system model outputs: Topics by Science.gov

Sample records for system model outputs

H∞ output tracking control of discrete-time nonlinear systems via standard neural network models.

PubMed

Liu, Meiqin; Zhang, Senlin; Chen, Haiyang; Sheng, Weihua

2014-10-01

This brief proposes an output tracking control for a class of discrete-time nonlinear systems with disturbances. A standard neural network model is used to represent discrete-time nonlinear systems whose nonlinearity satisfies the sector conditions. H∞ control performance for the closed-loop system including the standard neural network model, the reference model, and state feedback controller is analyzed using Lyapunov-Krasovskii stability theorem and linear matrix inequality (LMI) approach. The H∞ controller, of which the parameters are obtained by solving LMIs, guarantees that the output of the closed-loop system closely tracks the output of a given reference model well, and reduces the influence of disturbances on the tracking error. Three numerical examples are provided to show the effectiveness of the proposed H∞ output tracking design approach.
Global robust output regulation control for cascaded nonlinear systems using the internal model principle

NASA Astrophysics Data System (ADS)

Yu, Jiang-Bo; Zhao, Yan; Wu, Yu-Qiang

2014-04-01

This article considers the global robust output regulation problem via output feedback for a class of cascaded nonlinear systems with input-to-state stable inverse dynamics. The system uncertainties depend not only on the measured output but also all the unmeasurable states. By introducing an internal model, the output regulation problem is converted into a stabilisation problem for an appropriately augmented system. The designed dynamic controller could achieve the global asymptotic tracking control for a class of time-varying reference signals for the system output while keeping all other closed-loop signals bounded. It is of interest to note that the developed control approach can be applied to the speed tracking control of the fan speed control system. The simulation results demonstrate its effectiveness.
Uncertainty and sensitivity analysis for photovoltaic system modeling.

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

Hansen, Clifford W.; Pohl, Andrew Phillip; Jordan, Dirk

2013-12-01

We report an uncertainty and sensitivity analysis for modeling DC energy from photovoltaic systems. We consider two systems, each comprised of a single module using either crystalline silicon or CdTe cells, and located either at Albuquerque, NM, or Golden, CO. Output from a PV system is predicted by a sequence of models. Uncertainty in the output of each model is quantified by empirical distributions of each model's residuals. We sample these distributions to propagate uncertainty through the sequence of models to obtain an empirical distribution for each PV system's output. We considered models that: (1) translate measured global horizontal, directmore » and global diffuse irradiance to plane-of-array irradiance; (2) estimate effective irradiance from plane-of-array irradiance; (3) predict cell temperature; and (4) estimate DC voltage, current and power. We found that the uncertainty in PV system output to be relatively small, on the order of 1% for daily energy. Four alternative models were considered for the POA irradiance modeling step; we did not find the choice of one of these models to be of great significance. However, we observed that the POA irradiance model introduced a bias of upwards of 5% of daily energy which translates directly to a systematic difference in predicted energy. Sensitivity analyses relate uncertainty in the PV system output to uncertainty arising from each model. We found that the residuals arising from the POA irradiance and the effective irradiance models to be the dominant contributors to residuals for daily energy, for either technology or location considered. This analysis indicates that efforts to reduce the uncertainty in PV system output should focus on improvements to the POA and effective irradiance models.« less
SU-F-T-143: Implementation of a Correction-Based Output Model for a Compact Passively Scattered Proton Therapy System

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

Ferguson, S; Ahmad, S; Chen, Y

2016-06-15

Purpose: To commission and investigate the accuracy of an output (cGy/MU) prediction model for a compact passively scattered proton therapy system. Methods: A previously published output prediction model (Sahoo et al, Med Phys, 35, 5088–5097, 2008) was commissioned for our Mevion S250 proton therapy system. This model is a correction-based model that multiplies correction factors (d/MUwnc=ROFxSOBPF xRSFxSOBPOCFxOCRxFSFxISF). These factors accounted for changes in output due to options (12 large, 5 deep, and 7 small), modulation width M, range R, off-center, off-axis, field-size, and off-isocenter. In this study, the model was modified to ROFxSOBPFxRSFxOCRxFSFxISF-OCFxGACF by merging SOBPOCF and ISF for simplicitymore » and introducing a gantry angle correction factor (GACF). To commission the model, outputs over 1,000 data points were taken at the time of the system commissioning. The output was predicted by interpolation (1D for SOBPF, FSF, and GACF; 2D for RSF and OCR) with inverse-square calculation (ISF-OCR). The outputs of 273 combinations of R and M covering total 24 options were measured to test the model. To minimize fluence perturbation, scattered dose from range compensator and patient was not considered. The percent differences between the predicted (P) and measured (M) outputs were calculated to test the prediction accuracy ([P-M]/Mx100%). Results: GACF was required because of up to 3.5% output variation dependence on the gantry angle. A 2D interpolation was required for OCR because the dose distribution was not radially symmetric especially for the deep options. The average percent differences were −0.03±0.98% (mean±SD) and the differences of all the measurements fell within ±3%. Conclusion: It is concluded that the model can be clinically used for the compact passively scattered proton therapy system. However, great care should be taken when the field-size is less than 5×5 cm{sup 2} where a direct output measurement is required due to substantial output change by irregular block shape.« less
Method and system for detecting a failure or performance degradation in a dynamic system such as a flight vehicle

NASA Technical Reports Server (NTRS)

Miller, Robert H. (Inventor); Ribbens, William B. (Inventor)

2003-01-01

A method and system for detecting a failure or performance degradation in a dynamic system having sensors for measuring state variables and providing corresponding output signals in response to one or more system input signals are provided. The method includes calculating estimated gains of a filter and selecting an appropriate linear model for processing the output signals based on the input signals. The step of calculating utilizes one or more models of the dynamic system to obtain estimated signals. The method further includes calculating output error residuals based on the output signals and the estimated signals. The method also includes detecting one or more hypothesized failures or performance degradations of a component or subsystem of the dynamic system based on the error residuals. The step of calculating the estimated values is performed optimally with respect to one or more of: noise, uncertainty of parameters of the models and un-modeled dynamics of the dynamic system which may be a flight vehicle or financial market or modeled financial system.
Control and optimization system

DOEpatents

Xinsheng, Lou

2013-02-12

A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
System level analysis and control of manufacturing process variation

DOEpatents

Hamada, Michael S.; Martz, Harry F.; Eleswarpu, Jay K.; Preissler, Michael J.

2005-05-31

A computer-implemented method is implemented for determining the variability of a manufacturing system having a plurality of subsystems. Each subsystem of the plurality of subsystems is characterized by signal factors, noise factors, control factors, and an output response, all having mean and variance values. Response models are then fitted to each subsystem to determine unknown coefficients for use in the response models that characterize the relationship between the signal factors, noise factors, control factors, and the corresponding output response having mean and variance values that are related to the signal factors, noise factors, and control factors. The response models for each subsystem are coupled to model the output of the manufacturing system as a whole. The coefficients of the fitted response models are randomly varied to propagate variances through the plurality of subsystems and values of signal factors and control factors are found to optimize the output of the manufacturing system to meet a specified criterion.
Acceleration and Performance Modeling Workshop, Washington, DC, 14-17 May 79,

DTIC Science & Technology

1979-12-01

disturbance of the muscular systems, perhaps changes in spindle fiber output, and changes in the perceived weight of the muscle because of the acceleration...at this point either. The output models which are determining performance are essentially tied to muscular systems, through manual control (hand and...feet), and through speech, another muscular output. In normal activities the pilot, who senses changes in the visual system, the acceleration vector
Explicit least squares system parameter identification for exact differential input/output models

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1993-01-01

The equation error for a class of systems modeled by input/output differential operator equations has the potential to be integrated exactly, given the input/output data on a finite time interval, thereby opening up the possibility of using an explicit least squares estimation technique for system parameter identification. The paper delineates the class of models for which this is possible and shows how the explicit least squares cost function can be obtained in a way that obviates dealing with unknown initial and boundary conditions. The approach is illustrated by two examples: a second order chemical kinetics model and a third order system of Lorenz equations.
System parameter identification from projection of inverse analysis

NASA Astrophysics Data System (ADS)

Liu, K.; Law, S. S.; Zhu, X. Q.

2017-05-01

The output of a system due to a change of its parameters is often approximated with the sensitivity matrix from the first order Taylor series. The system output can be measured in practice, but the perturbation in the system parameters is usually not available. Inverse sensitivity analysis can be adopted to estimate the unknown system parameter perturbation from the difference between the observation output data and corresponding analytical output data calculated from the original system model. The inverse sensitivity analysis is re-visited in this paper with improvements based on the Principal Component Analysis on the analytical data calculated from the known system model. The identification equation is projected into a subspace of principal components of the system output, and the sensitivity of the inverse analysis is improved with an iterative model updating procedure. The proposed method is numerical validated with a planar truss structure and dynamic experiments with a seven-storey planar steel frame. Results show that it is robust to measurement noise, and the location and extent of stiffness perturbation can be identified with better accuracy compared with the conventional response sensitivity-based method.
System/observer/controller identification toolbox

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Horta, Lucas G.; Phan, Minh

1992-01-01

System Identification is the process of constructing a mathematical model from input and output data for a system under testing, and characterizing the system uncertainties and measurement noises. The mathematical model structure can take various forms depending upon the intended use. The SYSTEM/OBSERVER/CONTROLLER IDENTIFICATION TOOLBOX (SOCIT) is a collection of functions, written in MATLAB language and expressed in M-files, that implements a variety of modern system identification techniques. For an open loop system, the central features of the SOCIT are functions for identification of a system model and its corresponding forward and backward observers directly from input and output data. The system and observers are represented by a discrete model. The identified model and observers may be used for controller design of linear systems as well as identification of modal parameters such as dampings, frequencies, and mode shapes. For a closed-loop system, an observer and its corresponding controller gain directly from input and output data.
Optimized System Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Longman, Richard W.

1999-01-01

In system identification, one usually cares most about finding a model whose outputs are as close as possible to the true system outputs when the same input is applied to both. However, most system identification algorithms do not minimize this output error. Often they minimize model equation error instead, as in typical least-squares fits using a finite-difference model, and it is seen here that this distinction is significant. Here, we develop a set of system identification algorithms that minimize output error for multi-input/multi-output and multi-input/single-output systems. This is done with sequential quadratic programming iterations on the nonlinear least-squares problems, with an eigendecomposition to handle indefinite second partials. This optimization minimizes a nonlinear function of many variables, and hence can converge to local minima. To handle this problem, we start the iterations from the OKID (Observer/Kalman Identification) algorithm result. Not only has OKID proved very effective in practice, it minimizes an output error of an observer which has the property that as the data set gets large, it converges to minimizing the criterion of interest here. Hence, it is a particularly good starting point for the nonlinear iterations here. Examples show that the methods developed here eliminate the bias that is often observed using any system identification methods of either over-estimating or under-estimating the damping of vibration modes in lightly damped structures.
Photovoltaic System Modeling. Uncertainty and Sensitivity Analyses

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

Hansen, Clifford W.; Martin, Curtis E.

2015-08-01

We report an uncertainty and sensitivity analysis for modeling AC energy from ph otovoltaic systems . Output from a PV system is predicted by a sequence of models. We quantify u ncertainty i n the output of each model using empirical distribution s of each model's residuals. We propagate uncertainty through the sequence of models by sampli ng these distributions to obtain a n empirical distribution of a PV system's output. We consider models that: (1) translate measured global horizontal, direct and global diffuse irradiance to plane - of - array irradiance; (2) estimate effective irradiance; (3) predict cell temperature;more » (4) estimate DC voltage, current and power ; (5) reduce DC power for losses due to inefficient maximum power point tracking or mismatch among modules; and (6) convert DC to AC power . O ur analysis consider s a notional PV system com prising an array of FirstSolar FS - 387 modules and a 250 kW AC inverter ; we use measured irradiance and weather at Albuquerque, NM. We found the uncertainty in PV syste m output to be relatively small, on the order of 1% for daily energy. We found that unce rtainty in the models for POA irradiance and effective irradiance to be the dominant contributors to uncertainty in predicted daily energy. Our analysis indicates that efforts to reduce the uncertainty in PV system output predictions may yield the greatest improvements by focusing on the POA and effective irradiance models.« less
Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.
Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803
Using multi-criteria analysis of simulation models to understand complex biological systems

Treesearch

Maureen C. Kennedy; E. David Ford

2011-01-01

Scientists frequently use computer-simulation models to help solve complex biological problems. Typically, such models are highly integrated, they produce multiple outputs, and standard methods of model analysis are ill suited for evaluating them. We show how multi-criteria optimization with Pareto optimality allows for model outputs to be compared to multiple system...
Grid-connected wind and photovoltaic system

NASA Astrophysics Data System (ADS)

Devabakthuni, Sindhuja

The objective of this thesis is to design a grid connected wind and photovoltaic system. A new model of converter control was designed which maintains the voltage of the bus to grid as constant when combined system of solar and wind is connected to AC bus. The model is designed to track maximum power at each point irrespective of changes in irradiance, temperature and wind speed which affects the power supplied to grid. Solar power from the sun is not constant as it is affected by changes in irradiances and temperature. Even the wind power is affected by wind speed. A MPPT controller was designed for both systems. A boost converter is designed which uses the pulses from MPPT controller to boost the output. Wind system consists of wind turbine block from the MATLAB with a pitch angle controller to maintain optimum pitch angle. The output from wind turbine is connected to a permanent magnet synchronous generator. The unregulated DC output from the photovoltaic system is directly given to boost converter. The AC output from the wind system is given to an uncontrolled rectifier to get a unregulated DC output. The unregulated DC output goes to the boost converter. A voltage source inverter was designed which converts the rectified DC output from the boost converter to AC power. The inverter is designed to maintain constant AC bus voltage irrespective of the disturbances in the power supply. Photovoltaic and wind systems are individually designed for 5KW each in MATLAB-Simulink environment. In this thesis, the models were subjected to changes in irradiance, temperature and wind speed and the results were interpreted. The model was successful in tracking maximum at every instant and the AC bus voltage was maintained constant throughout the simulation.
Data-Based Predictive Control with Multirate Prediction Step

NASA Technical Reports Server (NTRS)

Barlow, Jonathan S.

2010-01-01

Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.
Combustion Control System Design of Diesel Engine via ASPR based Output Feedback Control Strategy with a PFC

NASA Astrophysics Data System (ADS)

Mizumoto, Ikuro; Tsunematsu, Junpei; Fujii, Seiya

2016-09-01

In this paper, a design method of an output feedback control system with a simple feedforward input for a combustion model of diesel engine will be proposed based on the almost strictly positive real-ness (ASPR-ness) of the controlled system for a combustion control of diesel engines. A parallel feedforward compensator (PFC) design scheme which renders the resulting augmented controlled system ASPR will also be proposed in order to design a stable output feedback control system for the considered combustion model. The effectiveness of our proposed method will be confirmed through numerical simulations.
Structural identifiability analysis of a cardiovascular system model.

PubMed

Pironet, Antoine; Dauby, Pierre C; Chase, J Geoffrey; Docherty, Paul D; Revie, James A; Desaive, Thomas

2016-05-01

The six-chamber cardiovascular system model of Burkhoff and Tyberg has been used in several theoretical and experimental studies. However, this cardiovascular system model (and others derived from it) are not identifiable from any output set. In this work, two such cases of structural non-identifiability are first presented. These cases occur when the model output set only contains a single type of information (pressure or volume). A specific output set is thus chosen, mixing pressure and volume information and containing only a limited number of clinically available measurements. Then, by manipulating the model equations involving these outputs, it is demonstrated that the six-chamber cardiovascular system model is structurally globally identifiable. A further simplification is made, assuming known cardiac valve resistances. Because of the poor practical identifiability of these four parameters, this assumption is usual. Under this hypothesis, the six-chamber cardiovascular system model is structurally identifiable from an even smaller dataset. As a consequence, parameter values computed from limited but well-chosen datasets are theoretically unique. This means that the parameter identification procedure can safely be performed on the model from such a well-chosen dataset. Thus, the model may be considered suitable for use in diagnosis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Distinguishing the Forest from the Trees: Synthesizing IHRMP Research

Treesearch

Gregory B. Greenwood

1991-01-01

A conceptual model of hardwood rangelands as multi-output resource system is developed and used to achieve a synthesis of Integrated Hardwood Range Management Program (IHRMP) research. The model requires the definition of state variables which characterize the system at any time, processes that move the system to different states, outputs...
Fuzzy model-based servo and model following control for nonlinear systems.

PubMed

Ohtake, Hiroshi; Tanaka, Kazuo; Wang, Hua O

2009-12-01

This correspondence presents servo and nonlinear model following controls for a class of nonlinear systems using the Takagi-Sugeno fuzzy model-based control approach. First, the construction method of the augmented fuzzy system for continuous-time nonlinear systems is proposed by differentiating the original nonlinear system. Second, the dynamic fuzzy servo controller and the dynamic fuzzy model following controller, which can make outputs of the nonlinear system converge to target points and to outputs of the reference system, respectively, are introduced. Finally, the servo and model following controller design conditions are given in terms of linear matrix inequalities. Design examples illustrate the utility of this approach.
Output Containment Control of Linear Heterogeneous Multi-Agent Systems Using Internal Model Principle.

PubMed

Zuo, Shan; Song, Yongduan; Lewis, Frank L; Davoudi, Ali

2017-01-04

This paper studies the output containment control of linear heterogeneous multi-agent systems, where the system dynamics and even the state dimensions can generally be different. Since the states can have different dimensions, standard results from state containment control do not apply. Therefore, the control objective is to guarantee the convergence of the output of each follower to the dynamic convex hull spanned by the outputs of leaders. This can be achieved by making certain output containment errors go to zero asymptotically. Based on this formulation, two different control protocols, namely, full-state feedback and static output-feedback, are designed based on internal model principles. Sufficient local conditions for the existence of the proposed control protocols are developed in terms of stabilizing the local followers' dynamics and satisfying a certain H∞ criterion. Unified design procedures to solve the proposed two control protocols are presented by formulation and solution of certain local state-feedback and static output-feedback problems, respectively. Numerical simulations are given to validate the proposed control protocols.
Method and apparatus for loss of control inhibitor systems

NASA Technical Reports Server (NTRS)

A'Harrah, Ralph C. (Inventor)

2007-01-01

Active and adaptive systems and methods to prevent loss of control incidents by providing tactile feedback to a vehicle operator are disclosed. According to the present invention, an operator gives a control input to an inceptor. An inceptor sensor measures an inceptor input value of the control input. The inceptor input is used as an input to a Steady-State Inceptor Input/Effector Output Model that models the vehicle control system design. A desired effector output from the inceptor input is generated from the model. The desired effector output is compared to an actual effector output to get a distortion metric. A feedback force is generated as a function of the distortion metric. The feedback force is used as an input to a feedback force generator which generates a loss of control inhibitor system (LOCIS) force back to the inceptor. The LOCIS force is felt by the operator through the inceptor.
Generative electronic background music system

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

Mazurowski, Lukasz

In this short paper-extended abstract the new approach to generation of electronic background music has been presented. The Generative Electronic Background Music System (GEBMS) has been located between other related approaches within the musical algorithm positioning framework proposed by Woller et al. The music composition process is performed by a number of mini-models parameterized by further described properties. The mini-models generate fragments of musical patterns used in output composition. Musical pattern and output generation are controlled by container for the mini-models - a host-model. General mechanism has been presented including the example of the synthesized output compositions.
Using quantum theory to simplify input-output processes

NASA Astrophysics Data System (ADS)

Thompson, Jayne; Garner, Andrew J. P.; Vedral, Vlatko; Gu, Mile

2017-02-01

All natural things process and transform information. They receive environmental information as input, and transform it into appropriate output responses. Much of science is dedicated to building models of such systems-algorithmic abstractions of their input-output behavior that allow us to simulate how such systems can behave in the future, conditioned on what has transpired in the past. Here, we show that classical models cannot avoid inefficiency-storing past information that is unnecessary for correct future simulation. We construct quantum models that mitigate this waste, whenever it is physically possible to do so. This suggests that the complexity of general input-output processes depends fundamentally on what sort of information theory we use to describe them.
Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

PubMed

Lam, H K

2012-02-01

This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.
Adaptive Identification of Fluid-Dynamic Systems

DTIC Science & Technology

2001-06-14

Fig. 1. Unknown System Adaptive Filter Σ _ + Input u Filter Output y Desired Output d Error e Fig. 1. Modeling of a SISO system using...2J E e n =   (12) Here [ ]. E is the expectation operator and ( ) ( ) ( ) e n d n y n= − is the error between the desired system output and...B … input vector ( ) ( ) ( ) ( )[ ], , ,1 1 Tn u n u n u n N= − − +U … output and error ( ) ( ) ( ) ( ) ( ) ( ) ( ) T T y n n n e n d n n n
A model for plant lighting system selection.

PubMed

Ciolkosz, D E; Albright, L D; Sager, J C; Langhans, R W

2002-01-01

A decision model is presented that compares lighting systems for a plant growth scenario and chooses the most appropriate system from a given set of possible choices. The model utilizes a Multiple Attribute Utility Theory approach, and incorporates expert input and performance simulations to calculate a utility value for each lighting system being considered. The system with the highest utility is deemed the most appropriate system. The model was applied to a greenhouse scenario, and analyses were conducted to test the model's output for validity. Parameter variation indicates that the model performed as expected. Analysis of model output indicates that differences in utility among the candidate lighting systems were sufficiently large to give confidence that the model's order of selection was valid.
Identification procedure for epistemic uncertainties using inverse fuzzy arithmetic

NASA Astrophysics Data System (ADS)

Haag, T.; Herrmann, J.; Hanss, M.

2010-10-01

For the mathematical representation of systems with epistemic uncertainties, arising, for example, from simplifications in the modeling procedure, models with fuzzy-valued parameters prove to be a suitable and promising approach. In practice, however, the determination of these parameters turns out to be a non-trivial problem. The identification procedure to appropriately update these parameters on the basis of a reference output (measurement or output of an advanced model) requires the solution of an inverse problem. Against this background, an inverse method for the computation of the fuzzy-valued parameters of a model with epistemic uncertainties is presented. This method stands out due to the fact that it only uses feedforward simulations of the model, based on the transformation method of fuzzy arithmetic, along with the reference output. An inversion of the system equations is not necessary. The advancement of the method presented in this paper consists of the identification of multiple input parameters based on a single reference output or measurement. An optimization is used to solve the resulting underdetermined problems by minimizing the uncertainty of the identified parameters. Regions where the identification procedure is reliable are determined by the computation of a feasibility criterion which is also based on the output data of the transformation method only. For a frequency response function of a mechanical system, this criterion allows a restriction of the identification process to some special range of frequency where its solution can be guaranteed. Finally, the practicability of the method is demonstrated by covering the measured output of a fluid-filled piping system by the corresponding uncertain FE model in a conservative way.
Feasibility study of dish/stirling power systems in Turkey

NASA Astrophysics Data System (ADS)

Zilanlı, Gülin Acarol; Eray, Aynur

2017-06-01

In this study, two different commercial dish/stirling systems, SES (Stirling Energy Systems) and WGA-ADDS (WGAssociates - Advanced Dish Development System), are modeled using the "System Advisor Model" (SAM) modeling software in designated settlement areas. Both systems are modeled for the US state of Albuquerque, where they were designed, and Turkish provinces of Ankara, Van, Muğla, Mersin, Urfa and Konya. At first, the dish/stirling system is optimized according to the power output values and the system loss parameters. Then, the layout of the solar field is designed with an installed capacity of 600kW both of SES and WGA-ADDS systems, Upon securing the most suitable layout, the system is modeled for the aforementioned settlements using the optimum output values gathered from the parametric analysis. As a result of the simulation studies, the applicability of this model is discussed according to the power output and the efficiency. Although Turkey is located in an area called "the sun belt" where solar energy technologies can be used, there is no advanced application of these systems. This study aims to discuss the application of these systems in detail and to pave the way for future studies in this field.
Two models for identification and predicting behaviour of an induction motor system

NASA Astrophysics Data System (ADS)

Kuo, Chien-Hsun

2018-01-01

System identification or modelling is the process of building mathematical models of dynamical systems based on the available input and output data from the systems. This paper introduces system identification by using ARX (Auto Regressive with eXogeneous input) and ARMAX (Auto Regressive Moving Average with eXogeneous input) models. Through the identified system model, the predicted output could be compared with the measured one to help prevent the motor faults from developing into a catastrophic machine failure and avoid unnecessary costs and delays caused by the need to carry out unscheduled repairs. The induction motor system is illustrated as an example. Numerical and experimental results are shown for the identified induction motor system.
Analysis and model on space-time characteristics of wind power output based on the measured wind speed data

NASA Astrophysics Data System (ADS)

Shi, Wenhui; Feng, Changyou; Qu, Jixian; Zha, Hao; Ke, Dan

2018-02-01

Most of the existing studies on wind power output focus on the fluctuation of wind farms and the spatial self-complementary of wind power output time series was ignored. Therefore the existing probability models can’t reflect the features of power system incorporating wind farms. This paper analyzed the spatial self-complementary of wind power and proposed a probability model which can reflect temporal characteristics of wind power on seasonal and diurnal timescales based on sufficient measured data and improved clustering method. This model could provide important reference for power system simulation incorporating wind farms.
Software Validation via Model Animation

NASA Technical Reports Server (NTRS)

Dutle, Aaron M.; Munoz, Cesar A.; Narkawicz, Anthony J.; Butler, Ricky W.

2015-01-01

This paper explores a new approach to validating software implementations that have been produced from formally-verified algorithms. Although visual inspection gives some confidence that the implementations faithfully reflect the formal models, it does not provide complete assurance that the software is correct. The proposed approach, which is based on animation of formal specifications, compares the outputs computed by the software implementations on a given suite of input values to the outputs computed by the formal models on the same inputs, and determines if they are equal up to a given tolerance. The approach is illustrated on a prototype air traffic management system that computes simple kinematic trajectories for aircraft. Proofs for the mathematical models of the system's algorithms are carried out in the Prototype Verification System (PVS). The animation tool PVSio is used to evaluate the formal models on a set of randomly generated test cases. Output values computed by PVSio are compared against output values computed by the actual software. This comparison improves the assurance that the translation from formal models to code is faithful and that, for example, floating point errors do not greatly affect correctness and safety properties.
Markov Modeling of Component Fault Growth over a Derived Domain of Feasible Output Control Effort Modifications

NASA Technical Reports Server (NTRS)

Bole, Brian; Goebel, Kai; Vachtsevanos, George

2012-01-01

This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adaptation. A metric representing the relative deviation between the nominal output of a system and the net output that is actually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formulated Markov process. The state space of the Markov process will be defined in terms of an abstracted metric representing the relative health remaining in each of the system s components. The proposed formulation of component fault dynamics will conveniently relate feasible system output performance modifications to predictions of future component health deterioration.
MIMO system identification using frequency response data

NASA Technical Reports Server (NTRS)

Medina, Enrique A.; Irwin, R. D.; Mitchell, Jerrel R.; Bukley, Angelia P.

1992-01-01

A solution to the problem of obtaining a multi-input, multi-output statespace model of a system from its individual input/output frequency responses is presented. The Residue Identification Algorithm (RID) identifies the system poles from a transfer function model of the determinant of the frequency response data matrix. Next, the residue matrices of the modes are computed guaranteeing that each input/output frequency response is fitted in the least squares sense. Finally, a realization of the system is computed. Results of the application of RID to experimental frequency responses of a large space structure ground test facility are presented and compared to those obtained via the Eigensystem Realization Algorithm.
Dynamic modeling and parameter estimation of a radial and loop type distribution system network

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

Jun Qui; Heng Chen; Girgis, A.A.

1993-05-01

This paper presents a new identification approach to three-phase power system modeling and model reduction taking power system network as multi-input, multi-output (MIMO) processes. The model estimate can be obtained in discrete-time input-output form, discrete- or continuous-time state-space variable form, or frequency-domain impedance transfer function matrix form. An algorithm for determining the model structure of this MIMO process is described. The effect of measurement noise on the approach is also discussed. This approach has been applied on a sample system and simulation results are also presented in this paper.
A diagnostic interface for the ICOsahedral Non-hydrostatic (ICON) modelling framework based on the Modular Earth Submodel System (MESSy v2.50)

NASA Astrophysics Data System (ADS)

Kern, Bastian; Jöckel, Patrick

2016-10-01

Numerical climate and weather models have advanced to finer scales, accompanied by large amounts of output data. The model systems hit the input and output (I/O) bottleneck of modern high-performance computing (HPC) systems. We aim to apply diagnostic methods online during the model simulation instead of applying them as a post-processing step to written output data, to reduce the amount of I/O. To include diagnostic tools into the model system, we implemented a standardised, easy-to-use interface based on the Modular Earth Submodel System (MESSy) into the ICOsahedral Non-hydrostatic (ICON) modelling framework. The integration of the diagnostic interface into the model system is briefly described. Furthermore, we present a prototype implementation of an advanced online diagnostic tool for the aggregation of model data onto a user-defined regular coarse grid. This diagnostic tool will be used to reduce the amount of model output in future simulations. Performance tests of the interface and of two different diagnostic tools show, that the interface itself introduces no overhead in form of additional runtime to the model system. The diagnostic tools, however, have significant impact on the model system's runtime. This overhead strongly depends on the characteristics and implementation of the diagnostic tool. A diagnostic tool with high inter-process communication introduces large overhead, whereas the additional runtime of a diagnostic tool without inter-process communication is low. We briefly describe our efforts to reduce the additional runtime from the diagnostic tools, and present a brief analysis of memory consumption. Future work will focus on optimisation of the memory footprint and the I/O operations of the diagnostic interface.
Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.
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.

Identification and modeling of the electrohydraulic systems of the main gun of a main battle tank

NASA Astrophysics Data System (ADS)

Campos, Luiz C. A.; Menegaldo, Luciano L.

2012-11-01

The black-box mathematical models of the electrohydraulic systems responsible for driving the two degrees of freedom (elevation and azimuth) of the main gun of a main battle tank (MBT) were identified. Such systems respond to gunner's inputs while acquiring and tracking targets. Identification experiments were designed to collect simultaneous data from two inertial measurement units (IMU) installed at the gunner's handle (input) and at the center of rotation of the turret (output), for the identification of the azimuth system. For the elevation system, IMUs were installed at the gunner's handle (input) and at the breech of the gun (output). Linear accelerations and angular rates were collected for both input and output. Several black-box model architectures were investigated. As a result, nonlinear autoregressive with exogenous variables (NARX) second order model and nonlinear finite impulse response (NFIR) fourth order model, demonstrate to best fit the experimental data, with low computational costs. The derived models are being employed in a broader research, aiming to reproduce such systems in a laboratory virtual main gun simulator.
State-Space System Realization with Input- and Output-Data Correlation

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan

1997-01-01

This paper introduces a general version of the information matrix consisting of the autocorrelation and cross-correlation matrices of the shifted input and output data. Based on the concept of data correlation, a new system realization algorithm is developed to create a model directly from input and output data. The algorithm starts by computing a special type of correlation matrix derived from the information matrix. The special correlation matrix provides information on the system-observability matrix and the state-vector correlation. A system model is then developed from the observability matrix in conjunction with other algebraic manipulations. This approach leads to several different algorithms for computing system matrices for use in representing the system model. The relationship of the new algorithms with other realization algorithms in the time and frequency domains is established with matrix factorization of the information matrix. Several examples are given to illustrate the validity and usefulness of these new algorithms.
Model reference adaptive control of flexible robots in the presence of sudden load changes

NASA Technical Reports Server (NTRS)

Steinvorth, Rodrigo; Kaufman, Howard; Neat, Gregory

1991-01-01

Direct command generator tracker based model reference adaptive control (MRAC) algorithms are applied to the dynamics for a flexible-joint arm in the presence of sudden load changes. Because of the need to satisfy a positive real condition, such MRAC procedures are designed so that a feedforward augmented output follows the reference model output, thus, resulting in an ultimately bounded rather than zero output error. Thus, modifications are suggested and tested that: (1) incorporate feedforward into the reference model's output as well as the plant's output, and (2) incorporate a derivative term into only the process feedforward loop. The results of these simulations give a response with zero steady state model following error, and thus encourage further use of MRAC for more complex flexibile robotic systems.
Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

PubMed

Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.
A mixed-unit input-output model for environmental life-cycle assessment and material flow analysis.

PubMed

Hawkins, Troy; Hendrickson, Chris; Higgins, Cortney; Matthews, H Scott; Suh, Sangwon

2007-02-01

Materials flow analysis models have traditionally been used to track the production, use, and consumption of materials. Economic input-output modeling has been used for environmental systems analysis, with a primary benefit being the capability to estimate direct and indirect economic and environmental impacts across the entire supply chain of production in an economy. We combine these two types of models to create a mixed-unit input-output model that is able to bettertrack economic transactions and material flows throughout the economy associated with changes in production. A 13 by 13 economic input-output direct requirements matrix developed by the U.S. Bureau of Economic Analysis is augmented with material flow data derived from those published by the U.S. Geological Survey in the formulation of illustrative mixed-unit input-output models for lead and cadmium. The resulting model provides the capabilities of both material flow and input-output models, with detailed material tracking through entire supply chains in response to any monetary or material demand. Examples of these models are provided along with a discussion of uncertainty and extensions to these models.
Analysis and compensation of an aircraft simulator control loading system with compliant linkage. [using hydraulic equipment

NASA Technical Reports Server (NTRS)

Johnson, P. R.; Bardusch, R. E.

1974-01-01

A hydraulic control loading system for aircraft simulation was analyzed to find the causes of undesirable low frequency oscillations and loading effects in the output. The hypothesis of mechanical compliance in the control linkage was substantiated by comparing the behavior of a mathematical model of the system with previously obtained experimental data. A compensation scheme based on the minimum integral of the squared difference between desired and actual output was shown to be effective in reducing the undesirable output effects. The structure of the proposed compensation was computed by use of a dynamic programing algorithm and a linear state space model of the fixed elements in the system.
Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part I: Theory and Simulations

PubMed Central

Marmarelis, Vasilis Z.; Zanos, Theodoros P.; Berger, Theodore W.

2010-01-01

This paper presents a new modeling approach for neural systems with point-process (spike) inputs and outputs that utilizes Boolean operators (i.e. modulo 2 multiplication and addition that correspond to the logical AND and OR operations respectively, as well as the AND_NOT logical operation representing inhibitory effects). The form of the employed mathematical models is akin to a “Boolean-Volterra” model that contains the product terms of all relevant input lags in a hierarchical order, where terms of order higher than first represent nonlinear interactions among the various lagged values of each input point-process or among lagged values of various inputs (if multiple inputs exist) as they reflect on the output. The coefficients of this Boolean-Volterra model are also binary variables that indicate the presence or absence of the respective term in each specific model/system. Simulations are used to explore the properties of such models and the feasibility of their accurate estimation from short data-records in the presence of noise (i.e. spurious spikes). The results demonstrate the feasibility of obtaining reliable estimates of such models, with excitatory and inhibitory terms, in the presence of considerable noise (spurious spikes) in the outputs and/or the inputs in a computationally efficient manner. A pilot application of this approach to an actual neural system is presented in the companion paper (Part II). PMID:19517238
Optimum systems design with random input and output applied to solar water heating

NASA Astrophysics Data System (ADS)

Abdel-Malek, L. L.

1980-03-01

Solar water heating systems are evaluated. Models were developed to estimate the percentage of energy supplied from the Sun to a household. Since solar water heating systems have random input and output queueing theory, birth and death processes were the major tools in developing the models of evaluation. Microeconomics methods help in determining the optimum size of the solar water heating system design parameters, i.e., the water tank volume and the collector area.
Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

PubMed Central

Coelho, Antonio Augusto Rodrigues

2016-01-01

This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723
Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form.

PubMed

Li, Dong-Juan; Li, Da-Peng

2017-09-14

In this paper, an adaptive output feedback control is framed for uncertain nonlinear discrete-time systems. The considered systems are a class of multi-input multioutput nonaffine nonlinear systems, and they are in the nested lower triangular form. Furthermore, the unknown dead-zone inputs are nonlinearly embedded into the systems. These properties of the systems will make it very difficult and challenging to construct a stable controller. By introducing a new diffeomorphism coordinate transformation, the controlled system is first transformed into a state-output model. By introducing a group of new variables, an input-output model is finally obtained. Based on the transformed model, the implicit function theorem is used to determine the existence of the ideal controllers and the approximators are employed to approximate the ideal controllers. By using the mean value theorem, the nonaffine functions of systems can become an affine structure but nonaffine terms still exist. The adaptation auxiliary terms are skillfully designed to cancel the effect of the dead-zone input. Based on the Lyapunov difference theorem, the boundedness of all the signals in the closed-loop system can be ensured and the tracking errors are kept in a bounded compact set. The effectiveness of the proposed technique is checked by a simulation study.
Peak expiratory flow profiles delivered by pump systems. Limitations due to wave action.

PubMed

Miller, M R; Jones, B; Xu, Y; Pedersen, O F; Quanjer, P H

2000-06-01

Pump systems are currently used to test the performance of both spirometers and peak expiratory flow (PEF) meters, but for certain flow profiles the input signal (i.e., requested profile) and the output profile can differ. We developed a mathematical model of wave action within a pump and compared the recorded flow profiles with both the input profiles and the output predicted by the model. Three American Thoracic Society (ATS) flow profiles and four artificial flow-versus-time profiles were delivered by a pump, first to a pneumotachograph (PT) on its own, then to the PT with a 32-cm upstream extension tube (which would favor wave action), and lastly with the PT in series with and immediately downstream to a mini-Wright peak flow meter. With the PT on its own, recorded flow for the seven profiles was 2.4 +/- 1.9% (mean +/- SD) higher than the pump's input flow, and similarly was 2.3 +/- 2.3% higher than the pump's output flow as predicted by the model. With the extension tube in place, the recorded flow was 6.6 +/- 6.4% higher than the input flow (range: 0.1 to 18.4%), but was only 1.2 +/- 2.5% higher than the output flow predicted by the model (range: -0.8 to 5.2%). With the mini-Wright meter in series, the flow recorded by the PT was on average 6.1 +/- 9.1% below the input flow (range: -23.8 to 2. 5%), but was only 0.6 +/- 3.3% above the pump's output flow predicted by the model (range: -5.5 to 3.9%). The mini-Wright meter's reading (corrected for its nonlinearity) was on average 1.3 +/- 3.6% below the model's predicted output flow (range: -9.0 to 1. 5%). The mini-Wright meter would be deemed outside ATS limits for accuracy for three of the seven profiles when compared with the pump's input PEF, but this would be true for only one profile when compared with the pump's output PEF as predicted by the model. Our study shows that the output flow from pump systems can differ from the input waveform depending on the operating configuration. This effect can be predicted with reasonable accuracy using a model based on nonsteady flow analysis that takes account of pressure wave reflections within pump systems.
Design of vaccination and fumigation on Host-Vector Model by input-output linearization method

NASA Astrophysics Data System (ADS)

Nugraha, Edwin Setiawan; Naiborhu, Janson; Nuraini, Nuning

2017-03-01

Here, we analyze the Host-Vector Model and proposed design of vaccination and fumigation to control infectious population by using feedback control especially input-output liniearization method. Host population is divided into three compartments: susceptible, infectious and recovery. Whereas the vector population is divided into two compartment such as susceptible and infectious. In this system, vaccination and fumigation treat as input factors and infectious population as output result. The objective of design is to stabilize of the output asymptotically tend to zero. We also present the examples to illustrate the design model.
One output function: a misconception of students studying digital systems - a case study

NASA Astrophysics Data System (ADS)

Trotskovsky, E.; Sabag, N.

2015-05-01

Background:Learning processes are usually characterized by students' misunderstandings and misconceptions. Engineering educators intend to help their students overcome their misconceptions and achieve correct understanding of the concept. This paper describes a misconception in digital systems held by many students who believe that combinational logic circuits should have only one output. Purpose:The current study aims to investigate the roots of the misconception about one-output function and the pedagogical methods that can help students overcome the misconception. Sample:Three hundred and eighty-one students in the Departments of Electrical and Electronics and Mechanical Engineering at an academic engineering college, who learned the same topics of a digital combinational system, participated in the research. Design and method:In the initial research stage, students were taught according to traditional method - first to design a one-output combinational logic system, and then to implement a system with a number of output functions. In the main stage, an experimental group was taught using a new method whereby they were shown how to implement a system with several output functions, prior to learning about one-output systems. A control group was taught using the traditional method. In the replication stage (the third stage), an experimental group was taught using the new method. A mixed research methodology was used to examine the results of the new learning method. Results:Quantitative research showed that the new teaching approach resulted in a statistically significant decrease in student errors, and qualitative research revealed students' erroneous thinking patterns. Conclusions:It can be assumed that the traditional teaching method generates an incorrect mental model of the one-output function among students. The new pedagogical approach prevented the creation of an erroneous mental model and helped students develop the correct conceptual understanding.
Obs4MIPS: Satellite Observations for Model Evaluation

NASA Astrophysics Data System (ADS)

Ferraro, R.; Waliser, D. E.; Gleckler, P. J.

2017-12-01

This poster will review the current status of the obs4MIPs project, whose purpose is to provide a limited collection of well-established and documented datasets for comparison with Earth system models (https://www.earthsystemcog.org/projects/obs4mips/). These datasets have been reformatted to correspond with the CMIP5 model output requirements, and include technical documentation specifically targeted for their use in model output evaluation. The project holdings now exceed 120 datasets with observations that directly correspond to CMIP5 model output variables, with new additions in response to the CMIP6 experiments. With the growth in climate model output data volume, it is increasing more difficult to bring the model output and the observations together to do evaluations. The positioning of the obs4MIPs datasets within the Earth System Grid Federation (ESGF) allows for the use of currently available and planned online tools within the ESGF to perform analysis using model output and observational datasets without necessarily downloading everything to a local workstation. This past year, obs4MIPs has updated its submission guidelines to closely align with changes in the CMIP6 experiments, and is implementing additional indicators and ancillary data to allow users to more easily determine the efficacy of an obs4MIPs dataset for specific evaluation purposes. This poster will present the new guidelines and indicators, and update the list of current obs4MIPs holdings and their connection to the ESGF evaluation and analysis tools currently available, and being developed for the CMIP6 experiments.
Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.

PubMed

Nabavi-Pelesaraei, Ashkan; Rafiee, Shahin; Mohtasebi, Seyed Saeid; Hosseinzadeh-Bandbafha, Homa; Chau, Kwok-Wing

2018-08-01

Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg -1 and 66,112.94MJkg -1 , respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy. Copyright © 2018 Elsevier B.V. All rights reserved.
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.
Fuzzy logic controller optimization

DOEpatents

Sepe, Jr., Raymond B; Miller, John Michael

2004-03-23

A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.
Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

2015-11-01

This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.
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.
Characteristic operator functions for quantum input-plant-output models and coherent control

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

Gough, John E.

We introduce the characteristic operator as the generalization of the usual concept of a transfer function of linear input-plant-output systems to arbitrary quantum nonlinear Markovian input-output models. This is intended as a tool in the characterization of quantum feedback control systems that fits in with the general theory of networks. The definition exploits the linearity of noise differentials in both the plant Heisenberg equations of motion and the differential form of the input-output relations. Mathematically, the characteristic operator is a matrix of dimension equal to the number of outputs times the number of inputs (which must coincide), but with entriesmore » that are operators of the plant system. In this sense, the characteristic operator retains details of the effective plant dynamical structure and is an essentially quantum object. We illustrate the relevance to model reduction and simplification definition by showing that the convergence of the characteristic operator in adiabatic elimination limit models requires the same conditions and assumptions appearing in the work on limit quantum stochastic differential theorems of Bouten and Silberfarb [Commun. Math. Phys. 283, 491-505 (2008)]. This approach also shows in a natural way that the limit coefficients of the quantum stochastic differential equations in adiabatic elimination problems arise algebraically as Schur complements and amounts to a model reduction where the fast degrees of freedom are decoupled from the slow ones and eliminated.« less

Optimization of output power and transmission efficiency of magnetically coupled resonance wireless power transfer system

NASA Astrophysics Data System (ADS)

Yan, Rongge; Guo, Xiaoting; Cao, Shaoqing; Zhang, Changgeng

2018-05-01

Magnetically coupled resonance (MCR) wireless power transfer (WPT) system is a promising technology in electric energy transmission. But, if its system parameters are designed unreasonably, output power and transmission efficiency will be low. Therefore, optimized parameters design of MCR WPT has important research value. In the MCR WPT system with designated coil structure, the main parameters affecting output power and transmission efficiency are the distance between the coils, the resonance frequency and the resistance of the load. Based on the established mathematical model and the differential evolution algorithm, the change of output power and transmission efficiency with parameters can be simulated. From the simulation results, it can be seen that output power and transmission efficiency of the two-coil MCR WPT system and four-coil one with designated coil structure are improved. The simulation results confirm the validity of the optimization method for MCR WPT system with designated coil structure.
Energy: Economic activity and energy demand; link to energy flow. Example: France

NASA Astrophysics Data System (ADS)

1980-10-01

The data derived from the EXPLOR and EPOM, Energy Flow Optimization Model are described. The core of the EXPLOR model is a circular system of relations involving consumer's demand, producer's outputs, and market prices. The solution of this system of relations is obtained by successive iterations; the final output is a coherent system of economic accounts. The computer program for this transition is described. The work conducted by comparing different energy demand models is summarized. The procedure is illustrated by a numerical projection to 1980 and 1985 using the existing version of the EXPLOR France model.
Fuzzy logic modeling of the resistivity parameter and topography features for aquifer assessment in hydrogeological investigation of a crystalline basement complex

NASA Astrophysics Data System (ADS)

Adabanija, M. A.; Omidiora, E. O.; Olayinka, A. I.

2008-05-01

A linguistic fuzzy logic system (LFLS)-based expert system model has been developed for the assessment of aquifers for the location of productive water boreholes in a crystalline basement complex. The model design employed a multiple input/single output (MISO) approach with geoelectrical parameters and topographic features as input variables and control crisp value as the output. The application of the method to the data acquired in Khondalitic terrain, a basement complex in Vizianagaram District, south India, shows that potential groundwater resource zones that have control output values in the range 0.3295-0.3484 have a yield greater than 6,000 liters per hour (LPH). The range 0.3174-0.3226 gives a yield less than 4,000 LPH. The validation of the control crisp value using data acquired from Oban Massif, a basement complex in southeastern Nigeria, indicates a yield less than 3,000 LPH for control output values in the range 0.2938-0.3065. This validation corroborates the ability of control output values to predict a yield, thereby vindicating the applicability of linguistic fuzzy logic system in siting productive water boreholes in a basement complex.
Life and reliability models for helicopter transmissions

NASA Technical Reports Server (NTRS)

Savage, M.; Knorr, R. J.; Coy, J. J.

1982-01-01

Computer models of life and reliability are presented for planetary gear trains with a fixed ring gear, input applied to the sun gear, and output taken from the planet arm. For this transmission the input and output shafts are co-axial and the input and output torques are assumed to be coaxial with these shafts. Thrust and side loading are neglected. The reliability model is based on the Weibull distributions of the individual reliabilities of the in transmission components. The system model is also a Weibull distribution. The load versus life model for the system is a power relationship as the models for the individual components. The load-life exponent and basic dynamic capacity are developed as functions of the components capacities. The models are used to compare three and four planet, 150 kW (200 hp), 5:1 reduction transmissions with 1500 rpm input speed to illustrate their use.
Modeling the control of the central nervous system over the cardiovascular system using support vector machines.

PubMed

Díaz, José; Acosta, Jesús; González, Rafael; Cota, Juan; Sifuentes, Ernesto; Nebot, Àngela

2018-02-01

The control of the central nervous system (CNS) over the cardiovascular system (CS) has been modeled using different techniques, such as fuzzy inductive reasoning, genetic fuzzy systems, neural networks, and nonlinear autoregressive techniques; the results obtained so far have been significant, but not solid enough to describe the control response of the CNS over the CS. In this research, support vector machines (SVMs) are used to predict the response of a branch of the CNS, specifically, the one that controls an important part of the cardiovascular system. To do this, five models are developed to emulate the output response of five controllers for the same input signal, the carotid sinus blood pressure (CSBP). These controllers regulate parameters such as heart rate, myocardial contractility, peripheral and coronary resistance, and venous tone. The models are trained using a known set of input-output response in each controller; also, there is a set of six input-output signals for testing each proposed model. The input signals are processed using an all-pass filter, and the accuracy performance of the control models is evaluated using the percentage value of the normalized mean square error (MSE). Experimental results reveal that SVM models achieve a better estimation of the dynamical behavior of the CNS control compared to others modeling systems. The main results obtained show that the best case is for the peripheral resistance controller, with a MSE of 1.20e-4%, while the worst case is for the heart rate controller, with a MSE of 1.80e-3%. These novel models show a great reliability in fitting the output response of the CNS which can be used as an input to the hemodynamic system models in order to predict the behavior of the heart and blood vessels in response to blood pressure variations. Copyright © 2017 Elsevier Ltd. All rights reserved.
Global sensitivity analysis for fuzzy inputs based on the decomposition of fuzzy output entropy

NASA Astrophysics Data System (ADS)

Shi, Yan; Lu, Zhenzhou; Zhou, Yicheng

2018-06-01

To analyse the component of fuzzy output entropy, a decomposition method of fuzzy output entropy is first presented. After the decomposition of fuzzy output entropy, the total fuzzy output entropy can be expressed as the sum of the component fuzzy entropy contributed by fuzzy inputs. Based on the decomposition of fuzzy output entropy, a new global sensitivity analysis model is established for measuring the effects of uncertainties of fuzzy inputs on the output. The global sensitivity analysis model can not only tell the importance of fuzzy inputs but also simultaneously reflect the structural composition of the response function to a certain degree. Several examples illustrate the validity of the proposed global sensitivity analysis, which is a significant reference in engineering design and optimization of structural systems.
Common evolutionary trends underlie the four-bar linkage systems of sunfish and mantis shrimp.

PubMed

Hu, Yinan; Nelson-Maney, Nathan; Anderson, Philip S L

2017-05-01

Comparative biomechanics offers an opportunity to explore the evolution of disparate biological systems that share common underlying mechanics. Four-bar linkage modeling has been applied to various biological systems such as fish jaws and crustacean appendages to explore the relationship between biomechanics and evolutionary diversification. Mechanical sensitivity states that the functional output of a mechanical system will show differential sensitivity to changes in specific morphological components. We document similar patterns of mechanical sensitivity in two disparate four-bar systems from different phyla: the opercular four-bar system in centrarchid fishes and the raptorial appendage of stomatopods. We built dynamic linkage models of 19 centrarchid and 36 stomatopod species and used phylogenetic generalized least squares regression (PGLS) to compare evolutionary shifts in linkage morphology and mechanical outputs derived from the models. In both systems, the kinematics of the four-bar mechanism show significant evolutionary correlation with the output link, while travel distance of the output arm is correlated with the coupler link. This common evolutionary pattern seen in both fish and crustacean taxa is a potential consequence of the mechanical principles underlying four-bar systems. Our results illustrate the potential influence of physical principles on morphological evolution across biological systems with different structures, behaviors, and ecologies. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.
Systems Operation Studies for Automated Guideway Transit Systems : System Availability Model User's Manual

DOT National Transportation Integrated Search

1981-01-01

The System Availability Model (SAM) is a system-level model which provides measures of vehicle and passenger availability. The SAM operates in conjunction with the AGT discrete Event Simulation Model (DESM). The DESM output is the normal source of th...
A Bayesian Approach to Evaluating Consistency between Climate Model Output and Observations

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Cressie, N.; Teixeira, J.

2010-12-01

Like other scientific and engineering problems that involve physical modeling of complex systems, climate models can be evaluated and diagnosed by comparing their output to observations of similar quantities. Though the global remote sensing data record is relatively short by climate research standards, these data offer opportunities to evaluate model predictions in new ways. For example, remote sensing data are spatially and temporally dense enough to provide distributional information that goes beyond simple moments to allow quantification of temporal and spatial dependence structures. In this talk, we propose a new method for exploiting these rich data sets using a Bayesian paradigm. For a collection of climate models, we calculate posterior probabilities its members best represent the physical system each seeks to reproduce. The posterior probability is based on the likelihood that a chosen summary statistic, computed from observations, would be obtained when the model's output is considered as a realization from a stochastic process. By exploring how posterior probabilities change with different statistics, we may paint a more quantitative and complete picture of the strengths and weaknesses of the models relative to the observations. We demonstrate our method using model output from the CMIP archive, and observations from NASA's Atmospheric Infrared Sounder.
Electric Propulsion System Modeling for the Proposed Prometheus 1 Mission

NASA Technical Reports Server (NTRS)

Fiehler, Douglas; Dougherty, Ryan; Manzella, David

2005-01-01

The proposed Prometheus 1 spacecraft would utilize nuclear electric propulsion to propel the spacecraft to its ultimate destination where it would perform its primary mission. As part of the Prometheus 1 Phase A studies, system models were developed for each of the spacecraft subsystems that were integrated into one overarching system model. The Electric Propulsion System (EPS) model was developed using data from the Prometheus 1 electric propulsion technology development efforts. This EPS model was then used to provide both performance and mass information to the Prometheus 1 system model for total system trades. Development of the EPS model is described, detailing both the performance calculations as well as its evolution over the course of Phase A through three technical baselines. Model outputs are also presented, detailing the performance of the model and its direct relationship to the Prometheus 1 technology development efforts. These EP system model outputs are also analyzed chronologically showing the response of the model development to the four technical baselines during Prometheus 1 Phase A.
Control Systems with Pulse Width Modulation in Matrix Converters

NASA Astrophysics Data System (ADS)

Bondarev, A. V.; Fedorov, S. V.; Muravyova, E. A.

2018-03-01

In this article, the matrix frequency converter for the system of the frequency control of the electric drive is considered. Algorithms of formation of an output signal on the basis of pulse width modulation were developed for the quantitative analysis of quality of an output signal on the basis of mathematical models. On the basis of simulation models of an output signal, assessment of quality of this signal was carried out. The analysis of harmonic composition of the voltage output received on the basis of pulse width modulation was made for the purpose of determination of opportunities of the control system for improving harmonic composition. The result of such analysis led to the fact that the device formation of switching functions of the control system on the basis of PWM does not lead to a distortion reduction of a harmonic of the control signal, and leads to offset of harmonic in the field of frequencies, the multiple relatively carrier frequency.
H∞ output tracking control of uncertain and disturbed nonlinear systems based on neural network model

NASA Astrophysics Data System (ADS)

Li, Chengcheng; Li, Yuefeng; Wang, Guanglin

2017-07-01

The work presented in this paper seeks to address the tracking problem for uncertain continuous nonlinear systems with external disturbances. The objective is to obtain a model that uses a reference-based output feedback tracking control law. The control scheme is based on neural networks and a linear difference inclusion (LDI) model, and a PDC structure and H∞ performance criterion are used to attenuate external disturbances. The stability of the whole closed-loop model is investigated using the well-known quadratic Lyapunov function. The key principles of the proposed approach are as follows: neural networks are first used to approximate nonlinearities, to enable a nonlinear system to then be represented as a linearised LDI model. An LMI (linear matrix inequality) formula is obtained for uncertain and disturbed linear systems. This formula enables a solution to be obtained through an interior point optimisation method for some nonlinear output tracking control problems. Finally, simulations and comparisons are provided on two practical examples to illustrate the validity and effectiveness of the proposed method.
Assessment of reservoir system variable forecasts

NASA Astrophysics Data System (ADS)

Kistenmacher, Martin; Georgakakos, Aris P.

