so, how?

Evolutionary genetics of plant adaptation.

PubMed

Anderson, Jill T; Willis, John H; Mitchell-Olds, Thomas

2011-07-01

Plants provide unique opportunities to study the mechanistic basis and evolutionary processes of adaptation to diverse environmental conditions. Complementary laboratory and field experiments are important for testing hypotheses reflecting long-term ecological and evolutionary history. For example, these approaches can infer whether local adaptation results from genetic tradeoffs (antagonistic pleiotropy), where native alleles are best adapted to local conditions, or if local adaptation is caused by conditional neutrality at many loci, where alleles show fitness differences in one environment, but not in a contrasting environment. Ecological genetics in natural populations of perennial or outcrossing plants can also differ substantially from model systems. In this review of the evolutionary genetics of plant adaptation, we emphasize the importance of field studies for understanding the evolutionary dynamics of model and nonmodel systems, highlight a key life history trait (flowering time) and discuss emerging conservation issues. Copyright © 2011 Elsevier Ltd. All rights reserved.

Human factors systems approach to healthcare quality and patient safety

PubMed Central

Carayon, Pascale; Wetterneck, Tosha B.; Rivera-Rodriguez, A. Joy; Hundt, Ann Schoofs; Hoonakker, Peter; Holden, Richard; Gurses, Ayse P.

2013-01-01

Human factors systems approaches are critical for improving healthcare quality and patient safety. The SEIPS (Systems Engineering Initiative for Patient Safety) model of work system and patient safety is a human factors systems approach that has been successfully applied in healthcare research and practice. Several research and practical applications of the SEIPS model are described. Important implications of the SEIPS model for healthcare system and process redesign are highlighted. Principles for redesigning healthcare systems using the SEIPS model are described. Balancing the work system and encouraging the active and adaptive role of workers are key principles for improving healthcare quality and patient safety. PMID:23845724

A disturbance observer-based adaptive control approach for flexure beam nano manipulators.

PubMed

Zhang, Yangming; Yan, Peng; Zhang, Zhen

2016-01-01

This paper presents a systematic modeling and control methodology for a two-dimensional flexure beam-based servo stage supporting micro/nano manipulations. Compared with conventional mechatronic systems, such systems have major control challenges including cross-axis coupling, dynamical uncertainties, as well as input saturations, which may have adverse effects on system performance unless effectively eliminated. A novel disturbance observer-based adaptive backstepping-like control approach is developed for high precision servo manipulation purposes, which effectively accommodates model uncertainties and coupling dynamics. An auxiliary system is also introduced, on top of the proposed control scheme, to compensate the input saturations. The proposed control architecture is deployed on a customized-designed nano manipulating system featured with a flexure beam structure and voice coil actuators (VCA). Real time experiments on various manipulating tasks, such as trajectory/contour tracking, demonstrate precision errors of less than 1%. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Application of the Modular Automated Reconfigurable Assembly System (MARAS) concept to adaptable vision gauging and parts feeding

NASA Technical Reports Server (NTRS)

By, Andre Bernard; Caron, Ken; Rothenberg, Michael; Sales, Vic

1994-01-01

This paper presents the first phase results of a collaborative effort between university researchers and a flexible assembly systems integrator to implement a comprehensive modular approach to flexible assembly automation. This approach, named MARAS (Modular Automated Reconfigurable Assembly System), has been structured to support multiple levels of modularity in terms of both physical components and system control functions. The initial focus of the MARAS development has been on parts gauging and feeding operations for cylinder lock assembly. This phase is nearing completion and has resulted in the development of a highly configurable system for vision gauging functions on a wide range of small components (2 mm to 100 mm in size). The reconfigurable concepts implemented in this adaptive Vision Gauging Module (VGM) are now being extended to applicable aspects of the singulating, selecting, and orienting functions required for the flexible feeding of similar mechanical components and assemblies.

Combined AIE/EBE/GMRES approach to incompressible flows. [Adaptive Implicit-Explicit/Grouped Element-by-Element/Generalized Minimum Residuals

NASA Technical Reports Server (NTRS)

Liou, J.; Tezduyar, T. E.

1990-01-01

Adaptive implicit-explicit (AIE), grouped element-by-element (GEBE), and generalized minimum residuals (GMRES) solution techniques for incompressible flows are combined. In this approach, the GEBE and GMRES iteration methods are employed to solve the equation systems resulting from the implicitly treated elements, and therefore no direct solution effort is involved. The benchmarking results demonstrate that this approach can substantially reduce the CPU time and memory requirements in large-scale flow problems. Although the description of the concepts and the numerical demonstration are based on the incompressible flows, the approach presented here is applicable to larger class of problems in computational mechanics.

Adaptive harvest management of North American waterfowl populations: a brief history and future prospects

USGS Publications Warehouse

Nichols, J.D.; Runge, M.C.; Johnson, F.A.; Williams, B.K.

2007-01-01

Since 1995, the US Fish and Wildlife Service has used an adaptive approach to the management of sport harvest of mid-continent Mallard ducks (Anas platyrhynchos) in North America. This approach differs from many current approaches to conservation and management in requiring close collaboration between managers and scientists. Key elements of this process are objectives, alternative management actions, models permitting prediction of system responses, and a monitoring program. The iterative process produces optimal management decisions and leads to reduction in uncertainty about response of populations to management. This general approach to management has a number of desirable features and is recommended for use in many other programs of management and conservation.

An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream

PubMed Central

Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.

2016-01-01

This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081

Plasma Exosome Profiling of Cancer Patients by a Next Generation Systems Biology Approach.

PubMed

Domenyuk, Valeriy; Zhong, Zhenyu; Stark, Adam; Xiao, Nianqing; O'Neill, Heather A; Wei, Xixi; Wang, Jie; Tinder, Teresa T; Tonapi, Sonal; Duncan, Janet; Hornung, Tassilo; Hunter, Andrew; Miglarese, Mark R; Schorr, Joachim; Halbert, David D; Quackenbush, John; Poste, George; Berry, Donald A; Mayer, Günter; Famulok, Michael; Spetzler, David

2017-02-20

Technologies capable of characterizing the full breadth of cellular systems need to be able to measure millions of proteins, isoforms, and complexes simultaneously. We describe an approach that fulfils this criterion: Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT). ADAPT employs an enriched library of single-stranded oligodeoxynucleotides (ssODNs) to profile complex biological samples, thus achieving an unprecedented coverage of system-wide, native biomolecules. We used ADAPT as a highly specific profiling tool that distinguishes women with or without breast cancer based on circulating exosomes in their blood. To develop ADAPT, we enriched a library of ~10 11 ssODNs for those associating with exosomes from breast cancer patients or controls. The resulting 10 6 enriched ssODNs were then profiled against plasma from independent groups of healthy and breast cancer-positive women. ssODN-mediated affinity purification and mass spectrometry identified low-abundance exosome-associated proteins and protein complexes, some with known significance in both normal homeostasis and disease. Sequencing of the recovered ssODNs provided quantitative measures that were used to build highly accurate multi-analyte signatures for patient classification. Probing plasma from 500 subjects with a smaller subset of 2000 resynthesized ssODNs stratified healthy, breast biopsy-negative, and -positive women. An AUC of 0.73 was obtained when comparing healthy donors with biopsy-positive patients.

Plasma Exosome Profiling of Cancer Patients by a Next Generation Systems Biology Approach

PubMed Central

Domenyuk, Valeriy; Zhong, Zhenyu; Stark, Adam; Xiao, Nianqing; O’Neill, Heather A.; Wei, Xixi; Wang, Jie; Tinder, Teresa T.; Tonapi, Sonal; Duncan, Janet; Hornung, Tassilo; Hunter, Andrew; Miglarese, Mark R.; Schorr, Joachim; Halbert, David D.; Quackenbush, John; Poste, George; Berry, Donald A.; Mayer, Günter; Famulok, Michael; Spetzler, David

2017-01-01

Technologies capable of characterizing the full breadth of cellular systems need to be able to measure millions of proteins, isoforms, and complexes simultaneously. We describe an approach that fulfils this criterion: Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT). ADAPT employs an enriched library of single-stranded oligodeoxynucleotides (ssODNs) to profile complex biological samples, thus achieving an unprecedented coverage of system-wide, native biomolecules. We used ADAPT as a highly specific profiling tool that distinguishes women with or without breast cancer based on circulating exosomes in their blood. To develop ADAPT, we enriched a library of ~1011 ssODNs for those associating with exosomes from breast cancer patients or controls. The resulting 106 enriched ssODNs were then profiled against plasma from independent groups of healthy and breast cancer-positive women. ssODN-mediated affinity purification and mass spectrometry identified low-abundance exosome-associated proteins and protein complexes, some with known significance in both normal homeostasis and disease. Sequencing of the recovered ssODNs provided quantitative measures that were used to build highly accurate multi-analyte signatures for patient classification. Probing plasma from 500 subjects with a smaller subset of 2000 resynthesized ssODNs stratified healthy, breast biopsy-negative, and -positive women. An AUC of 0.73 was obtained when comparing healthy donors with biopsy-positive patients. PMID:28218293

Robustness via Run-Time Adaptation of Contingent Plans

NASA Technical Reports Server (NTRS)

Bresina, John L.; Washington, Richard; Norvig, Peter (Technical Monitor)

2000-01-01

In this paper, we discuss our approach to making the behavior of planetary rovers more robust for the purpose of increased productivity. Due to the inherent uncertainty in rover exploration, the traditional approach to rover control is conservative, limiting the autonomous operation of the rover and sacrificing performance for safety. Our objective is to increase the science productivity possible within a single uplink by allowing the rover's behavior to be specified with flexible, contingent plans and by employing dynamic plan adaptation during execution. We have deployed a system exhibiting flexible, contingent execution; this paper concentrates on our ongoing efforts on plan adaptation, Plans can be revised in two ways: plan steps may be deleted, with execution continuing with the plan suffix; and the current plan may be merged with an "alternate plan" from an on-board library. The plan revision action is chosen to maximize the expected utility of the plan. Plan merging and action deletion constitute a more conservative general-purpose planning system; in return, our approach is more efficient and more easily verified, two important criteria for deployed rovers.

Adaptive Temporal Matched Filtering for Noise Suppression in Fiber Optic Distributed Acoustic Sensing.

PubMed

Ölçer, İbrahim; Öncü, Ahmet

2017-06-05

Distributed vibration sensing based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) is being widely used in several applications. However, one of the main challenges in coherent detection-based ϕ -OTDR systems is the fading noise, which impacts the detection performance. In addition, typical signal averaging and differentiating techniques are not suitable for detecting high frequency events. This paper presents a new approach for reducing the effect of fading noise in fiber optic distributed acoustic vibration sensing systems without any impact on the frequency response of the detection system. The method is based on temporal adaptive processing of ϕ -OTDR signals. The fundamental theory underlying the algorithm, which is based on signal-to-noise ratio (SNR) maximization, is presented, and the efficacy of our algorithm is demonstrated with laboratory experiments and field tests. With the proposed digital processing technique, the results show that more than 10 dB of SNR values can be achieved without any reduction in the system bandwidth and without using additional optical amplifier stages in the hardware. We believe that our proposed adaptive processing approach can be effectively used to develop fiber optic-based distributed acoustic vibration sensing systems.

Adaptive feedforward control of non-minimum phase structural systems

NASA Astrophysics Data System (ADS)

Vipperman, J. S.; Burdisso, R. A.

1995-06-01

Adaptive feedforward control algorithms have been effectively applied to stationary disturbance rejection. For structural systems, the ideal feedforward compensator is a recursive filter which is a function of the transfer functions between the disturbance and control inputs and the error sensor output. Unfortunately, most control configurations result in a non-minimum phase control path; even a collocated control actuator and error sensor will not necessarily produce a minimum phase control path in the discrete domain. Therefore, the common practice is to choose a suitable approximation of the ideal compensator. In particular, all-zero finite impulse response (FIR) filters are desirable because of their inherent stability for adaptive control approaches. However, for highly resonant systems, large order filters are required for broadband applications. In this work, a control configuration is investigated for controlling non-minimum phase lightly damped structural systems. The control approach uses low order FIR filters as feedforward compensators in a configuration that has one more control actuator than error sensors. The performance of the controller was experimentally evaluated on a simply supported plate under white noise excitation for a two-input, one-output (2I1O) system. The results show excellent error signal reduction, attesting to the effectiveness of the method.

Adaptive Temporal Matched Filtering for Noise Suppression in Fiber Optic Distributed Acoustic Sensing

PubMed Central

Ölçer, İbrahim; Öncü, Ahmet

2017-01-01

Distributed vibration sensing based on phase-sensitive optical time domain reflectometry (ϕ-OTDR) is being widely used in several applications. However, one of the main challenges in coherent detection-based ϕ-OTDR systems is the fading noise, which impacts the detection performance. In addition, typical signal averaging and differentiating techniques are not suitable for detecting high frequency events. This paper presents a new approach for reducing the effect of fading noise in fiber optic distributed acoustic vibration sensing systems without any impact on the frequency response of the detection system. The method is based on temporal adaptive processing of ϕ-OTDR signals. The fundamental theory underlying the algorithm, which is based on signal-to-noise ratio (SNR) maximization, is presented, and the efficacy of our algorithm is demonstrated with laboratory experiments and field tests. With the proposed digital processing technique, the results show that more than 10 dB of SNR values can be achieved without any reduction in the system bandwidth and without using additional optical amplifier stages in the hardware. We believe that our proposed adaptive processing approach can be effectively used to develop fiber optic-based distributed acoustic vibration sensing systems. PMID:28587240

Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming.

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

In this paper, the robust control problem for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-based control method. First, the robust control problem is transformed into a corresponding optimal control problem with an augmented control and an appropriate cost function. Under the event-based mechanism, we prove that the solution of the optimal control problem can asymptotically stabilize the uncertain system with an adaptive triggering condition. That is, the designed event-based controller is robust to the original uncertain system. Note that the event-based controller is updated only when the triggering condition is satisfied, which can save the communication resources between the plant and the controller. Then, a single network adaptive dynamic programming structure with experience replay technique is constructed to approach the optimal control policies. The stability of the closed-loop system with the event-based control policy and the augmented control policy is analyzed using the Lyapunov approach. Furthermore, we prove that the minimal intersample time is bounded by a nonzero positive constant, which excludes Zeno behavior during the learning process. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Adaptive robust fault tolerant control design for a class of nonlinear uncertain MIMO systems with quantization.

PubMed

Ao, Wei; Song, Yongdong; Wen, Changyun

2017-05-01

In this paper, we investigate the adaptive control problem for a class of nonlinear uncertain MIMO systems with actuator faults and quantization effects. Under some mild conditions, an adaptive robust fault-tolerant control is developed to compensate the affects of uncertainties, actuator failures and errors caused by quantization, and a range of the parameters for these quantizers is established. Furthermore, a Lyapunov-like approach is adopted to demonstrate that the ultimately uniformly bounded output tracking error is guaranteed by the controller, and the signals of the closed-loop system are ensured to be bounded, even in the presence of at most m-q actuators stuck or outage. Finally, numerical simulations are provided to verify and illustrate the effectiveness of the proposed adaptive schemes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Improved Information Retrieval Performance on SQL Database Using Data Adapter

NASA Astrophysics Data System (ADS)

Husni, M.; Djanali, S.; Ciptaningtyas, H. T.; Wicaksana, I. G. N. A.

2018-02-01

The NoSQL databases, short for Not Only SQL, are increasingly being used as the number of big data applications increases. Most systems still use relational databases (RDBs), but as the number of data increases each year, the system handles big data with NoSQL databases to analyze and access data more quickly. NoSQL emerged as a result of the exponential growth of the internet and the development of web applications. The query syntax in the NoSQL database differs from the SQL database, therefore requiring code changes in the application. Data adapter allow applications to not change their SQL query syntax. Data adapters provide methods that can synchronize SQL databases with NotSQL databases. In addition, the data adapter provides an interface which is application can access to run SQL queries. Hence, this research applied data adapter system to synchronize data between MySQL database and Apache HBase using direct access query approach, where system allows application to accept query while synchronization process in progress. From the test performed using data adapter, the results obtained that the data adapter can synchronize between SQL databases, MySQL, and NoSQL database, Apache HBase. This system spends the percentage of memory resources in the range of 40% to 60%, and the percentage of processor moving from 10% to 90%. In addition, from this system also obtained the performance of database NoSQL better than SQL database.

Adaptive interface for personalizing information seeking.

PubMed

Narayanan, S; Koppaka, Lavanya; Edala, Narasimha; Loritz, Don; Daley, Raymond

2004-12-01

An adaptive interface autonomously adjusts its display and available actions to current goals and abilities of the user by assessing user status, system task, and the context. Knowledge content adaptability is needed for knowledge acquisition and refinement tasks. In the case of knowledge content adaptability, the requirements of interface design focus on the elicitation of information from the user and the refinement of information based on patterns of interaction. In such cases, the emphasis on adaptability is on facilitating information search and knowledge discovery. In this article, we present research on adaptive interfaces that facilitates personalized information seeking from a large data warehouse. The resulting proof-of-concept system, called source recommendation system (SRS), assists users in locating and navigating data sources in the repository. Based on the initial user query and an analysis of the content of the search results, the SRS system generates a profile of the user tailored to the individual's context during information seeking. The user profiles are refined successively and are used in progressively guiding the user to the appropriate set of sources within the knowledge base. The SRS system is implemented as an Internet browser plug-in to provide a seamless and unobtrusive, personalized experience to the users during the information search process. The rationale behind our approach, system design, empirical evaluation, and implications for research on adaptive interfaces are described in this paper.

A resilience perspective to water risk management: case-study application of the adaptation tipping point method

NASA Astrophysics Data System (ADS)

Gersonius, Berry; Ashley, Richard; Jeuken, Ad; Nasruddin, Fauzy; Pathirana, Assela; Zevenbergen, Chris

2010-05-01

In a context of high uncertainty about hydrological variables due to climate change and other factors, the development of updated risk management approaches is as important as—if not more important than—the provision of improved data and forecasts of the future. Traditional approaches to adaptation attempt to manage future water risks to cities with the use of the predict-then-adapt method. This method uses hydrological change projections as the starting point to identify adaptive strategies, which is followed by analysing the cause-effect chain based on some sort of Pressures-State-Impact-Response (PSIR) scheme. The predict-then-adapt method presumes that it is possible to define a singular (optimal) adaptive strategy according to a most likely or average projection of future change. A key shortcoming of the method is, however, that the planning of water management structures is typically decoupled from forecast uncertainties and is, as such, inherently inflexible. This means that there is an increased risk of under- or over-adaptation, resulting in either mal-functioning or unnecessary costs. Rather than taking a traditional approach, responsible water risk management requires an alternative approach to adaptation that recognises and cultivates resiliency for change. The concept of resiliency relates to the capability of complex socio-technical systems to make aspirational levels of functioning attainable despite the occurrence of possible changes. Focusing on resiliency does not attempt to reduce uncertainty associated with future change, but rather to develop better ways of managing it. This makes it a particularly relevant perspective for adaptation to long-term hydrological change. Although resiliency is becoming more refined as a theory, the application of the concept to water risk management is still in an initial phase. Different methods are used in practice to support the implementation of a resilience-focused approach. Typically these approaches start the identification and analysis of adaptive strategies at the end of PSIR scheme: impact and examine whether, and for how long, current risk management strategies will continue to be effective under different future conditions. The most noteworthy application of this approach is the adaptation tipping point method. Adaptation tipping points (ATP) are defined as the points where the magnitude of change is such that the current risk management strategy can no longer meet its objectives. In the ATP method, policy objectives, determining aspirational functioning, are taken as the starting point. Also, the current measures to achieve these objectives are described. This is followed by a sensitivity analysis to determine the optimal and critical boundary conditions (state). Lastly, the state is related to pressures in terms of future change. It should be noted that in the ATP method the driver for adopting a new risk management strategy is not future change as such, but rather failing to meet the policy objectives. In the current paper, the ATP method is applied to the case study of an existing stormwater system in Dordrecht (the Netherlands). This application shows the potential of the ATP method to reduce the complexity of implementing a resilience-focused approach to water risk management. It is expected that this will help foster greater practical relevance of resilience as a perspective for the planning of water management structures.

An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation.

PubMed

Li, Zheng; Zhang, Hai; Zhou, Qifan; Che, Huan

2017-09-05

The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes.

An Adaptive Low-Cost INS/GNSS Tightly-Coupled Integration Architecture Based on Redundant Measurement Noise Covariance Estimation

PubMed Central

Li, Zheng; Zhang, Hai; Zhou, Qifan; Che, Huan

2017-01-01

The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance estimation (RMNCE) theorem, which adaptively estimates measurement noise properties by analyzing the difference sequences of system measurements. The proposed RMNCE approach is then applied to design both a modified weighted satellite selection algorithm and a type of adaptive unscented Kalman filter (UKF) to improve the performance of the tightly-coupled integration system. In addition, an adaptive measurement noise covariance expanding algorithm is developed to mitigate outliers when facing heavy multipath and other harsh situations. Both semi-physical simulation and field experiments were conducted to evaluate the performance of the proposed architecture and were compared with state-of-the-art algorithms. The results validate that the RMNCE provides a significant improvement in the measurement noise covariance estimation and the proposed architecture can improve the accuracy and reliability of the INS/GNSS tightly-coupled systems. The proposed architecture can effectively limit positioning errors under conditions of poor GNSS measurement quality and outperforms all the compared schemes. PMID:28872629

Generating Shifting Workloads to Benchmark Adaptability in Relational Database Systems

NASA Astrophysics Data System (ADS)

Rabl, Tilmann; Lang, Andreas; Hackl, Thomas; Sick, Bernhard; Kosch, Harald

A large body of research concerns the adaptability of database systems. Many commercial systems already contain autonomic processes that adapt configurations as well as data structures and data organization. Yet there is virtually no possibility for a just measurement of the quality of such optimizations. While standard benchmarks have been developed that simulate real-world database applications very precisely, none of them considers variations in workloads produced by human factors. Today’s benchmarks test the performance of database systems by measuring peak performance on homogeneous request streams. Nevertheless, in systems with user interaction access patterns are constantly shifting. We present a benchmark that simulates a web information system with interaction of large user groups. It is based on the analysis of a real online eLearning management system with 15,000 users. The benchmark considers the temporal dependency of user interaction. Main focus is to measure the adaptability of a database management system according to shifting workloads. We will give details on our design approach that uses sophisticated pattern analysis and data mining techniques.

