Investigation, development and application of optimal output feedback theory. Volume 2: Development of an optimal, limited state feedback outer-loop digital flight control system for 3-D terminal area operation

NASA Technical Reports Server (NTRS)

Broussard, J. R.; Halyo, N.

1984-01-01

This report contains the development of a digital outer-loop three dimensional radio navigation (3-D RNAV) flight control system for a small commercial jet transport. The outer-loop control system is designed using optimal stochastic limited state feedback techniques. Options investigated using the optimal limited state feedback approach include integrated versus hierarchical control loop designs, 20 samples per second versus 5 samples per second outer-loop operation and alternative Type 1 integration command errors. Command generator tracking techniques used in the digital control design enable the jet transport to automatically track arbitrary curved flight paths generated by waypoints. The performance of the design is demonstrated using detailed nonlinear aircraft simulations in the terminal area, frequency domain multi-input sigma plots, frequency domain single-input Bode plots and closed-loop poles. The response of the system to a severe wind shear during a landing approach is also presented.

Optimal inverse functions created via population-based optimization.

PubMed

Jennings, Alan L; Ordóñez, Raúl

2014-06-01

Finding optimal inputs for a multiple-input, single-output system is taxing for a system operator. Population-based optimization is used to create sets of functions that produce a locally optimal input based on a desired output. An operator or higher level planner could use one of the functions in real time. For the optimization, each agent in the population uses the cost and output gradients to take steps lowering the cost while maintaining their current output. When an agent reaches an optimal input for its current output, additional agents are generated in the output gradient directions. The new agents then settle to the local optima for the new output values. The set of associated optimal points forms an inverse function, via spline interpolation, from a desired output to an optimal input. In this manner, multiple locally optimal functions can be created. These functions are naturally clustered in input and output spaces allowing for a continuous inverse function. The operator selects the best cluster over the anticipated range of desired outputs and adjusts the set point (desired output) while maintaining optimality. This reduces the demand from controlling multiple inputs, to controlling a single set point with no loss in performance. Results are demonstrated on a sample set of functions and on a robot control problem.

Aerospace engineering design by systematic decomposition and multilevel optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; Barthelemy, J. F. M.; Giles, G. L.

1984-01-01

A method for systematic analysis and optimization of large engineering systems, by decomposition of a large task into a set of smaller subtasks that is solved concurrently is described. The subtasks may be arranged in hierarchical levels. Analyses are carried out in each subtask using inputs received from other subtasks, and are followed by optimizations carried out from the bottom up. Each optimization at the lower levels is augmented by analysis of its sensitivity to the inputs received from other subtasks to account for the couplings among the subtasks in a formal manner. The analysis and optimization operations alternate iteratively until they converge to a system design whose performance is maximized with all constraints satisfied. The method, which is still under development, is tentatively validated by test cases in structural applications and an aircraft configuration optimization.

Irrigation timing and volume affects growth of container grown maples

USDA-ARS?s Scientific Manuscript database

Container nursery production requires large inputs of water and nutrients but frequently irrigation inputs exceed plant demand and lack application precision or are not applied at optimal times for plant production. The results from this research can assist producers in developing irrigation manage...

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.

First Parity Evaluation of Body Condition, Weight, and Blood Beta-Hydroxybutyrate During Lactation of Range Cows Developed in the Same Ecophysiological System but Receiving Different Harvested Feed Inputs

USDA-ARS?s Scientific Manuscript database

Reduction of harvested feed inputs during heifer development could optimize range livestock production and improve economic feasibility for producers. The objective of this study was to measure body condition and weight as well as blood beta-hydroxybutyrate (BHB) concentrations for primiparous beef ...

First parity evaluation of body condition, weight, and blood beta-hydroxybutyrate during lactation of range cows developed in the same ecophysiological system but receiving different harvested feed inputs

USDA-ARS?s Scientific Manuscript database

Reduction of harvested feed inputs during heifer development could optimize range livestock production and improve economic feasibility for producers. The objective of this study was to measure body condition and weight as well as blood beta-hydroxybutyrate (BHB) concentrations for primiparous beef ...

First parity evaluation of peak milk yield for range cows developed in the same ecophysiological system but receiving different concentrations of harvested feed inputs

USDA-ARS?s Scientific Manuscript database

Reduction of harvested feed inputs during heifer development could optimize range livestock production and improve economic feasibility. The objective for this two year study was to measure milk production (kg/d) and milk constituent concentrations (g/d) for 16 primiparous beef cows each year that w...

A policy iteration approach to online optimal control of continuous-time constrained-input systems.

PubMed

Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

2013-09-01

This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. Copyright © 2013 ISA. All rights reserved.

Texas two-step: a framework for optimal multi-input single-output deconvolution.

PubMed

Neelamani, Ramesh; Deffenbaugh, Max; Baraniuk, Richard G

2007-11-01

Multi-input single-output deconvolution (MISO-D) aims to extract a deblurred estimate of a target signal from several blurred and noisy observations. This paper develops a new two step framework--Texas Two-Step--to solve MISO-D problems with known blurs. Texas Two-Step first reduces the MISO-D problem to a related single-input single-output deconvolution (SISO-D) problem by invoking the concept of sufficient statistics (SSs) and then solves the simpler SISO-D problem using an appropriate technique. The two-step framework enables new MISO-D techniques (both optimal and suboptimal) based on the rich suite of existing SISO-D techniques. In fact, the properties of SSs imply that a MISO-D algorithm is mean-squared-error optimal if and only if it can be rearranged to conform to the Texas Two-Step framework. Using this insight, we construct new wavelet- and curvelet-based MISO-D algorithms with asymptotically optimal performance. Simulated and real data experiments verify that the framework is indeed effective.

Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

PubMed

Zhang, Huaipin; Yue, Dong; Zhao, Wei; Hu, Songlin; Dou, Chunxia; Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou; Hu, Songlin; Zhang, Huaipin; Dou, Chunxia; Yue, Dong; Zhao, Wei

2018-06-01

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

Fuzzy logic control and optimization system

DOEpatents

Lou, Xinsheng [West Hartford, CT

2012-04-17

A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

Genetics-based control of a mimo boiler-turbine plant

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

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

Design and optimization of input shapers for liquid slosh suppression

NASA Astrophysics Data System (ADS)

Aboel-Hassan, Ameen; Arafa, Mustafa; Nassef, Ashraf

2009-02-01

The need for fast maneuvering and accurate positioning of flexible structures poses a control challenge. The inherent flexibility in these lightly damped systems creates large undesirable residual vibrations in response to rapid excitations. Several control approaches have been proposed to tackle this class of problems, of which the input shaping technique is appealing in many aspects. While input shaping has been widely investigated to attenuate residual vibrations in flexible structures, less attention was granted to expand its viability in further applications. The aim of this work is to develop a methodology for applying input shaping techniques to suppress sloshing effects in open moving containers to facilitate safe and fast point-to-point movements. The liquid behavior is modeled using finite element analysis. The input shaper parameters are optimized to find the commands that would result in minimum residual vibration. Other objectives, such as improved robustness, and motion constraints such as deflection limiting are also addressed in the optimization scheme. Numerical results are verified on an experimental setup consisting of a small motor-driven water tank undergoing rectilinear motion, while measuring both the tank motion and free surface displacement of the water. The results obtained suggest that input shaping is an effective method for liquid slosh suppression.

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.

Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation.

PubMed

Cheema, Jitender Jit Singh; Sankpal, Narendra V; Tambe, Sanjeev S; Kulkarni, Bhaskar D

2002-01-01

This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.

Evolving a Method to Capture Science Stakeholder Inputs to Optimize Instrument, Payload, and Program Design

NASA Astrophysics Data System (ADS)

Clark, P. E.; Rilee, M. L.; Curtis, S. A.; Bailin, S.

2012-03-01

We are developing Frontier, a highly adaptable, stably reconfigurable, web-accessible intelligent decision engine capable of optimizing design as well as the simulating operation of complex systems in response to evolving needs and environment.

A new approach of optimal control for a class of continuous-time chaotic systems by an online ADP algorithm

NASA Astrophysics Data System (ADS)

Song, Rui-Zhuo; Xiao, Wen-Dong; Wei, Qing-Lai

2014-05-01

We develop an online adaptive dynamic programming (ADP) based optimal control scheme for continuous-time chaotic systems. The idea is to use the ADP algorithm to obtain the optimal control input that makes the performance index function reach an optimum. The expression of the performance index function for the chaotic system is first presented. The online ADP algorithm is presented to achieve optimal control. In the ADP structure, neural networks are used to construct a critic network and an action network, which can obtain an approximate performance index function and the control input, respectively. It is proven that the critic parameter error dynamics and the closed-loop chaotic systems are uniformly ultimately bounded exponentially. Our simulation results illustrate the performance of the established optimal control method.

SU-F-J-105: Towards a Novel Treatment Planning Pipeline Delivering Pareto- Optimal Plans While Enabling Inter- and Intrafraction Plan Adaptation

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

Kontaxis, C; Bol, G; Lagendijk, J

2016-06-15

Purpose: To develop a new IMRT treatment planning methodology suitable for the new generation of MR-linear accelerator machines. The pipeline is able to deliver Pareto-optimal plans and can be utilized for conventional treatments as well as for inter- and intrafraction plan adaptation based on real-time MR-data. Methods: A Pareto-optimal plan is generated using the automated multicriterial optimization approach Erasmus-iCycle. The resulting dose distribution is used as input to the second part of the pipeline, an iterative process which generates deliverable segments that target the latest anatomical state and gradually converges to the prescribed dose. This process continues until a certainmore » percentage of the dose has been delivered. Under a conventional treatment, a Segment Weight Optimization (SWO) is then performed to ensure convergence to the prescribed dose. In the case of inter- and intrafraction adaptation, post-processing steps like SWO cannot be employed due to the changing anatomy. This is instead addressed by transferring the missing/excess dose to the input of the subsequent fraction. In this work, the resulting plans were delivered on a Delta4 phantom as a final Quality Assurance test. Results: A conventional static SWO IMRT plan was generated for two prostate cases. The sequencer faithfully reproduced the input dose for all volumes of interest. For the two cases the mean relative dose difference of the PTV between the ideal input and sequenced dose was 0.1% and −0.02% respectively. Both plans were delivered on a Delta4 phantom and passed the clinical Quality Assurance procedures by achieving 100% pass rate at a 3%/3mm gamma analysis. Conclusion: We have developed a new sequencing methodology capable of online plan adaptation. In this work, we extended the pipeline to support Pareto-optimal input and clinically validated that it can accurately achieve these ideal distributions, while its flexible design enables inter- and intrafraction plan adaptation. This research is financially supported by Elekta AB, Stockholm, Sweden.« less

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

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2012-01-01

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

Optimization of input parameters of acoustic-transfection for the intracellular delivery of macromolecules using FRET-based biosensors

NASA Astrophysics Data System (ADS)

Yoon, Sangpil; Wang, Yingxiao; Shung, K. K.

2016-03-01

Acoustic-transfection technique has been developed for the first time. We have developed acoustic-transfection by integrating a high frequency ultrasonic transducer and a fluorescence microscope. High frequency ultrasound with the center frequency over 150 MHz can focus acoustic sound field into a confined area with the diameter of 10 μm or less. This focusing capability was used to perturb lipid bilayer of cell membrane to induce intracellular delivery of macromolecules. Single cell level imaging was performed to investigate the behavior of a targeted single-cell after acoustic-transfection. FRET-based Ca2+ biosensor was used to monitor intracellular concentration of Ca2+ after acoustic-transfection and the fluorescence intensity of propidium iodide (PI) was used to observe influx of PI molecules. We changed peak-to-peak voltages and pulse duration to optimize the input parameters of an acoustic pulse. Input parameters that can induce strong perturbations on cell membrane were found and size dependent intracellular delivery of macromolecules was explored. To increase the amount of delivered molecules by acoustic-transfection, we applied several acoustic pulses and the intensity of PI fluorescence increased step wise. Finally, optimized input parameters of acoustic-transfection system were used to deliver pMax-E2F1 plasmid and GFP expression 24 hours after the intracellular delivery was confirmed using HeLa cells.

Input and language development in bilingually developing children.

PubMed

Hoff, Erika; Core, Cynthia

2013-11-01

Language skills in young bilingual children are highly varied as a result of the variability in their language experiences, making it difficult for speech-language pathologists to differentiate language disorder from language difference in bilingual children. Understanding the sources of variability in bilingual contexts and the resulting variability in children's skills will help improve language assessment practices by speech-language pathologists. In this article, we review literature on bilingual first language development for children under 5 years of age. We describe the rate of development in single and total language growth, we describe effects of quantity of input and quality of input on growth, and we describe effects of family composition on language input and language growth in bilingual children. We provide recommendations for language assessment of young bilingual children and consider implications for optimizing children's dual language development. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Optimal control of LQR for discrete time-varying systems with input delays

NASA Astrophysics Data System (ADS)

Yin, Yue-Zhu; Yang, Zhong-Lian; Yin, Zhi-Xiang; Xu, Feng

2018-04-01

In this work, we consider the optimal control problem of linear quadratic regulation for discrete time-variant systems with single input and multiple input delays. An innovative and simple method to derive the optimal controller is given. The studied problem is first equivalently converted into a problem subject to a constraint condition. Last, with the established duality, the problem is transformed into a static mathematical optimisation problem without input delays. The optimal control input solution to minimise performance index function is derived by solving this optimisation problem with two methods. A numerical simulation example is carried out and its results show that our two approaches are both feasible and very effective.