2015-05-01

Forecast ensembles are a convenient means to model water resources uncertainties and to inform planning and management processes. For multipurpose reservoir systems, forecast types include (i) forecasts of upcoming inflows and (ii) forecasts of system variables and outputs such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. Forecasts of system variables and outputs are conditional on forecasted inflows as well as on specific management policies and can provide useful information for decision-making processes. Unlike inflow forecasts (in ensemble or other forms), which have been the subject of many previous studies, reservoir system variable and output forecasts are not formally assessed in water resources management theory or practice. This article addresses this gap and develops methods to rectify potential reservoir system forecast inconsistencies and improve the quality of management-relevant information provided to stakeholders and managers. The overarching conclusion is that system variable and output forecast consistency is critical for robust reservoir management and needs to be routinely assessed for any management model used to inform planning and management processes. The above are demonstrated through an application from the Sacramento-American-San Joaquin reservoir system in northern California.
The future of climate science analysis in a coming era of exascale computing

NASA Astrophysics Data System (ADS)

Bates, S. C.; Strand, G.

2013-12-01

Projections of Community Earth System Model (CESM) output based on the growth of data archived over 2000-2012 at all of our computing sites (NCAR, NERSC, ORNL) show that we can expect to reach 1,000 PB (1 EB) sometime in the next decade or so. The current paradigms of using site-based archival systems to hold these data that are then accessed via portals or gateways, downloading the data to a local system, and then processing/analyzing the data will be irretrievably broken before then. From a climate modeling perspective, the expertise involved in making climate models themselves efficient on HPC systems will need to be applied to the data as well - providing fast parallel analysis tools co-resident in memory with the data, because the disk I/O bandwidth simply will not keep up with the expected arrival of exaflop systems. The ability of scientists, analysts, stakeholders and others to use climate model output to turn these data into understanding and knowledge will require significant advances in the current typical analysis tools and packages to enable these processes for these vast volumes of data. Allowing data users to enact their own analyses on model output is virtually a requirement as well - climate modelers cannot anticipate all the possibilities for analysis that users may want to do. In addition, the expertise of data scientists, and their knowledge of the model output and their knowledge of best practices in data management (metadata, curation, provenance and so on) will need to be rewarded and exploited to gain the most understanding possible from these volumes of data. In response to growing data size, demand, and future projections, the CESM output has undergone a structure evolution and the data management plan has been reevaluated and updated. The major evolution of the CESM data structure is presented here, along with the CESM experience and role within the CMIP3/CMIP5.
A Comparative Study of the Proposed Models for the Components of the National Health Information System

PubMed Central

Ahmadi, Maryam; Damanabi, Shahla; Sadoughi, Farahnaz

2014-01-01

Introduction: National Health Information System plays an important role in ensuring timely and reliable access to Health information, which is essential for strategic and operational decisions that improve health, quality and effectiveness of health care. In other words, using the National Health information system you can improve the quality of health data, information and knowledge used to support decision making at all levels and areas of the health sector. Since full identification of the components of this system – for better planning and management influential factors of performanceseems necessary, therefore, in this study different attitudes towards components of this system are explored comparatively. Methods: This is a descriptive and comparative kind of study. The society includes printed and electronic documents containing components of the national health information system in three parts: input, process and output. In this context, search for information using library resources and internet search were conducted, and data analysis was expressed using comparative tables and qualitative data. Results: The findings showed that there are three different perspectives presenting the components of national health information system Lippeveld and Sauerborn and Bodart model in 2000, Health Metrics Network (HMN) model from World Health Organization in 2008, and Gattini’s 2009 model. All three models outlined above in the input (resources and structure) require components of management and leadership, planning and design programs, supply of staff, software and hardware facilities and equipment. Plus, in the “process” section from three models, we pointed up the actions ensuring the quality of health information system, and in output section, except for Lippeveld Model, two other models consider information products and use and distribution of information as components of the national health information system. Conclusion: the results showed that all the three models have had a brief discussion about the components of health information in input section. But Lippeveld model has overlooked the components of national health information in process and output sections. Therefore, it seems that the health measurement model of network has a comprehensive presentation for the components of health system in all three sections-input, process and output. PMID:24825937
A comparative study of the proposed models for the components of the national health information system.

PubMed

Ahmadi, Maryam; Damanabi, Shahla; Sadoughi, Farahnaz

2014-04-01

National Health Information System plays an important role in ensuring timely and reliable access to Health information, which is essential for strategic and operational decisions that improve health, quality and effectiveness of health care. In other words, using the National Health information system you can improve the quality of health data, information and knowledge used to support decision making at all levels and areas of the health sector. Since full identification of the components of this system - for better planning and management influential factors of performanceseems necessary, therefore, in this study different attitudes towards components of this system are explored comparatively. This is a descriptive and comparative kind of study. The society includes printed and electronic documents containing components of the national health information system in three parts: input, process and output. In this context, search for information using library resources and internet search were conducted, and data analysis was expressed using comparative tables and qualitative data. The findings showed that there are three different perspectives presenting the components of national health information system Lippeveld and Sauerborn and Bodart model in 2000, Health Metrics Network (HMN) model from World Health Organization in 2008, and Gattini's 2009 model. All three models outlined above in the input (resources and structure) require components of management and leadership, planning and design programs, supply of staff, software and hardware facilities and equipment. Plus, in the "process" section from three models, we pointed up the actions ensuring the quality of health information system, and in output section, except for Lippeveld Model, two other models consider information products and use and distribution of information as components of the national health information system. the results showed that all the three models have had a brief discussion about the components of health information in input section. But Lippeveld model has overlooked the components of national health information in process and output sections. Therefore, it seems that the health measurement model of network has a comprehensive presentation for the components of health system in all three sections-input, process and output.
Study of Regional Downscaled Climate and Air Quality in the United States

NASA Astrophysics Data System (ADS)

Gao, Y.; Fu, J. S.; Drake, J.; Lamarque, J.; Lam, Y.; Huang, K.

2011-12-01

Due to the increasing anthropogenic greenhouse gas emissions, the global and regional climate patterns have significantly changed. Climate change has exerted strong impact on ecosystem, air quality and human life. The global model Community Earth System Model (CESM v1.0) was used to predict future climate and chemistry under projected emission scenarios. Two new emission scenarios, Representative Community Pathways (RCP) 4.5 and RCP 8.5, were used in this study for climate and chemistry simulations. The projected global mean temperature will increase 1.2 and 1.7 degree Celcius for the RCP 4.5 and RCP 8.5 scenarios in 2050s, respectively. In order to take advantage of local detailed topography, land use data and conduct local climate impact on air quality, we downscaled CESM outputs to 4 km by 4 km Eastern US domain using Weather Research and Forecasting (WRF) Model and Community Multi-scale Air Quality modeling system (CMAQ). The evaluations between regional model outputs and global model outputs, regional model outputs and observational data were conducted to verify the downscaled methodology. Future climate change and air quality impact were also examined on a 4 km by 4 km high resolution scale.
Caliver: An R package for CALIbration and VERification of forest fire gridded model outputs.

PubMed

Vitolo, Claudia; Di Giuseppe, Francesca; D'Andrea, Mirko

2018-01-01

The name caliver stands for CALIbration and VERification of forest fire gridded model outputs. This is a package developed for the R programming language and available under an APACHE-2 license from a public repository. In this paper we describe the functionalities of the package and give examples using publicly available datasets. Fire danger model outputs are taken from the modeling components of the European Forest Fire Information System (EFFIS) and observed burned areas from the Global Fire Emission Database (GFED). Complete documentation, including a vignette, is also available within the package.
Caliver: An R package for CALIbration and VERification of forest fire gridded model outputs

PubMed Central

Di Giuseppe, Francesca; D’Andrea, Mirko

2018-01-01

The name caliver stands for CALIbration and VERification of forest fire gridded model outputs. This is a package developed for the R programming language and available under an APACHE-2 license from a public repository. In this paper we describe the functionalities of the package and give examples using publicly available datasets. Fire danger model outputs are taken from the modeling components of the European Forest Fire Information System (EFFIS) and observed burned areas from the Global Fire Emission Database (GFED). Complete documentation, including a vignette, is also available within the package. PMID:29293536
Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

DOE PAGES

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

2017-09-10

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less

Direct model reference adaptive control with application to flexible robots

NASA Technical Reports Server (NTRS)

Steinvorth, Rodrigo; Kaufman, Howard; Neat, Gregory W.

1992-01-01

A modification to a direct command generator tracker-based model reference adaptive control (MRAC) system is suggested in this paper. This modification incorporates a feedforward into the reference model's output as well as the plant's output. Its purpose is to eliminate the bounded model following error present in steady state when previous MRAC systems were used. The algorithm was evaluated using the dynamics for a single-link flexible-joint arm. The results of these simulations show a response with zero steady state model following error. These results encourage further use of MRAC for various types of nonlinear plants.
A controls engineering approach for analyzing airplane input-output characteristics

NASA Technical Reports Server (NTRS)

Arbuckle, P. Douglas

1991-01-01

An engineering approach for analyzing airplane control and output characteristics is presented. State-space matrix equations describing the linear perturbation dynamics are transformed from physical coordinates into scaled coordinates. The scaling is accomplished by applying various transformations to the system to employ prior engineering knowledge of the airplane physics. Two different analysis techniques are then explained. Modal analysis techniques calculate the influence of each system input on each fundamental mode of motion and the distribution of each mode among the system outputs. The optimal steady state response technique computes the blending of steady state control inputs that optimize the steady state response of selected system outputs. Analysis of an example airplane model is presented to demonstrate the described engineering approach.
Verifiable fault tolerance in measurement-based quantum computation

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Hayashi, Masahito

2017-09-01

Quantum systems, in general, cannot be simulated efficiently by a classical computer, and hence are useful for solving certain mathematical problems and simulating quantum many-body systems. This also implies, unfortunately, that verification of the output of the quantum systems is not so trivial, since predicting the output is exponentially hard. As another problem, the quantum system is very delicate for noise and thus needs an error correction. Here, we propose a framework for verification of the output of fault-tolerant quantum computation in a measurement-based model. In contrast to existing analyses on fault tolerance, we do not assume any noise model on the resource state, but an arbitrary resource state is tested by using only single-qubit measurements to verify whether or not the output of measurement-based quantum computation on it is correct. Verifiability is equipped by a constant time repetition of the original measurement-based quantum computation in appropriate measurement bases. Since full characterization of quantum noise is exponentially hard for large-scale quantum computing systems, our framework provides an efficient way to practically verify the experimental quantum error correction.
Performance of an Advanced MOS System in the 1996-97 National Collegiate Weather Forecasting Contest.

NASA Astrophysics Data System (ADS)

Vislocky, Robert L.; Fritsch, J. Michael

1997-12-01

A prototype advanced model output statistics (MOS) forecast system that was entered in the 1996-97 National Collegiate Weather Forecast Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype system uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS forecasts, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the forecast site. The forecasts are totally objective and can be generated quickly on a personal computer. Other "objective" forms of guidance tracked in the contest are 1) the consensus forecast (i.e., the average of the forecasts from all of the human participants), 2) the combination of NGM raw output (for precipitation forecasts) and NGM MOS guidance (for temperature forecasts), and 3) the combination of Eta Model raw output (for precipitation forecasts) and AVN MOS guidance (for temperature forecasts).Results show that the advanced MOS system finished in 20th place out of 737 original entrants, or better than approximately 97% of the human forecasters who entered the contest. Moreover, the advanced MOS system was slightly better than consensus (23d place). The fact that an objective forecast system finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable "opponent" in forecast competitions. Equally significant is that the advanced MOS system was superior to the traditional guidance products available from the National Centers for Environmental Prediction (NCEP). Specifically, the combination of NGM raw output and NGM MOS guidance finished in 175th place, and the combination of Eta Model raw output and AVN MOS guidance finished in 266th place. The latter result is most intriguing since the proposed elimination of all NGM products would likely result in a serious degradation of objective products disseminated by NCEP, unless they are replaced with equal or better substitutes. On the other hand, the positive performance of the prototype advanced MOS system shows that it is possible to create a single objective product that is not only superior to currently available objective guidance products, but is also on par with some of the better human forecasters.
Improved system integration for integrated gasification combined cycle (IGCC) systems.

PubMed

Frey, H Christopher; Zhu, Yunhua

2006-03-01

Integrated gasification combined cycle (IGCC) systems are a promising technology for power generation. They include an air separation unit (ASU), a gasification system, and a gas turbine combined cycle power block, and feature competitive efficiency and lower emissions compared to conventional power generation technology. IGCC systems are not yet in widespread commercial use and opportunities remain to improve system feasibility via improved process integration. A process simulation model was developed for IGCC systems with alternative types of ASU and gas turbine integration. The model is applied to evaluate integration schemes involving nitrogen injection, air extraction, and combinations of both, as well as different ASU pressure levels. The optimal nitrogen injection only case in combination with an elevated pressure ASU had the highest efficiency and power output and approximately the lowest emissions per unit output of all cases considered, and thus is a recommended design option. The optimal combination of air extraction coupled with nitrogen injection had slightly worse efficiency, power output, and emissions than the optimal nitrogen injection only case. Air extraction alone typically produced lower efficiency, lower power output, and higher emissions than all other cases. The recommended nitrogen injection only case is estimated to provide annualized cost savings compared to a nonintegrated design. Process simulation modeling is shown to be a useful tool for evaluation and screening of technology options.
Neural network system for purposeful behavior based on foveal visual preprocessor

NASA Astrophysics Data System (ADS)

Golovan, Alexander V.; Shevtsova, Natalia A.; Klepatch, Arkadi A.

1996-10-01

Biologically plausible model of the system with an adaptive behavior in a priori environment and resistant to impairment has been developed. The system consists of input, learning, and output subsystems. The first subsystems classifies input patterns presented as n-dimensional vectors in accordance with some associative rule. The second one being a neural network determines adaptive responses of the system to input patterns. Arranged neural groups coding possible input patterns and appropriate output responses are formed during learning by means of negative reinforcement. Output subsystem maps a neural network activity into the system behavior in the environment. The system developed has been studied by computer simulation imitating a collision-free motion of a mobile robot. After some learning period the system 'moves' along a road without collisions. It is shown that in spite of impairment of some neural network elements the system functions reliably after relearning. Foveal visual preprocessor model developed earlier has been tested to form a kind of visual input to the system.
Estimating the Uncertain Mathematical Structure of Hydrological Model via Bayesian Data Assimilation

NASA Astrophysics Data System (ADS)

Bulygina, N.; Gupta, H.; O'Donell, G.; Wheater, H.

2008-12-01

The structure of hydrological model at macro scale (e.g. watershed) is inherently uncertain due to many factors, including the lack of a robust hydrological theory at the macro scale. In this work, we assume that a suitable conceptual model for the hydrologic system has already been determined - i.e., the system boundaries have been specified, the important state variables and input and output fluxes to be included have been selected, and the major hydrological processes and geometries of their interconnections have been identified. The structural identification problem then is to specify the mathematical form of the relationships between the inputs, state variables and outputs, so that a computational model can be constructed for making simulations and/or predictions of system input-state-output behaviour. We show how Bayesian data assimilation can be used to merge both prior beliefs in the form of pre-assumed model equations with information derived from the data to construct a posterior model. The approach, entitled Bayesian Estimation of Structure (BESt), is used to estimate a hydrological model for a small basin in England, at hourly time scales, conditioned on the assumption of 3-dimensional state - soil moisture storage, fast and slow flow stores - conceptual model structure. Inputs to the system are precipitation and potential evapotranspiration, and outputs are actual evapotranspiration and streamflow discharge. Results show the difference between prior and posterior mathematical structures, as well as provide prediction confidence intervals that reflect three types of uncertainty: due to initial conditions, due to input and due to mathematical structure.
Use of observational and model-derived fields and regime model output statistics in mesoscale forecasting

NASA Technical Reports Server (NTRS)

Forbes, G. S.; Pielke, R. A.

1985-01-01

Various empirical and statistical weather-forecasting studies which utilize stratification by weather regime are described. Objective classification was used to determine weather regime in some studies. In other cases the weather pattern was determined on the basis of a parameter representing the physical and dynamical processes relevant to the anticipated mesoscale phenomena, such as low level moisture convergence and convective precipitation, or the Froude number and the occurrence of cold-air damming. For mesoscale phenomena already in existence, new forecasting techniques were developed. The use of cloud models in operational forecasting is discussed. Models to calculate the spatial scales of forcings and resultant response for mesoscale systems are presented. The use of these models to represent the climatologically most prevalent systems, and to perform case-by-case simulations is reviewed. Operational implementation of mesoscale data into weather forecasts, using both actual simulation output and method-output statistics is discussed.
Analysis of transverse field distributions in Porro prism resonators

NASA Astrophysics Data System (ADS)

Litvin, Igor A.; Burger, Liesl; Forbes, Andrew

2007-05-01

A model to describe the transverse field distribution of the output beam from porro prism resonators is proposed. The model allows the prediction of the output transverse field distribution by assuming that the main areas of loss are located at the apexes of the porro prisms. Experimental work on a particular system showed some interested correlations between the time domain behavior of the resonator and the transverse field output. These findings are presented and discussed.
Closed-loop model identification of cooperative manipulators holding deformable objects

NASA Astrophysics Data System (ADS)

Alkathiri, A. A.; Akmeliawati, R.; Azlan, N. Z.

2017-11-01

This paper presents system identification to obtain the closed-loop models of a couple of cooperative manipulators in a system, which function to hold deformable objects. The system works using the master-slave principle. In other words, one of the manipulators is position-controlled through encoder feedback, while a force sensor gives feedback to the other force-controlled manipulator. Using the closed-loop input and output data, the closed-loop models, which are useful for model-based control design, are estimated. The criteria for model validation are a 95% fit between the measured and simulated output of the estimated models and residual analysis. The results show that for both position and force control respectively, the fits are 95.73% and 95.88%.
Use of output from high-resolution atmospheric models in landscape-scale hydrologic models: An assessment

USGS Publications Warehouse

Hostetler, S.W.; Giorgi, F.

1993-01-01

In this paper we investigate the feasibility of coupling regional climate models (RCMs) with landscape-scale hydrologic models (LSHMs) for studies of the effects of climate on hydrologic systems. The RCM used is the National Center for Atmospheric Research/Pennsylvania State University mesoscale model (MM4). Output from two year-round simulations (1983 and 1988) over the western United States is used to drive a lake model for Pyramid Lake in Nevada and a streamfiow model for Steamboat Creek in Oregon. Comparisons with observed data indicate that MM4 is able to produce meteorologic data sets that can be used to drive hydrologic models. Results from the lake model simulations indicate that the use of MM4 output produces reasonably good predictions of surface temperature and evaporation. Results from the streamflow simulations indicate that the use of MM4 output results in good simulations of the seasonal cycle of streamflow, but deficiencies in simulated wintertime precipitation resulted in underestimates of streamflow and soil moisture. Further work with climate (multiyear) simulations is necessary to achieve a complete analysis, but the results from this study indicate that coupling of LSHMs and RCMs may be a useful approach for evaluating the effects of climate change on hydrologic systems.
A biocatalytic cascade with several output signals—towards biosensors with different levels of confidence

PubMed Central

Guz, Nataliia; Halámek, Jan; Rusling, James F.; Katz, Evgeny

2014-01-01

The biocatalytic cascade based on enzyme-catalyzed reactions activated by several biomolecular input signals and producing output signal after each reaction step was developed as an example of a logically reversible information processing system. The model system was designed to mimic the operation of concatenated AND logic gates with optically readable output signals generated at each step of the logic operation. Implications include concurrent bioanalyses and data interpretation for medical diagnostics. PMID:24748446
System capacity and economic modeling computer tool for satellite mobile communications systems

NASA Technical Reports Server (NTRS)

Wiedeman, Robert A.; Wen, Doong; Mccracken, Albert G.

1988-01-01

A unique computer modeling tool that combines an engineering tool with a financial analysis program is described. The resulting combination yields a flexible economic model that can predict the cost effectiveness of various mobile systems. Cost modeling is necessary in order to ascertain if a given system with a finite satellite resource is capable of supporting itself financially and to determine what services can be supported. Personal computer techniques using Lotus 123 are used for the model in order to provide as universal an application as possible such that the model can be used and modified to fit many situations and conditions. The output of the engineering portion of the model consists of a channel capacity analysis and link calculations for several qualities of service using up to 16 types of earth terminal configurations. The outputs of the financial model are a revenue analysis, an income statement, and a cost model validation section.
Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks.

PubMed

Hovakimyan, N; Nardi, F; Calise, A; Kim, Nakwan

2002-01-01

We consider adaptive output feedback control of uncertain nonlinear systems, in which both the dynamics and the dimension of the regulated system may be unknown. However, the relative degree of the regulated output is assumed to be known. Given a smooth reference trajectory, the problem is to design a controller that forces the system measurement to track it with bounded errors. The classical approach requires a state observer. Finding a good observer for an uncertain nonlinear system is not an obvious task. We argue that it is sufficient to build an observer for the output tracking error. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. The theoretical results are illustrated in the design of a controller for a fourth-order nonlinear system of relative degree two and a high-bandwidth attitude command system for a model R-50 helicopter.
Identification of Low Order Equivalent System Models From Flight Test Data

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2000-01-01

Identification of low order equivalent system dynamic models from flight test data was studied. Inputs were pilot control deflections, and outputs were aircraft responses, so the models characterized the total aircraft response including bare airframe and flight control system. Theoretical investigations were conducted and related to results found in the literature. Low order equivalent system modeling techniques using output error and equation error parameter estimation in the frequency domain were developed and validated on simulation data. It was found that some common difficulties encountered in identifying closed loop low order equivalent system models from flight test data could be overcome using the developed techniques. Implications for data requirements and experiment design were discussed. The developed methods were demonstrated using realistic simulation cases, then applied to closed loop flight test data from the NASA F-18 High Alpha Research Vehicle.
Petri net modelling of buffers in automated manufacturing systems.

PubMed

Zhou, M; Dicesare, F

1996-01-01

This paper presents Petri net models of buffers and a methodology by which buffers can be included in a system without introducing deadlocks or overflows. The context is automated manufacturing. The buffers and models are classified as random order or order preserved (first-in-first-out or last-in-first-out), single-input-single-output or multiple-input-multiple-output, part type and/or space distinguishable or indistinguishable, and bounded or safe. Theoretical results for the development of Petri net models which include buffer modules are developed. This theory provides the conditions under which the system properties of boundedness, liveness, and reversibility are preserved. The results are illustrated through two manufacturing system examples: a multiple machine and multiple buffer production line and an automatic storage and retrieval system in the context of flexible manufacturing.
A downscaling scheme for atmospheric variables to drive soil-vegetation-atmosphere transfer models

NASA Astrophysics Data System (ADS)

Schomburg, A.; Venema, V.; Lindau, R.; Ament, F.; Simmer, C.

2010-09-01

For driving soil-vegetation-transfer models or hydrological models, high-resolution atmospheric forcing data is needed. For most applications the resolution of atmospheric model output is too coarse. To avoid biases due to the non-linear processes, a downscaling system should predict the unresolved variability of the atmospheric forcing. For this purpose we derived a disaggregation system consisting of three steps: (1) a bi-quadratic spline-interpolation of the low-resolution data, (2) a so-called `deterministic' part, based on statistical rules between high-resolution surface variables and the desired atmospheric near-surface variables and (3) an autoregressive noise-generation step. The disaggregation system has been developed and tested based on high-resolution model output (400m horizontal grid spacing). A novel automatic search-algorithm has been developed for deriving the deterministic downscaling rules of step 2. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying downscaling step 1 and 2, root mean square errors are decreased. Step 3 finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. The scheme can be applied to the output of atmospheric models, both for stand-alone offline simulations, and a fully coupled model system.
Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control.

PubMed

Chai, Tianyou; Zhang, Yajun; Wang, Hong; Su, Chun-Yi; Sun, Jing

2011-12-01

For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.
"One-Stop Shopping" for Ocean Remote-Sensing and Model Data

NASA Technical Reports Server (NTRS)

Li, P. Peggy; Vu, Quoc; Chao, Yi; Li, Zhi-Jin; Choi, Jei-Kook

2006-01-01

OurOcean Portal 2.0 (http:// ourocean.jpl.nasa.gov) is a software system designed to enable users to easily gain access to ocean observation data, both remote-sensing and in-situ, configure and run an Ocean Model with observation data assimilated on a remote computer, and visualize both the observation data and the model outputs. At present, the observation data and models focus on the California coastal regions and Prince William Sound in Alaska. This system can be used to perform both real-time and retrospective analyses of remote-sensing data and model outputs. OurOcean Portal 2.0 incorporates state-of-the-art information technologies (IT) such as MySQL database, Java Web Server (Apache/Tomcat), Live Access Server (LAS), interactive graphics with Java Applet at the Client site and MatLab/GMT at the server site, and distributed computing. OurOcean currently serves over 20 real-time or historical ocean data products. The data are served in pre-generated plots or their native data format. For some of the datasets, users can choose different plotting parameters and produce customized graphics. OurOcean also serves 3D Ocean Model outputs generated by ROMS (Regional Ocean Model System) using LAS. The Live Access Server (LAS) software, developed by the Pacific Marine Environmental Laboratory (PMEL) of the National Oceanic and Atmospheric Administration (NOAA), is a configurable Web-server program designed to provide flexible access to geo-referenced scientific data. The model output can be views as plots in horizontal slices, depth profiles or time sequences, or can be downloaded as raw data in different data formats, such as NetCDF, ASCII, Binary, etc. The interactive visualization is provided by graphic software, Ferret, also developed by PMEL. In addition, OurOcean allows users with minimal computing resources to configure and run an Ocean Model with data assimilation on a remote computer. Users may select the forcing input, the data to be assimilated, the simulation period, and the output variables and submit the model to run on a backend parallel computer. When the run is complete, the output will be added to the LAS server for
Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

NASA Astrophysics Data System (ADS)

Kumar, Sujay V.; Wang, Shugong; Mocko, David M.; Peters-Lidard, Christa D.; Xia, Youlong

2017-11-01

Multimodel ensembles are often used to produce ensemble mean estimates that tend to have increased simulation skill over any individual model output. If multimodel outputs are too similar, an individual LSM would add little additional information to the multimodel ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. The article presents a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multimodel ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/upgraded models that are under consideration for the next phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture, snow water equivalent, and terrestrial water storage. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer time scales. Trade-offs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity-accuracy space compared to the new LSMs. The results of the article indicate that simultaneous consideration of model similarity and accuracy at the relevant time scales is necessary in the development of multimodel ensemble.

Characterization of Magma-Driven Hydrothermal Systems at Oceanic Spreading Centers

NASA Astrophysics Data System (ADS)

Farough, A.; Lowell, R. P.; Corrigan, R.

2012-12-01

Fluid circulation in high-temperature hydrothermal systems involves complex water-rock chemical reactions and phase separation. Numerical modeling of reactive transport in multi-component, multiphase systems is required to obtain a full understanding of the characteristics and evolution of hydrothermal vent systems. We use a single-pass parameterized model of high-temperature hydrothermal circulation at oceanic spreading centers constrained by observational parameters such as vent temperature, heat output, and vent field area, together with surface area and depth of the sub-axial magma chamber, to deduce fundamental hydrothermal parameters such as mass flow rate, bulk permeability, conductive boundary layer thickness at the base of the system, magma replenishment rate, and residence time in the discharge zone. All of these key subsurface characteristics are known for fewer than 10 sites out of 300 known hydrothermal systems. The principal limitations of this approach stem from the uncertainty in heat output and vent field area. For systems where data are available on partitioning of heat and chemical output between focused and diffuse flow, we determined the fraction of high-temperature vent fluid incorporated into diffuse flow using a two-limb single pass model. For EPR 9°50` N and ASHES, the diffuse flow temperatures calculated assuming conservative mixing are nearly equal to the observed temperatures indicating that approximately 80%-90% of the hydrothermal heat output occurs as high-temperature flow derived from magmatic heat even though most of the heat output appears as low-temperature diffuse discharge. For the Main Endeavour Field and Lucky Strike, diffuse flow fluids show significant conductive cooling and heating respectively. Finally, we calculate the transport of various geochemical constituents in focused and diffuse flow at the vent field scale and compare the results with estimates of geochemical transports from the Rainbow hydrothermal field where diffuse flow is absent.
Assimilation of a knowledge base and physical models to reduce errors in passive-microwave classifications of sea ice

NASA Technical Reports Server (NTRS)

Maslanik, J. A.; Key, J.

1992-01-01

An expert system framework has been developed to classify sea ice types using satellite passive microwave data, an operational classification algorithm, spatial and temporal information, ice types estimated from a dynamic-thermodynamic model, output from a neural network that detects the onset of melt, and knowledge about season and region. The rule base imposes boundary conditions upon the ice classification, modifies parameters in the ice algorithm, determines a `confidence' measure for the classified data, and under certain conditions, replaces the algorithm output with model output. Results demonstrate the potential power of such a system for minimizing overall error in the classification and for providing non-expert data users with a means of assessing the usefulness of the classification results for their applications.
Method and system for monitoring and displaying engine performance parameters

NASA Technical Reports Server (NTRS)

Abbott, Terence S. (Inventor); Person, Jr., Lee H. (Inventor)

1991-01-01

The invention is a method and system for monitoring and directly displaying the actual thrust produced by a jet aircraft engine under determined operating conditions and the available thrust and predicted (commanded) thrust of a functional model of an ideal engine under the same determined operating conditions. A first set of actual value output signals representative of a plurality of actual performance parameters of the engine under the determined operating conditions is generated and compared with a second set of predicted value output signals representative of the predicted value of corresponding performance parameters of a functional model of the engine under the determined operating conditions to produce a third set of difference value output signals within a range of normal, caution, or warning limit values. A thrust indicator displays when any one of the actual value output signals is in the warning range while shaping function means shape each of the respective difference output signals as each approaches the limit of the respective normal, caution, and warning range limits.
Bias correction of temperature produced by the Community Climate System Model using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Moghim, S.; Hsu, K.; Bras, R. L.

2013-12-01

General Circulation Models (GCMs) are used to predict circulation and energy transfers between the atmosphere and the land. It is known that these models produce biased results that will have impact on their uses. This work proposes a new method for bias correction: the equidistant cumulative distribution function-artificial neural network (EDCDFANN) procedure. The method uses artificial neural networks (ANNs) as a surrogate model to estimate bias-corrected temperature, given an identification of the system derived from GCM models output variables. A two-layer feed forward neural network is trained with observations during a historical period and then the adjusted network can be used to predict bias-corrected temperature for future periods. To capture the extreme values this method is combined with the equidistant CDF matching method (EDCDF, Li et al. 2010). The proposed method is tested with the Community Climate System Model (CCSM3) outputs using air and skin temperature, specific humidity, shortwave and longwave radiation as inputs to the ANN. This method decreases the mean square error and increases the spatial correlation between the modeled temperature and the observed one. The results indicate the EDCDFANN has potential to remove the biases of the model outputs.
Experimental feedback linearisation of a vibrating system with a non-smooth nonlinearity

NASA Astrophysics Data System (ADS)

Lisitano, D.; Jiffri, S.; Bonisoli, E.; Mottershead, J. E.

2018-03-01

Input-output partial feedback linearisation is demonstrated experimentally for the first time on a system with non-smooth nonlinearity, a laboratory three degrees of freedom lumped mass system with a piecewise-linear spring. The output degree of freedom is located away from the nonlinearity so that the partial feedback linearisation possesses nonlinear internal dynamics. The dynamic behaviour of the linearised part is specified by eigenvalue assignment and an investigation of the zero dynamics is carried out to confirm stability of the overall system. A tuned numerical model is developed for use in the controller and to produce numerical outputs for comparison with experimental closed-loop results. A new limitation of the feedback linearisation method is discovered in the case of lumped mass systems - that the input and output must share the same degrees of freedom.
System Identification Methods for Aircraft Flight Control Development and Validation

DOT National Transportation Integrated Search

1995-10-01

System-identification methods compose a mathematical model, or series of models, : from measurements of inputs and outputs of dynamic systems. This paper : discusses the use of frequency-domain system-identification methods for the : development and ...
Thermal and optical performance of encapsulation systems for flat-plate photovoltaic modules

NASA Technical Reports Server (NTRS)

Minning, C. P.; Coakley, J. F.; Perrygo, C. M.; Garcia, A., III; Cuddihy, E. F.

1981-01-01

The electrical power output from a photovoltaic module is strongly influenced by the thermal and optical characteristics of the module encapsulation system. Described are the methodology and computer model for performing fast and accurate thermal and optical evaluations of different encapsulation systems. The computer model is used to evaluate cell temperature, solar energy transmittance through the encapsulation system, and electric power output for operation in a terrestrial environment. Extensive results are presented for both superstrate-module and substrate-module design schemes which include different types of silicon cell materials, pottants, and antireflection coatings.
Fuel Cell Stack Testing and Durability in Support of Ion Tiger UAV

DTIC Science & Technology

2010-06-02

N00173-08-2-C008 specified. In June 2008, the first M250 stack 242503 data were incorporated into the PEMFC system model as a look-up data table...control and operational model which implements the operational strategy by controlling the power from the PEMFC systems and battery pack for a total...Outputs PEMFC System Outputs <~~>*<*,yrx**i~yc*r»>r-’+**^^ FCS_P«wi_Dwn«l (W) 10 15 20 25 BfOfumon PCM« Cwnind Wi ) 5 10 15
Non-fragile observer-based output feedback control for polytopic uncertain system under distributed model predictive control approach

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

NASA Astrophysics Data System (ADS)

Shahab, Shima; Leadenham, Stephen; Guillot, François; Sabra, Karim; Erturk, Alper

2014-04-01

This paper investigates low-power electricity generation from ultrasound acoustic wave energy transfer combined with piezoelectric energy harvesting for wireless applications ranging from medical implants to naval sensor systems. The focus is placed on an underwater system that consists of a pulsating source for spherical wave generation and a harvester connected to an external resistive load for quantifying the electrical power output. An analytical electro-acoustic model is developed to relate the source strength to the electrical power output of the harvester located at a specific distance from the source. The model couples the energy harvester dynamics (piezoelectric device and electrical load) with the source strength through the acoustic-structure interaction at the harvester-fluid interface. Case studies are given for a detailed understanding of the coupled system dynamics under various conditions. Specifically the relationship between the electrical power output and system parameters, such as the distance of the harvester from the source, dimensions of the harvester, level of source strength, and electrical load resistance are explored. Sensitivity of the electrical power output to the excitation frequency in the neighborhood of the harvester's underwater resonance frequency is also reported.
Documentation of model input and output values for simulation of pumping effects in Paradise Valley, a basin tributary to the Humboldt River, Humboldt County, Nevada

USGS Publications Warehouse

Carey, A.E.; Prudic, David E.

1996-01-01

Documentation is provided of model input and sample output used in a previous report for analysis of ground-water flow and simulated pumping scenarios in Paradise Valley, Humboldt County, Nevada.Documentation includes files containing input values and listings of sample output. The files, in American International Standard Code for Information Interchange (ASCII) or binary format, are compressed and put on a 3-1/2-inch diskette. The decompressed files require approximately 8.4 megabytes of disk space on an International Business Machine (IBM)- compatible microcomputer using the MicroSoft Disk Operating System (MS-DOS) operating system version 5.0 or greater.
HAWQS Beta Flyer

EPA Pesticide Factsheets

HAWQS is a web-based interactive water quantity and quality modeling system that provides users with interactive web interfaces and maps; pre-loaded input data; outputs that include tables, charts, graphs and raw output data; and a user guide.
HAWQS beta flyer

EPA Pesticide Factsheets

HAWQS is a web-based interactive water quantity and quality modeling system that provides users with interactive web interfaces and maps; pre-loaded input data; outputs that include tables, charts, graphs and raw output data; and a user guide.
Current Source Based on H-Bridge Inverter with Output LCL Filter

NASA Astrophysics Data System (ADS)

Blahnik, Vojtech; Talla, Jakub; Peroutka, Zdenek

2015-09-01

The paper deals with a control of current source with an LCL output filter. The controlled current source is realized as a single-phase inverter and output LCL filter provides low ripple of output current. However, systems incorporating LCL filters require more complex control strategies and there are several interesting approaches to the control of this type of converter. This paper presents the inverter control algorithm, which combines model based control with a direct current control based on resonant controllers and single-phase vector control. The primary goal is to reduce the current ripple and distortion under required limits and provides fast and precise control of output current. The proposed control technique is verified by measurements on the laboratory model.
Impact of device level faults in a digital avionic processor

NASA Technical Reports Server (NTRS)

Suk, Ho Kim

1989-01-01

This study describes an experimental analysis of the impact of gate and device-level faults in the processor of a Bendix BDX-930 flight control system. Via mixed mode simulation, faults were injected at the gate (stuck-at) and at the transistor levels and, their propagation through the chip to the output pins was measured. The results show that there is little correspondence between a stuck-at and a device-level fault model, as far as error activity or detection within a functional unit is concerned. In so far as error activity outside the injected unit and at the output pins are concerned, the stuck-at and device models track each other. The stuck-at model, however, overestimates, by over 100 percent, the probability of fault propagation to the output pins. An evaluation of the Mean Error Durations and the Mean Time Between Errors at the output pins shows that the stuck-at model significantly underestimates (by 62 percent) the impact of an internal chip fault on the output pins. Finally, the study also quantifies the impact of device fault by location, both internally and at the output pins.
Analysis of material flow in a utillzation technology of low grade manganese ore and sulphur coal complementary

NASA Astrophysics Data System (ADS)

Wang, Bo-Zhi; Deng, Biao; Su, Shi-Jun; Ding, Sang-Lan; Sun, Wei-Yi

2018-03-01

Electrolytic manganese is conventionally produced through low-grade manganese ore leaching in SO2, with the combustion of high sulfur coal. Subsequently the coal ash and manganese slag, produced by the combustion of high sulfur coal and preparation of electrolytic manganese, can be used as raw ingredients for the preparation of sulphoaluminate cement. In order to realize the `coal-electricity-sulfur-manganese-building material' system of complementary resource utilization, the conditions of material inflow and outflow in each process were determined using material flow analysis. The material flow models in each unit and process can be obtained by analyzed of material flow for new technology, and the input-output model could be obtained. Through the model, it is possible to obtain the quantity of all the input and output material in the condition of limiting the quantity of a substance. Taking one ton electrolytic manganese as a basis, the quantity of other input material and cements can be determined with the input-output model. The whole system had thusly achieved a cleaner production level. Therefore, the input-output model can be used for guidance in practical production.
User Guide and Documentation for Five MODFLOW Ground-Water Modeling Utility Programs

USGS Publications Warehouse

Banta, Edward R.; Paschke, Suzanne S.; Litke, David W.

2008-01-01

This report documents five utility programs designed for use in conjunction with ground-water flow models developed with the U.S. Geological Survey's MODFLOW ground-water modeling program. One program extracts calculated flow values from one model for use as input to another model. The other four programs extract model input or output arrays from one model and make them available in a form that can be used to generate an ArcGIS raster data set. The resulting raster data sets may be useful for visual display of the data or for further geographic data processing. The utility program GRID2GRIDFLOW reads a MODFLOW binary output file of cell-by-cell flow terms for one (source) model grid and converts the flow values to input flow values for a different (target) model grid. The spatial and temporal discretization of the two models may differ. The four other utilities extract selected 2-dimensional data arrays in MODFLOW input and output files and write them to text files that can be imported into an ArcGIS geographic information system raster format. These four utilities require that the model cells be square and aligned with the projected coordinate system in which the model grid is defined. The four raster-conversion utilities are * CBC2RASTER, which extracts selected stress-package flow data from a MODFLOW binary output file of cell-by-cell flows; * DIS2RASTER, which extracts cell-elevation data from a MODFLOW Discretization file; * MFBIN2RASTER, which extracts array data from a MODFLOW binary output file of head or drawdown; and * MULT2RASTER, which extracts array data from a MODFLOW Multiplier file.
Design and Characterization of a High Resolution Microfluidic Heat Flux Sensor with Thermal Modulation

PubMed Central

Nam, Sung-Ki; Kim, Jung-Kyun; Cho, Sung-Cheon; Lee, Sun-Kyu

2010-01-01

A complementary metal-oxide semiconductor-compatible process was used in the design and fabrication of a suspended membrane microfluidic heat flux sensor with a thermopile for the purpose of measuring the heat flow rate. The combination of a thirty-junction gold and nickel thermoelectric sensor with an ultralow noise preamplifier, a low pass filter, and a lock-in amplifier can yield a resolution 20 nW with a sensitivity of 461 V/W. The thermal modulation method is used to eliminate low-frequency noise from the sensor output, and various amounts of fluidic heat were applied to the sensor to investigate its suitability for microfluidic applications. For sensor design and analysis of signal output, a method of modeling and simulating electro-thermal behavior in a microfluidic heat flux sensor with an integrated electronic circuit is presented and validated. The electro-thermal domain model was constructed by using system dynamics, particularly the bond graph. The electro-thermal domain system model in which the thermal and the electrical domains are coupled expresses the heat generation of samples and converts thermal input to electrical output. The proposed electro-thermal domain system model is in good agreement with the measured output voltage response in both the transient and the steady state. PMID:22163568
ANALYTICAL MODELING OF ELECTRON BACK-BOMBARDMENT INDUCED CURRENT INCREASE IN UN-GATED THERMIONIC CATHODE RF GUNS

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

Edelen, J. P.; Sun, Y.; Harris, J. R.

In this paper we derive analytical expressions for the output current of an un-gated thermionic cathode RF gun in the presence of back-bombardment heating. We provide a brief overview of back-bombardment theory and discuss comparisons between the analytical back-bombardment predictions and simulation models. We then derive an expression for the output current as a function of the RF repetition rate and discuss relationships between back-bombardment, fieldenhancement, and output current. We discuss in detail the relevant approximations and then provide predictions about how the output current should vary as a function of repetition rate for some given system configurations.
Input-output identification of controlled discrete manufacturing systems

NASA Astrophysics Data System (ADS)

Estrada-Vargas, Ana Paula; López-Mellado, Ernesto; Lesage, Jean-Jacques

2014-03-01

The automated construction of discrete event models from observations of external system's behaviour is addressed. This problem, often referred to as system identification, allows obtaining models of ill-known (or even unknown) systems. In this article, an identification method for discrete event systems (DESs) controlled by a programmable logic controller is presented. The method allows processing a large quantity of observed long sequences of input/output signals generated by the controller and yields an interpreted Petri net model describing the closed-loop behaviour of the automated DESs. The proposed technique allows the identification of actual complex systems because it is sufficiently efficient and well adapted to cope with both the technological characteristics of industrial controllers and data collection requirements. Based on polynomial-time algorithms, the method is implemented as an efficient software tool which constructs and draws the model automatically; an overview of this tool is given through a case study dealing with an automated manufacturing system.

An integrated prediction and optimization model of biogas production system at a wastewater treatment facility.

PubMed

Akbaş, Halil; Bilgen, Bilge; Turhan, Aykut Melih

2015-11-01

This study proposes an integrated prediction and optimization model by using multi-layer perceptron neural network and particle swarm optimization techniques. Three different objective functions are formulated. The first one is the maximization of methane percentage with single output. The second one is the maximization of biogas production with single output. The last one is the maximization of biogas quality and biogas production with two outputs. Methane percentage, carbon dioxide percentage, and other contents' percentage are used as the biogas quality criteria. Based on the formulated models and data from a wastewater treatment facility, optimal values of input variables and their corresponding maximum output values are found out for each model. It is expected that the application of the integrated prediction and optimization models increases the biogas production and biogas quality, and contributes to the quantity of electricity production at the wastewater treatment facility. Copyright © 2015 Elsevier Ltd. All rights reserved.
Input-output characterization of an ultrasonic testing system by digital signal analysis

NASA Technical Reports Server (NTRS)

Karaguelle, H.; Lee, S. S.; Williams, J., Jr.

1984-01-01

The input/output characteristics of an ultrasonic testing system used for stress wave factor measurements were studied. The fundamentals of digital signal processing are summarized. The inputs and outputs are digitized and processed in a microcomputer using digital signal processing techniques. The entire ultrasonic test system, including transducers and all electronic components, is modeled as a discrete-time linear shift-invariant system. Then the impulse response and frequency response of the continuous time ultrasonic test system are estimated by interpolating the defining points in the unit sample response and frequency response of the discrete time system. It is found that the ultrasonic test system behaves as a linear phase bandpass filter. Good results were obtained for rectangular pulse inputs of various amplitudes and durations and for tone burst inputs whose center frequencies are within the passband of the test system and for single cycle inputs of various amplitudes. The input/output limits on the linearity of the system are determined.
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.
Supervisory Control of Discrete Event Systems Modeled by Mealy Automata with Nondeterministic Output Functions

NASA Astrophysics Data System (ADS)

Ushio, Toshimitsu; Takai, Shigemasa

Supervisory control is a general framework of logical control of discrete event systems. A supervisor assigns a set of control-disabled controllable events based on observed events so that the controlled discrete event system generates specified languages. In conventional supervisory control, it is assumed that observed events are determined by internal events deterministically. But, this assumption does not hold in a discrete event system with sensor errors and a mobile system, where each observed event depends on not only an internal event but also a state just before the occurrence of the internal event. In this paper, we model such a discrete event system by a Mealy automaton with a nondeterministic output function. We introduce two kinds of supervisors: one assigns each control action based on a permissive policy and the other based on an anti-permissive one. We show necessary and sufficient conditions for the existence of each supervisor. Moreover, we discuss the relationship between the supervisors in the case that the output function is determinisitic.
Implications of freshwater flux data from the CMIP5 multimodel output across a set of Northern Hemisphere drainage basins

NASA Astrophysics Data System (ADS)

Bring, Arvid; Asokan, Shilpa M.; Jaramillo, Fernando; Jarsjö, Jerker; Levi, Lea; Pietroń, Jan; Prieto, Carmen; Rogberg, Peter; Destouni, Georgia

2015-06-01

The multimodel ensemble of the Coupled Model Intercomparison Project, Phase 5 (CMIP5) synthesizes the latest research in global climate modeling. The freshwater system on land, particularly runoff, has so far been of relatively low priority in global climate models, despite the societal and ecosystem importance of freshwater changes, and the science and policy needs for such model output on drainage basin scales. Here we investigate the implications of CMIP5 multimodel ensemble output data for the freshwater system across a set of drainage basins in the Northern Hemisphere. Results of individual models vary widely, with even ensemble mean results differing greatly from observations and implying unrealistic long-term systematic changes in water storage and level within entire basins. The CMIP5 projections of basin-scale freshwater fluxes differ considerably more from observations and among models for the warm temperate study basins than for the Arctic and cold temperate study basins. In general, the results call for concerted research efforts and model developments for improving the understanding and modeling of the freshwater system and its change drivers. Specifically, more attention to basin-scale water flux analyses should be a priority for climate model development, and an important focus for relevant model-based advice for adaptation to climate change.
Documentation of the dynamic parameter, water-use, stream and lake flow routing, and two summary output modules and updates to surface-depression storage simulation and initial conditions specification options with the Precipitation-Runoff Modeling System (PRMS)

USGS Publications Warehouse

Regan, R. Steve; LaFontaine, Jacob H.

2017-10-05

This report documents seven enhancements to the U.S. Geological Survey (USGS) Precipitation-Runoff Modeling System (PRMS) hydrologic simulation code: two time-series input options, two new output options, and three updates of existing capabilities. The enhancements are (1) new dynamic parameter module, (2) new water-use module, (3) new Hydrologic Response Unit (HRU) summary output module, (4) new basin variables summary output module, (5) new stream and lake flow routing module, (6) update to surface-depression storage and flow simulation, and (7) update to the initial-conditions specification. This report relies heavily upon U.S. Geological Survey Techniques and Methods, book 6, chapter B7, which documents PRMS version 4 (PRMS-IV). A brief description of PRMS is included in this report.
The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

PubMed

Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

2013-01-01

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.
Output feedback regulator design for jet engine control systems

NASA Technical Reports Server (NTRS)

Merrill, W. C.

1977-01-01

A multivariable control design procedure based on the output feedback regulator formulation is described and applied to turbofan engine model. Full order model dynamics, were incorporated in the example design. The effect of actuator dynamics on closed loop performance was investigaged. Also, the importance of turbine inlet temperature as an element of the dynamic feedback was studied. Step responses were given to indicate the improvement in system performance with this control. Calculation times for all experiments are given in CPU seconds for comparison purposes.
Evaluating Multi-Input/Multi-Output Digital Control Systems

NASA Technical Reports Server (NTRS)

Pototzky, Anthony S.; Wieseman, Carol D.; Hoadley, Sherwood T.; Mukhopadhyay, Vivek

1994-01-01

Controller-performance-evaluation (CPE) methodology for multi-input/multi-output (MIMO) digital control systems developed. Procedures identify potentially destabilizing controllers and confirm satisfactory performance of stabilizing ones. Methodology generic and used in many types of multi-loop digital-controller applications, including digital flight-control systems, digitally controlled spacecraft structures, and actively controlled wind-tunnel models. Also applicable to other complex, highly dynamic digital controllers, such as those in high-performance robot systems.
Thermospheric dynamics - A system theory approach

NASA Technical Reports Server (NTRS)

Codrescu, M.; Forbes, J. M.; Roble, R. G.

1990-01-01

A system theory approach to thermospheric modeling is developed, based upon a linearization method which is capable of preserving nonlinear features of a dynamical system. The method is tested using a large, nonlinear, time-varying system, namely the thermospheric general circulation model (TGCM) of the National Center for Atmospheric Research. In the linearized version an equivalent system, defined for one of the desired TGCM output variables, is characterized by a set of response functions that is constructed from corresponding quasi-steady state and unit sample response functions. The linearized version of the system runs on a personal computer and produces an approximation of the desired TGCM output field height profile at a given geographic location.
Adaptive model predictive process control using neural networks

DOEpatents

Buescher, K.L.; Baum, C.C.; Jones, R.D.

1997-08-19

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
Adaptive model predictive process control using neural networks

DOEpatents

Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

1997-01-01

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.
Re-using biological devices: a model-aided analysis of interconnected transcriptional cascades designed from the bottom-up.

PubMed

Pasotti, Lorenzo; Bellato, Massimo; Casanova, Michela; Zucca, Susanna; Cusella De Angelis, Maria Gabriella; Magni, Paolo

2017-01-01

The study of simplified, ad-hoc constructed model systems can help to elucidate if quantitatively characterized biological parts can be effectively re-used in composite circuits to yield predictable functions. Synthetic systems designed from the bottom-up can enable the building of complex interconnected devices via rational approach, supported by mathematical modelling. However, such process is affected by different, usually non-modelled, unpredictability sources, like cell burden. Here, we analyzed a set of synthetic transcriptional cascades in Escherichia coli . We aimed to test the predictive power of a simple Hill function activation/repression model (no-burden model, NBM) and of a recently proposed model, including Hill functions and the modulation of proteins expression by cell load (burden model, BM). To test the bottom-up approach, the circuit collection was divided into training and test sets, used to learn individual component functions and test the predicted output of interconnected circuits, respectively. Among the constructed configurations, two test set circuits showed unexpected logic behaviour. Both NBM and BM were able to predict the quantitative output of interconnected devices with expected behaviour, but only the BM was also able to predict the output of one circuit with unexpected behaviour. Moreover, considering training and test set data together, the BM captures circuits output with higher accuracy than the NBM, which is unable to capture the experimental output exhibited by some of the circuits even qualitatively. Finally, resource usage parameters, estimated via BM, guided the successful construction of new corrected variants of the two circuits showing unexpected behaviour. Superior descriptive and predictive capabilities were achieved considering resource limitation modelling, but further efforts are needed to improve the accuracy of models for biological engineering.
Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System

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

Goldhaber, Steve; Holland, Marika

The major goal of this project was to contribute improvements to the infrastructure of an Earth System Model in order to support research in the Multiscale Methods for Accurate, Efficient, and Scale-Aware models of the Earth System project. In support of this, the NCAR team accomplished two main tasks: improving input/output performance of the model and improving atmospheric model simulation quality. Improvement of the performance and scalability of data input and diagnostic output within the model required a new infrastructure which can efficiently handle the unstructured grids common in multiscale simulations. This allows for a more computationally efficient model, enablingmore » more years of Earth System simulation. The quality of the model simulations was improved by reducing grid-point noise in the spectral element version of the Community Atmosphere Model (CAM-SE). This was achieved by running the physics of the model using grid-cell data on a finite-volume grid.« less
Analysis of model output and science data in the Virtual Model Repository (VMR).

NASA Astrophysics Data System (ADS)

De Zeeuw, D.; Ridley, A. J.

2014-12-01

Big scientific data not only includes large repositories of data from scientific platforms like satelites and ground observation, but also the vast output of numerical models. The Virtual Model Repository (VMR) provides scientific analysis and visualization tools for a many numerical models of the Earth-Sun system. Individual runs can be analyzed in the VMR and compared to relevant data through relevant metadata, but larger collections of runs can also now be studied and statistics generated on the accuracy and tendancies of model output. The vast model repository at the CCMC with over 1000 simulations of the Earth's magnetosphere was used to look at overall trends in accuracy when compared to satelites such as GOES, Geotail, and Cluster. Methodology for this analysis as well as case studies will be presented.
Modeling a dielectric elastomer as driven by triboelectric nanogenerator

NASA Astrophysics Data System (ADS)

Chen, Xiangyu; Jiang, Tao; Wang, Zhong Lin

2017-01-01

By integrating a triboelectric nanogenerator (TENG) and a thin film dielectric elastomer actuator (DEA), the DEA can be directly powered and controlled by the output of the TENG, which demonstrates a self-powered actuation system toward various practical applications in the fields of electronic skin and soft robotics. This paper describes a method to construct a physical model for this integrated TENG-DEA system on the basis of nonequilibrium thermodynamics and electrostatics induction theory. The model can precisely simulate the influences from both the viscoelasticity and current leakage to the output performance of the TENG, which can help us to better understand the interaction between TENG and DEA devices. Accordingly, the established electric field, the deformation strain of the DEA, and the output current from the TENG are systemically analyzed by using this model. A comparison between real measurements and simulation results confirms that the proposed model can predict the dynamic response of the DEA driven by contact-electrification and can also quantitatively analyze the relaxation of the tribo-induced strain due to the leakage behavior. Hence, the proposed model in this work could serve as a guidance for optimizing the devices in the future studies.
The Ecological Model Web Concept: A Consultative Infrastructure for Decision Makers and Researchers

NASA Astrophysics Data System (ADS)

Geller, G.; Nativi, S.

2011-12-01

Rapid climate and socioeconomic changes may be outrunning society's ability to understand, predict, and respond to change effectively. Decision makers want better information about what these changes will be and how various resources will be affected, while researchers want better understanding of the components and processes of ecological systems, how they interact, and how they respond to change. Although there are many excellent models in ecology and related disciplines, there is only limited coordination among them, and accessible, openly shared models or model systems that can be consulted to gain insight on important ecological questions or assist with decision-making are rare. A "consultative infrastructure" that increased access to and sharing of models and model outputs would benefit decision makers, researchers, as well as modelers. Of course, envisioning such an ambitious system is much easier than building it, but several complementary approaches exist that could contribute. The one discussed here is called the Model Web. This is a concept for an open-ended system of interoperable computer models and databases based on making models and their outputs available as services ("model as a service"). Initially, it might consist of a core of several models from which it could grow gradually as new models or databases were added. However, a model web would not be a monolithic, rigidly planned and built system--instead, like the World Wide Web, it would grow largely organically, with limited central control, within a framework of broad goals and data exchange standards. One difference from the WWW is that a model web is much harder to create, and has more pitfalls, and thus is a long term vision. However, technology, science, observations, and models have advanced enough so that parts of an ecological model web can be built and utilized now, forming a framework for gradual growth as well as a broadly accessible infrastructure. Ultimately, the value of a model web lies in the increase in access to and sharing of both models and model outputs. By lowering access barriers to models and their outputs there is less reinvention, more efficient use of resources, greater interaction among researchers and across disciplines, as well as other benefits. The growth of such a system of models fits well with the concept and architecture of the Global Earth Observing System of Systems (GEOSS) as well as the Semantic Web. And, while framed here in the context of ecological forecasting, the same concept can be applied to any discipline utilizing models.
Comparative study of diode-pumped alkali vapor laser and exciplex-pumped alkali laser systems and selection principal of parameters

NASA Astrophysics Data System (ADS)

Huang, Wei; Tan, Rongqing; Li, Zhiyong; Han, Gaoce; Li, Hui

2017-03-01

A theoretical model based on common pump structure is proposed to analyze the output characteristics of a diode-pumped alkali vapor laser (DPAL) and XPAL (exciplex-pumped alkali laser). Cs-DPAL and Cs-Ar XPAL systems are used as examples. The model predicts that an optical-to-optical efficiency approaching 80% can be achieved for continuous-wave four- and five-level XPAL systems with broadband pumping, which is several times the pumped linewidth for DPAL. Operation parameters including pumped intensity, temperature, cell's length, mixed gas concentration, pumped linewidth, and output coupler are analyzed for DPAL and XPAL systems based on the kinetic model. In addition, the predictions of selection principal of temperature and cell's length are also presented. The concept of the equivalent "alkali areal density" is proposed. The result shows that the output characteristics with the same alkali areal density but different temperatures turn out to be equal for either the DPAL or the XPAL system. It is the areal density that reflects the potential of DPAL or XPAL systems directly. A more detailed analysis of similar influences of cavity parameters with the same areal density is also presented.
Development of model reference adaptive control theory for electric power plant control applications

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

Mabius, L.E.