Microfluidic approaches to malaria detection

PubMed Central

Gascoyne, Peter; Satayavivad, Jutamaad; Ruchirawat, Mathuros

2009-01-01

Microfluidic systems are under development to address a variety of medical problems. Key advantages of micrototal analysis systems based on microfluidic technology are the promise of small size and the integration of sample handling and measurement functions within a single, automated device having low mass-production costs. Here, we review the spectrum of methods currently used to detect malaria, consider their advantages and disadvantages, and discuss their adaptability towards integration into small, automated micro total analysis systems. Molecular amplification methods emerge as leading candidates for chip-based systems because they offer extremely high sensitivity, the ability to recognize malaria species and strain, and they will be adaptable to the detection of new genotypic signatures that will emerge from current genomic-based research of the disease. Current approaches to the development of chip-based molecular amplification are considered with special emphasis on flow-through PCR, and we present for the first time the method of malaria specimen preparation by dielectrophoretic field-flow-fractionation. Although many challenges must be addressed to realize a micrototal analysis system for malaria diagnosis, it is concluded that the potential benefits of the approach are well worth pursuing. PMID:14744562

Fully implicit adaptive mesh refinement MHD algorithm

NASA Astrophysics Data System (ADS)

Philip, Bobby

2005-10-01

In the macroscopic simulation of plasmas, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. The former results in stiffness due to the presence of very fast waves. The latter requires one to resolve the localized features that the system develops. Traditional approaches based on explicit time integration techniques and fixed meshes are not suitable for this challenge, as such approaches prevent the modeler from using realistic plasma parameters to keep the computation feasible. We propose here a novel approach, based on implicit methods and structured adaptive mesh refinement (SAMR). Our emphasis is on both accuracy and scalability with the number of degrees of freedom. To our knowledge, a scalable, fully implicit AMR algorithm has not been accomplished before for MHD. As a proof-of-principle, we focus on the reduced resistive MHD model as a basic MHD model paradigm, which is truly multiscale. The approach taken here is to adapt mature physics-based technologyootnotetextL. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002) to AMR grids, and employ AMR-aware multilevel techniques (such as fast adaptive composite --FAC-- algorithms) for scalability. We will demonstrate that the concept is indeed feasible, featuring optimal scalability under grid refinement. Results of fully-implicit, dynamically-adaptive AMR simulations will be presented on a variety of problems.

Evolution of adaptation mechanisms: Adaptation energy, stress, and oscillating death.

PubMed

Gorban, Alexander N; Tyukina, Tatiana A; Smirnova, Elena V; Pokidysheva, Lyudmila I

2016-09-21

In 1938, Selye proposed the notion of adaptation energy and published 'Experimental evidence supporting the conception of adaptation energy.' Adaptation of an animal to different factors appears as the spending of one resource. Adaptation energy is a hypothetical extensive quantity spent for adaptation. This term causes much debate when one takes it literally, as a physical quantity, i.e. a sort of energy. The controversial points of view impede the systematic use of the notion of adaptation energy despite experimental evidence. Nevertheless, the response to many harmful factors often has general non-specific form and we suggest that the mechanisms of physiological adaptation admit a very general and nonspecific description. We aim to demonstrate that Selye׳s adaptation energy is the cornerstone of the top-down approach to modelling of non-specific adaptation processes. We analyze Selye׳s axioms of adaptation energy together with Goldstone׳s modifications and propose a series of models for interpretation of these axioms. Adaptation energy is considered as an internal coordinate on the 'dominant path' in the model of adaptation. The phenomena of 'oscillating death' and 'oscillating remission' are predicted on the base of the dynamical models of adaptation. Natural selection plays a key role in the evolution of mechanisms of physiological adaptation. We use the fitness optimization approach to study of the distribution of resources for neutralization of harmful factors, during adaptation to a multifactor environment, and analyze the optimal strategies for different systems of factors. Copyright © 2016 Elsevier Ltd. All rights reserved.

An adaptive approach to the physical annealing strategy for simulated annealing

NASA Astrophysics Data System (ADS)

Hasegawa, M.

2013-02-01

A new and reasonable method for adaptive implementation of simulated annealing (SA) is studied on two types of random traveling salesman problems. The idea is based on the previous finding on the search characteristics of the threshold algorithms, that is, the primary role of the relaxation dynamics in their finite-time optimization process. It is shown that the effective temperature for optimization can be predicted from the system's behavior analogous to the stabilization phenomenon occurring in the heating process starting from a quenched solution. The subsequent slow cooling near the predicted point draws out the inherent optimizing ability of finite-time SA in more straightforward manner than the conventional adaptive approach.

Chaos control for the output-constrained system by using adaptive dynamic surface technology and application to the brushless DC motor

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

Luo, Shaohua, E-mail: hua66com@163.com; School of Automation, Chongqing University, Chongqing 400044; Hou, Zhiwei

2015-12-15

In this paper, chaos control is proposed for the output- constrained system with uncertain control gain and time delay and is applied to the brushless DC motor. Using the dynamic surface technology, the controller overcomes the repetitive differentiation of backstepping and boundedness hypothesis of pre-determined control gain by incorporating radial basis function neural network and adaptive technology. The tangent barrier Lyapunov function is employed for time-delay chaotic system to prevent constraint violation. It is proved that the proposed control approach can guarantee asymptotically stable in the sense of uniformly ultimate boundedness without constraint violation. Finally, the effectiveness of the proposedmore » approach is demonstrated on the brushless DC motor example.« less

Forest Management Under Uncertainty for Multiple Bird Population Objectives

Treesearch

Clinton T. Moore; W. Todd Plummer; Michael J. Conroy

2005-01-01

We advocate adaptive programs of decision making and monitoring for the management of forest birds when responses by populations to management, and particularly management trade-offs among populations, are uncertain. Models are necessary components of adaptive management. Under this approach, uncertainty about the behavior of a managed system is explicitly captured in...

On Event-Triggered Adaptive Architectures for Decentralized and Distributed Control of Large-Scale Modular Systems

PubMed Central

Albattat, Ali; Gruenwald, Benjamin C.; Yucelen, Tansel

2016-01-01

The last decade has witnessed an increased interest in physical systems controlled over wireless networks (networked control systems). These systems allow the computation of control signals via processors that are not attached to the physical systems, and the feedback loops are closed over wireless networks. The contribution of this paper is to design and analyze event-triggered decentralized and distributed adaptive control architectures for uncertain networked large-scale modular systems; that is, systems consist of physically-interconnected modules controlled over wireless networks. Specifically, the proposed adaptive architectures guarantee overall system stability while reducing wireless network utilization and achieving a given system performance in the presence of system uncertainties that can result from modeling and degraded modes of operation of the modules and their interconnections between each other. In addition to the theoretical findings including rigorous system stability and the boundedness analysis of the closed-loop dynamical system, as well as the characterization of the effect of user-defined event-triggering thresholds and the design parameters of the proposed adaptive architectures on the overall system performance, an illustrative numerical example is further provided to demonstrate the efficacy of the proposed decentralized and distributed control approaches. PMID:27537894

On Event-Triggered Adaptive Architectures for Decentralized and Distributed Control of Large-Scale Modular Systems.

PubMed

Albattat, Ali; Gruenwald, Benjamin C; Yucelen, Tansel

2016-08-16

The last decade has witnessed an increased interest in physical systems controlled over wireless networks (networked control systems). These systems allow the computation of control signals via processors that are not attached to the physical systems, and the feedback loops are closed over wireless networks. The contribution of this paper is to design and analyze event-triggered decentralized and distributed adaptive control architectures for uncertain networked large-scale modular systems; that is, systems consist of physically-interconnected modules controlled over wireless networks. Specifically, the proposed adaptive architectures guarantee overall system stability while reducing wireless network utilization and achieving a given system performance in the presence of system uncertainties that can result from modeling and degraded modes of operation of the modules and their interconnections between each other. In addition to the theoretical findings including rigorous system stability and the boundedness analysis of the closed-loop dynamical system, as well as the characterization of the effect of user-defined event-triggering thresholds and the design parameters of the proposed adaptive architectures on the overall system performance, an illustrative numerical example is further provided to demonstrate the efficacy of the proposed decentralized and distributed control approaches.

On the adaptivity and complexity embedded into differential evolution

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

Senkerik, Roman; Pluhacek, Michal; Jasek, Roman

2016-06-08

This research deals with the comparison of the two modern approaches for evolutionary algorithms, which are the adaptivity and complex chaotic dynamics. This paper aims on the investigations on the chaos-driven Differential Evolution (DE) concept. This paper is aimed at the embedding of discrete dissipative chaotic systems in the form of chaotic pseudo random number generators for the DE and comparing the influence to the performance with the state of the art adaptive representative jDE. This research is focused mainly on the possible disadvantages and advantages of both compared approaches. Repeated simulations for Lozi map driving chaotic systems were performedmore » on the simple benchmark functions set, which are more close to the real optimization problems. Obtained results are compared with the canonical not-chaotic and not adaptive DE. Results show that with used simple test functions, the performance of ChaosDE is better in the most cases than jDE and Canonical DE, furthermore due to the unique sequencing in CPRNG given by the hidden chaotic dynamics, thus better and faster selection of unique individuals from population, ChaosDE is faster.« less

Adaptive control of artificial pancreas systems - a review.

PubMed

Turksoy, Kamuran; Cinar, Ali

2014-01-01

Artificial pancreas (AP) systems offer an important improvement in regulating blood glucose concentration for patients with type 1 diabetes, compared to current approaches. AP consists of sensors, control algorithms and an insulin pump. Different AP control algorithms such as proportional-integral-derivative, model-predictive control, adaptive control, and fuzzy logic control have been investigated in simulation and clinical studies in the past three decades. The variability over time and complexity of the dynamics of blood glucose concentration, unsteady disturbances such as meals, time-varying delays on measurements and insulin infusion, and noisy data from sensors create a challenging system to AP. Adaptive control is a powerful control technique that can deal with such challenges. In this paper, a review of adaptive control techniques for blood glucose regulation with an AP system is presented. The investigations and advances in technology produced impressive results, but there is still a need for a reliable AP system that is both commercially viable and appealing to patients with type 1 diabetes.

Statistical Physics of T-Cell Development and Pathogen Specificity

NASA Astrophysics Data System (ADS)

Košmrlj, Andrej; Kardar, Mehran; Chakraborty, Arup K.

2013-04-01

In addition to an innate immune system that battles pathogens in a nonspecific fashion, higher organisms, such as humans, possess an adaptive immune system to combat diverse (and evolving) microbial pathogens. Remarkably, the adaptive immune system mounts pathogen-specific responses, which can be recalled upon reinfection with the same pathogen. It is difficult to see how the adaptive immune system can be preprogrammed to respond specifically to a vast and unknown set of pathogens. Although major advances have been made in understanding pertinent molecular and cellular phenomena, the precise principles that govern many aspects of an immune response are largely unknown. We discuss complementary approaches from statistical mechanics and cell biology that can shed light on how key components of the adaptive immune system, T cells, develop to enable pathogen-specific responses against many diverse pathogens. The mechanistic understanding that emerges has implications for how host genetics may influence the development of T cells with differing responses to the human immunodeficiency virus (HIV) infection.

Robot Competence Development by Constructive Learning

NASA Astrophysics Data System (ADS)

Meng, Q.; Lee, M. H.; Hinde, C. J.

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system’s adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.

Evolvable social agents for bacterial systems modeling.

PubMed

Paton, Ray; Gregory, Richard; Vlachos, Costas; Saunders, Jon; Wu, Henry

2004-09-01

We present two approaches to the individual-based modeling (IbM) of bacterial ecologies and evolution using computational tools. The IbM approach is introduced, and its important complementary role to biosystems modeling is discussed. A fine-grained model of bacterial evolution is then presented that is based on networks of interactivity between computational objects representing genes and proteins. This is followed by a coarser grained agent-based model, which is designed to explore the evolvability of adaptive behavioral strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of the two proposed individual-based bacterial models are discussed, and some results from simulation experiments are presented, illustrating their adaptive properties.

Adaptive envelope protection methods for aircraft

NASA Astrophysics Data System (ADS)

Unnikrishnan, Suraj

Carefree handling refers to the ability of a pilot to operate an aircraft without the need to continuously monitor aircraft operating limits. At the heart of all carefree handling or maneuvering systems, also referred to as envelope protection systems, are algorithms and methods for predicting future limit violations. Recently, envelope protection methods that have gained more acceptance, translate limit proximity information to its equivalent in the control channel. Envelope protection algorithms either use very small prediction horizon or are static methods with no capability to adapt to changes in system configurations. Adaptive approaches maximizing prediction horizon such as dynamic trim, are only applicable to steady-state-response critical limit parameters. In this thesis, a new adaptive envelope protection method is developed that is applicable to steady-state and transient response critical limit parameters. The approach is based upon devising the most aggressive optimal control profile to the limit boundary and using it to compute control limits. Pilot-in-the-loop evaluations of the proposed approach are conducted at the Georgia Tech Carefree Maneuver lab for transient longitudinal hub moment limit protection. Carefree maneuvering is the dual of carefree handling in the realm of autonomous Uninhabited Aerial Vehicles (UAVs). Designing a flight control system to fully and effectively utilize the operational flight envelope is very difficult. With the increasing role and demands for extreme maneuverability there is a need for developing envelope protection methods for autonomous UAVs. In this thesis, a full-authority automatic envelope protection method is proposed for limit protection in UAVs. The approach uses adaptive estimate of limit parameter dynamics and finite-time horizon predictions to detect impending limit boundary violations. Limit violations are prevented by treating the limit boundary as an obstacle and by correcting nominal control/command inputs to track a limit parameter safe-response profile near the limit boundary. The method is evaluated using software-in-the-loop and flight evaluations on the Georgia Tech unmanned rotorcraft platform---GTMax. The thesis also develops and evaluates an extension for calculating control margins based on restricting limit parameter response aggressiveness near the limit boundary.

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.

Artificial Immune System Approaches for Aerospace Applications

NASA Technical Reports Server (NTRS)

KrishnaKumar, Kalmanje; Koga, Dennis (Technical Monitor)

2002-01-01

Artificial Immune Systems (AIS) combine a priori knowledge with the adapting capabilities of biological immune system to provide a powerful alternative to currently available techniques for pattern recognition, modeling, design, and control. Immunology is the science of built-in defense mechanisms that are present in all living beings to protect against external attacks. A biological immune system can be thought of as a robust, adaptive system that is capable of dealing with an enormous variety of disturbances and uncertainties. Biological immune systems use a finite number of discrete "building blocks" to achieve this adaptiveness. These building blocks can be thought of as pieces of a puzzle which must be put together in a specific way-to neutralize, remove, or destroy each unique disturbance the system encounters. In this paper, we outline AIS models that are immediately applicable to aerospace problems and identify application areas that need further investigation.

Final Report - Regulatory Considerations for Adaptive Systems

NASA Technical Reports Server (NTRS)

Wilkinson, Chris; Lynch, Jonathan; Bharadwaj, Raj

2013-01-01

This report documents the findings of a preliminary research study into new approaches to the software design assurance of adaptive systems. We suggest a methodology to overcome the software validation and verification difficulties posed by the underlying assumption of non-adaptive software in the requirementsbased- testing verification methods in RTCA/DO-178B and C. An analysis of the relevant RTCA/DO-178B and C objectives is presented showing the reasons for the difficulties that arise in showing satisfaction of the objectives and suggested additional means by which they could be satisfied. We suggest that the software design assurance problem for adaptive systems is principally one of developing correct and complete high level requirements and system level constraints that define the necessary system functional and safety properties to assure the safe use of adaptive systems. We show how analytical techniques such as model based design, mathematical modeling and formal or formal-like methods can be used to both validate the high level functional and safety requirements, establish necessary constraints and provide the verification evidence for the satisfaction of requirements and constraints that supplements conventional testing. Finally the report identifies the follow-on research topics needed to implement this methodology.

On dynamical systems approaches and methods in f ( R ) cosmology

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

Alho, Artur; Carloni, Sante; Uggla, Claes, E-mail: aalho@math.ist.utl.pt, E-mail: sante.carloni@tecnico.ulisboa.pt, E-mail: claes.uggla@kau.se

We discuss dynamical systems approaches and methods applied to flat Robertson-Walker models in f ( R )-gravity. We argue that a complete description of the solution space of a model requires a global state space analysis that motivates globally covering state space adapted variables. This is shown explicitly by an illustrative example, f ( R ) = R + α R {sup 2}, α > 0, for which we introduce new regular dynamical systems on global compactly extended state spaces for the Jordan and Einstein frames. This example also allows us to illustrate several local and global dynamical systems techniquesmore » involving, e.g., blow ups of nilpotent fixed points, center manifold analysis, averaging, and use of monotone functions. As a result of applying dynamical systems methods to globally state space adapted dynamical systems formulations, we obtain pictures of the entire solution spaces in both the Jordan and the Einstein frames. This shows, e.g., that due to the domain of the conformal transformation between the Jordan and Einstein frames, not all the solutions in the Jordan frame are completely contained in the Einstein frame. We also make comparisons with previous dynamical systems approaches to f ( R ) cosmology and discuss their advantages and disadvantages.« less

A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate

NASA Astrophysics Data System (ADS)

Culley, S.; Noble, S.; Yates, A.; Timbs, M.; Westra, S.; Maier, H. R.; Giuliani, M.; Castelletti, A.

2016-09-01

Many water resource systems have been designed assuming that the statistical characteristics of future inflows are similar to those of the historical record. This assumption is no longer valid due to large-scale changes in the global climate, potentially causing declines in water resource system performance, or even complete system failure. Upgrading system infrastructure to cope with climate change can require substantial financial outlay, so it might be preferable to optimize existing system performance when possible. This paper builds on decision scaling theory by proposing a bottom-up approach to designing optimal feedback control policies for a water system exposed to a changing climate. This approach not only describes optimal operational policies for a range of potential climatic changes but also enables an assessment of a system's upper limit of its operational adaptive capacity, beyond which upgrades to infrastructure become unavoidable. The approach is illustrated using the Lake Como system in Northern Italy—a regulated system with a complex relationship between climate and system performance. By optimizing system operation under different hydrometeorological states, it is shown that the system can continue to meet its minimum performance requirements for more than three times as many states as it can under current operations. Importantly, a single management policy, no matter how robust, cannot fully utilize existing infrastructure as effectively as an ensemble of flexible management policies that are updated as the climate changes.

Using planned adaptation to implement evidence-based programs with new populations.

PubMed

Lee, Shawna J; Altschul, Inna; Mowbray, Carol T

2008-06-01

The Interactive Systems Framework (ISF) for Dissemination and Implementation (Wandersman et al. 2008) elaborates the functions and structures that move evidence-based programs (EBPs) from research to practice. Inherent in that process is the tension between implementing programs with fidelity and the need to tailor programs to fit the target population. We propose Planned Adaptation as one approach to resolve this tension, with the goal of guiding practitioners in adapting EBPs so that they maintain core components of program theory while taking into account the needs of particular populations. Planned Adaptation is a form of capacity building within the Prevention Support System that provides a framework to guide practitioners in adapting programs while encouraging researchers to provide information relevant to adaptation as a critical aspect of dissemination research, with the goal of promoting wider dissemination and better implementation of EBPs. We illustrate Planned Adaptation using the JOBS Program (Caplan et al. 1989), which was developed for recently laid-off, working- and middle-class workers and subsequently implemented with welfare recipients.

How to Assess Vulnerabilities of Water Policies to Global Change?

NASA Astrophysics Data System (ADS)

Kumar, A.; Haasnoot, M.; Weijs, S.

2017-12-01

Water managers are confronted with uncertainties arising from hydrological, societal, economical and political drivers. To manage these uncertainties two paradigms have been identified: top-down and bottom-up approaches. Top-down or prediction-based approaches use socio-economic scenarios together with a discrete set of GCM projections (often downscaled) to assess the expected impact of drivers and policies on water resource system through various hydrological and social systems models. Adaptation strategies to alleviate these impacts are then identified and tested against the scenarios. To address GCM and downscaling uncertainties, these approaches put more focus on climate predictions, rather than the decision problem itself. Triggered by the wish to have a more scenario-neutral approach and address downscaling uncertainties, recent analyses have been shifted towards vulnerability-based (bottom-up or decision-centric) approaches. They begin at the local scale by addressing socio-economic responses to climate, often involving stakeholder's input; identify vulnerabilities under a larger sample of plausible futures and evaluate sensitivity and robustness of possible adaptation options. Several bottom-up approaches have emerged so far and are increasingly recommended. Fundamentally they share several core ideas, however, subtle differences exist in vulnerability assessment, visualization tools for exploring vulnerabilities and computational methods used for identifying robust water policies. Through this study, we try to identify how these approaches are progressing, how the climate and non-climate uncertainties are being confronted and how to integrate existing and new tools. We find that choice of a method may depend on the number of vulnerability drivers identified and type of threshold levels (environmental conditions or policy objectives) defined. Certain approaches are suited well for assessing adaptive capacities, tipping points and sequencing of decisions. However, visualization of the vulnerability domain is still challenging if multiple drivers are present. New emerging tools are focused on generating synthetic scenarios, addressing multiple objectives, linking decision-making frameworks to adaptation pathways and communicating risks to the stakeholders.

Neural-Network-Based Adaptive Decentralized Fault-Tolerant Control for a Class of Interconnected Nonlinear Systems.

PubMed

Li, Xiao-Jian; Yang, Guang-Hong

2018-01-01

This paper is concerned with the adaptive decentralized fault-tolerant tracking control problem for a class of uncertain interconnected nonlinear systems with unknown strong interconnections. An algebraic graph theory result is introduced to address the considered interconnections. In addition, to achieve the desirable tracking performance, a neural-network-based robust adaptive decentralized fault-tolerant control (FTC) scheme is given to compensate the actuator faults and system uncertainties. Furthermore, via the Lyapunov analysis method, it is proven that all the signals of the resulting closed-loop system are semiglobally bounded, and the tracking errors of each subsystem exponentially converge to a compact set, whose radius is adjustable by choosing different controller design parameters. Finally, the effectiveness and advantages of the proposed FTC approach are illustrated with two simulated examples.

Peptide and protein-based nanotubes for nanobiotechnology.