Inverse optimal design of input-to-state stabilisation for affine nonlinear systems with input delays

NASA Astrophysics Data System (ADS)

Cai, Xiushan; Meng, Lingxin; Zhang, Wei; Liu, Leipo

2018-03-01

We establish robustness of the predictor feedback control law to perturbations appearing at the system input for affine nonlinear systems with time-varying input delay and additive disturbances. Furthermore, it is shown that it is inverse optimal with respect to a differential game problem. All of the stability and inverse optimality proofs are based on the infinite-dimensional backstepping transformation and an appropriate Lyapunov functional. A single-link manipulator subject to input delays and disturbances is given to illustrate the validity of the proposed method.

Optimization Under Uncertainty for Electronics Cooling Design

NASA Astrophysics Data System (ADS)

Bodla, Karthik K.; Murthy, Jayathi Y.; Garimella, Suresh V.

Optimization under uncertainty is a powerful methodology used in design and optimization to produce robust, reliable designs. Such an optimization methodology, employed when the input quantities of interest are uncertain, produces output uncertainties, helping the designer choose input parameters that would result in satisfactory thermal solutions. Apart from providing basic statistical information such as mean and standard deviation in the output quantities, auxiliary data from an uncertainty based optimization, such as local and global sensitivities, help the designer decide the input parameter(s) to which the output quantity of interest is most sensitive. This helps the design of experiments based on the most sensitive input parameter(s). A further crucial output of such a methodology is the solution to the inverse problem - finding the allowable uncertainty range in the input parameter(s), given an acceptable uncertainty range in the output quantity of interest...

Optimization of Systems with Uncertainty: Initial Developments for Performance, Robustness and Reliability Based Designs

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

This paper presents a study on the optimization of systems with structured uncertainties, whose inputs and outputs can be exhaustively described in the probabilistic sense. By propagating the uncertainty from the input to the output in the space of the probability density functions and the moments, optimization problems that pursue performance, robustness and reliability based designs are studied. Be specifying the desired outputs in terms of desired probability density functions and then in terms of meaningful probabilistic indices, we settle a computationally viable framework for solving practical optimization problems. Applications to static optimization and stability control are used to illustrate the relevance of incorporating uncertainty in the early stages of the design. Several examples that admit a full probabilistic description of the output in terms of the design variables and the uncertain inputs are used to elucidate the main features of the generic problem and its solution. Extensions to problems that do not admit closed form solutions are also evaluated. Concrete evidence of the importance of using a consistent probabilistic formulation of the optimization problem and a meaningful probabilistic description of its solution is provided in the examples. In the stability control problem the analysis shows that standard deterministic approaches lead to designs with high probability of running into instability. The implementation of such designs can indeed have catastrophic consequences.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

PubMed

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

NASA Astrophysics Data System (ADS)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints.

PubMed

Liu, Derong; Yang, Xiong; Wang, Ding; Wei, Qinglai

2015-07-01

The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. The constrained-input coupled with the inability to identify accurately the uncertainties motivates the design of stabilizing controller based on reinforcement-learning (RL) methods. In this paper, a novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints. The robust control problem is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system. Distinct from typical action-critic dual networks employed in RL, only one critic neural network (NN) is constructed to derive the approximate optimal control. Meanwhile, unlike initial stabilizing control often indispensable in RL, there is no special requirement imposed on the initial control. By utilizing Lyapunov's direct method, the closed-loop optimal control system and the estimated weights of the critic NN are proved to be uniformly ultimately bounded. In addition, the derived approximate optimal control is verified to guarantee the uncertain nonlinear system to be stable in the sense of uniform ultimate boundedness. Two simulation examples are provided to illustrate the effectiveness and applicability of the present approach.

The optimal input optical pulse shape for the self-phase modulation based chirp generator

NASA Astrophysics Data System (ADS)

Zachinyaev, Yuriy; Rumyantsev, Konstantin

2018-04-01

The work is aimed to obtain the optimal shape of the input optical pulse for the proper functioning of the self-phase modulation based chirp generator allowing to achieve high values of chirp frequency deviation. During the research, the structure of the device based on self-phase modulation effect using has been analyzed. The influence of the input optical pulse shape of the transmitting optical module on the chirp frequency deviation has been studied. The relationship between the frequency deviation of the generated chirp and frequency linearity for the three options for implementation of the pulse shape has been also estimated. The results of research are related to the development of the theory of radio processors based on fiber-optic structures and can be used in radars, secure communications, geolocation and tomography.

Transform methods for precision continuum and control models of flexible space structures

NASA Technical Reports Server (NTRS)

Lupi, Victor D.; Turner, James D.; Chun, Hon M.

1991-01-01

An open loop optimal control algorithm is developed for general flexible structures, based on Laplace transform methods. A distributed parameter model of the structure is first presented, followed by a derivation of the optimal control algorithm. The control inputs are expressed in terms of their Fourier series expansions, so that a numerical solution can be easily obtained. The algorithm deals directly with the transcendental transfer functions from control inputs to outputs of interest, and structural deformation penalties, as well as penalties on control effort, are included in the formulation. The algorithm is applied to several structures of increasing complexity to show its generality.

Aerospace engineering design by systematic decomposition and multilevel optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; Giles, G. L.; Barthelemy, J.-F. M.

1984-01-01

This paper describes a method for systematic analysis and optimization of large engineering systems, e.g., aircraft, by decomposition of a large task into a set of smaller, self-contained subtasks that can be solved concurrently. The subtasks may be arranged in many hierarchical levels with the assembled system at the top level. Analyses are carried out in each subtask using inputs received from other subtasks, and are followed by optimizations carried out from the bottom up. Each optimization at the lower levels is augmented by analysis of its sensitivity to the inputs received from other subtasks to account for the couplings among the subtasks in a formal manner. The analysis and optimization operations alternate iteratively until they converge to a system design whose performance is maximized with all constraints satisfied. The method, which is still under development, is tentatively validated by test cases in structural applications and an aircraft configuration optimization. It is pointed out that the method is intended to be compatible with the typical engineering organization and the modern technology of distributed computing.

Development and application of computer assisted optimal method for treatment of femoral neck fracture.

PubMed

Wang, Monan; Zhang, Kai; Yang, Ning

2018-04-09

To help doctors decide their treatment from the aspect of mechanical analysis, the work built a computer assisted optimal system for treatment of femoral neck fracture oriented to clinical application. The whole system encompassed the following three parts: Preprocessing module, finite element mechanical analysis module, post processing module. Preprocessing module included parametric modeling of bone, parametric modeling of fracture face, parametric modeling of fixed screw and fixed position and input and transmission of model parameters. Finite element mechanical analysis module included grid division, element type setting, material property setting, contact setting, constraint and load setting, analysis method setting and batch processing operation. Post processing module included extraction and display of batch processing operation results, image generation of batch processing operation, optimal program operation and optimal result display. The system implemented the whole operations from input of fracture parameters to output of the optimal fixed plan according to specific patient real fracture parameter and optimal rules, which demonstrated the effectiveness of the system. Meanwhile, the system had a friendly interface, simple operation and could improve the system function quickly through modifying single module.

Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes

NASA Astrophysics Data System (ADS)

Bauer, Johannes; Dávila-Chacón, Jorge; Wermter, Stefan

2015-10-01

Humans and other animals have been shown to perform near-optimally in multi-sensory integration tasks. Probabilistic population codes (PPCs) have been proposed as a mechanism by which optimal integration can be accomplished. Previous approaches have focussed on how neural networks might produce PPCs from sensory input or perform calculations using them, like combining multiple PPCs. Less attention has been given to the question of how the necessary organisation of neurons can arise and how the required knowledge about the input statistics can be learned. In this paper, we propose a model of learning multi-sensory integration based on an unsupervised learning algorithm in which an artificial neural network learns the noise characteristics of each of its sources of input. Our algorithm borrows from the self-organising map the ability to learn latent-variable models of the input and extends it to learning to produce a PPC approximating a probability density function over the latent variable behind its (noisy) input. The neurons in our network are only required to perform simple calculations and we make few assumptions about input noise properties and tuning functions. We report on a neurorobotic experiment in which we apply our algorithm to multi-sensory integration in a humanoid robot to demonstrate its effectiveness and compare it to human multi-sensory integration on the behavioural level. We also show in simulations that our algorithm performs near-optimally under certain plausible conditions, and that it reproduces important aspects of natural multi-sensory integration on the neural level.

JAGUAR developer's manual.

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

Chan, Ethan

2011-06-01

JAGUAR (JAva GUi for Applied Research) is a Java software tool providing an advanced text editor and graphical user interface (GUI) to manipulate DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) input specifications. This document focuses on the technical background necessary for a developer to understand JAGUAR.

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.

Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models

NASA Astrophysics Data System (ADS)

Rothenberger, Michael J.

This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input-output measurements, and is the approach used in this dissertation. Research in the literature studies optimal current input shaping for high-order electrochemical battery models and focuses on offline laboratory cycling. While this body of research highlights improvements in identifiability through optimal input shaping, each optimal input is a function of nominal parameters, which creates a tautology. The parameter values must be known a priori to determine the optimal input for maximizing estimation speed and accuracy. The system identification literature presents multiple studies containing methods that avoid the challenges of this tautology, but these methods are absent from the battery parameter estimation domain. The gaps in the above literature are addressed in this dissertation through the following five novel and unique contributions. First, this dissertation optimizes the parameter identifiability of a thermal battery model, which Sergio Mendoza experimentally validates through a close collaboration with this dissertation's author. Second, this dissertation extends input-shaping optimization to a linear and nonlinear equivalent-circuit battery model and illustrates the substantial improvements in Fisher identifiability for a periodic optimal signal when compared against automotive benchmark cycles. Third, this dissertation presents an experimental validation study of the simulation work in the previous contribution. The estimation study shows that the automotive benchmark cycles either converge slower than the optimized cycle, or not at all for certain parameters. Fourth, this dissertation examines how automotive battery packs with additional power electronic components that dynamically route current to individual cells/modules can be used for parameter identifiability optimization. While the user and vehicle supervisory controller dictate the current demand for these packs, the optimized internal allocation of current still improves identifiability. Finally, this dissertation presents a robust Bayesian sequential input shaping optimization study to maximize the conditional Fisher information of the battery model parameters without prior knowledge of the nominal parameter set. This iterative algorithm only requires knowledge of the prior parameter distributions to converge to the optimal input trajectory.

Program to Optimize Simulated Trajectories (POST). Volume 2: Utilization manual

NASA Technical Reports Server (NTRS)

Bauer, G. L.; Cornick, D. E.; Habeger, A. R.; Petersen, F. M.; Stevenson, R.

1975-01-01

Information pertinent to users of the program to optimize simulated trajectories (POST) is presented. The input required and output available is described for each of the trajectory and targeting/optimization options. A sample input listing and resulting output are given.

Reexamination of optimal quantum state estimation of pure states

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

Hayashi, A.; Hashimoto, T.; Horibe, M.

2005-09-15

A direct derivation is given for the optimal mean fidelity of quantum state estimation of a d-dimensional unknown pure state with its N copies given as input, which was first obtained by Hayashi in terms of an infinite set of covariant positive operator valued measures (POVM's) and by Bruss and Macchiavello establishing a connection to optimal quantum cloning. An explicit condition for POVM measurement operators for optimal estimators is obtained, by which we construct optimal estimators with finite POVMs using exact quadratures on a hypersphere. These finite optimal estimators are not generally universal, where universality means the fidelity is independentmore » of input states. However, any optimal estimator with finite POVM for M(>N) copies is universal if it is used for N copies as input.« less

Optimal input selection for neural machine interfaces predicting multiple non-explicit outputs.

PubMed

Krepkovich, Eileen T; Perreault, Eric J

2008-01-01

This study implemented a novel algorithm that optimally selects inputs for neural machine interface (NMI) devices intended to control multiple outputs and evaluated its performance on systems lacking explicit output. NMIs often incorporate signals from multiple physiological sources and provide predictions for multidimensional control, leading to multiple-input multiple-output systems. Further, NMIs often are used with subjects who have motor disabilities and thus lack explicit motor outputs. Our algorithm was tested on simulated multiple-input multiple-output systems and on electromyogram and kinematic data collected from healthy subjects performing arm reaches. Effects of output noise in simulated systems indicated that the algorithm could be useful for systems with poor estimates of the output states, as is true for systems lacking explicit motor output. To test efficacy on physiological data, selection was performed using inputs from one subject and outputs from a different subject. Selection was effective for these cases, again indicating that this algorithm will be useful for predictions where there is no motor output, as often is the case for disabled subjects. Further, prediction results generalized for different movement types not used for estimation. These results demonstrate the efficacy of this algorithm for the development of neural machine interfaces.

Optimization-Based Robust Nonlinear Control

DTIC Science & Technology

2006-08-01

ABSTRACT New control algorithms were developed for robust stabilization of nonlinear dynamical systems . Novel, linear matrix inequality-based synthesis...was to further advance optimization-based robust nonlinear control design, for general nonlinear systems (especially in discrete time ), for linear...Teel, IEEE Transactions on Control Systems Technology, vol. 14, no. 3, p. 398-407, May 2006. 3. "A unified framework for input-to-state stability in

Shape Optimization by Bayesian-Validated Computer-Simulation Surrogates

NASA Technical Reports Server (NTRS)

Patera, Anthony T.