1982-09-15

The scope of this effort includes the theoretical development of a multi-input, multi-output (MIMO) Model Reference Control (MRC) algorithm, (i.e., model following control law), Model Reference Adaptive Control (MRAC) algorithm and the formulation of a nonlinear model of a typical electric power plant. Previous single-input, single-output MRAC algorithm designs have been generalized to MIMO MRAC designs using the MIMO MRC algorithm. This MRC algorithm, which has been developed using Command Generator Tracker methodologies, represents the steady state behavior (in the adaptive sense) of the MRAC algorithm. The MRC algorithm is a fundamental component in the MRAC design and stability analysis.more » An enhanced MRC algorithm, which has been developed for systems with more controls than regulated outputs, alleviates the MRC stability constraint of stable plant transmission zeroes. The nonlinear power plant model is based on the Cromby model with the addition of a governor valve management algorithm, turbine dynamics and turbine interactions with extraction flows. An application of the MRC algorithm to a linearization of this model demonstrates its applicability to power plant systems. In particular, the generated power changes at 7% per minute while throttle pressure and temperature, reheat temperature and drum level are held constant with a reasonable level of control. The enhanced algorithm reduces significantly control fluctuations without modifying the output response.« less
Hierarchical multi-scale approach to validation and uncertainty quantification of hyper-spectral image modeling

NASA Astrophysics Data System (ADS)

Engel, Dave W.; Reichardt, Thomas A.; Kulp, Thomas J.; Graff, David L.; Thompson, Sandra E.

2016-05-01

Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensor level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.

Evaluation of simulated ocean carbon in the CMIP5 earth system models

NASA Astrophysics Data System (ADS)

Orr, James; Brockmann, Patrick; Seferian, Roland; Servonnat, Jérôme; Bopp, Laurent

2013-04-01

We maintain a centralized model output archive containing output from the previous generation of Earth System Models (ESMs), 7 models used in the IPCC AR4 assessment. Output is in a common format located on a centralized server and is publicly available through a web interface. Through the same interface, LSCE/IPSL has also made available output from the Coupled Model Intercomparison Project (CMIP5), the foundation for the ongoing IPCC AR5 assessment. The latter includes ocean biogeochemical fields from more than 13 ESMs. Modeling partners across 3 EU projects refer to the combined AR4-AR5 archive and comparison as OCMIP5, building on previous phases of OCMIP (Ocean Carbon Cycle Intercomparison Project) and making a clear link to IPCC AR5 (CMIP5). While now focusing on assessing the latest generation of results (AR5, CMIP5), this effort is also able to put them in context (AR4). For model comparison and evaluation, we have also stored computed derived variables (e.g., those needed to assess ocean acidification) and key fields regridded to a common 1°x1° grid, thus complementing the standard CMIP5 archive. The combined AR4-AR5 output (OCMIP5) has been used to compute standard quantitative metrics, both global and regional, and those have been synthesized with summary diagrams. In addition, for key biogeochemical fields we have deconvolved spatiotemporal components of the mean square error in order to constrain which models go wrong where. Here we will detail results from these evaluations which have exploited gridded climatological data. The archive, interface, and centralized evaluation provide a solid technical foundation, upon which collaboration and communication is being broadened in the ocean biogeochemical modeling community. Ultimately we aim to encourage wider use of the OCMIP5 archive.
Alternative to Ritt's pseudodivision for finding the input-output equations of multi-output models.

PubMed

Meshkat, Nicolette; Anderson, Chris; DiStefano, Joseph J

2012-09-01

Differential algebra approaches to structural identifiability analysis of a dynamic system model in many instances heavily depend upon Ritt's pseudodivision at an early step in analysis. The pseudodivision algorithm is used to find the characteristic set, of which a subset, the input-output equations, is used for identifiability analysis. A simpler algorithm is proposed for this step, using Gröbner Bases, along with a proof of the method that includes a reduced upper bound on derivative requirements. Efficacy of the new algorithm is illustrated with several biosystem model examples. Copyright © 2012 Elsevier Inc. All rights reserved.
Self tuning control of wind-diesel power systems

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

Mufti, M.D.; Balasubramanian, R.; Tripathy, S.C.

1995-12-31

This paper proposes some effective self-tuning control strategies for isolated Wind-Diesel power generation systems. Detailed modeling and studies on both single-input single-output (SISO) as well as multi-input multi-output (MIMO) self tuning regulators, applied to a typical system, are reported. Further, the effect of introducing a Super-conducting Magnetic Energy Storage (SMES) unit on the system performance has been investigated. The MIMO self-tuning regulator controlling the hybrid system and the SMES in a coordinated manner exhibits the best performance.
Experimental comparison of conventional and nonlinear model-based control of a mixing tank

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

Haeggblom, K.E.

1993-11-01

In this case study concerning control of a laboratory-scale mixing tank, conventional multiloop single-input single-output (SISO) control is compared with model-based'' control where the nonlinearity and multivariable characteristics of the process are explicitly taken into account. It is shown, especially if the operating range of the process is large, that the two outputs (level and temperature) cannot be adequately controlled by multiloop SISO control even if gain scheduling is used. By nonlinear multiple-input multiple-output (MIMO) control, on the other hand, very good control performance is obtained. The basic approach to nonlinear control used in this study is first to transformmore » the process into a globally linear and decoupled system, and then to design controllers for this system. Because of the properties of the resulting MIMO system, the controller design is very easy. Two nonlinear control system designs based on a steady-state and a dynamic model, respectively, are considered. In the dynamic case, both setpoint tracking and disturbance rejection can be addressed separately.« less
Theoretic aspects of the identification of the parameters in the optimal control model

NASA Technical Reports Server (NTRS)

Vanwijk, R. A.; Kok, J. J.

1977-01-01

The identification of the parameters of the optimal control model from input-output data of the human operator is considered. Accepting the basic structure of the model as a cascade of a full-order observer and a feedback law, and suppressing the inherent optimality of the human controller, the parameters to be identified are the feedback matrix, the observer gain matrix, and the intensity matrices of the observation noise and the motor noise. The identification of the parameters is a statistical problem, because the system and output are corrupted by noise, and therefore the solution must be based on the statistics (probability density function) of the input and output data of the human operator. However, based on the statistics of the input-output data of the human operator, no distinction can be made between the observation and the motor noise, which shows that the model suffers from overparameterization.
A programmable power processor for high power space applications

NASA Technical Reports Server (NTRS)

Lanier, J. R., Jr.; Graves, J. R.; Kapustka, R. E.; Bush, J. R., Jr.

1982-01-01

A Programmable Power Processor (P3) has been developed for application in future large space power systems. The P3 is capable of operation over a wide range of input voltage (26 to 375 Vdc) and output voltage (24 to 180 Vdc). The peak output power capability is 18 kW (180 V at 100 A). The output characteristics of the P3 can be programmed to any voltage and/or current level within the limits of the processor and may be controlled as a function of internal or external parameters. Seven breadboard P3s and one 'flight-type' engineering model P3 have been built and tested both individually and in electrical power systems. The programmable feature allows the P3 to be used in a variety of applications by changing the output characteristics. Test results, including efficiency at various input/output combinations, transient response, and output impedance, are presented.
The N-BOD2 user's and programmer's manual

NASA Technical Reports Server (NTRS)

Frisch, H. P.

1978-01-01

A general purpose digital computer program was developed and designed to aid in the analysis of spacecraft attitude dynamics. The program provides the analyst with the capability of automatically deriving and numerically solving the equations of motion of any system that can be modeled as a topological tree of coupled rigid bodies, flexible bodies, point masses, and symmetrical momentum wheels. Two modes of output are available. The composite system equations of motion may be outputted on a line printer in a symbolic form that may be easily translated into common vector-dyadic notation, or the composite system equations of motion may be solved numerically and any desirable set of system state variables outputted as a function of time.
Blind Deconvolution for Distributed Parameter Systems with Unbounded Input and Output and Determining Blood Alcohol Concentration from Transdermal Biosensor Data.

PubMed

Rosen, I G; Luczak, Susan E; Weiss, Jordan

2014-03-15

We develop a blind deconvolution scheme for input-output systems described by distributed parameter systems with boundary input and output. An abstract functional analytic theory based on results for the linear quadratic control of infinite dimensional systems with unbounded input and output operators is presented. The blind deconvolution problem is then reformulated as a series of constrained linear and nonlinear optimization problems involving infinite dimensional dynamical systems. A finite dimensional approximation and convergence theory is developed. The theory is applied to the problem of estimating blood or breath alcohol concentration (respectively, BAC or BrAC) from biosensor-measured transdermal alcohol concentration (TAC) in the field. A distributed parameter model with boundary input and output is proposed for the transdermal transport of ethanol from the blood through the skin to the sensor. The problem of estimating BAC or BrAC from the TAC data is formulated as a blind deconvolution problem. A scheme to identify distinct drinking episodes in TAC data based on a Hodrick Prescott filter is discussed. Numerical results involving actual patient data are presented.
Active structural acoustic control of helicopter interior multifrequency noise using input-output-based hybrid control

NASA Astrophysics Data System (ADS)

Ma, Xunjun; Lu, Yang; Wang, Fengjiao

2017-09-01

This paper presents the recent advances in reduction of multifrequency noise inside helicopter cabin using an active structural acoustic control system, which is based on active gearbox struts technical approach. To attenuate the multifrequency gearbox vibrations and resulting noise, a new scheme of discrete model predictive sliding mode control has been proposed based on controlled auto-regressive moving average model. Its implementation only needs input/output data, hence a broader frequency range of controlled system is modelled and the burden on the state observer design is released. Furthermore, a new iteration form of the algorithm is designed, improving the developing efficiency and run speed. To verify the algorithm's effectiveness and self-adaptability, experiments of real-time active control are performed on a newly developed helicopter model system. The helicopter model can generate gear meshing vibration/noise similar to a real helicopter with specially designed gearbox and active struts. The algorithm's control abilities are sufficiently checked by single-input single-output and multiple-input multiple-output experiments via different feedback strategies progressively: (1) control gear meshing noise through attenuating vibrations at the key points on the transmission path, (2) directly control the gear meshing noise in the cabin using the actuators. Results confirm that the active control system is practical for cancelling multifrequency helicopter interior noise, which also weakens the frequency-modulation of the tones. For many cases, the attenuations of the measured noise exceed the level of 15 dB, with maximum reduction reaching 31 dB. Also, the control process is demonstrated to be smoother and faster.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Moslehi, Salim; Reddy, T. Agami; Katipamula, Srinivas

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature.more » The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.« less
Analysis of inter-country input-output table based on bibliographic coupling network: How industrial sectors on the GVC compete for production resources

NASA Astrophysics Data System (ADS)

Guan, Jun; Xu, Xiaoyu; Xing, Lizhi

2018-03-01

The input-output table is comprehensive and detailed in describing national economic systems with abundance of economic relationships depicting information of supply and demand among industrial sectors. This paper focuses on how to quantify the degree of competition on the global value chain (GVC) from the perspective of econophysics. Global Industrial Strongest Relevant Network models are established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output (ICIO) tables and then have them transformed into Global Industrial Resource Competition Network models to analyze the competitive relationships based on bibliographic coupling approach. Three indicators well suited for the weighted and undirected networks with self-loops are introduced here, including unit weight for competitive power, disparity in the weight for competitive amplitude and weighted clustering coefficient for competitive intensity. Finally, these models and indicators were further applied empirically to analyze the function of industrial sectors on the basis of the latest World Input-Output Database (WIOD) in order to reveal inter-sector competitive status during the economic globalization.
Detection of no-model input-output pairs in closed-loop systems.

PubMed

Potts, Alain Segundo; Alvarado, Christiam Segundo Morales; Garcia, Claudio

2017-11-01

The detection of no-model input-output (IO) pairs is important because it can speed up the multivariable system identification process, since all the pairs with null transfer functions are previously discarded and it can also improve the identified model quality, thus improving the performance of model based controllers. In the available literature, the methods focus just on the open-loop case, since in this case there is not the effect of the controller forcing the main diagonal in the transfer matrix to one and all the other terms to zero. In this paper, a modification of a previous method able to detect no-model IO pairs in open-loop systems is presented, but adapted to perform this duty in closed-loop systems. Tests are performed by using the traditional methods and the proposed one to show its effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
ARCAS (ACACIA Regional Climate-data Access System) -- a Web Access System for Climate Model Data Access, Visualization and Comparison

NASA Astrophysics Data System (ADS)

Hakkarinen, C.; Brown, D.; Callahan, J.; hankin, S.; de Koningh, M.; Middleton-Link, D.; Wigley, T.

2001-05-01

A Web-based access system to climate model output data sets for intercomparison and analysis has been produced, using the NOAA-PMEL developed Live Access Server software as host server and Ferret as the data serving and visualization engine. Called ARCAS ("ACACIA Regional Climate-data Access System"), and publicly accessible at http://dataserver.ucar.edu/arcas, the site currently serves climate model outputs from runs of the NCAR Climate System Model for the 21st century, for Business as Usual and Stabilization of Greenhouse Gas Emission scenarios. Users can select, download, and graphically display single variables or comparisons of two variables from either or both of the CSM model runs, averaged for monthly, seasonal, or annual time resolutions. The time length of the averaging period, and the geographical domain for download and display, are fully selectable by the user. A variety of arithmetic operations on the data variables can be computed "on-the-fly", as defined by the user. Expansions of the user-selectable options for defining analysis options, and for accessing other DOD-compatible ("Distributed Ocean Data System-compatible") data sets, residing at locations other than the NCAR hardware server on which ARCAS operates, are planned for this year. These expansions are designed to allow users quick and easy-to-operate web-based access to the largest possible selection of climate model output data sets available throughout the world.
Modeling and vibration control of the flapping-wing robotic aircraft with output constraint

NASA Astrophysics Data System (ADS)

He, Wei; Mu, Xinxing; Chen, Yunan; He, Xiuyu; Yu, Yao

2018-06-01

In this paper, we propose the boundary control for undesired vibrations suppression with output constraint of the flapping-wing robotic aircraft (FWRA). We also present the dynamics of the flexible wing of FWRA with governing equations and boundary conditions, which are partial differential equations (PDEs) and ordinary differential equations (ODEs), respectively. An energy-based barrier Lyapunov function is introduced to analyze the system stability and prevent violation of output constraint. With the effect of the proposed boundary controller, distributed states of the system remain in the constrained spaces. Then the IBLF-based boundary controls are proposed to assess the stability of the FWRA in the presence of output constraint.
A new modelling and identification scheme for time-delay systems with experimental investigation: a relay feedback approach

NASA Astrophysics Data System (ADS)

Pandey, Saurabh; Majhi, Somanath; Ghorai, Prasenjit

2017-07-01

In this paper, the conventional relay feedback test has been modified for modelling and identification of a class of real-time dynamical systems in terms of linear transfer function models with time-delay. An ideal relay and unknown systems are connected through a negative feedback loop to bring the sustained oscillatory output around the non-zero setpoint. Thereafter, the obtained limit cycle information is substituted in the derived mathematical equations for accurate identification of unknown plants in terms of overdamped, underdamped, critically damped second-order plus dead time and stable first-order plus dead time transfer function models. Typical examples from the literature are included for the validation of the proposed identification scheme through computer simulations. Subsequently, the comparisons between estimated model and true system are drawn through integral absolute error criterion and frequency response plots. Finally, the obtained output responses through simulations are verified experimentally on real-time liquid level control system using Yokogawa Distributed Control System CENTUM CS3000 set up.
Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

NASA Astrophysics Data System (ADS)

Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

2014-05-01

While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.
A computer program for simulating geohydrologic systems in three dimensions

USGS Publications Warehouse

Posson, D.R.; Hearne, G.A.; Tracy, J.V.; Frenzel, P.F.

1980-01-01

This document is directed toward individuals who wish to use a computer program to simulate ground-water flow in three dimensions. The strongly implicit procedure (SIP) numerical method is used to solve the set of simultaneous equations. New data processing techniques and program input and output options are emphasized. The quifer system to be modeled may be heterogeneous and anisotropic, and may include both artesian and water-table conditions. Systems which consist of well defined alternating layers of highly permeable and poorly permeable material may be represented by a sequence of equations for two dimensional flow in each of the highly permeable units. Boundaries where head or flux is user-specified may be irregularly shaped. The program also allows the user to represent streams as limited-source boundaries when the streamflow is small in relation to the hydraulic stress on the system. The data-processing techniques relating to ' cube ' input and output, to swapping of layers, to restarting of simulation, to free-format NAMELIST input, to the details of each sub-routine 's logic, and to the overlay program structure are discussed. The program is capable of processing large models that might overflow computer memories with conventional programs. Detailed instructions for selecting program options, for initializing the data arrays, for defining ' cube ' output lists and maps, and for plotting hydrographs of calculated and observed heads and/or drawdowns are provided. Output may be restricted to those nodes of particular interest, thereby reducing the volumes of printout for modelers, which may be critical when working at remote terminals. ' Cube ' input commands allow the modeler to set aquifer parameters and initialize the model with very few input records. Appendixes provide instructions to compile the program, definitions and cross-references for program variables, summary of the FLECS structured FORTRAN programming language, listings of the FLECS and FORTRAN source code, and samples of input and output for example simulations. (USGS)
Agreement Between Institutional Measurements and Treatment Planning System Calculations for Basic Dosimetric Parameters as Measured by the Imaging and Radiation Oncology Core-Houston

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

Kerns, James R.; Followill, David S.; Imaging and Radiation Oncology Core-Houston, The University of Texas Health Science Center-Houston, Houston, Texas

Purpose: To compare radiation machine measurement data collected by the Imaging and Radiation Oncology Core at Houston (IROC-H) with institutional treatment planning system (TPS) values, to identify parameters with large differences in agreement; the findings will help institutions focus their efforts to improve the accuracy of their TPS models. Methods and Materials: Between 2000 and 2014, IROC-H visited more than 250 institutions and conducted independent measurements of machine dosimetric data points, including percentage depth dose, output factors, off-axis factors, multileaf collimator small fields, and wedge data. We compared these data with the institutional TPS values for the same points bymore » energy, class, and parameter to identify differences and similarities using criteria involving both the medians and standard deviations for Varian linear accelerators. Distributions of differences between machine measurements and institutional TPS values were generated for basic dosimetric parameters. Results: On average, intensity modulated radiation therapy–style and stereotactic body radiation therapy–style output factors and upper physical wedge output factors were the most problematic. Percentage depth dose, jaw output factors, and enhanced dynamic wedge output factors agreed best between the IROC-H measurements and the TPS values. Although small differences were shown between 2 common TPS systems, neither was superior to the other. Parameter agreement was constant over time from 2000 to 2014. Conclusions: Differences in basic dosimetric parameters between machine measurements and TPS values vary widely depending on the parameter, although agreement does not seem to vary by TPS and has not changed over time. Intensity modulated radiation therapy–style output factors, stereotactic body radiation therapy–style output factors, and upper physical wedge output factors had the largest disagreement and should be carefully modeled to ensure accuracy.« less
Advances in a distributed approach for ocean model data interoperability

USGS Publications Warehouse

Signell, Richard P.; Snowden, Derrick P.

2014-01-01

An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.
Simulation of medical Q-switch flash-pumped Er:YAG laser

NASA Astrophysics Data System (ADS)

-Yan-lin, Wang; Huang-Chuyun; Yao-Yucheng; Xiaolin, Zou

2011-01-01

Er: YAG laser, the wavelength is 2940nm, can be absorbed strongly by water. The absorption coefficient is as high as 13000 cm-1. As the water strong absorption, Erbium laser can bring shallow penetration depth and smaller surrounding tissue injury in most soft tissue and hard tissue. At the same time, the interaction between 2940nm radiation and biological tissue saturated with water is equivalent to instantaneous heating within limited volume, thus resulting in the phenomenon of micro-explosion to removal organization. Different parameters can be set up to cut enamel, dentin, caries and soft tissue. For the development and optimization of laser system, it is a practical choice to use laser modeling to predict the influence of various parameters for laser performance. Aim at the status of low Erbium laser output power, flash-pumped Er: YAG laser performance was simulated to obtain optical output in theory. the rate equation model was obtained and used to predict the change of population densities in various manifolds and use the technology of Q-switch the simulate laser output for different design parameters and results showed that Er: YAG laser output energy can achieve the maximum average output power of 9.8W under the given parameters. The model can be used to find the potential laser systems that meet application requirements.

Modeling and control of non-square MIMO system using relay feedback.

PubMed

Kalpana, D; Thyagarajan, T; Gokulraj, N

2015-11-01

This paper proposes a systematic approach for the modeling and control of non-square MIMO systems in time domain using relay feedback. Conventionally, modeling, selection of the control configuration and controller design of non-square MIMO systems are performed using input/output information of direct loop, while the output of undesired responses that bears valuable information on interaction among the loops are not considered. However, in this paper, the undesired response obtained from relay feedback test is also taken into consideration to extract the information about the interaction between the loops. The studies are performed on an Air Path Scheme of Turbocharged Diesel Engine (APSTDE) model, which is a typical non-square MIMO system, with input and output variables being 3 and 2 respectively. From the relay test response, the generalized analytical expressions are derived and these analytical expressions are used to estimate unknown system parameters and also to evaluate interaction measures. The interaction is analyzed by using Block Relative Gain (BRG) method. The model thus identified is later used to design appropriate controller to carry out closed loop studies. Closed loop simulation studies were performed for both servo and regulatory operations. Integral of Squared Error (ISE) performance criterion is employed to quantitatively evaluate performance of the proposed scheme. The usefulness of the proposed method is demonstrated on a lab-scale Two-Tank Cylindrical Interacting System (TTCIS), which is configured as a non-square system. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Control design methods for floating wind turbines for optimal disturbance rejection

NASA Astrophysics Data System (ADS)

Lemmer, Frank; Schlipf, David; Cheng, Po Wen

2016-09-01

An analysis of the floating wind turbine as a multi-input-multi-output system investigating the effect of the control inputs on the system outputs is shown. These effects are compared to the ones of the disturbances from wind and waves in order to give insights for the selection of the control layout. The frequencies with the largest impact on the outputs due to limited effect of the controlled variables are identified. Finally, an optimal controller is designed as a benchmark and compared to a conventional PI-controller using only the rotor speed as input. Here, the previously found system properties, especially the difficulties to damp responses to wave excitation, are confirmed and verified through a spectral analysis with realistic environmental conditions. This comparison also assesses the quality of the employed simplified linear simulation model compared to the nonlinear model and shows that such an efficient frequency-domain evaluation for control design is feasible.
New nonlinear control algorithms for multiple robot arms

NASA Technical Reports Server (NTRS)

Tarn, T. J.; Bejczy, A. K.; Yun, X.

1988-01-01

Multiple coordinated robot arms are modeled by considering the arms as closed kinematic chains and as a force-constrained mechanical system working on the same object simultaneously. In both formulations, a novel dynamic control method is discussed. It is based on feedback linearization and simultaneous output decoupling technique. By applying a nonlinear feedback and a nonlinear coordinate transformation, the complicated model of the multiple robot arms in either formulation is converted into a linear and output decoupled system. The linear system control theory and optimal control theory are used to design robust controllers in the task space. The first formulation has the advantage of automatically handling the coordination and load distribution among the robot arms. In the second formulation, it was found that by choosing a general output equation it became possible simultaneously to superimpose the position and velocity error feedback with the force-torque error feedback in the task space.
System identification using Nuclear Norm & Tabu Search optimization

NASA Astrophysics Data System (ADS)

Ahmed, Asif A.; Schoen, Marco P.; Bosworth, Ken W.

2018-01-01

In recent years, subspace System Identification (SI) algorithms have seen increased research, stemming from advanced minimization methods being applied to the Nuclear Norm (NN) approach in system identification. These minimization algorithms are based on hard computing methodologies. To the authors’ knowledge, as of now, there has been no work reported that utilizes soft computing algorithms to address the minimization problem within the nuclear norm SI framework. A linear, time-invariant, discrete time system is used in this work as the basic model for characterizing a dynamical system to be identified. The main objective is to extract a mathematical model from collected experimental input-output data. Hankel matrices are constructed from experimental data, and the extended observability matrix is employed to define an estimated output of the system. This estimated output and the actual - measured - output are utilized to construct a minimization problem. An embedded rank measure assures minimum state realization outcomes. Current NN-SI algorithms employ hard computing algorithms for minimization. In this work, we propose a simple Tabu Search (TS) algorithm for minimization. TS algorithm based SI is compared with the iterative Alternating Direction Method of Multipliers (ADMM) line search optimization based NN-SI. For comparison, several different benchmark system identification problems are solved by both approaches. Results show improved performance of the proposed SI-TS algorithm compared to the NN-SI ADMM algorithm.
Two graphical user interfaces for managing and analyzing MODFLOW groundwater-model scenarios

USGS Publications Warehouse

Banta, Edward R.

2014-01-01

Scenario Manager and Scenario Analyzer are graphical user interfaces that facilitate the use of calibrated, MODFLOW-based groundwater models for investigating possible responses to proposed stresses on a groundwater system. Scenario Manager allows a user, starting with a calibrated model, to design and run model scenarios by adding or modifying stresses simulated by the model. Scenario Analyzer facilitates the process of extracting data from model output and preparing such display elements as maps, charts, and tables. Both programs are designed for users who are familiar with the science on which groundwater modeling is based but who may not have a groundwater modeler’s expertise in building and calibrating a groundwater model from start to finish. With Scenario Manager, the user can manipulate model input to simulate withdrawal or injection wells, time-variant specified hydraulic heads, recharge, and such surface-water features as rivers and canals. Input for stresses to be simulated comes from user-provided geographic information system files and time-series data files. A Scenario Manager project can contain multiple scenarios and is self-documenting. Scenario Analyzer can be used to analyze output from any MODFLOW-based model; it is not limited to use with scenarios generated by Scenario Manager. Model-simulated values of hydraulic head, drawdown, solute concentration, and cell-by-cell flow rates can be presented in display elements. Map data can be represented as lines of equal value (contours) or as a gradated color fill. Charts and tables display time-series data obtained from output generated by a transient-state model run or from user-provided text files of time-series data. A display element can be based entirely on output of a single model run, or, to facilitate comparison of results of multiple scenarios, an element can be based on output from multiple model runs. Scenario Analyzer can export display elements and supporting metadata as a Portable Document Format file.
Evaluation of the performance of a passive-active vibration isolation system

NASA Astrophysics Data System (ADS)

Sun, L. L.; Hansen, C. H.; Doolan, C.

2015-01-01

The behavior of a feedforward active isolation system subjected to actuator output constraints is investigated. Distributed parameter models are developed to analyze the system response, and to produce a transfer matrix for the design of an integrated passive-active isolation system. Cost functions considered here comprise a combination of the vibration transmission energy and the sum of the squared control forces. The example system considered is a rigid body connected to a simply supported plate via two isolation mounts. The overall isolation performance is evaluated by numerical simulation. The results show that the control strategies which rely on unconstrained actuator outputs may give substantial power transmission reductions over a wide frequency range, but also require large control force amplitudes to control excited vibration modes of the system. Expected power transmission reductions for modified control strategies that incorporate constrained actuator outputs are considerably less than typical reductions with unconstrained actuator outputs. The active system with constrained control force outputs is shown to be more effective at the resonance frequencies of the supporting plate. However, in the frequency range in which rigid body modes are present, the control strategies employed using constrained actuator outputs can only achieve 5-10 dB power transmission reduction, while at off-resonance frequencies, little or no power transmission reduction can be obtained with realistic control forces. Analysis of the wave effects in the passive mounts is also presented.
Design and experiment of vehicular charger AC/DC system based on predictive control algorithm

NASA Astrophysics Data System (ADS)

He, Guangbi; Quan, Shuhai; Lu, Yuzhang

2018-06-01

For the car charging stage rectifier uncontrollable system, this paper proposes a predictive control algorithm of DC/DC converter based on the prediction model, established by the state space average method and its prediction model, obtained by the optimal mathematical description of mathematical calculation, to analysis prediction algorithm by Simulink simulation. The design of the structure of the car charging, at the request of the rated output power and output voltage adjustable control circuit, the first stage is the three-phase uncontrolled rectifier DC voltage Ud through the filter capacitor, after by using double-phase interleaved buck-boost circuit with wide range output voltage required value, analyzing its working principle and the the parameters for the design and selection of components. The analysis of current ripple shows that the double staggered parallel connection has the advantages of reducing the output current ripple and reducing the loss. The simulation experiment of the whole charging circuit is carried out by software, and the result is in line with the design requirements of the system. Finally combining the soft with hardware circuit to achieve charging of the system according to the requirements, experimental platform proved the feasibility and effectiveness of the proposed predictive control algorithm based on the car charging of the system, which is consistent with the simulation results.
Modeling and simulation of queuing system for customer service improvement: A case study

NASA Astrophysics Data System (ADS)

Xian, Tan Chai; Hong, Chai Weng; Hawari, Nurul Nazihah

2016-10-01

This study aims to develop a queuing model at UniMall by using discrete event simulation approach in analyzing the service performance that affects customer satisfaction. The performance measures that considered in this model are such as the average time in system, the total number of student served, the number of student in waiting queue, the waiting time in queue as well as the maximum length of buffer. ARENA simulation software is used to develop a simulation model and the output is analyzed. Based on the analysis of output, it is recommended that management of UniMall consider introducing shifts and adding another payment counter in the morning.
Estimation of hysteretic damping of structures by stochastic subspace identification

NASA Astrophysics Data System (ADS)

Bajrić, Anela; Høgsberg, Jan

2018-05-01

Output-only system identification techniques can estimate modal parameters of structures represented by linear time-invariant systems. However, the extension of the techniques to structures exhibiting non-linear behavior has not received much attention. This paper presents an output-only system identification method suitable for random response of dynamic systems with hysteretic damping. The method applies the concept of Stochastic Subspace Identification (SSI) to estimate the model parameters of a dynamic system with hysteretic damping. The restoring force is represented by the Bouc-Wen model, for which an equivalent linear relaxation model is derived. Hysteretic properties can be encountered in engineering structures exposed to severe cyclic environmental loads, as well as in vibration mitigation devices, such as Magneto-Rheological (MR) dampers. The identification technique incorporates the equivalent linear damper model in the estimation procedure. Synthetic data, representing the random vibrations of systems with hysteresis, validate the estimated system parameters by the presented identification method at low and high-levels of excitation amplitudes.
Rodent Studies of Cardiovascular Deconditioning

NASA Technical Reports Server (NTRS)

Shoukas, Artin A.

1999-01-01

Changes in blood pressure can occur for two reasons: 1) A decrease in cardiac output resulting from the altered contractility of the heart or through changes in venous filling pressure via the Frank Starling mechanism or; 2) A change in systemic vascular resistance. The observed changes in cardiac output and blood pressure after long term space flight cannot be entirely explained through changes in contractility or heart rate alone. Therefore, alterations in filling pressure mediated through changes in systemic venous capacitance and arterial resistance function may be important determinants of cardiac output and blood pressure after long term space flight. Our laboratory and previous studies have shown the importance of veno-constriction mediated by the carotid sinus baroreceptor reflex system on overall circulatory homeostasis and in the regulation of cardiac output. Our proposed experiments test the overall hypothesis that alterations in venous capacitance function and arterial resistance by the carotid sinus baroreceptor reflex system are an important determinant of the cardiac output and blood pressure response seen in astronauts after returning to earth from long term exposure to microgravity. This hypothesis is important to our overall understanding of circulatory adjustments made during long term space flight. It also provides a framework for investigating counter measures to reduce the incidence of orthostatic hypotension caused by an attenuation of cardiac output. We continue to use hind limb unweighted (HLU) rat model to simulate the patho physiological effects as they relate to cardiovascular deconditioning in microgravity. We have used this model to address the hypothesis that microgravity induced cardiovascular deconditioning results in impaired vascular responses and that these impaired vascular responses result from abnormal alpha-1 AR signaling. The impaired vascular reactivity results in attenuated blood pressure and cardiac output responses to an orthostatic challenge. We have used in vitro vascular reactivity assays to explore abnormalities in vascular responses in vessels from HLU animals and, cardiac output (CO), blood pressure (BP) and heart rate (HR) measurements to characterize changes in hemodynamics following HLU.
Hierarchical Multi-Scale Approach To Validation and Uncertainty Quantification of Hyper-Spectral Image Modeling

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

Engel, David W.; Reichardt, Thomas A.; Kulp, Thomas J.

Validating predictive models and quantifying uncertainties inherent in the modeling process is a critical component of the HARD Solids Venture program [1]. Our current research focuses on validating physics-based models predicting the optical properties of solid materials for arbitrary surface morphologies and characterizing the uncertainties in these models. We employ a systematic and hierarchical approach by designing physical experiments and comparing the experimental results with the outputs of computational predictive models. We illustrate this approach through an example comparing a micro-scale forward model to an idealized solid-material system and then propagating the results through a system model to the sensormore » level. Our efforts should enhance detection reliability of the hyper-spectral imaging technique and the confidence in model utilization and model outputs by users and stakeholders.« less
HiRel: Hybrid Automated Reliability Predictor (HARP) integrated reliability tool system, (version 7.0). Volume 4: HARP Output (HARPO) graphics display user's guide

NASA Technical Reports Server (NTRS)

Sproles, Darrell W.; Bavuso, Salvatore J.

1994-01-01

The Hybrid Automated Reliability Predictor (HARP) integrated Reliability (HiRel) tool system for reliability/availability prediction offers a toolbox of integrated reliability/availability programs that can be used to customize the user's application in a workstation or nonworkstation environment. HiRel consists of interactive graphical input/output programs and four reliability/availability modeling engines that provide analytical and simulative solutions to a wide host of highly reliable fault-tolerant system architectures and is also applicable to electronic systems in general. The tool system was designed at the outset to be compatible with most computing platforms and operating systems and some programs have been beta tested within the aerospace community for over 8 years. This document is a user's guide for the HiRel graphical postprocessor program HARPO (HARP Output). HARPO reads ASCII files generated by HARP. It provides an interactive plotting capability that can be used to display alternate model data for trade-off analyses. File data can also be imported to other commercial software programs.
A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
TKKMOD: A computer simulation program for an integrated wind diesel system. Version 1.0: Document and user guide

NASA Astrophysics Data System (ADS)

Manninen, L. M.

1993-12-01

The document describes TKKMOD, a simulation model developed at Helsinki University of Technology for a specific wind-diesel system layout, with special emphasis on the battery submodel and its use in simulation. The model has been included into the European wind-diesel modeling software package WDLTOOLS under the CEC JOULE project 'Engineering Design Tools for Wind-Diesel Systems' (JOUR-0078). WDLTOOLS serves as the user interface and processes the input and output data of different logistic simulation models developed by the project participants. TKKMOD cannot be run without this shell. The report only describes the simulation principles and model specific parameters of TKKMOD and gives model specific user instructions. The input and output data processing performed outside this model is described in the documentation of the shell. The simulation model is utilized for calculation of long-term performance of the reference system configuration for given wind and load conditions. The main results are energy flows, losses in the system components, diesel fuel consumption, and the number of diesel engine starts.
Modeling of a multileaf collimator

NASA Astrophysics Data System (ADS)

Kim, Siyong

A comprehensive physics model of a multileaf collimator (MLC) field for treatment planning was developed. Specifically, an MLC user interface module that includes a geometric optimization tool and a general method of in- air output factor calculation were developed. An automatic tool for optimization of MLC conformation is needed to realize the potential benefits of MLC. It is also necessary that a radiation therapy treatment planning (RTTP) system is capable of modeling MLC completely. An MLC geometric optimization and user interface module was developed. The planning time has been reduced significantly by incorporating the MLC module into the main RTTP system, Radiation Oncology Computer System (ROCS). The dosimetric parameter that has the most profound effect on the accuracy of the dose delivered with an MLC is the change in the in-air output factor that occurs with field shaping. It has been reported that the conventional method of calculating an in-air output factor cannot be used for MLC shaped fields accurately. Therefore, it is necessary to develop algorithms that allow accurate calculation of the in-air output factor. A generalized solution for an in-air output factor calculation was developed. Three major contributors of scatter to the in-air output-flattening filter, wedge, and tertiary collimator-were considered separately. By virtue of a field mapping method, in which a source plane field determined by detector's eye view is mapped into a detector plane field, no additional dosimetric data acquisition other than the standard data set for a range of square fields is required for the calculation of head scatter. Comparisons of in-air output factors between calculated and measured values show a good agreement for both open and wedge fields. For rectangular fields, a simple equivalent square formula was derived based on the configuration of a linear accelerator treatment head. This method predicts in-air output to within 1% accuracy. A two-effective-source algorithm was developed to account for the effect of source to detector distance on in-air output for wedge fields. Two effective sources, one for head scatter and the other for wedge scatter, were dealt with independently. Calculations provided less than 1% difference of in-air output factors from measurements. This approach offers the best comprehensive accuracy in radiation delivery with field shapes defined using MLC. This generalized model works equally well with fields shaped by any type of tertiary collimator and have the necessary framework to extend its application to intensity modulated radiation therapy.
A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

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

Zhang Yumin; Lum, Kai-Yew; Wang Qingguo

In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus,more » the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.« less
A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

NASA Astrophysics Data System (ADS)

Zhang, Yumin; Wang, Qing-Guo; Lum, Kai-Yew

2009-03-01

In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.
Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification

NASA Technical Reports Server (NTRS)

Shafer, Jaclyn; Watson, Leela R.

2015-01-01

NASA's Launch Services Program, Ground Systems Development and Operations, Space Launch System and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). Examples include determining if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 km Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the high-resolution WRF Environmental Modeling System (EMS) model configured by the AMU (Watson 2013) in real time. Implementing a real-time version of the ER WRF-EMS would generate a larger database of model output than in the previous AMU task for determining model performance, and allows the AMU more control over and access to the model output archive. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The AMU also calculated verification statistics to determine model performance compared to observational data. Finally, the AMU made the model output available on the AMU Advanced Weather Interactive Processing System II (AWIPS II) servers, which allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations (RWO) AWIPS II client computers and conduct real-time subjective analyses.
Probabilistic Physics-Based Risk Tools Used to Analyze the International Space Station Electrical Power System Output

NASA Technical Reports Server (NTRS)

Patel, Bhogila M.; Hoge, Peter A.; Nagpal, Vinod K.; Hojnicki, Jeffrey S.; Rusick, Jeffrey J.

2004-01-01

This paper describes the methods employed to apply probabilistic modeling techniques to the International Space Station (ISS) power system. These techniques were used to quantify the probabilistic variation in the power output, also called the response variable, due to variations (uncertainties) associated with knowledge of the influencing factors called the random variables. These uncertainties can be due to unknown environmental conditions, variation in the performance of electrical power system components or sensor tolerances. Uncertainties in these variables, cause corresponding variations in the power output, but the magnitude of that effect varies with the ISS operating conditions, e.g. whether or not the solar panels are actively tracking the sun. Therefore, it is important to quantify the influence of these uncertainties on the power output for optimizing the power available for experiments.
Optimal fixed-finite-dimensional compensator for Burgers' equation with unbounded input/output operators

NASA Technical Reports Server (NTRS)

Burns, John A.; Marrekchi, Hamadi

1993-01-01

The problem of using reduced order dynamic compensators to control a class of nonlinear parabolic distributed parameter systems was considered. Concentration was on a system with unbounded input and output operators governed by Burgers' equation. A linearized model was used to compute low-order-finite-dimensional control laws by minimizing certain energy functionals. Then these laws were applied to the nonlinear model. Standard approaches to this problem employ model/controller reduction techniques in conjunction with linear quadratic Gaussian (LQG) theory. The approach used is based on the finite dimensional Bernstein/Hyland optimal projection theory which yields a fixed-finite-order controller.

A Review of Models of the Human Temperature Regulation System.

DTIC Science & Technology

1992-02-18

experiments to challenge the model and building new models, the researcher will benefit cptimally from its use. It appears that this process has been...during Cooling OUTPUTS: WWe W, Decreased by ?. W H~eating .. ___ > - W Constnt during Coolng IT OUTPUTS: U Increased by 7 We Haoling and Coaling Y. L.AT,)0...12, pp. 709-903, 1976. 43. Slonim, N.B., Environmental Physiology, Mosby, St.Louis, MO, 1974. 44. Sulman, S.G., Health , Weather, and Climate, Karger
Fundamental Mechanisms of NeuroInformation Processing: Inverse Problems and Spike Processing

DTIC Science & Technology

2016-08-04

platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution...example. We investigated the following nonlinear identification problem: given both the input signal u and the time sequence (tk)k2Z at the output of...from a time sequence is to be contrasted with existing methods for rate-based models in neuroscience. In such models the output of the system is taken
On energy harvesting from a vibro-impact oscillator with dielectric membranes

NASA Astrophysics Data System (ADS)

Lai, Z. H.; Thomson, G.; Yurchenko, D.; Val, D. V.; Rodgers, E.

2018-07-01

A vibro-impact mechanical system comprising of a ball moving freely between two dielectric membranes located at a certain distance from each other is studied. The system generates electricity when the ball moving due ambient vibrations impacts one of the membranes. The energy harvesting principle of the proposed system is explained and then used to formulate a numerical model for estimating the system output voltage. The dynamic behavior and output performance of the system are thoroughly studied under a harmonic excitation, as well as different initial conditions and various values of the restitution coefficient of the membranes. The delivered research results are useful for selecting the system parameters to achieve its optimal output performance in a realistic vibrational environment. Potential application of the proposed system for energy harvesting from car engine vibrations is presented.
Dynamic output feedback control of a flexible air-breathing hypersonic vehicle via T-S fuzzy approach

NASA Astrophysics Data System (ADS)

Hu, Xiaoxiang; Wu, Ligang; Hu, Changhua; Wang, Zhaoqiang; Gao, Huijun

2014-08-01

By utilising Takagi-Sugeno (T-S) fuzzy set approach, this paper addresses the robust H∞ dynamic output feedback control for the non-linear longitudinal model of flexible air-breathing hypersonic vehicles (FAHVs). The flight control of FAHVs is highly challenging due to the unique dynamic characteristics, and the intricate couplings between the engine and fight dynamics and external disturbance. Because of the dynamics' enormous complexity, currently, only the longitudinal dynamics models of FAHVs have been used for controller design. In this work, T-S fuzzy modelling technique is utilised to approach the non-linear dynamics of FAHVs, then a fuzzy model is developed for the output tracking problem of FAHVs. The fuzzy model contains parameter uncertainties and disturbance, which can approach the non-linear dynamics of FAHVs more exactly. The flexible models of FAHVs are difficult to measure because of the complex dynamics and the strong couplings, thus a full-order dynamic output feedback controller is designed for the fuzzy model. A robust H∞ controller is designed for the obtained closed-loop system. By utilising the Lyapunov functional approach, sufficient solvability conditions for such controllers are established in terms of linear matrix inequalities. Finally, the effectiveness of the proposed T-S fuzzy dynamic output feedback control method is demonstrated by numerical simulations.
Land surface Verification Toolkit (LVT)

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.

2017-01-01

LVT is a framework developed to provide an automated, consolidated environment for systematic land surface model evaluation Includes support for a range of in-situ, remote-sensing and other model and reanalysis products. Supports the analysis of outputs from various LIS subsystems, including LIS-DA, LIS-OPT, LIS-UE. Note: The Land Information System Verification Toolkit (LVT) is a NASA software tool designed to enable the evaluation, analysis and comparison of outputs generated by the Land Information System (LIS). The LVT software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.1 or later. Land Information System Verification Toolkit (LVT) NOSA.
Applications of the DOE/NASA wind turbine engineering information system

NASA Technical Reports Server (NTRS)

Neustadter, H. E.; Spera, D. A.

1981-01-01

A statistical analysis of data obtained from the Technology and Engineering Information Systems was made. The systems analyzed consist of the following elements: (1) sensors which measure critical parameters (e.g., wind speed and direction, output power, blade loads and component vibrations); (2) remote multiplexing units (RMUs) on each wind turbine which frequency-modulate, multiplex and transmit sensor outputs; (3) on-site instrumentation to record, process and display the sensor output; and (4) statistical analysis of data. Two examples of the capabilities of these systems are presented. The first illustrates the standardized format for application of statistical analysis to each directly measured parameter. The second shows the use of a model to estimate the variability of the rotor thrust loading, which is a derived parameter.
Networked iterative learning control design for discrete-time systems with stochastic communication delay in input and output channels

NASA Astrophysics Data System (ADS)

Liu, Jian; Ruan, Xiaoe

2017-07-01

This paper develops two kinds of derivative-type networked iterative learning control (NILC) schemes for repetitive discrete-time systems with stochastic communication delay occurred in input and output channels and modelled as 0-1 Bernoulli-type stochastic variable. In the two schemes, the delayed signal of the current control input is replaced by the synchronous input utilised at the previous iteration, whilst for the delayed signal of the system output the one scheme substitutes it by the synchronous predetermined desired trajectory and the other takes it by the synchronous output at the previous operation, respectively. In virtue of the mathematical expectation, the tracking performance is analysed which exhibits that for both the linear time-invariant and nonlinear affine systems the two kinds of NILCs are convergent under the assumptions that the probabilities of communication delays are adequately constrained and the product of the input-output coupling matrices is full-column rank. Last, two illustrative examples are presented to demonstrate the effectiveness and validity of the proposed NILC schemes.
Numerical investigation of output beam quality in efficient broadband optical parametric chirped pulse amplification

NASA Astrophysics Data System (ADS)

Liu, Xiao-Di; Xu, Lu; Liang, Xiao-Yan

2017-01-01

We theoretically analyzed output beam quality of broad bandwidth non-collinear optical parametric chirped pulse amplification (NOPCPA) in LiB3O5 (LBO) centered at 800 nm. With a three-dimensional numerical model, the influence of the pump intensity, pump and signal spatial modulations, and the walk-off effect on the OPCPA output beam quality are presented, together with conversion efficiency and the gain spectrum. The pump modulation is a dominant factor that affects the output beam quality. Comparatively, the influence of signal modulation is insignificant. For a low-energy system with small beam sizes, walk-off effect has to be considered. Pump modulation and walk-off effect lead to asymmetric output beam profile with increased modulation. A special pump modulation type is found to optimize output beam quality and efficiency. For a high-energy system with large beam sizes, the walk-off effect can be neglected, certain back conversion is beneficial to reduce the output modulation. A trade-off must be made between the output beam quality and the conversion efficiency, especially when the pump modulation is large since. A relatively high conversion efficiency and a low output modulation are both achievable by controlling the pump modulation and intensity.
Nonlinear system identification of smart structures under high impact loads

NASA Astrophysics Data System (ADS)

Sarp Arsava, Kemal; Kim, Yeesock; El-Korchi, Tahar; Park, Hyo Seon

2013-05-01

The main purpose of this paper is to develop numerical models for the prediction and analysis of the highly nonlinear behavior of integrated structure control systems subjected to high impact loading. A time-delayed adaptive neuro-fuzzy inference system (TANFIS) is proposed for modeling of the complex nonlinear behavior of smart structures equipped with magnetorheological (MR) dampers under high impact forces. Experimental studies are performed to generate sets of input and output data for training and validation of the TANFIS models. The high impact load and current signals are used as the input disturbance and control signals while the displacement and acceleration responses from the structure-MR damper system are used as the output signals. The benchmark adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. Comparisons of the trained TANFIS models with experimental results demonstrate that the TANFIS modeling framework is an effective way to capture nonlinear behavior of integrated structure-MR damper systems under high impact loading. In addition, the performance of the TANFIS model is much better than that of ANFIS in both the training and the validation processes.
Mixed H2/Hinfinity output-feedback control of second-order neutral systems with time-varying state and input delays.

PubMed

Karimi, Hamid Reza; Gao, Huijun

2008-07-01

A mixed H2/Hinfinity output-feedback control design methodology is presented in this paper for second-order neutral linear systems with time-varying state and input delays. Delay-dependent sufficient conditions for the design of a desired control are given in terms of linear matrix inequalities (LMIs). A controller, which guarantees asymptotic stability and a mixed H2/Hinfinity performance for the closed-loop system of the second-order neutral linear system, is then developed directly instead of coupling the model to a first-order neutral system. A Lyapunov-Krasovskii method underlies the LMI-based mixed H2/Hinfinity output-feedback control design using some free weighting matrices. The simulation results illustrate the effectiveness of the proposed methodology.
Principal Dynamic Mode Analysis of the Hodgkin–Huxley Equations

PubMed Central

Eikenberry, Steffen E.; Marmarelis, Vasilis Z.

2015-01-01

We develop an autoregressive model framework based on the concept of Principal Dynamic Modes (PDMs) for the process of action potential (AP) generation in the excitable neuronal membrane described by the Hodgkin–Huxley (H–H) equations. The model's exogenous input is injected current, and whenever the membrane potential output exceeds a specified threshold, it is fed back as a second input. The PDMs are estimated from the previously developed Nonlinear Autoregressive Volterra (NARV) model, and represent an efficient functional basis for Volterra kernel expansion. The PDM-based model admits a modular representation, consisting of the forward and feedback PDM bases as linear filterbanks for the exogenous and autoregressive inputs, respectively, whose outputs are then fed to a static nonlinearity composed of polynomials operating on the PDM outputs and cross-terms of pair-products of PDM outputs. A two-step procedure for model reduction is performed: first, influential subsets of the forward and feedback PDM bases are identified and selected as the reduced PDM bases. Second, the terms of the static nonlinearity are pruned. The first step reduces model complexity from a total of 65 coefficients to 27, while the second further reduces the model coefficients to only eight. It is demonstrated that the performance cost of model reduction in terms of out-of-sample prediction accuracy is minimal. Unlike the full model, the eight coefficient pruned model can be easily visualized to reveal the essential system components, and thus the data-derived PDM model can yield insight into the underlying system structure and function. PMID:25630480
Optimal Frequency-Domain System Realization with Weighting

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Maghami, Peiman G.

1999-01-01

Several approaches are presented to identify an experimental system model directly from frequency response data. The formulation uses a matrix-fraction description as the model structure. Frequency weighting such as exponential weighting is introduced to solve a weighted least-squares problem to obtain the coefficient matrices for the matrix-fraction description. A multi-variable state-space model can then be formed using the coefficient matrices of the matrix-fraction description. Three different approaches are introduced to fine-tune the model using nonlinear programming methods to minimize the desired cost function. The first method uses an eigenvalue assignment technique to reassign a subset of system poles to improve the identified model. The second method deals with the model in the real Schur or modal form, reassigns a subset of system poles, and adjusts the columns (rows) of the input (output) influence matrix using a nonlinear optimizer. The third method also optimizes a subset of poles, but the input and output influence matrices are refined at every optimization step through least-squares procedures.
Nonlinear feedback model attitude control using CCD in magnetic suspension system

NASA Technical Reports Server (NTRS)

Lin, CHIN-E.; Hou, Ann-San

1994-01-01

A model attitude control system for a CCD camera magnetic suspension system is studied in this paper. In a recent work, a position and attitude sensing method was proposed. From this result, model position and attitude of a magnetic suspension system can be detected by generating digital outputs. Based on this achievement, a control system design using nonlinear feedback techniques for magnetic suspended model attitude control is proposed.
Nuclear thermal propulsion engine system design analysis code development

NASA Astrophysics Data System (ADS)

Pelaccio, Dennis G.; Scheil, Christine M.; Petrosky, Lyman J.; Ivanenok, Joseph F.

1992-01-01

A Nuclear Thermal Propulsion (NTP) Engine System Design Analyis Code has recently been developed to characterize key NTP engine system design features. Such a versatile, standalone NTP system performance and engine design code is required to support ongoing and future engine system and vehicle design efforts associated with proposed Space Exploration Initiative (SEI) missions of interest. Key areas of interest in the engine system modeling effort were the reactor, shielding, and inclusion of an engine multi-redundant propellant pump feed system design option. A solid-core nuclear thermal reactor and internal shielding code model was developed to estimate the reactor's thermal-hydraulic and physical parameters based on a prescribed thermal output which was integrated into a state-of-the-art engine system design model. The reactor code module has the capability to model graphite, composite, or carbide fuels. Key output from the model consists of reactor parameters such as thermal power, pressure drop, thermal profile, and heat generation in cooled structures (reflector, shield, and core supports), as well as the engine system parameters such as weight, dimensions, pressures, temperatures, mass flows, and performance. The model's overall analysis methodology and its key assumptions and capabilities are summarized in this paper.
Administrative Decision Making and Resource Allocation.

ERIC Educational Resources Information Center

Sardy, Susan; Sardy, Hyman

This paper considers selected aspects of the systems analysis of administrative decisionmaking regarding resource allocations in an educational system. A model of the instructional materials purchase system is presented. The major components of this model are: environment, input, decision process, conversion structure, conversion process, output,…
A GIS-based modeling system for petroleum waste management. Geographical information system.

PubMed

Chen, Z; Huang, G H; Li, J B

2003-01-01

With an urgent need for effective management of petroleum-contaminated sites, a GIS-aided simulation (GISSIM) system is presented in this study. The GISSIM contains two components: an advanced 3D numerical model and a geographical information system (GIS), which are integrated within a general framework. The modeling component undertakes simulation for the fate of contaminants in subsurface unsaturated and saturated zones. The GIS component is used in three areas throughout the system development and implementation process: (i) managing spatial and non-spatial databases; (ii) linking inputs, model, and outputs; and (iii) providing an interface between the GISSIM and its users. The developed system is applied to a North American case study. Concentrations of benzene, toluene, and xylenes in groundwater under a petroleum-contaminated site are dynamically simulated. Reasonable outputs have been obtained and presented graphically. They provide quantitative and scientific bases for further assessment of site-contamination impacts and risks, as well as decisions on practical remediation actions.
Flow Simulation of Modified Duct System Wind Turbines Installed on Vehicle

NASA Astrophysics Data System (ADS)

Rosly, N.; Mohd, S.; Zulkafli, M. F.; Ghafir, M. F. Abdul; Shamsudin, S. S.; Muhammad, W. N. A. Wan

2017-10-01

This study investigates the characteristics of airflow with a flow guide installed and output power generated by wind turbine system being installed on a pickup truck. The wind turbine models were modelled by using SolidWorks 2015 software. In order to investigate the characteristic of air flow inside the wind turbine system, a computer simulation (by using ANSYS Fluent software) is used. There were few models being designed and simulated, one without the rotor installed and another two with rotor installed in the wind turbine system. Three velocities being used for the simulation which are 16.7 m/s (60 km/h), 25 m/s (90 km/h) and 33.33 m/s (120 km/h). The study proved that the flow guide did give an impact to the output power produced by the wind turbine system. The predicted result from this study is the velocity of the air inside the ducting system of the present model is better that reference model. Besides, the flow guide implemented in the ducting system gives a big impact on the characteristics of the air flow.
Modifications of the U.S. Geological Survey modular, finite-difference, ground-water flow model to read and write geographic information system files

USGS Publications Warehouse

Orzol, Leonard L.; McGrath, Timothy S.