PubMed

Petrov, Anna; Audette, Gerald F

2012-01-01

The development of biologically relevant nanosystems such as biomolecular probes and sensors requires systems that effectively interface specific biochemical environments with abiotic architectures. The most widely studied nanomaterial, carbon nanotubes, has proven challenging in their adaptation for biomedical applications despite their numerous advantageous physical and electrochemical properties. On the other hand, development of bionanosystems through adaptation of existing biological systems has several advantages including their adaptability through modern recombinant DNA strategies. Indeed, the use of peptides, proteins and protein assemblies as nanotubes, scaffolds, and nanowires has shown much promise as a bottom-up approach to the development of novel bionanosystems. We highlight several unique peptide and protein systems that generate protein nanotubes (PNTs) that are being explored for the development of biosensors, probes, bionanowires, and drug delivery systems. Copyright © 2012 Wiley Periodicals, Inc.

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

NASA Astrophysics Data System (ADS)

Wang, Jing; Yang, Tianyu; Staskevich, Gennady; Abbe, Brian

2017-04-01

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton-Jacobi-Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.

On Cognition, Structured Sequence Processing, and Adaptive Dynamical Systems

NASA Astrophysics Data System (ADS)

Petersson, Karl Magnus

2008-11-01

Cognitive neuroscience approaches the brain as a cognitive system: a system that functionally is conceptualized in terms of information processing. We outline some aspects of this concept and consider a physical system to be an information processing device when a subclass of its physical states can be viewed as representational/cognitive and transitions between these can be conceptualized as a process operating on these states by implementing operations on the corresponding representational structures. We identify a generic and fundamental problem in cognition: sequentially organized structured processing. Structured sequence processing provides the brain, in an essential sense, with its processing logic. In an approach addressing this problem, we illustrate how to integrate levels of analysis within a framework of adaptive dynamical systems. We note that the dynamical system framework lends itself to a description of asynchronous event-driven devices, which is likely to be important in cognition because the brain appears to be an asynchronous processing system. We use the human language faculty and natural language processing as a concrete example through out.

Multilevel adaptive control of nonlinear interconnected systems.

PubMed

Motallebzadeh, Farzaneh; Ozgoli, Sadjaad; Momeni, Hamid Reza

2015-01-01

This paper presents an adaptive backstepping-based multilevel approach for the first time to control nonlinear interconnected systems with unknown parameters. The system consists of a nonlinear controller at the first level to neutralize the interaction terms, and some adaptive controllers at the second level, in which the gains are optimally tuned using genetic algorithm. The presented scheme can be used in systems with strong couplings where completely ignoring the interactions leads to problems in performance or stability. In order to test the suitability of the method, two case studies are provided: the uncertain double and triple coupled inverted pendulums connected by springs with unknown parameters. The simulation results show that the method is capable of controlling the system effectively, in both regulation and tracking tasks. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Summary of a Competency Based, Field Centered, Systems Approach to Elementary Teacher Education. Summary of the Final Report.

ERIC Educational Resources Information Center

Northwest Regional Educational Lab., Portland, OR.

A competency-based, field-centered systems approach to elementary school teacher education was designed to bring about specified, measurable outcomes, to have evidence of its effectiveness continually available, and to be adaptive in the light of that evidence. The model was separated into two interdependent parts, the instructional model and the…

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.

Optomechanical design of TMT NFIRAOS Subsystems at INO

NASA Astrophysics Data System (ADS)

Lamontagne, Frédéric; Desnoyers, Nichola; Grenier, Martin; Cottin, Pierre; Leclerc, Mélanie; Martin, Olivier; Buteau-Vaillancourt, Louis; Boucher, Marc-André; Nash, Reston; Lardière, Olivier; Andersen, David; Atwood, Jenny; Hill, Alexis; Byrnes, Peter W. G.; Herriot, Glen; Fitzsimmons, Joeleff; Véran, Jean-Pierre

2017-08-01

The adaptive optics system for the Thirty Meter Telescope (TMT) is the Narrow-Field InfraRed Adaptive Optics System (NFIRAOS). Recently, INO has been involved in the optomechanical design of several subsystems of NFIRAOS, including the Instrument Selection Mirror (ISM), the NFIRAOS Beamsplitters (NBS), and the NFIRAOS Source Simulator system (NSS) comprising the Focal Plane Mask (FPM), the Laser Guide Star (LGS) sources, and the Natural Guide Star (NGS) sources. This paper presents an overview of these subsystems and the optomechanical design approaches used to meet the optical performance requirements under environmental constraints.

An adaptive software defined radio design based on a standard space telecommunication radio system API

NASA Astrophysics Data System (ADS)

Xiong, Wenhao; Tian, Xin; Chen, Genshe; Pham, Khanh; Blasch, Erik

2017-05-01

Software defined radio (SDR) has become a popular tool for the implementation and testing for communications performance. The advantage of the SDR approach includes: a re-configurable design, adaptive response to changing conditions, efficient development, and highly versatile implementation. In order to understand the benefits of SDR, the space telecommunication radio system (STRS) was proposed by NASA Glenn research center (GRC) along with the standard application program interface (API) structure. Each component of the system uses a well-defined API to communicate with other components. The benefit of standard API is to relax the platform limitation of each component for addition options. For example, the waveform generating process can support a field programmable gate array (FPGA), personal computer (PC), or an embedded system. As long as the API defines the requirements, the generated waveform selection will work with the complete system. In this paper, we demonstrate the design and development of adaptive SDR following the STRS and standard API protocol. We introduce step by step the SDR testbed system including the controlling graphic user interface (GUI), database, GNU radio hardware control, and universal software radio peripheral (USRP) tranceiving front end. In addition, a performance evaluation in shown on the effectiveness of the SDR approach for space telecommunication.

Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters.

PubMed

Chiang, Austin W T; Liu, Wei-Chung; Charusanti, Pep; Hwang, Ming-Jing

2014-01-15

A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system's dynamics. We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are governed by constraints of the specified system dynamics. A systematic approach based on enrichment statistical tests has been developed to elucidate the relationships between model parameters and the roles they play in affecting system dynamics of a prototype biological network. The proposed approach is generally applicable and therefore can find wide use in systems biology modeling research.

Catalyzing Genetic Thinking in Undergraduate Mathematics Education

ERIC Educational Resources Information Center

King, Samuel Olugbenga

2016-01-01

In undergraduate mathematics education, atypical problem solving approaches are usually discouraged because they are not adaptive to systematic deduction on which undergraduate instructional systems are predicated. I present preliminary qualitative research evidence that indicates that these atypical approaches, such as genetic guessing, which…

Requirements for Pseudomonas aeruginosa Type I-F CRISPR-Cas Adaptation Determined Using a Biofilm Enrichment Assay.

PubMed

Heussler, Gary E; Miller, Jon L; Price, Courtney E; Collins, Alan J; O'Toole, George A

2016-11-15

CRISPR (clustered regularly interspaced short palindromic repeat)-Cas (CRISPR-associated protein) systems are diverse and found in many archaea and bacteria. These systems have mainly been characterized as adaptive immune systems able to protect against invading mobile genetic elements, including viruses. The first step in this protection is acquisition of spacer sequences from the invader DNA and incorporation of those sequences into the CRISPR array, termed CRISPR adaptation. Progress in understanding the mechanisms and requirements of CRISPR adaptation has largely been accomplished using overexpression of cas genes or plasmid loss assays; little work has focused on endogenous CRISPR-acquired immunity from viral predation. Here, we developed a new biofilm-based assay system to enrich for Pseudomonas aeruginosa strains with new spacer acquisition. We used this assay to demonstrate that P. aeruginosa rapidly acquires spacers protective against DMS3vir, an engineered lytic variant of the Mu-like bacteriophage DMS3, through primed CRISPR adaptation from spacers present in the native CRISPR2 array. We found that for the P. aeruginosa type I-F system, the cas1 gene is required for CRISPR adaptation, recG contributes to (but is not required for) primed CRISPR adaptation, recD is dispensable for primed CRISPR adaptation, and finally, the ability of a putative priming spacer to prime can vary considerably depending on the specific sequences of the spacer. Our understanding of CRISPR adaptation has expanded largely through experiments in type I CRISPR systems using plasmid loss assays, mutants of Escherichia coli, or cas1-cas2 overexpression systems, but there has been little focus on studying the adaptation of endogenous systems protecting against a lytic bacteriophage. Here we describe a biofilm system that allows P. aeruginosa to rapidly gain spacers protective against a lytic bacteriophage. This approach has allowed us to probe the requirements for CRISPR adaptation in the endogenous type I-F system of P. aeruginosa Our data suggest that CRISPR-acquired immunity in a biofilm may be one reason that many P. aeruginosa strains maintain a CRISPR-Cas system. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

The Resilience Assessment Framework: a common indicator for land management?

NASA Astrophysics Data System (ADS)

Cowie, Annette; Metternicht, Graciela; O'Connell, Deborah

2015-04-01

At the Rio+20 conference in June 2013, the United Nations Convention to Combat Desertification (UNCCD), the Convention on Biological Diversity (CBD), and the United Nations Framework Convention on Climate Change (UNFCCC) reinforced their mutual interests in building linkages between biodiversity conservation, sustainable land management, and climate change mitigation and adaptation. The UNCCD sees building resilience of agro-ecosystems as a common interest that could strengthen linkages between the conventions and deliver synergies in progressing goals of each of the conventions. Furthermore, enhancing resilience of productive agro-ecosystems is fundamental to food security and sustainable development, and thus aligns with the Sustainable Development Goals (SDGs). The Global Environment Facility (GEF) shares the interest of the conventions in building resilience in agro-ecosystems. Indicators of resilience are required for monitoring progress in these endeavors, application of a common indicator between the UNCCD, UNFCCC and CBD as a measure of both land-based adaptation and ecosystem resilience, could strengthen links between the conventions and increase attention to the broad benefits of improved land management. Consequently, the Scientific and Technical Advisory Panel (STAP) to the GEF commissioned the Commonwealth Scientific and Industrial Research Organisation (CSIRO) to produce a report reviewing the conceptual basis for resilience, and proposing an indicator approach that could meet the needs of the Conventions and the GEF for an indicator of agro-ecosystem resilience and land-based adaption. The paper presents a synthesis of scientific understanding of resilience in agro-ecosystems, reviews indicators that have been proposed, and, having concluded that none of the extant indicator approaches adequately assesses resilience of agro-ecosystems, proposes a new approach to the assessment of resilience. Recognizing that no single indicator of resilience is applicable to all agro-ecosystems, and that involvement of stakeholders is critical to discerning the critical variables to be assessed, the proposed framework uses an iterative participatory approach to characterise the system, considering also interactions across and within scales; identify the controlling variables, and assess proximity to thresholds, and adaptive capacity. The framework consists of four elements: Element A: System description; Element B Assessing the system; Element C Adaptive governance and management; Element D Participatory process. Element D is intended as a cross-cutting element, applying across Elements A to C, although Elements A and B can be applied as a desktop activity in a preliminary assessment. The results of the assessment are synthesised in "Resilience action indicators", that summarise the state of the system with respect to the need to adapt or transform. The presentation will summarise the framework and the responses of expert reviewers who identified strengths of the approach, and challenges for implementation, particularly at program and national scales. The presentation will emphasise the conceptual basis for the approach, and the role of scientists in testing, refining and operationalizing the approach.

Thinking shift on health systems: from blueprint health programmes towards resilience of health systems Comment on "Constraints to applying systems thinking concepts in health systems: A regional perspective from surveying stakeholders in Eastern Mediterranean countries".

PubMed

Blanchet, Karl

2015-03-03

International health is still highly dominated by equilibrium approaches. The emergence of systems thinking in international health provides a great avenue to develop innovative health interventions adapted to changing contexts. The public health community, nevertheless, has the responsibility to translate concepts related to systems thinking and complexity into concrete research methods and interventions. One possibility is to consider the properties of systems such as resilience and adaptability as entry points to better understand how health systems react to shocks. © 2015 by Kerman University of Medical Sciences.

Using Multi-threading for the Automatic Load Balancing of 2D Adaptive Finite Element Meshes

NASA Technical Reports Server (NTRS)

Heber, Gerd; Biswas, Rupak; Thulasiraman, Parimala; Gao, Guang R.; Saini, Subhash (Technical Monitor)

1998-01-01

In this paper, we present a multi-threaded approach for the automatic load balancing of adaptive finite element (FE) meshes The platform of our choice is the EARTH multi-threaded system which offers sufficient capabilities to tackle this problem. We implement the adaption phase of FE applications oil triangular meshes and exploit the EARTH token mechanism to automatically balance the resulting irregular and highly nonuniform workload. We discuss the results of our experiments oil EARTH-SP2, on implementation of EARTH on the IBM SP2 with different load balancing strategies that are built into the runtime system.

Where you stand depends on where you sit: Qualitative inquiry into notions of fire adaptation

Treesearch

Hannah Brenkert-Smith; James R. Meldrum; Patricia A. Champ; Christopher M. Barth

2017-01-01

Wildfire and the threat it poses to society represents an example of the complex, dynamic relationship between social and ecological systems. Increasingly, wildfire adaptation is posited as a pathway to shift the approach to fire from a suppression paradigm that seeks to control fire to a paradigm that focuses on “living with” and “adapting to” wildfire. In this study...

Reciprocal Interactions of the Intestinal Microbiota and Immune System

PubMed Central

Maynard, Craig L.; Elson, Charles O.; Hatton, Robin D.; Weaver, Casey T.

2013-01-01

Preface Emergence of the adaptive immune system in vertebrates set the stage for evolution of an advanced symbiotic relationship with the intestinal microbiota. The defining features of specificity and memory that characterize adaptive immunity have afforded vertebrates mechanisms for efficiently tailoring immune responses to diverse types of microbes, whether to promote mutualism or host defense. These same attributes carry risk for immune-mediated diseases that are increasingly linked to the intestinal microbiota. Understanding how the adaptive immune system copes with the remarkable number and diversity of microbes that colonize the digestive tract, and how it integrates with more primitive innate immune mechanisms to maintain immune homeostasis, holds considerable promise for new approaches to modulate immune networks in order to treat and prevent disease. PMID:22972296

Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.

PubMed

Spüler, Martin; Rosenstiel, Wolfgang; Bogdan, Martin

2012-01-01

The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.

DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems.

PubMed

Dos Reis, Julio Cesar; Pruski, Cédric; Da Silveira, Marcos; Reynaud-Delaître, Chantal

2015-06-01

Knowledge Organization Systems (KOS) and their associated mappings play a central role in several decision support systems. However, by virtue of knowledge evolution, KOS entities are modified over time, impacting mappings and potentially turning them invalid. This requires semi-automatic methods to maintain such semantic correspondences up-to-date at KOS evolution time. We define a complete and original framework based on formal heuristics that drives the adaptation of KOS mappings. Our approach takes into account the definition of established mappings, the evolution of KOS and the possible changes that can be applied to mappings. This study experimentally evaluates the proposed heuristics and the entire framework on realistic case studies borrowed from the biomedical domain, using official mappings between several biomedical KOSs. We demonstrate the overall performance of the approach over biomedical datasets of different characteristics and sizes. Our findings reveal the effectiveness in terms of precision, recall and F-measure of the suggested heuristics and methods defining the framework to adapt mappings affected by KOS evolution. The obtained results contribute and improve the quality of mappings over time. The proposed framework can adapt mappings largely automatically, facilitating thus the maintenance task. The implemented algorithms and tools support and minimize the work of users in charge of KOS mapping maintenance. Copyright © 2015 Elsevier Inc. All rights reserved.

Investigation of signal models and methods for evaluating structures of processing telecommunication information exchange systems under acoustic noise conditions

NASA Astrophysics Data System (ADS)

Kropotov, Y. A.; Belov, A. A.; Proskuryakov, A. Y.; Kolpakov, A. A.

2018-05-01

The paper considers models and methods for estimating signals during the transmission of information messages in telecommunication systems of audio exchange. One-dimensional probability distribution functions that can be used to isolate useful signals, and acoustic noise interference are presented. An approach to the estimation of the correlation and spectral functions of the parameters of acoustic signals is proposed, based on the parametric representation of acoustic signals and the components of the noise components. The paper suggests an approach to improving the efficiency of interference cancellation and highlighting the necessary information when processing signals from telecommunications systems. In this case, the suppression of acoustic noise is based on the methods of adaptive filtering and adaptive compensation. The work also describes the models of echo signals and the structure of subscriber devices in operational command telecommunications systems.

A Dynamic Time Warping Approach to Real-Time Activity Recognition for Food Preparation

NASA Astrophysics Data System (ADS)

Pham, Cuong; Plötz, Thomas; Olivier, Patrick

We present a dynamic time warping based activity recognition system for the analysis of low-level food preparation activities. Accelerometers embedded into kitchen utensils provide continuous sensor data streams while people are using them for cooking. The recognition framework analyzes frames of contiguous sensor readings in real-time with low latency. It thereby adapts to the idiosyncrasies of utensil use by automatically maintaining a template database. We demonstrate the effectiveness of the classification approach by a number of real-world practical experiments on a publically available dataset. The adaptive system shows superior performance compared to a static recognizer. Furthermore, we demonstrate the generalization capabilities of the system by gradually reducing the amount of training samples. The system achieves excellent classification results even if only a small number of training samples is available, which is especially relevant for real-world scenarios.

Intelligent data management for real-time spacecraft monitoring

NASA Technical Reports Server (NTRS)

Schwuttke, Ursula M.; Gasser, Les; Abramson, Bruce

1992-01-01

Real-time AI systems have begun to address the challenge of restructuring problem solving to meet real-time constraints by making key trade-offs that pursue less than optimal strategies with minimal impact on system goals. Several approaches for adapting to dynamic changes in system operating conditions are known. However, simultaneously adapting system decision criteria in a principled way has been difficult. Towards this end, a general technique for dynamically making such trade-offs using a combination of decision theory and domain knowledge has been developed. Multi-attribute utility theory (MAUT), a decision theoretic approach for making one-time decisions is discussed and dynamic trade-off evaluation is described as a knowledge-based extension of MAUT that is suitable for highly dynamic real-time environments, and provides an example of dynamic trade-off evaluation applied to a specific data management trade-off in a real-world spacecraft monitoring application.

Expert system and process optimization techniques for real-time monitoring and control of plasma processes

NASA Astrophysics Data System (ADS)

Cheng, Jie; Qian, Zhaogang; Irani, Keki B.; Etemad, Hossein; Elta, Michael E.

1991-03-01

To meet the ever-increasing demand of the rapidly-growing semiconductor manufacturing industry it is critical to have a comprehensive methodology integrating techniques for process optimization real-time monitoring and adaptive process control. To this end we have accomplished an integrated knowledge-based approach combining latest expert system technology machine learning method and traditional statistical process control (SPC) techniques. This knowledge-based approach is advantageous in that it makes it possible for the task of process optimization and adaptive control to be performed consistently and predictably. Furthermore this approach can be used to construct high-level and qualitative description of processes and thus make the process behavior easy to monitor predict and control. Two software packages RIST (Rule Induction and Statistical Testing) and KARSM (Knowledge Acquisition from Response Surface Methodology) have been developed and incorporated with two commercially available packages G2 (real-time expert system) and ULTRAMAX (a tool for sequential process optimization).

A software architecture for automating operations processes

NASA Technical Reports Server (NTRS)

Miller, Kevin J.

1994-01-01

The Operations Engineering Lab (OEL) at JPL has developed a software architecture based on an integrated toolkit approach for simplifying and automating mission operations tasks. The toolkit approach is based on building adaptable, reusable graphical tools that are integrated through a combination of libraries, scripts, and system-level user interface shells. The graphical interface shells are designed to integrate and visually guide a user through the complex steps in an operations process. They provide a user with an integrated system-level picture of an overall process, defining the required inputs and possible output through interactive on-screen graphics. The OEL has developed the software for building these process-oriented graphical user interface (GUI) shells. The OEL Shell development system (OEL Shell) is an extension of JPL's Widget Creation Library (WCL). The OEL Shell system can be used to easily build user interfaces for running complex processes, applications with extensive command-line interfaces, and tool-integration tasks. The interface shells display a logical process flow using arrows and box graphics. They also allow a user to select which output products are desired and which input sources are needed, eliminating the need to know which program and its associated command-line parameters must be executed in each case. The shells have also proved valuable for use as operations training tools because of the OEL Shell hypertext help environment. The OEL toolkit approach is guided by several principles, including the use of ASCII text file interfaces with a multimission format, Perl scripts for mission-specific adaptation code, and programs that include a simple command-line interface for batch mode processing. Projects can adapt the interface shells by simple changes to the resources configuration file. This approach has allowed the development of sophisticated, automated software systems that are easy, cheap, and fast to build. This paper will discuss our toolkit approach and the OEL Shell interface builder in the context of a real operations process example. The paper will discuss the design and implementation of a Ulysses toolkit for generating the mission sequence of events. The Sequence of Events Generation (SEG) system provides an adaptable multimission toolkit for producing a time-ordered listing and timeline display of spacecraft commands, state changes, and required ground activities.

Adaptive-numerical-bias metadynamics.

PubMed

Khanjari, Neda; Eslami, Hossein; Müller-Plathe, Florian

2017-12-05

A metadynamics scheme is presented in which the free energy surface is filled with progressively adding adaptive biasing potentials, obtained from the accumulated probability distribution of the collective variables. Instead of adding Gaussians with assigned height and width in conventional metadynamics method, here we add a more realistic adaptive biasing potential to the Hamiltonian of the system. The shape of the adaptive biasing potential is adjusted on the fly by sampling over the visited states. As the top of the barrier is approached, the biasing potentials become wider. This decreases the problem of trapping the system in the niches, introduced by the addition of Gaussians of fixed height in metadynamics. Our results for the free energy profiles of three test systems show that this method is more accurate and converges more quickly than the conventional metadynamics, and is quite comparable (in accuracy and convergence rate) with the well-tempered metadynamics method. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Intelligent Tracking Control for a Class of Uncertain High-Order Nonlinear Systems.

PubMed

Zhao, Xudong; Shi, Peng; Zheng, Xiaolong; Zhang, Jianhua

2016-09-01

This brief is concerned with the problem of intelligent tracking control for a class of high-order nonlinear systems with completely unknown nonlinearities. An intelligent adaptive control algorithm is presented by combining the adaptive backstepping technique with the neural networks' approximation ability. It is shown that the practical output tracking performance of the system is achieved using the proposed state-feedback controller under two mild assumptions. In particular, by introducing a parameter in the derivations, the tracking error between the time-varying target signal and the output can be reduced via tuning the controller design parameters. Moreover, in order to solve the problem of overparameterization, which is a common issue in adaptive control design, a controller with one adaptive law is also designed. Finally, simulation results are given to show the effectiveness of the theoretical approaches and the potential of the proposed new design techniques.