1997-01-01

A nonparametric-validated, surrogate approach to optimization has been applied to the computational optimization of eddy-promoter heat exchangers and to the experimental optimization of a multielement airfoil. In addition to the baseline surrogate framework, a surrogate-Pareto framework has been applied to the two-criteria, eddy-promoter design problem. The Pareto analysis improves the predictability of the surrogate results, preserves generality, and provides a means to rapidly determine design trade-offs. Significant contributions have been made in the geometric description used for the eddy-promoter inclusions as well as to the surrogate framework itself. A level-set based, geometric description has been developed to define the shape of the eddy-promoter inclusions. The level-set technique allows for topology changes (from single-body,eddy-promoter configurations to two-body configurations) without requiring any additional logic. The continuity of the output responses for input variations that cross the boundary between topologies has been demonstrated. Input-output continuity is required for the straightforward application of surrogate techniques in which simplified, interpolative models are fitted through a construction set of data. The surrogate framework developed previously has been extended in a number of ways. First, the formulation for a general, two-output, two-performance metric problem is presented. Surrogates are constructed and validated for the outputs. The performance metrics can be functions of both outputs, as well as explicitly of the inputs, and serve to characterize the design preferences. By segregating the outputs and the performance metrics, an additional level of flexibility is provided to the designer. The validated outputs can be used in future design studies and the error estimates provided by the output validation step still apply, and require no additional appeals to the expensive analysis. Second, a candidate-based a posteriori error analysis capability has been developed which provides probabilistic error estimates on the true performance for a design randomly selected near the surrogate-predicted optimal design.

Optimization of process parameters in drilling of fibre hybrid composite using Taguchi and grey relational analysis

NASA Astrophysics Data System (ADS)

Vijaya Ramnath, B.; Sharavanan, S.; Jeykrishnan, J.

2017-03-01

Nowadays quality plays a vital role in all the products. Hence, the development in manufacturing process focuses on the fabrication of composite with high dimensional accuracy and also incurring low manufacturing cost. In this work, an investigation on machining parameters has been performed on jute-flax hybrid composite. Here, the two important responses characteristics like surface roughness and material removal rate are optimized by employing 3 machining input parameters. The input variables considered are drill bit diameter, spindle speed and feed rate. Machining is done on CNC vertical drilling machine at different levels of drilling parameters. Taguchi’s L16 orthogonal array is used for optimizing individual tool parameters. Analysis Of Variance is used to find the significance of individual parameters. The simultaneous optimization of the process parameters is done by grey relational analysis. The results of this investigation shows that, spindle speed and drill bit diameter have most effect on material removal rate and surface roughness followed by feed rate.

Optimal reorientation of asymmetric underactuated spacecraft using differential flatness and receding horizon control

NASA Astrophysics Data System (ADS)

Cai, Wei-wei; Yang, Le-ping; Zhu, Yan-wei

2015-01-01

This paper presents a novel method integrating nominal trajectory optimization and tracking for the reorientation control of an underactuated spacecraft with only two available control torque inputs. By employing a pseudo input along the uncontrolled axis, the flatness property of a general underactuated spacecraft is extended explicitly, by which the reorientation trajectory optimization problem is formulated into the flat output space with all the differential constraints eliminated. Ultimately, the flat output optimization problem is transformed into a nonlinear programming problem via the Chebyshev pseudospectral method, which is improved by the conformal map and barycentric rational interpolation techniques to overcome the side effects of the differential matrix's ill-conditions on numerical accuracy. Treating the trajectory tracking control as a state regulation problem, we develop a robust closed-loop tracking control law using the receding-horizon control method, and compute the feedback control at each control cycle rapidly via the differential transformation method. Numerical simulation results show that the proposed control scheme is feasible and effective for the reorientation maneuver.

Contingency Contractor Optimization Phase 3 Sustainment Database Design Document - Contingency Contractor Optimization Tool - Prototype

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

Frazier, Christopher Rawls; Durfee, Justin David; Bandlow, Alisa

The Contingency Contractor Optimization Tool – Prototype (CCOT-P) database is used to store input and output data for the linear program model described in [1]. The database allows queries to retrieve this data and updating and inserting new input data.

Academic Branch Libraries: Assessment and Collection Development

ERIC Educational Resources Information Center

Poole, Julie

2009-01-01

An ongoing project at Mercer University's Regional Academic Center Libraries illustrates how utilizing established assessment guidelines, stakeholder input, and a clear understanding of audience and curriculum needs may all be used to optimize a collection. Academic branch libraries often have clear collection development limitations in terms of…

Optimal input sizes for neural network de-interlacing

NASA Astrophysics Data System (ADS)

Choi, Hyunsoo; Seo, Guiwon; Lee, Chulhee

2009-02-01

Neural network de-interlacing has shown promising results among various de-interlacing methods. In this paper, we investigate the effects of input size for neural networks for various video formats when the neural networks are used for de-interlacing. In particular, we investigate optimal input sizes for CIF, VGA and HD video formats.

SU-E-T-488: An Iso-Dose Curve Based Interactive IMRT Optimization System for Physician-Driven Plan Tuning

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

Shi, F; Tian, Z; Jia, X

Purpose: In treatment plan optimization for Intensity Modulated Radiation Therapy (IMRT), after a plan is initially developed by a dosimetrist, the attending physician evaluates its quality and often would like to improve it. As opposed to having the dosimetrist implement the improvements, it is desirable to have the physician directly and efficiently modify the plan for a more streamlined and effective workflow. In this project, we developed an interactive optimization system for physicians to conveniently and efficiently fine-tune iso-dose curves. Methods: An interactive interface is developed under C++/Qt. The physician first examines iso-dose lines. S/he then picks an iso-dose curvemore » to be improved and drags it to a more desired configuration using a computer mouse or touchpad. Once the mouse is released, a voxel-based optimization engine is launched. The weighting factors corresponding to voxels between the iso-dose lines before and after the dragging are modified. The underlying algorithm then takes these factors as input to re-optimize the plan in near real-time on a GPU platform, yielding a new plan best matching the physician's desire. The re-optimized DVHs and iso-dose curves are then updated for the next iteration of modifications. This process is repeated until a physician satisfactory plan is achieved. Results: We have tested this system for a series of IMRT plans. Results indicate that our system provides the physicians an intuitive and efficient tool to edit the iso-dose curves according to their preference. The input information is used to guide plan re-optimization, which is achieved in near real-time using our GPU-based optimization engine. Typically, a satisfactory plan can be developed by a physician in a few minutes using this tool. Conclusion: With our system, physicians are able to manipulate iso-dose curves according to their preferences. Preliminary results demonstrate the feasibility and effectiveness of this tool.« less

The Development and Use of a Flight Optimization System Model of a C-130E Transport Aircraft

NASA Technical Reports Server (NTRS)

Desch, Jeremy D.

1995-01-01

The Systems Analysis Branch at NASA Langley Research Center conducts a variety of aircraft design and analyses studies. These studies include the prediction of characteristics of a particular conceptual design, analyses of designs that already exist, and assessments of the impact of technology on current and future aircraft. The FLight OPtimization System (FLOPS) is a tool used for aircraft systems analysis and design. A baseline input model of a Lockheed C-130E was generated for the Flight Optimization System. This FLOPS model can be used to conduct design-trade studies and technology impact assessments. The input model was generated using standard input data such as basic geometries and mission specifications. All of the other data needed to determine the airplane performance is computed internally by FLOPS. The model was then calibrated to reproduce the actual airplane performance from flight test data. This allows a systems analyzer to change a specific item of geometry or mission definition in the FLOPS input file and evaluate the resulting change in performance from the output file. The baseline model of the C-130E was used to analyze the effects of implementing upper wing surface blowing on the airplane. This involved removing the turboprop engines that were on the C-130E and replacing them with turbofan engines. An investigation of the improvements in airplane performance with the new engines could be conducted within the Flight Optimization System. Although a thorough analysis was not completed, the impact of this change on basic mission performance was investigated.

Theory of optimal information transmission in E. coli chemotaxis pathway

NASA Astrophysics Data System (ADS)

Micali, Gabriele; Endres, Robert G.

Bacteria live in complex microenvironments where they need to make critical decisions fast and reliably. These decisions are inherently affected by noise at all levels of the signaling pathway, and cells are often modeled as an input-output device that transmits extracellular stimuli (input) to internal proteins (channel), which determine the final behavior (output). Increasing the amount of transmitted information between input and output allows cells to better infer extracellular stimuli and respond accordingly. However, in contrast to electronic devices, the separation into input, channel, and output is not always clear in biological systems. Output might feed back into the input, and the channel, made by proteins, normally interacts with the input. Furthermore, a biological channel is affected by mutations and can change under evolutionary pressure. Here, we present a novel approach to maximize information transmission: given cell-external and internal noise, we analytically identify both input distributions and input-output relations that optimally transmit information. Using E. coli chemotaxis as an example, we conclude that its pathway is compatible with an optimal information transmission device despite the ultrasensitive rotary motors.

An Optimization Framework for Dynamic Hybrid Energy Systems

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

Wenbo Du; Humberto E Garcia; Christiaan J.J. Paredis

A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problemmore » takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.« less

Optimisation Of Cutting Parameters Of Composite Material Laser Cutting Process By Taguchi Method

NASA Astrophysics Data System (ADS)

Lokesh, S.; Niresh, J.; Neelakrishnan, S.; Rahul, S. P. Deepak

2018-03-01

The aim of this work is to develop a laser cutting process model that can predict the relationship between the process input parameters and resultant surface roughness, kerf width characteristics. The research conduct is based on the Design of Experiment (DOE) analysis. Response Surface Methodology (RSM) is used in this work. It is one of the most practical and most effective techniques to develop a process model. Even though RSM has been used for the optimization of the laser process, this research investigates laser cutting of materials like Composite wood (veneer)to be best circumstances of laser cutting using RSM process. The input parameters evaluated are focal length, power supply and cutting speed, the output responses being kerf width, surface roughness, temperature. To efficiently optimize and customize the kerf width and surface roughness characteristics, a machine laser cutting process model using Taguchi L9 orthogonal methodology was proposed.

A generalised optimal linear quadratic tracker with universal applications. Part 2: discrete-time systems

NASA Astrophysics Data System (ADS)

Ebrahimzadeh, Faezeh; Tsai, Jason Sheng-Hong; Chung, Min-Ching; Liao, Ying Ting; Guo, Shu-Mei; Shieh, Leang-San; Wang, Li

2017-01-01

Contrastive to Part 1, Part 2 presents a generalised optimal linear quadratic digital tracker (LQDT) with universal applications for the discrete-time (DT) systems. This includes (1) a generalised optimal LQDT design for the system with the pre-specified trajectories of the output and the control input and additionally with both the input-to-output direct-feedthrough term and known/estimated system disturbances or extra input/output signals; (2) a new optimal filter-shaped proportional plus integral state-feedback LQDT design for non-square non-minimum phase DT systems to achieve a minimum-phase-like tracking performance; (3) a new approach for computing the control zeros of the given non-square DT systems; and (4) a one-learning-epoch input-constrained iterative learning LQDT design for the repetitive DT systems.

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.

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

Analysis of a Two-Dimensional Thermal Cloaking Problem on the Basis of Optimization

NASA Astrophysics Data System (ADS)

Alekseev, G. V.

2018-04-01

For a two-dimensional model of thermal scattering, inverse problems arising in the development of tools for cloaking material bodies on the basis of a mixed thermal cloaking strategy are considered. By applying the optimization approach, these problems are reduced to optimization ones in which the role of controls is played by variable parameters of the medium occupying the cloaking shell and by the heat flux through a boundary segment of the basic domain. The solvability of the direct and optimization problems is proved, and an optimality system is derived. Based on its analysis, sufficient conditions on the input data are established that ensure the uniqueness and stability of optimal solutions.

New control concepts for uncertain water resources systems: 1. Theory

NASA Astrophysics Data System (ADS)

Georgakakos, Aris P.; Yao, Huaming

1993-06-01

A major complicating factor in water resources systems management is handling unknown inputs. Stochastic optimization provides a sound mathematical framework but requires that enough data exist to develop statistical input representations. In cases where data records are insufficient (e.g., extreme events) or atypical of future input realizations, stochastic methods are inadequate. This article presents a control approach where input variables are only expected to belong in certain sets. The objective is to determine sets of admissible control actions guaranteeing that the system will remain within desirable bounds. The solution is based on dynamic programming and derived for the case where all sets are convex polyhedra. A companion paper (Yao and Georgakakos, this issue) addresses specific applications and problems in relation to reservoir system management.

Approach for Input Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Putko, Michele M.; Taylor, Arthur C., III; Newman, Perry A.; Green, Lawrence L.

2002-01-01

An implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for quasi 3-D Euler CFD code is presented. Given uncertainties in statistically independent, random, normally distributed input variables, first- and second-order statistical moment procedures are performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, these moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Comparative analysis of the operation efficiency of the continuous and relay control systems of a multi-axle wheeled vehicle suspension

NASA Astrophysics Data System (ADS)

Zhileykin, M. M.; Kotiev, G. O.; Nagatsev, M. V.

2018-02-01

In order to improve the efficiency of the multi-axle wheeled vehicles (MWV) automotive engineers are increasing their cruising speed. One of the promising ways to improve ride comfort of the MWV is the development of the dynamic active suspension systems and control laws for such systems. Here, by the dynamic control systems we mean the systems operating in real time mode and using current (instantaneous) values of the state variables. The aim of the work is to develop the MWV suspension optimal control laws that would reduce vibrations on the driver’s seat at kinematic excitation. The authors have developed the optimal control laws for damping the oscillations of the MWV body. The developed laws allow reduction of the vibrations on the driver’s seat and increase in the maximum speed of the vehicle. The laws are characterized in that they allow generating the control inputs in real time mode. The authors have demonstrated the efficiency of the proposed control laws by means of mathematical simulation of the MWV driving over unpaved road with kinematic excitation. The proposed optimal control laws can be used in the MWV suspension control systems with magnetorheological shock absorbers or controlled hydropneumatic springs. Further evolution of the research line can be the development of the energy-efficient MWV suspension control systems with continuous control input on the vehicle body.

Reproducibility, Controllability, and Optimization of Lenr Experiments

NASA Astrophysics Data System (ADS)

Nagel, David J.