1992-01-01

This report documents modifications to the U.S. Geological Survey modular, three-dimensional, finite-difference, ground-water flow model, commonly called MODFLOW, so that it can read and write files used by a geographic information system (GIS). The modified model program is called MODFLOWARC. Simulation programs such as MODFLOW generally require large amounts of input data and produce large amounts of output data. Viewing data graphically, generating head contours, and creating or editing model data arrays such as hydraulic conductivity are examples of tasks that currently are performed either by the use of independent software packages or by tedious manual editing, manipulating, and transferring data. Programs such as GIS programs are commonly used to facilitate preparation of the model input data and analyze model output data; however, auxiliary programs are frequently required to translate data between programs. Data translations are required when different programs use different data formats. Thus, the user might use GIS techniques to create model input data, run a translation program to convert input data into a format compatible with the ground-water flow model, run the model, run a translation program to convert the model output into the correct format for GIS, and use GIS to display and analyze this output. MODFLOWARC, avoids the two translation steps and transfers data directly to and from the ground-water-flow model. This report documents the design and use of MODFLOWARC and includes instructions for data input/output of the Basic, Block-centered flow, River, Recharge, Well, Drain, Evapotranspiration, General-head boundary, and Streamflow-routing packages. The modification to MODFLOW and the Streamflow-Routing package was minimized. Flow charts and computer-program code describe the modifications to the original computer codes for each of these packages. Appendix A contains a discussion on the operation of MODFLOWARC using a sample problem.
Transport coefficient computation based on input/output reduced order models

NASA Astrophysics Data System (ADS)

Hurst, Joshua L.

The guiding purpose of this thesis is to address the optimal material design problem when the material description is a molecular dynamics model. The end goal is to obtain a simplified and fast model that captures the property of interest such that it can be used in controller design and optimization. The approach is to examine model reduction analysis and methods to capture a specific property of interest, in this case viscosity, or more generally complex modulus or complex viscosity. This property and other transport coefficients are defined by a input/output relationship and this motivates model reduction techniques that are tailored to preserve input/output behavior. In particular Singular Value Decomposition (SVD) based methods are investigated. First simulation methods are identified that are amenable to systems theory analysis. For viscosity, these models are of the Gosling and Lees-Edwards type. They are high order nonlinear Ordinary Differential Equations (ODEs) that employ Periodic Boundary Conditions. Properties can be calculated from the state trajectories of these ODEs. In this research local linear approximations are rigorously derived and special attention is given to potentials that are evaluated with Periodic Boundary Conditions (PBC). For the Gosling description LTI models are developed from state trajectories but are found to have limited success in capturing the system property, even though it is shown that full order LTI models can be well approximated by reduced order LTI models. For the Lees-Edwards SLLOD type model nonlinear ODEs will be approximated by a Linear Time Varying (LTV) model about some nominal trajectory and both balanced truncation and Proper Orthogonal Decomposition (POD) will be used to assess the plausibility of reduced order models to this system description. An immediate application of the derived LTV models is Quasilinearization or Waveform Relaxation. Quasilinearization is a Newton's method applied to the ODE operator equation. Its a recursive method that solves nonlinear ODE's by solving a LTV systems at each iteration to obtain a new closer solution. LTV models are derived for both Gosling and Lees-Edwards type models. Particular attention is given to SLLOD Lees-Edwards models because they are in a form most amenable to performing Taylor series expansion, and the most commonly used model to examine viscosity. With linear models developed a method is presented to calculate viscosity based on LTI Gosling models but is shown to have some limitations. To address these issues LTV SLLOD models are analyzed with both Balanced Truncation and POD and both show that significant order reduction is possible. By examining the singular values of both techniques it is shown that Balanced Truncation has a potential to offer greater reduction, which should be expected as it is based on the input/output mapping instead of just the state information as in POD. Obtaining reduced order systems that capture the property of interest is challenging. For Balanced Truncation reduced order models for 1-D LJ and FENE systems are obtained and are shown to capture the output of interest fairly well. However numerical challenges currently limit this analysis to small order systems. Suggestions are presented to extend this method to larger systems. In addition reduced 2nd order systems are obtained from POD. Here the challenge is extending the solution beyond the original period used for the projection, in particular identifying the manifold the solution travels along. The remaining challenges are presented and discussed.
User's guide to the western spruce budworm modeling system

Treesearch

Nicholas L. Crookston; J. J. Colbert; Paul W. Thomas; Katharine A. Sheehan; William P. Kemp

1990-01-01

The Budworm Modeling System is a set of four computer programs: The Budworm Dynamics Model, the Prognosis-Budworm Dynamics Model, the Prognosis-Budworm Damage Model, and the Parallel Processing-Budworm Dynamics Model. Input to the first three programs and the output produced are described in this guide. A guide to the fourth program will be published separately....

A proposed Kalman filter algorithm for estimation of unmeasured output variables for an F100 turbofan engine

NASA Technical Reports Server (NTRS)

Alag, Gurbux S.; Gilyard, Glenn B.

1990-01-01

To develop advanced control systems for optimizing aircraft engine performance, unmeasurable output variables must be estimated. The estimation has to be done in an uncertain environment and be adaptable to varying degrees of modeling errors and other variations in engine behavior over its operational life cycle. This paper represented an approach to estimate unmeasured output variables by explicitly modeling the effects of off-nominal engine behavior as biases on the measurable output variables. A state variable model accommodating off-nominal behavior is developed for the engine, and Kalman filter concepts are used to estimate the required variables. Results are presented from nonlinear engine simulation studies as well as the application of the estimation algorithm on actual flight data. The formulation presented has a wide range of application since it is not restricted or tailored to the particular application described.
The SLH framework for modeling quantum input-output networks

DOE PAGES

Combes, Joshua; Kerckhoff, Joseph; Sarovar, Mohan

2017-09-04

Here, many emerging quantum technologies demand precise engineering and control over networks consisting of quantum mechanical degrees of freedom connected by propagating electromagnetic fields, or quantum input-output networks. Here we review recent progress in theory and experiment related to such quantum input-output networks, with a focus on the SLH framework, a powerful modeling framework for networked quantum systems that is naturally endowed with properties such as modularity and hierarchy. We begin by explaining the physical approximations required to represent any individual node of a network, e.g. atoms in cavity or a mechanical oscillator, and its coupling to quantum fields bymore » an operator triple ( S,L,H). Then we explain how these nodes can be composed into a network with arbitrary connectivity, including coherent feedback channels, using algebraic rules, and how to derive the dynamics of network components and output fields. The second part of the review discusses several extensions to the basic SLH framework that expand its modeling capabilities, and the prospects for modeling integrated implementations of quantum input-output networks. In addition to summarizing major results and recent literature, we discuss the potential applications and limitations of the SLH framework and quantum input-output networks, with the intention of providing context to a reader unfamiliar with the field.« less
The SLH framework for modeling quantum input-output networks

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

Combes, Joshua; Kerckhoff, Joseph; Sarovar, Mohan

Here, many emerging quantum technologies demand precise engineering and control over networks consisting of quantum mechanical degrees of freedom connected by propagating electromagnetic fields, or quantum input-output networks. Here we review recent progress in theory and experiment related to such quantum input-output networks, with a focus on the SLH framework, a powerful modeling framework for networked quantum systems that is naturally endowed with properties such as modularity and hierarchy. We begin by explaining the physical approximations required to represent any individual node of a network, e.g. atoms in cavity or a mechanical oscillator, and its coupling to quantum fields bymore » an operator triple ( S,L,H). Then we explain how these nodes can be composed into a network with arbitrary connectivity, including coherent feedback channels, using algebraic rules, and how to derive the dynamics of network components and output fields. The second part of the review discusses several extensions to the basic SLH framework that expand its modeling capabilities, and the prospects for modeling integrated implementations of quantum input-output networks. In addition to summarizing major results and recent literature, we discuss the potential applications and limitations of the SLH framework and quantum input-output networks, with the intention of providing context to a reader unfamiliar with the field.« less
Adaptive model reduction for continuous systems via recursive rational interpolation

NASA Technical Reports Server (NTRS)

Lilly, John H.

1994-01-01

A method for adaptive identification of reduced-order models for continuous stable SISO and MIMO plants is presented. The method recursively finds a model whose transfer function (matrix) matches that of the plant on a set of frequencies chosen by the designer. The algorithm utilizes the Moving Discrete Fourier Transform (MDFT) to continuously monitor the frequency-domain profile of the system input and output signals. The MDFT is an efficient method of monitoring discrete points in the frequency domain of an evolving function of time. The model parameters are estimated from MDFT data using standard recursive parameter estimation techniques. The algorithm has been shown in simulations to be quite robust to additive noise in the inputs and outputs. A significant advantage of the method is that it enables a type of on-line model validation. This is accomplished by simultaneously identifying a number of models and comparing each with the plant in the frequency domain. Simulations of the method applied to an 8th-order SISO plant and a 10-state 2-input 2-output plant are presented. An example of on-line model validation applied to the SISO plant is also presented.
NEMS Freight Transportation Module Improvement Study

EIA Publications

2015-01-01

The U.S. Energy Information Administration (EIA) contracted with IHS Global, Inc. (IHS) to analyze the relationship between the value of industrial output, physical output, and freight movement in the United States for use in updating analytic assumptions and modeling structure within the National Energy Modeling System (NEMS) freight transportation module, including forecasting methodologies and processes to identify possible alternative approaches that would improve multi-modal freight flow and fuel consumption estimation.
Research on the output bit error rate of 2DPSK signal based on stochastic resonance theory

NASA Astrophysics Data System (ADS)

Yan, Daqin; Wang, Fuzhong; Wang, Shuo

2017-12-01

Binary differential phase-shift keying (2DPSK) signal is mainly used for high speed data transmission. However, the bit error rate of digital signal receiver is high in the case of wicked channel environment. In view of this situation, a novel method based on stochastic resonance (SR) is proposed, which is aimed to reduce the bit error rate of 2DPSK signal by coherent demodulation receiving. According to the theory of SR, a nonlinear receiver model is established, which is used to receive 2DPSK signal under small signal-to-noise ratio (SNR) circumstances (between -15 dB and 5 dB), and compared with the conventional demodulation method. The experimental results demonstrate that when the input SNR is in the range of -15 dB to 5 dB, the output bit error rate of nonlinear system model based on SR has a significant decline compared to the conventional model. It could reduce 86.15% when the input SNR equals -7 dB. Meanwhile, the peak value of the output signal spectrum is 4.25 times as that of the conventional model. Consequently, the output signal of the system is more likely to be detected and the accuracy can be greatly improved.
Modeling of processes of formation of the images in optical-electronic systems

NASA Astrophysics Data System (ADS)

Grudin, B. N.; Plotnikov, V. S.; Fischenko, V. K.

2001-08-01

The digital model of the multicomponent coherent optical system with arbitrary layout of optical elements (lasers, lenses, phototransparencies with recording of the function of transmission of a specimens or filters, photoregistrars), constructed with usage of fast algorithms is considered. The model is realized as the program for personal computers in operational systems Windows 95, 98 and Windows NT. At simulation, for example, coherent system consisting of twenty elementary optical cascades a relative error in the output image as a rule does not exceed 0.25% when N >= 256 (N x N - the number of discrete samples on the image), and time of calculation of the output image on a computer (Pentium-2, 300 MHz) for N = 512 does not exceed one minute. The program of simulation of coherent optical systems will be utilized in scientific researches and at tutoring the students of Far East State University.
Super short term forecasting of photovoltaic power generation output in micro grid

NASA Astrophysics Data System (ADS)

Gong, Cheng; Ma, Longfei; Chi, Zhongjun; Zhang, Baoqun; Jiao, Ran; Yang, Bing; Chen, Jianshu; Zeng, Shuang

2017-01-01

The prediction model combining data mining and support vector machine (SVM) was built. Which provide information of photovoltaic (PV) power generation output for economic operation and optimal control of micro gird, and which reduce influence of power system from PV fluctuation. Because of the characteristic which output of PV rely on radiation intensity, ambient temperature, cloudiness, etc., so data mining was brought in. This technology can deal with large amounts of historical data and eliminate superfluous data, by using fuzzy classifier of daily type and grey related degree. The model of SVM was built, which can dock with information from data mining. Based on measured data from a small PV station, the prediction model was tested. The numerical example shows that the prediction model is fast and accurate.
Analysis of inter-country input-output table based on citation network: How to measure the competition and collaboration between industrial sectors on the global value chain

PubMed Central

2017-01-01

The input-output table is comprehensive and detailed in describing the national economic system with complex economic relationships, which embodies information of supply and demand among industrial sectors. This paper aims to scale the degree of competition/collaboration on the global value chain from the perspective of econophysics. Global Industrial Strongest Relevant Network models were established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output tables and then transformed into Global Industrial Resource Competition Network/Global Industrial Production Collaboration Network models embodying the competitive/collaborative relationships based on bibliographic coupling/co-citation approach. Three indicators well suited for these two kinds of weighted and non-directed networks with self-loops were introduced, including unit weight for competitive/collaborative power, disparity in the weight for competitive/collaborative amplitude and weighted clustering coefficient for competitive/collaborative intensity. Finally, these models and indicators were further applied to empirically analyze the function of sectors in the latest World Input-Output Database, to reveal inter-sector competitive/collaborative status during the economic globalization. PMID:28873432
Analysis of inter-country input-output table based on citation network: How to measure the competition and collaboration between industrial sectors on the global value chain.

PubMed

Xing, Lizhi

2017-01-01

The input-output table is comprehensive and detailed in describing the national economic system with complex economic relationships, which embodies information of supply and demand among industrial sectors. This paper aims to scale the degree of competition/collaboration on the global value chain from the perspective of econophysics. Global Industrial Strongest Relevant Network models were established by extracting the strongest and most immediate industrial relevance in the global economic system with inter-country input-output tables and then transformed into Global Industrial Resource Competition Network/Global Industrial Production Collaboration Network models embodying the competitive/collaborative relationships based on bibliographic coupling/co-citation approach. Three indicators well suited for these two kinds of weighted and non-directed networks with self-loops were introduced, including unit weight for competitive/collaborative power, disparity in the weight for competitive/collaborative amplitude and weighted clustering coefficient for competitive/collaborative intensity. Finally, these models and indicators were further applied to empirically analyze the function of sectors in the latest World Input-Output Database, to reveal inter-sector competitive/collaborative status during the economic globalization.
Earth System Model Development and Analysis using FRE-Curator and Live Access Servers: On-demand analysis of climate model output with data provenance.

NASA Astrophysics Data System (ADS)

Radhakrishnan, A.; Balaji, V.; Schweitzer, R.; Nikonov, S.; O'Brien, K.; Vahlenkamp, H.; Burger, E. F.

2016-12-01

There are distinct phases in the development cycle of an Earth system model. During the model development phase, scientists make changes to code and parameters and require rapid access to results for evaluation. During the production phase, scientists may make an ensemble of runs with different settings, and produce large quantities of output, that must be further analyzed and quality controlled for scientific papers and submission to international projects such as the Climate Model Intercomparison Project (CMIP). During this phase, provenance is a key concern:being able to track back from outputs to inputs. We will discuss one of the paths taken at GFDL in delivering tools across this lifecycle, offering on-demand analysis of data by integrating the use of GFDL's in-house FRE-Curator, Unidata's THREDDS and NOAA PMEL's Live Access Servers (LAS).Experience over this lifecycle suggests that a major difficulty in developing analysis capabilities is only partially the scientific content, but often devoted to answering the questions "where is the data?" and "how do I get to it?". "FRE-Curator" is the name of a database-centric paradigm used at NOAA GFDL to ingest information about the model runs into an RDBMS (Curator database). The components of FRE-Curator are integrated into Flexible Runtime Environment workflow and can be invoked during climate model simulation. The front end to FRE-Curator, known as the Model Development Database Interface (MDBI) provides an in-house web-based access to GFDL experiments: metadata, analysis output and more. In order to provide on-demand visualization, MDBI uses Live Access Servers which is a highly configurable web server designed to provide flexible access to geo-referenced scientific data, that makes use of OPeNDAP. Model output saved in GFDL's tape archive, the size of the database and experiments, continuous model development initiatives with more dynamic configurations add complexity and challenges in providing an on-demand visualization experience to our GFDL users.
Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles

PubMed Central

Zanos, Theodoros P.; Courellis, Spiros H.; Berger, Theodore W.; Hampson, Robert E.; Deadwyler, Sam A.; Marmarelis, Vasilis Z.

2009-01-01

The increasing availability of multiunit recordings gives new urgency to the need for effective analysis of “multidimensional” time-series data that are derived from the recorded activity of neuronal ensembles in the form of multiple sequences of action potentials—treated mathematically as point-processes and computationally as spike-trains. Whether in conditions of spontaneous activity or under conditions of external stimulation, the objective is the identification and quantification of possible causal links among the neurons generating the observed binary signals. A multiple-input/multiple-output (MIMO) modeling methodology is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons. These causal interrelationships are modeled as transformations of spike-trains recorded from a set of neurons designated as the “inputs” into spike-trains recorded from another set of neurons designated as the “outputs.” The MIMO model is composed of a set of multiinput/single-output (MISO) modules, one for each output. Each module is the cascade of a MISO Volterra model and a threshold operator generating the output spikes. The Laguerre expansion approach is used to estimate the Volterra kernels of each MISO module from the respective input–output data using the least-squares method. The predictive performance of the model is evaluated with the use of the receiver operating characteristic (ROC) curve, from which the optimum threshold is also selected. The Mann–Whitney statistic is used to select the significant inputs for each output by examining the statistical significance of improvements in the predictive accuracy of the model when the respective inputs is included. Illustrative examples are presented for a simulated system and for an actual application using multiunit data recordings from the hippocampus of a behaving rat. PMID:18701382
Combination of Alternative Models by Mutual Data Assimilation: Supermodeling With A Suite of Primitive Equation Models

NASA Astrophysics Data System (ADS)

Duane, G. S.; Selten, F.

2016-12-01

Different models of climate and weather commonly give projections/predictions that differ widely in their details. While averaging of model outputs almost always improves results, nonlinearity implies that further improvement can be obtained from model interaction in run time, as has already been demonstrated with toy systems of ODEs and idealized quasigeostrophic models. In the supermodeling scheme, models effectively assimilate data from one another and partially synchronize with one another. Spread among models is manifest as a spread in possible inter-model connection coefficients, so that the models effectively "agree to disagree". Here, we construct a supermodel formed from variants of the SPEEDO model, a primitive-equation atmospheric model (SPEEDY) coupled to ocean and land. A suite of atmospheric models, coupled to the same ocean and land, is chosen to represent typical differences among climate models by varying model parameters. Connections are introduced between all pairs of corresponding independent variables at synoptic-scale intervals. Strengths of the inter-atmospheric connections can be considered to represent inverse inter-model observation error. Connection strengths are adapted based on an established procedure that extends the dynamical equations of a pair of synchronizing systems to synchronize parameters as well. The procedure is applied to synchronize the suite of SPEEDO models with another SPEEDO model regarded as "truth", adapting the inter-model connections along the way. The supermodel with trained connections gives marginally lower error in all fields than any weighted combination of the separate model outputs when used in "weather-prediction mode", i.e. with constant nudging to truth. Stronger results are obtained if a supermodel is used to predict the formation of coherent structures or the frequency of such. Partially synchronized SPEEDO models give a better representation of the blocked-zonal index cycle than does a weighted average of the constituent model outputs. We have thus shown that supermodeling and the synchronization-based procedure to adapt inter-model connections give results superior to output averaging not only with highly nonlinear toy systems, but with smaller nonlinearities as occur in climate models.
Research on laser detonation pulse circuit with low-power based on super capacitor

NASA Astrophysics Data System (ADS)

Wang, Hao-yu; Hong, Jin; He, Aifeng; Jing, Bo; Cao, Chun-qiang; Ma, Yue; Chu, En-yi; Hu, Ya-dong

2018-03-01

According to the demand of laser initiating device miniaturization and low power consumption of weapon system, research on the low power pulse laser detonation circuit with super capacitor. Established a dynamic model of laser output based on super capacitance storage capacity, discharge voltage and programmable output pulse width. The output performance of the super capacitor under different energy storage capacity and discharge voltage is obtained by simulation. The experimental test system was set up, and the laser diode of low power pulsed laser detonation circuit was tested and the laser output waveform of laser diode in different energy storage capacity and discharge voltage was collected. Experiments show that low power pulse laser detonation based on super capacitor energy storage circuit discharge with high efficiency, good transient performance, for a low power consumption requirement, for laser detonation system and low power consumption and provide reference light miniaturization of engineering practice.
Functional expansion representations of artificial neural networks

NASA Technical Reports Server (NTRS)

Gray, W. Steven

1992-01-01

In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.
Goal Directed Model Inversion: Learning Within Domain Constraints

NASA Technical Reports Server (NTRS)

Colombano, Silvano P.; Compton, Michael; Raghavan, Bharathi; Friedland, Peter (Technical Monitor)

1994-01-01

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: (1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e., would not need to learn everything from scratch as normally required at present; and (2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.
Goal Directed Model Inversion: Learning Within Domain Constraints

NASA Technical Reports Server (NTRS)

Colombano, Silvano P.; Compton, Michael; Raghavan, Bharathi; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: 1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e. would not need to learn everything from scratch as normally required at present, and 2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.
Designing ecological climate change impact assessments to reflect key climatic drivers

USGS Publications Warehouse

Sofaer, Helen R.; Barsugli, Joseph J.; Jarnevich, Catherine S.; Abatzoglou, John T.; Talbert, Marian; Miller, Brian W.; Morisette, Jeffrey T.

2017-01-01

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.
Designing ecological climate change impact assessments to reflect key climatic drivers.

PubMed

Sofaer, Helen R; Barsugli, Joseph J; Jarnevich, Catherine S; Abatzoglou, John T; Talbert, Marian K; Miller, Brian W; Morisette, Jeffrey T

2017-07-01

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive - such as means or extremes - can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the 'model space' approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling. © 2017 John Wiley & Sons Ltd.
The Description and Validation of a Computationally-Efficient CH4-CO-OH (ECCOHv1.01) Chemistry Module for 3D Model Applications

NASA Technical Reports Server (NTRS)

Elshorbany, Yasin F.; Duncan, Bryan N.; Strode, Sarah A.; Wang, James S.; Kouatchou, Jules

2016-01-01

We present the Efficient CH4-CO-OH (ECCOH) chemistry module that allows for the simulation of the methane, carbon monoxide, and hydroxyl radical (CH4-CO- OH) system, within a chemistry climate model, carbon cycle model, or Earth system model. The computational efficiency of the module allows many multi-decadal sensitivity simulations of the CH4-CO-OH system, which primarily determines the global atmospheric oxidizing capacity. This capability is important for capturing the nonlinear feedbacks of the CH4-CO-OH system and understanding the perturbations to methane, CO, and OH, and the concomitant impacts on climate. We implemented the ECCOH chemistry module in the NASA GEOS-5 atmospheric global circulation model (AGCM), performed multiple sensitivity simulations of the CH4-CO-OH system over 2 decades, and evaluated the model output with surface and satellite data sets of methane and CO. The favorable comparison of output from the ECCOH chemistry module (as configured in the GEOS- 5 AGCM) with observations demonstrates the fidelity of the module for use in scientific research.

Finite element based N-Port model for preliminary design of multibody systems

NASA Astrophysics Data System (ADS)

Sanfedino, Francesco; Alazard, Daniel; Pommier-Budinger, Valérie; Falcoz, Alexandre; Boquet, Fabrice

2018-02-01

This article presents and validates a general framework to build a linear dynamic Finite Element-based model of large flexible structures for integrated Control/Structure design. An extension of the Two-Input Two-Output Port (TITOP) approach is here developed. The authors had already proposed such framework for simple beam-like structures: each beam was considered as a TITOP sub-system that could be interconnected to another beam thanks to the ports. The present work studies bodies with multiple attaching points by allowing complex interconnections among several sub-structures in tree-like assembly. The TITOP approach is extended to generate NINOP (N-Input N-Output Port) models. A Matlab toolbox is developed integrating beam and bending plate elements. In particular a NINOP formulation of bending plates is proposed to solve analytic two-dimensional problems. The computation of NINOP models using the outputs of a MSC/Nastran modal analysis is also investigated in order to directly use the results provided by a commercial finite element software. The main advantage of this tool is to provide a model of a multibody system under the form of a block diagram with a minimal number of states. This model is easy to operate for preliminary design and control. An illustrative example highlights the potential of the proposed approach: the synthesis of the dynamical model of a spacecraft with two deployable and flexible solar arrays.
Divvy Economies Based On (An Abstract) Temperature

NASA Astrophysics Data System (ADS)

Collins, Dennis G.

2004-04-01

The Leontief Input-Output economic system can provide a model for a one-parameter family of economic systems based on an abstract temperature T. In particular, given a normalized input-output matrix R and taking R= R(1), a family of economic systems R(1/T)=R(α) is developed that represents heating (T>1) and cooling (T<1) of the economy relative to T=1. .The economy for a given value of T represents the solution of a constrained maximum entropy problem.
Digital Avionics Information System (DAIS): Training Requirements Analysis Model Users Guide. Final Report.

ERIC Educational Resources Information Center

Czuchry, Andrew J.; And Others

This user's guide describes the functions, logical operations and subroutines, input data requirements, and available outputs of the Training Requirements Analysis Model (TRAMOD), a computerized analytical life cycle cost modeling system for use in the early stages of system design. Operable in a stand-alone mode, TRAMOD can be used for the…
Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems

PubMed Central

Li, Zhining; Zhang, Yingtang; Yin, Gang

2018-01-01

The measurement error of the differencing (i.e., using two homogenous field sensors at a known baseline distance) magnetic gradient tensor system includes the biases, scale factors, nonorthogonality of the single magnetic sensor, and the misalignment error between the sensor arrays, all of which can severely affect the measurement accuracy. In this paper, we propose a low-cost artificial vector calibration method for the tensor system. Firstly, the error parameter linear equations are constructed based on the single-sensor’s system error model to obtain the artificial ideal vector output of the platform, with the total magnetic intensity (TMI) scalar as a reference by two nonlinear conversions, without any mathematical simplification. Secondly, the Levenberg–Marquardt algorithm is used to compute the integrated model of the 12 error parameters by nonlinear least-squares fitting method with the artificial vector output as a reference, and a total of 48 parameters of the system is estimated simultaneously. The calibrated system outputs along the reference platform-orthogonal coordinate system. The analysis results show that the artificial vector calibrated output can track the orientation fluctuations of TMI accurately, effectively avoiding the “overcalibration” problem. The accuracy of the error parameters’ estimation in the simulation is close to 100%. The experimental root-mean-square error (RMSE) of the TMI and tensor components is less than 3 nT and 20 nT/m, respectively, and the estimation of the parameters is highly robust. PMID:29373544
Comparative study of DPAL and XPAL systems and selection principal of parameters

NASA Astrophysics Data System (ADS)

Huang, Wei; Tan, Rongqing; Li, Zhiyong; Han, Gaoce; Li, Hui

2016-10-01

A theoretical model based on common pump structure is proposed to analyze the laser output characteristics of DPAL (Diode pumped alkali vapor laser) and XPAL (Exciplex pumped alkali laser) in this paper. The model predicts that an optical-to-optical efficiency approaching 80% can be achieved for continuous-wave four- and five-XPAL systems with broadband pumping which is several times of pumped linewidth for DPAL. Operation parameters including pumped intensity, temperature, cell' s length, mixed gas concentration, pumped linewidth and output mirror reflectivity are analyzed for DPAL and XPAL systems basing on the kinetic model. The result shows a better performance in Cs-Ar XPAL laser with requirements of relatively high Ar concentration, high pumped intensity and high temperature. Comparatively, for Cs-DPAL laser, lower temperature and lower pumped intensity should be acquired. In addition, the predictions of selection principal of temperature and cell's length are also presented. The conception of the equivalent "alkali areal density" is proposed in this paper. It is defined as the product of the alkali density and cell's length. The result shows that the output characteristics of DPAL (or XPAL) system with the same alkali areal density but different temperatures turn out to be equal. It is the areal density that reflects the potential of DPAL or XPAL systems directly. A more detailed analysis of similar influences of cavity parameters with the same areal density is also presented. The detailed results of continuous-wave DPAL and XPAL performances as a function of pumped laser linewidth and mixed gas pressure are presented along with an analysis of influences of output coupler.
A space transportation system operations model

NASA Technical Reports Server (NTRS)

Morris, W. Douglas; White, Nancy H.

1987-01-01

Presented is a description of a computer program which permits assessment of the operational support requirements of space transportation systems functioning in both a ground- and space-based environment. The scenario depicted provides for the delivery of payloads from Earth to a space station and beyond using upper stages based at the station. Model results are scenario dependent and rely on the input definitions of delivery requirements, task times, and available resources. Output is in terms of flight rate capabilities, resource requirements, and facility utilization. A general program description, program listing, input requirements, and sample output are included.
Information Architecture for Interactive Archives at the Community Coordianted Modeling Center

NASA Astrophysics Data System (ADS)

De Zeeuw, D.; Wiegand, C.; Kuznetsova, M.; Mullinix, R.; Boblitt, J. M.

2017-12-01

The Community Coordinated Modeling Center (CCMC) is upgrading its meta-data system for model simulations to be compliant with the SPASE meta-data standard. This work is helping to enhance the SPASE standards for simulations to better describe the wide variety of models and their output. It will enable much more sophisticated and automated metrics and validation efforts at the CCMC, as well as much more robust searches for specific types of output. The new meta-data will also allow much more tailored run submissions as it will allow some code options to be selected for Run-On-Request models. We will also demonstrate data accessibility through an implementation of the Heliophysics Application Programmer's Interface (HAPI) protocol of data otherwise available throught the integrated space weather analysis system (iSWA).
Adaptive nonlinear control for autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Black, William S.

We present the background and motivation for ground vehicle autonomy, and focus on uses for space-exploration. Using a simple design example of an autonomous ground vehicle we derive the equations of motion. After providing the mathematical background for nonlinear systems and control we present two common methods for exactly linearizing nonlinear systems, feedback linearization and backstepping. We use these in combination with three adaptive control methods: model reference adaptive control, adaptive sliding mode control, and extremum-seeking model reference adaptive control. We show the performances of each combination through several simulation results. We then consider disturbances in the system, and design nonlinear disturbance observers for both single-input-single-output and multi-input-multi-output systems. Finally, we show the performance of these observers with simulation results.
Assigning Quantitative Function to Post-Translational Modifications Reveals Multiple Sites of Phosphorylation That Tune Yeast Pheromone Signaling Output

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

Pincus, David; Ryan, Christopher J.; Smith, Richard D.

2013-03-12

Cell signaling systems transmit information by post-translationally modifying signaling proteins, often via phosphorylation. While thousands of sites of phosphorylation have been identified in proteomic studies, the vast majority of sites have no known function. Assigning functional roles to the catalog of uncharacterized phosphorylation sites is a key research challenge. Here we present a general approach to address this challenge and apply it to a prototypical signaling pathway, the pheromone response pathway in Saccharomyces cerevisiae. The pheromone pathway includes a mitogen activated protein kinase (MAPK) cascade activated by a G-protein coupled receptor (GPCR). We used mass spectrometry-based proteomics to identify sitesmore » whose phosphorylation changed when the system was active, and evolutionary conservation to assign priority to a list of candidate MAPK regulatory sites. We made targeted alterations in those sites, and measured the effects of the mutations on pheromone pathway output in single cells. Our work identified six new sites that quantitatively tuned system output. We developed simple computational models to find system architectures that recapitulated the quantitative phenotypes of the mutants. Our results identify a number of regulated phosphorylation events that contribute to adjust the input-output relationship of this model eukaryotic signaling system. We believe this combined approach constitutes a general means not only to reveal modification sites required to turn a pathway on and off, but also those required for more subtle quantitative effects that tune pathway output. Our results further suggest that relatively small quantitative influences from individual regulatory phosphorylation events endow signaling systems with plasticity that evolution may exploit to quantitatively tailor signaling outcomes.« less
Input and output constraints-based stabilisation of switched nonlinear systems with unstable subsystems and its application

NASA Astrophysics Data System (ADS)

Chen, Chao; Liu, Qian; Zhao, Jun

2018-01-01

This paper studies the problem of stabilisation of switched nonlinear systems with output and input constraints. We propose a recursive approach to solve this issue. None of the subsystems are assumed to be stablisable while the switched system is stabilised by dual design of controllers for subsystems and a switching law. When only dealing with bounded input, we provide nested switching controllers using an extended backstepping procedure. If both input and output constraints are taken into consideration, a Barrier Lyapunov Function is employed during operation to construct multiple Lyapunov functions for switched nonlinear system in the backstepping procedure. As a practical example, the control design of an equilibrium manifold expansion model of aero-engine is given to demonstrate the effectiveness of the proposed design method.
Multi input single output model predictive control of non-linear bio-polymerization process

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

Arumugasamy, Senthil Kumar; Ahmad, Z.

This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state spacemore » model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.« less
Information-System Structure by Communication-Technology Concepts: A Cybernetic Model Approach.

ERIC Educational Resources Information Center

Reisig, Gerhard H. R.

1978-01-01

Presents the "Evidence-of-Existence" information system in which the structure is developed, with application of cybernetic concepts, as an isomorphic model in analogy to the system structure of communication technology. Three criteria of structuring are postulated: (1) source-channel-sink, with input-output characteristics, (2) filter-type…
Method for Predicting and Optimizing System Parameters for Electrospinning System

NASA Technical Reports Server (NTRS)

Wincheski, Russell A. (Inventor)

2011-01-01

An electrospinning system using a spinneret and a counter electrode is first operated for a fixed amount of time at known system and operational parameters to generate a fiber mat having a measured fiber mat width associated therewith. Next, acceleration of the fiberizable material at the spinneret is modeled to determine values of mass, drag, and surface tension associated with the fiberizable material at the spinneret output. The model is then applied in an inversion process to generate predicted values of an electric charge at the spinneret output and an electric field between the spinneret and electrode required to fabricate a selected fiber mat design. The electric charge and electric field are indicative of design values for system and operational parameters needed to fabricate the selected fiber mat design.
Feature-based data assimilation in geophysics

NASA Astrophysics Data System (ADS)

Morzfeld, Matthias; Adams, Jesse; Lunderman, Spencer; Orozco, Rafael

2018-05-01

Many applications in science require that computational models and data be combined. In a Bayesian framework, this is usually done by defining likelihoods based on the mismatch of model outputs and data. However, matching model outputs and data in this way can be unnecessary or impossible. For example, using large amounts of steady state data is unnecessary because these data are redundant. It is numerically difficult to assimilate data in chaotic systems. It is often impossible to assimilate data of a complex system into a low-dimensional model. As a specific example, consider a low-dimensional stochastic model for the dipole of the Earth's magnetic field, while other field components are ignored in the model. The above issues can be addressed by selecting features of the data, and defining likelihoods based on the features, rather than by the usual mismatch of model output and data. Our goal is to contribute to a fundamental understanding of such a feature-based approach that allows us to assimilate selected aspects of data into models. We also explain how the feature-based approach can be interpreted as a method for reducing an effective dimension and derive new noise models, based on perturbed observations, that lead to computationally efficient solutions. Numerical implementations of our ideas are illustrated in four examples.
High-output LED-based light engine for profile lighting fixtures with high color uniformity using freeform reflectors.

PubMed

Gadegaard, Jesper; Jensen, Thøger Kari; Jørgensen, Dennis Thykjær; Kristensen, Peter Kjær; Søndergaard, Thomas; Pedersen, Thomas Garm; Pedersen, Kjeld

2016-02-20

In the stage lighting and entertainment market, light engines (LEs) for lighting fixtures are often based on high-intensity discharge (HID) bulbs. Switching to LED-based light engines gives possibilities for fast switching, additive color mixing, a longer lifetime, and potentially, more energy-efficient systems. The lumen output of a single LED is still not sufficient to replace an HID source in high-output profile fixtures, but combining multiple LEDs can create an LE with a similar output, but with added complexity. This paper presents the results of modeling and testing such a light engine. Custom ray-tracing software was used to design a high-output red, green and blue LED-based light engine with twelve CBT-90 LEDs using a dual-reflector principle. The simulated optical system efficiency was 0.626 with a perfect (R=1) reflector coating for light delivered on a target surface through the entire optical system. A profile lighting fixture prototype was created, and provided an output of 6744 lumen and an efficiency of 0.412. The lower efficiency was mainly due to a non-optimal reflector coating, and the optimized design is expected to reach a significantly higher efficiency.
Model-free adaptive control of supercritical circulating fluidized-bed boilers

DOEpatents

Cheng, George Shu-Xing; Mulkey, Steven L

2014-12-16

A novel 3-Input-3-Output (3.times.3) Fuel-Air Ratio Model-Free Adaptive (MFA) controller is introduced, which can effectively control key process variables including Bed Temperature, Excess O2, and Furnace Negative Pressure of combustion processes of advanced boilers. A novel 7-input-7-output (7.times.7) MFA control system is also described for controlling a combined 3-Input-3-Output (3.times.3) process of Boiler-Turbine-Generator (BTG) units and a 5.times.5 CFB combustion process of advanced boilers. Those boilers include Circulating Fluidized-Bed (CFB) Boilers and Once-Through Supercritical Circulating Fluidized-Bed (OTSC CFB) Boilers.
Gradient-based controllers for timed continuous Petri nets

NASA Astrophysics Data System (ADS)

Lefebvre, Dimitri; Leclercq, Edouard; Druaux, Fabrice; Thomas, Philippe

2015-07-01

This paper is about control design for timed continuous Petri nets that are described as piecewise affine systems. In this context, the marking vector is considered as the state space vector, weighted marking of place subsets are defined as the model outputs and the model inputs correspond to multiplicative control actions that slow down the firing rate of some controllable transitions. Structural and functional sensitivity of the outputs with respect to the inputs are discussed in terms of Petri nets. Then, gradient-based controllers (GBC) are developed in order to adapt the control actions of the controllable transitions according to desired trajectories of the outputs.
Study of cumulative fatigue damage detection for used parts with nonlinear output frequency response functions based on NARMAX modelling

NASA Astrophysics Data System (ADS)

Huang, Honglan; Mao, Hanying; Mao, Hanling; Zheng, Weixue; Huang, Zhenfeng; Li, Xinxin; Wang, Xianghong

2017-12-01

Cumulative fatigue damage detection for used parts plays a key role in the process of remanufacturing engineering and is related to the service safety of the remanufactured parts. In light of the nonlinear properties of used parts caused by cumulative fatigue damage, the based nonlinear output frequency response functions detection approach offers a breakthrough to solve this key problem. First, a modified PSO-adaptive lasso algorithm is introduced to improve the accuracy of the NARMAX model under impulse hammer excitation, and then, an effective new algorithm is derived to estimate the nonlinear output frequency response functions under rectangular pulse excitation, and a based nonlinear output frequency response functions index is introduced to detect the cumulative fatigue damage in used parts. Then, a novel damage detection approach that integrates the NARMAX model and the rectangular pulse is proposed for nonlinear output frequency response functions identification and cumulative fatigue damage detection of used parts. Finally, experimental studies of fatigued plate specimens and used connecting rod parts are conducted to verify the validity of the novel approach. The obtained results reveal that the new approach can detect cumulative fatigue damages of used parts effectively and efficiently and that the various values of the based nonlinear output frequency response functions index can be used to detect the different fatigue damages or working time. Since the proposed new approach can extract nonlinear properties of systems by only a single excitation of the inspected system, it shows great promise for use in remanufacturing engineering applications.
Assessment of Effectiveness of Geologic Isolation Systems. Variable thickness transient ground-water flow model. Volume 2. Users' manual

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

Reisenauer, A.E.

1979-12-01

A system of computer codes to aid in the preparation and evaluation of ground-water model input, as well as in the computer codes and auxillary programs developed and adapted for use in modeling major ground-water aquifers is described. The ground-water model is interactive, rather than a batch-type model. Interactive models have been demonstrated to be superior to batch in the ground-water field. For example, looking through reams of numerical lists can be avoided with the much superior graphical output forms or summary type numerical output. The system of computer codes permits the flexibility to develop rapidly the model-required data filesmore » from engineering data and geologic maps, as well as efficiently manipulating the voluminous data generated. Central to these codes is the Ground-water Model, which given the boundary value problem, produces either the steady-state or transient time plane solutions. A sizeable part of the codes available provide rapid evaluation of the results. Besides contouring the new water potentials, the model allows graphical review of streamlines of flow, travel times, and detailed comparisons of surfaces or points at designated wells. Use of the graphics scopes provide immediate, but temporary displays which can be used for evaluation of input and output and which can be reproduced easily on hard copy devices, such as a line printer, Calcomp plotter and image photographs.« less
A network-base analysis of CMIP5 "historical" experiments

NASA Astrophysics Data System (ADS)

Bracco, A.; Foudalis, I.; Dovrolis, C.

2012-12-01

In computer science, "complex network analysis" refers to a set of metrics, modeling tools and algorithms commonly used in the study of complex nonlinear dynamical systems. Its main premise is that the underlying topology or network structure of a system has a strong impact on its dynamics and evolution. By allowing to investigate local and non-local statistical interaction, network analysis provides a powerful, but only marginally explored, framework to validate climate models and investigate teleconnections, assessing their strength, range, and impacts on the climate system. In this work we propose a new, fast, robust and scalable methodology to examine, quantify, and visualize climate sensitivity, while constraining general circulation models (GCMs) outputs with observations. The goal of our novel approach is to uncover relations in the climate system that are not (or not fully) captured by more traditional methodologies used in climate science and often adopted from nonlinear dynamical systems analysis, and to explain known climate phenomena in terms of the network structure or its metrics. Our methodology is based on a solid theoretical framework and employs mathematical and statistical tools, exploited only tentatively in climate research so far. Suitably adapted to the climate problem, these tools can assist in visualizing the trade-offs in representing global links and teleconnections among different data sets. Here we present the methodology, and compare network properties for different reanalysis data sets and a suite of CMIP5 coupled GCM outputs. With an extensive model intercomparison in terms of the climate network that each model leads to, we quantify how each model reproduces major teleconnections, rank model performances, and identify common or specific errors in comparing model outputs and observations.

Volterra-series-based nonlinear system modeling and its engineering applications: A state-of-the-art review

NASA Astrophysics Data System (ADS)

Cheng, C. M.; Peng, Z. K.; Zhang, W. M.; Meng, G.

2017-03-01

Nonlinear problems have drawn great interest and extensive attention from engineers, physicists and mathematicians and many other scientists because most real systems are inherently nonlinear in nature. To model and analyze nonlinear systems, many mathematical theories and methods have been developed, including Volterra series. In this paper, the basic definition of the Volterra series is recapitulated, together with some frequency domain concepts which are derived from the Volterra series, including the general frequency response function (GFRF), the nonlinear output frequency response function (NOFRF), output frequency response function (OFRF) and associated frequency response function (AFRF). The relationship between the Volterra series and other nonlinear system models and nonlinear problem solving methods are discussed, including the Taylor series, Wiener series, NARMAX model, Hammerstein model, Wiener model, Wiener-Hammerstein model, harmonic balance method, perturbation method and Adomian decomposition. The challenging problems and their state of arts in the series convergence study and the kernel identification study are comprehensively introduced. In addition, a detailed review is then given on the applications of Volterra series in mechanical engineering, aeroelasticity problem, control engineering, electronic and electrical engineering.
A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control.

PubMed

Wang, Tong; Gao, Huijun; Qiu, Jianbin

2016-02-01

This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.
Water planning in a mixed land use Mediterranean area: point-source abstraction and pollution scenarios by a numerical model of varying stream-aquifer regime.

PubMed

Du, Mingxuan; Fouché, Olivier; Zavattero, Elodie; Ma, Qiang; Delestre, Olivier; Gourbesville, Philippe

2018-02-22

Integrated hydrodynamic modelling is an efficient approach for making semi-quantitative scenarios reliable enough for groundwater management, provided that the numerical simulations are from a validated model. The model set-up, however, involves many inputs due to the complexity of both the hydrological system and the land use. The case study of a Mediterranean alluvial unconfined aquifer in the lower Var valley (Southern France) is useful to test a method to estimate lacking data on water abstraction by small farms in urban context. With this estimation of the undocumented pumping volumes, and after calibration of the exchange parameters of the stream-aquifer system with the help of a river model, the groundwater flow model shows a high goodness of fit with the measured potentiometric levels. The consistency between simulated results and real behaviour of the system, with regard to the observed effects of lowering weirs and previously published hydrochemistry data, confirms reliability of the groundwater flow model. On the other hand, accuracy of the transport model output may be influenced by many parameters, many of which are not derived from field measurements. In this case study, for which river-aquifer feeding is the main control, the partition coefficient between direct recharge and runoff does not show a significant effect on the transport model output, and therefore, uncertainty of the hydrological terms such as evapotranspiration and runoff is not a first-rank issue to the pollution propagation. The simulation of pollution scenarios with the model returns expected pessimistic outputs, with regard to hazard management. The model is now ready to be used in a decision support system by the local water supply managers.
Visualizing Terrestrial and Aquatic Systems in 3-D

EPA Science Inventory

The environmental modeling community has a long-standing need for affordable, easy-to-use tools that support 3-D visualization of complex spatial and temporal model output. The Visualization of Terrestrial and Aquatic Systems project (VISTAS) aims to help scientists produce effe...
Analysis of information systems for hydropower operations

NASA Technical Reports Server (NTRS)

Sohn, R. L.; Becker, L.; Estes, J.; Simonett, D.; Yeh, W. W. G.

1976-01-01

The operations of hydropower systems were analyzed with emphasis on water resource management, to determine how aerospace derived information system technologies can increase energy output. Better utilization of water resources was sought through improved reservoir inflow forecasting based on use of hydrometeorologic information systems with new or improved sensors, satellite data relay systems, and use of advanced scheduling techniques for water release. Specific mechanisms for increased energy output were determined, principally the use of more timely and accurate short term (0-7 days) inflow information to reduce spillage caused by unanticipated dynamic high inflow events. The hydrometeorologic models used in predicting inflows were examined to determine the sensitivity of inflow prediction accuracy to the many variables employed in the models, and the results used to establish information system requirements. Sensor and data handling system capabilities were reviewed and compared to the requirements, and an improved information system concept outlined.
Analysis of information systems for hydropower operations: Executive summary

NASA Technical Reports Server (NTRS)

Sohn, R. L.; Becker, L.; Estes, J.; Simonett, D.; Yeh, W.

1976-01-01

An analysis was performed of the operations of hydropower systems, with emphasis on water resource management, to determine how aerospace derived information system technologies can effectively increase energy output. Better utilization of water resources was sought through improved reservoir inflow forecasting based on use of hydrometeorologic information systems with new or improved sensors, satellite data relay systems, and use of advanced scheduling techniques for water release. Specific mechanisms for increased energy output were determined, principally the use of more timely and accurate short term (0-7 days) inflow information to reduce spillage caused by unanticipated dynamic high inflow events. The hydrometeorologic models used in predicting inflows were examined in detail to determine the sensitivity of inflow prediction accuracy to the many variables employed in the models, and the results were used to establish information system requirements. Sensor and data handling system capabilities were reviewed and compared to the requirements, and an improved information system concept was outlined.
Adaptive Data-based Predictive Control for Short Take-off and Landing (STOL) Aircraft

NASA Technical Reports Server (NTRS)

Barlow, Jonathan Spencer; Acosta, Diana Michelle; Phan, Minh Q.

2010-01-01

Data-based Predictive Control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. The characteristics of adaptive data-based predictive control are particularly appropriate for the control of nonlinear and time-varying systems, such as Short Take-off and Landing (STOL) aircraft. STOL is a capability of interest to NASA because conceptual Cruise Efficient Short Take-off and Landing (CESTOL) transport aircraft offer the ability to reduce congestion in the terminal area by utilizing existing shorter runways at airports, as well as to lower community noise by flying steep approach and climb-out patterns that reduce the noise footprint of the aircraft. In this study, adaptive data-based predictive control is implemented as an integrated flight-propulsion controller for the outer-loop control of a CESTOL-type aircraft. Results show that the controller successfully tracks velocity while attempting to maintain a constant flight path angle, using longitudinal command, thrust and flap setting as the control inputs.
Model reduction of nonsquare linear MIMO systems using multipoint matrix continued-fraction expansions

NASA Technical Reports Server (NTRS)

Guo, Tong-Yi; Hwang, Chyi; Shieh, Leang-San

1994-01-01

This paper deals with the multipoint Cauer matrix continued-fraction expansion (MCFE) for model reduction of linear multi-input multi-output (MIMO) systems with various numbers of inputs and outputs. A salient feature of the proposed MCFE approach to model reduction of MIMO systems with square transfer matrices is its equivalence to the matrix Pade approximation approach. The Cauer second form of the ordinary MCFE for a square transfer function matrix is generalized in this paper to a multipoint and nonsquare-matrix version. An interesting connection of the multipoint Cauer MCFE method to the multipoint matrix Pade approximation method is established. Also, algorithms for obtaining the reduced-degree matrix-fraction descriptions and reduced-dimensional state-space models from a transfer function matrix via the multipoint Cauer MCFE algorithm are presented. Practical advantages of using the multipoint Cauer MCFE are discussed and a numerical example is provided to illustrate the algorithms.
Balancing the stochastic description of uncertainties as a function of hydrologic model complexity

NASA Astrophysics Data System (ADS)

Del Giudice, D.; Reichert, P.; Albert, C.; Kalcic, M.; Logsdon Muenich, R.; Scavia, D.; Bosch, N. S.; Michalak, A. M.

2016-12-01

Uncertainty analysis is becoming an important component of forecasting water and pollutant fluxes in urban and rural environments. Properly accounting for errors in the modeling process can help to robustly assess the uncertainties associated with the inputs (e.g. precipitation) and outputs (e.g. runoff) of hydrological models. In recent years we have investigated several Bayesian methods to infer the parameters of a mechanistic hydrological model along with those of the stochastic error component. The latter describes the uncertainties of model outputs and possibly inputs. We have adapted our framework to a variety of applications, ranging from predicting floods in small stormwater systems to nutrient loads in large agricultural watersheds. Given practical constraints, we discuss how in general the number of quantities to infer probabilistically varies inversely with the complexity of the mechanistic model. Most often, when evaluating a hydrological model of intermediate complexity, we can infer the parameters of the model as well as of the output error model. Describing the output errors as a first order autoregressive process can realistically capture the "downstream" effect of inaccurate inputs and structure. With simpler runoff models we can additionally quantify input uncertainty by using a stochastic rainfall process. For complex hydrologic transport models, instead, we show that keeping model parameters fixed and just estimating time-dependent output uncertainties could be a viable option. The common goal across all these applications is to create time-dependent prediction intervals which are both reliable (cover the nominal amount of validation data) and precise (are as narrow as possible). In conclusion, we recommend focusing both on the choice of the hydrological model and of the probabilistic error description. The latter can include output uncertainty only, if the model is computationally-expensive, or, with simpler models, it can separately account for different sources of errors like in the inputs and the structure of the model.
Approximate Bayesian Computation by Subset Simulation using hierarchical state-space models

NASA Astrophysics Data System (ADS)

Vakilzadeh, Majid K.; Huang, Yong; Beck, James L.; Abrahamsson, Thomas

2017-02-01

A new multi-level Markov Chain Monte Carlo algorithm for Approximate Bayesian Computation, ABC-SubSim, has recently appeared that exploits the Subset Simulation method for efficient rare-event simulation. ABC-SubSim adaptively creates a nested decreasing sequence of data-approximating regions in the output space that correspond to increasingly closer approximations of the observed output vector in this output space. At each level, multiple samples of the model parameter vector are generated by a component-wise Metropolis algorithm so that the predicted output corresponding to each parameter value falls in the current data-approximating region. Theoretically, if continued to the limit, the sequence of data-approximating regions would converge on to the observed output vector and the approximate posterior distributions, which are conditional on the data-approximation region, would become exact, but this is not practically feasible. In this paper we study the performance of the ABC-SubSim algorithm for Bayesian updating of the parameters of dynamical systems using a general hierarchical state-space model. We note that the ABC methodology gives an approximate posterior distribution that actually corresponds to an exact posterior where a uniformly distributed combined measurement and modeling error is added. We also note that ABC algorithms have a problem with learning the uncertain error variances in a stochastic state-space model and so we treat them as nuisance parameters and analytically integrate them out of the posterior distribution. In addition, the statistical efficiency of the original ABC-SubSim algorithm is improved by developing a novel strategy to regulate the proposal variance for the component-wise Metropolis algorithm at each level. We demonstrate that Self-regulated ABC-SubSim is well suited for Bayesian system identification by first applying it successfully to model updating of a two degree-of-freedom linear structure for three cases: globally, locally and un-identifiable model classes, and then to model updating of a two degree-of-freedom nonlinear structure with Duffing nonlinearities in its interstory force-deflection relationship.
Non-parametric identification of multivariable systems: A local rational modeling approach with application to a vibration isolation benchmark

NASA Astrophysics Data System (ADS)

Voorhoeve, Robbert; van der Maas, Annemiek; Oomen, Tom

2018-05-01

Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF identification of lightly damped mechanical systems with improved speed and accuracy. The proposed method is based on local rational models, which can efficiently handle the lightly-damped resonant dynamics. A key aspect herein is the freedom in the multivariable rational model parametrizations. Several choices for such multivariable rational model parametrizations are proposed and investigated. For systems with many inputs and outputs the required number of model parameters can rapidly increase, adversely affecting the performance of the local modeling approach. Therefore, low-order model structures are investigated. The structure of these low-order parametrizations leads to an undesired directionality in the identification problem. To address this, an iterative local rational modeling algorithm is proposed. As a special case recently developed SISO algorithms are recovered. The proposed approach is successfully demonstrated on simulations and on an active vibration isolation system benchmark, confirming good performance of the method using significantly less parameters compared with alternative approaches.
Analytically tractable climate-carbon cycle feedbacks under 21st century anthropogenic forcing

NASA Astrophysics Data System (ADS)

Lade, Steven J.; Donges, Jonathan F.; Fetzer, Ingo; Anderies, John M.; Beer, Christian; Cornell, Sarah E.; Gasser, Thomas; Norberg, Jon; Richardson, Katherine; Rockström, Johan; Steffen, Will

2018-05-01

Changes to climate-carbon cycle feedbacks may significantly affect the Earth system's response to greenhouse gas emissions. These feedbacks are usually analysed from numerical output of complex and arguably opaque Earth system models. Here, we construct a stylised global climate-carbon cycle model, test its output against comprehensive Earth system models, and investigate the strengths of its climate-carbon cycle feedbacks analytically. The analytical expressions we obtain aid understanding of carbon cycle feedbacks and the operation of the carbon cycle. Specific results include that different feedback formalisms measure fundamentally the same climate-carbon cycle processes; temperature dependence of the solubility pump, biological pump, and CO2 solubility all contribute approximately equally to the ocean climate-carbon feedback; and concentration-carbon feedbacks may be more sensitive to future climate change than climate-carbon feedbacks. Simple models such as that developed here also provide workbenches for simple but mechanistically based explorations of Earth system processes, such as interactions and feedbacks between the planetary boundaries, that are currently too uncertain to be included in comprehensive Earth system models.
Finding identifiable parameter combinations in nonlinear ODE models and the rational reparameterization of their input-output equations.