An adaptive trajectory tracking control of four rotor hover vehicle using extended normalized radial basis function network

NASA Astrophysics Data System (ADS)

ul Amin, Rooh; Aijun, Li; Khan, Muhammad Umer; Shamshirband, Shahaboddin; Kamsin, Amirrudin

2017-01-01

In this paper, an adaptive trajectory tracking controller based on extended normalized radial basis function network (ENRBFN) is proposed for 3-degree-of-freedom four rotor hover vehicle subjected to external disturbance i.e. wind turbulence. Mathematical model of four rotor hover system is developed using equations of motions and a new computational intelligence based technique ENRBFN is introduced to approximate the unmodeled dynamics of the hover vehicle. The adaptive controller based on the Lyapunov stability approach is designed to achieve tracking of the desired attitude angles of four rotor hover vehicle in the presence of wind turbulence. The adaptive weight update based on the Levenberg-Marquardt algorithm is used to avoid weight drift in case the system is exposed to external disturbances. The closed-loop system stability is also analyzed using Lyapunov stability theory. Simulations and experimental results are included to validate the effectiveness of the proposed control scheme.

Joint U.S./Japan Conference on Adaptive Structures, 1st, Maui, HI, Nov. 13-15, 1990, Proceedings

NASA Technical Reports Server (NTRS)

Wada, Ben K. (Editor); Fanson, James L. (Editor); Miura, Koryo (Editor)

1991-01-01

The present volume of adaptive structures discusses the development of control laws for an orbiting tethered antenna/reflector system test scale model, the sizing of active piezoelectric struts for vibration suppression on a space-based interferometer, the control design of a space station mobile transporter with multiple constraints, and optimum configuration control of an intelligent truss structure. Attention is given to the formulation of full state feedback for infinite order structural systems, robustness issues in the design of smart structures, passive piezoelectric vibration damping, shape control experiments with a functional model for large optical reflectors, and a mathematical basis for the design optimization of adaptive trusses in precision control. Topics addressed include approaches to the optimal adaptive geometries of intelligent truss structures, the design of an automated manufacturing system for tubular smart structures, the Sandia structural control experiments, and the zero-gravity dynamics of space structures in parabolic aircraft flight.

Joint U.S./Japan Conference on Adaptive Structures, 1st, Maui, HI, Nov. 13-15, 1990, Proceedings

NASA Astrophysics Data System (ADS)

Wada, Ben K.; Fanson, James L.; Miura, Koryo

1991-11-01

The present volume of adaptive structures discusses the development of control laws for an orbiting tethered antenna/reflector system test scale model, the sizing of active piezoelectric struts for vibration suppression on a space-based interferometer, the control design of a space station mobile transporter with multiple constraints, and optimum configuration control of an intelligent truss structure. Attention is given to the formulation of full state feedback for infinite order structural systems, robustness issues in the design of smart structures, passive piezoelectric vibration damping, shape control experiments with a functional model for large optical reflectors, and a mathematical basis for the design optimization of adaptive trusses in precision control. Topics addressed include approaches to the optimal adaptive geometries of intelligent truss structures, the design of an automated manufacturing system for tubular smart structures, the Sandia structural control experiments, and the zero-gravity dynamics of space structures in parabolic aircraft flight.

A Systems Approach to the Physiology of Weightlessness

NASA Technical Reports Server (NTRS)

White, Ronald J.; Leonard, Joel I.; Rummel, John A.; Leach, Carolyn S.

1991-01-01

A systems approach to the unraveling of the complex response pattern of the human subjected to weightlessness is presented. The major goal of this research is to obtain an understanding of the role that each of the major components of the human system plays following the transition to and from space. The cornerstone of this approach is the utilization of a variety of mathematical models in order to pose and test alternative hypotheses concerned with the adaptation process. An integrated hypothesis for the human physiological response to weightlessness is developed.

Climate change adaptation strategies for resource management and conservation planning.

PubMed

Lawler, Joshua J

2009-04-01

Recent rapid changes in the Earth's climate have altered ecological systems around the globe. Global warming has been linked to changes in physiology, phenology, species distributions, interspecific interactions, and disturbance regimes. Projected future climate change will undoubtedly result in even more dramatic shifts in the states of many ecosystems. These shifts will provide one of the largest challenges to natural resource managers and conservation planners. Managing natural resources and ecosystems in the face of uncertain climate requires new approaches. Here, the many adaptation strategies that have been proposed for managing natural systems in a changing climate are reviewed. Most of the recommended approaches are general principles and many are tools that managers are already using. What is new is a turning toward a more agile management perspective. To address climate change, managers will need to act over different spatial and temporal scales. The focus of restoration will need to shift from historic species assemblages to potential future ecosystem services. Active adaptive management based on potential future climate impact scenarios will need to be a part of everyday operations. And triage will likely become a critical option. Although many concepts and tools for addressing climate change have been proposed, key pieces of information are still missing. To successfully manage for climate change, a better understanding will be needed of which species and systems will likely be most affected by climate change, how to preserve and enhance the evolutionary capacity of species, how to implement effective adaptive management in new systems, and perhaps most importantly, in which situations and systems will the general adaptation strategies that have been proposed work and how can they be effectively applied.

A Multi-Agent System Approach for Distance Learning Architecture

ERIC Educational Resources Information Center

Turgay, Safiye

2005-01-01

The goal of this study is to suggest the agent systems by intelligence and adaptability properties in distance learning environment. The suggested system has flexible, agile, intelligence and cooperation features. System components are teachers, students (learners), and resources. Inter component relations are modeled and reviewed by using the…

Climate change adaptation in regulated water utilities

NASA Astrophysics Data System (ADS)

Vicuna, S.; Melo, O.; Harou, J. J.; Characklis, G. W.; Ricalde, I.

2017-12-01

Concern about climate change impacts on water supply systems has grown in recent years. However, there are still few examples of pro-active interventions (e.g. infrastructure investment or policy changes) meant to address plausible future changes. Deep uncertainty associated with climate impacts, future demands, and regulatory constraints might explain why utility planning in a range of contexts doesn't explicitly consider climate change scenarios and potential adaptive responses. Given the importance of water supplies for economic development and the cost and longevity of many water infrastructure investments, large urban water supply systems could suffer from lack of pro-active climate change adaptation. Water utilities need to balance the potential for high regret stranded assets on the one side, with insufficient supplies leading to potentially severe socio-economic, political and environmental failures on the other, and need to deal with a range of interests and constraints. This work presents initial findings from a project looking at how cities in Chile, the US and the UK are developing regulatory frameworks that incorporate utility planning under uncertainty. Considering for example the city of Santiago, Chile, recent studies have shown that although high scarcity cost scenarios are plausible, pre-emptive investment to guard from possible water supply failures is still remote and not accommodated by current planning practice. A first goal of the project is to compare and contrast regulatory approaches to utility risks considering climate change adaptation measures. Subsequently we plan to develop and propose a custom approach for the city of Santiago based on lessons learned from other contexts. The methodological approach combines institutional assessment of water supply regulatory frameworks with simulation-based decision-making under uncertainty approaches. Here we present initial work comparing the regulatory frameworks in Chile, UK and USA evaluating their ability to incorporate uncertain climate and other changes into long-term infrastructure investment planning. The potential for regulatory and financial adaptive measures is explored in addition to a discussion on evaluating their appropriateness via various modelling-based intervention decision-making approaches.

Assessment of a User's Time Pressure and Cognitive Load on the Basis of Features of Speech

NASA Astrophysics Data System (ADS)

Jameson, Anthony; Kiefer, Juergen; Müller, Christian; Großmann-Hutter, Barbara; Wittig, Frank; Rummer, Ralf

The project READY (1996-2004) approached the topic of resource-adaptive cognitive processes from a different angle than most of the other projects represented in this volume: The resources in question were the cognitive resources of computer users; the adaptation was done by the system that they were using.

A Multiple Objective Test Assembly Approach for Exposure Control Problems in Computerized Adaptive Testing

ERIC Educational Resources Information Center

Veldkamp, Bernard P.; Verschoor, Angela J.; Eggen, Theo J. H. M.

2010-01-01

Overexposure and underexposure of items in the bank are serious problems in operational computerized adaptive testing (CAT) systems. These exposure problems might result in item compromise, or point at a waste of investments. The exposure control problem can be viewed as a test assembly problem with multiple objectives. Information in the test has…

Recent Contributions to a Generic Architecture Design that Supports Learning Objects Interoperability

ERIC Educational Resources Information Center

Botsios, Sotirios; Georgiou, Dimitrios A.

2009-01-01

Adaptation and personalization services in e-learning environments are considered the turning point of recent research efforts, as the "one-size-fits-all" approach has some important drawbacks, from the educational point of view. Adaptive Educational Hypermedia Systems in World Wide Web became a very active research field and the need of…

CDP - Adaptive Supervisory Control and Data Acquisition (SCADA) Technology for Infrastructure Protection

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

Marco Carvalho; Richard Ford

2012-05-14

Supervisory Control and Data Acquisition (SCADA) Systems are a type of Industrial Control System characterized by the centralized (or hierarchical) monitoring and control of geographically dispersed assets. SCADA systems combine acquisition and network components to provide data gathering, transmission, and visualization for centralized monitoring and control. However these integrated capabilities, especially when built over legacy systems and protocols, generally result in vulnerabilities that can be exploited by attackers, with potentially disastrous consequences. Our research project proposal was to investigate new approaches for secure and survivable SCADA systems. In particular, we were interested in the resilience and adaptability of large-scale mission-criticalmore » monitoring and control infrastructures. Our research proposal was divided in two main tasks. The first task was centered on the design and investigation of algorithms for survivable SCADA systems and a prototype framework demonstration. The second task was centered on the characterization and demonstration of the proposed approach in illustrative scenarios (simulated or emulated).« less

Adaptive Optical System for Retina Imaging Approaches Clinic Applications

NASA Astrophysics Data System (ADS)

Ling, N.; Zhang, Y.; Rao, X.; Wang, C.; Hu, Y.; Jiang, W.; Jiang, C.

We presented "A small adaptive optical system on table for human retinal imaging" at the 3rd Workshop on Adaptive Optics for Industry and Medicine. In this system, a 19 element small deformable mirror was used as wavefront correction element. High resolution images of photo receptors and capillaries of human retina were obtained. In recent two years, at the base of this system a new adaptive optical system for human retina imaging has been developed. The wavefront correction element is a newly developed 37 element deformable mirror. Some modifications have been adopted for easy operation. Experiments for different imaging wavelengths and axial positions were conducted. Mosaic pictures of photoreceptors and capillaries were obtained. 100 normal and abnormal eyes of different ages have been inspected.The first report in the world concerning the most detailed capillary distribution images cover ±3° by ± 3° field around the fovea has been demonstrated. Some preliminary very early diagnosis experiment has been tried in laboratory. This system is being planned to move to the hospital for clinic experiments.

Air-Breathing Hypersonic Vehicle Tracking Control Based on Adaptive Dynamic Programming.

PubMed

Mu, Chaoxu; Ni, Zhen; Sun, Changyin; He, Haibo

2017-03-01

In this paper, we propose a data-driven supplementary control approach with adaptive learning capability for air-breathing hypersonic vehicle tracking control based on action-dependent heuristic dynamic programming (ADHDP). The control action is generated by the combination of sliding mode control (SMC) and the ADHDP controller to track the desired velocity and the desired altitude. In particular, the ADHDP controller observes the differences between the actual velocity/altitude and the desired velocity/altitude, and then provides a supplementary control action accordingly. The ADHDP controller does not rely on the accurate mathematical model function and is data driven. Meanwhile, it is capable to adjust its parameters online over time under various working conditions, which is very suitable for hypersonic vehicle system with parameter uncertainties and disturbances. We verify the adaptive supplementary control approach versus the traditional SMC in the cruising flight, and provide three simulation studies to illustrate the improved performance with the proposed approach.

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

NASA Astrophysics Data System (ADS)

Cai, Jingxiao; Zhang, Yan

2017-07-01

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

NASA Astrophysics Data System (ADS)

Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

Schools as social complex adaptive systems: a new way to understand the challenges of introducing the health promoting schools concept.

PubMed

Keshavarz, Nastaran; Nutbeam, Don; Rowling, Louise; Khavarpour, Freidoon

2010-05-01

Achieving system-wide implementation of health promotion programs in schools and sustaining both the program and its health related benefits have proved challenging. This paper reports on a qualitative study examining the implementation of health promoting schools programs in primary schools in Sydney, Australia. It draw upon insights from systems science to examine the relevance and usefulness of the concept of "complex adaptive systems" as a framework to better understand ways in which health promoting school interventions could be introduced and sustained. The primary data for the study were collected by semi-structured interviews with 26 school principals and teachers. Additional information was extracted from publicly available school management plans and annual reports. We examined the data from these sources to determine whether schools exhibit characteristics of complex adaptive systems. The results confirmed that schools do exhibit most, but not all of the characteristics of social complex adaptive systems, and exhibit significant differences with artificial and natural systems. Understanding schools as social complex adaptive systems may help to explain some of the challenges of introducing and sustaining change in schools. These insights may, in turn, lead us to adopt more sophisticated approaches to the diffusion of new programs in school systems that account for the diverse, complex and context specific nature of individual school systems. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Development as adaptation: a paradigm for gravitational and space biology

NASA Technical Reports Server (NTRS)

Alberts, Jeffrey R.; Ronca, April E.

2005-01-01

Adaptation is a central precept of biology; it provides a framework for identifying functional significance. We equate mammalian development with adaptation, by viewing the developmental sequence as a series of adaptations to a stereotyped sequence of habitats. In this way development is adaptation. The Norway rat is used as a mammalian model, and the sequence of habitats that is used to define its adaptive-developmental sequence is (a) the uterus, (b) the mother's body, (c) the huddle, and (d) the coterie of pups as they gain independence. Then, within this framework and in relation to each of the habitats, we consider problems of organismal responses to altered gravitational forces (micro-g to hyper-g), especially those encountered during space flight and centrifugation. This approach enables a clearer identification of simple "effects" and active "responses" with respect to gravity. It focuses our attention on functional systems and brings to the fore the manner in which experience shapes somatic adaptation. We argue that this basic developmental approach is not only central to basic issues in gravitational biology, but that it provides a natural tool for understanding the underlying processes that are vital to astronaut health and well-being during long duration flights that will involve adaptation to space flight conditions and eventual re-adaptation to Earth's gravity.

Adaptive control of a millimeter-scale flapping-wing robot.

PubMed

Chirarattananon, Pakpong; Ma, Kevin Y; Wood, Robert J

2014-06-01

Challenges for the controlled flight of a robotic insect are due to the inherent instability of the system, complex fluid-structure interactions, and the general lack of a complete system model. In this paper, we propose theoretical models of the system based on the limited information available from previous work and a comprehensive flight controller. The modular flight controller is derived from Lyapunov function candidates with proven stability over a large region of attraction. Moreover, it comprises adaptive components that are capable of coping with uncertainties in the system that arise from manufacturing imperfections. We have demonstrated that the proposed methods enable the robot to achieve sustained hovering flights with relatively small errors compared to a non-adaptive approach. Simple lateral maneuvers and vertical takeoff and landing flights are also shown to illustrate the fidelity of the flight controller. The analysis suggests that the adaptive scheme is crucial in order to achieve millimeter-scale precision in flight control as observed in natural insect flight.

Adaptive Biomedical Innovation: Evolving Our Global System to Sustainably and Safely Bring New Medicines to Patients in Need

PubMed Central

Trusheim, M; Cobbs, E; Bala, M; Garner, S; Hartman, D; Isaacs, K; Lumpkin, M; Lim, R; Oye, K; Pezalla, E; Saltonstall, P; Selker, H

2016-01-01

The current system of biomedical innovation is unable to keep pace with scientific advancements. We propose to address this gap by reengineering innovation processes to accelerate reliable delivery of products that address unmet medical needs. Adaptive biomedical innovation (ABI) provides an integrative, strategic approach for process innovation. Although the term “ABI” is new, it encompasses fragmented “tools” that have been developed across the global pharmaceutical industry, and could accelerate the evolution of the system through more coordinated application. ABI involves bringing stakeholders together to set shared objectives, foster trust, structure decision‐making, and manage expectations through rapid‐cycle feedback loops that maximize product knowledge and reduce uncertainty in a continuous, adaptive, and sustainable learning healthcare system. Adaptive decision‐making, a core element of ABI, provides a framework for structuring decision‐making designed to manage two types of uncertainty – the maturity of scientific and clinical knowledge, and the behaviors of other critical stakeholders. PMID:27626610

Adaptive Neural Control for a Class of Pure-Feedback Nonlinear Systems via Dynamic Surface Technique.

PubMed

Liu, Zongcheng; Dong, Xinmin; Xue, Jianping; Li, Hongbo; Chen, Yong

2016-09-01

This brief addresses the adaptive control problem for a class of pure-feedback systems with nonaffine functions possibly being nondifferentiable. Without using the mean value theorem, the difficulty of the control design for pure-feedback systems is overcome by modeling the nonaffine functions appropriately. With the help of neural network approximators, an adaptive neural controller is developed by combining the dynamic surface control (DSC) and minimal learning parameter (MLP) techniques. The key features of our approach are that, first, the restrictive assumptions on the partial derivative of nonaffine functions are removed, second, the DSC technique is used to avoid "the explosion of complexity" in the backstepping design, and the number of adaptive parameters is reduced significantly using the MLP technique, third, smooth robust compensators are employed to circumvent the influences of approximation errors and disturbances. Furthermore, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, the simulation results are provided to demonstrate the effectiveness of the designed method.

Assessing the components of adaptive capacity to improve conservation and management efforts under global change

USGS Publications Warehouse

Nicotra, Adrienne; Beever, Erik; Robertson, Amanda; Hofmann, Gretchen; O’Leary, John

2015-01-01

Natural-resource managers and other conservation practitioners are under unprecedented pressure to categorize and quantify the vulnerability of natural systems based on assessment of the exposure, sensitivity, and adaptive capacity of species to climate change. Despite the urgent need for these assessments, neither the theoretical basis of adaptive capacity nor the practical issues underlying its quantification has been articulated in a manner that is directly applicable to natural-resource management. Both are critical for researchers, managers, and other conservation practitioners to develop reliable strategies for assessing adaptive capacity. Drawing from principles of classical and contemporary research and examples from terrestrial, marine, plant, and animal systems, we examined broadly the theory behind the concept of adaptive capacity. We then considered how interdisciplinary, trait- and triage-based approaches encompassing the oft-overlooked interactions among components of adaptive capacity can be used to identify species and populations likely to have higher (or lower) adaptive capacity. We identified the challenges and value of such endeavors and argue for a concerted interdisciplinary research approach that combines ecology, ecological genetics, and eco-physiology to reflect the interacting components of adaptive capacity. We aimed to provide a basis for constructive discussion between natural-resource managers and researchers, discussions urgently needed to identify research directions that will deliver answers to real-world questions facing resource managers, other conservation practitioners, and policy makers. Directing research to both seek general patterns and identify ways to facilitate adaptive capacity of key species and populations within species, will enable conservation ecologists and resource managers to maximize returns on research and management investment and arrive at novel and dynamic management and policy decisions.

Assessing the components of adaptive capacity to improve conservation and management efforts under global change.

PubMed

Nicotra, Adrienne B; Beever, Erik A; Robertson, Amanda L; Hofmann, Gretchen E; O'Leary, John

2015-10-01

Natural-resource managers and other conservation practitioners are under unprecedented pressure to categorize and quantify the vulnerability of natural systems based on assessment of the exposure, sensitivity, and adaptive capacity of species to climate change. Despite the urgent need for these assessments, neither the theoretical basis of adaptive capacity nor the practical issues underlying its quantification has been articulated in a manner that is directly applicable to natural-resource management. Both are critical for researchers, managers, and other conservation practitioners to develop reliable strategies for assessing adaptive capacity. Drawing from principles of classical and contemporary research and examples from terrestrial, marine, plant, and animal systems, we examined broadly the theory behind the concept of adaptive capacity. We then considered how interdisciplinary, trait- and triage-based approaches encompassing the oft-overlooked interactions among components of adaptive capacity can be used to identify species and populations likely to have higher (or lower) adaptive capacity. We identified the challenges and value of such endeavors and argue for a concerted interdisciplinary research approach that combines ecology, ecological genetics, and eco-physiology to reflect the interacting components of adaptive capacity. We aimed to provide a basis for constructive discussion between natural-resource managers and researchers, discussions urgently needed to identify research directions that will deliver answers to real-world questions facing resource managers, other conservation practitioners, and policy makers. Directing research to both seek general patterns and identify ways to facilitate adaptive capacity of key species and populations within species, will enable conservation ecologists and resource managers to maximize returns on research and management investment and arrive at novel and dynamic management and policy decisions. © 2015 Society for Conservation Biology.

A quality improvement approach to capacity building in low- and middle-income countries.

PubMed

Bardfield, Joshua; Agins, Bruce; Akiyama, Matthew; Basenero, Apollo; Luphala, Patience; Kaindjee-Tjituka, Francina; Natanael, Salomo; Hamunime, Ndapewa

2015-07-01

To describe the HEALTHQUAL framework consisting of the following three components: performance measurement, quality improvement and the quality management program, representing an adaptive approach to building capacity in national quality management programs in low and middle-income countries. We present a case study from Namibia illustrating how this approach is adapted to country context. HEALTHQUAL partners with Ministries of Health to build knowledge and expertise in modern improvement methods, including data collection, analysis and reporting, process analysis and the use of data to implement quality improvement projects that aim to improve systems and processes of care. Clinical performance measures are selected in each country by the Ministry of Health on the basis of national guidelines. Patient records are sampled using a standardized statistical table to achieve a minimum confidence interval of 90%, with a spread of ±8% in participating facilities. Data are routinely reviewed to identify gaps in patient care, and aggregated to produce facility mean scores that are trended over time. A formal organizational assessment is conducted at facility and national levels to review the implementation progress. Aggregate mean rates of performance for 10 of 11 indicators of HIV care improved for adult HIV-positive patients between 2008 and 2013. Quality improvement is an approach to capacity building and health systems strengthening that offers adaptive methodology. Synergistic implementation of elements of a national quality program can lead to improvements in care, in parallel with systematic capacity development for measurement, improvement and quality management throughout the healthcare delivery system.