2006-02-01

Low-energy nuclear reaction (LENR) measurements are significantly and increasingly reproducible. Practical control of the production of energy or materials by LENR has yet to be demonstrated. Minimization of costly inputs and maximization of desired outputs of LENR remain for future developments.

PATENTS AND RESEARCH INVESTMENTS: ASSESSING THE EMPIRICAL EVIDENCE.

PubMed

Budish, Eric; Roin, Benjamin N; Williams, Heidi L

2016-05-01

A well-developed theoretical literature - dating back at least to Nordhaus (1969) - has analyzed optimal patent policy design. We re-present the core trade-off of the Nordhaus model and highlight an empirical question which emerges from the Nordhaus framework as a key input into optimal patent policy design: namely, what is the elasticity of R&D investment with respect to the patent term? We then review the - surprisingly small - body of empirical evidence that has been developed on this question over the nearly half century since the publication of Nordhaus's book.

Efficient Strategies for Predictive Cell-Level Control of Lithium-Ion Batteries

NASA Astrophysics Data System (ADS)

Xavier, Marcelo A.

This dissertation introduces a set of state-space based model predictive control (MPC) algorithms tailored to a non-zero feedthrough term to account for the ohmic resistance that is inherent to the battery dynamics. MPC is herein applied to the problem of regulating cell-level measures of performance for lithium-ion batteries; the control methodologies are used first to compute a fast charging profile that respects input, output, and state constraints, i.e., input current, terminal voltage, and state of charge for an equivalent circuit model of the battery cell, and extended later to a linearized physics-based reduced-order model. The novelty of this work can summarized as follows: (1) the MPC variants are employed to a physics based reduce-order model in order to make use of the available set of internal electrochemical variables and mitigate internal mechanisms of cell degradation. (e.g., lithium plating); (2) we developed a dual-mode MPC closed-loop paradigm that suits the battery control problem with the objective of reducing computational effort by solving simpler optimization routines and guaranteeing stability; and finally (3) we developed a completely new approach of the use of a predictive control strategy where MPC is employed as a "smart sensor" for power estimation. Results are presented that show the comparative performance of the MPC algorithms for both EMC and PBROM These results highlight that dual-mode MPC can deliver optimal input current profiles by using a shorter horizon while still guaranteeing stability. Additionally, rigorous mathematical developments are presented for the development of the MPC algorithms. The use of MPC as a "smart sensor" presents it self as an appealing method for power estimation, since MPC permits a fully dynamic input profile that is able to achieve performance right at the proper constraint boundaries. Therefore, MPC is expected to produce accurate power limits for each computed sample time when compared to the Bisection method [1] which assumes constant input values over the prediction interval.

Stiffness modeling of compliant parallel mechanisms and applications in the performance analysis of a decoupled parallel compliant stage

NASA Astrophysics Data System (ADS)

Jiang, Yao; Li, Tie-Min; Wang, Li-Ping

2015-09-01

This paper investigates the stiffness modeling of compliant parallel mechanism (CPM) based on the matrix method. First, the general compliance matrix of a serial flexure chain is derived. The stiffness modeling of CPMs is next discussed in detail, considering the relative positions of the applied load and the selected displacement output point. The derived stiffness models have simple and explicit forms, and the input, output, and coupling stiffness matrices of the CPM can easily be obtained. The proposed analytical model is applied to the stiffness modeling and performance analysis of an XY parallel compliant stage with input and output decoupling characteristics. Then, the key geometrical parameters of the stage are optimized to obtain the minimum input decoupling degree. Finally, a prototype of the compliant stage is developed and its input axial stiffness, coupling characteristics, positioning resolution, and circular contouring performance are tested. The results demonstrate the excellent performance of the compliant stage and verify the effectiveness of the proposed theoretical model. The general stiffness models provided in this paper will be helpful for performance analysis, especially in determining coupling characteristics, and the structure optimization of the CPM.

Estimation of the longitudinal and lateral-directional aerodynamic parameters from flight data for the NASA F/A-18 HARV

NASA Technical Reports Server (NTRS)

Napolitano, Marcello R.

1996-01-01

This progress report presents the results of an investigation focused on parameter identification for the NASA F/A-18 HARV. This aircraft was used in the high alpha research program at the NASA Dryden Flight Research Center. In this study the longitudinal and lateral-directional stability derivatives are estimated from flight data using the Maximum Likelihood method coupled with a Newton-Raphson minimization technique. The objective is to estimate an aerodynamic model describing the aircraft dynamics over a range of angle of attack from 5 deg to 60 deg. The mathematical model is built using the traditional static and dynamic derivative buildup. Flight data used in this analysis were from a variety of maneuvers. The longitudinal maneuvers included large amplitude multiple doublets, optimal inputs, frequency sweeps, and pilot pitch stick inputs. The lateral-directional maneuvers consisted of large amplitude multiple doublets, optimal inputs and pilot stick and rudder inputs. The parameter estimation code pEst, developed at NASA Dryden, was used in this investigation. Results of the estimation process from alpha = 5 deg to alpha = 60 deg are presented and discussed.

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

PubMed Central

Ma, Suliang; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

2018-01-01

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods. PMID:29659548

A method for obtaining reduced-order control laws for high-order systems using optimization techniques

NASA Technical Reports Server (NTRS)

Mukhopadhyay, V.; Newsom, J. R.; Abel, I.

1981-01-01

A method of synthesizing reduced-order optimal feedback control laws for a high-order system is developed. A nonlinear programming algorithm is employed to search for the control law design variables that minimize a performance index defined by a weighted sum of mean-square steady-state responses and control inputs. An analogy with the linear quadractic Gaussian solution is utilized to select a set of design variables and their initial values. To improve the stability margins of the system, an input-noise adjustment procedure is used in the design algorithm. The method is applied to the synthesis of an active flutter-suppression control law for a wind tunnel model of an aeroelastic wing. The reduced-order controller is compared with the corresponding full-order controller and found to provide nearly optimal performance. The performance of the present method appeared to be superior to that of two other control law order-reduction methods. It is concluded that by using the present algorithm, nearly optimal low-order control laws with good stability margins can be synthesized.

Robustness-Based Design Optimization Under Data Uncertainty

NASA Technical Reports Server (NTRS)

Zaman, Kais; McDonald, Mark; Mahadevan, Sankaran; Green, Lawrence

2010-01-01

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.

Fusion of Hard and Soft Information in Nonparametric Density Estimation

DTIC Science & Technology

2015-06-10

and stochastic optimization models, in analysis of simulation output, and when instantiating probability models. We adopt a constrained maximum...particular, density estimation is needed for generation of input densities to simulation and stochastic optimization models, in analysis of simulation output...an essential step in simulation analysis and stochastic optimization is the generation of probability densities for input random variables; see for

Development and application of an optimization procedure for flutter suppression using the aerodynamic energy concept

NASA Technical Reports Server (NTRS)

Nissim, E.; Abel, I.

1978-01-01

An optimization procedure is developed based on the responses of a system to continuous gust inputs. The procedure uses control law transfer functions which have been partially determined by using the relaxed aerodynamic energy approach. The optimization procedure yields a flutter suppression system which minimizes control surface activity in a gust environment. The procedure is applied to wing flutter of a drone aircraft to demonstrate a 44 percent increase in the basic wing flutter dynamic pressure. It is shown that a trailing edge control system suppresses the flutter instability over a wide range of subsonic mach numbers and flight altitudes. Results of this study confirm the effectiveness of the relaxed energy approach.

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

NASA Technical Reports Server (NTRS)

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

2017-01-01

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

Character Recognition Using Genetically Trained Neural Networks

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

Diniz, C.; Stantz, K.M.; Trahan, M.W.

1998-10-01

Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfidmore » recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the amount of noise significantly degrades character recognition efficiency, some of which can be overcome by adding noise during training and optimizing the form of the network's activation fimction.« less

Overview and Software Architecture of the Copernicus Trajectory Design and Optimization System

NASA Technical Reports Server (NTRS)

Williams, Jacob; Senent, Juan S.; Ocampo, Cesar; Mathur, Ravi; Davis, Elizabeth C.

2010-01-01

The Copernicus Trajectory Design and Optimization System represents an innovative and comprehensive approach to on-orbit mission design, trajectory analysis and optimization. Copernicus integrates state of the art algorithms in optimization, interactive visualization, spacecraft state propagation, and data input-output interfaces, allowing the analyst to design spacecraft missions to all possible Solar System destinations. All of these features are incorporated within a single architecture that can be used interactively via a comprehensive GUI interface, or passively via external interfaces that execute batch processes. This paper describes the Copernicus software architecture together with the challenges associated with its implementation. Additionally, future development and planned new capabilities are discussed. Key words: Copernicus, Spacecraft Trajectory Optimization Software.

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.

Studies of HZE particle interactions and transport for space radiation protection purposes

NASA Technical Reports Server (NTRS)

Townsend, Lawrence W.; Wilson, John W.; Schimmerling, Walter; Wong, Mervyn

1987-01-01

The main emphasis is on developing general methods for accurately predicting high-energy heavy ion (HZE) particle interactions and transport for use by researchers in mission planning studies, in evaluating astronaut self-shielding factors, and in spacecraft shield design and optimization studies. The two research tasks are: (1) to develop computationally fast and accurate solutions to the Boltzmann (transport) equation; and (2) to develop accurate HZE interaction models, from fundamental physical considerations, for use as inputs into these transport codes. Accurate solutions to the HZE transport problem have been formulated through a combination of analytical and numerical techniques. In addition, theoretical models for the input interaction parameters are under development: stopping powers, nuclear absorption cross sections, and fragmentation parameters.

Neural dynamic optimization for control systems. I. Background.

PubMed

Seong, C Y; Widrow, B

2001-01-01

The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the background and motivations for the development of NDO, while the two other subsequent papers of this topic present the theory of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

Neural dynamic optimization for control systems.III. Applications.

PubMed

Seong, C Y; Widrow, B

2001-01-01

For pt.II. see ibid., p. 490-501. The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper demonstrates NDO with several applications including control of autonomous vehicles and of a robot-arm, while the two other companion papers of this topic describes the background for the development of NDO and present the theory of the method, respectively.

Neural dynamic optimization for control systems.II. Theory.

PubMed

Seong, C Y; Widrow, B

2001-01-01

The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the theory of NDO, while the two other companion papers of this topic explain the background for the development of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

Computational techniques for design optimization of thermal protection systems for the space shuttle vehicle. Volume 1: Final report

NASA Technical Reports Server (NTRS)

1971-01-01

Computational techniques were developed and assimilated for the design optimization. The resulting computer program was then used to perform initial optimization and sensitivity studies on a typical thermal protection system (TPS) to demonstrate its application to the space shuttle TPS design. The program was developed in Fortran IV for the CDC 6400 but was subsequently converted to the Fortran V language to be used on the Univac 1108. The program allows for improvement and update of the performance prediction techniques. The program logic involves subroutines which handle the following basic functions: (1) a driver which calls for input, output, and communication between program and user and between the subroutines themselves; (2) thermodynamic analysis; (3) thermal stress analysis; (4) acoustic fatigue analysis; and (5) weights/cost analysis. In addition, a system total cost is predicted based on system weight and historical cost data of similar systems. Two basic types of input are provided, both of which are based on trajectory data. These are vehicle attitude (altitude, velocity, and angles of attack and sideslip), for external heat and pressure loads calculation, and heating rates and pressure loads as a function of time.

Optimal Output Trajectory Redesign for Invertible Systems

NASA Technical Reports Server (NTRS)

Devasia, S.

1996-01-01

Given a desired output trajectory, inversion-based techniques find input-state trajectories required to exactly track the output. These inversion-based techniques have been successfully applied to the endpoint tracking control of multijoint flexible manipulators and to aircraft control. The specified output trajectory uniquely determines the required input and state trajectories that are found through inversion. These input-state trajectories exactly track the desired output; however, they might not meet acceptable performance requirements. For example, during slewing maneuvers of flexible structures, the structural deformations, which depend on the required state trajectories, may be unacceptably large. Further, the required inputs might cause actuator saturation during an exact tracking maneuver, for example, in the flight control of conventional takeoff and landing aircraft. In such situations, a compromise is desired between the tracking requirement and other goals such as reduction of internal vibrations and prevention of actuator saturation; the desired output trajectory needs to redesigned. Here, we pose the trajectory redesign problem as an optimization of a general quadratic cost function and solve it in the context of linear systems. The solution is obtained as an off-line prefilter of the desired output trajectory. An advantage of our technique is that the prefilter is independent of the particular trajectory. The prefilter can therefore be precomputed, which is a major advantage over other optimization approaches. Previous works have addressed the issue of preshaping inputs to minimize residual and in-maneuver vibrations for flexible structures; Since the command preshaping is computed off-line. Further minimization of optimal quadratic cost functions has also been previously use to preshape command inputs for disturbance rejection. All of these approaches are applicable when the inputs to the system are known a priori. Typically, outputs (not inputs) are specified in tracking problems, and hence the input trajectories have to be computed. The inputs to the system are however, difficult to determine for non-minimum phase systems like flexible structures. One approach to solve this problem is to (1) choose a tracking controller (the desired output trajectory is now an input to the closed-loop system and (2) redesign this input to the closed-loop system. Thus we effectively perform output redesign. These redesigns are however, dependent on the choice of the tracking controllers. Thus the controller optimization and trajectory redesign problems become coupled; this coupled optimization is still an open problem. In contrast, we decouple the trajectory redesign problem from the choice of feedback-based tracking controller. It is noted that our approach remains valid when a particular tracking controller is chosen. In addition, the formulation of our problem not only allows for the minimization of residual vibration as in available techniques but also allows for the optimal reduction fo vibrations during the maneuver, e.g., the altitude control of flexible spacecraft. We begin by formulating the optimal output trajectory redesign problem and then solve it in the context of general linear systems. This theory is then applied to an example flexible structure, and simulation results are provided.