PubMed

Meshkat, Nicolette; Anderson, Chris; Distefano, Joseph J

2011-09-01

When examining the structural identifiability properties of dynamic system models, some parameters can take on an infinite number of values and yet yield identical input-output data. These parameters and the model are then said to be unidentifiable. Finding identifiable combinations of parameters with which to reparameterize the model provides a means for quantitatively analyzing the model and computing solutions in terms of the combinations. In this paper, we revisit and explore the properties of an algorithm for finding identifiable parameter combinations using Gröbner Bases and prove useful theoretical properties of these parameter combinations. We prove a set of M algebraically independent identifiable parameter combinations can be found using this algorithm and that there exists a unique rational reparameterization of the input-output equations over these parameter combinations. We also demonstrate application of the procedure to a nonlinear biomodel. Copyright © 2011 Elsevier Inc. All rights reserved.
Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Lin; Gupta, Hoshin V.; Gao, Xiaogang; Sorooshian, Soroosh; Imam, Bisher

2002-12-01

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling.
On a Model of a Nonlinear Feedback System for River Flow Prediction

NASA Astrophysics Data System (ADS)

Ozaki, T.

1980-02-01

A nonlinear system with feedback is proposed as a dynamic model for the hydrological system, whose input is the rainfall and whose output is the discharge of river flow. Parameters and orders of the model are estimated using Akaike's information criterion. Its application to the prediction of daily discharges of Kanna River and Bird Creek is discussed.
Software Users Manual (SUM): Extended Testability Analysis (ETA) Tool

NASA Technical Reports Server (NTRS)

Maul, William A.; Fulton, Christopher E.

2011-01-01

This software user manual describes the implementation and use the Extended Testability Analysis (ETA) Tool. The ETA Tool is a software program that augments the analysis and reporting capabilities of a commercial-off-the-shelf (COTS) testability analysis software package called the Testability Engineering And Maintenance System (TEAMS) Designer. An initial diagnostic assessment is performed by the TEAMS Designer software using a qualitative, directed-graph model of the system being analyzed. The ETA Tool utilizes system design information captured within the diagnostic model and testability analysis output from the TEAMS Designer software to create a series of six reports for various system engineering needs. The ETA Tool allows the user to perform additional studies on the testability analysis results by determining the detection sensitivity to the loss of certain sensors or tests. The ETA Tool was developed to support design and development of the NASA Ares I Crew Launch Vehicle. The diagnostic analysis provided by the ETA Tool was proven to be valuable system engineering output that provided consistency in the verification of system engineering requirements. This software user manual provides a description of each output report generated by the ETA Tool. The manual also describes the example diagnostic model and supporting documentation - also provided with the ETA Tool software release package - that were used to generate the reports presented in the manual
Satellite Remote Sensing is Key to Water Cycle Integrator

NASA Astrophysics Data System (ADS)

Koike, T.

2016-12-01

To promote effective multi-sectoral, interdisciplinary collaboration based on coordinated and integrated efforts, the Global Earth Observation System of Systems (GEOSS) is now developing a "GEOSS Water Cycle Integrator (WCI)", which integrates "Earth observations", "modeling", "data and information", "management systems" and "education systems". GEOSS/WCI sets up "work benches" by which partners can share data, information and applications in an interoperable way, exchange knowledge and experiences, deepen mutual understanding and work together effectively to ultimately respond to issues of both mitigation and adaptation. (A work bench is a virtual geographical or phenomenological space where experts and managers collaborate to use information to address a problem within that space). GEOSS/WCI enhances the coordination of efforts to strengthen individual, institutional and infrastructure capacities, especially for effective interdisciplinary coordination and integration. GEOSS/WCI archives various satellite data to provide various hydrological information such as cloud, rainfall, soil moisture, or land-surface snow. These satellite products were validated using land observation in-situ data. Water cycle models can be developed by coupling in-situ and satellite data. River flows and other hydrological parameters can be simulated and validated by in-situ data. Model outputs from weather-prediction, seasonal-prediction, and climate-prediction models are archived. Some of these model outputs are archived on an online basis, but other models, e.g., climate-prediction models are archived on an offline basis. After models are evaluated and biases corrected, the outputs can be used as inputs into the hydrological models for predicting the hydrological parameters. Additionally, we have already developed a data-assimilation system by combining satellite data and the models. This system can improve our capability to predict hydrological phenomena. The WCI can provide better predictions of the hydrological parameters for integrated water resources management (IWRM) and also assess the impact of climate change and calculate adaptation needs.
Main control computer security model of closed network systems protection against cyber attacks

NASA Astrophysics Data System (ADS)

Seymen, Bilal

2014-06-01

The model that brings the data input/output under control in closed network systems, that maintains the system securely, and that controls the flow of information through the Main Control Computer which also brings the network traffic under control against cyber-attacks. The network, which can be controlled single-handedly thanks to the system designed to enable the network users to make data entry into the system or to extract data from the system securely, intends to minimize the security gaps. Moreover, data input/output record can be kept by means of the user account assigned for each user, and it is also possible to carry out retroactive tracking, if requested. Because the measures that need to be taken for each computer on the network regarding cyber security, do require high cost; it has been intended to provide a cost-effective working environment with this model, only if the Main Control Computer has the updated hardware.
An effective drift correction for dynamical downscaling of decadal global climate predictions

NASA Astrophysics Data System (ADS)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.
Nonlinear Modeling and Control of a Propellant Mixer

NASA Technical Reports Server (NTRS)

Barbieri, Enrique; Richter, Hanz; Figueroa, Fernando

2003-01-01

A mixing chamber used in rocket engine combustion testing at NASA Stennis Space Center is modeled by a second order nonlinear MIMO system. The mixer is used to condition the thermodynamic properties of cryogenic liquid propellant by controlled injection of the same substance in the gaseous phase. The three inputs of the mixer are the positions of the valves regulating the liquid and gas flows at the inlets, and the position of the exit valve regulating the flow of conditioned propellant. The outputs to be tracked and/or regulated are mixer internal pressure, exit mass flow, and exit temperature. The outputs must conform to test specifications dictated by the type of rocket engine or component being tested downstream of the mixer. Feedback linearization is used to achieve tracking and regulation of the outputs. It is shown that the system is minimum-phase provided certain conditions on the parameters are satisfied. The conditions are shown to have physical interpretation.

A dynamic model using monitoring data and watershed characteristics to project fish tissue mercury concentrations in stream systems.

PubMed

Chan, Caroline; Heinbokel, John F; Myers, John A; Jacobs, Robert R

2012-10-01

A complex interplay of factors determines the degree of bioaccumulation of Hg in fish in any particular basin. Although certain watershed characteristics have been associated with higher or lower bioaccumulation rates, the relationships between these characteristics are poorly understood. To add to this understanding, a dynamic model was built to examine these relationships in stream systems. The model follows Hg from the water column, through microbial conversion and subsequent concentration, through the food web to piscivorous fish. The model was calibrated to 7 basins in Kentucky and further evaluated by comparing output to 7 sites in, or proximal to, the Ohio River Valley, an underrepresented region in the bioaccumulation literature. Water quality and basin characteristics were inputs into the model, with tissue concentrations of Hg of generic trophic level 3, 3.5, and 4 fish the output. Regulatory and monitoring data were used to calibrate and evaluate the model. Mean average prediction error for Kentucky sites was 26%, whereas mean error for evaluation sites was 51%. Variability within natural systems can be substantial and was quantified for fish tissue by analysis of the US Geological Survey National Fish Database. This analysis pointed to the need for more systematic sampling of fish tissue. Analysis of model output indicated that parameters that had the greatest impact on bioaccumulation influenced the system at several points. These parameters included forested and wetlands coverage and nutrient levels. Factors that were less sensitive modified the system at only 1 point and included the unfiltered total Hg input and the portion of the basin that is developed. Copyright © 2012 SETAC.
User's manual for a parameter identification technique. [with options for model simulation for fixed input forcing functions and identification from wind tunnel and flight measurements

NASA Technical Reports Server (NTRS)

Kanning, G.

1975-01-01

A digital computer program written in FORTRAN is presented that implements the system identification theory for deterministic systems using input-output measurements. The user supplies programs simulating the mathematical model of the physical plant whose parameters are to be identified. The user may choose any one of three options. The first option allows for a complete model simulation for fixed input forcing functions. The second option identifies up to 36 parameters of the model from wind tunnel or flight measurements. The third option performs a sensitivity analysis for up to 36 parameters. The use of each option is illustrated with an example using input-output measurements for a helicopter rotor tested in a wind tunnel.
GDP and efficiency of Russian economy

NASA Astrophysics Data System (ADS)

Borodachev, Sergey M.

2018-01-01

The goal is to study GDP (gross domestic product) as an unobservable characteristic of the Russian national economy state on the basis of more reliable observed data on gross output (systems output) and final consumption (systems control). To do this, the dynamic Leontief model is presented in a system-like form and its parameters and GDP dynamics are estimated by the Kalman filter (KF). We consider that all previous year's investments affect the growth of the gross output by the next year. The weights of these investments in the sum are equal to unity and decrease in geometric progression. The estimation of the model parameters was carried out by the maximum likelihood method. The original data on the gross output and final consumption in the period from 1995 to 2015 years where taken from the Rosstat website, where maximally aggregated economy of Russia is reflected in the system of national accounts. The growth of direct costs and capital expenditures at gross output increase has been discovered, which indicates the extensive character of the development of the economy. Investments are being absorbed 2 - 4 years; any change of them causes a surge of commissioned fixed assets fluctuation with a period of 2 years. Then these parameter values were used in the KF to estimate the states of the system. The emerging tendency of the transition of GDP growth to its fall means that the rate of growth of final consumption is higher than the rate of GDP growth. In general, the behavior of the curve of Rosstat GDP obviously follows the declared investments, whereas in the present calculation it is closer to the behavior of final consumption. Estimated GDP and investments that really increased it were significantly less after the crisis of 2008-2009 years than officially published data.
Neural Systems with Numerically Matched Input-Output Statistic: Isotonic Bivariate Statistical Modeling

PubMed Central

Fiori, Simone

2007-01-01

Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not match (i.e., there are “holes” in the data) or when the data sets have been acquired independently. Also, statistical modeling is useful when the amount of available data is enough to show relevant statistical features of the phenomenon underlying the data. We propose to tackle the problem of statistical modeling via a neural (nonlinear) system that is able to match its input-output statistic to the statistic of the available data sets. A key point of the new implementation proposed here is that it is based on look-up-table (LUT) neural systems, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure. PMID:18566641
Team performance in the Italian NHS: the role of reflexivity.

PubMed

Urbini, Flavio; Callea, Antonino; Chirumbolo, Antonio; Talamo, Alessandra; Ingusci, Emanuela; Ciavolino, Enrico

2018-04-09

Purpose The purpose of this paper is twofold: first, to investigate the goodness of the input-process-output (IPO) model in order to evaluate work team performance within the Italian National Health Care System (NHS); and second, to test the mediating role of reflexivity as an overarching process factor between input and output. Design/methodology/approach The Italian version of the Aston Team Performance Inventory was administered to 351 employees working in teams in the Italian NHS. Mediation analyses with latent variables were performed via structural equation modeling (SEM); the significance of total, direct, and indirect effect was tested via bootstrapping. Findings Underpinned by the IPO framework, the results of SEM supported mediational hypotheses. First, the application of the IPO model in the Italian NHS showed adequate fit indices, showing that the process mediates the relationship between input and output factors. Second, reflexivity mediated the relationship between input and output, influencing some aspects of team performance. Practical implications The results provide useful information for HRM policies improving process dimensions of the IPO model via the mediating role of reflexivity as a key role in team performance. Originality/value This study is one of a limited number of studies that applied the IPO model in the Italian NHS. Moreover, no study has yet examined the role of reflexivity as a mediator between input and output factors in the IPO model.
A model for a continuous-wave iodine laser

NASA Technical Reports Server (NTRS)

Hwang, In H.; Tabibi, Bagher M.

1990-01-01

A model for a continuous-wave (CW) iodine laser has been developed and compared with the experimental results obtained from a solar-simulator-pumped CW iodine laser. The agreement between the calculated laser power output and the experimental results is generally good for various laser parameters even when the model includes only prominent rate coefficients. The flow velocity dependence of the output power shows that the CW iodine laser cannot be achieved with a flow velocity below 1 m/s for the present solar-simulator-pumped CW iodine laser system.
A Novel Degradation Identification Method for Wind Turbine Pitch System

NASA Astrophysics Data System (ADS)

Guo, Hui-Dong

2018-04-01

It’s difficult for traditional threshold value method to identify degradation of operating equipment accurately. An novel degradation evaluation method suitable for wind turbine condition maintenance strategy implementation was proposed in this paper. Based on the analysis of typical variable-speed pitch-to-feather control principle and monitoring parameters for pitch system, a multi input multi output (MIMO) regression model was applied to pitch system, where wind speed, power generation regarding as input parameters, wheel rotation speed, pitch angle and motor driving currency for three blades as output parameters. Then, the difference between the on-line measurement and the calculated value from the MIMO regression model applying least square support vector machines (LSSVM) method was defined as the Observed Vector of the system. The Gaussian mixture model (GMM) was applied to fitting the distribution of the multi dimension Observed Vectors. Applying the model established, the Degradation Index was calculated using the SCADA data of a wind turbine damaged its pitch bearing retainer and rolling body, which illustrated the feasibility of the provided method.
Nonlinear aeroservoelastic analysis of a controlled multiple-actuated-wing model with free-play

NASA Astrophysics Data System (ADS)

Huang, Rui; Hu, Haiyan; Zhao, Yonghui

2013-10-01

In this paper, the effects of structural nonlinearity due to free-play in both leading-edge and trailing-edge outboard control surfaces on the linear flutter control system are analyzed for an aeroelastic model of three-dimensional multiple-actuated-wing. The free-play nonlinearities in the control surfaces are modeled theoretically by using the fictitious mass approach. The nonlinear aeroelastic equations of the presented model can be divided into nine sub-linear modal-based aeroelastic equations according to the different combinations of deflections of the leading-edge and trailing-edge outboard control surfaces. The nonlinear aeroelastic responses can be computed based on these sub-linear aeroelastic systems. To demonstrate the effects of nonlinearity on the linear flutter control system, a single-input and single-output controller and a multi-input and multi-output controller are designed based on the unconstrained optimization techniques. The numerical results indicate that the free-play nonlinearity can lead to either limit cycle oscillations or divergent motions when the linear control system is implemented.
On the modelling of linear-assisted DC-DC voltage regulators for photovoltaic solar energy systems

NASA Astrophysics Data System (ADS)

Martínez-García, Herminio; García-Vílchez, Encarna

2017-11-01

This paper shows the modelling of linear-assisted or hybrid (linear & switching) DC/DC voltage regulators. In this kind of regulators, an auxiliary linear regulator is used, which objective is to cancel the ripple at the output voltage and provide fast responses for load variations. On the other hand, a switching DC/DC converter, connected in parallel with the linear regulator, allows to supply almost the whole output current demanded by the load. The objective of this topology is to take advantage of the suitable regulation characteristics that series linear voltage regulators have, but almost achieving the high efficiency that switching DC/DC converters provide. Linear-assisted DC/DC regulators are feedback systems with potential instability. Therefore, their modelling is mandatory in order to obtain design guidelines and assure stability of the implemented power supply system.
Climate Model Ensemble Methodology: Rationale and Challenges

NASA Astrophysics Data System (ADS)

Vezer, M. A.; Myrvold, W.

2012-12-01

A tractable model of the Earth's atmosphere, or, indeed, any large, complex system, is inevitably unrealistic in a variety of ways. This will have an effect on the model's output. Nonetheless, we want to be able to rely on certain features of the model's output in studies aiming to detect, attribute, and project climate change. For this, we need assurance that these features reflect the target system, and are not artifacts of the unrealistic assumptions that go into the model. One technique for overcoming these limitations is to study ensembles of models which employ different simplifying assumptions and different methods of modelling. One then either takes as reliable certain outputs on which models in the ensemble agree, or takes the average of these outputs as the best estimate. Since the Intergovernmental Panel on Climate Change's Fourth Assessment Report (IPCC AR4) modellers have aimed to improve ensemble analysis by developing techniques to account for dependencies among models, and to ascribe unequal weights to models according to their performance. The goal of this paper is to present as clearly and cogently as possible the rationale for climate model ensemble methodology, the motivation of modellers to account for model dependencies, and their efforts to ascribe unequal weights to models. The method of our analysis is as follows. We will consider a simpler, well-understood case of taking the mean of a number of measurements of some quantity. Contrary to what is sometimes said, it is not a requirement of this practice that the errors of the component measurements be independent; one must, however, compensate for any lack of independence. We will also extend the usual accounts to include cases of unknown systematic error. We draw parallels between this simpler illustration and the more complex example of climate model ensembles, detailing how ensembles can provide more useful information than any of their constituent models. This account emphasizes the epistemic importance of considering degrees of model dependence, and the practice of ascribing unequal weights to models of unequal skill.
Modeling and simulation of a hybrid ship power system

NASA Astrophysics Data System (ADS)

Doktorcik, Christopher J.

2011-12-01

Optimizing the performance of naval ship power systems requires integrated design and coordination of the respective subsystems (sources, converters, and loads). A significant challenge in the system-level integration is solving the Power Management Control Problem (PMCP). The PMCP entails deciding on subsystem power usages for achieving a trade-off between the error in tracking a desired position/velocity profile, minimizing fuel consumption, and ensuring stable system operation, while at the same time meeting performance limitations of each subsystem. As such, the PMCP naturally arises at a supervisory level of a ship's operation. In this research, several critical steps toward the solution of the PMCP for surface ships have been undertaken. First, new behavioral models have been developed for gas turbine engines, wound rotor synchronous machines, DC super-capacitors, induction machines, and ship propulsion systems. Conventional models describe system inputs and outputs in terms of physical variables such as voltage, current, torque, and force. In contrast, the behavioral models developed herein express system inputs and outputs in terms of power whenever possible. Additionally, the models have been configured to form a hybrid system-level power model (HSPM) of a proposed ship electrical architecture. Lastly, several simulation studies have been completed to expose the capabilities and limitations of the HSPM.
Terminal Area Simulation System User's Guide - Version 10.0

NASA Technical Reports Server (NTRS)

Switzer, George F.; Proctor, Fred H.

2014-01-01

The Terminal Area Simulation System (TASS) is a three-dimensional, time-dependent, large eddy simulation model that has been developed for studies of wake vortex and weather hazards to aviation, along with other atmospheric turbulence, and cloud-scale weather phenomenology. This document describes the source code for TASS version 10.0 and provides users with needed documentation to run the model. The source code is programed in Fortran language and is formulated to take advantage of vector and efficient multi-processor scaling for execution on massively-parallel supercomputer clusters. The code contains different initialization modules allowing the study of aircraft wake vortex interaction with the atmosphere and ground, atmospheric turbulence, atmospheric boundary layers, precipitating convective clouds, hail storms, gust fronts, microburst windshear, supercell and mesoscale convective systems, tornadic storms, and ring vortices. The model is able to operate in either two- or three-dimensions with equations numerically formulated on a Cartesian grid. The primary output from the TASS is time-dependent domain fields generated by the prognostic equations and diagnosed variables. This document will enable a user to understand the general logic of TASS, and will show how to configure and initialize the model domain. Also described are the formats of the input and output files, as well as the parameters that control the input and output.
Human Activity Recognition by Combining a Small Number of Classifiers.

PubMed

Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

2016-09-01

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.
Application of SIGGS to Project PRIME: A General Systems Approach to Evaluation of Mainstreaming.

ERIC Educational Resources Information Center

Frick, Ted

The use of the systems approach in educational inquiry is not new, and the models of input/output, input/process/product, and cybernetic systems have been widely used. The general systems model is an extension of all these, adding the dimension of environmental influence on the system as well as system influence on the environment. However, if the…
Estimates of Embodied Global Energy and Air-Emission Intensities of Japanese Products for Building a Japanese Input–Output Life Cycle Assessment Database with a Global System Boundary

PubMed Central

2012-01-01

To build a life cycle assessment (LCA) database of Japanese products embracing their global supply chains in a manner requiring lower time and labor burdens, this study estimates the intensity of embodied global environmental burden for commodities produced in Japan. The intensity of embodied global environmental burden is a measure of the environmental burden generated globally by unit production of the commodity and can be used as life cycle inventory data in LCA. The calculation employs an input–output LCA method with a global link input–output model that defines a global system boundary grounded in a simplified multiregional input–output framework. As results, the intensities of embodied global environmental burden for 406 Japanese commodities are determined in terms of energy consumption, greenhouse-gas emissions (carbon dioxide, methane, nitrous oxide, perfluorocarbons, hydrofluorocarbons, sulfur hexafluoride, and their summation), and air-pollutant emissions (nitrogen oxide and sulfur oxide). The uncertainties in the intensities of embodied global environmental burden attributable to the simplified structure of the global link input–output model are quantified using Monte Carlo simulation. In addition, by analyzing the structure of the embodied global greenhouse-gas intensities we characterize Japanese commodities in the context of LCA embracing global supply chains. PMID:22881452
Robust Real-Time Wide-Area Differential GPS Navigation

NASA Technical Reports Server (NTRS)

Yunck, Thomas P. (Inventor); Bertiger, William I. (Inventor); Lichten, Stephen M. (Inventor); Mannucci, Anthony J. (Inventor); Muellerschoen, Ronald J. (Inventor); Wu, Sien-Chong (Inventor)

1998-01-01

The present invention provides a method and a device for providing superior differential GPS positioning data. The system includes a group of GPS receiving ground stations covering a wide area of the Earth's surface. Unlike other differential GPS systems wherein the known position of each ground station is used to geometrically compute an ephemeris for each GPS satellite. the present system utilizes real-time computation of satellite orbits based on GPS data received from fixed ground stations through a Kalman-type filter/smoother whose output adjusts a real-time orbital model. ne orbital model produces and outputs orbital corrections allowing satellite ephemerides to be known with considerable greater accuracy than from die GPS system broadcasts. The modeled orbits are propagated ahead in time and differenced with actual pseudorange data to compute clock offsets at rapid intervals to compensate for SA clock dither. The orbital and dock calculations are based on dual frequency GPS data which allow computation of estimated signal delay at each ionospheric point. These delay data are used in real-time to construct and update an ionospheric shell map of total electron content which is output as part of the orbital correction data. thereby allowing single frequency users to estimate ionospheric delay with an accuracy approaching that of dual frequency users.
Examination of Airborne FDEM System Attributes for UXO Mapping and Detection

DTIC Science & Technology

2009-11-01

quadrature output should only occur when there is a distortion in the transmitter waveform signal that correlates with the quadrature part of the...suggested that the S/N performance of the quadrature output of the two FDEM designs would be similar to the observed S/N of TEM systems, though...the semi-airborne configuration. We propose to extend the current SAIC codes to address this need, and to perform additional modeling using codes
Air Force Global Weather Central System Architecture Study. Final System/Subsystem Summary Report. Volume 4. Systems Analysis and Trade Studies

DTIC Science & Technology

1976-03-01

atmosphere,as well as very fine grid cloud models and cloud probability models. Some of the new requirements that will be supported with this system are a...including the Advanced Prediction Model for the global atmosphere, as well as very fine grid cloud models and cloud proba- bility models. Some of the new...with the mapping and gridding function (imput and output)? Should the capability exist to interface raw ungridded data with the SID interface
Evaluating the performance of a fault detection and diagnostic system for vapor compression equipment

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

Breuker, M.S.; Braun, J.E.

This paper presents a detailed evaluation of the performance of a statistical, rule-based fault detection and diagnostic (FDD) technique presented by Rossi and Braun (1997). Steady-state and transient tests were performed on a simple rooftop air conditioner over a range of conditions and fault levels. The steady-state data without faults were used to train models that predict outputs for normal operation. The transient data with faults were used to evaluate FDD performance. The effect of a number of design variables on FDD sensitivity for different faults was evaluated and two prototype systems were specified for more complete evaluation. Good performancemore » was achieved in detecting and diagnosing five faults using only six temperatures (2 input and 4 output) and linear models. The performance improved by about a factor of two when ten measurements (three input and seven output) and higher order models were used. This approach for evaluating and optimizing the performance of the statistical, rule-based FDD technique could be used as a design and evaluation tool when applying this FDD method to other packaged air-conditioning systems. Furthermore, the approach could also be modified to evaluate the performance of other FDD methods.« less
Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2018-02-01

This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Simplified adaptive control of an orbiting flexible spacecraft

NASA Astrophysics Data System (ADS)

Maganti, Ganesh B.; Singh, Sahjendra N.

2007-10-01

The paper presents the design of a new simple adaptive system for the rotational maneuver and vibration suppression of an orbiting spacecraft with flexible appendages. A moment generating device located on the central rigid body of the spacecraft is used for the attitude control. It is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension. In addition, only the pitch angle and its derivative are measured and elastic modes are not available for feedback. The control output variable is chosen as the linear combination of the pitch angle and the pitch rate. Exploiting the hyper minimum phase nature of the spacecraft, a simple adaptive control law is derived for the pitch angle control and elastic mode stabilization. The adaptation rule requires only four adjustable parameters and the structure of the control system does not depend on the order of the truncated spacecraft model. For the synthesis of control system, the measured output error and the states of a third-order command generator are used. Simulation results are presented which show that in the closed-loop system adaptive output regulation is accomplished in spite of large parameter uncertainties and disturbance input.
End-to-End Modeling with the Heimdall Code to Scope High-Power Microwave Systems

DTIC Science & Technology

2007-06-01

END-TO-END MODELING WITH THE HEIMDALL CODE TO SCOPE HIGH - POWER MICROWAVE SYSTEMS ∗ John A. Swegleξ Savannah River National Laboratory, 743A...describe the expert-system code HEIMDALL, which is used to model full high - power microwave systems using over 60 systems-engineering models, developed in...of our calculations of the mass of a Supersystem producing 500-MW, 15-ns output pulses in the X band for bursts of 1 s , interspersed with 10- s
Scientific Benefits of Space Science Models Archiving at Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Kuznetsova, Maria M.; Berrios, David; Chulaki, Anna; Hesse, Michael; MacNeice, Peter J.; Maddox, Marlo M.; Pulkkinen, Antti; Rastaetter, Lutz; Taktakishvili, Aleksandre

2009-01-01

The Community Coordinated Modeling Center (CCMC) hosts a set of state-of-the-art space science models ranging from the solar atmosphere to the Earth's upper atmosphere. CCMC provides a web-based Run-on-Request system, by which the interested scientist can request simulations for a broad range of space science problems. To allow the models to be driven by data relevant to particular events CCMC developed a tool that automatically downloads data from data archives and transform them to required formats. CCMC also provides a tailored web-based visualization interface for the model output, as well as the capability to download the simulation output in portable format. CCMC offers a variety of visualization and output analysis tools to aid scientists in interpretation of simulation results. During eight years since the Run-on-request system became available the CCMC archived the results of almost 3000 runs that are covering significant space weather events and time intervals of interest identified by the community. The simulation results archived at CCMC also include a library of general purpose runs with modeled conditions that are used for education and research. Archiving results of simulations performed in support of several Modeling Challenges helps to evaluate the progress in space weather modeling over time. We will highlight the scientific benefits of CCMC space science model archive and discuss plans for further development of advanced methods to interact with simulation results.
An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube

PubMed Central

2009-01-01

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input–output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input–output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down. PMID:20596382
An Artificial Intelligence Approach for Modeling and Prediction of Water Diffusion Inside a Carbon Nanotube.

PubMed

Ahadian, Samad; Kawazoe, Yoshiyuki

2009-06-04

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input-output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input-output data pairs. Good performance of the designed ANFIS ensures its capability as a promising tool for modeling and prediction of fluid flow at nanoscale where the continuum models of fluid dynamics tend to break down.
Control theory-based regulation of hippocampal CA1 nonlinear dynamics.

PubMed

Hsiao, Min-Chi; Song, Dong; Berger, Theodore W

2008-01-01

We are developing a biomimetic electronic neural prosthesis to replace regions of the hippocampal brain area that have been damaged by disease or insult. Our previous study has shown that the VLSI implementation of a CA3 nonlinear dynamic model can functionally replace the CA3 subregion of the hippocampal slice. As a result, the propagation of temporal patterns of activity from DG-->VLSI-->CA1 reproduces the activity observed experimentally in the biological DG-->CA3-->CA1 circuit. In this project, we incorporate an open-loop controller to optimize the output (CA1) response. Specifically, we seek to optimize the stimulation signal to CA1 using a predictive dentate gyrus (DG)-CA1 nonlinear model (i.e., DG-CA1 trajectory model) and a CA1 input-output model (i.e., CA1 plant model), such that the ultimate CA1 response (i.e., desired output) can be first predicted by the DG-CA1 trajectory model and then transformed to the desired stimulation through the inversed CA1 plant model. Lastly, the desired CA1 output is evoked by the estimated optimal stimulation. This study will be the first stage of formulating an integrated modeling-control strategy for the hippocampal neural prosthetic system.
Information visualization of the minority game

NASA Astrophysics Data System (ADS)

Jiang, W.; Herbert, R. D.; Webber, R.

2008-02-01

Many dynamical systems produce large quantities of data. How can the system be understood from the output data? Often people are simply overwhelmed by the data. Traditional tools such as tables and plots are often not adequate, and new techniques are needed to help people to analyze the system. In this paper, we propose the use of two spacefilling visualization tools to examine the output from a complex agent-based financial model. We measure the effectiveness and performance of these tools through usability experiments. Based on the experimental results, we develop two new visualization techniques that combine the advantages and discard the disadvantages of the information visualization tools. The model we use is an evolutionary version of the Minority Game which simulates a financial market.
Development and testing of controller performance evaluation methodology for multi-input/multi-output digital control systems

NASA Technical Reports Server (NTRS)

Pototzky, Anthony; Wieseman, Carol; Hoadley, Sherwood Tiffany; Mukhopadhyay, Vivek

1991-01-01

Described here is the development and implementation of on-line, near real time controller performance evaluation (CPE) methods capability. Briefly discussed are the structure of data flow, the signal processing methods used to process the data, and the software developed to generate the transfer functions. This methodology is generic in nature and can be used in any type of multi-input/multi-output (MIMO) digital controller application, including digital flight control systems, digitally controlled spacecraft structures, and actively controlled wind tunnel models. Results of applying the CPE methodology to evaluate (in near real time) MIMO digital flutter suppression systems being tested on the Rockwell Active Flexible Wing (AFW) wind tunnel model are presented to demonstrate the CPE capability.
Coordination between Understanding Historic Buildings and BIM Modelling: A 3D-Output Oriented and typological Data Capture Method

NASA Astrophysics Data System (ADS)

Li, K.; Li, S. J.; Liu, Y.; Wang, W.; Wu, C.

2015-08-01

At the present, in trend of shifting the old 2D-output oriented survey to a new 3D-output oriented survey based on BIM technology, the corresponding working methods and workflow for data capture, process, representation, etc. have to be changed.Based on case study of two buildings in the Summer Palace of Beijing, and Jiayuguan Pass at the west end of the Great Wall (both World Heritage sites), this paper puts forward a "structure-and-type method" by means of typological method used in archaeology, Revit family system, and the tectonic logic of building to realize a good coordination between understanding of historic buildings and BIM modelling.
Linear and quadratic models of point process systems: contributions of patterned input to output.

PubMed

Lindsay, K A; Rosenberg, J R

2012-08-01

In the 1880's Volterra characterised a nonlinear system using a functional series connecting continuous input and continuous output. Norbert Wiener, in the 1940's, circumvented problems associated with the application of Volterra series to physical problems by deriving from it a new series of terms that are mutually uncorrelated with respect to Gaussian processes. Subsequently, Brillinger, in the 1970's, introduced a point-process analogue of Volterra's series connecting point-process inputs to the instantaneous rate of point-process output. We derive here a new series from this analogue in which its terms are mutually uncorrelated with respect to Poisson processes. This new series expresses how patterned input in a spike train, represented by third-order cross-cumulants, is converted into the instantaneous rate of an output point-process. Given experimental records of suitable duration, the contribution of arbitrary patterned input to an output process can, in principle, be determined. Solutions for linear and quadratic point-process models with one and two inputs and a single output are investigated. Our theoretical results are applied to isolated muscle spindle data in which the spike trains from the primary and secondary endings from the same muscle spindle are recorded in response to stimulation of one and then two static fusimotor axons in the absence and presence of a random length change imposed on the parent muscle. For a fixed mean rate of input spikes, the analysis of the experimental data makes explicit which patterns of two input spikes contribute to an output spike. Copyright © 2012 Elsevier Ltd. All rights reserved.
Impact of nonzero boresight pointing error on ergodic capacity of MIMO FSO communication systems.

PubMed

Boluda-Ruiz, Rubén; García-Zambrana, Antonio; Castillo-Vázquez, Beatriz; Castillo-Vázquez, Carmen

2016-02-22

A thorough investigation of the impact of nonzero boresight pointing errors on the ergodic capacity of multiple-input/multiple-output (MIMO) free-space optical (FSO) systems with equal gain combining (EGC) reception under different turbulence models, which are modeled as statistically independent, but not necessarily identically distributed (i.n.i.d.) is addressed in this paper. Novel closed-form asymptotic expressions at high signal-to-noise ratio (SNR) for the ergodic capacity of MIMO FSO systems are derived when different geometric arrangements of the receive apertures at the receiver are considered in order to reduce the effect of nonzero inherent boresight displacement, which is inevitably present when more than one receive aperture is considered. As a result, the asymptotic ergodic capacity of MIMO FSO systems is evaluated over log-normal (LN), gamma-gamma (GG) and exponentiated Weibull (EW) atmospheric turbulence in order to study different turbulence conditions, different sizes of receive apertures as well as different aperture averaging conditions. It is concluded that the use of single-input/multiple-output (SIMO) and MIMO techniques can significantly increase the ergodic capacity respect to the direct path link when the inherent boresight displacement takes small values, i.e. when the spacing among receive apertures is not too big. The effect of nonzero additional boresight errors, which is due to the thermal expansion of the building, is evaluated in multiple-input/single-output (MISO) and single-input/single-output (SISO) FSO systems. Simulation results are further included to confirm the analytical results.
Army Manpower Cost System (AMCOS) Economic and Budget Cost Models.

DTIC Science & Technology

1985-06-30

STANDARDS - 963 - A ZRDMServices0opaiy in ( o FINAL REPORT Tm ARMY MANPOWER COST SYSTEM4 (ANCOS) ECONOMIC AND BUDGET I COST MODELS CONTRACT NO. N00014...Organization .. .. . . . . . . . . . 1 2.0 AN APPLICATION OF ANCOS: UNIT-LEVEL MANPOWER COST COMPARISON . . . . o ...13 3.0 MODEL OVERVIEW . . . . o . . . . . . .. . .. . o . . 21 3.1 Cost Definitions ........ ............ 22 3.2 Model Output
Splines and polynomial tools for flatness-based constrained motion planning

NASA Astrophysics Data System (ADS)

Suryawan, Fajar; De Doná, José; Seron, María

2012-08-01

This article addresses the problem of trajectory planning for flat systems with constraints. Flat systems have the useful property that the input and the state can be completely characterised by the so-called flat output. We propose a spline parametrisation for the flat output, the performance output, the states and the inputs. Using this parametrisation the problem of constrained trajectory planning can be cast into a simple quadratic programming problem. An important result is that the B-spline parametrisation used gives exact results for constrained linear continuous-time system. The result is exact in the sense that the constrained signal can be made arbitrarily close to the boundary without having intersampling issues (as one would have in sampled-data systems). Simulation examples are presented, involving the generation of rest-to-rest trajectories. In addition, an experimental result of the method is also presented, where two methods to generate trajectories for a magnetic-levitation (maglev) system in the presence of constraints are compared and each method's performance is discussed. The first method uses the nonlinear model of the plant, which turns out to belong to the class of flat systems. The second method uses a linearised version of the plant model around an operating point. In every case, a continuous-time description is used. The experimental results on a real maglev system reported here show that, in most scenarios, the nonlinear and linearised models produce almost similar, indistinguishable trajectories.
Applications of active adaptive noise control to jet engines

NASA Technical Reports Server (NTRS)

Shoureshi, Rahmat; Brackney, Larry

1993-01-01

During phase 2 research on the application of active noise control to jet engines, the development of multiple-input/multiple-output (MIMO) active adaptive noise control algorithms and acoustic/controls models for turbofan engines were considered. Specific goals for this research phase included: (1) implementation of a MIMO adaptive minimum variance active noise controller; and (2) turbofan engine model development. A minimum variance control law for adaptive active noise control has been developed, simulated, and implemented for single-input/single-output (SISO) systems. Since acoustic systems tend to be distributed, multiple sensors, and actuators are more appropriate. As such, the SISO minimum variance controller was extended to the MIMO case. Simulation and experimental results are presented. A state-space model of a simplified gas turbine engine is developed using the bond graph technique. The model retains important system behavior, yet is of low enough order to be useful for controller design. Expansion of the model to include multiple stages and spools is also discussed.
Fragmentation of urban forms and the environmental consequences: results from a high-spatial resolution model system

NASA Astrophysics Data System (ADS)

Tang, U. W.; Wang, Z. S.

2008-10-01

Each city has its unique urban form. The importance of urban form on sustainable development has been recognized in recent years. Traditionally, air quality modelling in a city is in a mesoscale with grid resolution of kilometers, regardless of its urban form. This paper introduces a GIS-based air quality and noise model system developed to study the built environment of highly compact urban forms. Compared with traditional mesoscale air quality model system, the present model system has a higher spatial resolution down to individual buildings along both sides of the street. Applying the developed model system in the Macao Peninsula with highly compact urban forms, the average spatial resolution of input and output data is as high as 174 receptor points per km2. Based on this input/output dataset with a high spatial resolution, this study shows that even the highly compact urban forms can be fragmented into a very small geographic scale of less than 3 km2. This is due to the significant temporal variation of urban development. The variation of urban form in each fragment in turn affects air dispersion, traffic condition, and thus air quality and noise in a measurable scale.
Negative autoregulation matches production and demand in synthetic transcriptional networks.

PubMed

Franco, Elisa; Giordano, Giulia; Forsberg, Per-Ola; Murray, Richard M

2014-08-15

We propose a negative feedback architecture that regulates activity of artificial genes, or "genelets", to meet their output downstream demand, achieving robustness with respect to uncertain open-loop output production rates. In particular, we consider the case where the outputs of two genelets interact to form a single assembled product. We show with analysis and experiments that negative autoregulation matches the production and demand of the outputs: the magnitude of the regulatory signal is proportional to the "error" between the circuit output concentration and its actual demand. This two-device system is experimentally implemented using in vitro transcriptional networks, where reactions are systematically designed by optimizing nucleic acid sequences with publicly available software packages. We build a predictive ordinary differential equation (ODE) model that captures the dynamics of the system and can be used to numerically assess the scalability of this architecture to larger sets of interconnected genes. Finally, with numerical simulations we contrast our negative autoregulation scheme with a cross-activation architecture, which is less scalable and results in slower response times.
An adaptive tracking observer for failure-detection systems

NASA Technical Reports Server (NTRS)

Sidar, M.

1982-01-01

The design problem of adaptive observers applied to linear, constant and variable parameters, multi-input, multi-output systems, is considered. It is shown that, in order to keep the observer's (or Kalman filter) false-alarm rate (FAR) under a certain specified value, it is necessary to have an acceptable proper matching between the observer (or KF) model and the system parameters. An adaptive observer algorithm is introduced in order to maintain desired system-observer model matching, despite initial mismatching and/or system parameter variations. Only a properly designed adaptive observer is able to detect abrupt changes in the system (actuator, sensor failures, etc.) with adequate reliability and FAR. Conditions for convergence for the adaptive process were obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors and accurate and fast parameter identification, in both deterministic and stochastic cases.
Climate impacts on palm oil yields in the Nigerian Niger Delta

NASA Astrophysics Data System (ADS)

Okoro, Stanley U.; Schickhoff, Udo; Boehner, Juergen; Schneider, Uwe A.; Huth, Neil

2016-04-01

Palm oil production has increased in recent decades and is estimated to increase further. The optimal role of palm oil production, however, is controversial because of resource conflicts with alternative land uses. Local conditions and climate change affect resource competition and the desirability of palm oil production. Based on this, crop yield simulations using different climate model output under different climate scenarios could be important tool in addressing the problem of uncertainty quantification among different climate model outputs. Previous studies on this region have focused mostly on single experimental fields, not considering variations in Agro-Ecological Zones, climatic conditions, varieties and management practices and, in most cases not extending to various IPCC climate scenarios and were mostly based on single climate model output. Furthermore, the uncertainty quantification of the climate- impact model has rarely been investigated on this region. To this end we use the biophysical simulation model APSIM (Agricultural Production Systems Simulator) to simulate the regional climate impact on oil palm yield over the Nigerian Niger Delta. We also examine whether the use of crop yield model output ensemble reduces the uncertainty rather than the use of climate model output ensemble. The results could serve as a baseline for policy makers in this region in understanding the interaction between potentials of energy crop production of the region as well as its food security and other negative feedbacks that could be associated with bioenergy from oil palm. Keywords: Climate Change, Climate impacts, Land use and Crop yields.
An Integrated Gulf Coast Monitoring System Using Field, Remote Sensing and Model Results (Invited)

NASA Astrophysics Data System (ADS)

D'Sa, E. J.; Ko, D. S.; Stone, G.; Walker, N. D.

2010-12-01

The northern Gulf of Mexico is strongly influenced by the discharge of water, nutrients, dissolved and suspended particulate matter from the Mississippi-Atchafalaya River system, the largest in North America. It is also frequently impacted by energetic meteorological events that cause storm surge, high waves and affects water quality along its coastal waters. We describe the components of an integrated web-based Gulf Coast Information System (GCIS) (http://gulf-coast.lsu.edu) developed to serve remotely sensed products from a number of NASA satellite sensors such as the SeaWiFS and MODIS ocean color and the QuikSCAT wind sensors. GCIS also serves high-resolution nowcast and 48-hour forecast outputs (sea level variations, temperature, salinity and currents) from a 3-dimensional NCOM coastal circulation model for the coastal states of Mississippi, Louisiana and Texas. The GCIS is coupled to the near real-time outputs of a field monitoring and satellite receiving system, the Wave-Current Information System (WAVCIS) (http://www.wavcis.lsu.edu) and Earth Scan Laboratory (ESL) (www.esl.lsu.edu), respectively that provide critical decision support during hurricanes to the Gulf Coast. We present results on the use of the combined field, satellite and model outputs to monitor the effects of fronts, hurricanes, oil spill and the potential to study longer term climate impacts along the Gulf coast.
Computing the structural influence matrix for biological systems.

PubMed

Giordano, Giulia; Cuba Samaniego, Christian; Franco, Elisa; Blanchini, Franco

2016-06-01

We consider the problem of identifying structural influences of external inputs on steady-state outputs in a biological network model. We speak of a structural influence if, upon a perturbation due to a constant input, the ensuing variation of the steady-state output value has the same sign as the input (positive influence), the opposite sign (negative influence), or is zero (perfect adaptation), for any feasible choice of the model parameters. All these signs and zeros can constitute a structural influence matrix, whose (i, j) entry indicates the sign of steady-state influence of the jth system variable on the ith variable (the output caused by an external persistent input applied to the jth variable). Each entry is structurally determinate if the sign does not depend on the choice of the parameters, but is indeterminate otherwise. In principle, determining the influence matrix requires exhaustive testing of the system steady-state behaviour in the widest range of parameter values. Here we show that, in a broad class of biological networks, the influence matrix can be evaluated with an algorithm that tests the system steady-state behaviour only at a finite number of points. This algorithm also allows us to assess the structural effect of any perturbation, such as variations of relevant parameters. Our method is applied to nontrivial models of biochemical reaction networks and population dynamics drawn from the literature, providing a parameter-free insight into the system dynamics.

An efficient approach to ARMA modeling of biological systems with multiple inputs and delays

NASA Technical Reports Server (NTRS)

Perrott, M. H.; Cohen, R. J.

1996-01-01

This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.
Identification of linear system models and state estimators for controls

NASA Technical Reports Server (NTRS)

Chen, Chung-Wen

1992-01-01

The following paper is presented in viewgraph format and covers topics including: (1) linear state feedback control system; (2) Kalman filter state estimation; (3) relation between residual and stochastic part of output; (4) obtaining Kalman filter gain; (5) state estimation under unknown system model and unknown noises; and (6) relationship between filter Markov parameters and system Markov parameters.
The Correlation of Human Capital on Costs of Air Force Acquisition Programs

DTIC Science & Technology

2009-03-01

6.78 so our model does not exhibit the presence of multi-collinearity. We empirically tested for heteroskedasticity using the Breusch - Pagan -Godfrey...inputs to outputs. The output in this study is the average cost overrun of Aeronautical Systems Center research, development, test , and evaluation...32 Pre-Estimation Specification Tests ............................................................................34 Post
Observer-Based Adaptive Fault-Tolerant Tracking Control of Nonlinear Nonstrict-Feedback Systems.

PubMed

Wu, Chengwei; Liu, Jianxing; Xiong, Yongyang; Wu, Ligang

2017-06-28

This paper studies an output-based adaptive fault-tolerant control problem for nonlinear systems with nonstrict-feedback form. Neural networks are utilized to identify the unknown nonlinear characteristics in the system. An observer and a general fault model are constructed to estimate the unavailable states and describe the fault, respectively. Adaptive parameters are constructed to overcome the difficulties in the design process for nonstrict-feedback systems. Meanwhile, dynamic surface control technique is introduced to avoid the problem of ''explosion of complexity''. Furthermore, based on adaptive backstepping control method, an output-based adaptive neural tracking control strategy is developed for the considered system against actuator fault, which can ensure that all the signals in the resulting closed-loop system are bounded, and the system output signal can be regulated to follow the response of the given reference signal with a small error. Finally, the simulation results are provided to validate the effectiveness of the control strategy proposed in this paper.
Methods to Register Models and Input/Output Parameters for Integrated Modeling

EPA Science Inventory

Significant resources can be required when constructing integrated modeling systems. In a typical application, components (e.g., models and databases) created by different developers are assimilated, requiring the framework’s functionality to bridge the gap between the user’s kno...
Water quality modelling of an impacted semi-arid catchment using flow data from the WEAP model

NASA Astrophysics Data System (ADS)

Slaughter, Andrew R.; Mantel, Sukhmani K.

2018-04-01

The continuous decline in water quality in many regions is forcing a shift from quantity-based water resources management to a greater emphasis on water quality management. Water quality models can act as invaluable tools as they facilitate a conceptual understanding of processes affecting water quality and can be used to investigate the water quality consequences of management scenarios. In South Africa, the Water Quality Systems Assessment Model (WQSAM) was developed as a management-focussed water quality model that is relatively simple to be able to utilise the small amount of available observed data. Importantly, WQSAM explicitly links to systems (yield) models routinely used in water resources management in South Africa by using their flow output to drive water quality simulations. Although WQSAM has been shown to be able to represent the variability of water quality in South African rivers, its focus on management from a South African perspective limits its use to within southern African regions for which specific systems model setups exist. Facilitating the use of WQSAM within catchments outside of southern Africa and within catchments for which these systems model setups to not exist would require WQSAM to be able to link to a simple-to-use and internationally-applied systems model. One such systems model is the Water Evaluation and Planning (WEAP) model, which incorporates a rainfall-runoff component (natural hydrology), and reservoir storage, return flows and abstractions (systems modelling), but within which water quality modelling facilities are rudimentary. The aims of the current study were therefore to: (1) adapt the WQSAM model to be able to use as input the flow outputs of the WEAP model and; (2) provide an initial assessment of how successful this linkage was by application of the WEAP and WQSAM models to the Buffalo River for historical conditions; a small, semi-arid and impacted catchment in the Eastern Cape of South Africa. The simulations of the two models were compared to the available observed data, with the initial focus within WQSAM on a simulation of instream total dissolved solids (TDS) and nutrient concentrations. The WEAP model was able to adequately simulate flow in the Buffalo River catchment, with consideration of human inputs and outputs. WQSAM was adapted to successfully take as input the flow output of the WEAP model, and the simulations of nutrients by WQSAM provided a good representation of the variability of observed nutrient concentrations in the catchment. This study showed that the WQSAM model is able to accept flow inputs from the WEAP model, and that this approach is able to provide satisfactory estimates of both flow and water quality for a small, semi-arid and impacted catchment. It is hoped that this research will encourage the application of WQSAM to an increased number of catchments within southern Africa and beyond.
Determining A Purely Symbolic Transfer Function from Symbol Streams: Theory and Algorithms

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

Griffin, Christopher H

Transfer function modeling is a \\emph{standard technique} in classical Linear Time Invariant and Statistical Process Control. The work of Box and Jenkins was seminal in developing methods for identifying parameters associated with classicalmore » $(r,s,k)$$ transfer functions. Discrete event systems are often \\emph{used} for modeling hybrid control structures and high-level decision problems. \\emph{Examples include} discrete time, discrete strategy repeated games. For these games, a \\emph{discrete transfer function in the form of} an accurate hidden Markov model of input-output relations \\emph{could be used to derive optimal response strategies.} In this paper, we develop an algorithm \\emph{for} creating probabilistic \\textit{Mealy machines} that act as transfer function models for discrete event dynamic systems (DEDS). Our models are defined by three parameters, $$(l_1, l_2, k)$ just as the Box-Jenkins transfer function models. Here $$l_1$$ is the maximal input history lengths to consider, $$l_2$$ is the maximal output history lengths to consider and $k$ is the response lag. Using related results, We show that our Mealy machine transfer functions are optimal in the sense that they maximize the mutual information between the current known state of the DEDS and the next observed input/output pair.« less
Infrared radiometer for measuring thermophysical properties of wind tunnel models

NASA Technical Reports Server (NTRS)

Corwin, R. R.; Moorman, S. L.; Becker, E. C.

1978-01-01

An infrared radiometer is described which was developed to measure temperature rises of wind tunnel models undergoing transient heating over a temperature range of -17.8 C to 260 C. This radiometer interfaces directly with a system which measures the effective thermophysical property square root of rho ck. It has an output temperature fluctuation of 0.26 C at low temperatures and 0.07 C at high temperatures, and the output frequency response of the radiometer is from dc to 400 hertz.
Simulation Framework for Teaching in Modeling and Simulation Areas

ERIC Educational Resources Information Center

De Giusti, Marisa Raquel; Lira, Ariel Jorge; Villarreal, Gonzalo Lujan

2008-01-01

Simulation is the process of executing a model that describes a system with enough detail; this model has its entities, an internal state, some input and output variables and a list of processes bound to these variables. Teaching a simulation language such as general purpose simulation system (GPSS) is always a challenge, because of the way it…
Output-Feedback Model Predictive Control of a Pasteurization Pilot Plant based on an LPV model

NASA Astrophysics Data System (ADS)

Karimi Pour, Fatemeh; Ocampo-Martinez, Carlos; Puig, Vicenç

2017-01-01

This paper presents a model predictive control (MPC) of a pasteurization pilot plant based on an LPV model. Since not all the states are measured, an observer is also designed, which allows implementing an output-feedback MPC scheme. However, the model of the plant is not completely observable when augmented with the disturbance models. In order to solve this problem, the following strategies are used: (i) the whole system is decoupled into two subsystems, (ii) an inner state-feedback controller is implemented into the MPC control scheme. A real-time example based on the pasteurization pilot plant is simulated as a case study for testing the behavior of the approaches.
Modeling the Afferent Dynamics of the Baroreflex Control System

PubMed Central

Mahdi, Adam; Sturdy, Jacob; Ottesen, Johnny T.; Olufsen, Mette S.

2013-01-01

In this study we develop a modeling framework for predicting baroreceptor firing rate as a function of blood pressure. We test models within this framework both quantitatively and qualitatively using data from rats. The models describe three components: arterial wall deformation, stimulation of mechanoreceptors located in the BR nerve-endings, and modulation of the action potential frequency. The three sub-systems are modeled individually following well-established biological principles. The first submodel, predicting arterial wall deformation, uses blood pressure as an input and outputs circumferential strain. The mechanoreceptor stimulation model, uses circumferential strain as an input, predicting receptor deformation as an output. Finally, the neural model takes receptor deformation as an input predicting the BR firing rate as an output. Our results show that nonlinear dependence of firing rate on pressure can be accounted for by taking into account the nonlinear elastic properties of the artery wall. This was observed when testing the models using multiple experiments with a single set of parameters. We find that to model the response to a square pressure stimulus, giving rise to post-excitatory depression, it is necessary to include an integrate-and-fire model, which allows the firing rate to cease when the stimulus falls below a given threshold. We show that our modeling framework in combination with sensitivity analysis and parameter estimation can be used to test and compare models. Finally, we demonstrate that our preferred model can exhibit all known dynamics and that it is advantageous to combine qualitative and quantitative analysis methods. PMID:24348231
RCHILD - an R-package for flexible use of the landscape evolution model CHILD

NASA Astrophysics Data System (ADS)

Dietze, Michael

2014-05-01

Landscape evolution models provide powerful approaches to numerically assess earth surface processes, to quantify rates of landscape change, infer sediment transfer rates, estimate sediment budgets, investigate the consequences of changes in external drivers on a geomorphic system, to provide spatio-temporal interpolations between known landscape states or to test conceptual hypotheses. CHILD (Channel-Hillslope Integrated Landscape Development Model) is one of the most-used models of landscape change in the context of at least tectonic and geomorphologic process interactions. Running CHILD from command line and working with the model output can be a rather awkward task (static model control via text input file, only numeric output in text files). The package RCHILD is a collection of functions for the free statistical software R that help using CHILD in a flexible, dynamic and user-friendly way. The comprised functions allow creating maps, real-time scenes, animations and further thematic plots from model output. The model input files can be modified dynamically and, hence, (feedback-related) changes in external factors can be implemented iteratively. Output files can be written to common formats that can be readily imported to standard GIS software. This contribution presents the basic functionality of the model CHILD as visualised and modified by the package. A rough overview of the available functions is given. Application examples help to illustrate the great potential of numeric modelling of geomorphologic processes.
Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

DOE PAGES

Schryver, Jack; Nutaro, James; Shankar, Mallikarjun

2015-10-30

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease statesmore » in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model. The performances of the baseline agent-based model and its extensions illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.« less
Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

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

Schryver, Jack; Nutaro, James; Shankar, Mallikarjun

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease statesmore » in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model. The performances of the baseline agent-based model and its extensions illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.« less
SU-F-T-479: Estimation of the Accuracy in Respiratory-Gated Radiotherapy

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

Kurosawa, T; Miyakawa, S; Sato, M

Purpose: Irregular respiratory patterns affects dose outputs in respiratorygated radiotherapy and there is no commercially available quality assurance (QA) system for it. We designed and developed a patient specific QA system for respiratory-gated radiotherapy to estimate irradiated output. Methods: Our in-house QA system for gating was composed of a personal computer with the USB-FSIO electronic circuit connecting to the linear accelerator (ONCOR-K, Toshiba Medical Systems). The linac implements a respiratory gating system (AZ-733V, Anzai Medical). During the beam was on, 4.2 V square-wave pulses were continually sent to the system. Our system can receive and count the pulses. At first,more » our system and an oscilloscope were compared to check the performance of our system. Next, basic estimation models were generated when ionization-chamber measurements were performed in gating using regular sinusoidal wave patterns (2.0, 2.5, 4.0, 8.0, 15 sec/cycle). During gated irradiation with the regular patterns, the number of the pulses per one gating window was measured using our system. Correlation between the number of the pulses per one gating and dose per the gating window were assessed to generate the estimation model. Finally, two irregular respiratory patterns were created and the accuracy of the estimation was evaluated. Results: Compared to the oscilloscope, our system worked similarly. The basic models were generated with the accuracy within 0.1%. The results of the gated irradiations with two irregular respiratory patterns show good agreement within 0.4% estimation accuracy. Conclusion: Our developed system shows good estimation for even irregular respiration patterns. The system would be a useful tool to verify the output for respiratory-gated radiotherapy.« less
Performance characteristics of plane-wall venturi-like reverse flow diverters

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

Smith, G.V.; Counce, R.M.