Adaptive Gaussian mixture models for pre-screening in GPR data

NASA Astrophysics Data System (ADS)

Torrione, Peter; Morton, Kenneth, Jr.; Besaw, Lance E.

2011-06-01

Due to the large amount of data generated by vehicle-mounted ground penetrating radar (GPR) antennae arrays, advanced feature extraction and classification can only be performed on a small subset of data during real-time operation. As a result, most GPR based landmine detection systems implement "pre-screening" algorithms to processes all of the data generated by the antennae array and identify locations with anomalous signatures for more advanced processing. These pre-screening algorithms must be computationally efficient and obtain high probability of detection, but can permit a false alarm rate which might be higher than the total system requirements. Many approaches to prescreening have previously been proposed, including linear prediction coefficients, the LMS algorithm, and CFAR-based approaches. Similar pre-screening techniques have also been developed in the field of video processing to identify anomalous behavior or anomalous objects. One such algorithm, an online k-means approximation to an adaptive Gaussian mixture model (GMM), is particularly well-suited to application for pre-screening in GPR data due to its computational efficiency, non-linear nature, and relevance of the logic underlying the algorithm to GPR processing. In this work we explore the application of an adaptive GMM-based approach for anomaly detection from the video processing literature to pre-screening in GPR data. Results with the ARA Nemesis landmine detection system demonstrate significant pre-screening performance improvements compared to alternative approaches, and indicate that the proposed algorithm is a complimentary technique to existing methods.

A systems approach to assess farm-scale nutrient and trace element dynamics: a case study at the Ojebyn dairy farm.

PubMed

Oborn, Ingrid; Modin-Edman, Anna-Karin; Bengtsson, Helena; Gustafson, Gunnela M; Salomon, Eva; Nilsson, S Ingvar; Holmqvist, Johan; Jonsson, Simon; Sverdrup, Harald

2005-06-01

A systems analysis approach was used to assess farmscale nutrient and trace element sustainability by combining full-scale field experiments with specific studies of nutrient release from mineral weathering and trace-element cycling. At the Ojebyn dairy farm in northern Sweden, a farm-scale case study including phosphorus (P), potassium (K), and zinc (Zn) was run to compare organic and conventional agricultural management practices. By combining different element-balance approaches (at farmgate, barn, and field scales) and further adapting these to the FARMFLOW model, we were able to combine mass flows and pools within the subsystems and establish links between subsystems in order to make farm-scale predictions. It was found that internal element flows on the farm are large and that there are farm internal sources (Zn) and loss terms (K). The approaches developed and tested at the Ojebyn farm are promising and considered generally adaptable to any farm.

Real-time artificial intelligence issues in the development of the adaptive tactical navigator

NASA Technical Reports Server (NTRS)

Green, Peter E.; Glasson, Douglas P.; Pomarede, Jean-Michel L.; Acharya, Narayan A.

1987-01-01

Adaptive Tactical Navigation (ATN) is a laboratory prototype of a knowledge based system to provide navigation system management and decision aiding in the next generation of tactical aircraft. ATN's purpose is to manage a set of multimode navigation equipment, dynamically selecting the best equipment to use in accordance with mission goals and phase, threat environment, equipment malfunction status, and battle damage. ATN encompasses functions as diverse as sensor data interpretation, diagnosis, and planning. Real time issues that were identified in ATN and the approaches used to address them are addressed. Functional requirements and a global architecture for the ATN system are described. Decision making with time constraints are discussed. Two subproblems are identified; making decisions with incomplete information and with limited resources. Approaches used in ATN to address real time performance are described and simulation results are discussed.

Dynamic optimization and adaptive controller design

NASA Astrophysics Data System (ADS)

Inamdar, S. R.

2010-10-01

In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

An approach enabling adaptive FEC for OFDM in fiber-VLLC system

NASA Astrophysics Data System (ADS)

Wei, Yiran; He, Jing; Deng, Rui; Shi, Jin; Chen, Shenghai; Chen, Lin

2017-12-01

In this paper, we propose an orthogonal circulant matrix transform (OCT)-based adaptive frame-level-forward error correction (FEC) scheme for fiber-visible laser light communication (VLLC) system and experimentally demonstrate by Reed-Solomon (RS) Code. In this method, no extra bits are spent for adaptive message, except training sequence (TS), which is simultaneously used for synchronization and channel estimation. Therefore, RS-coding can be adaptively performed frames by frames via the last received codeword-error-rate (CER) feedback estimated by the TSs of the previous few OFDM frames. In addition, the experimental results exhibit that over 20 km standard single-mode fiber (SSMF) and 8 m visible light transmission, the costs of RS codewords are at most 14.12% lower than those of conventional adaptive subcarrier-RS-code based 16-QAM OFDM at bit error rate (BER) of 10-5.

A Conceptual Framework for Planning Systemic Human Adaptation to Global Warming

PubMed Central

Tait, Peter W.; Hanna, Elizabeth G.

2015-01-01

Human activity is having multiple, inter-related effects on ecosystems. Greenhouse gas emissions persisting along current trajectories threaten to significantly alter human society. At 0.85 °C of anthropogenic warming, deleterious human impacts are acutely evident. Additional warming of 0.5 °C–1.0 °C from already emitted CO2 will further intensify extreme heat and damaging storm events. Failing to sufficiently address this trend will have a heavy human toll directly and indirectly on health. Along with mitigation efforts, societal adaptation to a warmer world is imperative. Adaptation efforts need to be significantly upscaled to prepare society to lessen the public health effects of rising temperatures. Modifying societal behaviour is inherently complex and presents a major policy challenge. We propose a social systems framework for conceptualizing adaptation that maps out three domains within the adaptation policy landscape: acclimatisation, behavioural adaptation and technological adaptation, which operate at societal and personal levels. We propose that overlaying this framework on a systems approach to societal change planning methods will enhance governments’ capacity and efficacy in strategic planning for adaptation. This conceptual framework provides a policy oriented planning assessment tool that will help planners match interventions to the behaviours being targeted for change. We provide illustrative examples to demonstrate the framework’s application as a planning tool. PMID:26334285

A Conceptual Framework for Planning Systemic Human Adaptation to Global Warming.

PubMed

Tait, Peter W; Hanna, Elizabeth G

2015-08-31

Human activity is having multiple, inter-related effects on ecosystems. Greenhouse gas emissions persisting along current trajectories threaten to significantly alter human society. At 0.85 °C of anthropogenic warming, deleterious human impacts are acutely evident. Additional warming of 0.5 °C-1.0 °C from already emitted CO₂ will further intensify extreme heat and damaging storm events. Failing to sufficiently address this trend will have a heavy human toll directly and indirectly on health. Along with mitigation efforts, societal adaptation to a warmer world is imperative. Adaptation efforts need to be significantly upscaled to prepare society to lessen the public health effects of rising temperatures. Modifying societal behaviour is inherently complex and presents a major policy challenge. We propose a social systems framework for conceptualizing adaptation that maps out three domains within the adaptation policy landscape: acclimatisation, behavioural adaptation and technological adaptation, which operate at societal and personal levels. We propose that overlaying this framework on a systems approach to societal change planning methods will enhance governments' capacity and efficacy in strategic planning for adaptation. This conceptual framework provides a policy oriented planning assessment tool that will help planners match interventions to the behaviours being targeted for change. We provide illustrative examples to demonstrate the framework's application as a planning tool.

Defining adaptation measures collaboratively: A participatory approach in the Doñana socio-ecological system, Spain.

PubMed

De Stefano, Lucia; Hernández-Mora, Nuria; Iglesias, Ana; Sánchez, Berta

2017-06-15

The uncertainty associated with the definition of strategies for climate change adaptation poses a challenge that cannot be faced by science alone. We present a participatory experience where, instead of having science defining solutions and eliciting stakeholders' feedback, local actors actually drove the process. While principles and methods of the approach are easily adaptable to different local contexts, this paper shows the contribution of participatory dynamics to the design of adaptation measures in the biodiversity-rich socio-ecological region surrounding the Doñana wetlands (Southern Spain). During the process, stakeholders and scientists collaboratively designed a common scenario for the future in which to define and assess a portfolio of potential adaptation measures, and found a safe, informal space for open dialogue and information exchange. Through this dialogue, points of connection among local actors emerged around the need for more integrated, transparent design of adaptation measures; for strengthening local capacity; and for strategies to diversify economic activities in order to increase the resilience of the region. Copyright © 2016 Elsevier Ltd. All rights reserved.

Follow and Facilitate: What Music Educators Can Learn from the Reggio Emilia Approach

ERIC Educational Resources Information Center

Bond, Vanessa L.

2013-01-01

The educational practices of the municipality of Reggio Emilia, Italy, are celebrated as among the best in the world. Inspired by this educational system, schools across America have adapted the Reggio Emilia approach. Yet, music educators may be unaware of its principles as the approach is not often discussed in music education literature. The…

Technologies for Inclusive Education: Beyond Traditional Integration Approaches. Advances in Educational Technologies and Instructional Design

ERIC Educational Resources Information Center

Barres, David Griol; Carrion, Zoraida Callejas; Lopez-Cozar Delgado, Ramon

2013-01-01

By providing students with the opportunities to receive a high quality education regardless of their social or cultural background, inclusive education is a new area that goes beyond traditional integration approaches. These approaches hope to provide the educative system with the ability to adapt to the diversity of its students. Technologies for…

Modal-space reference-model-tracking fuzzy control of earthquake excited structures

NASA Astrophysics Data System (ADS)

Park, Kwan-Soon; Ok, Seung-Yong

2015-01-01

This paper describes an adaptive modal-space reference-model-tracking fuzzy control technique for the vibration control of earthquake-excited structures. In the proposed approach, the fuzzy logic is introduced to update optimal control force so that the controlled structural response can track the desired response of a reference model. For easy and practical implementation, the reference model is constructed by assigning the target damping ratios to the first few dominant modes in modal space. The numerical simulation results demonstrate that the proposed approach successfully achieves not only the adaptive fault-tolerant control system against partial actuator failures but also the robust performance against the variations of the uncertain system properties by redistributing the feedback control forces to the available actuators.

Adaptive AOA-aided TOA self-positioning for mobile wireless sensor networks.

PubMed

Wen, Chih-Yu; Chan, Fu-Kai

2010-01-01

Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival (TOA) and angle of arrival (AOA) information employing multiple seeds in the line-of-sight scenario. By receiving the periodic broadcasts from the seeds, the mobile target sensors can obtain adequate observations and localize themselves automatically. The proposed positioning scheme performs location estimation in three phases: (I) AOA-aided TOA measurement, (II) Geometrical positioning with particle filter, and (III) Adaptive fuzzy control. Based on the distance measurements and the initial position estimate, adaptive fuzzy control scheme is applied to solve the localization adjustment problem. The simulations show that the proposed approach provides adaptive flexibility and robust improvement in position estimation.

Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems.

PubMed

Yan-Jun Liu; Shu Li; Shaocheng Tong; Chen, C L Philip

2017-07-01

In this paper, an adaptive control approach-based neural approximation is developed for a class of uncertain nonlinear discrete-time (DT) systems. The main characteristic of the considered systems is that they can be viewed as a class of multi-input multioutput systems in the nonstrict feedback structure. The similar control problem of this class of systems has been addressed in the past, but it focused on the continuous-time systems. Due to the complicacies of the system structure, it will become more difficult for the controller design and the stability analysis. To stabilize this class of systems, a new recursive procedure is developed, and the effect caused by the noncausal problem in the nonstrict feedback DT structure can be solved using a semirecurrent neural approximation. Based on the Lyapunov difference approach, it is proved that all the signals of the closed-loop system are semiglobal, ultimately uniformly bounded, and a good tracking performance can be guaranteed. The feasibility of the proposed controllers can be validated by setting a simulation example.

Making sense in a complex landscape: how the Cynefin Framework from Complex Adaptive Systems Theory can inform health promotion practice.

PubMed

Van Beurden, Eric K; Kia, Annie M; Zask, Avigdor; Dietrich, Uta; Rose, Lauren

2013-03-01

Health promotion addresses issues from the simple (with well-known cause/effect links) to the highly complex (webs and loops of cause/effect with unpredictable, emergent properties). Yet there is no conceptual framework within its theory base to help identify approaches appropriate to the level of complexity. The default approach favours reductionism--the assumption that reducing a system to its parts will inform whole system behaviour. Such an approach can yield useful knowledge, yet is inadequate where issues have multiple interacting causes, such as social determinants of health. To address complex issues, there is a need for a conceptual framework that helps choose action that is appropriate to context. This paper presents the Cynefin Framework, informed by complexity science--the study of Complex Adaptive Systems (CAS). It introduces key CAS concepts and reviews the emergence and implications of 'complex' approaches within health promotion. It explains the framework and its use with examples from contemporary practice, and sets it within the context of related bodies of health promotion theory. The Cynefin Framework, especially when used as a sense-making tool, can help practitioners understand the complexity of issues, identify appropriate strategies and avoid the pitfalls of applying reductionist approaches to complex situations. The urgency to address critical issues such as climate change and the social determinants of health calls for us to engage with complexity science. The Cynefin Framework helps practitioners make the shift, and enables those already engaged in complex approaches to communicate the value and meaning of their work in a system that privileges reductionist approaches.

Adaptive sensor-fault tolerant control for a class of multivariable uncertain nonlinear systems.

PubMed

Khebbache, Hicham; Tadjine, Mohamed; Labiod, Salim; Boulkroune, Abdesselem

2015-03-01

This paper deals with the active fault tolerant control (AFTC) problem for a class of multiple-input multiple-output (MIMO) uncertain nonlinear systems subject to sensor faults and external disturbances. The proposed AFTC method can tolerate three additive (bias, drift and loss of accuracy) and one multiplicative (loss of effectiveness) sensor faults. By employing backstepping technique, a novel adaptive backstepping-based AFTC scheme is developed using the fact that sensor faults and system uncertainties (including external disturbances and unexpected nonlinear functions caused by sensor faults) can be on-line estimated and compensated via robust adaptive schemes. The stability analysis of the closed-loop system is rigorously proven using a Lyapunov approach. The effectiveness of the proposed controller is illustrated by two simulation examples. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Multiple Model Adaptive Attitude Control of LEO Satellite with Angular Velocity Constraints

NASA Astrophysics Data System (ADS)

Shahrooei, Abolfazl; Kazemi, Mohammad Hosein

2018-04-01

In this paper, the multiple model adaptive control is utilized to improve the transient response of attitude control system for a rigid spacecraft. An adaptive output feedback control law is proposed for attitude control under angular velocity constraints and its almost global asymptotic stability is proved. The multiple model adaptive control approach is employed to counteract large uncertainty in parameter space of the inertia matrix. The nonlinear dynamics of a low earth orbit satellite is simulated and the proposed control algorithm is implemented. The reported results show the effectiveness of the suggested scheme.

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Introducing sampling entropy in repository based adaptive umbrella sampling

NASA Astrophysics Data System (ADS)

Zheng, Han; Zhang, Yingkai

2009-12-01

Determining free energy surfaces along chosen reaction coordinates is a common and important task in simulating complex systems. Due to the complexity of energy landscapes and the existence of high barriers, one widely pursued objective to develop efficient simulation methods is to achieve uniform sampling among thermodynamic states of interest. In this work, we have demonstrated sampling entropy (SE) as an excellent indicator for uniform sampling as well as for the convergence of free energy simulations. By introducing SE and the concentration theorem into the biasing-potential-updating scheme, we have further improved the adaptivity, robustness, and applicability of our recently developed repository based adaptive umbrella sampling (RBAUS) approach [H. Zheng and Y. Zhang, J. Chem. Phys. 128, 204106 (2008)]. Besides simulations of one dimensional free energy profiles for various systems, the generality and efficiency of this new RBAUS-SE approach have been further demonstrated by determining two dimensional free energy surfaces for the alanine dipeptide in gas phase as well as in water.

Formation tracker design of multiple mobile robots with wheel perturbations: adaptive output-feedback approach

NASA Astrophysics Data System (ADS)

Yoo, Sung Jin

2016-11-01

This paper presents a theoretical design approach for output-feedback formation tracking of multiple mobile robots under wheel perturbations. It is assumed that these perturbations are unknown and the linear and angular velocities of the robots are unmeasurable. First, adaptive state observers for estimating unmeasurable velocities of the robots are developed under the robots' kinematics and dynamics including wheel perturbation effects. Then, we derive a virtual-structure-based formation tracker scheme according to the observer dynamic surface design procedure. The main difficulty of the output-feedback control design is to manage the coupling problems between unmeasurable velocities and unknown wheel perturbation effects. These problems are avoided by using the adaptive technique and the function approximation property based on fuzzy logic systems. From the Lyapunov stability analysis, it is shown that point tracking errors of each robot and synchronisation errors for the desired formation converge to an adjustable neighbourhood of the origin, while all signals in the controlled closed-loop system are semiglobally uniformly ultimately bounded.

Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection

DOE PAGES

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max; ...

2016-08-04

This work proposes a hyperspherical sparse approximation framework for detecting jump discontinuities in functions in high-dimensional spaces. The need for a novel approach results from the theoretical and computational inefficiencies of well-known approaches, such as adaptive sparse grids, for discontinuity detection. Our approach constructs the hyperspherical coordinate representation of the discontinuity surface of a function. Then sparse approximations of the transformed function are built in the hyperspherical coordinate system, with values at each point estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the new technique can identify jump discontinuities with significantly reduced computationalmore » cost, compared to existing methods. Several approaches are used to approximate the transformed discontinuity surface in the hyperspherical system, including adaptive sparse grid and radial basis function interpolation, discrete least squares projection, and compressed sensing approximation. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. In conclusion, rigorous complexity analyses of the new methods are provided, as are several numerical examples that illustrate the effectiveness of our approach.« less

Adaptive filtering in biological signal processing.

PubMed

Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A

1990-01-01

The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.

Performance enhancement of low-cost, high-accuracy, state estimation for vehicle collision prevention system using ANFIS

NASA Astrophysics Data System (ADS)

Saadeddin, Kamal; Abdel-Hafez, Mamoun F.; Jaradat, Mohammad A.; Jarrah, Mohammad Amin

2013-12-01

In this paper, a low-cost navigation system that fuses the measurements of the inertial navigation system (INS) and the global positioning system (GPS) receiver is developed. First, the system's dynamics are obtained based on a vehicle's kinematic model. Second, the INS and GPS measurements are fused using an extended Kalman filter (EKF) approach. Subsequently, an artificial intelligence based approach for the fusion of INS/GPS measurements is developed based on an Input-Delayed Adaptive Neuro-Fuzzy Inference System (IDANFIS). Experimental tests are conducted to demonstrate the performance of the two sensor fusion approaches. It is found that the use of the proposed IDANFIS approach achieves a reduction in the integration development time and an improvement in the estimation accuracy of the vehicle's position and velocity compared to the EKF based approach.

State-space self-tuner for on-line adaptive control

NASA Technical Reports Server (NTRS)

Shieh, L. S.

1994-01-01

Dynamic systems, such as flight vehicles, satellites and space stations, operating in real environments, constantly face parameter and/or structural variations owing to nonlinear behavior of actuators, failure of sensors, changes in operating conditions, disturbances acting on the system, etc. In the past three decades, adaptive control has been shown to be effective in dealing with dynamic systems in the presence of parameter uncertainties, structural perturbations, random disturbances and environmental variations. Among the existing adaptive control methodologies, the state-space self-tuning control methods, initially proposed by us, are shown to be effective in designing advanced adaptive controllers for multivariable systems. In our approaches, we have embedded the standard Kalman state-estimation algorithm into an online parameter estimation algorithm. Thus, the advanced state-feedback controllers can be easily established for digital adaptive control of continuous-time stochastic multivariable systems. A state-space self-tuner for a general multivariable stochastic system has been developed and successfully applied to the space station for on-line adaptive control. Also, a technique for multistage design of an optimal momentum management controller for the space station has been developed and reported in. Moreover, we have successfully developed various digital redesign techniques which can convert a continuous-time controller to an equivalent digital controller. As a result, the expensive and unreliable continuous-time controller can be implemented using low-cost and high performance microprocessors. Recently, we have developed a new hybrid state-space self tuner using a new dual-rate sampling scheme for on-line adaptive control of continuous-time uncertain systems.

An adaptive optics approach for laser beam correction in turbulence utilizing a modified plenoptic camera

NASA Astrophysics Data System (ADS)

Ko, Jonathan; Wu, Chensheng; Davis, Christopher C.

2015-09-01

Adaptive optics has been widely used in the field of astronomy to correct for atmospheric turbulence while viewing images of celestial bodies. The slightly distorted incoming wavefronts are typically sensed with a Shack-Hartmann sensor and then corrected with a deformable mirror. Although this approach has proven to be effective for astronomical purposes, a new approach must be developed when correcting for the deep turbulence experienced in ground to ground based optical systems. We propose the use of a modified plenoptic camera as a wavefront sensor capable of accurately representing an incoming wavefront that has been significantly distorted by strong turbulence conditions (C2n <10-13 m- 2/3). An intelligent correction algorithm can then be developed to reconstruct the perturbed wavefront and use this information to drive a deformable mirror capable of correcting the major distortions. After the large distortions have been corrected, a secondary mode utilizing more traditional adaptive optics algorithms can take over to fine tune the wavefront correction. This two-stage algorithm can find use in free space optical communication systems, in directed energy applications, as well as for image correction purposes.

Adaptive dynamic programming approach to experience-based systems identification and control.

PubMed

Lendaris, George G

2009-01-01

Humans have the ability to make use of experience while selecting their control actions for distinct and changing situations, and their process speeds up and have enhanced effectiveness as more experience is gained. In contrast, current technological implementations slow down as more knowledge is stored. A novel way of employing Approximate (or Adaptive) Dynamic Programming (ADP) is described that shifts the underlying Adaptive Critic type of Reinforcement Learning method "up a level", away from designing individual (optimal) controllers to that of developing on-line algorithms that efficiently and effectively select designs from a repository of existing controller solutions (perhaps previously developed via application of ADP methods). The resulting approach is called Higher-Level Learning Algorithm. The approach and its rationale are described and some examples of its application are given. The notions of context and context discernment are important to understanding the human abilities noted above. These are first defined, in a manner appropriate to controls and system-identification, and as a foundation relating to the application arena, a historical view of the various phases during development of the controls field is given, organized by how the notion 'context' was, or was not, involved in each phase.

Advancing national climate change risk assessment to deliver national adaptation plans

NASA Astrophysics Data System (ADS)

Warren, R. F.; Wilby, R. L.; Brown, K.; Watkiss, P.; Betts, Richard A.; Murphy, James M.; Lowe, Jason A.