Development of small bore, high speed tapered roller bearing

NASA Technical Reports Server (NTRS)

Morrison, F. R.; Gassel, S. S.; Bovenkerk, R. L.

1981-01-01

The performance of four rolling bearing configurations for use on the input pinion shaft of a proposed commercial helicopter transmission was evaluated. The performance characteristics of a high speed tapered roller bearing operating under conditions comparable to those existing at this input pinion shaft were defined. The tapered roller bearing shaft support configuration was developed for the gearbox using commercially available bearing designings. The configuration was optimized and interactive thermomechanically system analyzed. Automotive pinion quality tapered roller bearings were found to be reliable under load and speed conditions in excess of those anticipated in the helicopter transmission. However, it is indicated that the elastohydrodynamic lubricant films are inadequate.

Derated ion thruster design issues

NASA Technical Reports Server (NTRS)

Patterson, Michael J.; Rawlin, Vincent K.

1991-01-01

Preliminary activities to develop and refine a lightweight 30 cm engineering model ion thruster are discussed. The approach is to develop a 'derated' ion thruster capable of performing both auxiliary and primary propulsion roles over an input power range of at least 0.5 to 5.0 kilo-W. Design modifications to a baseline thruster to reduce mass and volume are discussed. Performance data over an order of magnitude input power range are presented, with emphasis on the performance impact of engine throttling. Thruster design modifications to optimize performance over specific power envelopes are discussed. Additionally, lifetime estimates based on wear test measurements are made for the operation envelope of the engine.

Optimal simulations of ultrasonic fields produced by large thermal therapy arrays using the angular spectrum approach

PubMed Central

Zeng, Xiaozheng; McGough, Robert J.

2009-01-01

The angular spectrum approach is evaluated for the simulation of focused ultrasound fields produced by large thermal therapy arrays. For an input pressure or normal particle velocity distribution in a plane, the angular spectrum approach rapidly computes the output pressure field in a three dimensional volume. To determine the optimal combination of simulation parameters for angular spectrum calculations, the effect of the size, location, and the numerical accuracy of the input plane on the computed output pressure is evaluated. Simulation results demonstrate that angular spectrum calculations performed with an input pressure plane are more accurate than calculations with an input velocity plane. Results also indicate that when the input pressure plane is slightly larger than the array aperture and is located approximately one wavelength from the array, angular spectrum simulations have very small numerical errors for two dimensional planar arrays. Furthermore, the root mean squared error from angular spectrum simulations asymptotically approaches a nonzero lower limit as the error in the input plane decreases. Overall, the angular spectrum approach is an accurate and robust method for thermal therapy simulations of large ultrasound phased arrays when the input pressure plane is computed with the fast nearfield method and an optimal combination of input parameters. PMID:19425640

Noise in Charge Amplifiers— A gm/ID Approach

NASA Astrophysics Data System (ADS)

Alvarez, Enrique; Avila, Diego; Campillo, Hernan; Dragone, Angelo; Abusleme, Angel

2012-10-01

Charge amplifiers represent the standard solution to amplify signals from capacitive detectors in high energy physics experiments. In a typical front-end, the noise due to the charge amplifier, and particularly from its input transistor, limits the achievable resolution. The classic approach to attenuate noise effects in MOSFET charge amplifiers is to use the maximum power available, to use a minimum-length input device, and to establish the input transistor width in order to achieve the optimal capacitive matching at the input node. These conclusions, reached by analysis based on simple noise models, lead to sub-optimal results. In this work, a new approach on noise analysis for charge amplifiers based on an extension of the gm/ID methodology is presented. This method combines circuit equations and results from SPICE simulations, both valid for all operation regions and including all noise sources. The method, which allows to find the optimal operation point of the charge amplifier input device for maximum resolution, shows that the minimum device length is not necessarily the optimal, that flicker noise is responsible for the non-monotonic noise versus current function, and provides a deeper insight on the noise limits mechanism from an alternative and more design-oriented point of view.

NIF Ignition Target 3D Point Design

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

Jones, O; Marinak, M; Milovich, J

2008-11-05

We have developed an input file for running 3D NIF hohlraums that is optimized such that it can be run in 1-2 days on parallel computers. We have incorporated increasing levels of automation into the 3D input file: (1) Configuration controlled input files; (2) Common file for 2D and 3D, different types of capsules (symcap, etc.); and (3) Can obtain target dimensions, laser pulse, and diagnostics settings automatically from NIF Campaign Management Tool. Using 3D Hydra calculations to investigate different problems: (1) Intrinsic 3D asymmetry; (2) Tolerance to nonideal 3D effects (e.g. laser power balance, pointing errors); and (3) Syntheticmore » diagnostics.« less

Optimal control of nonlinear continuous-time systems in strict-feedback form.

PubMed

Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani

2015-10-01

This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

Structural tailoring of engine blades (STAEBL)

NASA Technical Reports Server (NTRS)

Platt, C. E.; Pratt, T. K.; Brown, K. W.

1982-01-01

A mathematical optimization procedure was developed for the structural tailoring of engine blades and was used to structurally tailor two engine fan blades constructed of composite materials without midspan shrouds. The first was a solid blade made from superhybrid composites, and the second was a hollow blade with metal matrix composite inlays. Three major computerized functions were needed to complete the procedure: approximate analysis with the established input variables, optimization of an objective function, and refined analysis for design verification.

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

Hearing AIDS and music.

PubMed

Chasin, Marshall; Russo, Frank A

2004-01-01

Historically, the primary concern for hearing aid design and fitting is optimization for speech inputs. However, increasingly other types of inputs are being investigated and this is certainly the case for music. Whether the hearing aid wearer is a musician or merely someone who likes to listen to music, the electronic and electro-acoustic parameters described can be optimized for music as well as for speech. That is, a hearing aid optimally set for music can be optimally set for speech, even though the converse is not necessarily true. Similarities and differences between speech and music as inputs to a hearing aid are described. Many of these lead to the specification of a set of optimal electro-acoustic characteristics. Parameters such as the peak input-limiting level, compression issues-both compression ratio and knee-points-and number of channels all can deleteriously affect music perception through hearing aids. In other cases, it is not clear how to set other parameters such as noise reduction and feedback control mechanisms. Regardless of the existence of a "music program,'' unless the various electro-acoustic parameters are available in a hearing aid, music fidelity will almost always be less than optimal. There are many unanswered questions and hypotheses in this area. Future research by engineers, researchers, clinicians, and musicians will aid in the clarification of these questions and their ultimate solutions.

Optimal control of first order distributed systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Johnson, T. L.

1972-01-01

The problem of characterizing optimal controls for a class of distributed-parameter systems is considered. The system dynamics are characterized mathematically by a finite number of coupled partial differential equations involving first-order time and space derivatives of the state variables, which are constrained at the boundary by a finite number of algebraic relations. Multiple control inputs, extending over the entire spatial region occupied by the system ("distributed controls') are to be designed so that the response of the system is optimal. A major example involving boundary control of an unstable low-density plasma is developed from physical laws.

A thermal vacuum test optimization procedure

NASA Technical Reports Server (NTRS)

Kruger, R.; Norris, H. P.

1979-01-01

An analytical model was developed that can be used to establish certain parameters of a thermal vacuum environmental test program based on an optimization of program costs. This model is in the form of a computer program that interacts with a user insofar as the input of certain parameters. The program provides the user a list of pertinent information regarding an optimized test program and graphs of some of the parameters. The model is a first attempt in this area and includes numerous simplifications. The model appears useful as a general guide and provides a way for extrapolating past performance to future missions.

Structural Tailoring of Advanced Turboprops (STAT)

NASA Technical Reports Server (NTRS)

Brown, Kenneth W.

1988-01-01

This interim report describes the progress achieved in the structural Tailoring of Advanced Turboprops (STAT) program which was developed to perform numerical optimizations on highly swept propfan blades. The optimization procedure seeks to minimize an objective function, defined as either direct operating cost or aeroelastic differences between a blade and its scaled model, by tuning internal and external geometry variables that must satisfy realistic blade design constraints. This report provides a detailed description of the input, optimization procedures, approximate analyses and refined analyses, as well as validation test cases for the STAT program. In addition, conclusions and recommendations are summarized.

Preliminary Development of an Object-Oriented Optimization Tool

NASA Technical Reports Server (NTRS)

Pak, Chan-gi

2011-01-01

The National Aeronautics and Space Administration Dryden Flight Research Center has developed a FORTRAN-based object-oriented optimization (O3) tool that leverages existing tools and practices and allows easy integration and adoption of new state-of-the-art software. The object-oriented framework can integrate the analysis codes for multiple disciplines, as opposed to relying on one code to perform analysis for all disciplines. Optimization can thus take place within each discipline module, or in a loop between the central executive module and the discipline modules, or both. Six sample optimization problems are presented. The first four sample problems are based on simple mathematical equations; the fifth and sixth problems consider a three-bar truss, which is a classical example in structural synthesis. Instructions for preparing input data for the O3 tool are presented.

Comparative study of pulsed Nd:YAG laser welding of AISI 304 and AISI 316 stainless steels

NASA Astrophysics Data System (ADS)

Kumar, Nikhil; Mukherjee, Manidipto; Bandyopadhyay, Asish

2017-02-01

Laser welding is a potentially useful technique for joining two pieces of similar or dissimilar materials with high precision. In the present work, comparative studies on laser welding of similar metal of AISI 304SS and AISI 316SS have been conducted forming butt joints. A robotic control 600 W pulsed Nd:YAG laser source has been used for welding purpose. The effects of laser power, scanning speed and pulse width on the ultimate tensile strength and weld width have been investigated using the empirical models developed by RSM. The results of ANOVA indicate that the developed models predict the responses adequately within the limits of input parameters. 3-D response surface and contour plots have been developed to find out the combined effects of input parameters on responses. Furthermore, microstructural analysis as well as hardness and tensile behavior of the selected weld of 304SS and 316SS have been carried out to understand the metallurgical and mechanical behavior of the weld. The selection criteria are based on the maximum and minimum strength achieved by the respective weld. It has been observed that the current pulsation, base metal composition and variation in heat input have significant influence on controlling the microstructural constituents (i.e. phase fraction, grain size etc.). The result suggests that the low energy input pulsation generally produce fine grain structure and improved mechanical properties than the high energy input pulsation irrespective of base material composition. However, among the base materials, 304SS depict better microstructural and mechanical properties than the 316SS for a given parametric condition. Finally, desirability function analysis has been applied for multi-objective optimization for maximization of ultimate tensile strength and minimization of weld width simultaneously. Confirmatory tests have been conducted at optimum parametric conditions to validate the optimization techniques.

Directional hearing by linear summation of binaural inputs at the medial superior olive

PubMed Central

van der Heijden, Marcel; Lorteije, Jeannette A. M.; Plauška, Andrius; Roberts, Michael T.; Golding, Nace L.; Borst, J. Gerard G.

2013-01-01

SUMMARY Neurons in the medial superior olive (MSO) enable sound localization by their remarkable sensitivity to submillisecond interaural time differences (ITDs). Each MSO neuron has its own “best ITD” to which it responds optimally. A difference in physical path length of the excitatory inputs from both ears cannot fully account for the ITD tuning of MSO neurons. As a result, it is still debated how these inputs interact and whether the segregation of inputs to opposite dendrites, well-timed synaptic inhibition, or asymmetries in synaptic potentials or cellular morphology further optimize coincidence detection or ITD tuning. Using in vivo whole-cell and juxtacellular recordings, we show here that ITD tuning of MSO neurons is determined by the timing of their excitatory inputs. The inputs from both ears sum linearly, whereas spike probability depends nonlinearly on the size of synaptic inputs. This simple coincidence detection scheme thus makes accurate sound localization possible. PMID:23764292

Actor-critic-based optimal tracking for partially unknown nonlinear discrete-time systems.

PubMed

Kiumarsi, Bahare; Lewis, Frank L

2015-01-01

This paper presents a partially model-free adaptive optimal control solution to the deterministic nonlinear discrete-time (DT) tracking control problem in the presence of input constraints. The tracking error dynamics and reference trajectory dynamics are first combined to form an augmented system. Then, a new discounted performance function based on the augmented system is presented for the optimal nonlinear tracking problem. In contrast to the standard solution, which finds the feedforward and feedback terms of the control input separately, the minimization of the proposed discounted performance function gives both feedback and feedforward parts of the control input simultaneously. This enables us to encode the input constraints into the optimization problem using a nonquadratic performance function. The DT tracking Bellman equation and tracking Hamilton-Jacobi-Bellman (HJB) are derived. An actor-critic-based reinforcement learning algorithm is used to learn the solution to the tracking HJB equation online without requiring knowledge of the system drift dynamics. That is, two neural networks (NNs), namely, actor NN and critic NN, are tuned online and simultaneously to generate the optimal bounded control policy. A simulation example is given to show the effectiveness of the proposed method.

jFuzz: A Concolic Whitebox Fuzzer for Java

NASA Technical Reports Server (NTRS)

Jayaraman, Karthick; Harvison, David; Ganesh, Vijay; Kiezun, Adam

2009-01-01

We present jFuzz, a automatic testing tool for Java programs. jFuzz is a concolic whitebox fuzzer, built on the NASA Java PathFinder, an explicit-state Java model checker, and a framework for developing reliability and analysis tools for Java. Starting from a seed input, jFuzz automatically and systematically generates inputs that exercise new program paths. jFuzz uses a combination of concrete and symbolic execution, and constraint solving. Time spent on solving constraints can be significant. We implemented several well-known optimizations and name-independent caching, which aggressively normalizes the constraints to reduce the number of calls to the constraint solver. We present preliminary results due to the optimizations, and demonstrate the effectiveness of jFuzz in creating good test inputs. The source code of jFuzz is available as part of the NASA Java PathFinder. jFuzz is intended to be a research testbed for investigating new testing and analysis techniques based on concrete and symbolic execution. The source code of jFuzz is available as part of the NASA Java PathFinder.