1984-02-01

The results of an analytical and experimental study of plane-wall venturi-like reverse flow diverters (RFD) are presented. In general, the flow characteristics of the RFD are reasonably well predicted by the mathematical model of the RFD, although a divergence between theory and data is observed for the output characteristics in the reverse flow mode as the output impedance is reduced. Overall, the performance of these devices indicates their usefulness in fluid control and fluid power systems, such as displacement pumping systems.
Performance characteristics of plane-wall venturi-like reverse flow diverters

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

Smith, G.V.; Counce, R.M.

1982-01-01

The results of an analytical and experimental study of plane-wall venturi-like reverse flow diverters (RFD) are presented. In general, the flow characteristics of the RFD are reasonably well predicted by the mathematical model of the RFD, although a divergence between theory and data is observed for the output characteristics in the reverse flow mode as the output impedance is reduced. Overall, the performance of these devices indicates their usefulness in fluid control and fluid power systems, such as displacement pumping systems.
The Aegean Sea marine security decision support system

NASA Astrophysics Data System (ADS)

Perivoliotis, L.; Krokos, G.; Nittis, K.; Korres, G.

2011-05-01

As part of the integrated ECOOP (European Coastal Sea Operational observing and Forecasting System) project, HCMR upgraded the already existing standalone Oil Spill Forecasting System for the Aegean Sea, initially developed for the Greek Operational Oceanography System (POSEIDON), into an active element of the European Decision Support System (EuroDeSS). The system is accessible through a user friendly web interface where the case scenarios can be fed into the oil spill drift model component, while the synthetic output contains detailed information about the distribution of oil spill particles and the oil spill budget and it is provided both in text based ECOOP common output format and as a series of sequential graphics. The main development steps that were necessary for this transition were the modification of the forcing input data module in order to allow the import of other system products which are usually provided in standard formats such as NetCDF and the transformation of the model's calculation routines to allow use of current, density and diffusivities data in z instead of sigma coordinates. During the implementation of the Aegean DeSS, the system was used in operational mode in order support the Greek marine authorities in handling a real accident that took place in North Aegean area. Furthermore, the introduction of common input and output files by all the partners of EuroDeSS extended the system's interoperability thus facilitating data exchanges and comparison experiments.
The Aegean sea marine security decision support system

NASA Astrophysics Data System (ADS)

Perivoliotis, L.; Krokos, G.; Nittis, K.; Korres, G.

2011-10-01

As part of the integrated ECOOP (European Coastal Sea Operational observing and Forecasting System) project, HCMR upgraded the already existing standalone Oil Spill Forecasting System for the Aegean Sea, initially developed for the Greek Operational Oceanography System (POSEIDON), into an active element of the European Decision Support System (EuroDeSS). The system is accessible through a user friendly web interface where the case scenarios can be fed into the oil spill drift model component, while the synthetic output contains detailed information about the distribution of oil spill particles and the oil spill budget and it is provided both in text based ECOOP common output format and as a series of sequential graphics. The main development steps that were necessary for this transition were the modification of the forcing input data module in order to allow the import of other system products which are usually provided in standard formats such as NetCDF and the transformation of the model's calculation routines to allow use of current, density and diffusivities data in z instead of sigma coordinates. During the implementation of the Aegean DeSS, the system was used in operational mode in order to support the Greek marine authorities in handling a real accident that took place in North Aegean area. Furthermore, the introduction of common input and output files by all the partners of EuroDeSS extended the system's interoperability thus facilitating data exchanges and comparison experiments.
Effects of range-wide variation in climate and isolation on floral traits and reproductive output of Clarkia pulchella.

PubMed

Bontrager, Megan; Angert, Amy L

2016-01-01

Plant mating systems and geographic range limits are conceptually linked by shared underlying drivers, including landscape-level heterogeneity in climate and in species' abundance. Studies of how geography and climate interact to affect plant traits that influence mating system and population dynamics can lend insight to ecological and evolutionary processes shaping ranges. Here, we examined how spatiotemporal variation in climate affects reproductive output of a mixed-mating annual, Clarkia pulchella. We also tested the effects of population isolation and climate on mating-system-related floral traits across the range. We measured reproductive output and floral traits on herbarium specimens collected across the range of C. pulchella. We extracted climate data associated with specimens and derived a population isolation metric from a species distribution model. We then examined how predictors of reproductive output and floral traits vary among populations of increasing distance from the range center. Finally, we tested whether reproductive output and floral traits vary with increasing distance from the center of the range. Reproductive output decreased as summer precipitation decreased, and low precipitation may contribute to limiting the southern and western range edges of C. pulchella. High spring and summer temperatures are correlated with low herkogamy, but these climatic factors show contrasting spatial patterns in different quadrants of the range. Limiting factors differ among different parts of the range. Due to the partial decoupling of geography and environment, examining relationships between climate, reproductive output, and mating-system-related floral traits reveals spatial patterns that might be missed when focusing solely on geographic position. © 2016 Botanical Society of America.

A High Voltage Ratio and Low Ripple Interleaved DC-DC Converter for Fuel Cell Applications

PubMed Central

Chang, Long-Yi; Chao, Kuei-Hsiang; Chang, Tsang-Chih

2012-01-01

This paper proposes a high voltage ratio and low ripple interleaved boost DC-DC converter, which can be used to reduce the output voltage ripple. This converter transfers the low DC voltage of fuel cell to high DC voltage in DC link. The structure of the converter is parallel with two voltage-doubler boost converters by interleaving their output voltages to reduce the voltage ripple ratio. Besides, it can lower the current stress for the switches and inductors in the system. First, the PSIM software was used to establish a proton exchange membrane fuel cell and a converter circuit model. The simulated and measured results of the fuel cell output characteristic curve are made to verify the correctness of the established simulation model. In addition, some experimental results are made to validate the effectiveness in improving output voltage ripple of the proposed high voltage ratio interleaved boost DC-DC converters. PMID:23365536
A high voltage ratio and low ripple interleaved DC-DC converter for fuel cell applications.

PubMed

Chang, Long-Yi; Chao, Kuei-Hsiang; Chang, Tsang-Chih

2012-01-01

This paper proposes a high voltage ratio and low ripple interleaved boost DC-DC converter, which can be used to reduce the output voltage ripple. This converter transfers the low DC voltage of fuel cell to high DC voltage in DC link. The structure of the converter is parallel with two voltage-doubler boost converters by interleaving their output voltages to reduce the voltage ripple ratio. Besides, it can lower the current stress for the switches and inductors in the system. First, the PSIM software was used to establish a proton exchange membrane fuel cell and a converter circuit model. The simulated and measured results of the fuel cell output characteristic curve are made to verify the correctness of the established simulation model. In addition, some experimental results are made to validate the effectiveness in improving output voltage ripple of the proposed high voltage ratio interleaved boost DC-DC converters.
A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations.

PubMed

Eikenberry, Steffen E; Marmarelis, Vasilis Z

2013-02-01

We propose a new variant of Volterra-type model with a nonlinear auto-regressive (NAR) component that is a suitable framework for describing the process of AP generation by the neuron membrane potential, and we apply it to input-output data generated by the Hodgkin-Huxley (H-H) equations. Volterra models use a functional series expansion to describe the input-output relation for most nonlinear dynamic systems, and are applicable to a wide range of physiologic systems. It is difficult, however, to apply the Volterra methodology to the H-H model because is characterized by distinct subthreshold and suprathreshold dynamics. When threshold is crossed, an autonomous action potential (AP) is generated, the output becomes temporarily decoupled from the input, and the standard Volterra model fails. Therefore, in our framework, whenever membrane potential exceeds some threshold, it is taken as a second input to a dual-input Volterra model. This model correctly predicts membrane voltage deflection both within the subthreshold region and during APs. Moreover, the model naturally generates a post-AP afterpotential and refractory period. It is known that the H-H model converges to a limit cycle in response to a constant current injection. This behavior is correctly predicted by the proposed model, while the standard Volterra model is incapable of generating such limit cycle behavior. The inclusion of cross-kernels, which describe the nonlinear interactions between the exogenous and autoregressive inputs, is found to be absolutely necessary. The proposed model is general, non-parametric, and data-derived.
Method and system to estimate variables in an integrated gasification combined cycle (IGCC) plant

DOEpatents

Kumar, Aditya; Shi, Ruijie; Dokucu, Mustafa

2013-09-17

System and method to estimate variables in an integrated gasification combined cycle (IGCC) plant are provided. The system includes a sensor suite to measure respective plant input and output variables. An extended Kalman filter (EKF) receives sensed plant input variables and includes a dynamic model to generate a plurality of plant state estimates and a covariance matrix for the state estimates. A preemptive-constraining processor is configured to preemptively constrain the state estimates and covariance matrix to be free of constraint violations. A measurement-correction processor may be configured to correct constrained state estimates and a constrained covariance matrix based on processing of sensed plant output variables. The measurement-correction processor is coupled to update the dynamic model with corrected state estimates and a corrected covariance matrix. The updated dynamic model may be configured to estimate values for at least one plant variable not originally sensed by the sensor suite.
Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.

PubMed

Bastian, Nathaniel D; Ekin, Tahir; Kang, Hyojung; Griffin, Paul M; Fulton, Lawrence V; Grannan, Benjamin C

2017-06-01

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.
Measurement uncertainty and feasibility study of a flush airdata system for a hypersonic flight experiment

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A.; Moes, Timothy R.

1994-01-01

Presented is a feasibility and error analysis for a hypersonic flush airdata system on a hypersonic flight experiment (HYFLITE). HYFLITE heating loads make intrusive airdata measurement impractical. Although this analysis is specifically for the HYFLITE vehicle and trajectory, the problems analyzed are generally applicable to hypersonic vehicles. A layout of the flush-port matrix is shown. Surface pressures are related airdata parameters using a simple aerodynamic model. The model is linearized using small perturbations and inverted using nonlinear least-squares. Effects of various error sources on the overall uncertainty are evaluated using an error simulation. Error sources modeled include boundarylayer/viscous interactions, pneumatic lag, thermal transpiration in the sensor pressure tubing, misalignment in the matrix layout, thermal warping of the vehicle nose, sampling resolution, and transducer error. Using simulated pressure data for input to the estimation algorithm, effects caused by various error sources are analyzed by comparing estimator outputs with the original trajectory. To obtain ensemble averages the simulation is run repeatedly and output statistics are compiled. Output errors resulting from the various error sources are presented as a function of Mach number. Final uncertainties with all modeled error sources included are presented as a function of Mach number.
Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.

PubMed

Cheng, Ching-An; Huang, Han-Pang

2016-12-01

We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space. We reformulate Lagrangian systems with kernels that embed the governing Euler-Lagrange equation-the Lagrangian kernels-and show that these kernels span a subspace capturing the Lagrangian's projection as inverse dynamics. By such property, our model uses only inputs and outputs as in machine learning and inherits the structured form as in system dynamics, thereby removing the need for the mundane derivations for new systems as well as the generalization problem in learning from scratches. In effect, it learns the system's Lagrangian, a simpler task than directly learning the dynamics. To demonstrate, we applied the proposed kernel to identify the robot inverse dynamics in simulations and experiments. Our results present a competitive novel approach to identifying Lagrangian systems, despite using only inputs and outputs.
Life cycle assessment modelling of waste-to-energy incineration in Spain and Portugal.

PubMed

Margallo, M; Aldaco, R; Irabien, A; Carrillo, V; Fischer, M; Bala, A; Fullana, P

2014-06-01

In recent years, waste management systems have been evaluated using a life cycle assessment (LCA) approach. A main shortcoming of prior studies was the focus on a mixture of waste with different characteristics. The estimation of emissions and consumptions associated with each waste fraction in these studies presented allocation problems. Waste-to-energy (WTE) incineration is a clear example in which municipal solid waste (MSW), comprising many types of materials, is processed to produce several outputs. This paper investigates an approach to better understand incineration processes in Spain and Portugal by applying a multi-input/output allocation model. The application of this model enabled predictions of WTE inputs and outputs, including the consumption of ancillary materials and combustibles, air emissions, solid wastes, and the energy produced during the combustion of each waste fraction. © The Author(s) 2014.
A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation

PubMed Central

Berger, Theodore W.; Song, Dong; Chan, Rosa H. M.; Marmarelis, Vasilis Z.; LaCoss, Jeff; Wills, Jack; Hampson, Robert E.; Deadwyler, Sam A.; Granacki, John J.

2012-01-01

This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the “core” of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals. PMID:22438335
Robust non-fragile finite-frequency H∞ static output-feedback control for active suspension systems

NASA Astrophysics Data System (ADS)

Wang, Gang; Chen, Changzheng; Yu, Shenbo

2017-07-01

This paper deals with the problem of non-fragile H∞ static output-feedback control of vehicle active suspension systems with finite-frequency constraint. The control objective is to improve ride comfort within the given frequency range and ensure the hard constraints in the time-domain. Moreover, in order to enhance the robustness of the controller, the control gain perturbation is also considered in controller synthesis. Firstly, a new non-fragile H∞ finite-frequency control condition is established by using generalized Kalman-Yakubovich-Popov (GKYP) lemma. Secondly, the static output-feedback control gain is directly derived by using a non-iteration algorithm. Different from the existing iteration LMI results, the static output-feedback design is simple and less conservative. Finally, the proposed control algorithm is applied to a quarter-car active suspension model with actuator dynamics, numerical results are made to show the effectiveness and merits of the proposed method.
Network Capacity Assessment of CHP-based Distributed Generation on Urban Energy Distribution Networks

NASA Astrophysics Data System (ADS)

Zhang, Xianjun

The combined heat and power (CHP)-based distributed generation (DG) or dis-tributed energy resources (DERs) are mature options available in the present energy market, considered to be an effective solution to promote energy efficiency. In the urban environment, the electricity, water and natural gas distribution networks are becoming increasingly interconnected with the growing penetration of the CHP-based DG. Subsequently, this emerging interdependence leads to new topics meriting serious consideration: how much of the CHP-based DG can be accommodated and where to locate these DERs, and given preexisting constraints, how to quantify the mutual impacts on operation performances between these urban energy distribution networks and the CHP-based DG. The early research work was conducted to investigate the feasibility and design methods for one residential microgrid system based on existing electricity, water and gas infrastructures of a residential community, mainly focusing on the economic planning. However, this proposed design method cannot determine the optimal DG sizing and siting for a larger test bed with the given information of energy infrastructures. In this context, a more systematic as well as generalized approach should be developed to solve these problems. In the later study, the model architecture that integrates urban electricity, water and gas distribution networks, and the CHP-based DG system was developed. The proposed approach addressed the challenge of identifying the optimal sizing and siting of the CHP-based DG on these urban energy networks and the mutual impacts on operation performances were also quantified. For this study, the overall objective is to maximize the electrical output and recovered thermal output of the CHP-based DG units. The electricity, gas, and water system models were developed individually and coupled by the developed CHP-based DG system model. The resultant integrated system model is used to constrain the DG's electrical output and recovered thermal output, which are affected by multiple factors and thus analyzed in different case studies. The results indicate that the designed typical gas system is capable of supplying sufficient natural gas for the DG normal operation, while the present water system cannot support the complete recovery of the exhaust heat from the DG units.
Rocketdyne/Westinghouse nuclear thermal rocket engine modeling

NASA Technical Reports Server (NTRS)

Glass, James F.

1993-01-01

The topics are presented in viewgraph form and include the following: systems approach needed for nuclear thermal rocket (NTR) design optimization; generic NTR engine power balance codes; rocketdyne nuclear thermal system code; software capabilities; steady state model; NTR engine optimizer code-logic; reactor power calculation logic; sample multi-component configuration; NTR design code output; generic NTR code at Rocketdyne; Rocketdyne NTR model; and nuclear thermal rocket modeling directions.
How to optimize tuberculosis case finding: explorations for Indonesia with a health system model

PubMed Central

2009-01-01

Background A mathematical model was designed to explore the impact of three strategies for better tuberculosis case finding. Strategies included: (1) reducing the number of tuberculosis patients who do not seek care; (2) reducing diagnostic delay; and (3) engaging non-DOTS providers in the referral of tuberculosis suspects to DOTS services in the Indonesian health system context. The impact of these strategies on tuberculosis mortality and treatment outcome was estimated using a mathematical model of the Indonesian health system. Methods The model consists of multiple compartments representing logical movement of a respiratory symptomatic (tuberculosis suspect) through the health system, including patient- and health system delays. Main outputs of the model are tuberculosis death rate and treatment outcome (i.e. full or partial cure). We quantified the model parameters for the Jogjakarta province context, using a two round Delphi survey with five Indonesian tuberculosis experts. Results The model validation shows that four critical model outputs (average duration of symptom onset to treatment, detection rate, cure rate, and death rate) were reasonably close to existing available data, erring towards more optimistic outcomes than are actually reported. The model predicted that an intervention to reduce the proportion of tuberculosis patients who never seek care would have the biggest impact on tuberculosis death prevention, while an intervention resulting in more referrals of tuberculosis suspects to DOTS facilities would yield higher cure rates. This finding is similar for situations where the alternative sector is a more important health resource, such as in most other parts of Indonesia. Conclusion We used mathematical modeling to explore the impact of Indonesian health system interventions on tuberculosis treatment outcome and deaths. Because detailed data were not available regarding the current Indonesian population, we relied on expert opinion to quantify the parameters. The fact that the model output showed similar results to epidemiological data suggests that the experts had an accurate understanding of this subject, thereby reassuring the quality of our predictions. The model highlighted the potential effectiveness of active case finding of tuberculosis patients with limited access to DOTS facilities in the developing country setting. PMID:19505296
Modelling of Two-Stage Methane Digestion With Pretreatment of Biomass

NASA Astrophysics Data System (ADS)

Dychko, A.; Remez, N.; Opolinskyi, I.; Kraychuk, S.; Ostapchuk, N.; Yevtieieva, L.

2018-04-01

Systems of anaerobic digestion should be used for processing of organic waste. Managing the process of anaerobic recycling of organic waste requires reliable predicting of biogas production. Development of mathematical model of process of organic waste digestion allows determining the rate of biogas output at the two-stage process of anaerobic digestion considering the first stage. Verification of Konto's model, based on the studied anaerobic processing of organic waste, is implemented. The dependencies of biogas output and its rate from time are set and may be used to predict the process of anaerobic processing of organic waste.
Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

PubMed

Kamesh, Reddi; Rani, K Yamuna

2016-09-01

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Monte Carlo based electron treatment planning and cutout output factor calculations

NASA Astrophysics Data System (ADS)

Mitrou, Ellis

Electron radiotherapy (RT) offers a number of advantages over photons. The high surface dose, combined with a rapid dose fall-off beyond the target volume presents a net increase in tumor control probability and decreases the normal tissue complication for superficial tumors. Electron treatments are normally delivered clinically without previously calculated dose distributions due to the complexity of the electron transport involved and greater error in planning accuracy. This research uses Monte Carlo (MC) methods to model clinical electron beams in order to accurately calculate electron beam dose distributions in patients as well as calculate cutout output factors, reducing the need for a clinical measurement. The present work is incorporated into a research MC calculation system: McGill Monte Carlo Treatment Planning (MMCTP) system. Measurements of PDDs, profiles and output factors in addition to 2D GAFCHROMICRTM EBT2 film measurements in heterogeneous phantoms were obtained to commission the electron beam model. The use of MC for electron TP will provide more accurate treatments and yield greater knowledge of the electron dose distribution within the patient. The calculation of output factors could invoke a clinical time saving of up to 1 hour per patient.
Validation of a new modal performance measure for flexible controllers design

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

Simo, J.B.; Tahan, S.A.; Kamwa, I.

1996-05-01

A new modal performance measure for power system stabilizer (PSS) optimization is proposed in this paper. The new method is based on modifying the square envelopes of oscillating modes, in order to take into account their damping ratios while minimizing the performance index. This criteria is applied to flexible controllers optimal design, on a multi-input-multi-output (MIMO) reduced-order model of a prototype power system. The multivariable model includes four generators, each having one input and one output. Linear time-response simulation and transient stability analysis with a nonlinear package confirm the superiority of the proposed criteria and illustrate its effectiveness in decentralizedmore » control.« less
Input-output characterization of an ultrasonic testing system by digital signal analysis

NASA Technical Reports Server (NTRS)

Williams, J. H., Jr.; Lee, S. S.; Karagulle, H.

1986-01-01

Ultrasonic test system input-output characteristics were investigated by directly coupling the transmitting and receiving transducers face to face without a test specimen. Some of the fundamentals of digital signal processing were summarized. Input and output signals were digitized by using a digital oscilloscope, and the digitized data were processed in a microcomputer by using digital signal-processing techniques. The continuous-time test system was modeled as a discrete-time, linear, shift-invariant system. In estimating the unit-sample response and frequency response of the discrete-time system, it was necessary to use digital filtering to remove low-amplitude noise, which interfered with deconvolution calculations. A digital bandpass filter constructed with the assistance of a Blackman window and a rectangular time window were used. Approximations of the impulse response and the frequency response of the continuous-time test system were obtained by linearly interpolating the defining points of the unit-sample response and the frequency response of the discrete-time system. The test system behaved as a linear-phase bandpass filter in the frequency range 0.6 to 2.3 MHz. These frequencies were selected in accordance with the criterion that they were 6 dB below the maximum peak of the amplitude of the frequency response. The output of the system to various inputs was predicted and the results were compared with the corresponding measurements on the system.
DFB laser array driver circuit controlled by adjustable signal

NASA Astrophysics Data System (ADS)

Du, Weikang; Du, Yinchao; Guo, Yu; Li, Wei; Wang, Hao

2018-01-01

In order to achieve the intelligent controlling of DFB laser array, this paper presents the design of an intelligence and high precision numerical controlling electric circuit. The system takes MCU and FPGA as the main control chip, with compact, high-efficiency, no impact, switching protection characteristics. The output of the DFB laser array can be determined by an external adjustable signal. The system transforms the analog control model into a digital control model, which improves the performance of the driver. The system can monitor the temperature and current of DFB laser array in real time. The output precision of the current can reach ± 0.1mA, which ensures the stable and reliable operation of the DFB laser array. Such a driver can benefit the flexible usage of the DFB laser array.
Design and test hardware for a solar array switching unit

NASA Technical Reports Server (NTRS)

Patil, A. R.; Cho, B. H.; Sable, D.; Lee, F. C.

1992-01-01

This paper describes the control of a pulse width modulated (PWM) type sequential shunt switching unit (SSU) for spacecraft applications. It is found that the solar cell output capacitance has a significant impact on SSU design. Shorting of this cell capacitance by the PWM switch causes input current surges. These surges are minimized by the use of a series filter inductor. The system with a filter is analyzed for ripple and the control to output-voltage transfer function. Stable closed loop design considerations are discussed. The results are supported by modeling and measurements of loop gain and of closed-loop bus impedance on test hardware for NASA's 120 V Earth Observation System (EOS). The analysis and modeling are also applicable to NASA's 160 V Space Station power system.

Evaluation Applied to Reliability Analysis of Reconfigurable, Highly Reliable, Fault-Tolerant, Computing Systems for Avionics

NASA Technical Reports Server (NTRS)

Migneault, G. E.

1979-01-01

Emulation techniques are proposed as a solution to a difficulty arising in the analysis of the reliability of highly reliable computer systems for future commercial aircraft. The difficulty, viz., the lack of credible precision in reliability estimates obtained by analytical modeling techniques are established. The difficulty is shown to be an unavoidable consequence of: (1) a high reliability requirement so demanding as to make system evaluation by use testing infeasible, (2) a complex system design technique, fault tolerance, (3) system reliability dominated by errors due to flaws in the system definition, and (4) elaborate analytical modeling techniques whose precision outputs are quite sensitive to errors of approximation in their input data. The technique of emulation is described, indicating how its input is a simple description of the logical structure of a system and its output is the consequent behavior. The use of emulation techniques is discussed for pseudo-testing systems to evaluate bounds on the parameter values needed for the analytical techniques.
Transient Turbine Engine Modeling with Hardware-in-the-Loop Power Extraction (PREPRINT)

DTIC Science & Technology

2008-07-01

Furthermore, it must be compatible with a real - time operating system that is capable of running the simulation. For some models, especially those that use...problem of interfacing the engine/control model to a real - time operating system and associated lab hardware becomes a problem of interfacing these...model in real-time. This requires the use of a real - time operating system and a compatible I/O (input/output) board. Figure 1 illustrates the HIL
Observer-based output feedback control of networked control systems with non-uniform sampling and time-varying delay

NASA Astrophysics Data System (ADS)

Meng, Su; Chen, Jie; Sun, Jian

2017-10-01

This paper investigates the problem of observer-based output feedback control for networked control systems with non-uniform sampling and time-varying transmission delay. The sampling intervals are assumed to vary within a given interval. The transmission delay belongs to a known interval. A discrete-time model is first established, which contains time-varying delay and norm-bounded uncertainties coming from non-uniform sampling intervals. It is then converted to an interconnection of two subsystems in which the forward channel is delay-free. The scaled small gain theorem is used to derive the stability condition for the closed-loop system. Moreover, the observer-based output feedback controller design method is proposed by utilising a modified cone complementary linearisation algorithm. Finally, numerical examples illustrate the validity and superiority of the proposed method.
The Description and Validation of a Computationally-Efficient CH4-CO-OH (ECCOH) Module for 3D Model Applications

NASA Technical Reports Server (NTRS)

Elshorbany, Yasin F.; Duncan, Bryan N.; Strode, Sarah A.; Wang, James S.; Kouatchou, Jules

2015-01-01

We present the Efficient CH4-CO-OH Module (ECCOH) that allows for the simulation of the methane, carbon monoxide and hydroxyl radical (CH4-CO-OH cycle, within a chemistry climate model, carbon cycle model, or earth system model. The computational efficiency of the module allows many multi-decadal, sensitivity simulations of the CH4-CO-OH cycle, which primarily determines the global tropospheric oxidizing capacity. This capability is important for capturing the nonlinear feedbacks of the CH4-CO-OH system and understanding the perturbations to relatively long-lived methane and the concomitant impacts on climate. We implemented the ECCOH module into the NASA GEOS-5 Atmospheric Global Circulation Model (AGCM), performed multiple sensitivity simulations of the CH4-CO-OH system over two decades, and evaluated the model output with surface and satellite datasets of methane and CO. The favorable comparison of output from the ECCOH module (as configured in the GEOS-5 AGCM) with observations demonstrates the fidelity of the module for use in scientific research.
Optimal pulse design for communication-oriented slow-light pulse detection.

PubMed

Stenner, Michael D; Neifeld, Mark A

2008-01-21

We present techniques for designing pulses for linear slow-light delay systems which are optimal in the sense that they maximize the signal-to-noise ratio (SNR) and signal-to-noise-plus-interference ratio (SNIR) of the detected pulse energy. Given a communication model in which input pulses are created in a finite temporal window and output pulse energy in measured in a temporally-offset output window, the SNIR-optimal pulses achieve typical improvements of 10 dB compared to traditional pulse shapes for a given output window offset. Alternatively, for fixed SNR or SNIR, window offset (detection delay) can be increased by 0.3 times the window width. This approach also invites a communication-based model for delay and signal fidelity.
Modelling of a Convecting, Crystallizing, and Replenished Diopside-Anorthite Axial Magma Chamber beneath Mid Ocean Ridges

NASA Astrophysics Data System (ADS)

Lowell, R. P.; Lata, C.

2016-12-01

The aim of this work is to model heat output from a cooling, convective, crystallizing, and replenished basaltic magma sill, representing an axial magma lens (AML) at mid oceanic ridges. As a simplified version of basaltic melt, we have assumed the melt to be a two-component eutectic system composed of diopside and anorthite. Convective vigor is expressed through the Rayleigh number and heat flux is scaled through a classical relationship between the Rayleigh number and Nusselt number, where the temperature difference driving the convective heat flux is derived from a "viscous" temperature scale reflecting the strong temperature dependent viscosity of the system. Viscosity is modeled as a function of melt composition and temperature using the Tammann-Vogel-Fulcher equation, with parameters fit to the values of observed viscosities along the diopside-anorthite liquidus. It was observed for the un-replenished case, in which crystals fall rapidly to the floor of the AML, model results show that the higher initial concentration of diopside, the more vigorous the convection and the faster the rate of crystallization and decay of heat output. Replenishment of the AML accompanied by modest thickening of the melt layer stabilizes the heat output at values similar to those observed at ridge-axis hydrothermal systems. This study is an important step forward in quantitative understanding of thermal evolution of the axial magma lens at a mid-ocean ridge and the corresponding effect on high-temperature hydrothermal systems. Future work could involve improved replenishment mechanisms, more complex melts, and direct coupling with hydrothermal circulation models.
Observer-Based Discrete-Time Nonnegative Edge Synchronization of Networked Systems.

PubMed

Su, Housheng; Wu, Han; Chen, Xia

2017-10-01

This paper studies the multi-input and multi-output discrete-time nonnegative edge synchronization of networked systems based on neighbors' output information. The communication relationship among the edges of networked systems is modeled by well-known line graph. Two observer-based edge synchronization algorithms are designed, for which some necessary and sufficient synchronization conditions are derived. Moreover, some computable sufficient synchronization conditions are obtained, in which the feedback matrix and the observer matrix are computed by solving the linear programming problems. We finally design several simulation examples to demonstrate the validity of the given nonnegative edge synchronization algorithms.
Coordination of heterogeneous nonlinear multi-agent systems with prescribed behaviours

NASA Astrophysics Data System (ADS)

Tang, Yutao

2017-10-01

In this paper, we consider a coordination problem for a class of heterogeneous nonlinear multi-agent systems with a prescribed input-output behaviour which was represented by another input-driven system. In contrast to most existing multi-agent coordination results with an autonomous (virtual) leader, this formulation takes possible control inputs of the leader into consideration. First, the coordination was achieved by utilising a group of distributed observers based on conventional assumptions of model matching problem. Then, a fully distributed adaptive extension was proposed without using the input of this input-output behaviour. An example was given to verify their effectiveness.
Jet impingement heat transfer enhancement for the GPU-3 Stirling engine

NASA Technical Reports Server (NTRS)

Johnson, D. C.; Congdon, C. W.; Begg, L. L.; Britt, E. J.; Thieme, L. G.

1981-01-01

A computer model of the combustion-gas-side heat transfer was developed to predict the effects of a jet impingement system and the possible range of improvements available. Using low temperature (315 C (600 F)) pretest data in an updated model, a high temperature silicon carbide jet impingement heat transfer system was designed and fabricated. The system model predicted that at the theoretical maximum limit, jet impingement enhanced heat transfer can: (1) reduce the flame temperature by 275 C (500 F); (2) reduce the exhaust temperature by 110 C (200 F); and (3) increase the overall heat into the working fluid by 10%, all for an increase in required pumping power of less than 0.5% of the engine power output. Initial tests on the GPU-3 Stirling engine at NASA-Lewis demonstrated that the jet impingement system increased the engine output power and efficiency by 5% - 8% with no measurable increase in pumping power. The overall heat transfer coefficient was increased by 65% for the maximum power point of the tests.
Asymmetry in Signal Oscillations Contributes to Efficiency of Periodic Systems.

PubMed

Bae, Seul-A; Acevedo, Alison; Androulakis, Ioannis P

2016-01-01

Oscillations are an important feature of cellular signaling that result from complex combinations of positive- and negative-feedback loops. The encoding and decoding mechanisms of oscillations based on amplitude and frequency have been extensively discussed in the literature in the context of intercellular and intracellular signaling. However, the fundamental questions of whether and how oscillatory signals offer any competitive advantages-and, if so, what-have not been fully answered. We investigated established oscillatory mechanisms and designed a study to analyze the oscillatory characteristics of signaling molecules and system output in an effort to answer these questions. Two classic oscillators, Goodwin and PER, were selected as the model systems, and corresponding no-feedback models were created for each oscillator to discover the advantage of oscillating signals. Through simulating the original oscillators and the matching no-feedback models, we show that oscillating systems have the capability to achieve better resource-to-output efficiency, and we identify oscillatory characteristics that lead to improved efficiency.
Parameter Optimization and Operating Strategy of a TEG System for Railway Vehicles

NASA Astrophysics Data System (ADS)

Heghmanns, A.; Wilbrecht, S.; Beitelschmidt, M.; Geradts, K.

2016-03-01

A thermoelectric generator (TEG) system demonstrator for diesel electric locomotives with the objective of reducing the mechanical load on the thermoelectric modules (TEM) is developed and constructed to validate a one-dimensional thermo-fluid flow simulation model. The model is in good agreement with the measurements and basis for the optimization of the TEG's geometry by a genetic multi objective algorithm. The best solution has a maximum power output of approx. 2.7 kW and does not exceed the maximum back pressure of the diesel engine nor the maximum TEM hot side temperature. To maximize the reduction of the fuel consumption, an operating strategy regarding the system power output for the TEG system is developed. Finally, the potential consumption reduction in passenger and freight traffic operating modes is estimated under realistic driving conditions by means of a power train and lateral dynamics model. The fuel savings are between 0.5% and 0.7%, depending on the driving style.
Multi-muscle FES force control of the human arm for arbitrary goals.

PubMed

Schearer, Eric M; Liao, Yu-Wei; Perreault, Eric J; Tresch, Matthew C; Memberg, William D; Kirsch, Robert F; Lynch, Kevin M

2014-05-01

We present a method for controlling a neuroprosthesis for a paralyzed human arm using functional electrical stimulation (FES) and characterize the errors of the controller. The subject has surgically implanted electrodes for stimulating muscles in her shoulder and arm. Using input/output data, a model mapping muscle stimulations to isometric endpoint forces measured at the subject's hand was identified. We inverted the model of this redundant and coupled multiple-input multiple-output system by minimizing muscle activations and used this inverse for feedforward control. The magnitude of the total root mean square error over a grid in the volume of achievable isometric endpoint force targets was 11% of the total range of achievable forces. Major sources of error were random error due to trial-to-trial variability and model bias due to nonstationary system properties. Because the muscles working collectively are the actuators of the skeletal system, the quantification of errors in force control guides designs of motion controllers for multi-joint, multi-muscle FES systems that can achieve arbitrary goals.
Enhancement of Local Climate Analysis Tool

NASA Astrophysics Data System (ADS)

Horsfall, F. M.; Timofeyeva, M. M.; Dutton, J.

2012-12-01

The National Oceanographic and Atmospheric Administration (NOAA) National Weather Service (NWS) will enhance its Local Climate Analysis Tool (LCAT) to incorporate specific capabilities to meet the needs of various users including energy, health, and other communities. LCAT is an online interactive tool that provides quick and easy access to climate data and allows users to conduct analyses at the local level such as time series analysis, trend analysis, compositing, correlation and regression techniques, with others to be incorporated as needed. LCAT uses principles of Artificial Intelligence in connecting human and computer perceptions on application of data and scientific techniques in multiprocessing simultaneous users' tasks. Future development includes expanding the type of data currently imported by LCAT (historical data at stations and climate divisions) to gridded reanalysis and General Circulation Model (GCM) data, which are available on global grids and thus will allow for climate studies to be conducted at international locations. We will describe ongoing activities to incorporate NOAA Climate Forecast System (CFS) reanalysis data (CFSR), NOAA model output data, including output from the National Multi Model Ensemble Prediction System (NMME) and longer term projection models, and plans to integrate LCAT into the Earth System Grid Federation (ESGF) and its protocols for accessing model output and observational data to ensure there is no redundancy in development of tools that facilitate scientific advancements and use of climate model information in applications. Validation and inter-comparison of forecast models will be included as part of the enhancement to LCAT. To ensure sustained development, we will investigate options for open sourcing LCAT development, in particular, through the University Corporation for Atmospheric Research (UCAR).
Neural node network and model, and method of teaching same

DOEpatents

Parlos, A.G.; Atiya, A.F.; Fernandez, B.; Tsai, W.K.; Chong, K.T.

1995-12-26

The present invention is a fully connected feed forward network that includes at least one hidden layer. The hidden layer includes nodes in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device occurring in the feedback path (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit from all the other nodes within the same layer. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing. 21 figs.
Neural node network and model, and method of teaching same

DOEpatents

Parlos, Alexander G.; Atiya, Amir F.; Fernandez, Benito; Tsai, Wei K.; Chong, Kil T.

1995-01-01

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.
Implications of conceptual channel representation on SWAT streamflow and sediment modeling

USDA-ARS?s Scientific Manuscript database

Hydrologic modeling outputs are influenced by how a watershed system is represented. Channel routing is a typical example of the mathematical conceptualization of watershed landscape and processes in hydrologic modeling. We investigated the sensitivity of accuracy, equifinality, and uncertainty of...
Computerized Adaptive Testing System Design: Preliminary Design Considerations.

ERIC Educational Resources Information Center

Croll, Paul R.

A functional design model for a computerized adaptive testing (CAT) system was developed and presented through a series of hierarchy plus input-process-output (HIPO) diagrams. System functions were translated into system structure: specifically, into 34 software components. Implementation of the design in a physical system was addressed through…
Dynamic Simulation of Human Gait Model With Predictive Capability.

PubMed

Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

2018-03-01

In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.
Flyback CCM inverter for AC module applications: iterative learning control and convergence analysis

NASA Astrophysics Data System (ADS)

Lee, Sung-Ho; Kim, Minsung

2017-12-01

This paper presents an iterative learning controller (ILC) for an interleaved flyback inverter operating in continuous conduction mode (CCM). The flyback CCM inverter features small output ripple current, high efficiency, and low cost, and hence it is well suited for photovoltaic power applications. However, it exhibits the non-minimum phase behaviour, because its transfer function from control duty to output current has the right-half-plane (RHP) zero. Moreover, the flyback CCM inverter suffers from the time-varying grid voltage disturbance. Thus, conventional control scheme results in inaccurate output tracking. To overcome these problems, the ILC is first developed and applied to the flyback inverter operating in CCM. The ILC makes use of both predictive and current learning terms which help the system output to converge to the reference trajectory. We take into account the nonlinear averaged model and use it to construct the proposed controller. It is proven that the system output globally converges to the reference trajectory in the absence of state disturbances, output noises, or initial state errors. Numerical simulations are performed to validate the proposed control scheme, and experiments using 400-W AC module prototype are carried out to demonstrate its practical feasibility.
Complex Dynamics in a Triopoly Game with Multiple Delays in the Competition of Green Product Level

NASA Astrophysics Data System (ADS)

Si, Fengshan; Ma, Junhai

Research on the output game behavior of oligopoly has greatly advanced in recent years. But many unknowns remain, particularly the influence of consumers’ willingness to buy green products on the oligopoly output game. This paper constructs a triopoly output game model with multiple delays in the competition of green products. The influence of the parameters on the stability and complexity of the system is studied by analyzing the existence and local asymptotic stability of the equilibrium point. It is found that the system loses stability and increases complexity if delay parameters exceed a certain range. In the unstable or chaotic game market, the decisions of oligopoly will be counterproductive. It is also observed that the influence of weight and output adjustment speed on the firm itself is obviously stronger than the influence of other firms. In addition, it is important that weight and output adjustment speed cannot increase indefinitely, otherwise it will bring unnecessary losses to the firm. Finally, chaos control is realized by using the variable feedback control method. The research results of this paper can provide a reference for decision-making for the output of the game of oligopoly.

U1108 performance model

NASA Technical Reports Server (NTRS)

Trachta, G.

1976-01-01

A model of Univac 1108 work flow has been developed to assist in performance evaluation studies and configuration planning. Workload profiles and system configurations are parameterized for ease of experimental modification. Outputs include capacity estimates and performance evaluation functions. The U1108 system is conceptualized as a service network; classical queueing theory is used to evaluate network dynamics.
Applications products of aviation forecast models

NASA Technical Reports Server (NTRS)

Garthner, John P.

1988-01-01

A service called the Optimum Path Aircraft Routing System (OPARS) supplies products based on output data from the Naval Oceanographic Global Atmospheric Prediction System (NOGAPS), a model run on a Cyber-205 computer. Temperatures and winds are extracted from the surface to 100 mb, approximately 55,000 ft. Forecast winds are available in six-hour time steps.
Stability and Performance Metrics for Adaptive Flight Control

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje; Nguyen, Nhan; VanEykeren, Luarens

2009-01-01

This paper addresses the problem of verifying adaptive control techniques for enabling safe flight in the presence of adverse conditions. Since the adaptive systems are non-linear by design, the existing control verification metrics are not applicable to adaptive controllers. Moreover, these systems are in general highly uncertain. Hence, the system's characteristics cannot be evaluated by relying on the available dynamical models. This necessitates the development of control verification metrics based on the system's input-output information. For this point of view, a set of metrics is introduced that compares the uncertain aircraft's input-output behavior under the action of an adaptive controller to that of a closed-loop linear reference model to be followed by the aircraft. This reference model is constructed for each specific maneuver using the exact aerodynamic and mass properties of the aircraft to meet the stability and performance requirements commonly accepted in flight control. The proposed metrics are unified in the sense that they are model independent and not restricted to any specific adaptive control methods. As an example, we present simulation results for a wing damaged generic transport aircraft with several existing adaptive controllers.
Modeling of four-terminal solar photovoltaic systems for field application

NASA Astrophysics Data System (ADS)

Vahanka, Harikrushna; Purohit, Zeel; Tripathi, Brijesh

2018-05-01

In this article a theoretical framework for mechanically stacked four-terminal solar photovoltaic (FTSPV) system has been proposed. In a mechanical stack arrangement, a semitransparent CdTe panel has been used as a top sub-module, whereas a μc-Si solar panel has been used as bottom sub-module. Theoretical modeling has been done to analyze the physical processes in the system and to estimate reliable prediction of the performance. To incorporate the effect of material, the band gap and the absorption coefficient data for CdTe and μc-Si panels have been considered. The electrical performance of the top and bottom panels operated in a mechanical stack has been obtained experimentally for various inter-panel separations in the range of 0-3 cm. Maximum output power density has been obtained for a separation of 0.75 cm. The mean value of output power density from CdTe (top panel) has been calculated as 32.3 Wm-2 and the mean value of output power density from μc-Si, the bottom panel of four-terminal photovoltaic system has been calculated as ˜3.5 Wm-2. Results reported in this study reveal the potential of mechanically stacked four-terminal tandem solar photovoltaic system towards an energy-efficient configuration.
Multivariable control of vapor compression systems

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

He, X.D.; Liu, S.; Asada, H.H.

1999-07-01

This paper presents the results of a study of multi-input multi-output (MIMO) control of vapor compression cycles that have multiple actuators and sensors for regulating multiple outputs, e.g., superheat and evaporating temperature. The conventional single-input single-output (SISO) control was shown to have very limited performance. A low order lumped-parameter model was developed to describe the significant dynamics of vapor compression cycles. Dynamic modes were analyzed based on the low order model to provide physical insight of system dynamic behavior. To synthesize a MIMO control system, the Linear-Quadratic Gaussian (LQG) technique was applied to coordinate compressor speed and expansion valve openingmore » with guaranteed stability robustness in the design. Furthermore, to control a vapor compression cycle over a wide range of operating conditions where system nonlinearities become evident, a gain scheduling scheme was used so that the MIMO controller could adapt to changing operating conditions. Both analytical studies and experimental tests showed that the MIMO control could significantly improve the transient behavior of vapor compression cycles compared to the conventional SISO control scheme. The MIMO control proposed in this paper could be extended to the control of vapor compression cycles in a variety of HVAC and refrigeration applications to improve system performance and energy efficiency.« less
DOE Office of Scientific and Technical Information (OSTI.GOV)

Markidis, S.; Rizwan, U.

The use of virtual nuclear control room can be an effective and powerful tool for training personnel working in the nuclear power plants. Operators could experience and simulate the functioning of the plant, even in critical situations, without being in a real power plant or running any risk. 3D models can be exported to Virtual Reality formats and then displayed in the Virtual Reality environment providing an immersive 3D experience. However, two major limitations of this approach are that 3D models exhibit static textures, and they are not fully interactive and therefore cannot be used effectively in training personnel. Inmore » this paper we first describe a possible solution for embedding the output of a computer application in a 3D virtual scene, coupling real-world applications and VR systems. The VR system reported here grabs the output of an application running on an X server; creates a texture with the output and then displays it on a screen or a wall in the virtual reality environment. We then propose a simple model for providing interaction between the user in the VR system and the running simulator. This approach is based on the use of internet-based application that can be commanded by a laptop or tablet-pc added to the virtual environment. (authors)« less
Automatic detection of echolocation clicks based on a Gabor model of their waveform.

PubMed

Madhusudhana, Shyam; Gavrilov, Alexander; Erbe, Christine

2015-06-01

Prior research has shown that echolocation clicks of several species of terrestrial and marine fauna can be modelled as Gabor-like functions. Here, a system is proposed for the automatic detection of a variety of such signals. By means of mathematical formulation, it is shown that the output of the Teager-Kaiser Energy Operator (TKEO) applied to Gabor-like signals can be approximated by a Gaussian function. Based on the inferences, a detection algorithm involving the post-processing of the TKEO outputs is presented. The ratio of the outputs of two moving-average filters, a Gaussian and a rectangular filter, is shown to be an effective detection parameter. Detector performance is assessed using synthetic and real (taken from MobySound database) recordings. The detection method is shown to work readily with a variety of echolocation clicks and in various recording scenarios. The system exhibits low computational complexity and operates several times faster than real-time. Performance comparisons are made to other publicly available detectors including pamguard.
Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data

NASA Astrophysics Data System (ADS)

Pohjoranta, Antti; Halinen, Matias; Pennanen, Jari; Kiviaho, Jari

2015-03-01

Generalized predictive control (GPC) is applied to control the maximum temperature in a solid oxide fuel cell (SOFC) stack and the temperature difference over the stack. GPC is a model predictive control method and the models utilized in this work are ARX-type (autoregressive with extra input), multiple input-multiple output, polynomial models that were identified from experimental data obtained from experiments with a complete SOFC system. The proposed control is evaluated by simulation with various input-output combinations, with and without constraints. A comparison with conventional proportional-integral-derivative (PID) control is also made. It is shown that if only the stack maximum temperature is controlled, a standard PID controller can be used to obtain output performance comparable to that obtained with the significantly more complex model predictive controller. However, in order to control the temperature difference over the stack, both the stack minimum and the maximum temperature need to be controlled and this cannot be done with a single PID controller. In such a case the model predictive controller provides a feasible and effective solution.
Allan deviation computations of a linear frequency synthesizer system using frequency domain techniques

NASA Technical Reports Server (NTRS)

Wu, Andy

1995-01-01

Allan Deviation computations of linear frequency synthesizer systems have been reported previously using real-time simulations. Even though it takes less time compared with the actual measurement, it is still very time consuming to compute the Allan Deviation for long sample times with the desired confidence level. Also noises, such as flicker phase noise and flicker frequency noise, can not be simulated precisely. The use of frequency domain techniques can overcome these drawbacks. In this paper the system error model of a fictitious linear frequency synthesizer is developed and its performance using a Cesium (Cs) atomic frequency standard (AFS) as a reference is evaluated using frequency domain techniques. For a linear timing system, the power spectral density at the system output can be computed with known system transfer functions and known power spectral densities from the input noise sources. The resulting power spectral density can then be used to compute the Allan Variance at the system output. Sensitivities of the Allan Variance at the system output to each of its independent input noises are obtained, and they are valuable for design trade-off and trouble-shooting.
Flatness-based model inverse for feed-forward braking control

NASA Astrophysics Data System (ADS)

de Vries, Edwin; Fehn, Achim; Rixen, Daniel

2010-12-01

For modern cars an increasing number of driver assistance systems have been developed. Some of these systems interfere/assist with the braking of a car. Here, a brake actuation algorithm for each individual wheel that can respond to both driver inputs and artificial vehicle deceleration set points is developed. The algorithm consists of a feed-forward control that ensures, within the modelled system plant, the optimal behaviour of the vehicle. For the quarter-car model with LuGre-tyre behavioural model, an inverse model can be derived using v x as the 'flat output', that is, the input for the inverse model. A number of time derivatives of the flat output are required to calculate the model input, brake torque. Polynomial trajectory planning provides the needed time derivatives of the deceleration request. The transition time of the planning can be adjusted to meet actuator constraints. It is shown that the output of the trajectory planning would ripple and introduce a time delay when a gradual continuous increase of deceleration is requested by the driver. Derivative filters are then considered: the Bessel filter provides the best symmetry in its step response. A filter of same order and with negative real-poles is also used, exhibiting no overshoot nor ringing. For these reasons, the 'real-poles' filter would be preferred over the Bessel filter. The half-car model can be used to predict the change in normal load on the front and rear axle due to the pitching of the vehicle. The anticipated dynamic variation of the wheel load can be included in the inverse model, even though it is based on a quarter-car. Brake force distribution proportional to normal load is established. It provides more natural and simpler equations than a fixed force ratio strategy.
A whole-body mathematical model for intracranial pressure dynamics.

PubMed

Lakin, William D; Stevens, Scott A; Tranmer, Bruce I; Penar, Paul L

2003-04-01

Most attempts to study intracranial pressure using lumped-parameter models have adopted the classical "Kellie-Monro Doctrine," which considers the intracranial space to be a closed system that is confined within the nearly-rigid skull, conserves mass, and has equal inflow and outflow. The present work revokes this Doctrine and develops a mathematical model for the dynamics of intracranial pressures, volumes, and flows that embeds the intracranial system in extensive whole-body physiology. The new model consistently introduces compartments representing the tissues and vasculature of the extradural portions of the body, including both the thoracic region and the lower extremities. In addition to vascular connections, a spinal-subarachnoid cerebrospinal fluid (CSF) compartment bridges intracranial and extracranial physiology allowing explict buffering of intracranial pressure fluctuations by the spinal theca. The model contains cerebrovascular autoregulation, regulation of systemic vascular pressures by the sympathetic nervous system, regulation of CSF production in the choroid plexus, a lymphatic system, colloid osmotic pressure effects, and realistic descriptions of cardiac output. To validate the model in situations involving normal physiology, the model's response to a realistic pulsatile cardiac output is examined. A well-known experimentally-derived intracranial pressure-volume relationship is recovered by using the model to simulate CSF infusion tests, and the effect on cerebral blood flow of a change in body position is also examined. Cardiac arrest and hemorrhagic shock are simulated to demonstrate the predictive capabilities of the model in pathological conditions.
Nodal-line dynamics via exact polynomial solutions for coherent waves traversing aberrated imaging systems.

PubMed

Paganin, David M; Beltran, Mario A; Petersen, Timothy C

2018-03-01

We obtain exact polynomial solutions for two-dimensional coherent complex scalar fields propagating through arbitrary aberrated shift-invariant linear imaging systems. These solutions are used to model nodal-line dynamics of coherent fields output by such systems.
Global Sensitivity Analysis of Environmental Systems via Multiple Indices based on Statistical Moments of Model Outputs

NASA Astrophysics Data System (ADS)

Guadagnini, A.; Riva, M.; Dell'Oca, A.

2017-12-01

We propose to ground sensitivity of uncertain parameters of environmental models on a set of indices based on the main (statistical) moments, i.e., mean, variance, skewness and kurtosis, of the probability density function (pdf) of a target model output. This enables us to perform Global Sensitivity Analysis (GSA) of a model in terms of multiple statistical moments and yields a quantification of the impact of model parameters on features driving the shape of the pdf of model output. Our GSA approach includes the possibility of being coupled with the construction of a reduced complexity model that allows approximating the full model response at a reduced computational cost. We demonstrate our approach through a variety of test cases. These include a commonly used analytical benchmark, a simplified model representing pumping in a coastal aquifer, a laboratory-scale tracer experiment, and the migration of fracturing fluid through a naturally fractured reservoir (source) to reach an overlying formation (target). Our strategy allows discriminating the relative importance of model parameters to the four statistical moments considered. We also provide an appraisal of the error associated with the evaluation of our sensitivity metrics by replacing the original system model through the selected surrogate model. Our results suggest that one might need to construct a surrogate model with increasing level of accuracy depending on the statistical moment considered in the GSA. The methodological framework we propose can assist the development of analysis techniques targeted to model calibration, design of experiment, uncertainty quantification and risk assessment.
Modeling and validation of single-chamber microbial fuel cell cathode biofilm growth and response to oxidant gas composition

NASA Astrophysics Data System (ADS)

Ou, Shiqi; Zhao, Yi; Aaron, Douglas S.; Regan, John M.; Mench, Matthew M.

2016-10-01

This work describes experiments and computational simulations to analyze single-chamber, air-cathode microbial fuel cell (MFC) performance and cathodic limitations in terms of current generation, power output, mass transport, biomass competition, and biofilm growth. Steady-state and transient cathode models were developed and experimentally validated. Two cathode gas mixtures were used to explore oxygen transport in the cathode: the MFCs exposed to a helium-oxygen mixture (heliox) produced higher current and power output than the group of MFCs exposed to air or a nitrogen-oxygen mixture (nitrox), indicating a dependence on gas-phase transport in the cathode. Multi-substance transport, biological reactions, and electrochemical reactions in a multi-layer and multi-biomass cathode biofilm were also simulated in a transient model. The transient model described biofilm growth over 15 days while providing insight into mass transport and cathodic dissolved species concentration profiles during biofilm growth. Simulation results predict that the dissolved oxygen content and diffusion in the cathode are key parameters affecting the power output of the air-cathode MFC system, with greater oxygen content in the cathode resulting in increased power output and fully-matured biomass.
Modeling and validation of single-chamber microbial fuel cell cathode biofilm growth and response to oxidant gas composition

DOE PAGES

Ou, Shiqi; Zhao, Yi; Aaron, Douglas S.; ...

2016-08-15

This work describes experiments and computational simulations to analyze single-chamber, air-cathode microbial fuel cell (MFC) performance and cathodic limitations in terms of current generation, power output, mass transport, biomass competition, and biofilm growth. Steady-state and transient cathode models were developed and experimentally validated. Two cathode gas mixtures were used to explore oxygen transport in the cathode: the MFCs exposed to a helium-oxygen mixture (heliox) produced higher current and power output than the group of MFCs exposed to air or a nitrogen-oxygen mixture (nitrox), indicating a dependence on gas-phase transport in the cathode. Multi-substance transport, biological reactions, and electrochemical reactions inmore » a multi-layer and multi-biomass cathode biofilm were also simulated in a transient model. The transient model described biofilm growth over 15 days while providing insight into mass transport and cathodic dissolved species concentration profiles during biofilm growth. Lastly, simulation results predict that the dissolved oxygen content and diffusion in the cathode are key parameters affecting the power output of the air-cathode MFC system, with greater oxygen content in the cathode resulting in increased power output and fully-matured biomass.« less
The effect of signal variability on the histograms of anthropomorphic channel outputs: factors resulting in non-normally distributed data

NASA Astrophysics Data System (ADS)

Elshahaby, Fatma E. A.; Ghaly, Michael; Jha, Abhinav K.; Frey, Eric C.

2015-03-01

Model Observers are widely used in medical imaging for the optimization and evaluation of instrumentation, acquisition parameters and image reconstruction and processing methods. The channelized Hotelling observer (CHO) is a commonly used model observer in nuclear medicine and has seen increasing use in other modalities. An anthropmorphic CHO consists of a set of channels that model some aspects of the human visual system and the Hotelling Observer, which is the optimal linear discriminant. The optimality of the CHO is based on the assumption that the channel outputs for data with and without the signal present have a multivariate normal distribution with equal class covariance matrices. The channel outputs result from the dot product of channel templates with input images and are thus the sum of a large number of random variables. The central limit theorem is thus often used to justify the assumption that the channel outputs are normally distributed. In this work, we aim to examine this assumption for realistically simulated nuclear medicine images when various types of signal variability are present.
Estimating the Fully Burdened Cost of Fuel Using an Input-Output Model - A Micro-Level Analysis

DTIC Science & Technology

2011-09-01

The multilocation distribution model used by Lu and Rencheng to evaluate an international supply chain (From: Lu & Rencheng, 2007...IO model to evaluate an international supply chain specifically for a multilocation production system. Figure 2 illustrates such a system. vendor...vendor vendor Target markets Production plants Material vendor Figure 2. The multilocation distribution model used by Lu and Rencheng to
Modeling and performance assessment in QinetiQ of EO and IR airborne reconnaissance systems

NASA Astrophysics Data System (ADS)

Williams, John W.; Potter, Gary E.