2018-06-01

A wide range of climate vulnerability and risk assessments have been implemented using different approaches at different scales, some with a broad multi-sectoral scope and others focused on single risks or sectors. This paper describes the novel approach to vulnerability and risk assessment which was designed and put into practice in the United Kingdom's Second Climate Change Risk Assessment (CCRA2) so as to build upon its earlier assessment (CCRA1). First, we summarize and critique the CCRA1 approach, and second describe the steps taken in the CCRA2 approach in detail, providing examples of how each was applied in practice. Novel elements of the approach include assessment of both present day and future vulnerability, a focus on the urgency of adaptation action, and a structure focused around systems of receptors rather than conventional sectors. Both stakeholders and reviewers generally regarded the approach as successful in providing advice on current risks and future opportunities to the UK from climate change, and the fulfilment of statutory duty. The need for a well-supported and open suite of impact indicators going forward is highlighted. This article is part of the theme issue `Advances in risk assessment for climate change adaptation policy'.

Adaptive guidance and control for future remote sensing systems

NASA Technical Reports Server (NTRS)

Lowrie, J. W.; Myers, J. E.

1980-01-01

A unique approach to onboard processing was developed that is capable of acquiring high quality image data for users in near real time. The approach is divided into two steps: the development of an onboard cloud detection system; and the development of a landmark tracker. The results of these two developments are outlined and the requirements of an operational guidance and control system capable of providing continuous estimation of the sensor boresight position are summarized.

The Dynamics of Vulnerability and Implications for Climate Change Adaptation: Lessons from Urban Water Management

NASA Astrophysics Data System (ADS)

Dilling, L.; Daly, M.; Travis, W.; Wilhelmi, O.; Klein, R.; Kenney, D.; Ray, A. J.; Miller, K.

2013-12-01

Recent reports and scholarship have suggested that adapting to current climate variability may represent a "no regrets" strategy for adapting to climate change. Filling "adaptation deficits" and other approaches that rely on addressing current vulnerabilities are of course helpful for responding to current climate variability, but we find here that they are not sufficient for adapting to climate change. First, following a comprehensive review and unique synthesis of the natural hazards and climate adaptation literatures, we advance six reasons why adapting to climate variability is not sufficient for adapting to climate change: 1) Vulnerability is different at different levels of exposure; 2) Coping with climate variability is not equivalent to adaptation to longer term change; 3) The socioeconomic context for vulnerability is constantly changing; 4) The perception of risk associated with climate variability does not necessarily promote adaptive behavior in the face of climate change; 5) Adaptations made to short term climate variability may reduce the flexibility of the system in the long term; and 6) Adaptive actions may shift vulnerabilities to other parts of the system or to other people. Instead we suggest that decision makers faced with choices to adapt to climate change must consider the dynamics of vulnerability in a connected system-- how choices made in one part of the system might impact other valued outcomes or even create new vulnerabilities. Furthermore we suggest that rather than expressing climate change adaptation as an extension of adaptation to climate variability, the research and practice communities would do well to articulate adaptation as an imperfect policy, with tradeoffs and consequences and that decisions be prioritized to preserve flexibility be revisited often as climate change unfolds. We then present the results of a number of empirical studies of decision making for drought in urban water systems in the United States to understand: a) the variety of actions taken; b) the limitations of actions available to water managers; and c) the effectiveness of actions taken to date. Time permitting, we briefly present the results of 3 in-depth case studies of drought response and current perception of preparedness with respect to future drought and climate change among urban water system managers. We examine the role of governance, system connectivity, public perceptions and other factors in driving decision making and outcomes.

Vision-Based People Detection System for Heavy Machine Applications

PubMed Central

Fremont, Vincent; Bui, Manh Tuan; Boukerroui, Djamal; Letort, Pierrick

2016-01-01

This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance. PMID:26805838

Vision-Based People Detection System for Heavy Machine Applications.

PubMed

Fremont, Vincent; Bui, Manh Tuan; Boukerroui, Djamal; Letort, Pierrick

2016-01-20

This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance.

Fast correction approach for wavefront sensorless adaptive optics based on a linear phase diversity technique.

PubMed

Yue, Dan; Nie, Haitao; Li, Ye; Ying, Changsheng

2018-03-01

Wavefront sensorless (WFSless) adaptive optics (AO) systems have been widely studied in recent years. To reach optimum results, such systems require an efficient correction method. This paper presents a fast wavefront correction approach for a WFSless AO system mainly based on the linear phase diversity (PD) technique. The fast closed-loop control algorithm is set up based on the linear relationship between the drive voltage of the deformable mirror (DM) and the far-field images of the system, which is obtained through the linear PD algorithm combined with the influence function of the DM. A large number of phase screens under different turbulence strengths are simulated to test the performance of the proposed method. The numerical simulation results show that the method has fast convergence rate and strong correction ability, a few correction times can achieve good correction results, and can effectively improve the imaging quality of the system while needing fewer measurements of CCD data.

Full Quantum Dynamics Simulation of a Realistic Molecular System Using the Adaptive Time-Dependent Density Matrix Renormalization Group Method.

PubMed

Yao, Yao; Sun, Ke-Wei; Luo, Zhen; Ma, Haibo

2018-01-18

The accurate theoretical interpretation of ultrafast time-resolved spectroscopy experiments relies on full quantum dynamics simulations for the investigated system, which is nevertheless computationally prohibitive for realistic molecular systems with a large number of electronic and/or vibrational degrees of freedom. In this work, we propose a unitary transformation approach for realistic vibronic Hamiltonians, which can be coped with using the adaptive time-dependent density matrix renormalization group (t-DMRG) method to efficiently evolve the nonadiabatic dynamics of a large molecular system. We demonstrate the accuracy and efficiency of this approach with an example of simulating the exciton dissociation process within an oligothiophene/fullerene heterojunction, indicating that t-DMRG can be a promising method for full quantum dynamics simulation in large chemical systems. Moreover, it is also shown that the proper vibronic features in the ultrafast electronic process can be obtained by simulating the two-dimensional (2D) electronic spectrum by virtue of the high computational efficiency of the t-DMRG method.

Contextual adaptation of the Personnel Evaluation Standards for assessing faculty evaluation systems in developing countries: the case of Iran

PubMed Central

Ahmady, Soleiman; Changiz, Tahereh; Brommels, Mats; Gaffney, F Andrew; Thor, Johan; Masiello, Italo

2009-01-01

Background Faculty evaluations can identify needs to be addressed in effective development programs. Generic evaluation models exist, but these require adaptation to a particular context of interest. We report on one approach to such adaptation in the context of medical education in Iran, which is integrated into the delivery and management of healthcare services nationwide. Methods Using a triangulation design, interviews with senior faculty leaders were conducted to identify relevant areas for faculty evaluation. We then adapted the published checklist of the Personnel Evaluation Standards to fit the Iranian medical universities' context by considering faculty members' diverse roles. Then the adapted instrument was administered to faculty at twelve medical schools in Iran. Results The interviews revealed poor linkages between existing forms of development and evaluation, imbalance between the faculty work components and evaluated areas, inappropriate feedback and use of information in decision making. The principles of Personnel Evaluation Standards addressed almost all of these concerns and were used to assess the existing faculty evaluation system and also adapted to evaluate the core faculty roles. The survey response rate was 74%. Responses showed that the four principles in all faculty members' roles were met occasionally to frequently. Evaluation of teaching and research had the highest mean scores, while clinical and healthcare services, institutional administration, and self-development had the lowest mean scores. There were statistically significant differences between small medium and large medical schools (p < 0.000). Conclusion The adapted Personnel Evaluation Standards appears to be valid and applicable for monitoring and continuous improvement of a faculty evaluation system in the context of medical universities in Iran. The approach developed here provides a more balanced assessment of multiple faculty roles, including educational, clinical and healthcare services. In order to address identified deficiencies, the evaluation system should recognize, document, and uniformly reward those activities that are vital to the academic mission. Inclusion of personal developmental concerns in the evaluation discussion is essential for evaluation systems. PMID:19400932

An Adaptive E-Learning System Based on Students' Learning Styles: An Empirical Study

ERIC Educational Resources Information Center

Drissi, Samia; Amirat, Abdelkrim

2016-01-01

Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia.…

Creating Domain Independent Adaptive E-Learning Systems Using the Sharable Content Object Reference Model

ERIC Educational Resources Information Center

Watson, Jason; Ahmed, Pervaiz K.; Hardaker, Glenn

2007-01-01

Purpose: This research aims to investigate how a generic web-based ITS can be created which will adapt the training content in real time, to the needs of the individual trainee across any domain. Design/methodology/approach: After examining the various alternatives SCORM was adopted in this project because it provided an infrastructure that makes…

An Approach for Automatic Generation of Adaptive Hypermedia in Education with Multilingual Knowledge Discovery Techniques

ERIC Educational Resources Information Center

Alfonseca, Enrique; Rodriguez, Pilar; Perez, Diana

2007-01-01

This work describes a framework that combines techniques from Adaptive Hypermedia and Natural Language processing in order to create, in a fully automated way, on-line information systems from linear texts in electronic format, such as textbooks. The process is divided into two steps: an "off-line" processing step, which analyses the source text,…

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

Cololla, P.

This review describes a structured approach to adaptivity. The Automated Mesh Refinement (ARM) algorithms developed by M Berger are described, touching on hyperbolic and parabolic applications. Adaptivity is achieved by overlaying finer grids only in areas flagged by a generalized error criterion. The author discusses some of the issues involved in abutting disparate-resolution grids, and demonstrates that suitable algorithms exist for dissipative as well as hyperbolic systems.

Distributed robust adaptive control of high order nonlinear multi agent systems.

PubMed

Hashemi, Mahnaz; Shahgholian, Ghazanfar

2018-03-01

In this paper, a robust adaptive neural network based controller is presented for multi agent high order nonlinear systems with unknown nonlinear functions, unknown control gains and unknown actuator failures. At first, Neural Network (NN) is used to approximate the nonlinear uncertainty terms derived from the controller design procedure for the followers. Then, a novel distributed robust adaptive controller is developed by combining the backstepping method and the Dynamic Surface Control (DSC) approach. The proposed controllers are distributed in the sense that the designed controller for each follower agent only requires relative state information between itself and its neighbors. By using the Young's inequality, only few parameters need to be tuned regardless of NN nodes number. Accordingly, the problems of dimensionality curse and explosion of complexity are counteracted, simultaneously. New adaptive laws are designed by choosing the appropriate Lyapunov-Krasovskii functionals. The proposed approach proves the boundedness of all the closed-loop signals in addition to the convergence of the distributed tracking errors to a small neighborhood of the origin. Simulation results indicate that the proposed controller is effective and robust. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Control with Reference Model Modification

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

This paper presents a modification of the conventional model reference adaptive control (MRAC) architecture in order to improve transient performance of the input and output signals of uncertain systems. A simple modification of the reference model is proposed by feeding back the tracking error signal. It is shown that the proposed approach guarantees tracking of the given reference command and the reference control signal (one that would be designed if the system were known) not only asymptotically but also in transient. Moreover, it prevents generation of high frequency oscillations, which are unavoidable in conventional MRAC systems for large adaptation rates. The provided design guideline makes it possible to track a reference commands of any magnitude from any initial position without re-tuning. The benefits of the method are demonstrated with a simulation example

A New Method for 3D Radiative Transfer with Adaptive Grids

NASA Astrophysics Data System (ADS)

Folini, D.; Walder, R.; Psarros, M.; Desboeufs, A.

2003-01-01

We present a new method for 3D NLTE radiative transfer in moving media, including an adaptive grid, along with some test examples and first applications. The central features of our approach we briefly outline in the following. For the solution of the radiative transfer equation, we make use of a generalized mean intensity approach. In this approach, the transfer eqation is solved directly, instead of using the moments of the transfer equation, thus avoiding the associated closure problem. In a first step, a system of equations for the transfer of each directed intensity is set up, using short characteristics. Next, the entity of systems of equations for each directed intensity is re-formulated in the form of one system of equations for the angle-integrated mean intensity. This system then is solved by a modern, fast BiCGStab iterative solver. An additional advantage of this procedure is that convergence rates barely depend on the spatial discretization. For the solution of the rate equations we use Housholder transformations. Lines are treated by a 3D generalization of the well-known Sobolev-approximation. The two parts, solution of the transfer equation and solution of the rate equations, are iteratively coupled. We recently have implemented an adaptive grid, which allows for recursive refinement on a cell-by-cell basis. The spatial resolution, which is always a problematic issue in 3D simulations, we can thus locally reduce or augment, depending on the problem to be solved.

Climate change and Development of a methodological approach for the implementation of a strategy of resilience in the face of floods (Tangier North of Morocco)

NASA Astrophysics Data System (ADS)

Satour, N.; Kassou, N.; Kacimi, I.; BEN-Daoud, M.; Maatouk, M.

2016-12-01

Tangier is one of the 14 priority cities, cited in the National Plan for the fight against the floods in Morocco with the specifics linked to its geographical position and socio-economic factors. The city needs more strategies to improve it response and confidence in front of floods risk. Climate change exacerbates this problem by causing more frequent extreme precipitation events. Several different traditional methods were used to modelling, understanding and anticipating the phenomenon but the urban resilience is studied by few authors in Morocco. The approach should uncover the role of all components of the urban system, economic, institutional and natural. The central aim of this study is to develop a new resilience strategy of urban system of Tangier to floods across temporal and spatial scales to maintain or rapidly return to desired functions in the face of disturbance to adapt to change, and to quickly transform systems that limit current or future adaptive capacity. The Flood resilience index (FRI) is developed as an approach for evaluation of flood resilience using Geographic Information Systems. The research for this study started from the findings and conclusion of Meerow, Sara. 2016 and Batica, Jelena 2015. . Here we seek to introduce the factor "Climate Change" in the forecast scenarios of the resilience of the city by 2030. This suggests additional thinks about flood risks and adaptation strategies in Morocco.

Innovations in Educational System: Mobile Learning Applications

ERIC Educational Resources Information Center

Rokhvadze, Roza F.; Yelashkina, Natalya V.

2013-01-01

This article presents the analysis of the current changes in the higher educational system of the Russian Federation. The stated issues are accompanied with the advice and possible solutions. Authors offer their own approaches and techniques for the academic staff of higher educational institutions in order to adapt to the new system.

Therapy for Family Systems.

ERIC Educational Resources Information Center

Rosmann, Michael R.

A family therapy model, based on a conceptualization of the family as a behavioral system whose members interact adaptively so that an optimal level of functioning is maintained within the system, is described. The divergent roots of this conceptualization are discussed briefly, as are the treatment approaches based on it. The author's model,…

Multi-Armed Bandits for Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Clement, Benjamin; Roy, Didier; Oudeyer, Pierre-Yves; Lopes, Manuel

2015-01-01

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two…

USAF Evaluation of an Automated Adaptive Flight Training System

DTIC Science & Technology

1975-10-01

system. C. What is the most effective wav to utilize the system in ^jierational training’ Student opinion for this question JS equally divided...None Utility hydraulic failure Flap failure left engine failure Right engine failure Stah 2 aug failure No g\\ ro approach procedure, no MIDI

An accuracy aware low power wireless EEG unit with information content based adaptive data compression.

PubMed

Tolbert, Jeremy R; Kabali, Pratik; Brar, Simeranjit; Mukhopadhyay, Saibal

2009-01-01

We present a digital system for adaptive data compression for low power wireless transmission of Electroencephalography (EEG) data. The proposed system acts as a base-band processor between the EEG analog-to-digital front-end and RF transceiver. It performs a real-time accuracy energy trade-off for multi-channel EEG signal transmission by controlling the volume of transmitted data. We propose a multi-core digital signal processor for on-chip processing of EEG signals, to detect signal information of each channel and perform real-time adaptive compression. Our analysis shows that the proposed approach can provide significant savings in transmitter power with minimal impact on the overall signal accuracy.

Decentralized adaptive control of robot manipulators with robust stabilization design

NASA Technical Reports Server (NTRS)

Yuan, Bau-San; Book, Wayne J.

1988-01-01

Due to geometric nonlinearities and complex dynamics, a decentralized technique for adaptive control for multilink robot arms is attractive. Lyapunov-function theory for stability analysis provides an approach to robust stabilization. Each joint of the arm is treated as a component subsystem. The adaptive controller is made locally stable with servo signals including proportional and integral gains. This results in the bound on the dynamical interactions with other subsystems. A nonlinear controller which stabilizes the system with uniform boundedness is used to improve the robustness properties of the overall system. As a result, the robot tracks the reference trajectories with convergence. This strategy makes computation simple and therefore facilitates real-time implementation.

Cockpit Adaptive Automation and Pilot Performance

NASA Technical Reports Server (NTRS)

Parasuraman, Raja

2001-01-01

The introduction of high-level automated systems in the aircraft cockpit has provided several benefits, e.g., new capabilities, enhanced operational efficiency, and reduced crew workload. At the same time, conventional 'static' automation has sometimes degraded human operator monitoring performance, increased workload, and reduced situation awareness. Adaptive automation represents an alternative to static automation. In this approach, task allocation between human operators and computer systems is flexible and context-dependent rather than static. Adaptive automation, or adaptive task allocation, is thought to provide for regulation of operator workload and performance, while preserving the benefits of static automation. In previous research we have reported beneficial effects of adaptive automation on the performance of both pilots and non-pilots of flight-related tasks. For adaptive systems to be viable, however, such benefits need to be examined jointly in the context of a single set of tasks. The studies carried out under this project evaluated a systematic method for combining different forms of adaptive automation. A model for effective combination of different forms of adaptive automation, based on matching adaptation to operator workload was proposed and tested. The model was evaluated in studies using IFR-rated pilots flying a general-aviation simulator. Performance, subjective, and physiological (heart rate variability, eye scan-paths) measures of workload were recorded. The studies compared workload-based adaptation to to non-adaptive control conditions and found evidence for systematic benefits of adaptive automation. The research provides an empirical basis for evaluating the effectiveness of adaptive automation in the cockpit. The results contribute to the development of design principles and guidelines for the implementation of adaptive automation in the cockpit, particularly in general aviation, and in other human-machine systems. Project goals were met or exceeded. The results of the research extended knowledge of automation-related performance decrements in pilots and demonstrated the positive effects of adaptive task allocation. In addition, several practical implications for cockpit automation design were drawn from the research conducted. A total of 12 articles deriving from the project were published.

Biologically inspired dynamic material systems.

PubMed

Studart, André R

2015-03-09

Numerous examples of material systems that dynamically interact with and adapt to the surrounding environment are found in nature, from hair-based mechanoreceptors in animals to self-shaping seed dispersal units in plants to remodeling bone in vertebrates. Inspired by such fascinating biological structures, a wide range of synthetic material systems have been created to replicate the design concepts of dynamic natural architectures. Examples of biological structures and their man-made counterparts are herein revisited to illustrate how dynamic and adaptive responses emerge from the intimate microscale combination of building blocks with intrinsic nanoscale properties. By using top-down photolithographic methods and bottom-up assembly approaches, biologically inspired dynamic material systems have been created 1) to sense liquid flow with hair-inspired microelectromechanical systems, 2) to autonomously change shape by utilizing plantlike heterogeneous architectures, 3) to homeostatically influence the surrounding environment through self-regulating adaptive surfaces, and 4) to spatially concentrate chemical species by using synthetic microcompartments. The ever-increasing complexity and remarkable functionalities of such synthetic systems offer an encouraging perspective to the rich set of dynamic and adaptive properties that can potentially be implemented in future man-made material systems. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Adaptive integral backstepping sliding mode control for opto-electronic tracking system based on modified LuGre friction model

NASA Astrophysics Data System (ADS)

Yue, Fengfa; Li, Xingfei; Chen, Cheng; Tan, Wenbin

2017-12-01

In order to improve the control accuracy and stability of opto-electronic tracking system fixed on reef or airport under friction and external disturbance conditions, adaptive integral backstepping sliding mode control approach with friction compensation is developed to achieve accurate and stable tracking for fast moving target. The nonlinear observer and slide mode controller based on modified LuGre model with friction compensation can effectively reduce the influence of nonlinear friction and disturbance of this servo system. The stability of the closed-loop system is guaranteed by Lyapunov theory. The steady-state error of the system is eliminated by integral action. The adaptive integral backstepping sliding mode controller and its performance are validated by a nonlinear modified LuGre dynamic model of the opto-electronic tracking system in simulation and practical experiments. The experiment results demonstrate that the proposed controller can effectively realise the accuracy and stability control of opto-electronic tracking system.

Complex adaptive systems: concept analysis.

PubMed

Holden, Lela M

2005-12-01

The aim of this paper is to explicate the concept of complex adaptive systems through an analysis that provides a description, antecedents, consequences, and a model case from the nursing and health care literature. Life is more than atoms and molecules--it is patterns of organization. Complexity science is the latest generation of systems thinking that investigates patterns and has emerged from the exploration of the subatomic world and quantum physics. A key component of complexity science is the concept of complex adaptive systems, and active research is found in many disciplines--from biology to economics to health care. However, the research and literature related to these appealing topics have generated confusion. A thorough explication of complex adaptive systems is needed. A modified application of the methods recommended by Walker and Avant for concept analysis was used. A complex adaptive system is a collection of individual agents with freedom to act in ways that are not always totally predictable and whose actions are interconnected. Examples include a colony of termites, the financial market, and a surgical team. It is often referred to as chaos theory, but the two are not the same. Chaos theory is actually a subset of complexity science. Complexity science offers a powerful new approach--beyond merely looking at clinical processes and the skills of healthcare professionals. The use of complex adaptive systems as a framework is increasing for a wide range of scientific applications, including nursing and healthcare management research. When nursing and other healthcare managers focus on increasing connections, diversity, and interactions they increase information flow and promote creative adaptation referred to as self-organization. Complexity science builds on the rich tradition in nursing that views patients and nursing care from a systems perspective.

Adaptive cyber-attack modeling system

NASA Astrophysics Data System (ADS)

Gonsalves, Paul G.; Dougherty, Edward T.