Optimizations of Human Restraint Systems for Short-Period Acceleration

NASA Technical Reports Server (NTRS)

Payne, P. R.

1963-01-01

A restraint system's main function is to restrain its occupant when his vehicle is subjected to acceleration. If the restraint system is rigid and well-fitting (to eliminate slack) then it will transmit the vehicle acceleration to its occupant without modifying it in any way. Few present-day restraint systems are stiff enough to give this one-to-one transmission characteristic, and depending upon their dynamic characteristics and the nature of the vehicle's acceleration-time history, they will either magnify or attenuate the acceleration. Obviously an optimum restraint system will give maximum attenuation of an input acceleration. In the general case of an arbitrary acceleration input, a computer must be used to determine the optimum dynamic characteristics for the restraint system. Analytical solutions can be obtained for certain simple cases, however, and these cases are considered in this paper, after the concept of dynamic models of the human body is introduced. The paper concludes with a description of an analog computer specially developed for the Air Force to handle completely general mechanical restraint optimization programs of this type, where the acceleration input may be any arbitrary function of time.

The Linear Quadratic Optimal Control Problem for Infinite Dimensional Systems with Unbounded Input and Output Operators.

DTIC Science & Technology

1984-01-01

5.5) respectively (5.3) and (5.8) which means that u(t) - 0 respectively v(t) - 0 for t > o. The evolution of the systems (5.1) and (5.3) in terms ...input/output delays; and b) the general theory is presented in such a way that it applies to both . . ... ..- Lr parabolic and hyperbolic I P9es as...particular emphasis is placed on the development of the duality theory by means of two different state concepts. The resulting evolution equations

Optimization of MR fluid Yield stress using Taguchi Method and Response Surface Methodology Techniques

NASA Astrophysics Data System (ADS)

Mangal, S. K.; Sharma, Vivek

2018-02-01

Magneto rheological fluids belong to a class of smart materials whose rheological characteristics such as yield stress, viscosity etc. changes in the presence of applied magnetic field. In this paper, optimization of MR fluid constituents is obtained with on-state yield stress as response parameter. For this, 18 samples of MR fluids are prepared using L-18 Orthogonal Array. These samples are experimentally tested on a developed & fabricated electromagnet setup. It has been found that the yield stress of MR fluid mainly depends on the volume fraction of the iron particles and type of carrier fluid used in it. The optimal combination of the input parameters for the fluid are found to be as Mineral oil with a volume percentage of 67%, iron powder of 300 mesh size with a volume percentage of 32%, oleic acid with a volume percentage of 0.5% and tetra-methyl-ammonium-hydroxide with a volume percentage of 0.7%. This optimal combination of input parameters has given the on-state yield stress as 48.197 kPa numerically. An experimental confirmation test on the optimized MR fluid sample has been then carried out and the response parameter thus obtained has found matching quite well (less than 1% error) with the numerically obtained values.

Development of free-piston Stirling engine performance and optimization codes based on Martini simulation technique

NASA Technical Reports Server (NTRS)

Martini, William R.

1989-01-01

A FORTRAN computer code is described that could be used to design and optimize a free-displacer, free-piston Stirling engine similar to the RE-1000 engine made by Sunpower. The code contains options for specifying displacer and power piston motion or for allowing these motions to be calculated by a force balance. The engine load may be a dashpot, inertial compressor, hydraulic pump or linear alternator. Cycle analysis may be done by isothermal analysis or adiabatic analysis. Adiabatic analysis may be done using the Martini moving gas node analysis or the Rios second-order Runge-Kutta analysis. Flow loss and heat loss equations are included. Graphical display of engine motions and pressures and temperatures are included. Programming for optimizing up to 15 independent dimensions is included. Sample performance results are shown for both specified and unconstrained piston motions; these results are shown as generated by each of the two Martini analyses. Two sample optimization searches are shown using specified piston motion isothermal analysis. One is for three adjustable input and one is for four. Also, two optimization searches for calculated piston motion are presented for three and for four adjustable inputs. The effect of leakage is evaluated. Suggestions for further work are given.

Reliability evaluation of high-performance, low-power FinFET standard cells based on mixed RBB/FBB technique

NASA Astrophysics Data System (ADS)

Wang, Tian; Cui, Xiaoxin; Ni, Yewen; Liao, Kai; Liao, Nan; Yu, Dunshan; Cui, Xiaole

2017-04-01

With shrinking transistor feature size, the fin-type field-effect transistor (FinFET) has become the most promising option in low-power circuit design due to its superior capability to suppress leakage. To support the VLSI digital system flow based on logic synthesis, we have designed an optimized high-performance low-power FinFET standard cell library based on employing the mixed FBB/RBB technique in the existing stacked structure of each cell. This paper presents the reliability evaluation of the optimized cells under process and operating environment variations based on Monte Carlo analysis. The variations are modelled with Gaussian distribution of the device parameters and 10000 sweeps are conducted in the simulation to obtain the statistical properties of the worst-case delay and input-dependent leakage for each cell. For comparison, a set of non-optimal cells that adopt the same topology without employing the mixed biasing technique is also generated. Experimental results show that the optimized cells achieve standard deviation reduction of 39.1% and 30.7% at most in worst-case delay and input-dependent leakage respectively while the normalized deviation shrinking in worst-case delay and input-dependent leakage can be up to 98.37% and 24.13%, respectively, which demonstrates that our optimized cells are less sensitive to variability and exhibit more reliability. Project supported by the National Natural Science Foundation of China (No. 61306040), the State Key Development Program for Basic Research of China (No. 2015CB057201), the Beijing Natural Science Foundation (No. 4152020), and Natural Science Foundation of Guangdong Province, China (No. 2015A030313147).

F-18 High Alpha Research Vehicle (HARV) parameter identification flight test maneuvers for optimal input design validation and lateral control effectiveness

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1995-01-01

Flight test maneuvers are specified for the F-18 High Alpha Research Vehicle (HARV). The maneuvers were designed for open loop parameter identification purposes, specifically for optimal input design validation at 5 degrees angle of attack, identification of individual strake effectiveness at 40 and 50 degrees angle of attack, and study of lateral dynamics and lateral control effectiveness at 40 and 50 degrees angle of attack. Each maneuver is to be realized by applying square wave inputs to specific control effectors using the On-Board Excitation System (OBES). Maneuver descriptions and complete specifications of the time/amplitude points define each input are included, along with plots of the input time histories.

Numerical Simulation and Optimization of Directional Solidification Process of Single Crystal Superalloy Casting

PubMed Central

Zhang, Hang; Xu, Qingyan; Liu, Baicheng

2014-01-01

The rapid development of numerical modeling techniques has led to more accurate results in modeling metal solidification processes. In this study, the cellular automaton-finite difference (CA-FD) method was used to simulate the directional solidification (DS) process of single crystal (SX) superalloy blade samples. Experiments were carried out to validate the simulation results. Meanwhile, an intelligent model based on fuzzy control theory was built to optimize the complicate DS process. Several key parameters, such as mushy zone width and temperature difference at the cast-mold interface, were recognized as the input variables. The input variables were functioned with the multivariable fuzzy rule to get the output adjustment of withdrawal rate (v) (a key technological parameter). The multivariable fuzzy rule was built, based on the structure feature of casting, such as the relationship between section area, and the delay time of the temperature change response by changing v, and the professional experience of the operator as well. Then, the fuzzy controlling model coupled with CA-FD method could be used to optimize v in real-time during the manufacturing process. The optimized process was proven to be more flexible and adaptive for a steady and stray-grain free DS process. PMID:28788535

A New Hybrid BFOA-PSO Optimization Technique for Decoupling and Robust Control of Two-Coupled Distillation Column Process.

PubMed

Abdelkarim, Noha; Mohamed, Amr E; El-Garhy, Ahmed M; Dorrah, Hassen T

2016-01-01

The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostly, such a process is to be decoupled into several input/output pairings (loops), so that a single controller can be assigned for each loop. In the frame of this research, the Brain Emotional Learning Based Intelligent Controller (BELBIC) forms the control structure for each decoupled loop. The paper's main objective is to develop a parameterization technique for decoupling and control schemes, which ensures robust control behavior. In this regard, the novel optimization technique Bacterial Swarm Optimization (BSO) is utilized for the minimization of summation of the integral time-weighted squared errors (ITSEs) for all control loops. This optimization technique constitutes a hybrid between two techniques, which are the Particle Swarm and Bacterial Foraging algorithms. According to the simulation results, this hybridized technique ensures low mathematical burdens and high decoupling and control accuracy. Moreover, the behavior analysis of the proposed BELBIC shows a remarkable improvement in the time domain behavior and robustness over the conventional PID controller.

A New Hybrid BFOA-PSO Optimization Technique for Decoupling and Robust Control of Two-Coupled Distillation Column Process

PubMed Central

Mohamed, Amr E.; Dorrah, Hassen T.

2016-01-01

The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostly, such a process is to be decoupled into several input/output pairings (loops), so that a single controller can be assigned for each loop. In the frame of this research, the Brain Emotional Learning Based Intelligent Controller (BELBIC) forms the control structure for each decoupled loop. The paper's main objective is to develop a parameterization technique for decoupling and control schemes, which ensures robust control behavior. In this regard, the novel optimization technique Bacterial Swarm Optimization (BSO) is utilized for the minimization of summation of the integral time-weighted squared errors (ITSEs) for all control loops. This optimization technique constitutes a hybrid between two techniques, which are the Particle Swarm and Bacterial Foraging algorithms. According to the simulation results, this hybridized technique ensures low mathematical burdens and high decoupling and control accuracy. Moreover, the behavior analysis of the proposed BELBIC shows a remarkable improvement in the time domain behavior and robustness over the conventional PID controller. PMID:27807444

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

PubMed

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-09-01

This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor-critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically once every triggered instant. An adaptive event-trigger condition to decide the trigger instants is derived. Thus, a suitable number of events are generated to ensure a desired accuracy of approximation. A near optimal performance is achieved without using value and/or policy iterations. A detailed analysis of nontrivial inter-event times with an explicit formula to show the reduction in computation is also derived. The Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system. The simulation results are included to verify the performance of the controller. The net result is the development of event-driven NDP.

Thermal/structural Tailoring of Engine Blades (T/STAEBL) User's Manual

NASA Technical Reports Server (NTRS)

Brown, K. W.; Clevenger, W. B.; Arel, J. D.

1994-01-01

The Thermal/Structural Tailoring of Engine Blades (T/STAEBL) system is a family of computer programs executed by a control program. The T/STAEBL system performs design optimizations of cooled, hollow turbine blades and vanes. This manual contains an overview of the system, fundamentals of the data block structure, and detailed descriptions of the inputs required by the optimizer. Additionally, the thermal analysis input requirements are described as well as the inputs required to perform a finite element blade vibrations analysis.

The effects of Poynting-Robertson drag on solar sails

NASA Astrophysics Data System (ADS)

Abd El-Salam, F. A.

2018-06-01

In the present work, the concept of solar sailing and its developing spacecraft are presented. The effects of Poynting-Robertson drag on solar sails are considered. Some analytical control laws with some mentioned input constraints for optimizing solar sails dynamics in heliocentric orbit using Lagrange's planetary equations are obtained. Optimum force vector in a required direction is maximized by deriving optimal sail cone angle. New control laws that maximize thrust to obtain certain required maximization in some particular orbital element are obtained.

Tactile Sensory Supplementation of Gravitational References to Optimize Sensorimotor Recovery

NASA Technical Reports Server (NTRS)

Black, F. O.; Paloski, W. H.; Bloomberg, J. J.; Wood, S. J.

2007-01-01

Integration of multi-sensory inputs to detect tilts relative to gravity is critical for sensorimotor control of upright orientation. Displaying body orientation using electrotactile feedback to the tongue has been developed by Bach-y- Rita and colleagues as a sensory aid to maintain upright stance with impaired vestibular feedback. This investigation has explored the effects of Tongue Elecrotactile Feedback (TEF) for control of posture and movement as a sensorimotor countermeasure, specifically addressing the optimal location of movement sensors.

The Development and Application of Random Matrix Theory in Adaptive Signal Processing in the Sample Deficient Regime

DTIC Science & Technology

2014-09-01

optimal diagonal loading which minimizes the MSE. The be- havior of optimal diagonal loading when the arrival process is composed of plane waves embedded...observation vectors. The examples of the ensemble correlation matrix corresponding to the input process consisting of a single or multiple plane waves...Y ∗ij is a complex-conjugate of Yij. This result is used in order to evaluate the expectations of different quadratic forms. The Poincare -Nash

[The development of endoscope workstation].

PubMed

Qi, L; Qi, L; Qiou, Q J; Yu, Q L

2001-01-01

This paper introduces an endoscope workstation, which solved the weak points of multimedia endoscope database used by most hospitals. The endoscope workstation was built on pedal-switch and NTFS file system. This paper also Introduces how to make program optimal and quick inputting. The workstation has promoted the efficiency of the doctor's operation.

Recommendations and settings of word prediction software by health-related professionals for patients with spinal cord injury: a prospective observational study.