2002-11-01

QinetiQ are the technical authority responsible for specifying the performance requirements for the procurement of airborne reconnaissance systems, on behalf of the UK MoD. They are also responsible for acceptance of delivered systems, overseeing and verifying the installed system performance as predicted and then assessed by the contractor. Measures of functional capability are central to these activities. The conduct of these activities utilises the broad technical insight and wide range of analysis tools and models available within QinetiQ. This paper focuses on the tools, methods and models that are applicable to systems based on EO and IR sensors. The tools, methods and models are described, and representative output for systems that QinetiQ has been responsible for is presented. The principle capability applicable to EO and IR airborne reconnaissance systems is the STAR (Simulation Tools for Airborne Reconnaissance) suite of models. STAR generates predictions of performance measures such as GRD (Ground Resolved Distance) and GIQE (General Image Quality) NIIRS (National Imagery Interpretation Rating Scales). It also generates images representing sensor output, using the scene generation software CAMEO-SIM and the imaging sensor model EMERALD. The simulated image 'quality' is fully correlated with the predicted non-imaging performance measures. STAR also generates image and table data that is compliant with STANAG 7023, which may be used to test ground station functionality.
System dynamic modeling: an alternative method for budgeting.

PubMed

Srijariya, Witsanuchai; Riewpaiboon, Arthorn; Chaikledkaew, Usa

2008-03-01

To construct, validate, and simulate a system dynamic financial model and compare it against the conventional method. The study was a cross-sectional analysis of secondary data retrieved from the National Health Security Office (NHSO) in the fiscal year 2004. The sample consisted of all emergency patients who received emergency services outside their registered hospital-catchments area. The dependent variable used was the amount of reimbursed money. Two types of model were constructed, namely, the system dynamic model using the STELLA software and the multiple linear regression model. The outputs of both methods were compared. The study covered 284,716 patients from various levels of providers. The system dynamic model had the capability of producing various types of outputs, for example, financial and graphical analyses. For the regression analysis, statistically significant predictors were composed of service types (outpatient or inpatient), operating procedures, length of stay, illness types (accident or not), hospital characteristics, age, and hospital location (adjusted R(2) = 0.74). The total budget arrived at from using the system dynamic model and regression model was US$12,159,614.38 and US$7,301,217.18, respectively, whereas the actual NHSO reimbursement cost was US$12,840,805.69. The study illustrated that the system dynamic model is a useful financial management tool, although it is not easy to construct. The model is not only more accurate in prediction but is also more capable of analyzing large and complex real-world situations than the conventional method.
Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.

PubMed

Ligmann-Zielinska, Arika; Kramer, Daniel B; Spence Cheruvelil, Kendra; Soranno, Patricia A

2014-01-01

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system.

Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance

PubMed Central

Ligmann-Zielinska, Arika; Kramer, Daniel B.; Spence Cheruvelil, Kendra; Soranno, Patricia A.

2014-01-01

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system. PMID:25340764
Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: computational study

PubMed Central

Marmarelis, Vasilis Z.; Berger, Theodore W.

2009-01-01

Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-parametric models accurately and efficiently replicate the input–output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP. PMID:18506609
Neural control and transient analysis of the LCL-type resonant converter

NASA Astrophysics Data System (ADS)

Zouggar, S.; Nait Charif, H.; Azizi, M.

2000-07-01

This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.
The Copernicus Atmosphere Monitoring Service: facilitating the prediction of air quality from global to local scales

NASA Astrophysics Data System (ADS)

Engelen, R. J.; Peuch, V. H.

2017-12-01

The European Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition and regional air quality. The global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for numerical weather prediction and which has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The regional forecasts are produced by an ensemble of seven operational European air quality models that take their boundary conditions from the global system and provide an ensemble median with ensemble spread as their main output. Both the global and regional forecasting systems are feeding their output into air quality models on a variety of scales in various parts of the world. We will introduce the CAMS service chain and provide illustrations of its use in downstream applications. Both the usage of the daily forecasts and the usage of global and regional reanalyses will be addressed.
A parametric investigation on a solar dish-Stirling system

NASA Astrophysics Data System (ADS)

Gholamalizadeh, Ehsan; Chung, Jae Dong

2018-06-01

The aim of this study is to analyze the performance of a solar dish-Stirling system. A mathematical model for the overall thermal efficiency of the solar-powered high-temperature-differential dish-Stirling engine is described. This model takes into account pressure losses due to fluid friction which is internal to the engine, mechanical friction between the moving parts, actual heat transfer includes the effects of both internal and external irreversibilities of the cycle and finite regeneration processes time. Validation was done through comparison with the actual power output of the "EuroDish" system. Moreover, the effects of dish diameter and working fluid on the performance of the system were studied. An increase of about 7.2% was observed for the power output using hydrogen as the working fluid rather than helium. Also, the focal distance for any diameter of dish was calculated.
Modelling and simulation of fuel cell dynamics for electrical energy usage of Hercules airplanes.

PubMed

Radmanesh, Hamid; Heidari Yazdi, Seyed Saeid; Gharehpetian, G B; Fathi, S H

2014-01-01

Dynamics of proton exchange membrane fuel cells (PEMFC) with hydrogen storage system for generating part of Hercules airplanes electrical energy is presented. Feasibility of using fuel cell (FC) for this airplane is evaluated by means of simulations. Temperature change and dual layer capacity effect are considered in all simulations. Using a three-level 3-phase inverter, FC's output voltage is connected to the essential bus of the airplane. Moreover, it is possible to connect FC's output voltage to airplane DC bus alternatively. PID controller is presented to control flow of hydrogen and oxygen to FC and improve transient and steady state responses of the output voltage to load disturbances. FC's output voltage is regulated via an ultracapacitor. Simulations are carried out via MATLAB/SIMULINK and results show that the load tracking and output voltage regulation are acceptable. The proposed system utilizes an electrolyser to generate hydrogen and a tank for storage. Therefore, there is no need for batteries. Moreover, the generated oxygen could be used in other applications in airplane.
Modelling and Simulation of Fuel Cell Dynamics for Electrical Energy Usage of Hercules Airplanes

PubMed Central

Radmanesh, Hamid; Heidari Yazdi, Seyed Saeid; Gharehpetian, G. B.; Fathi, S. H.

2014-01-01

Dynamics of proton exchange membrane fuel cells (PEMFC) with hydrogen storage system for generating part of Hercules airplanes electrical energy is presented. Feasibility of using fuel cell (FC) for this airplane is evaluated by means of simulations. Temperature change and dual layer capacity effect are considered in all simulations. Using a three-level 3-phase inverter, FC's output voltage is connected to the essential bus of the airplane. Moreover, it is possible to connect FC's output voltage to airplane DC bus alternatively. PID controller is presented to control flow of hydrogen and oxygen to FC and improve transient and steady state responses of the output voltage to load disturbances. FC's output voltage is regulated via an ultracapacitor. Simulations are carried out via MATLAB/SIMULINK and results show that the load tracking and output voltage regulation are acceptable. The proposed system utilizes an electrolyser to generate hydrogen and a tank for storage. Therefore, there is no need for batteries. Moreover, the generated oxygen could be used in other applications in airplane. PMID:24782664
COMPARISON OF SPATIAL PATTERNS OF POLLUTANT DISTRIBUTION WITH CMAQ PREDICTIONS

EPA Science Inventory

To evaluate the Models-3/Community Multiscale Air Quality (CMAQ) modeling system in reproducing the spatial patterns of aerosol concentrations over the country on timescales of months and years, the spatial patterns of model output are compared with those derived from observation...
Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation

NASA Astrophysics Data System (ADS)

Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi

2016-09-01

We propose a statistical modeling method of wind power output for very short-term prediction. The modeling method with a nonlinear model has cascade structure composed of two parts. One is a linear dynamic part that is driven by a Gaussian white noise and described by an autoregressive model. The other is a nonlinear static part that is driven by the output of the linear part. This nonlinear part is designed for output distribution matching: we shape the distribution of the model output to match with that of the wind power output. The constructed model is utilized for one-step ahead prediction of the wind power output. Furthermore, we study the relation between the prediction accuracy and the prediction horizon.
Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

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

Melin, Alexander M.; Olama, Mohammed M.; Dong, Jin

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed tomore » estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.« less
Assessment of effectiveness of geologic isolation systems. CIRMIS data system. Volume 3. Generator routines

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

Friedrichs, D.R.; Argo, R.S.

The Assessment of Effectiveness of Geologic Isolation Systems (AEGIS) Program is developing and applying the methodology for assessing the far-field, long-term post-closure safety of deep geologic nuclear waste repositories. AEGIS is being performed by Pacific Northwest Laboratory (PNL) under contract with the Office of Nuclear Waste Isolation (ONWI) for the Department of Energy (DOE). One task within AEGIS is the development of methodology for analysis of the consequences (water pathway) from loss of repository containment as defined by various release scenarios. The various input parameters required in the analysis are compiled in data systems. The data are organized and preparedmore » by various input subroutines for utilization by the hydraulic and transport codes. The hydrologic models simulate the groundwater flow systems and provide water flow directions, rates, and velocities as inputs to the transport models. Outputs from the transport models are basically graphs of radionuclide concentration in the groundwater plotted against time. After dilution in the receiving surface-water body (e.g., lake, river, bay), these data are the input source terms for the dose models, if dose assessments are required. The dose models calculate radiation dose to individuals and populations. CIRMIS (Comprehensive Information Retrieval and Model Input Sequence) Data System, a storage and retrieval system for model input and output data, including graphical interpretation and display is described. This is the third of four volumes of the description of the CIRMIS Data System.« less
Assessment of effectiveness of geologic isolation systems. CIRMIS data system. Volume 1. Initialization, operation, and documentation

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

Friedrichs, D.R.

1980-01-01

The Assessment of Effectiveness of Geologic Isolation Systems (AEGIS) Program is developing and applying the methodology for assessing the far-field, long-term post-closure safety of deep geologic nuclear waste repositories. AEGIS is being performed by Pacific Northwest Laboratory (PNL) under contract with the Office of Nuclear Waste Isolation (ONWI) for the Department of Energy (DOE). One task within AEGIS is the development of methodology for analysis of the consequences (water pathway) from loss of repository containment as defined by various release scenarios. The various input parameters required in the analysis are compiled in data systems. The data are organized and preparedmore » by various input subroutines for use by the hydrologic and transport codes. The hydrologic models simulate the groundwater flow systems and provide water flow directions, rates, and velocities as inputs to the transport models. Outputs from the transport models are basically graphs of radionuclide concentration in the groundwater plotted against time. After dilution in the receiving surface-water body (e.g., lake, river, bay), these data are the input source terms for the dose models, if dose assessments are required. The dose models calculate radiation dose to individuals and populations. CIRMIS (Comprehensive Information Retrieval and Model Input Sequence) Data System, a storage and retrieval system for model input and output data, including graphical interpretation and display is described. This is the first of four volumes of the description of the CIRMIS Data System.« less
Probing dynamical symmetry breaking using quantum-entangled photons

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

Li, Hao; Piryatinski, Andrei; Jerke, Jonathan

Here, we present an input/output analysis of photon-correlation experiments whereby a quantum mechanically entangled bi-photon state interacts with a material sample placed in one arm of a Hong–Ou–Mandel apparatus. We show that the output signal contains detailed information about subsequent entanglement with the microscopic quantum states in the sample. In particular, we apply the method to an ensemble of emitters interacting with a common photon mode within the open-system Dicke model. Our results indicate considerable dynamical information concerning spontaneous symmetry breaking can be revealed with such an experimental system.
Probing dynamical symmetry breaking using quantum-entangled photons

DOE PAGES

Li, Hao; Piryatinski, Andrei; Jerke, Jonathan; ...

2017-11-15

Here, we present an input/output analysis of photon-correlation experiments whereby a quantum mechanically entangled bi-photon state interacts with a material sample placed in one arm of a Hong–Ou–Mandel apparatus. We show that the output signal contains detailed information about subsequent entanglement with the microscopic quantum states in the sample. In particular, we apply the method to an ensemble of emitters interacting with a common photon mode within the open-system Dicke model. Our results indicate considerable dynamical information concerning spontaneous symmetry breaking can be revealed with such an experimental system.
Biometric identification

NASA Astrophysics Data System (ADS)

Syryamkim, V. I.; Kuznetsov, D. N.; Kuznetsova, A. S.

2018-05-01

Image recognition is an information process implemented by some information converter (intelligent information channel, recognition system) having input and output. The input of the system is fed with information about the characteristics of the objects being presented. The output of the system displays information about which classes (generalized images) the recognized objects are assigned to. When creating and operating an automated system for pattern recognition, a number of problems are solved, while for different authors the formulations of these tasks, and the set itself, do not coincide, since it depends to a certain extent on the specific mathematical model on which this or that recognition system is based. This is the task of formalizing the domain, forming a training sample, learning the recognition system, reducing the dimensionality of space.
Strategic preparedness for recovery from catastrophic risks to communities and infrastructure systems of systems.

PubMed

Haimes, Yacov Y

2012-11-01

Natural and human-induced disasters affect organizations in myriad ways because of the inherent interconnectedness and interdependencies among human, cyber, and physical infrastructures, but more importantly, because organizations depend on the effectiveness of people and on the leadership they provide to the organizations they serve and represent. These human-organizational-cyber-physical infrastructure entities are termed systems of systems. Given the multiple perspectives that characterize them, they cannot be modeled effectively with a single model. The focus of this article is: (i) the centrality of the states of a system in modeling; (ii) the efficacious role of shared states in modeling systems of systems, in identification, and in the meta-modeling of systems of systems; and (iii) the contributions of the above to strategic preparedness, response to, and recovery from catastrophic risk to such systems. Strategic preparedness connotes a decision-making process and its associated actions. These must be: implemented in advance of a natural or human-induced disaster, aimed at reducing consequences (e.g., recovery time, community suffering, and cost), and/or controlling their likelihood to a level considered acceptable (through the decisionmakers' implicit and explicit acceptance of various risks and tradeoffs). The inoperability input-output model (IIM), which is grounded on Leontief's input/output model, has enabled the modeling of interdependent subsystems. Two separate modeling structures are introduced. These are: phantom system models (PSM), where shared states constitute the essence of modeling coupled systems; and the IIM, where interdependencies among sectors of the economy are manifested by the Leontief matrix of technological coefficients. This article demonstrates the potential contributions of these two models to each other, and thus to more informative modeling of systems of systems schema. The contributions of shared states to this modeling and to systems identification are presented with case studies. © 2012 Society for Risk Analysis.
An Analysis of Input/Output Paradigms for Real-Time Systems

DTIC Science & Technology

1990-07-01

timing and concurrency aspects of real - time systems . This paper illustrates how to build a mathematical model of the schedulability of a real-time...various design alternatives. The primary characteristic that distinguishes real-time system from non- real - time systems is the importance of time. The
Effects of Voice Coding and Speech Rate on a Synthetic Speech Display in a Telephone Information System

DTIC Science & Technology

1988-05-01

Seeciv Limited- System for varying Senses term filter capacity output until some Figure 2. Original limited-capacity channel model (Frim Broadbent, 1958) S...2 Figure 2. Original limited-capacity channel model (From Broadbent, 1958) .... 10 Figure 3. Experimental...unlimited variety of human voices for digital recording sources. Synthesis by Analysis Analysis-synthesis methods electronically model the human voice
Digital Earth system based river basin data integration

NASA Astrophysics Data System (ADS)

Zhang, Xin; Li, Wanqing; Lin, Chao

2014-12-01

Digital Earth is an integrated approach to build scientific infrastructure. The Digital Earth systems provide a three-dimensional visualization and integration platform for river basin data which include the management data, in situ observation data, remote sensing observation data and model output data. This paper studies the Digital Earth system based river basin data integration technology. Firstly, the construction of the Digital Earth based three-dimensional river basin data integration environment is discussed. Then the river basin management data integration technology is presented which is realized by general database access interface, web service and ActiveX control. Thirdly, the in situ data stored in database tables as records integration is realized with three-dimensional model of the corresponding observation apparatus display in the Digital Earth system by a same ID code. In the next two parts, the remote sensing data and the model output data integration technologies are discussed in detail. The application in the Digital Zhang River basin System of China shows that the method can effectively improve the using efficiency and visualization effect of the data.
Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)

USGS Publications Warehouse

Gurdak, Jason J.; Qi, Sharon L.; Geisler, Michael L.

2009-01-01

The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a custom tool for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. Similar to other geoprocessing tools available in ArcGIS Desktop, REPTool can be run from a dialog window, from the ArcMap command line, or from a Python script. REPTool consists of public-domain, Python-based packages that implement Latin Hypercube Sampling within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution method to quantify the relative error contribution from the two primary components in the geospatial model - errors in the model input data and coefficients of the model variables. REPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data.

Anomaly Detection in Test Equipment via Sliding Mode Observers

NASA Technical Reports Server (NTRS)

Solano, Wanda M.; Drakunov, Sergey V.

2012-01-01

Nonlinear observers were originally developed based on the ideas of variable structure control, and for the purpose of detecting disturbances in complex systems. In this anomaly detection application, these observers were designed for estimating the distributed state of fluid flow in a pipe described by a class of advection equations. The observer algorithm uses collected data in a piping system to estimate the distributed system state (pressure and velocity along a pipe containing liquid gas propellant flow) using only boundary measurements. These estimates are then used to further estimate and localize possible anomalies such as leaks or foreign objects, and instrumentation metering problems such as incorrect flow meter orifice plate size. The observer algorithm has the following parts: a mathematical model of the fluid flow, observer control algorithm, and an anomaly identification algorithm. The main functional operation of the algorithm is in creating the sliding mode in the observer system implemented as software. Once the sliding mode starts in the system, the equivalent value of the discontinuous function in sliding mode can be obtained by filtering out the high-frequency chattering component. In control theory, "observers" are dynamic algorithms for the online estimation of the current state of a dynamic system by measurements of an output of the system. Classical linear observers can provide optimal estimates of a system state in case of uncertainty modeled by white noise. For nonlinear cases, the theory of nonlinear observers has been developed and its success is mainly due to the sliding mode approach. Using the mathematical theory of variable structure systems with sliding modes, the observer algorithm is designed in such a way that it steers the output of the model to the output of the system obtained via a variety of sensors, in spite of possible mismatches between the assumed model and actual system. The unique properties of sliding mode control allow not only control of the model internal states to the states of the real-life system, but also identification of the disturbance or anomaly that may occur.
Equivalent ZF precoding scheme for downlink indoor MU-MIMO VLC systems

NASA Astrophysics Data System (ADS)

Fan, YangYu; Zhao, Qiong; Kang, BoChao; Deng, LiJun

2018-01-01

In indoor visible light communication (VLC) systems, the channels of photo detectors (PDs) at one user are highly correlated, which determines the choice of spatial diversity model for individual users. In a spatial diversity model, the signals received by PDs belonging to one user carry the same information, and can be combined directly. Based on the above, we propose an equivalent zero-forcing (ZF) precoding scheme for multiple-user multiple-input single-output (MU-MIMO) VLC systems by transforming an indoor MU-MIMO VLC system into an indoor multiple-user multiple-input single-output (MU-MISO) VLC system through simply processing. The power constraints of light emitting diodes (LEDs) are also taken into account. Comprehensive computer simulations in three scenarios indicate that our scheme can not only reduce the computational complexity, but also guarantee the system performance. Furthermore, the proposed scheme does not require noise information in the calculating of the precoding weights, and has no restrictions on the numbers of APs and PDs.
Attitude determination of a high altitude balloon system. Part 2: Development of the parameter determination process

NASA Technical Reports Server (NTRS)

Nigro, N. J.; Elkouh, A. F.

1975-01-01

The attitude of the balloon system is determined as a function of time if: (a) a method for simulating the motion of the system is available, and (b) the initial state is known. The initial state is obtained by fitting the system motion (as measured by sensors) to the corresponding output predicted by the mathematical model. In the case of the LACATE experiment the sensors consisted of three orthogonally oriented rate gyros and a magnetometer all mounted on the research platform. The initial state was obtained by fitting the angular velocity components measured with the gyros to the corresponding values obtained from the solution of the math model. A block diagram illustrating the attitude determination process employed for the LACATE experiment is shown. The process consists of three essential parts; a process for simulating the balloon system, an instrumentation system for measuring the output, and a parameter estimation process for systematically and efficiently solving the initial state. Results are presented and discussed.
Dynamics of nonlinear feedback control.

PubMed

Snippe, H P; van Hateren, J H

2007-05-01

Feedback control in neural systems is ubiquitous. Here we study the mathematics of nonlinear feedback control. We compare models in which the input is multiplied by a dynamic gain (multiplicative control) with models in which the input is divided by a dynamic attenuation (divisive control). The gain signal (resp. the attenuation signal) is obtained through a concatenation of an instantaneous nonlinearity and a linear low-pass filter operating on the output of the feedback loop. For input steps, the dynamics of gain and attenuation can be very different, depending on the mathematical form of the nonlinearity and the ordering of the nonlinearity and the filtering in the feedback loop. Further, the dynamics of feedback control can be strongly asymmetrical for increment versus decrement steps of the input. Nevertheless, for each of the models studied, the nonlinearity in the feedback loop can be chosen such that immediately after an input step, the dynamics of feedback control is symmetric with respect to increments versus decrements. Finally, we study the dynamics of the output of the control loops and find conditions under which overshoots and undershoots of the output relative to the steady-state output occur when the models are stimulated with low-pass filtered steps. For small steps at the input, overshoots and undershoots of the output do not occur when the filtering in the control path is faster than the low-pass filtering at the input. For large steps at the input, however, results depend on the model, and for some of the models, multiple overshoots and undershoots can occur even with a fast control path.
Performance and Feasibility Analysis of a Wind Turbine Power System for Use on Mars

NASA Technical Reports Server (NTRS)

Lichter, Matthew D.; Viterna, Larry

1999-01-01

A wind turbine power system for future missions to the Martian surface was studied for performance and feasibility. A C++ program was developed from existing FORTRAN code to analyze the power capabilities of wind turbines under different environments and design philosophies. Power output, efficiency, torque, thrust, and other performance criteria could be computed given design geometries, atmospheric conditions, and airfoil behavior. After reviewing performance of such a wind turbine, a conceptual system design was modeled to evaluate feasibility. More analysis code was developed to study and optimize the overall structural design. Findings of this preliminary study show that turbine power output on Mars could be as high as several hundred kilowatts. The optimized conceptual design examined here would have a power output of 104 kW, total mass of 1910 kg, and specific power of 54.6 W/kg.
Power quality control of an autonomous wind-diesel power system based on hybrid intelligent controller.

PubMed

Ko, Hee-Sang; Lee, Kwang Y; Kang, Min-Jae; Kim, Ho-Chan

2008-12-01

Wind power generation is gaining popularity as the power industry in the world is moving toward more liberalized trade of energy along with public concerns of more environmentally friendly mode of electricity generation. The weakness of wind power generation is its dependence on nature-the power output varies in quite a wide range due to the change of wind speed, which is difficult to model and predict. The excess fluctuation of power output and voltages can influence negatively the quality of electricity in the distribution system connected to the wind power generation plant. In this paper, the authors propose an intelligent adaptive system to control the output of a wind power generation plant to maintain the quality of electricity in the distribution system. The target wind generator is a cost-effective induction generator, while the plant is equipped with a small capacity energy storage based on conventional batteries, heater load for co-generation and braking, and a voltage smoothing device such as a static Var compensator (SVC). Fuzzy logic controller provides a flexible controller covering a wide range of energy/voltage compensation. A neural network inverse model is designed to provide compensating control amount for a system. The system can be optimized to cope with the fluctuating market-based electricity price conditions to lower the cost of electricity consumption or to maximize the power sales opportunities from the wind generation plant.
W3MAMCAT: a world wide web based tool for mammillary and catenary compartmental modeling and expert system distinguishability.

PubMed

Russell, Solomon; Distefano, Joseph J

2006-07-01

W(3)MAMCAT is a new web-based and interactive system for building and quantifying the parameters or parameter ranges of n-compartment mammillary and catenary model structures, with input and output in the first compartment, from unstructured multiexponential (sum-of-n-exponentials) models. It handles unidentifiable as well as identifiable models and, as such, provides finite parameter interval solutions for unidentifiable models, whereas direct parameter search programs typically do not. It also tutorially develops the theory of model distinguishability for same order mammillary versus catenary models, as did its desktop application predecessor MAMCAT+. This includes expert system analysis for distinguishing mammillary from catenary structures, given input and output in similarly numbered compartments. W(3)MAMCAT provides for universal deployment via the internet and enhanced application error checking. It uses supported Microsoft technologies to form an extensible application framework for maintaining a stable and easily updatable application. Most important, anybody, anywhere, is welcome to access it using Internet Explorer 6.0 over the internet for their teaching or research needs. It is available on the Biocybernetics Laboratory website at UCLA: www.biocyb.cs.ucla.edu.
A Sensitivity Analysis Method to Study the Behavior of Complex Process-based Models

NASA Astrophysics Data System (ADS)

Brugnach, M.; Neilson, R.; Bolte, J.

2001-12-01

The use of process-based models as a tool for scientific inquiry is becoming increasingly relevant in ecosystem studies. Process-based models are artificial constructs that simulate the system by mechanistically mimicking the functioning of its component processes. Structurally, a process-based model can be characterized, in terms of its processes and the relationships established among them. Each process comprises a set of functional relationships among several model components (e.g., state variables, parameters and input data). While not encoded explicitly, the dynamics of the model emerge from this set of components and interactions organized in terms of processes. It is the task of the modeler to guarantee that the dynamics generated are appropriate and semantically equivalent to the phenomena being modeled. Despite the availability of techniques to characterize and understand model behavior, they do not suffice to completely and easily understand how a complex process-based model operates. For example, sensitivity analysis studies model behavior by determining the rate of change in model output as parameters or input data are varied. One of the problems with this approach is that it considers the model as a "black box", and it focuses on explaining model behavior by analyzing the relationship input-output. Since, these models have a high degree of non-linearity, understanding how the input affects an output can be an extremely difficult task. Operationally, the application of this technique may constitute a challenging task because complex process-based models are generally characterized by a large parameter space. In order to overcome some of these difficulties, we propose a method of sensitivity analysis to be applicable to complex process-based models. This method focuses sensitivity analysis at the process level, and it aims to determine how sensitive the model output is to variations in the processes. Once the processes that exert the major influence in the output are identified, the causes of its variability can be found. Some of the advantages of this approach are that it reduces the dimensionality of the search space, it facilitates the interpretation of the results and it provides information that allows exploration of uncertainty at the process level, and how it might affect model output. We present an example using the vegetation model BIOME-BGC.
Enhancement of the output emission efficiency of thin-film photoluminescence composite structures based on PbSe

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

Anisimova, N. P.; Tropina, N. E., E-mail: Mazina_ne@mail.ru; Tropin, A. N.

2010-12-15

The opportunity to increase the output emission efficiency of PbSe-based photoluminescence structures by depositing an antireflection layer is analyzed. A model of a three-layer thin film where the central layer is formed of a composite medium is proposed to calculate the reflectance spectra of the system. In von Bruggeman's approximation of the effective medium theory, the effective permittivity of the composite layer is calculated. The model proposed in the study is used to calculate the thickness of the arsenic chalcogenide (AsS{sub 4}) antireflection layer. The optimal AsS{sub 4} layer thickness determined experimentally is close to the results of calculation, andmore » the corresponding gain in the output photoluminescence efficiency is as high as 60%.« less
The WRF-CMAQ Integrated On-Line Modeling System: Development, Testing, and Initial Applications

EPA Science Inventory

Traditionally, atmospheric chemistry-transport and meteorology models have been applied in an off-line paradigm, in which archived output on the dynamical state of the atmosphere simulated using the meteorology model is used to drive transport and chemistry calculations of atmos...
The geospatial modeling interface (GMI) framework for deploying and assessing environmental models

USDA-ARS?s Scientific Manuscript database

Geographical information systems (GIS) software packages have been used for close to three decades as analytical tools in environmental management for geospatial data assembly, processing, storage, and visualization of input data and model output. However, with increasing availability and use of ful...
Guide to the Stand-Damage Model interface management system

Treesearch

George Racin; J. J. Colbert

1995-01-01

This programmer's support document describes the Gypsy Moth Stand-Damage Model interface management system. Management of stand-damage data made it necessary to define structures to store data and provide the mechanisms to manipulate these data. The software provides a user-friendly means to manipulate files, graph and manage outputs, and edit input data. The...
Analysis of a utility-interactive wind-photovoltaic hybrid system with battery storage using neural network

NASA Astrophysics Data System (ADS)

Giraud, Francois

1999-10-01

This dissertation investigates the application of neural network theory to the analysis of a 4-kW Utility-interactive Wind-Photovoltaic System (WPS) with battery storage. The hybrid system comprises a 2.5-kW photovoltaic generator and a 1.5-kW wind turbine. The wind power generator produces power at variable speed and variable frequency (VSVF). The wind energy is converted into dc power by a controlled, tree-phase, full-wave, bridge rectifier. The PV power is maximized by a Maximum Power Point Tracker (MPPT), a dc-to-dc chopper, switching at a frequency of 45 kHz. The whole dc power of both subsystems is stored in the battery bank or conditioned by a single-phase self-commutated inverter to be sold to the utility at a predetermined amount. First, the PV is modeled using Artificial Neural Network (ANN). To reduce model uncertainty, the open-circuit voltage VOC and the short-circuit current ISC of the PV are chosen as model input variables of the ANN. These input variables have the advantage of incorporating the effects of the quantifiable and non-quantifiable environmental variants affecting the PV power. Then, a simplified way to predict accurately the dynamic responses of the grid-linked WPS to gusty winds using a Recurrent Neural Network (RNN) is investigated. The RNN is a single-output feedforward backpropagation network with external feedback, which allows past responses to be fed back to the network input. In the third step, a Radial Basis Functions (RBF) Network is used to analyze the effects of clouds on the Utility-Interactive WPS. Using the irradiance as input signal, the network models the effects of random cloud movement on the output current, the output voltage, the output power of the PV system, as well as the electrical output variables of the grid-linked inverter. Fourthly, using RNN, the combined effects of a random cloud and a wind gusts on the system are analyzed. For short period intervals, the wind speed and the solar radiation are considered as the sole sources of power, whose variations influence the system variables. Since both subsystems have different dynamics, their respective responses are expected to impact differently the whole system behavior. The dispatchability of the battery-supported system as well as its stability and reliability during gusts and/or cloud passage is also discussed. In the fifth step, the goal is to determine to what extent the overall power quality of the grid would be affected by a proliferation of Utility-interactive hybrid system and whether recourse to bulky or individual filtering and voltage controller is necessary. The final stage of the research includes a steady-state analysis of two-year operation (May 96--Apr 98) of the system, with a discussion on system reliability, on any loss of supply probability, and on the effects of the randomness in the wind and solar radiation upon the system design optimization.
A Novel Approach to Implement Takagi-Sugeno Fuzzy Models.

PubMed

Chang, Chia-Wen; Tao, Chin-Wang

2017-09-01

This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.
Information processing in bacteria: memory, computation, and statistical physics: a key issues review

NASA Astrophysics Data System (ADS)

Lan, Ganhui; Tu, Yuhai

2016-05-01

Living systems have to constantly sense their external environment and adjust their internal state in order to survive and reproduce. Biological systems, from as complex as the brain to a single E. coli cell, have to process these data in order to make appropriate decisions. How do biological systems sense external signals? How do they process the information? How do they respond to signals? Through years of intense study by biologists, many key molecular players and their interactions have been identified in different biological machineries that carry out these signaling functions. However, an integrated, quantitative understanding of the whole system is still lacking for most cellular signaling pathways, not to say the more complicated neural circuits. To study signaling processes in biology, the key thing to measure is the input-output relationship. The input is the signal itself, such as chemical concentration, external temperature, light (intensity and frequency), and more complex signals such as the face of a cat. The output can be protein conformational changes and covalent modifications (phosphorylation, methylation, etc), gene expression, cell growth and motility, as well as more complex output such as neuron firing patterns and behaviors of higher animals. Due to the inherent noise in biological systems, the measured input-output dependence is often noisy. These noisy data can be analysed by using powerful tools and concepts from information theory such as mutual information, channel capacity, and the maximum entropy hypothesis. This information theory approach has been successfully used to reveal the underlying correlations between key components of biological networks, to set bounds for network performance, and to understand possible network architecture in generating observed correlations. Although the information theory approach provides a general tool in analysing noisy biological data and may be used to suggest possible network architectures in preserving information, it does not reveal the underlying mechanism that leads to the observed input-output relationship, nor does it tell us much about which information is important for the organism and how biological systems use information to carry out specific functions. To do that, we need to develop models of the biological machineries, e.g. biochemical networks and neural networks, to understand the dynamics of biological information processes. This is a much more difficult task. It requires deep knowledge of the underlying biological network—the main players (nodes) and their interactions (links)—in sufficient detail to build a model with predictive power, as well as quantitative input-output measurements of the system under different perturbations (both genetic variations and different external conditions) to test the model predictions to guide further development of the model. Due to the recent growth of biological knowledge thanks in part to high throughput methods (sequencing, gene expression microarray, etc) and development of quantitative in vivo techniques such as various florescence technology, these requirements are starting to be realized in different biological systems. The possible close interaction between quantitative experimentation and theoretical modeling has made systems biology an attractive field for physicists interested in quantitative biology. In this review, we describe some of the recent work in developing a quantitative predictive model of bacterial chemotaxis, which can be considered as the hydrogen atom of systems biology. Using statistical physics approaches, such as the Ising model and Langevin equation, we study how bacteria, such as E. coli, sense and amplify external signals, how they keep a working memory of the stimuli, and how they use these data to compute the chemical gradient. In particular, we will describe how E. coli cells avoid cross-talk in a heterogeneous receptor cluster to keep a ligand-specific memory. We will also study the thermodynamic costs of adaptation for cells to maintain an accurate memory. The statistical physics based approach described here should be useful in understanding design principles for cellular biochemical circuits in general.
Information processing in bacteria: memory, computation, and statistical physics: a key issues review.

PubMed

Lan, Ganhui; Tu, Yuhai

2016-05-01

Living systems have to constantly sense their external environment and adjust their internal state in order to survive and reproduce. Biological systems, from as complex as the brain to a single E. coli cell, have to process these data in order to make appropriate decisions. How do biological systems sense external signals? How do they process the information? How do they respond to signals? Through years of intense study by biologists, many key molecular players and their interactions have been identified in different biological machineries that carry out these signaling functions. However, an integrated, quantitative understanding of the whole system is still lacking for most cellular signaling pathways, not to say the more complicated neural circuits. To study signaling processes in biology, the key thing to measure is the input-output relationship. The input is the signal itself, such as chemical concentration, external temperature, light (intensity and frequency), and more complex signals such as the face of a cat. The output can be protein conformational changes and covalent modifications (phosphorylation, methylation, etc), gene expression, cell growth and motility, as well as more complex output such as neuron firing patterns and behaviors of higher animals. Due to the inherent noise in biological systems, the measured input-output dependence is often noisy. These noisy data can be analysed by using powerful tools and concepts from information theory such as mutual information, channel capacity, and the maximum entropy hypothesis. This information theory approach has been successfully used to reveal the underlying correlations between key components of biological networks, to set bounds for network performance, and to understand possible network architecture in generating observed correlations. Although the information theory approach provides a general tool in analysing noisy biological data and may be used to suggest possible network architectures in preserving information, it does not reveal the underlying mechanism that leads to the observed input-output relationship, nor does it tell us much about which information is important for the organism and how biological systems use information to carry out specific functions. To do that, we need to develop models of the biological machineries, e.g. biochemical networks and neural networks, to understand the dynamics of biological information processes. This is a much more difficult task. It requires deep knowledge of the underlying biological network-the main players (nodes) and their interactions (links)-in sufficient detail to build a model with predictive power, as well as quantitative input-output measurements of the system under different perturbations (both genetic variations and different external conditions) to test the model predictions to guide further development of the model. Due to the recent growth of biological knowledge thanks in part to high throughput methods (sequencing, gene expression microarray, etc) and development of quantitative in vivo techniques such as various florescence technology, these requirements are starting to be realized in different biological systems. The possible close interaction between quantitative experimentation and theoretical modeling has made systems biology an attractive field for physicists interested in quantitative biology. In this review, we describe some of the recent work in developing a quantitative predictive model of bacterial chemotaxis, which can be considered as the hydrogen atom of systems biology. Using statistical physics approaches, such as the Ising model and Langevin equation, we study how bacteria, such as E. coli, sense and amplify external signals, how they keep a working memory of the stimuli, and how they use these data to compute the chemical gradient. In particular, we will describe how E. coli cells avoid cross-talk in a heterogeneous receptor cluster to keep a ligand-specific memory. We will also study the thermodynamic costs of adaptation for cells to maintain an accurate memory. The statistical physics based approach described here should be useful in understanding design principles for cellular biochemical circuits in general.
General model and control of an n rotor helicopter

NASA Astrophysics Data System (ADS)

Sidea, A. G.; Yding Brogaard, R.; Andersen, N. A.; Ravn, O.

2014-12-01

The purpose of this study was to create a dynamic, nonlinear mathematical model of a multirotor that would be valid for different numbers of rotors. Furthermore, a set of Single Input Single Output (SISO) controllers were implemented for attitude control. Both model and controllers were tested experimentally on a quadcopter. Using the combined model and controllers, simple system simulation and control is possible, by replacing the physical values for the individual systems.
Assessing the Operational Robustness of the Homer Model for Marine Corps Use in Expeditionary Environments

DTIC Science & Technology

2014-06-01

systems. It can model systems including both conventional, diesel powered generators and renewable power sources such as photovoltaic arrays and wind...conducted an experiment where he assessed the capabilities of the HOMER model in forecasting the power output of a solar panel at NPS [32]. In his ex...energy efficiency in expeditionary operations, the HOMER micropower optimization model provides potential to serve as a powerful tool for improving
The Impact of Input and Output Prices on The Household Economic Behavior of Rice-Livestock Integrated Farming System (Rlifs) and Non Rlifs Farmers

NASA Astrophysics Data System (ADS)

Lindawati, L.; Kusnadi, N.; Kuntjoro, S. U.; Swastika, D. K. S.

2018-02-01

Integrated farming system is a system that emphasized linkages and synergism of farming units waste utilization. The objective of the study was to analyze the impact of input and output prices on both Rice Livestock Integrated Farming System (RLIFS) and non RLIFS farmers. The study used econometric model in the form of a simultaneous equations system consisted of 36 equations (18 behavior and 18 identity equations). The impact of changes in some variables was obtained through simulation of input and output prices on simultaneous equations. The results showed that the price increasing of the seed, SP-36, urea, medication/vitamins, manure, bran, straw had negative impact on production of the rice, cow, manure, bran, straw and household income. The decrease in the rice and cow production, production input usage, allocation of family labor, rice and cow business income was greater in RLIFS than non RLIFS farmers. The impact of rising rice and cow cattle prices in the two groups of farmers was not too much different because (1) farming waste wasn’t used effectively (2) manure and straw had small proportion of production costs. The increase of input and output price didn’t have impact on production costs and household expenditures on RLIFS.
Robust Fault Detection and Isolation for Stochastic Systems

NASA Technical Reports Server (NTRS)

George, Jemin; Gregory, Irene M.

2010-01-01

This paper outlines the formulation of a robust fault detection and isolation scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves precise reconstruction of sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the robust fault detection and isolation system.

Modal Parameter Identification of a Flexible Arm System

NASA Technical Reports Server (NTRS)

Barrington, Jason; Lew, Jiann-Shiun; Korbieh, Edward; Wade, Montanez; Tantaris, Richard

1998-01-01

In this paper an experiment is designed for the modal parameter identification of a flexible arm system. This experiment uses a function generator to provide input signal and an oscilloscope to save input and output response data. For each vibrational mode, many sets of sine-wave inputs with frequencies close to the natural frequency of the arm system are used to excite the vibration of this mode. Then a least-squares technique is used to analyze the experimental input/output data to obtain the identified parameters for this mode. The identified results are compared with the analytical model obtained by applying finite element analysis.
Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

NASA Technical Reports Server (NTRS)

Doll, Petra; Douville, Herve; Guntner, Andreas; Schmied, Hannes Muller; Wada, Yoshihide

2015-01-01

Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) Uncertainty of meteorological input data strongly affects model outputs. (3) The reaction of vegetation to changing climate and CO2 concentrations is uncertain and not taken into account in most GHMs that serve to estimate climate change impacts. (4) Reasons for discrepant responses of GHMs to changing climate have yet to be identified. (5) More accurate estimates of monthly time series of water availability and use are needed to provide good indicators of water scarcity. (6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater-surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyper resolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy.
Modelling of cantilever based on piezoelectric energy harvester

NASA Astrophysics Data System (ADS)

Rahim, N. F.; Ong, N. R.; Aziz, M. H. A.; Alcain, J. B.; Haimi, W. M. W. N.; Sauli, Z.

2017-09-01

Recent technology allows devices to become smaller and with more functions. However, the battery size remained the same and for some devices, the battery must be larger in order to accommodate the greater power demands by the portable device. Piezoelectric energy harvester has been suggested as a substitute for the batteries in coming future. In this paper, a cantilever based piezoelectric energy harvester was modelled and simulated using COMSOL software. The analysis focused on the mechanical part of the harvesting system such as output power, output voltage and vibration frequency. Results of the simulations proved that flexible piezoelectric energy harvesters using nano-materials had remarkable strength under the large strain. However, although the large strain was induced on the flexible energy harvesters, the output power was still lower than the bulk and MEMS piezoelectric energy harvesters that operated at the resonance frequency. The off-resonance operation and very lower packing density of the active piezoelectric materials of the flexible energy harvesters resulted in a low output power.
Transient Stability Output Margin Estimation Based on Energy Function Method

NASA Astrophysics Data System (ADS)

Miwa, Natsuki; Tanaka, Kazuyuki

In this paper, a new method of estimating critical generation margin (CGM) in power systems is proposed from the viewpoint of transient stability diagnostic. The proposed method has the capability to directly compute the stability limit output for a given contingency based on transient energy function method (TEF). Since CGM can be directly obtained by the limit output using estimated P-θ curves and is easy to understand, it is more useful rather than conventional critical clearing time (CCT) of energy function method. The proposed method can also estimate CGM as its negative value that means unstable in present load profile, then negative CGM can be directly utilized as generator output restriction. The proposed method is verified its accuracy and fast solution ability by applying to simple 3-machine model and IEEJ EAST10-machine standard model. Furthermore the useful application to severity ranking of transient stability for a lot of contingency cases is discussed by using CGM.
Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2018-05-11

Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. We herein introduce an adaptive framework in which optimal input design is integrated with Square root Cubature Kalman Filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 msec in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications. © 2018 IOP Publishing Ltd.
Hydrologic response to multimodel climate output using a physically based model of groundwater/surface water interactions

NASA Astrophysics Data System (ADS)

Sulis, M.; Paniconi, C.; Marrocu, M.; Huard, D.; Chaumont, D.

2012-12-01

General circulation models (GCMs) are the primary instruments for obtaining projections of future global climate change. Outputs from GCMs, aided by dynamical and/or statistical downscaling techniques, have long been used to simulate changes in regional climate systems over wide spatiotemporal scales. Numerous studies have acknowledged the disagreements between the various GCMs and between the different downscaling methods designed to compensate for the mismatch between climate model output and the spatial scale at which hydrological models are applied. Very little is known, however, about the importance of these differences once they have been input or assimilated by a nonlinear hydrological model. This issue is investigated here at the catchment scale using a process-based model of integrated surface and subsurface hydrologic response driven by outputs from 12 members of a multimodel climate ensemble. The data set consists of daily values of precipitation and min/max temperatures obtained by combining four regional climate models and five GCMs. The regional scenarios were downscaled using a quantile scaling bias-correction technique. The hydrologic response was simulated for the 690 km2des Anglais catchment in southwestern Quebec, Canada. The results show that different hydrological components (river discharge, aquifer recharge, and soil moisture storage) respond differently to precipitation and temperature anomalies in the multimodel climate output, with greater variability for annual discharge compared to recharge and soil moisture storage. We also find that runoff generation and extreme event-driven peak hydrograph flows are highly sensitive to any uncertainty in climate data. Finally, the results show the significant impact of changing sequences of rainy days on groundwater recharge fluxes and the influence of longer dry spells in modifying soil moisture spatial variability.
CIRMIS Data system. Volume 2. Program listings

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

Friedrichs, D.R.

1980-01-01

The Assessment of Effectiveness of Geologic Isolation Systems (AEGIS) Program is developing and applying the methodology for assessing the far-field, long-term post-closure safety of deep geologic nuclear waste repositories. AEGIS is being performed by Pacific Northwest Laboratory (PNL) under contract with the Office of Nuclear Waste Isolation (OWNI) for the Department of Energy (DOE). One task within AEGIS is the development of methodology for analysis of the consequences (water pathway) from loss of repository containment as defined by various release scenarios. Analysis of the long-term, far-field consequences of release scenarios requires the application of numerical codes which simulate the hydrologicmore » systems, model the transport of released radionuclides through the hydrologic systems, model the transport of released radionuclides through the hydrologic systems to the biosphere, and, where applicable, assess the radiological dose to humans. The various input parameters required in the analysis are compiled in data systems. The data are organized and prepared by various input subroutines for utilization by the hydraulic and transport codes. The hydrologic models simulate the groundwater flow systems and provide water flow directions, rates, and velocities as inputs to the transport models. Outputs from the transport models are basically graphs of radionuclide concentration in the groundwater plotted against time. After dilution in the receiving surface-water body (e.g., lake, river, bay), these data are the input source terms for the dose models, if dose assessments are required.The dose models calculate radiation dose to individuals and populations. CIRMIS (Comprehensive Information Retrieval and Model Input Sequence) Data System is a storage and retrieval system for model input and output data, including graphical interpretation and display. This is the second of four volumes of the description of the CIRMIS Data System.« less
General Circulation Model Output for Forest Climate Change Research and Applications

Treesearch

Ellen J. Cooter; Brian K. Eder; Sharon K. LeDuc; Lawrence Truppi

1993-01-01

This report reviews technical aspects of and summarizes output from four climate models. Recommendations concerning the use of these outputs in forest impact assessments are made. This report reviews technical aspects of and summarizes output from four climate models. Recommendations concerning the use of these outputs in forest impact assessments are made.
Two time scale output feedback regulation for ill-conditioned systems

NASA Technical Reports Server (NTRS)

Calise, A. J.; Moerder, D. D.

1986-01-01

Issues pertaining to the well-posedness of a two time scale approach to the output feedback regulator design problem are examined. An approximate quadratic performance index which reflects a two time scale decomposition of the system dynamics is developed. It is shown that, under mild assumptions, minimization of this cost leads to feedback gains providing a second-order approximation of optimal full system performance. A simplified approach to two time scale feedback design is also developed, in which gains are separately calculated to stabilize the slow and fast subsystem models. By exploiting the notion of combined control and observation spillover suppression, conditions are derived assuring that these gains will stabilize the full-order system. A sequential numerical algorithm is described which obtains output feedback gains minimizing a broad class of performance indices, including the standard LQ case. It is shown that the algorithm converges to a local minimum under nonrestrictive assumptions. This procedure is adapted to and demonstrated for the two time scale design formulations.
Computation of output feedback gains for linear stochastic systems using the Zangwill-Powell method

NASA Technical Reports Server (NTRS)

Kaufman, H.

1977-01-01

Because conventional optimal linear regulator theory results in a controller which requires the capability of measuring and/or estimating the entire state vector, it is of interest to consider procedures for computing controls which are restricted to be linear feedback functions of a lower dimensional output vector and which take into account the presence of measurement noise and process uncertainty. To this effect a stochastic linear model has been developed that accounts for process parameter and initial uncertainty, measurement noise, and a restricted number of measurable outputs. Optimization with respect to the corresponding output feedback gains was then performed for both finite and infinite time performance indices without gradient computation by using Zangwill's modification of a procedure originally proposed by Powell.
Decision- rather than scenario-centred downscaling: Towards smarter use of climate model outputs

NASA Astrophysics Data System (ADS)

Wilby, Robert L.

2013-04-01

Climate model output has been used for hydrological impact assessments for at least 25 years. Scenario-led methods raise awareness about risks posed by climate variability and change to the security of supplies, performance of water infrastructure, and health of freshwater ecosystems. However, it is less clear how these analyses translate into actionable information for adaptation. One reason is that scenario-led methods typically yield very large uncertainty bounds in projected impacts at regional and river catchment scales. Consequently, there is growing interest in vulnerability-based frameworks and strategies for employing climate model output in decision-making contexts. This talk begins by summarising contrasting perspectives on climate models and principles for testing their utility for water sector applications. Using selected examples it is then shown how water resource systems may be adapted with varying levels of reliance on climate model information. These approaches include the conventional scenario-led risk assessment, scenario-neutral strategies, safety margins and sensitivity testing, and adaptive management of water systems. The strengths and weaknesses of each approach are outlined and linked to selected water management activities. These cases show that much progress can be made in managing water systems without dependence on climate models. Low-regret measures such as improved forecasting, better inter-agency co-operation, and contingency planning, yield benefits regardless of the climate outlook. Nonetheless, climate model scenarios are useful for evaluating adaptation portfolios, identifying system thresholds and fixing weak links, exploring the timing of investments, improving operating rules, or developing smarter licensing regimes. The most problematic application remains the climate change safety margin because of the very low confidence in extreme precipitation and river flows generated by climate models. In such cases, it is necessary to understand the trade-offs that exist between the additional costs of a scheme and the level of risk that is accommodated.
MARSTHERM: A Web-based System Providing Thermophysical Analysis Tools for Mars Research

NASA Astrophysics Data System (ADS)

Putzig, N. E.; Barratt, E. M.; Mellon, M. T.; Michaels, T. I.

2013-12-01

We introduce MARSTHERM, a web-based system that will allow researchers access to a standard numerical thermal model of the Martian near-surface and atmosphere. In addition, the system will provide tools for the derivation, mapping, and analysis of apparent thermal inertia from temperature observations by the Mars Global Surveyor Thermal Emission Spectrometer (TES) and the Mars Odyssey Thermal Emission Imaging System (THEMIS). Adjustable parameters for the thermal model include thermal inertia, albedo, surface pressure, surface emissivity, atmospheric dust opacity, latitude, surface slope angle and azimuth, season (solar longitude), and time steps for calculations and output. The model computes diurnal surface and brightness temperatures for either a single day or a full Mars year. Output options include text files and plots of seasonal and diurnal surface, brightness, and atmospheric temperatures. The tools for the derivation and mapping of apparent thermal inertia from spacecraft data are project-based, wherein the user provides an area of interest (AOI) by specifying latitude and longitude ranges. The system will then extract results within the AOI from prior global mapping of elevation (from the Mars Orbiter Laser Altimeter, for calculating surface pressure), TES annual albedo, and TES seasonal and annual-mean 2AM and 2PM apparent thermal inertia (Putzig and Mellon, 2007, Icarus 191, 68-94). In addition, a history of TES dust opacity within the AOI is computed. For each project, users may then provide a list of THEMIS images to process for apparent thermal inertia, optionally overriding the TES-derived dust opacity with a fixed value. Output from the THEMIS derivation process includes thumbnail and context images, GeoTIFF raster data, and HDF5 files containing arrays of input and output data (radiance, brightness temperature, apparent thermal inertia, elevation, quality flag, latitude, and longitude) and ancillary information. As a demonstration of capabilities, we will present results from a thermophysical study of Gale Crater (Barratt and Putzig, 2013, EPSC abstract 613), for which TES and THEMIS mapping has been carried out during system development. Public access to the MARSTHERM system will be provided in conjunction with the 2013 AGU Fall Meeting and will feature the numerical thermal model and thermal-inertia derivation algorithm developed by Mellon et al. (2000, Icarus 148, 437-455) as modified by Putzig and Mellon (2007, Icarus 191, 68-94). Updates to the thermal model and derivation algorithm that include a more sophisticated representation of the atmosphere and a layered subsurface are presently in development, and these will be incorporated into the system when they are available. Other planned enhancements include tools for modeling temperatures from horizontal mixtures of materials and slope facets, for comparing heterogeneity modeling results to TES and THEMIS results, and for mosaicking THEMIS images.
Modeling of a VMJ PV array under Gaussian high intensity laser power beam condition

NASA Astrophysics Data System (ADS)

Eom, Jeongsook; Kim, Gunzung; Park, Yongwan

2018-02-01

The high intensity laser power beaming (HILPB) system is one of the most promising systems in the long-rang wireless power transfer field. The vertical multi-junction photovoltaic (VMJ PV) array converts the HILPB into electricity to power the load or charges a battery. The output power of a VMJ PV array depends mainly on irradiance values of each VMJ PV cells. For simulating an entire VMJ PV array, the irradiance profile of the Gaussian HILPB and the irradiance level of the VMJ PV cell are mathematically modeled first. The VMJ PV array is modeled as a network with dimension m*n, where m represents the number of VMJ PV cells in a column, and n represents the number of VMJ PV cells in a row. In order to validate the results obtained in modeling and simulation, a laboratory setup was developed using 55 VMJ PV array. By using the output power model of VMJ PV array, we can establish an optimal power transmission path by the receiver based on the received signal strength. When the laser beam from multiple transmitters aimed at a VMJ PV array at the same time, the received power is the sum of all energy at a VMJ PV array. The transmitter sends its power characteristics as optically coded laser pulses and powers as HILPB. Using the attenuated power model and output power model of VMJ PV array, the receiver can estimate the maximum receivable powers from the transmitters and select optimal transmitters.
Integrating pixel- and polygon-based approaches to wildfire risk assessment: Application to a high-value watershed on the Pike and San Isabel National Forests, Colorado, USA

Treesearch

Matthew P. Thompson; Julie W. Gilbertson-Day; Joe H. Scott

2015-01-01

We develop a novel risk assessment approach that integrates complementary, yet distinct, spatial modeling approaches currently used in wildfire risk assessment. Motivation for this work stems largely from limitations of existing stochastic wildfire simulation systems, which can generate pixel-based outputs of fire behavior as well as polygon-based outputs of simulated...
Two-Speed Gearbox Dynamic Simulation Predictions and Test Validation

NASA Technical Reports Server (NTRS)

Lewicki, David G.; DeSmidt, Hans; Smith, Edward C.; Bauman, Steven W.

2010-01-01

Dynamic simulations and experimental validation tests were performed on a two-stage, two-speed gearbox as part of the drive system research activities of the NASA Fundamental Aeronautics Subsonics Rotary Wing Project. The gearbox was driven by two electromagnetic motors and had two electromagnetic, multi-disk clutches to control output speed. A dynamic model of the system was created which included a direct current electric motor with proportional-integral-derivative (PID) speed control, a two-speed gearbox with dual electromagnetically actuated clutches, and an eddy current dynamometer. A six degree-of-freedom model of the gearbox accounted for the system torsional dynamics and included gear, clutch, shaft, and load inertias as well as shaft flexibilities and a dry clutch stick-slip friction model. Experimental validation tests were performed on the gearbox in the NASA Glenn gear noise test facility. Gearbox output speed and torque as well as drive motor speed and current were compared to those from the analytical predictions. The experiments correlate very well with the predictions, thus validating the dynamic simulation methodologies.
A robust and flexible Geospatial Modeling Interface (GMI) for deploying and evaluating natural resource models

USDA-ARS?s Scientific Manuscript database

Geographical information systems (GIS) software packages have been used for nearly three decades as analytical tools in natural resource management for geospatial data assembly, processing, storage, and visualization of input data and model output. However, with increasing availability and use of fu...
Optical linear algebra processors: noise and error-source modeling.