2006-05-01

The pervasiveness of software and networked information systems is evident across a broad spectrum of business and government sectors. Such reliance provides an ample opportunity not only for the nefarious exploits of lone wolf computer hackers, but for more systematic software attacks from organized entities. Much effort and focus has been placed on preventing and ameliorating network and OS attacks, a concomitant emphasis is required to address protection of mission critical software. Typical software protection technique and methodology evaluation and verification and validation (V&V) involves the use of a team of subject matter experts (SMEs) to mimic potential attackers or hackers. This manpower intensive, time-consuming, and potentially cost-prohibitive approach is not amenable to performing the necessary multiple non-subjective analyses required to support quantifying software protection levels. To facilitate the evaluation and V&V of software protection solutions, we have designed and developed a prototype adaptive cyber attack modeling system. Our approach integrates an off-line mechanism for rapid construction of Bayesian belief network (BN) attack models with an on-line model instantiation, adaptation and knowledge acquisition scheme. Off-line model construction is supported via a knowledge elicitation approach for identifying key domain requirements and a process for translating these requirements into a library of BN-based cyber-attack models. On-line attack modeling and knowledge acquisition is supported via BN evidence propagation and model parameter learning.

When ICT Meets Schools: Differentiation, Complexity and Adaptability

ERIC Educational Resources Information Center

Tubin, Dorit

2007-01-01

Purpose: The purpose of this study is to explore the interaction between information communication technology (ICT) and the school's organizational structure, and propose an analytical model based both on Luhmann's system theory and empirical findings. Design/methodology/approach: The approach of building a theory from a case study research along…

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

ERIC Educational Resources Information Center

Harley, Jason M.; Lajoie, Susanne P.; Frasson, Claude; Hall, Nathan C.

2017-01-01

A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners' experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to…

Methods for correcting tilt anisoplanatism in laser-guide-star-based multiconjugate adaptive optics.

PubMed

Ellerbroek, B L; Rigaut, F

2001-10-01

Multiconjugate adaptive optics (MCAO) is a technique for correcting turbulence-induced phase distortions in three dimensions instead of two, thereby greatly expanding the corrected field of view of an adaptive optics system. This is accomplished with use of multiple deformable mirrors conjugate to distinct ranges in the atmosphere, with actuator commands computed from wave-front sensor (WFS) measurements from multiple guide stars. Laser guide stars (LGSs) must be used (at least for the forseeable future) to achieve a useful degree of sky coverage in an astronomical MCAO system. Much as a single LGS cannot be used to measure overall wave-front tilt, a constellation of multiple LGSs at a common range cannot detect tilt anisoplanatism. This error alone will significantly degrade the performance of a MCAO system based on a single tilt-only natural guide star (NGS) and multiple tilt-removed LGSs at a common altitude. We present a heuristic, low-order model for the principal source of tilt anisoplanatism that suggests four possible approaches to eliminating this defect in LGS MCAO: (i) tip/tilt measurements from multiple NGS, (ii) a solution to the LGS tilt uncertainty problem, (iii) additional higher-order WFS measurements from a single NGS, or (iv) higher-order WFS measurements from both sodium and Rayleigh LGSs at different ranges. Sample numerical results for one particular MCAO system configuration indicate that approach (ii), if feasible, would provide the highest degree of tilt anisoplanatism compensation. Approaches (i) and (iv) also provide very useful levels of performance and do not require unrealistically low levels of WFS measurement noise. For a representative set of parameters for an 8-m telescope, the additional laser power required for approach (iv) is on the order of 2 W per Rayleigh LGS.

The impact of user- and system-initiated personalization on the user experience at large sports events.

PubMed

Sun, Xu; May, Andrew; Wang, Qingfeng

2016-05-01

This article describes an experimental study investigating the impact on user experience of two approaches of personalization of content provided on a mobile device, for spectators at large sports events. A lab-based experiment showed that a system-driven approach to personalization was generally preferable, but that there were advantages to retaining some user control over the process. Usability implications for a hybrid approach, and design implications are discussed, with general support for countermeasures designed to overcome recognised limitations of adaptive systems. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Human Health and Climate Change: Leverage Points for Adaptation in Urban Environments

PubMed Central

Proust, Katrina; Newell, Barry; Brown, Helen; Capon, Anthony; Browne, Chris; Burton, Anthony; Dixon, Jane; Mu, Lisa; Zarafu, Monica

2012-01-01

The design of adaptation strategies that promote urban health and well-being in the face of climate change requires an understanding of the feedback interactions that take place between the dynamical state of a city, the health of its people, and the state of the planet. Complexity, contingency and uncertainty combine to impede the growth of such systemic understandings. In this paper we suggest that the collaborative development of conceptual models can help a group to identify potential leverage points for effective adaptation. We describe a three-step procedure that leads from the development of a high-level system template, through the selection of a problem space that contains one or more of the group’s adaptive challenges, to a specific conceptual model of a sub-system of importance to the group. This procedure is illustrated by a case study of urban dwellers’ maladaptive dependence on private motor vehicles. We conclude that a system dynamics approach, revolving around the collaborative construction of a set of conceptual models, can help communities to improve their adaptive capacity, and so better meet the challenge of maintaining, and even improving, urban health in the face of climate change. PMID:22829795

Adaptation of a Filter Assembly to Assess Microbial Bioburden of Pressurant Within a Propulsion System

NASA Technical Reports Server (NTRS)

Benardini, James N.; Koukol, Robert C.; Schubert, Wayne W.; Morales, Fabian; Klatte, Marlin F.

2012-01-01

A report describes an adaptation of a filter assembly to enable it to be used to filter out microorganisms from a propulsion system. The filter assembly has previously been used for particulates greater than 2 micrometers. Projects that utilize large volumes of nonmetallic materials of planetary protection concern pose a challenge to their bioburden budget, as a conservative specification value of 30 spores per cubic centimeter is typically used. Helium was collected utilizing an adapted filtration approach employing an existing Millipore filter assembly apparatus used by the propulsion team for particulate analysis. The filter holder on the assembly has a 47-mm diameter, and typically a 1.2-5 micrometer pore-size filter is used for particulate analysis making it compatible with commercially available sterilization filters (0.22 micrometers) that are necessary for biological sampling. This adaptation to an existing technology provides a proof-of-concept and a demonstration of successful use in a ground equipment system. This adaptation has demonstrated that the Millipore filter assembly can be utilized to filter out microorganisms from a propulsion system, whereas in previous uses the filter assembly was utilized for particulates greater than 2 micrometers.

Foundations of resilience thinking.

PubMed

Curtin, Charles G; Parker, Jessica P

2014-08-01

Through 3 broad and interconnected streams of thought, resilience thinking has influenced the science of ecology and natural resource management by generating new multidisciplinary approaches to environmental problem solving. Resilience science, adaptive management (AM), and ecological policy design (EPD) contributed to an internationally unified paradigm built around the realization that change is inevitable and that science and management must approach the world with this assumption, rather than one of stability. Resilience thinking treats actions as experiments to be learned from, rather than intellectual propositions to be defended or mistakes to be ignored. It asks what is novel and innovative and strives to capture the overall behavior of a system, rather than seeking static, precise outcomes from discrete action steps. Understanding the foundations of resilience thinking is an important building block for developing more holistic and adaptive approaches to conservation. We conducted a comprehensive review of the history of resilience thinking because resilience thinking provides a working context upon which more effective, synergistic, and systems-based conservation action can be taken in light of rapid and unpredictable change. Together, resilience science, AM, and EPD bridge the gaps between systems analysis, ecology, and resource management to provide an interdisciplinary approach to solving wicked problems. © 2014 Society for Conservation Biology.

Systems identification and the adaptive management of waterfowl in the United States

USGS Publications Warehouse

Williams, B.K.; Nichols, J.D.

2001-01-01

Waterfowl management in the United States is one of the more visible conservation success stories in the United States. It is authorized and supported by appropriate legislative authorities, based on large-scale monitoring programs, and widely accepted by the public. The process is one of only a limited number of large-scale examples of effective collaboration between research and management, integrating scientific information with management in a coherent framework for regulatory decision-making. However, harvest management continues to face some serious technical problems, many of which focus on sequential identification of the resource system in a context of optimal decision-making. The objective of this paper is to provide a theoretical foundation of adaptive harvest management, the approach currently in use in the United States for regulatory decision-making. We lay out the legal and institutional framework for adaptive harvest management and provide a formal description of regulatory decision-making in terms of adaptive optimization. We discuss some technical and institutional challenges in applying adaptive harvest management and focus specifically on methods of estimating resource states for linear resource systems.

On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear $H_{\\infty}$ Control.

PubMed

Wang, Ding; Mu, Chaoxu; Liu, Derong; Ma, Hongwen

2018-04-01

In this paper, based on the adaptive critic learning technique, the control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. The novelty lies in that the data driven learning identifier is combined with the event driven design formulation, in order to develop the adaptive critic controller, thereby accomplishing the nonlinear control. The event driven optimal control law and the time driven worst case disturbance law are approximated by constructing and tuning a critic neural network. Applying the event driven feedback control, the closed-loop system is built with stability analysis. Simulation studies are conducted to verify the theoretical results and illustrate the control performance. It is significant to observe that the present research provides a new avenue of integrating data-based control and event-triggering mechanism into establishing advanced adaptive critic systems.

Towards Time Automata and Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Hutzler, G.; Klaudel, H.; Wang, D. Y.

2004-01-01

The design of reactive systems must comply with logical correctness (the system does what it is supposed to do) and timeliness (the system has to satisfy a set of temporal constraints) criteria. In this paper, we propose a global approach for the design of adaptive reactive systems, i.e., systems that dynamically adapt their architecture depending on the context. We use the timed automata formalism for the design of the agents' behavior. This allows evaluating beforehand the properties of the system (regarding logical correctness and timeliness), thanks to model-checking and simulation techniques. This model is enhanced with tools that we developed for the automatic generation of code, allowing to produce very quickly a running multi-agent prototype satisfying the properties of the model.

Output feedback control for a class of nonlinear systems with actuator degradation and sensor noise.

PubMed

Ai, Weiqing; Lu, Zhenli; Li, Bin; Fei, Shumin

2016-11-01

This paper investigates the output feedback control problem of a class of nonlinear systems with sensor noise and actuator degradation. Firstly, by using the descriptor observer approach, the origin system is transformed into a descriptor system. On the basis of the descriptor system, a novel Proportional Derivative (PD) observer is developed to asymptotically estimate sensor noise and system state simultaneously. Then, by designing an adaptive law to estimate the effectiveness of actuator, an adaptive observer-based controller is constructed to ensure that system state can be regulated to the origin asymptotically. Finally, the design scheme is applied to address a flexible joint robot link problem. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Model-based aberration correction in a closed-loop wavefront-sensor-less adaptive optics system.

PubMed

Song, H; Fraanje, R; Schitter, G; Kroese, H; Vdovin, G; Verhaegen, M

2010-11-08

In many scientific and medical applications, such as laser systems and microscopes, wavefront-sensor-less (WFSless) adaptive optics (AO) systems are used to improve the laser beam quality or the image resolution by correcting the wavefront aberration in the optical path. The lack of direct wavefront measurement in WFSless AO systems imposes a challenge to achieve efficient aberration correction. This paper presents an aberration correction approach for WFSlss AO systems based on the model of the WFSless AO system and a small number of intensity measurements, where the model is identified from the input-output data of the WFSless AO system by black-box identification. This approach is validated in an experimental setup with 20 static aberrations having Kolmogorov spatial distributions. By correcting N=9 Zernike modes (N is the number of aberration modes), an intensity improvement from 49% of the maximum value to 89% has been achieved in average based on N+5=14 intensity measurements. With the worst initial intensity, an improvement from 17% of the maximum value to 86% has been achieved based on N+4=13 intensity measurements.

Camouflage predicts survival in ground-nesting birds

PubMed Central

Troscianko, Jolyon; Wilson-Aggarwal, Jared; Stevens, Martin; Spottiswoode, Claire N.

2016-01-01

Evading detection by predators is crucial for survival. Camouflage is therefore a widespread adaptation, but despite substantial research effort our understanding of different camouflage strategies has relied predominantly on artificial systems and on experiments disregarding how camouflage is perceived by predators. Here we show for the first time in a natural system, that survival probability of wild animals is directly related to their level of camouflage as perceived by the visual systems of their main predators. Ground-nesting plovers and coursers flee as threats approach, and their clutches were more likely to survive when their egg contrast matched their surrounds. In nightjars – which remain motionless as threats approach – clutch survival depended on plumage pattern matching between the incubating bird and its surrounds. Our findings highlight the importance of pattern and luminance based camouflage properties, and the effectiveness of modern techniques in capturing the adaptive properties of visual phenotypes. PMID:26822039

Camouflage predicts survival in ground-nesting birds.

PubMed

Troscianko, Jolyon; Wilson-Aggarwal, Jared; Stevens, Martin; Spottiswoode, Claire N

2016-01-29

Evading detection by predators is crucial for survival. Camouflage is therefore a widespread adaptation, but despite substantial research effort our understanding of different camouflage strategies has relied predominantly on artificial systems and on experiments disregarding how camouflage is perceived by predators. Here we show for the first time in a natural system, that survival probability of wild animals is directly related to their level of camouflage as perceived by the visual systems of their main predators. Ground-nesting plovers and coursers flee as threats approach, and their clutches were more likely to survive when their egg contrast matched their surrounds. In nightjars - which remain motionless as threats approach - clutch survival depended on plumage pattern matching between the incubating bird and its surrounds. Our findings highlight the importance of pattern and luminance based camouflage properties, and the effectiveness of modern techniques in capturing the adaptive properties of visual phenotypes.

Practical adaptive quantum tomography

NASA Astrophysics Data System (ADS)

Granade, Christopher; Ferrie, Christopher; Flammia, Steven T.

2017-11-01

We introduce a fast and accurate heuristic for adaptive tomography that addresses many of the limitations of prior methods. Previous approaches were either too computationally intensive or tailored to handle special cases such as single qubits or pure states. By contrast, our approach combines the efficiency of online optimization with generally applicable and well-motivated data-processing techniques. We numerically demonstrate these advantages in several scenarios including mixed states, higher-dimensional systems, and restricted measurements. http://cgranade.com complete data and source code for this work are available online [1], and can be previewed at https://goo.gl/koiWxR.

Tile-based Level of Detail for the Parallel Age

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

Niski, K; Cohen, J D

Today's PCs incorporate multiple CPUs and GPUs and are easily arranged in clusters for high-performance, interactive graphics. We present an approach based on hierarchical, screen-space tiles to parallelizing rendering with level of detail. Adapt tiles, render tiles, and machine tiles are associated with CPUs, GPUs, and PCs, respectively, to efficiently parallelize the workload with good resource utilization. Adaptive tile sizes provide load balancing while our level of detail system allows total and independent management of the load on CPUs and GPUs. We demonstrate our approach on parallel configurations consisting of both single PCs and a cluster of PCs.

Computer aided segmentation of kidneys using locally shape constrained deformable models on CT images

NASA Astrophysics Data System (ADS)

Erdt, Marius; Sakas, Georgios

2010-03-01

This work presents a novel approach for model based segmentation of the kidney in images acquired by Computed Tomography (CT). The developed computer aided segmentation system is expected to support computer aided diagnosis and operation planning. We have developed a deformable model based approach based on local shape constraints that prevents the model from deforming into neighboring structures while allowing the global shape to adapt freely to the data. Those local constraints are derived from the anatomical structure of the kidney and the presence and appearance of neighboring organs. The adaptation process is guided by a rule-based deformation logic in order to improve the robustness of the segmentation in areas of diffuse organ boundaries. Our work flow consists of two steps: 1.) a user guided positioning and 2.) an automatic model adaptation using affine and free form deformation in order to robustly extract the kidney. In cases which show pronounced pathologies, the system also offers real time mesh editing tools for a quick refinement of the segmentation result. Evaluation results based on 30 clinical cases using CT data sets show an average dice correlation coefficient of 93% compared to the ground truth. The results are therefore in most cases comparable to manual delineation. Computation times of the automatic adaptation step are lower than 6 seconds which makes the proposed system suitable for an application in clinical practice.

Climate Risk Informed Decision Analysis: A Hypothetical Application to the Waas Region

NASA Astrophysics Data System (ADS)

Gilroy, Kristin; Mens, Marjolein; Haasnoot, Marjolijn; Jeuken, Ad

2016-04-01

More frequent and intense hydrologic events under climate change are expected to enhance water security and flood risk management challenges worldwide. Traditional planning approaches must be adapted to address climate change and develop solutions with an appropriate level of robustness and flexibility. The Climate Risk Informed Decision Analysis (CRIDA) method is a novel planning approach embodying a suite of complementary methods, including decision scaling and adaptation pathways. Decision scaling offers a bottom-up approach to assess risk and tailors the complexity of the analysis to the problem at hand and the available capacity. Through adaptation pathway,s an array of future strategies towards climate robustness are developed, ranging in flexibility and immediacy of investments. Flexible pathways include transfer points to other strategies to ensure that the system can be adapted if future conditions vary from those expected. CRIDA combines these two approaches in a stakeholder driven process which guides decision makers through the planning and decision process, taking into account how the confidence in the available science, the consequences in the system, and the capacity of institutions should influence strategy selection. In this presentation, we will explain the CRIDA method and compare it to existing planning processes, such as the US Army Corps of Engineers Principles and Guidelines as well as Integrated Water Resources Management Planning. Then, we will apply the approach to a hypothetical case study for the Waas Region, a large downstream river basin facing rapid development threatened by increased flood risks. Through the case study, we will demonstrate how a stakeholder driven process can be used to evaluate system robustness to climate change; develop adaptation pathways for multiple objectives and criteria; and illustrate how varying levels of confidence, consequences, and capacity would play a role in the decision making process, specifically in regards to the level of robustness and flexibility in the selected strategy. This work will equip practitioners and decision makers with an example of a structured process for decision making under climate uncertainty that can be scaled as needed to the problem at hand. This presentation builds further on another submitted abstract "Climate Risk Informed Decision Analysis (CRIDA): A novel practical guidance for Climate Resilient Investments and Planning" by Jeuken et al.

Adaptive distributed outlier detection for WSNs.

PubMed

De Paola, Alessandra; Gaglio, Salvatore; Lo Re, Giuseppe; Milazzo, Fabrizio; Ortolani, Marco

2015-05-01

The paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication complexity, and also considering externally imposed constraints on such conflicting goals. The performed experimental evaluation showed that our approach is able to improve the considered metrics for latency and energy consumption, with limited impact on classification accuracy.

Pathology and failure in the design and implementation of adaptive management

USGS Publications Warehouse

Allen, Craig R.; Gunderson, Lance H.

2011-01-01

The conceptual underpinnings for adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex ecological systems as a result non-linear interactions among components and emergence, yet management decisions must still be made. The strength of adaptive management is in the recognition and confrontation of such uncertainty. Rather than ignore uncertainty, or use it to preclude management actions, adaptive management can foster resilience and flexibility to cope with an uncertain future, and develop safe to fail management approaches that acknowledge inevitable changes and surprises. Since its initial introduction, adaptive management has been hailed as a solution to endless trial and error approaches to complex natural resource management challenges. However, its implementation has failed more often than not. It does not produce easy answers, and it is appropriate in only a subset of natural resource management problems. Clearly adaptive management has great potential when applied appropriately. Just as clearly adaptive management has seemingly failed to live up to its high expectations. Why? We outline nine pathologies and challenges that can lead to failure in adaptive management programs. We focus on general sources of failures in adaptive management, so that others can avoid these pitfalls in the future. Adaptive management can be a powerful and beneficial tool when applied correctly to appropriate management problems; the challenge is to keep the concept of adaptive management from being hijacked for inappropriate use.

EDITORIAL: Adaptive and Active Materials: Selected Papers from the ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems (SMASIS 08) (Maryland, USA, 28-30 October 2008) Adaptive and Active Materials: Selected Papers from the ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems (SMASIS 08) (Maryland, USA, 28-30 October 2008)

NASA Astrophysics Data System (ADS)

Lynch, Christopher

2009-10-01

The rapid development of the field of Smart Materials, Adaptive Structures, and Materials Systems led the Aerospace Division ASMS TC to launch the new annual SMASIS conference in 2008. The conference focuses on the multi-disciplinary challenges of developing new multifunctional materials and implementing them in advanced systems. The research spans length scales from nano-structured materials to civil, air, and space structures. The first conference consisted of six symposia, each focusing on a different research area. This special issue of Smart Materials and Structures summarizes some of the top research presented at the 2008 SMASIS conference in the materials-focused symposia. These symposia focused on the behavior and mechanics of active materials, on multifunctional materials, and on bio-inspired materials. The behavior and mechanics of active materials is an approach that combines observed material behavior with mechanism-based models that not only give insight into the observed behavior, but guide the development of new materials. This approach has been applied to shape memory metals and polymers, ferroelectrics, ferromagnetics, and recently to multiferroic materials, and has led to considerable improvements in our understanding of multi-field phenomena. Multifunctional materials are the next generation of active materials. These materials include structural, sensing, and actuation components integrated into a material system. A natural extension of multifunctional materials is a new class of bio-inspired materials. Bio-inspired materials range from detailed bio-mimicry of sensing and self healing materials to nano and microstructures that take advantage of features observed in biological systems. The Editors would like to express their sincere thanks to all of the authors for their contributions to this special issue on 'Adaptive and Active Materials' for Smart Materials and Structures. We convey our gratitude to all of the reviewers for their time and dedication. We thank IOP Publishing for their support and encouragement of this special issue and the staff for their special attention and timely response.

Managing adaptively for multifunctionality in agricultural systems

USGS Publications Warehouse

Hodbod, Jennifer; Barreteau, Olivier; Allen, Craig R.; Magda, Danièle

2016-01-01

The critical importance of agricultural systems for food security and as a dominant global landcover requires management that considers the full dimensions of system functions at appropriate scales, i.e. multifunctionality. We propose that adaptive management is the most suitable management approach for such goals, given its ability to reduce uncertainty over time and support multiple objectives within a system, for multiple actors. As such, adaptive management may be the most appropriate method for sustainably intensifying production whilst increasing the quantity and quality of ecosystem services. However, the current assessment of performance of agricultural systems doesn’t reward ecosystem service provision. Therefore, we present an overview of the ecosystem functions agricultural systems should and could provide, coupled with a revised definition for assessing the performance of agricultural systems from a multifunctional perspective that, when all satisfied, would create adaptive agricultural systems that can increase production whilst ensuring food security and the quantity and quality of ecosystem services. The outcome of this high level of performance is the capacity to respond to multiple shocks without collapse, equity and triple bottom line sustainability. Through the assessment of case studies, we find that alternatives to industrialized agricultural systems incorporate more functional goals, but that there are mixed findings as to whether these goals translate into positive measurable outcomes. We suggest that an adaptive management perspective would support the implementation of a systematic analysis of the social, ecological and economic trade-offs occurring within such systems, particularly between ecosystem services and functions, in order to provide suitable and comparable assessments. We also identify indicators to monitor performance at multiple scales in agricultural systems which can be used within an adaptive management framework to increase resilience at multiple scales.