PubMed

Pouplin, Samuel; Roche, Nicolas; Hugeron, Caroline; Vaugier, Isabelle; Bensmail, Djamel

2016-02-01

For people with cervical spinal cord injury (SCI), access to computers can be difficult, thus several devices have been developed to facilitate their use. However, text input speed remains very slow compared to users who do not have a disability, even with these devices. Several methods have been developed to increase text input speed, such as word prediction software (WPS). Health-related professionals (HRP) often recommend this type of software to people with cervical SCI. WPS can be customized using different settings. It is likely that the settings used will influence the effectiveness of the software on text input speed. However, there is currently a lack of literature regarding professional practices for the setting of WPS as well as the impact for users. To analyze word prediction software settings used by HRP for people with cervical SCI. Prospective observational study. Garches, France; health-related professionals who recommend Word Prediction Software. A questionnaire was submitted to HRP who advise tetraplegic people regarding the use of communication devices. A total of 93 professionals responded to the survey. The most frequently recommended software was Skippy, a commercially available software. HRP rated the importance of the possibility to customise the settings as high. Moreover, they rated some settings as more important than others (P<0.001). However, except for the number of words displayed, each setting was configured by less than 50% of HRP. The results showed that there was a difference between the perception of the importance of some settings and data in the literature regarding the optimization of settings. Moreover, although some parameters were considered as very important, they were rarely specifically configured. Confidence in default settings and lack of information regarding optimal settings seem to be the main reasons for this discordance. This could also explain the disparate results of studies which evaluated the impact of WPS on text input speed in people with cervical SCI. The results showed that there was a difference between the perception of the importance of some settings and data in the literature regarding the optimization of settings. Moreover, although some parameters were considered as very important, they were rarely specifically configured. Confidence in default settings and lack of information regarding optimal settings seem to be the main reasons for this discordance. This could also explain the disparate results of studies which evaluated the impact of WPS on text input speed in people with cervical SCI. Professionals tend to have confidence in default settings, despite the fact they are not always appropriate for users. It thus seems essential to develop information networks and training to disseminate the results of studies and in consequence possibly improve communication for people with cervical SCI who use such devices.

Datasets for supplier selection and order allocation with green criteria, all-unit quantity discounts and varying number of suppliers.

PubMed

Hamdan, Sadeque; Cheaitou, Ali

2017-08-01

This data article provides detailed optimization input and output datasets and optimization code for the published research work titled "Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability" (Hamdan and Cheaitou, 2017, In press) [1]. Researchers may use these datasets as a baseline for future comparison and extensive analysis of the green supplier selection and order allocation problem with all-unit quantity discount and varying number of suppliers. More particularly, the datasets presented in this article allow researchers to generate the exact optimization outputs obtained by the authors of Hamdan and Cheaitou (2017, In press) [1] using the provided optimization code and then to use them for comparison with the outputs of other techniques or methodologies such as heuristic approaches. Moreover, this article includes the randomly generated optimization input data and the related outputs that are used as input data for the statistical analysis presented in Hamdan and Cheaitou (2017 In press) [1] in which two different approaches for ranking potential suppliers are compared. This article also provides the time analysis data used in (Hamdan and Cheaitou (2017, In press) [1] to study the effect of the problem size on the computation time as well as an additional time analysis dataset. The input data for the time study are generated randomly, in which the problem size is changed, and then are used by the optimization problem to obtain the corresponding optimal outputs as well as the corresponding computation time.

Hearing Aids and Music

PubMed Central

Chasin, Marshall; Russo, Frank A.

2004-01-01

Historically, the primary concern for hearing aid design and fitting is optimization for speech inputs. However, increasingly other types of inputs are being investigated and this is certainly the case for music. Whether the hearing aid wearer is a musician or merely someone who likes to listen to music, the electronic and electro-acoustic parameters described can be optimized for music as well as for speech. That is, a hearing aid optimally set for music can be optimally set for speech, even though the converse is not necessarily true. Similarities and differences between speech and music as inputs to a hearing aid are described. Many of these lead to the specification of a set of optimal electro-acoustic characteristics. Parameters such as the peak input-limiting level, compression issues—both compression ratio and knee-points—and number of channels all can deleteriously affect music perception through hearing aids. In other cases, it is not clear how to set other parameters such as noise reduction and feedback control mechanisms. Regardless of the existence of a “music program,” unless the various electro-acoustic parameters are available in a hearing aid, music fidelity will almost always be less than optimal. There are many unanswered questions and hypotheses in this area. Future research by engineers, researchers, clinicians, and musicians will aid in the clarification of these questions and their ultimate solutions. PMID:15497032

The role of size of input box, location of input box, input method and display size in Chinese handwriting performance and preference on mobile devices.

PubMed

Chen, Zhe; Rau, Pei-Luen Patrick

2017-03-01

This study presented two experiments on Chinese handwriting performance (time, accuracy, the number of protruding strokes and number of rewritings) and subjective ratings (mental workload, satisfaction, and preference) on mobile devices. Experiment 1 evaluated the effects of size of the input box, input method and display size on Chinese handwriting performance and preference. It was indicated that the optimal input sizes were 30.8 × 30.8 mm, 46.6 × 46.6 mm, 58.9 × 58.9 mm and 84.6 × 84.6 mm for devices with 3.5-inch, 5.5-inch, 7.0-inch and 9.7-inch display sizes, respectively. Experiment 2 proved the significant effects of location of the input box, input method and display size on Chinese handwriting performance and subjective ratings. It was suggested that the optimal location was central regardless of display size and input method. Copyright © 2016 Elsevier Ltd. All rights reserved.

Formal development of a clock synchronization circuit

NASA Technical Reports Server (NTRS)

Miner, Paul S.

1995-01-01

This talk presents the latest stage in formal development of a fault-tolerant clock synchronization circuit. The development spans from a high level specification of the required properties to a circuit realizing the core function of the system. An abstract description of an algorithm has been verified to satisfy the high-level properties using the mechanical verification system EHDM. This abstract description is recast as a behavioral specification input to the Digital Design Derivation system (DDD) developed at Indiana University. DDD provides a formal design algebra for developing correct digital hardware. Using DDD as the principle design environment, a core circuit implementing the clock synchronization algorithm was developed. The design process consisted of standard DDD transformations augmented with an ad hoc refinement justified using the Prototype Verification System (PVS) from SRI International. Subsequent to the above development, Wilfredo Torres-Pomales discovered an area-efficient realization of the same function. Establishing correctness of this optimization requires reasoning in arithmetic, so a general verification is outside the domain of both DDD transformations and model-checking techniques. DDD represents digital hardware by systems of mutually recursive stream equations. A collection of PVS theories was developed to aid in reasoning about DDD-style streams. These theories include a combinator for defining streams that satisfy stream equations, and a means for proving stream equivalence by exhibiting a stream bisimulation. DDD was used to isolate the sub-system involved in Torres-Pomales' optimization. The equivalence between the original design and the optimized verified was verified in PVS by exhibiting a suitable bisimulation. The verification depended upon type constraints on the input streams and made extensive use of the PVS type system. The dependent types in PVS provided a useful mechanism for defining an appropriate bisimulation.

A risk explicit interval linear programming model for uncertainty-based environmental economic optimization in the Lake Fuxian watershed, China.

PubMed

Zhang, Xiaoling; Huang, Kai; Zou, Rui; Liu, Yong; Yu, Yajuan

2013-01-01

The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP) method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP) and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers' preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of "low risk and high return efficiency" in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.

A Risk Explicit Interval Linear Programming Model for Uncertainty-Based Environmental Economic Optimization in the Lake Fuxian Watershed, China

PubMed Central

Zou, Rui; Liu, Yong; Yu, Yajuan

2013-01-01

The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP) method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP) and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers' preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of “low risk and high return efficiency” in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management. PMID:24191144

Simulation based analysis of laser beam brazing

NASA Astrophysics Data System (ADS)

Dobler, Michael; Wiethop, Philipp; Schmid, Daniel; Schmidt, Michael

2016-03-01

Laser beam brazing is a well-established joining technology in car body manufacturing with main applications in the joining of divided tailgates and the joining of roof and side panels. A key advantage of laser brazed joints is the seam's visual quality which satisfies highest requirements. However, the laser beam brazing process is very complex and process dynamics are only partially understood. In order to gain deeper knowledge of the laser beam brazing process, to determine optimal process parameters and to test process variants, a transient three-dimensional simulation model of laser beam brazing is developed. This model takes into account energy input, heat transfer as well as fluid and wetting dynamics that lead to the formation of the brazing seam. A validation of the simulation model is performed by metallographic analysis and thermocouple measurements for different parameter sets of the brazing process. These results show that the multi-physical simulation model not only can be used to gain insight into the laser brazing process but also offers the possibility of process optimization in industrial applications. The model's capabilities in determining optimal process parameters are exemplarily shown for the laser power. Small deviations in the energy input can affect the brazing results significantly. Therefore, the simulation model is used to analyze the effect of the lateral laser beam position on the energy input and the resulting brazing seam.

ALARA in European nuclear installations

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

Lefaure, C.; Croft, J.; Pfeffer, W.

1995-03-01

For over a decade the Commission of the European Community has sponsored research projects on the development and practical implementation of the Optimization principle, or as it is often referred to, ALARA. These projects have given rise to a series of successful international Optimization training courses and have provided a significant input to the periodic European Seminars on Optimization, the last one of which took place in April 1993. This paper reviews the approaches to Optimization that have development within Europe and describes the areas of work in the current project. The on-going CEC research project addresses the problem ofmore » ALARA and internal exposures, and tries to define procedures for ALARA implementation, taking account of the perception of the hazard as well as the levels of probability of exposure. The relationships between ALARA and work management, and ALARA and decommissioning of installations appear to be other fruitful research areas. Finally, this paper introduces some software for using ALARA decision aiding techniques and databases containing feed back experience developed in Europe.« less

Resonant-Type Smooth Impact Drive Mechanism Actuator Operating at Lower Input Voltages

NASA Astrophysics Data System (ADS)

Morita, Takeshi; Nishimura, Takuma; Yoshida, Ryuichi; Hosaka, Hiroshi

2013-07-01

We report on the design and fabrication of a resonant-type smooth impact drive mechanism (SIDM) actuator based on a multilayered piezoelectric ceramic transducer. Conventional SIDMs use off-resonant sawtooth-shaped displacement in developing stick-slip motion of a slider, but require large input voltages for high-speed operation. In contrast, in resonant-type SIDMs, a quasi-sawtooth-shaped displacement is obtained by combining two resonant vibrational modes. This driving principle enables low input voltage operations. In combining the modes, their frequency ratio must be 1:2. To design and optimize the stator transducer to generate sawtooth-shaped displacements, a transfer matrix method was adopted. With a preload of 270 mN, the no-load speed was 40 mm/s under a driving voltage of 1.6 V (peak to peak). This input voltage was one-sixth that of previous SIDMs for the same performance. Concurrently, heat generation was significantly reduced because dielectric losses were suppressed under the lower input voltage operation.

Passive states as optimal inputs for single-jump lossy quantum channels

NASA Astrophysics Data System (ADS)

De Palma, Giacomo; Mari, Andrea; Lloyd, Seth; Giovannetti, Vittorio

2016-06-01

The passive states of a quantum system minimize the average energy among all the states with a given spectrum. We prove that passive states are the optimal inputs of single-jump lossy quantum channels. These channels arise from a weak interaction of the quantum system of interest with a large Markovian bath in its ground state, such that the interaction Hamiltonian couples only consecutive energy eigenstates of the system. We prove that the output generated by any input state ρ majorizes the output generated by the passive input state ρ0 with the same spectrum of ρ . Then, the output generated by ρ can be obtained applying a random unitary operation to the output generated by ρ0. This is an extension of De Palma et al. [IEEE Trans. Inf. Theory 62, 2895 (2016)], 10.1109/TIT.2016.2547426, where the same result is proved for one-mode bosonic Gaussian channels. We also prove that for finite temperature this optimality property can fail already in a two-level system, where the best input is a coherent superposition of the two energy eigenstates.

Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

Oyama, Akira; Liou, Meng-Sing

2001-01-01

A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

PubMed Central

Arab, Mohammad M.; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

2017-01-01

The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation. PMID:29163583

Heat transfer mechanisms in poplar wood undergoing torrefaction

NASA Astrophysics Data System (ADS)

Sule, Idris O.; Mahmud, Shohel; Dutta, Animesh; Tasnim, Syeda Humaira

2016-03-01

Torrefaction, a thermal treatment process of biomass, has been proved to improve biomass combustible properties. Torrefaction is defined as a thermochemical process in reduced oxygen condition and at temperature range from 200 to 300 °C for shorter residence time whereby energy yield is maximized, can be a bridging technology that can lead the conventional system (e.g. coal-fired plants) towards a sustainable energy system. In efforts to develop a commercial operable torrefaction reactor, the present study examines the minimum input condition at which biomass is torrefied and explores the heat transfer mechanisms during torrefaction in poplar wood samples. The heat transfer through the wood sample is numerically modeled and analyzed. Each poplar wood is torrefied at temperature of 250, 270, and 300 °C. The experimental study shows that the 270 °C-treatment can be deduced as the optimal input condition for torrefaction of poplar wood. A good understanding of heat transfer mechanisms can facilitate the upscaling and downscaling of torrefaction process equipment to fit the feedstock input criteria and can help to develop treatment input specifications that can maximize process efficiency.

Optimal allocation of testing resources for statistical simulations

NASA Astrophysics Data System (ADS)

Quintana, Carolina; Millwater, Harry R.; Singh, Gulshan; Golden, Patrick

2015-07-01

Statistical estimates from simulation involve uncertainty caused by the variability in the input random variables due to limited data. Allocating resources to obtain more experimental data of the input variables to better characterize their probability distributions can reduce the variance of statistical estimates. The methodology proposed determines the optimal number of additional experiments required to minimize the variance of the output moments given single or multiple constraints. The method uses multivariate t-distribution and Wishart distribution to generate realizations of the population mean and covariance of the input variables, respectively, given an amount of available data. This method handles independent and correlated random variables. A particle swarm method is used for the optimization. The optimal number of additional experiments per variable depends on the number and variance of the initial data, the influence of the variable in the output function and the cost of each additional experiment. The methodology is demonstrated using a fretting fatigue example.