PubMed

Casasent, D; Ghosh, A

1985-06-01

The modeling of system and component noise and error sources in optical linear algebra processors (OLAP's) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.
Optical linear algebra processors - Noise and error-source modeling

NASA Technical Reports Server (NTRS)

Casasent, D.; Ghosh, A.

1985-01-01

The modeling of system and component noise and error sources in optical linear algebra processors (OLAPs) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.
An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models
Internal combustion engine control for series hybrid electric vehicles by parallel and distributed genetic programming/multiobjective genetic algorithms

NASA Astrophysics Data System (ADS)

Gladwin, D.; Stewart, P.; Stewart, J.

2011-02-01

This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to utilise the miriad of existing control blocks in the Simulink libraries to automatically evolve optimal control structures.

Modeling Future Fire danger over North America in a Changing Climate

NASA Astrophysics Data System (ADS)

Jain, P.; Paimazumder, D.; Done, J.; Flannigan, M.

2016-12-01

Fire danger ratings are used to determine wildfire potential due to weather and climate factors. The Fire Weather Index (FWI), part of the Canadian Forest Fire Danger Rating System (CFFDRS), incorporates temperature, relative humidity, windspeed and precipitation to give a daily fire danger rating that is used by wildfire management agencies in an operational context. Studies using GCM output have shown that future wildfire danger will increase in a warming climate. However, these studies are somewhat limited by the coarse spatial resolution (typically 100-400km) and temporal resolution (typically 6-hourly to monthly) of the model output. Future wildfire potential over North America based on FWI is calculated using output from the Weather, Research and Forecasting (WRF) model, which is used to downscale future climate scenarios from the bias-corrected Community Climate System Model (CCSM) under RCP8.5 scenarios at a spatial resolution of 36km. We consider five eleven year time slices: 1990-2000, 2020-2030, 2030-2040, 2050-2060 and 2080-2090. The dynamically downscaled simulation improves determination of future extreme weather by improving both spatial and temporal resolution over most GCM models. To characterize extreme fire weather we calculate annual numbers of spread days (days for which FWI > 19) and annual 99th percentile of FWI. Additionally, an extreme value analysis based on the peaks-over-threshold method allows us to calculate the return values for extreme FWI values.
Aircraft dual-shaft jet engine with indirect action fuel flow controller

NASA Astrophysics Data System (ADS)

Tudosie, Alexandru-Nicolae

2017-06-01

The paper deals with an aircraft single-jet engine's control system, based on a fuel flow controller. Considering the engine as controlled object and its thrust the most important operation effect, from the multitude of engine's parameters only its rotational speed n is measurable and proportional to its thrust, so engine's speed has become the most important controlled parameter. Engine's control system is based on fuel injection Qi dosage, while the output is engine's speed n. Based on embedded system's main parts' mathematical models, the author has described the system by its block diagram with transfer functions; furthermore, some Simulink-Matlab simulations are performed, concerning embedded system quality (its output parameters time behavior) and, meanwhile, some conclusions concerning engine's parameters mutual influences are revealed. Quantitative determinations are based on author's previous research results and contributions, as well as on existing models (taken from technical literature). The method can be extended for any multi-spool engine, single- or twin-jet.
Power Control for Direct-Driven Permanent Magnet Wind Generator System with Battery Storage

PubMed Central

Guang, Chu Xiao; Ying, Kong

2014-01-01

The objective of this paper is to construct a wind generator system (WGS) loss model that addresses the loss of the wind turbine and the generator. It aims to optimize the maximum effective output power and turbine speed. Given that the wind generator system has inertia and is nonlinear, the dynamic model of the wind generator system takes the advantage of the duty of the Buck converter and employs feedback linearization to design the optimized turbine speed tracking controller and the load power controller. According to that, this paper proposes a dual-mode dynamic coordination strategy based on the auxiliary load to reduce the influence of mode conversion on the lifetime of the battery. Optimized speed and power rapid tracking as well as the reduction of redundant power during mode conversion have gone through the test based on a 5 kW wind generator system test platform. The generator output power as the capture target has also been proved to be efficient. PMID:25050405
Power control for direct-driven permanent magnet wind generator system with battery storage.

PubMed

Guang, Chu Xiao; Ying, Kong

2014-01-01

The objective of this paper is to construct a wind generator system (WGS) loss model that addresses the loss of the wind turbine and the generator. It aims to optimize the maximum effective output power and turbine speed. Given that the wind generator system has inertia and is nonlinear, the dynamic model of the wind generator system takes the advantage of the duty of the Buck converter and employs feedback linearization to design the optimized turbine speed tracking controller and the load power controller. According to that, this paper proposes a dual-mode dynamic coordination strategy based on the auxiliary load to reduce the influence of mode conversion on the lifetime of the battery. Optimized speed and power rapid tracking as well as the reduction of redundant power during mode conversion have gone through the test based on a 5 kW wind generator system test platform. The generator output power as the capture target has also been proved to be efficient.
Speckle noise in satellite based lidar systems

NASA Technical Reports Server (NTRS)

Gardner, C. S.

1977-01-01

The lidar system model was described, and the statistics of the signal and noise at the receiver output were derived. Scattering media effects were discussed along with polarization and atmospheric turbulence. The major equations were summarized and evaluated for some typical parameters.
Mathematical modeling and characteristic analysis for over-under turbine based combined cycle engine

NASA Astrophysics Data System (ADS)

Ma, Jingxue; Chang, Juntao; Ma, Jicheng; Bao, Wen; Yu, Daren

2018-07-01

The turbine based combined cycle engine has become the most promising hypersonic airbreathing propulsion system for its superiority of ground self-starting, wide flight envelop and reusability. The simulation model of the turbine based combined cycle engine plays an important role in the research of performance analysis and control system design. In this paper, a turbine based combined cycle engine mathematical model is built on the Simulink platform, including a dual-channel air intake system, a turbojet engine and a ramjet. It should be noted that the model of the air intake system is built based on computational fluid dynamics calculation, which provides valuable raw data for modeling of the turbine based combined cycle engine. The aerodynamic characteristics of turbine based combined cycle engine in turbojet mode, ramjet mode and mode transition process are studied by the mathematical model, and the influence of dominant variables on performance and safety of the turbine based combined cycle engine is analyzed. According to the stability requirement of thrust output and the safety in the working process of turbine based combined cycle engine, a control law is proposed that could guarantee the steady output of thrust by controlling the control variables of the turbine based combined cycle engine in the whole working process.
An experimental approach to identify dynamical models of transcriptional regulation in living cells

NASA Astrophysics Data System (ADS)

Fiore, G.; Menolascina, F.; di Bernardo, M.; di Bernardo, D.

2013-06-01

We describe an innovative experimental approach, and a proof of principle investigation, for the application of System Identification techniques to derive quantitative dynamical models of transcriptional regulation in living cells. Specifically, we constructed an experimental platform for System Identification based on a microfluidic device, a time-lapse microscope, and a set of automated syringes all controlled by a computer. The platform allows delivering a time-varying concentration of any molecule of interest to the cells trapped in the microfluidics device (input) and real-time monitoring of a fluorescent reporter protein (output) at a high sampling rate. We tested this platform on the GAL1 promoter in the yeast Saccharomyces cerevisiae driving expression of a green fluorescent protein (Gfp) fused to the GAL1 gene. We demonstrated that the System Identification platform enables accurate measurements of the input (sugars concentrations in the medium) and output (Gfp fluorescence intensity) signals, thus making it possible to apply System Identification techniques to obtain a quantitative dynamical model of the promoter. We explored and compared linear and nonlinear model structures in order to select the most appropriate to derive a quantitative model of the promoter dynamics. Our platform can be used to quickly obtain quantitative models of eukaryotic promoters, currently a complex and time-consuming process.
Modeling static and dynamic human cardiovascular responses to exercise.

PubMed

Stremel, R W; Bernauer, E M; Harter, L W; Schultz, R A; Walters, R F

1975-08-01

A human performance model has been developed and described [9] which portrays the human circulatory, thermo regulatory and energy-exchange systems as an intercoupled set. In this model, steady state or static relationships are used to describe oxygen consumption and blood flow. For example, heart rate (HTRT) is calculated as a function of the oxygen and the thermo-regulatory requirements of each body compartment, using the steady state work values of cardiac output (CO, sum of all compartment blood flows) and stroke volume (SV, assumed maximal after 40% maximal oxygen consumption): HTRT=CO/SV. The steady state model has proven to be an acceptable first approximation, but the inclusion of transient characteristics are essential in describing the overall systems' adjustment to exercise stress. In the present study, the dynamic transient characteristics of heart rate, stroke volume and cardiac output were obtained from experiments utilizing step and sinusoidal forcing of work. The gain and phase relationships reveal a probable first order system with a six minute time constant, and are utilized to model the transient characteristics of these parameters. This approach leads to a more complex model but a more accurate representation of the physiology involved. The instrumentation and programming essential to these experiments are described.
Design and Simulation of Horn Antenna Using CST Software for GPR System

NASA Astrophysics Data System (ADS)

Joret, Ariffuddin; Sulong, M. S.; Abdullah, M. F. L.; Madun, Aziman; Haimi Dahlan, Samsul

2018-04-01

Detection of underground object can be made using a GPR system. This system is classified as a non-destructive technique (NDT) where the ground areas need not to be excavated. The technique used by the GPR system is by measuring the reflection of electromagnetic wave signal produced and detected by antenna which is known as the transmitter and the receiver antenna. In this study, a GPR system was studied by means of simulation using a Horn antenna as a transceiver antenna. The electromagnetic wave signal in this simulation is produced by current signal of an antenna which having a shape of modulation of Gaussian pulse which is having spectrum from 8 GHz until 12 GHz. CST and MATLAB Software are used in this GPR system simulation. A model of a Horn antenna has been designed using the CST software before the GPR’s system simulation modeled by adding a model of background in front of the Horn antenna. The simulation results show that the output signal of the Horn antenna can be used in detecting embedded object which are made from material of wood and iron. In addition, the simulation result has successfully developed a 3D model image of the GPR system using output signal of the Horn antenna. The embedded iron object in the GPR system simulation can be seen clearly by using this 3D image.
Rhetorical Consequences of the Computer Society: Expert Systems and Human Communication.

ERIC Educational Resources Information Center

Skopec, Eric Wm.

Expert systems are computer programs that solve selected problems by modelling domain-specific behaviors of human experts. These computer programs typically consist of an input/output system that feeds data into the computer and retrieves advice, an inference system using the reasoning and heuristic processes of human experts, and a knowledge…
Improved first-order uncertainty method for water-quality modeling

USGS Publications Warehouse

Melching, C.S.; Anmangandla, S.

1992-01-01

Uncertainties are unavoidable in water-quality modeling and subsequent management decisions. Monte Carlo simulation and first-order uncertainty analysis (involving linearization at central values of the uncertain variables) have been frequently used to estimate probability distributions for water-quality model output due to their simplicity. Each method has its drawbacks: Monte Carlo simulation's is mainly computational time; and first-order analysis are mainly questions of accuracy and representativeness, especially for nonlinear systems and extreme conditions. An improved (advanced) first-order method is presented, where the linearization point varies to match the output level whose exceedance probability is sought. The advanced first-order method is tested on the Streeter-Phelps equation to estimate the probability distribution of critical dissolved-oxygen deficit and critical dissolved oxygen using two hypothetical examples from the literature. The advanced first-order method provides a close approximation of the exceedance probability for the Streeter-Phelps model output estimated by Monte Carlo simulation using less computer time - by two orders of magnitude - regardless of the probability distributions assumed for the uncertain model parameters.
Stochastic process approximation for recursive estimation with guaranteed bound on the error covariance

NASA Technical Reports Server (NTRS)

Menga, G.

1975-01-01

An approach, is proposed for the design of approximate, fixed order, discrete time realizations of stochastic processes from the output covariance over a finite time interval, was proposed. No restrictive assumptions are imposed on the process; it can be nonstationary and lead to a high dimension realization. Classes of fixed order models are defined, having the joint covariance matrix of the combined vector of the outputs in the interval of definition greater or equal than the process covariance; (the difference matrix is nonnegative definite). The design is achieved by minimizing, in one of those classes, a measure of the approximation between the model and the process evaluated by the trace of the difference of the respective covariance matrices. Models belonging to these classes have the notable property that, under the same measurement system and estimator structure, the output estimation error covariance matrix computed on the model is an upper bound of the corresponding covariance on the real process. An application of the approach is illustrated by the modeling of random meteorological wind profiles from the statistical analysis of historical data.
An Assessment of Magma-Hydrothermal Heat Output at the Costa Rica Rift

NASA Astrophysics Data System (ADS)

Lowell, R. P.; Morales Maqueda, M. A.; Banyte, D.; Zhang, L.; Tong, V.; Hobbs, R. W.; Harris, R. N.

2016-12-01

A joint geophysical/physical oceanographic investigation of the Costa Rica Ridge as part of the OSCAR (Oceanographic and Seismic Characterization of heat dissipation and alteration by hydrothermal fluids at an Axial Ridge) research program enables us to estimate hydrothermal heat output and its likely link to a sub-axial magma lens (AML). In December 2014, a number of tow-yo casts were made along and near the ridge axis where seismic reflection data collected in 1994 showed the presence of seismic reflector interpreted to be an AML at a depth of about 2800 m below the seafloor. A decline in beam transmission in a ≈ 200 m thick region located approximately 800 to 900 meters above the seafloor indicated the presence of a hydrothermal plume. CTD data collected above the ridge yielded a weighted average buoyancy frequency of approximately 19.3 x 10-8 s-2. Assuming a mean hydrothermal vent temperature of 350°C, buoyant plume theory yields a heat output between 400 and 600 MW. Application of the single-pass modeling approach to the hydrothermal system, yields an estimated mass flow between 210 and 337 kg/s, and the mean product of crustal permeability x discharge area ranges between 6 and 10 x 10-9 m4. A multichannel seismic profile collected in 2015 indicates the presence of a reflector 5 km along-axis and < 100 m wide, in approximately the same location as the 1994 survey, suggesting that magma-driven hydrothermal heat output may have exhibited stability on a decadal time scale. The relatively small size of the inferred AML, when coupled to the heat output estimate and the single-pass model, suggests that the conductive boundary layer at the top the AML is 2m thick and that the AML must be frequently replenished to maintain stable heat output. Assuming the hydrothermal system is driven by magmatic latent heat, a 100 m thick AML could have powered a 100 MW hydrothermal system for 20 years, while inputting 5 x 107 m3 of melt into the axis. These results indicate hydrothermal heat output at the Costa Rica Ridge is time-varying.
Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

2011-12-01

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.
Emulation applied to reliability analysis of reconfigurable, highly reliable, fault-tolerant computing systems

NASA Technical Reports Server (NTRS)

Migneault, G. E.

1979-01-01

Emulation techniques applied to the analysis of the reliability of highly reliable computer systems for future commercial aircraft are described. The lack of credible precision in reliability estimates obtained by analytical modeling techniques is first established. The difficulty is shown to be an unavoidable consequence of: (1) a high reliability requirement so demanding as to make system evaluation by use testing infeasible; (2) a complex system design technique, fault tolerance; (3) system reliability dominated by errors due to flaws in the system definition; and (4) elaborate analytical modeling techniques whose precision outputs are quite sensitive to errors of approximation in their input data. Next, the technique of emulation is described, indicating how its input is a simple description of the logical structure of a system and its output is the consequent behavior. Use of emulation techniques is discussed for pseudo-testing systems to evaluate bounds on the parameter values needed for the analytical techniques. Finally an illustrative example is presented to demonstrate from actual use the promise of the proposed application of emulation.
A novel auto-tuning PID control mechanism for nonlinear systems.

PubMed

Cetin, Meric; Iplikci, Serdar

2015-09-01

In this paper, a novel Runge-Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Dynamic impedance compensation for wireless power transfer using conjugate power

NASA Astrophysics Data System (ADS)

Liu, Suqi; Tan, Jianping; Wen, Xue

2018-02-01

Wireless power transfer (WPT) via coupled magnetic resonances has been in development for over a decade. However, the frequency splitting phenomenon occurs in the over-coupled region. Thus, the output power of the two-coil system achieves the maximum output power at the two splitting angular frequencies, and not at the natural resonant angular frequency. According to the maximum power transfer theorem, the impedance compensation method was adopted in many WPT projects. However, it remains a challenge to achieve the maximum output power and transmission efficiency in a fixed-frequency mode. In this study, dynamic impedance compensation for WPT was presented by utilizing the compensator within a virtual three-coil WPT system. First, the circuit model was established and transfer characteristics of a system were studied by utilizing circuit theories. Second, the power superposition of the WPT system was carefully researched. When a pair of compensating coils was inserted into the transmitter loop, the conjugate power of the compensator loop was created via magnetic coupling of the two compensating coils that insert into the transmitter loop. The mechanism for dynamic impedance compensation for wireless power transfer was then provided by investigating a virtual three-coil WPT system. Finally, the experimental circuit of a virtual three-coil WPT system was designed, and experimental results are consistent with the theoretical analysis, which achieves the maximum output power and transmission efficiency.
Goal Directed Model Inversion: A Study of Dynamic Behavior

NASA Technical Reports Server (NTRS)

Colombano, Silvano P.; Compton, Michael; Raghavan, Bharathi; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome 0 "would have been right if the outcome had been the desired one." The algorithm then proceeds as follows: (1) store the action that produced the wrong outcome as a "target" (2) redefine the wrong outcome as a desired goal (3) submit the new desired goal to the system (4) compare the new action with the target action and modify the system by using a suitable algorithm for credit assignment (Back propagation in our example) (5) resubmit the original goal. Prior publications by our group in this area focused on demonstrating empirical results based on the inverse kinematic problem for a simulated robotic arm. In this paper we apply the inversion process to much simpler analytic functions in order to elucidate the dynamic behavior of the system and to determine the sensitivity of the learning process to various parameters. This understanding will be necessary for the acceptance of GDMI as a practical tool.
Hierarchical resilience with lightweight threads.

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

Wheeler, Kyle Bruce

2011-10-01

This paper proposes methodology for providing robustness and resilience for a highly threaded distributed- and shared-memory environment based on well-defined inputs and outputs to lightweight tasks. These inputs and outputs form a failure 'barrier', allowing tasks to be restarted or duplicated as necessary. These barriers must be expanded based on task behavior, such as communication between tasks, but do not prohibit any given behavior. One of the trends in high-performance computing codes seems to be a trend toward self-contained functions that mimic functional programming. Software designers are trending toward a model of software design where their core functions are specifiedmore » in side-effect free or low-side-effect ways, wherein the inputs and outputs of the functions are well-defined. This provides the ability to copy the inputs to wherever they need to be - whether that's the other side of the PCI bus or the other side of the network - do work on that input using local memory, and then copy the outputs back (as needed). This design pattern is popular among new distributed threading environment designs. Such designs include the Barcelona STARS system, distributed OpenMP systems, the Habanero-C and Habanero-Java systems from Vivek Sarkar at Rice University, the HPX/ParalleX model from LSU, as well as our own Scalable Parallel Runtime effort (SPR) and the Trilinos stateless kernels. This design pattern is also shared by CUDA and several OpenMP extensions for GPU-type accelerators (e.g. the PGI OpenMP extensions).« less
Prediction of Francis Turbine Prototype Part Load Pressure and Output Power Fluctuations with Hydroelectric Model

NASA Astrophysics Data System (ADS)

Alligné, S.; Nicolet, C.; Béguin, A.; Landry, C.; Gomes, J.; Avellan, F.

2017-04-01

The prediction of pressure and output power fluctuations amplitudes on Francis turbine prototype is a challenge for hydro-equipment industry since it is subjected to guarantees to ensure smooth and reliable operation of the hydro units. The European FP7 research project Hyperbole aims to setup a methodology to transpose the pressure fluctuations induced by the cavitation vortex rope from the reduced scale model to the prototype generating units. A Francis turbine unit of 444MW with a specific speed value of ν = 0.29, is considered as case study. A SIMSEN model of the power station including electrical system, controllers, rotating train and hydraulic system with transposed draft tube excitation sources is setup. Based on this model, a frequency analysis of the hydroelectric system is performed for all technologies to analyse potential interactions between hydraulic excitation sources and electrical components. Three technologies have been compared: the classical fixed speed configuration with Synchronous Machine (SM) and the two variable speed technologies which are Doubly Fed Induction Machine (DFIM) and Full Size Frequency Converter (FSFC).

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

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

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reductionmore » in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.« less
Assessment of effectiveness of geologic isolation systems. CIRMIS data system. Volume 4. Driller's logs, stratigraphic cross section and utility routines

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

Friedrichs, D.R.

1980-01-01

The Assessment of Effectiveness of Geologic Isolation Systems (AEGIS) Program is developing and applying the methodology for assessing the far-field, long-term post-closure safety of deep geologic nuclear waste repositories. AEGIS is being performed by Pacific Northwest Laboratory (PNL) under contract with the Office of Nuclear Waste Isolation (ONWI) for the Department of Energy (DOE). One task within AEGIS is the development of methodology for analysis of the consequences (water pathway) from loss of repository containment as defined by various release scenarios. Analysis of the long-term, far-field consequences of release scenarios requires the application of numerical codes which simulate the hydrologicmore » systems, model the transport of released radionuclides through the hydrologic systems to the biosphere, and, where applicable, assess the radiological dose to humans. The various input parameters required in the analysis are compiled in data systems. The data are organized and prepared by various input subroutines for use by the hydrologic and transport codes. The hydrologic models simulate the groundwater flow systems and provide water flow directions, rates, and velocities as inputs to the transport models. Outputs from the transport models are basically graphs of radionuclide concentration in the groundwater plotted against time. After dilution in the receiving surface-water body (e.g., lake, river, bay), these data are the input source terms for the dose models, if dose assessments are required. The dose models calculate radiation dose to individuals and populations. CIRMIS (Comprehensive Information Retrieval and Model Input Sequence) Data System is a storage and retrieval system for model input and output data, including graphical interpretation and display. This is the fourth of four volumes of the description of the CIRMIS Data System.« less
Heavy-Tailed Fluctuations in the Spiking Output Intensity of Semiconductor Lasers with Optical Feedback

PubMed Central

2016-01-01

Although heavy-tailed fluctuations are ubiquitous in complex systems, a good understanding of the mechanisms that generate them is still lacking. Optical complex systems are ideal candidates for investigating heavy-tailed fluctuations, as they allow recording large datasets under controllable experimental conditions. A dynamical regime that has attracted a lot of attention over the years is the so-called low-frequency fluctuations (LFFs) of semiconductor lasers with optical feedback. In this regime, the laser output intensity is characterized by abrupt and apparently random dropouts. The statistical analysis of the inter-dropout-intervals (IDIs) has provided many useful insights into the underlying dynamics. However, the presence of large temporal fluctuations in the IDI sequence has not yet been investigated. Here, by applying fluctuation analysis we show that the experimental distribution of IDI fluctuations is heavy-tailed, and specifically, is well-modeled by a non-Gaussian stable distribution. We find a good qualitative agreement with simulations of the Lang-Kobayashi model. Moreover, we uncover a transition from a less-heavy-tailed state at low pump current to a more-heavy-tailed state at higher pump current. Our results indicate that fluctuation analysis can be a useful tool for investigating the output signals of complex optical systems; it can be used for detecting underlying regime shifts, for model validation and parameter estimation. PMID:26901346
Phase 1 Free Air CO2 Enrichment Model-Data Synthesis (FACE-MDS): Model Output Data (2015)

DOE Data Explorer

Walker, A. P.; De Kauwe, M. G.; Medlyn, B. E.; Zaehle, S.; Asao, S.; Dietze, M.; El-Masri, B.; Hanson, P. J.; Hickler, T.; Jain, A.; Luo, Y.; Parton, W. J.; Prentice, I. C.; Ricciuto, D. M.; Thornton, P. E.; Wang, S.; Wang, Y -P; Warlind, D.; Weng, E.; Oren, R.; Norby, R. J.

2015-01-01

These datasets comprise the model output from phase 1 of the FACE-MDS. These include simulations of the Duke and Oak Ridge experiments and also idealised long-term (300 year) simulations at both sites (please see the modelling protocol for details). Included as part of this dataset are modelling and output protocols. The model datasets are formatted according to the output protocols. Phase 1 datasets are reproduced here for posterity and reproducibility although the model output for the experimental period have been somewhat superseded by the Phase 2 datasets.
Tropical Cyclone Information System

NASA Technical Reports Server (NTRS)

Li, P. Peggy; Knosp, Brian W.; Vu, Quoc A.; Yi, Chao; Hristova-Veleva, Svetla M.

2009-01-01

The JPL Tropical Cyclone Infor ma tion System (TCIS) is a Web portal (http://tropicalcyclone.jpl.nasa.gov) that provides researchers with an extensive set of observed hurricane parameters together with large-scale and convection resolving model outputs. It provides a comprehensive set of high-resolution satellite (see figure), airborne, and in-situ observations in both image and data formats. Large-scale datasets depict the surrounding environmental parameters such as SST (Sea Surface Temperature) and aerosol loading. Model outputs and analysis tools are provided to evaluate model performance and compare observations from different platforms. The system pertains to the thermodynamic and microphysical structure of the storm, the air-sea interaction processes, and the larger-scale environment as depicted by ocean heat content and the aerosol loading of the environment. Currently, the TCIS is populated with satellite observations of all tropical cyclones observed globally during 2005. There is a plan to extend the database both forward in time till present as well as backward to 1998. The portal is powered by a MySQL database and an Apache/Tomcat Web server on a Linux system. The interactive graphic user interface is provided by Google Map.
Supervision of dynamic systems: Monitoring, decision-making and control

NASA Technical Reports Server (NTRS)

White, T. N.

1982-01-01

Effects of task variables on the performance of the human supervisor by means of modelling techniques are discussed. The task variables considered are: The dynamics of the system, the task to be performed, the environmental disturbances and the observation noise. A relationship between task variables and parameters of a supervisory model is assumed. The model consists of three parts: (1) The observer part is thought to be a full order optimal observer, (2) the decision-making part is stated as a set of decision rules, and (3) the controller part is given by a control law. The observer part generates, on the basis of the system output and the control actions, an estimate of the state of the system and its associated variance. The outputs of the observer part are then used by the decision-making part to determine the instants in time of the observation actions on the one hand and the controls actions on the other. The controller part makes use of the estimated state to derive the amplitude(s) of the control action(s).
Development of a magneto-rheological fluid based hybrid actuation system

NASA Astrophysics Data System (ADS)

John, Shaju

A hybrid hydraulic actuation system is proposed as an active pitch link for rotorcraft applications. Such an active pitch link can be used to implement Individual Blade Control (IBC) techniques for vibration and noise reduction, in addition to providing primary control for the helicopter. Conventional technologies like electric motors and hydraulic actuators have major disadvantages when it come to applications on a rotating environment. Centralized hydraulic system require the use of mechanically complex hydraulic slip rings and electric motors have high precision mechanical moving parts that make them unattractive in application with high centrifugal load. The high energy density of smart materials can be used to design hydraulic actuators in a compact package. MagnetoRheological (MR) fluids can be used as the working fluid in such a hybrid hydraulic actuation system to implement a valving system with no moving parts. Thus, such an actuation system can be theoretically well-suited for application in a rotating environment. To develop an actuation system based on an active material stack and MR fluidic valves, a fundamental understanding of the hydraulic circuit is essential. In order to address this issue, a theoretical model was developed to understand the effect of pumping chamber geometry on the pressure losses in the pumping chamber. Three dimensional analytical models were developed for steady and unsteady flow and the results were correlated to results obtained from Computation Fluid Dynamic simulation of fluid flow inside the pumping chamber. Fundamental understanding regarding the pressure losses in a pumping chamber are obtained from the modeling process. Vortices that form in the pumping chamber (during intake) and the discharge tube (during discharge) are identified as a major cause of pressure loss in the chamber. The role of vortices during dynamic operation is also captured through a frequency domain model. Extensive experimental studies were conducted on a hybrid hydraulic system driven by a pump (actuated by a 2" long and 1/4" diameter Terfenol-D rod) and a Wheatstone bridge network of MR fluidic valves. The Wheatstone bridge network is used to provide bi-directionality to the load. Through a variety of experimental studies, the main performance metrics of the actuation system, like output power, blocked force, maximum no-load velocity and efficiency, are obtained. The actuation system exhibits a blocked force of 30 N and a maximum no-load velocity of 50 mm/s. Extensive bi-directional tests were also done for cases of no-load, inertial load and spring load to establish the frequency bandwidth of the actuator. The actuation system can output a stroke of 9 mm at an output actuator frequency of 4 Hz. An analytical model was developed to predict the performance of the hybrid hydraulic actuation system. A state space representation of the system was derived using equations derived from the control volume considerations. The results of the analytical model show that the model predicts the frequency peak of the system to within 20 Hz of the actual resonance frequency. In the third part of this dissertation, the effectiveness of the hybrid hydraulic actuation system is evaluated in a rotating environment. A piezoelectric stack that is driven by three PI-804.10 stacks was attached at the end of a spin bar. After balancing the spin bar using a counterweight, the spin bar is spun to an RPM of 300. This simulates a centrifugal loading of 400 g, which is slightly higher than the full-scale centrifugal loads experienced by a pitch link on a UH-60. The performance of the actuator was measured in terms of velocity of an output cylinder shaft. Since some deterioration of performance was expected at 300 RPM, the output cylinder was redesigned to include roller bearings to support the excess force. Through no load and load tests, the effectiveness of the current hybrid actuation system design was shown as the performance of the system did not deteriorate in performance with greater centrifugal acceleration.
A new process sensitivity index to identify important system processes under process model and parametric uncertainty

DOE PAGES

Dai, Heng; Ye, Ming; Walker, Anthony P.; ...

2017-03-28

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less
A new process sensitivity index to identify important system processes under process model and parametric uncertainty

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

Dai, Heng; Ye, Ming; Walker, Anthony P.

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less
The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

NASA Astrophysics Data System (ADS)

Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter

2018-02-01

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. About 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). The relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.
Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters. Part 1; Equivalent Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, Galina; DaSilva, Arlindo M.; Norris, Peter M.; Platnick, Steven E.

2013-01-01

In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probably density function of total water (vapor and cloud condensate.) The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products.) We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement.
Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters . Pt. 1; Synthetic Sensor Radiance Formulation; [Synthetic Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, G.; DaSilva, A. M.; Norris, P. M.; Platnick, S.

2013-01-01

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.
Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force.

PubMed

Potluri, Chandrasekhar; Anugolu, Madhavi; Chiu, Steve; Urfer, Alex; Schoen, Marco P; Naidu, D Subbaram

2012-01-01

In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.
Robust fuzzy output feedback controller for affine nonlinear systems via T-S fuzzy bilinear model: CSTR benchmark.

PubMed

Hamdy, M; Hamdan, I

2015-07-01

In this paper, a robust H∞ fuzzy output feedback controller is designed for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T-S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI. A continuous stirred tank reactor (CSTR), which is a benchmark problem in nonlinear process control, is discussed in detail to verify the effectiveness of the proposed approach with a comparative study. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Sensitivity of geographic information system outputs to errors in remotely sensed data

NASA Technical Reports Server (NTRS)

Ramapriyan, H. K.; Boyd, R. K.; Gunther, F. J.; Lu, Y. C.

1981-01-01

The sensitivity of the outputs of a geographic information system (GIS) to errors in inputs derived from remotely sensed data (RSD) is investigated using a suitability model with per-cell decisions and a gridded geographic data base whose cells are larger than the RSD pixels. The process of preparing RSD as input to a GIS is analyzed, and the errors associated with classification and registration are examined. In the case of the model considered, it is found that the errors caused during classification and registration are partially compensated by the aggregation of pixels. The compensation is quantified by means of an analytical model, a Monte Carlo simulation, and experiments with Landsat data. The results show that error reductions of the order of 50% occur because of aggregation when 25 pixels of RSD are used per cell in the geographic data base.
A Decentralized Event-Triggered Dissipative Control Scheme for Systems With Multiple Sensors to Sample the System Outputs.

PubMed

Zhang, Xian-Ming; Han, Qing-Long

2016-12-01

This paper is concerned with decentralized event-triggered dissipative control for systems with the entries of the system outputs having different physical properties. Depending on these different physical properties, the entries of the system outputs are grouped into multiple nodes. A number of sensors are used to sample the signals from different nodes. A decentralized event-triggering scheme is introduced to select those necessary sampled-data packets to be transmitted so that communication resources can be saved significantly while preserving the prescribed closed-loop performance. First, in order to organize the decentralized data packets transmitted from the sensor nodes, a data packet processor (DPP) is used to generate a new signal to be held by the zero-order-hold once the signal stored by the DPP is updated at some time instant. Second, under the mechanism of the DPP, the resulting closed-loop system is modeled as a linear system with an interval time-varying delay. A sufficient condition is derived such that the closed-loop system is asymptotically stable and strictly (Q 0 ,S 0 ,R 0 ) -dissipative, where Q 0 ,S 0 , and R 0 are real matrices of appropriate dimensions with Q 0 and R 0 symmetric. Third, suitable output-based controllers can be designed based on solutions to a set of a linear matrix inequality. Finally, two examples are given to demonstrate the effectiveness of the proposed method.
Multilingual Generalization of the ModelCreator Software for Math Item Generation. Research Report. ETS RR-05-02

ERIC Educational Resources Information Center

Higgins, Derrick; Futagi, Yoko; Deane, Paul

2005-01-01

This paper reports on the process of modifying the ModelCreator item generation system to produce output in multiple languages. In particular, Japanese and Spanish are now supported in addition to English. The addition of multilingual functionality was considerably facilitated by the general formulation of our natural language generation system,…
Econometric Model for Optimizing Troop Dining Facility Operations. User’s and Programmer’s Reference Manual,

DTIC Science & Technology

1982-12-01

recipe-menu cross reference list is dependent on the ability to sort various files. At the time the model was first implemented, a FORTRAN callable system...the printer. e. As mentioned in paragraph 2-2d(5), a FORTRAN callable system sort was not available at the time the model was first implemented, and...absence of a FORTRAN callable system sort at the time the menu planning model was placed on the Burroughs meant that most output was not dis- played in
Making Sense of Complexity with FRE, a Scientific Workflow System for Climate Modeling (Invited)

NASA Astrophysics Data System (ADS)

Langenhorst, A. R.; Balaji, V.; Yakovlev, A.

2010-12-01

A workflow is a description of a sequence of activities that is both precise and comprehensive. Capturing the workflow of climate experiments provides a record which can be queried or compared, and allows reproducibility of the experiments - sometimes even to the bit level of the model output. This reproducibility helps to verify the integrity of the output data, and enables easy perturbation experiments. GFDL's Flexible Modeling System Runtime Environment (FRE) is a production-level software project which defines and implements building blocks of the workflow as command line tools. The scientific, numerical and technical input needed to complete the workflow of an experiment is recorded in an experiment description file in XML format. Several key features add convenience and automation to the FRE workflow: ● Experiment inheritance makes it possible to define a new experiment with only a reference to the parent experiment and the parameters to override. ● Testing is a basic element of the FRE workflow: experiments define short test runs which are verified before the main experiment is run, and a set of standard experiments are verified with new code releases. ● FRE is flexible enough to support short runs with mere megabytes of data, to high-resolution experiments that run on thousands of processors for months, producing terabytes of output data. Experiments run in segments of model time; after each segment, the state is saved and the model can be checkpointed at that level. Segment length is defined by the user, but the number of segments per system job is calculated to fit optimally in the batch scheduler requirements. FRE provides job control across multiple segments, and tools to monitor and alter the state of long-running experiments. ● Experiments are entered into a Curator Database, which stores query-able metadata about the experiment and the experiment's output. ● FRE includes a set of standardized post-processing functions as well as the ability to incorporate user-level functions. FRE post-processing can take us all the way to the preparing of graphical output for a scientific audience, and publication of data on a public portal. ● Recent FRE development includes incorporating a distributed workflow to support remote computing.
Network simulation using the simulation language for alternate modeling (SLAM 2)

NASA Technical Reports Server (NTRS)

Shen, S.; Morris, D. W.

1983-01-01

The simulation language for alternate modeling (SLAM 2) is a general purpose language that combines network, discrete event, and continuous modeling capabilities in a single language system. The efficacy of the system's network modeling is examined and discussed. Examples are given of the symbolism that is used, and an example problem and model are derived. The results are discussed in terms of the ease of programming, special features, and system limitations. The system offers many features which allow rapid model development and provides an informative standardized output. The system also has limitations which may cause undetected errors and misleading reports unless the user is aware of these programming characteristics.

A Scalable Cloud Library Empowering Big Data Management, Diagnosis, and Visualization of Cloud-Resolving Models

NASA Astrophysics Data System (ADS)

Zhou, S.; Tao, W. K.; Li, X.; Matsui, T.; Sun, X. H.; Yang, X.

2015-12-01

A cloud-resolving model (CRM) is an atmospheric numerical model that can numerically resolve clouds and cloud systems at 0.25~5km horizontal grid spacings. The main advantage of the CRM is that it can allow explicit interactive processes between microphysics, radiation, turbulence, surface, and aerosols without subgrid cloud fraction, overlapping and convective parameterization. Because of their fine resolution and complex physical processes, it is challenging for the CRM community to i) visualize/inter-compare CRM simulations, ii) diagnose key processes for cloud-precipitation formation and intensity, and iii) evaluate against NASA's field campaign data and L1/L2 satellite data products due to large data volume (~10TB) and complexity of CRM's physical processes. We have been building the Super Cloud Library (SCL) upon a Hadoop framework, capable of CRM database management, distribution, visualization, subsetting, and evaluation in a scalable way. The current SCL capability includes (1) A SCL data model enables various CRM simulation outputs in NetCDF, including the NASA-Unified Weather Research and Forecasting (NU-WRF) and Goddard Cumulus Ensemble (GCE) model, to be accessed and processed by Hadoop, (2) A parallel NetCDF-to-CSV converter supports NU-WRF and GCE model outputs, (3) A technique visualizes Hadoop-resident data with IDL, (4) A technique subsets Hadoop-resident data, compliant to the SCL data model, with HIVE or Impala via HUE's Web interface, (5) A prototype enables a Hadoop MapReduce application to dynamically access and process data residing in a parallel file system, PVFS2 or CephFS, where high performance computing (HPC) simulation outputs such as NU-WRF's and GCE's are located. We are testing Apache Spark to speed up SCL data processing and analysis.With the SCL capabilities, SCL users can conduct large-domain on-demand tasks without downloading voluminous CRM datasets and various observations from NASA Field Campaigns and Satellite data to a local computer, and inter-compare CRM output and data with GCE and NU-WRF.
Partial automation of database processing of simulation outputs from L-systems models of plant morphogenesis.

PubMed

Chen, Yi- Ping Phoebe; Hanan, Jim

2002-01-01

Models of plant architecture allow us to explore how genotype environment interactions effect the development of plant phenotypes. Such models generate masses of data organised in complex hierarchies. This paper presents a generic system for creating and automatically populating a relational database from data generated by the widely used L-system approach to modelling plant morphogenesis. Techniques from compiler technology are applied to generate attributes (new fields) in the database, to simplify query development for the recursively-structured branching relationship. Use of biological terminology in an interactive query builder contributes towards making the system biologist-friendly.
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.
Serving ocean model data on the cloud

USGS Publications Warehouse

Meisinger, Michael; Farcas, Claudiu; Farcas, Emilia; Alexander, Charles; Arrott, Matthew; de La Beaujardiere, Jeff; Hubbard, Paul; Mendelssohn, Roy; Signell, Richard P.

2010-01-01

The NOAA-led Integrated Ocean Observing System (IOOS) and the NSF-funded Ocean Observatories Initiative Cyberinfrastructure Project (OOI-CI) are collaborating on a prototype data delivery system for numerical model output and other gridded data using cloud computing. The strategy is to take an existing distributed system for delivering gridded data and redeploy on the cloud, making modifications to the system that allow it to harness the scalability of the cloud as well as adding functionality that the scalability affords.
Motion control of musculoskeletal systems with redundancy.

PubMed

Park, Hyunjoo; Durand, Dominique M

2008-12-01

Motion control of musculoskeletal systems for functional electrical stimulation (FES) is a challenging problem due to the inherent complexity of the systems. These include being highly nonlinear, strongly coupled, time-varying, time-delayed, and redundant. The redundancy in particular makes it difficult to find an inverse model of the system for control purposes. We have developed a control system for multiple input multiple output (MIMO) redundant musculoskeletal systems with little prior information. The proposed method separates the steady-state properties from the dynamic properties. The dynamic control uses a steady-state inverse model and is implemented with both a PID controller for disturbance rejection and an artificial neural network (ANN) feedforward controller for fast trajectory tracking. A mechanism to control the sum of the muscle excitation levels is also included. To test the performance of the proposed control system, a two degree of freedom ankle-subtalar joint model with eight muscles was used. The simulation results show that separation of steady-state and dynamic control allow small output tracking errors for different reference trajectories such as pseudo-step, sinusoidal and filtered random signals. The proposed control method also demonstrated robustness against system parameter and controller parameter variations. A possible application of this control algorithm is FES control using multiple contact cuff electrodes where mathematical modeling is not feasible and the redundancy makes the control of dynamic movement difficult.
Grid Integrated Distributed PV (GridPV) Version 2.

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

Reno, Matthew J.; Coogan, Kyle

2014-12-01

This manual provides the documentation of the MATLAB toolbox of functions for using OpenDSS to simulate the impact of solar energy on the distribution system. The majority of the functio ns are useful for interfacing OpenDSS and MATLAB, and they are of generic use for commanding OpenDSS from MATLAB and retrieving information from simulations. A set of functions is also included for modeling PV plant output and setting up the PV plant in th e OpenDSS simulation. The toolbox contains functions for modeling the OpenDSS distribution feeder on satellite images with GPS coordinates. Finally, example simulations functions are included tomore » show potential uses of the toolbox functions. Each function i n the toolbox is documented with the function use syntax, full description, function input list, function output list, example use, and example output.« less
Field experience and performance evaluation of a medium-concentration CPV system

NASA Astrophysics Data System (ADS)

Norton, Matthew; Bentley, Roger; Georghiou, George E.; Chonavel, Sylvain; De Mutiis, Alfredo

2012-10-01

With the aim of gaining experience and performance data from location with a harsh summer climate, a 70 X concentrating photovoltaic (CPV) system was installed in Janurary 2009 in Nicosia, Cyprus. The performance of this system has been monitored using regular current-voltage characterisations for three years. Over this period, the output of the system has remained fairly constant. Measured performance ratios varied from 0.79 to 0.86 in the winter, but fell to 0.64 over the year when left uncleaned. Operating cell temperatures were modeled and found to be similar to those of flat plate modules. The most significant causes of energy loss have been identified as originating from tracking issues and soiling. Losses due to soiling could account for a drop in output of 0.2% per day. When cleaned and properly oriented, the normalized output of the system has remained constant, suggesting that this particular design is tolerant to the physical strain of long-term outdoor exposure in harsh summer conditions. Regular cleaning and reliable tracker operation are shown to be essential for maximizing energy yield.
Climate Science's Globally Distributed Infrastructure

NASA Astrophysics Data System (ADS)

Williams, D. N.

2016-12-01

The Earth System Grid Federation (ESGF) is primarily funded by the Department of Energy's (DOE's) Office of Science (the Office of Biological and Environmental Research [BER] Climate Data Informatics Program and the Office of Advanced Scientific Computing Research Next Generation Network for Science Program), the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF), the European Infrastructure for the European Network for Earth System Modeling (IS-ENES), and the Australian National University (ANU). Support also comes from other U.S. federal and international agencies. The federation works across multiple worldwide data centers and spans seven international network organizations to provide users with the ability to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a series of geographically distributed peer nodes that are independently administered and united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP; output used by the Intergovernmental Panel on Climate Change assessment reports), multiple model intercomparison projects (MIPs; endorsed by the World Climate Research Programme [WCRP]), and the Accelerated Climate Modeling for Energy (ACME; ESGF is included in the overarching ACME workflow process to store model output). ESGF is a successful example of integration of disparate open-source technologies into a cohesive functional system that serves the needs the global climate science community. Data served by ESGF includes not only model output but also observational data from satellites and instruments, reanalysis, and generated images.
Simple, accurate formula for the average bit error probability of multiple-input multiple-output free-space optical links over negative exponential turbulence channels.

PubMed

Peppas, Kostas P; Lazarakis, Fotis; Alexandridis, Antonis; Dangakis, Kostas

2012-08-01

In this Letter we investigate the error performance of multiple-input multiple-output free-space optical communication systems employing intensity modulation/direct detection and operating over strong atmospheric turbulence channels. Atmospheric-induced strong turbulence fading is modeled using the negative exponential distribution. For the considered system, an approximate yet accurate analytical expression for the average bit error probability is derived and an efficient method for its numerical evaluation is proposed. Numerically evaluated and computer simulation results are further provided to demonstrate the validity of the proposed mathematical analysis.
A data-driven soft sensor for needle deflection in heterogeneous tissue using just-in-time modelling.

PubMed

Rossa, Carlos; Lehmann, Thomas; Sloboda, Ronald; Usmani, Nawaid; Tavakoli, Mahdi

2017-08-01

Global modelling has traditionally been the approach taken to estimate needle deflection in soft tissue. In this paper, we propose a new method based on local data-driven modelling of needle deflection. External measurement of needle-tissue interactions is collected from several insertions in ex vivo tissue to form a cloud of data. Inputs to the system are the needle insertion depth, axial rotations, and the forces and torques measured at the needle base by a force sensor. When a new insertion is performed, the just-in-time learning method estimates the model outputs given the current inputs to the needle-tissue system and the historical database. The query is compared to every observation in the database and is given weights according to some similarity criteria. Only a subset of historical data that is most relevant to the query is selected and a local linear model is fit to the selected points to estimate the query output. The model outputs the 3D deflection of the needle tip and the needle insertion force. The proposed approach is validated in ex vivo multilayered biological tissue in different needle insertion scenarios. Experimental results in five different case studies indicate an accuracy in predicting needle deflection of 0.81 and 1.24 mm in the horizontal and vertical lanes, respectively, and an accuracy of 0.5 N in predicting the needle insertion force over 216 needle insertions.
Representing Information in Patient Reports Using Natural Language Processing and the Extensible Markup Language

PubMed Central

Friedman, Carol; Hripcsak, George; Shagina, Lyuda; Liu, Hongfang

1999-01-01

Objective: To design a document model that provides reliable and efficient access to clinical information in patient reports for a broad range of clinical applications, and to implement an automated method using natural language processing that maps textual reports to a form consistent with the model. Methods: A document model that encodes structured clinical information in patient reports while retaining the original contents was designed using the extensible markup language (XML), and a document type definition (DTD) was created. An existing natural language processor (NLP) was modified to generate output consistent with the model. Two hundred reports were processed using the modified NLP system, and the XML output that was generated was validated using an XML validating parser. Results: The modified NLP system successfully processed all 200 reports. The output of one report was invalid, and 199 reports were valid XML forms consistent with the DTD. Conclusions: Natural language processing can be used to automatically create an enriched document that contains a structured component whose elements are linked to portions of the original textual report. This integrated document model provides a representation where documents containing specific information can be accurately and efficiently retrieved by querying the structured components. If manual review of the documents is desired, the salient information in the original reports can also be identified and highlighted. Using an XML model of tagging provides an additional benefit in that software tools that manipulate XML documents are readily available. PMID:9925230
Stochastic Resonance in an Underdamped System with Pinning Potential for Weak Signal Detection

PubMed Central

Zhang, Haibin; He, Qingbo; Kong, Fanrang

2015-01-01

Stochastic resonance (SR) has been proved to be an effective approach for weak sensor signal detection. This study presents a new weak signal detection method based on a SR in an underdamped system, which consists of a pinning potential model. The model was firstly discovered from magnetic domain wall (DW) in ferromagnetic strips. We analyze the principle of the proposed underdamped pinning SR (UPSR) system, the detailed numerical simulation and system performance. We also propose the strategy of selecting the proper damping factor and other system parameters to match a weak signal, input noise and to generate the highest output signal-to-noise ratio (SNR). Finally, we have verified its effectiveness with both simulated and experimental input signals. Results indicate that the UPSR performs better in weak signal detection than the conventional SR (CSR) with merits of higher output SNR, better anti-noise and frequency response capability. Besides, the system can be designed accurately and efficiently owing to the sensibility of parameters and potential diversity. The features also weaken the limitation of small parameters on SR system. PMID:26343662
Stochastic Resonance in an Underdamped System with Pinning Potential for Weak Signal Detection.

PubMed

Zhang, Haibin; He, Qingbo; Kong, Fanrang

2015-08-28

Stochastic resonance (SR) has been proved to be an effective approach for weak sensor signal detection. This study presents a new weak signal detection method based on a SR in an underdamped system, which consists of a pinning potential model. The model was firstly discovered from magnetic domain wall (DW) in ferromagnetic strips. We analyze the principle of the proposed underdamped pinning SR (UPSR) system, the detailed numerical simulation and system performance. We also propose the strategy of selecting the proper damping factor and other system parameters to match a weak signal, input noise and to generate the highest output signal-to-noise ratio (SNR). Finally, we have verified its effectiveness with both simulated and experimental input signals. Results indicate that the UPSR performs better in weak signal detection than the conventional SR (CSR) with merits of higher output SNR, better anti-noise and frequency response capability. Besides, the system can be designed accurately and efficiently owing to the sensibility of parameters and potential diversity. The features also weaken the limitation of small parameters on SR system.
A Novel Nonlinear Piezoelectric Energy Harvesting System Based on Linear-Element Coupling: Design, Modeling and Dynamic Analysis.

PubMed

Zhou, Shengxi; Yan, Bo; Inman, Daniel J

2018-05-09

This paper presents a novel nonlinear piezoelectric energy harvesting system which consists of linear piezoelectric energy harvesters connected by linear springs. In principle, the presented nonlinear system can improve broadband energy harvesting efficiency where magnets are forbidden. The linear spring inevitably produces the nonlinear spring force on the connected harvesters, because of the geometrical relationship and the time-varying relative displacement between two adjacent harvesters. Therefore, the presented nonlinear system has strong nonlinear characteristics. A theoretical model of the presented nonlinear system is deduced, based on Euler-Bernoulli beam theory, Kirchhoff’s law, piezoelectric theory and the relevant geometrical relationship. The energy harvesting enhancement of the presented nonlinear system (when n = 2, 3) is numerically verified by comparing with its linear counterparts. In the case study, the output power area of the presented nonlinear system with two and three energy harvesters is 268.8% and 339.8% of their linear counterparts, respectively. In addition, the nonlinear dynamic response characteristics are analyzed via bifurcation diagrams, Poincare maps of the phase trajectory, and the spectrum of the output voltage.
Behavioral Implications of Piezoelectric Stack Actuators for Control of Micromanipulation

NASA Technical Reports Server (NTRS)

Goldfarb, Michael; Celanovic, Nikola

1996-01-01

A lumped-parameter model of a piezoelectric stack actuator has been developed to describe actuator behavior for purposes of control system analysis and design, and in particular for microrobotic applications requiring accurate position and/or force control. In addition to describing the input-output dynamic behavior, the proposed model explains aspects of non-intuitive behavioral phenomena evinced by piezoelectric actuators, such as the input-output rate-independent hysteresis and the change in mechanical stiffness that results from altering electrical load. The authors incorporate a generalized Maxwell resistive capacitor as a lumped-parameter causal representation of rate-independent hysteresis. Model formulation is validated by comparing results of numerical simulations to experimental data.
On cup anemometer rotor aerodynamics.

PubMed

Pindado, Santiago; Pérez, Javier; Avila-Sanchez, Sergio

2012-01-01

The influence of anemometer rotor shape parameters, such as the cups' front area or their center rotation radius on the anemometer's performance was analyzed. This analysis was based on calibrations performed on two different anemometers (one based on magnet system output signal, and the other one based on an opto-electronic system output signal), tested with 21 different rotors. The results were compared to the ones resulting from classical analytical models. The results clearly showed a linear dependency of both calibration constants, the slope and the offset, on the cups' center rotation radius, the influence of the front area of the cups also being observed. The analytical model of Kondo et al. was proved to be accurate if it is based on precise data related to the aerodynamic behavior of a rotor's cup.
GPC-Based Stable Reconfigurable Control

NASA Technical Reports Server (NTRS)

Soloway, Don; Shi, Jian-Jun; Kelkar, Atul

2004-01-01

This paper presents development of multi-input multi-output (MIMO) Generalized Pre-dictive Control (GPC) law and its application to reconfigurable control design in the event of actuator saturation. A Controlled Auto-Regressive Integrating Moving Average (CARIMA) model is used to describe the plant dynamics. The control law is derived using input-output description of the system and is also related to the state-space form of the model. The stability of the GPC control law without reconfiguration is first established using Riccati-based approach and state-space formulation. A novel reconfiguration strategy is developed for the systems which have actuator redundancy and are faced with actuator saturation type failure. An elegant reconfigurable control design is presented with stability proof. Several numerical examples are presented to demonstrate the application of various results.
A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems

NASA Astrophysics Data System (ADS)

Farrell, Kathryn; Oden, J. Tinsley; Faghihi, Danial

2015-08-01

A general adaptive modeling algorithm for selection and validation of coarse-grained models of atomistic systems is presented. A Bayesian framework is developed to address uncertainties in parameters, data, and model selection. Algorithms for computing output sensitivities to parameter variances, model evidence and posterior model plausibilities for given data, and for computing what are referred to as Occam Categories in reference to a rough measure of model simplicity, make up components of the overall approach. Computational results are provided for representative applications.
A two-stage DEA approach for environmental efficiency measurement.

PubMed

Song, Malin; Wang, Shuhong; Liu, Wei

2014-05-01

The slacks-based measure (SBM) model based on the constant returns to scale has achieved some good results in addressing the undesirable outputs, such as waste water and water gas, in measuring environmental efficiency. However, the traditional SBM model cannot deal with the scenario in which desirable outputs are constant. Based on the axiomatic theory of productivity, this paper carries out a systematic research on the SBM model considering undesirable outputs, and further expands the SBM model from the perspective of network analysis. The new model can not only perform efficiency evaluation considering undesirable outputs, but also calculate desirable and undesirable outputs separately. The latter advantage successfully solves the "dependence" problem of outputs, that is, we can not increase the desirable outputs without producing any undesirable outputs. The following illustration shows that the efficiency values obtained by two-stage approach are smaller than those obtained by the traditional SBM model. Our approach provides a more profound analysis on how to improve environmental efficiency of the decision making units.
Evaluation of energy in heated water vapor for the application of lung volume reduction in patients with severe emphysema.

PubMed

Henne, Erik; Kesten, Steven; Herth, Felix J F

2013-01-01

A method of achieving endoscopic lung volume reduction for emphysema has been developed that utilizes precise amounts of thermal energy in the form of water vapor to ablate lung tissue. This study evaluates the energy output and implications of the commercial InterVapor system and compares it to the clinical trial system. Two methods of evaluating the energy output of the vapor systems were used, a direct energy measurement and a quantification of resultant thermal profile in a lung model. Direct measurement of total energy and the component attributable to gas (vapor energy) was performed by condensing vapor in a water bath and measuring the temperature and mass changes. Infrared images of a lung model were taken after vapor delivery. The images were quantified to characterize the thermal profile. The total energy and vapor energy of the InterVapor system was measured at various dose levels and compared to the clinical trial system at a dose of 10.0 cal/g. An InterVapor dose of 8.5 cal/g was found to have the most similar vapor energy output with the smallest associated reduction in total energy. This was supported by characterization of the thermal profile in the lung model that demonstrated the profile of InterVapor at 8.5 cal/g to not exceed the profile of the clinical trial system. Considering both total energy and vapor energy is important during the development of clinical vapor applications. For InterVapor, a closer study of both energy types justified a reduced target vapor-dosing range for lung volume reduction. The clinical implication is a potential improvement for benefiting the risk profile. Copyright © 2013 S. Karger AG, Basel.

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