Managing adaptively for multifunctionality in agricultural systems.

PubMed

Hodbod, Jennifer; Barreteau, Olivier; Allen, Craig; Magda, Danièle

2016-12-01

The critical importance of agricultural systems for food security and as a dominant global landcover requires management that considers the full dimensions of system functions at appropriate scales, i.e. multifunctionality. We propose that adaptive management is the most suitable management approach for such goals, given its ability to reduce uncertainty over time and support multiple objectives within a system, for multiple actors. As such, adaptive management may be the most appropriate method for sustainably intensifying production whilst increasing the quantity and quality of ecosystem services. However, the current assessment of performance of agricultural systems doesn't reward ecosystem service provision. Therefore, we present an overview of the ecosystem functions agricultural systems should and could provide, coupled with a revised definition for assessing the performance of agricultural systems from a multifunctional perspective that, when all satisfied, would create adaptive agricultural systems that can increase production whilst ensuring food security and the quantity and quality of ecosystem services. The outcome of this high level of performance is the capacity to respond to multiple shocks without collapse, equity and triple bottom line sustainability. Through the assessment of case studies, we find that alternatives to industrialized agricultural systems incorporate more functional goals, but that there are mixed findings as to whether these goals translate into positive measurable outcomes. We suggest that an adaptive management perspective would support the implementation of a systematic analysis of the social, ecological and economic trade-offs occurring within such systems, particularly between ecosystem services and functions, in order to provide suitable and comparable assessments. We also identify indicators to monitor performance at multiple scales in agricultural systems which can be used within an adaptive management framework to increase resilience at multiple scales. Copyright © 2016 Elsevier Ltd. All rights reserved.

Nonlinear versus Ordinary Adaptive Control of Continuous Stirred-Tank Reactor

PubMed Central

Dostal, Petr

2015-01-01

Unfortunately, the major group of the systems in industry has nonlinear behavior and control of such processes with conventional control approaches with fixed parameters causes problems and suboptimal or unstable control results. An adaptive control is one way to how we can cope with nonlinearity of the system. This contribution compares classic adaptive control and its modification with Wiener system. This configuration divides nonlinear controller into the dynamic linear part and the static nonlinear part. The dynamic linear part is constructed with the use of polynomial synthesis together with the pole-placement method and the spectral factorization. The static nonlinear part uses static analysis of the controlled plant for introducing the mathematical nonlinear description of the relation between the controlled output and the change of the control input. Proposed controller is tested by the simulations on the mathematical model of the continuous stirred-tank reactor with cooling in the jacket as a typical nonlinear system. PMID:26346878

Distributed Adaptive Fuzzy Control for Nonlinear Multiagent Systems Via Sliding Mode Observers.

PubMed

Shen, Qikun; Shi, Peng; Shi, Yan

2016-12-01

In this paper, the problem of distributed adaptive fuzzy control is investigated for high-order uncertain nonlinear multiagent systems on directed graph with a fixed topology. It is assumed that only the outputs of each follower and its neighbors are available in the design of its distributed controllers. Equivalent output injection sliding mode observers are proposed for each follower to estimate the states of itself and its neighbors, and an observer-based distributed adaptive controller is designed for each follower to guarantee that it asymptotically synchronizes to a leader with tracking errors being semi-globally uniform ultimate bounded, in which fuzzy logic systems are utilized to approximate unknown functions. Based on algebraic graph theory and Lyapunov function approach, using Filippov-framework, the closed-loop system stability analysis is conducted. Finally, numerical simulations are provided to illustrate the effectiveness and potential of the developed design techniques.

Effect of vergence adaptation on convergence-accommodation: model simulations.

PubMed

Sreenivasan, Vidhyapriya; Bobier, William R; Irving, Elizabeth L; Lakshminarayanan, Vasudevan

2009-10-01

Several theoretical control models depict the adaptation effects observed in the accommodation and vergence mechanisms of the human visual system. Two current quantitative models differ in their approach of defining adaptation and in identifying the effect of controller adaptation on their respective cross-links between the vergence and accommodative systems. Here, we compare the simulation results of these adaptation models with empirical data obtained from emmetropic adults when they performed sustained near task through + 2D lens addition. The results of our experimental study showed an initial increase in exophoria (a divergent open-loop vergence position) and convergence-accommodation (CA) when viewing through +2D lenses. Prolonged fixation through the near addition lenses initiated vergence adaptation, which reduced the lens-induced exophoria and resulted in a concurrent reduction of CA. Both models showed good agreement with empirical measures of vergence adaptation. However, only one model predicted the experimental time course of reduction in CA. The pattern of our empirical results seem to be best described by the adaptation model that indicates the total vergence response to be a sum of two controllers, phasic and tonic, with the output of phasic controller providing input to the cross-link interactions.

A qualitative assessment of cross-cultural adaptation of intermediate measures for schizophrenia in multisite international studies.

PubMed

Gonzalez, Jodi M; Rubin, Maureen; Fredrick, Megan M; Velligan, Dawn I

2013-04-30

In this substudy of the Measurement and Treatment Research to Improve Cognition in Schizophrenia we examined qualitative feedback on the cross-cultural adaptability of four intermediate measures of functional outcome (Independent Living Scales, UCSD Performance-Based Skills Assessment, Test of Adaptive Behavior in Schizophrenia, and Cognitive Assessment Interview). Feedback was provided by experienced English-fluent clinical researchers at 31 sites in eight countries familiar with medication trials. Researchers provided feedback on test subscales and items which were rated as having adaptation challenges. They noted the specific concern and made suggestions for adaptation to their culture. We analyzed the qualitative data using a modified Grounded Theory approach guided by the International Testing Commission Guidelines model for test adaptation. For each measure except the Cognitive Assessment Interview (CAI), the majority of subscales were reported to require major adaptations in terms of content and concepts contained in the subscale. In particular, social, financial, transportation and health care systems varied widely across countries-systems which are often used to assess performance capacity in the U.S. We provide suggestions for how to address future international test development and adaptation. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

PubMed

Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

2011-07-01

In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

A Bio-Inspired Model-Based Approach for Context-Aware Post-WIMP Tele-Rehabilitation.

PubMed

López-Jaquero, Víctor; Rodríguez, Arturo C; Teruel, Miguel A; Montero, Francisco; Navarro, Elena; Gonzalez, Pascual

2016-10-13

Tele-rehabilitation is one of the main domains where Information and Communication Technologies (ICT) have been proven useful to move healthcare from care centers to patients' home. Moreover, patients, especially those carrying out a physical therapy, cannot use a traditional Window, Icon, Menu, Pointer (WIMP) system, but they need to interact in a natural way, that is, there is a need to move from WIMP systems to Post-WIMP ones. Moreover, tele-rehabilitation systems should be developed following the context-aware approach, so that they are able to adapt to the patients' context to provide them with usable and effective therapies. In this work a model-based approach is presented to assist stakeholders in the development of context-aware Post-WIMP tele-rehabilitation systems. It entails three different models: (i) a task model for designing the rehabilitation tasks; (ii) a context model to facilitate the adaptation of these tasks to the context; and (iii) a bio-inspired presentation model to specify thoroughly how such tasks should be performed by the patients. Our proposal overcomes one of the limitations of the model-based approach for the development of context-aware systems supporting the specification of non-functional requirements. Finally, a case study is used to illustrate how this proposal can be put into practice to design a real world rehabilitation task.

Adaptation and Mitigation in Agriculture: A Review of Synergies and Tradeoffs and How EO Could Improve Understanding and Outcomes

NASA Astrophysics Data System (ADS)

Barbieri, L.; Wollenberg, E.

2017-12-01

We present a review of the published literature on agricultural adaptation and mitigation, and report on the current evidence as to whether changes in agricultural practices meant to achieve mitigation or adaptation goals can be dual purpose: simultaneously reducing greenhouse gas (GHG) emissions and helping to facilitate adaptation. We characterize the spatio-temporal and system trends in how adaptation and mitigation outcomes are being achieved, and report on the current technical and knowledge gaps that exist and where Earth observations (EO) could improve our understanding. Agriculture contributes 12% GHG emissions globally, roughly one third from the developing world. Nearly 70% of the technical mitigation potential in agriculture sector occurs in these countries, however, while the mitigation potential is high, agricultural productivity also relies heavily on climate factors. With climate change, agricultural systems already, and will increasingly, need to adapt to extreme events and variability in temperatures and precipitation. This underscores the importance of implementing agricultural practices that can both reduce GHG emissions and help facilitate adaptation. Until recently, these objectives have been treated separately, but policy makers are increasingly calling for a joint approach to improve synergies, and avoid tradeoffs. There remain many complications in considering a joint approach: lack of clear conceptual frameworks, knowledge gaps in scientific understanding and evidence associated with adaptation and mitigation outcomes, and the abilities and motivations of stakeholders to consider both objectives. We review 56 peer-reviewed publications and present results from an in-depth analysis to answer two major concerns: to what extent is evidence provided for claims of synergistic outcomes, and what uncertainty surrounds this evidence. Our results show that only 21% of studies empirically measured both mitigation and adaptation outcomes, and claims of synergies are not well substantiated, and evidence is provided at questionable spatio-temporal scales. We highlight information that could be provided by coordinated, comprehensive and sustained EO which could benefit this critical goal of simultaneously achieving agricultural adaptation and mitigation.

A Network Based Theory of Health Systems and Cycles of Well-being

PubMed Central

Rhodes, Michael Grant

2013-01-01

There are two dominant approaches to describe and understand the anatomy of complete health and well-being systems internationally. Yet, neither approach has been able to either predict or explain occasional but dramatic crises in health and well-being systems around the world and in developed emerging market or developing country contexts. As the impacts of such events can be measured not simply in terms of their social and economic consequences but also public health crises, there is a clear need to look for and formulate an alternative approach. This paper examines multi-disciplinary theoretical evidence to suggest that health systems exhibit natural and observable systemic and long cycle characteristics that can be modelled. A health and well-being system model of two slowly evolving anthropological network sub-systems is defined. The first network sub-system consists of organised professional networks of exclusive suppliers of health and well-being services. The second network sub-system consists of communities organising themselves to resource those exclusive services. Together these two network sub-systems interact to form the specific (sovereign) health and well-being systems we know today. But the core of a truly ‘complex adaptive system’ can also be identified and a simplified two sub-system model of recurring Lotka-Volterra predator-prey cycles is specified. The implications of such an adaptive and evolving model of system anatomy for effective public health, social security insurance and well-being systems governance could be considerable. PMID:24596831

Using adapted quality-improvement approaches to strengthen community-based health systems and improve care in high HIV-burden sub-Saharan African countries.

PubMed

Horwood, Christiane M; Youngleson, Michele S; Moses, Edward; Stern, Amy F; Barker, Pierre M

2015-07-01

Achieving long-term retention in HIV care is an important challenge for HIV management and achieving elimination of mother-to-child transmission. Sustainable, affordable strategies are required to achieve this, including strengthening of community-based interventions. Deployment of community-based health workers (CHWs) can improve health outcomes but there is a need to identify systems to support and maintain high-quality performance. Quality-improvement strategies have been successfully implemented to improve quality and coverage of healthcare in facilities and could provide a framework to support community-based interventions. Four community-based quality-improvement projects from South Africa, Malawi and Mozambique are described. Community-based improvement teams linked to the facility-based health system participated in learning networks (modified Breakthrough Series), and used quality-improvement methods to improve process performance. Teams were guided by trained quality mentors who used local data to help nurses and CHWs identify gaps in service provision and test solutions. Learning network participants gathered at intervals to share progress and identify successful strategies for improvement. CHWs demonstrated understanding of quality-improvement concepts, tools and methods, and implemented quality-improvement projects successfully. Challenges of using quality-improvement approaches in community settings included adapting processes, particularly data reporting, to the education level and first language of community members. Quality-improvement techniques can be implemented by CHWs to improve outcomes in community settings but these approaches require adaptation and additional mentoring support to be successful. More research is required to establish the effectiveness of this approach on processes and outcomes of care.

True-slime-mould-inspired hydrostatically coupled oscillator system exhibiting versatile behaviours.

PubMed

Umedachi, Takuya; Idei, Ryo; Ito, Kentaro; Ishiguro, Akio

2013-09-01

Behavioural diversity is an indispensable attribute of living systems, which makes them intrinsically adaptive and responsive to the demands of a dynamically changing environment. In contrast, conventional engineering approaches struggle to suppress behavioural diversity in artificial systems to reach optimal performance in given environments for desired tasks. The goals of this research include understanding the essential mechanism that endows living systems with behavioural diversity and implementing the mechanism in robots to exhibit adaptive behaviours. For this purpose, we have focused on an amoeba-like unicellular organism: the plasmodium of true slime mould. Despite the absence of a central nervous system, the plasmodium exhibits versatile spatiotemporal oscillatory patterns and switches spontaneously among these patterns. By exploiting this behavioural diversity, it is able to exhibit adaptive behaviour according to the situation encountered. Inspired by this organism, we built a real physical robot using hydrostatically coupled oscillators that produce versatile oscillatory patterns and spontaneous transitions among the patterns. The experimental results show that exploiting physical hydrostatic interplay—the physical dynamics of the robot—allows simple phase oscillators to promote versatile behaviours. The results can contribute to an understanding of how a living system generates versatile and adaptive behaviours with physical interplays among body parts.

Hybrid Decompositional Verification for Discovering Failures in Adaptive Flight Control Systems

NASA Technical Reports Server (NTRS)

Thompson, Sarah; Davies, Misty D.; Gundy-Burlet, Karen

2010-01-01

Adaptive flight control systems hold tremendous promise for maintaining the safety of a damaged aircraft and its passengers. However, most currently proposed adaptive control methodologies rely on online learning neural networks (OLNNs), which necessarily have the property that the controller is changing during the flight. These changes tend to be highly nonlinear, and difficult or impossible to analyze using standard techniques. In this paper, we approach the problem with a variant of compositional verification. The overall system is broken into components. Undesirable behavior is fed backwards through the system. Components which can be solved using formal methods techniques explicitly for the ranges of safe and unsafe input bounds are treated as white box components. The remaining black box components are analyzed with heuristic techniques that try to predict a range of component inputs that may lead to unsafe behavior. The composition of these component inputs throughout the system leads to overall system test vectors that may elucidate the undesirable behavior

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors.

PubMed

Villaverde, Monica; Perez, David; Moreno, Felix

2015-11-17

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor's infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Expansionary Adaptive Transformations of Socio-Hydrological Systems (SHSs): The Case of Drought in Messara Plain, Crete, Greece.

PubMed

Sapountzaki, Kalliopi; Daskalakis, Ioannis

2018-05-01

The paper attempts to document the ontology of socio-hydrological systems (SHSs), propose approaches of delimitation of SHSs' (territorial) boundaries, and investigate operational aspects of their adaptation to drought including repercussions on sustainability. To this end, a series of hypotheses are tested: (a) SHSs contain social subsystems with different expectations regarding water resources, different adaptive capacities, adaptation limits and prospects of sustainability, (b) SHSs do not adapt homogenously; some of their subsystems manage optimum adaptation, others fail to adapt and (c) territorial transformation of SHSs (e.g., through expansion of SHSs) may be the result of differential adaptation and sustainability potential within the SHS owing to power relations. After testing above hypotheses in the SHS of Messara Plain, Crete, the authors found out that powerful and dynamic sub-SHSs expand or break the boundaries of the initial SHS by establishing new relationships with other SHSs for the sake of resilience resources. Conversely, powerless sub-SHSs incapable to adapt descend and disappear. Therefore, territorial transformation of SHSs comes about from a combination of successful and failed adaptations, or in other words from different adaptation limits within SHSs. Consequently, water management and local development planning to guarantee adaptability to drought for all should be based on SHSs' analysis and management, not on jurisdictional areas or hydrological basins.

Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.

PubMed

Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing

2011-01-01

In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction

PubMed Central

Xing, Li; Hang, Yijun; Xiong, Zhi; Liu, Jianye; Wan, Zhong

2016-01-01

This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions. PMID:27754469

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction.

PubMed

Xing, Li; Hang, Yijun; Xiong, Zhi; Liu, Jianye; Wan, Zhong

2016-10-16

This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions.

A Novel Approach for Enhancing Lifelong Learning Systems by Using Hybrid Recommender System

ERIC Educational Resources Information Center

Kardan, Ahmad A.; Speily, Omid R. B.; Modaberi, Somayyeh

2011-01-01

The majority of current web-based learning systems are closed learning environments where courses and learning materials are fixed, and the only dynamic aspect is the organization of the material that can be adapted to allow a relatively individualized learning environment. In this paper, we propose an evolving web-based learning system which can…

Providing Adaptation and Guidance for Design Learning by Problem Solving: The Design Planning Approach in DomoSim-TPC Environment

ERIC Educational Resources Information Center

Redondo, Miguel A.; Bravo, Crescencio; Ortega, Manuel; Verdejo, M. Felisa

2007-01-01

Experimental learning environments based on simulation usually require monitoring and adaptation to the actions the users carry out. Some systems provide this functionality, but they do so in a way which is static or cannot be applied to problem solving tasks. In response to this problem, we propose a method based on the use of intermediate…

The Kinzua Quality Deer Cooperative: can adaptive management and local stakeholder engagement sustain reduced impact of ungulate browsers in forest systems?

Treesearch

Susan L. Stout; Alejandro A. Royo; David S. deCalesta; Kevin McAleese; James C. Finley

2013-01-01

The Kinzua Quality Deer Cooperative (KQDC) was established in 2000 to test new approaches to stewardship of white-tailed deer and forest habitat on a 30 000 hectare landscape in northwest Pennsylvania, USA. Partners included land managers, scientists, educators, tourism promoters,and hunters. KQDC goals were adaptive management of the deer herd, improved habitat...

The Quality Adaptation Model: Adaptation and Adoption of the Quality Standard ISO/IEC 19796-1 for Learning, Education, and Training

ERIC Educational Resources Information Center

Pawlowski, Jan M.

2007-01-01

In 2005, the new quality standard for learning, education, and training, ISO/IEC 19796-1, was published. Its purpose is to help educational organizations to develop quality systems and to improve the quality of their processes, products, and services. In this article, the standard is presented and compared to existing approaches, showing the…

Flexible Multi agent Algorithm for Distributed Decision Making

DTIC Science & Technology

2015-01-01

How, J. P. Consensus - Based Auction Approaches for Decentralized task Assignment. Proceedings of the AIAA Guidance, Navigation, and Control...G. ; Kim, Y. Market- based Decentralized Task Assignment for Cooperative UA V Mission Including Rendezvous. Proceedings of the AIAA Guidance...scalable and adaptable to a variety of specific mission tasks . Additionally, the algorithm could easily be adapted for use on land or sea- based systems

Life cycle assessment of stormwater management in the context of climate change adaptation.

PubMed

Brudler, Sarah; Arnbjerg-Nielsen, Karsten; Hauschild, Michael Zwicky; Rygaard, Martin

2016-12-01

Expected increases in pluvial flooding, due to climatic changes, require large investments in the retrofitting of cities to keep damage at an acceptable level. Many cities have investigated the possibility of implementing stormwater management (SWM) systems which are multi-functional and consist of different elements interacting to achieve desired safety levels. Typically, an economic assessment is carried out in the planning phase, while environmental sustainability is given little or no attention. In this paper, life cycle assessment is used to quantify environmental impacts of climate change adaptation strategies. The approach is tested using a climate change adaptation strategy for a catchment in Copenhagen, Denmark. A stormwater management system, using green infrastructure and local retention measures in combination with planned routing of stormwater on the surfaces to manage runoff, is compared to a traditional, sub-surface approach. Flood safety levels based on the Three Points Approach are defined as the functional unit to ensure comparability between systems. The adaptation plan has significantly lower impacts (3-18 person equivalents/year) than the traditional alternative (14-103 person equivalents/year) in all analysed impact categories. The main impacts are caused by managing rain events with return periods between 0.2 and 10 years. The impacts of handling smaller events with a return period of up to 0.2 years and extreme events with a return period of up to 100 years are lower in both alternatives. The uncertainty analysis shows the advantages of conducting an environmental assessment in the early stages of the planning process, when the design can still be optimised, but it also highlights the importance of detailed and site-specific data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Phase Contrast Wavefront Sensing for Adaptive Optics

NASA Technical Reports Server (NTRS)

Bloemhof, E. E.; Wallace, J. K.; Bloemhof, E. E.

2004-01-01

Most ground-based adaptive optics systems use one of a small number of wavefront sensor technologies, notably (for relatively high-order systems) the Shack-Hartmann sensor, which provides local measurements of the phase slope (first-derivative) at a number of regularly-spaced points across the telescope pupil. The curvature sensor, with response proportional to the second derivative of the phase, is also sometimes used, but has undesirable noise propagation properties during wavefront reconstruction as the number of actuators becomes large. It is interesting to consider the use for astronomical adaptive optics of the "phase contrast" technique, originally developed for microscopy by Zemike to allow convenient viewing of phase objects. In this technique, the wavefront sensor provides a direct measurement of the local value of phase in each sub-aperture of the pupil. This approach has some obvious disadvantages compared to Shack-Hartmann wavefront sensing, but has some less obvious but substantial advantages as well. Here we evaluate the relative merits in a practical ground-based adaptive optics system.

Evaluating the Ability of Heart Rate and EEG to Control Alertness during Performance

NASA Technical Reports Server (NTRS)

Freeman, Fred

2002-01-01

The major focus of the present proposal was to examine psychophysiological indices that show promise for invoking different modes of automation in an adaptive automation system. With the increased use of automation in today's work environment, people's roles in the work place are being redefined from that of active participant to one of passive monitor. Although the introduction of automated systems has a number of benefits, there are also a number of disadvantages regarding worker performance. Byrne and Parasuraman have argued for the use of psychophysiological measures in the development and the implementation of adaptive automation. While performance based, model based, and psychophysiologically based adaptive automation systems have been studied, the combined use of several psychophysiological measures has never been investigated. Such a combination provides the advantage of real time evaluation of the state of the subject in two relevant dimensions and offers a more realistic approach to the implementation of adaptive automation compared to the use of either dimension by itself.