Employing Sensitivity Derivatives for Robust Optimization under Uncertainty in CFD

NASA Technical Reports Server (NTRS)

Newman, Perry A.; Putko, Michele M.; Taylor, Arthur C., III

2004-01-01

A robust optimization is demonstrated on a two-dimensional inviscid airfoil problem in subsonic flow. Given uncertainties in statistically independent, random, normally distributed flow parameters (input variables), an approximate first-order statistical moment method is employed to represent the Computational Fluid Dynamics (CFD) code outputs as expected values with variances. These output quantities are used to form the objective function and constraints. The constraints are cast in probabilistic terms; that is, the probability that a constraint is satisfied is greater than or equal to some desired target probability. Gradient-based robust optimization of this stochastic problem is accomplished through use of both first and second-order sensitivity derivatives. For each robust optimization, the effect of increasing both input standard deviations and target probability of constraint satisfaction are demonstrated. This method provides a means for incorporating uncertainty when considering small deviations from input mean values.

Maximize, minimize or target - optimization for a fitted response from a designed experiment

DOE PAGES

Anderson-Cook, Christine Michaela; Cao, Yongtao; Lu, Lu

2016-04-01

One of the common goals of running and analyzing a designed experiment is to find a location in the design space that optimizes the response of interest. Depending on the goal of the experiment, we may seek to maximize or minimize the response, or set the process to hit a particular target value. After the designed experiment, a response model is fitted and the optimal settings of the input factors are obtained based on the estimated response model. Furthermore, the suggested optimal settings of the input factors are then used in the production environment.

Takagi-Sugeno fuzzy model based robust dissipative control for uncertain flexible spacecraft with saturated time-delay input.

PubMed

Xu, Shidong; Sun, Guanghui; Sun, Weichao

2017-01-01

In this paper, the problem of robust dissipative control is investigated for uncertain flexible spacecraft based on Takagi-Sugeno (T-S) fuzzy model with saturated time-delay input. Different from most existing strategies, T-S fuzzy approximation approach is used to model the nonlinear dynamics of flexible spacecraft. Simultaneously, the physical constraints of system, like input delay, input saturation, and parameter uncertainties, are also taken care of in the fuzzy model. By employing Lyapunov-Krasovskii method and convex optimization technique, a novel robust controller is proposed to implement rest-to-rest attitude maneuver for flexible spacecraft, and the guaranteed dissipative performance enables the uncertain closed-loop system to reject the influence of elastic vibrations and external disturbances. Finally, an illustrative design example integrated with simulation results are provided to confirm the applicability and merits of the developed control strategy. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Nonlinear system modeling based on bilinear Laguerre orthonormal bases.

PubMed

Garna, Tarek; Bouzrara, Kais; Ragot, José; Messaoud, Hassani

2013-05-01

This paper proposes a new representation of discrete bilinear model by developing its coefficients associated to the input, to the output and to the crossed product on three independent Laguerre orthonormal bases. Compared to classical bilinear model, the resulting model entitled bilinear-Laguerre model ensures a significant parameter number reduction as well as simple recursive representation. However, such reduction still constrained by an optimal choice of Laguerre pole characterizing each basis. To do so, we develop a pole optimization algorithm which constitutes an extension of that proposed by Tanguy et al.. The bilinear-Laguerre model as well as the proposed pole optimization algorithm are illustrated and tested on a numerical simulations and validated on the Continuous Stirred Tank Reactor (CSTR) System. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

DISCO: Distance and Spectrum Correlation Optimization Alignment for Two Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry-based Metabolomics

PubMed Central

Wang, Bing; Fang, Aiqin; Heim, John; Bogdanov, Bogdan; Pugh, Scott; Libardoni, Mark; Zhang, Xiang

2010-01-01

A novel peak alignment algorithm using a distance and spectrum correlation optimization (DISCO) method has been developed for two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) based metabolomics. This algorithm uses the output of the instrument control software, ChromaTOF, as its input data. It detects and merges multiple peak entries of the same metabolite into one peak entry in each input peak list. After a z-score transformation of metabolite retention times, DISCO selects landmark peaks from all samples based on both two-dimensional retention times and mass spectrum similarity of fragment ions measured by Pearson’s correlation coefficient. A local linear fitting method is employed in the original two-dimensional retention time space to correct retention time shifts. A progressive retention time map searching method is used to align metabolite peaks in all samples together based on optimization of the Euclidean distance and mass spectrum similarity. The effectiveness of the DISCO algorithm is demonstrated using data sets acquired under different experiment conditions and a spiked-in experiment. PMID:20476746

Some Insights of Spectral Optimization in Ocean Color Inversion

NASA Technical Reports Server (NTRS)

Lee, Zhongping; Franz, Bryan; Shang, Shaoling; Dong, Qiang; Arnone, Robert

2011-01-01

In the past decades various algorithms have been developed for the retrieval of water constituents from the measurement of ocean color radiometry, and one of the approaches is spectral optimization. This approach defines an error target (or error function) between the input remote sensing reflectance and the output remote sensing reflectance, with the latter modeled with a few variables that represent the optically active properties (such as the absorption coefficient of phytoplankton and the backscattering coefficient of particles). The values of the variables when the error reach a minimum (optimization is achieved) are considered the properties that form the input remote sensing reflectance; or in other words, the equations are solved numerically. The applications of this approach implicitly assume that the error is a monotonic function of the various variables. Here, with data from numerical simulation and field measurements, we show the shape of the error surface, in a way to justify the possibility of finding a solution of the various variables. In addition, because the spectral properties could be modeled differently, impacts of such differences on the error surface as well as on the retrievals are also presented.

Limit characteristics of digital optoelectronic processor

NASA Astrophysics Data System (ADS)

Kolobrodov, V. G.; Tymchik, G. S.; Kolobrodov, M. S.

2018-01-01

In this article, the limiting characteristics of a digital optoelectronic processor are explored. The limits are defined by diffraction effects and a matrix structure of the devices for input and output of optical signals. The purpose of a present research is to optimize the parameters of the processor's components. The developed physical and mathematical model of DOEP allowed to establish the limit characteristics of the processor, restricted by diffraction effects and an array structure of the equipment for input and output of optical signals, as well as to optimize the parameters of the processor's components. The diameter of the entrance pupil of the Fourier lens is determined by the size of SLM and the pixel size of the modulator. To determine the spectral resolution, it is offered to use a concept of an optimum phase when the resolved diffraction maxima coincide with the pixel centers of the radiation detector.

Optimizing noise control strategy in a forging workshop.

PubMed

Razavi, Hamideh; Ramazanifar, Ehsan; Bagherzadeh, Jalal

2014-01-01

In this paper, a computer program based on a genetic algorithm is developed to find an economic solution for noise control in a forging workshop. Initially, input data, including characteristics of sound sources, human exposure, abatement techniques, and production plans are inserted into the model. Using sound pressure levels at working locations, the operators who are at higher risk are identified and picked out for the next step. The program is devised in MATLAB such that the parameters can be easily defined and changed for comparison. The final results are structured into 4 sections that specify an appropriate abatement method for each operator and machine, minimum allowance time for high-risk operators, required damping material for enclosures, and minimum total cost of these treatments. The validity of input data in addition to proper settings in the optimization model ensures the final solution is practical and economically reasonable.

Geometrical quality evaluation in laser cutting of Inconel-718 sheet by using Taguchi based regression analysis and particle swarm optimization

NASA Astrophysics Data System (ADS)

Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-03-01

The Inconel-718 is one of the most demanding advanced engineering materials because of its superior quality. The conventional machining techniques are facing many problems to cut intricate profiles on these materials due to its minimum thermal conductivity, minimum elastic property and maximum chemical affinity at magnified temperature. The laser beam cutting is one of the advanced cutting method that may be used to achieve the geometrical accuracy with more precision by the suitable management of input process parameters. In this research work, the experimental investigation during the pulsed Nd:YAG laser cutting of Inconel-718 has been carried out. The experiments have been conducted by using the well planned orthogonal array L27. The experimentally measured values of different quality characteristics have been used for developing the second order regression models of bottom kerf deviation (KD), bottom kerf width (KW) and kerf taper (KT). The developed models of different quality characteristics have been utilized as a quality function for single-objective optimization by using particle swarm optimization (PSO) method. The optimum results obtained by the proposed hybrid methodology have been compared with experimental results. The comparison of optimized results with the experimental results shows that an individual improvement of 75%, 12.67% and 33.70% in bottom kerf deviation, bottom kerf width, and kerf taper has been observed. The parametric effects of different most significant input process parameters on quality characteristics have also been discussed.

Real-Time System for Water Modeling and Management

NASA Astrophysics Data System (ADS)

Lee, J.; Zhao, T.; David, C. H.; Minsker, B.

2012-12-01

Working closely with the Texas Commission on Environmental Quality (TCEQ) and the University of Texas at Austin (UT-Austin), we are developing a real-time system for water modeling and management using advanced cyberinfrastructure, data integration and geospatial visualization, and numerical modeling. The state of Texas suffered a severe drought in 2011 that cost the state $7.62 billion in agricultural losses (crops and livestock). Devastating situations such as this could potentially be avoided with better water modeling and management strategies that incorporate state of the art simulation and digital data integration. The goal of the project is to prototype a near-real-time decision support system for river modeling and management in Texas that can serve as a national and international model to promote more sustainable and resilient water systems. The system uses National Weather Service current and predicted precipitation data as input to the Noah-MP Land Surface model, which forecasts runoff, soil moisture, evapotranspiration, and water table levels given land surface features. These results are then used by a river model called RAPID, along with an error model currently under development at UT-Austin, to forecast stream flows in the rivers. Model forecasts are visualized as a Web application for TCEQ decision makers, who issue water diversion (withdrawal) permits and any needed drought restrictions; permit holders; and reservoir operation managers. Users will be able to adjust model parameters to predict the impacts of alternative curtailment scenarios or weather forecasts. A real-time optimization system under development will help TCEQ to identify optimal curtailment strategies to minimize impacts on permit holders and protect health and safety. To develop the system we have implemented RAPID as a remotely-executed modeling service using the Cyberintegrator workflow system with input data downloaded from the North American Land Data Assimilation System. The Cyberintegrator workflow system provides RESTful web services for users to provide inputs, execute workflows, and retrieve outputs. Along with REST endpoints, PAW (Publishable Active Workflows) provides the web user interface toolkit for us to develop web applications with scientific workflows. The prototype web application is built on top of workflows with PAW, so that users will have a user-friendly web environment to provide input parameters, execute the model, and visualize/retrieve the results using geospatial mapping tools. In future work the optimization model will be developed and integrated into the workflow.; Real-Time System for Water Modeling and Management

Modified optimal control pilot model for computer-aided design and analysis

NASA Technical Reports Server (NTRS)

Davidson, John B.; Schmidt, David K.

1992-01-01

This paper presents the theoretical development of a modified optimal control pilot model based upon the optimal control model (OCM) of the human operator developed by Kleinman, Baron, and Levison. This model is input compatible with the OCM and retains other key aspects of the OCM, such as a linear quadratic solution for the pilot gains with inclusion of control rate in the cost function, a Kalman estimator, and the ability to account for attention allocation and perception threshold effects. An algorithm designed for each implementation in current dynamic systems analysis and design software is presented. Example results based upon the analysis of a tracking task using three basic dynamic systems are compared with measured results and with similar analyses performed with the OCM and two previously proposed simplified optimal pilot models. The pilot frequency responses and error statistics obtained with this modified optimal control model are shown to compare more favorably to the measured experimental results than the other previously proposed simplified models evaluated.

Reconstruction of an input function from a dynamic PET water image using multiple tissue curves

NASA Astrophysics Data System (ADS)

Kudomi, Nobuyuki; Maeda, Yukito; Yamamoto, Yuka; Nishiyama, Yoshihiro

2016-08-01

Quantification of cerebral blood flow (CBF) is important for the understanding of normal and pathologic brain physiology. When CBF is assessed using PET with {{\\text{H}}2} 15O or C15O2, its calculation requires an arterial input function, which generally requires invasive arterial blood sampling. The aim of the present study was to develop a new technique to reconstruct an image derived input function (IDIF) from a dynamic {{\\text{H}}2} 15O PET image as a completely non-invasive approach. Our technique consisted of using a formula to express the input using tissue curve with rate constant parameter. For multiple tissue curves extracted from the dynamic image, the rate constants were estimated so as to minimize the sum of the differences of the reproduced inputs expressed by the extracted tissue curves. The estimated rates were used to express the inputs and the mean of the estimated inputs was used as an IDIF. The method was tested in human subjects (n  =  29) and was compared to the blood sampling method. Simulation studies were performed to examine the magnitude of potential biases in CBF and to optimize the number of multiple tissue curves used for the input reconstruction. In the PET study, the estimated IDIFs were well reproduced against the measured ones. The difference between the calculated CBF values obtained using the two methods was small as around  <8% and the calculated CBF values showed a tight correlation (r  =  0.97). The simulation showed that errors associated with the assumed parameters were  <10%, and that the optimal number of tissue curves to be used was around 500. Our results demonstrate that IDIF can be reconstructed directly from tissue curves obtained through {{\\text{H}}2} 15O PET imaging. This suggests the possibility of using a completely non-invasive technique to assess CBF in patho-physiological studies.

Exact algorithms for haplotype assembly from whole-genome sequence data.

PubMed

Chen, Zhi-Zhong; Deng, Fei; Wang, Lusheng

2013-08-15

Haplotypes play a crucial role in genetic analysis and have many applications such as gene disease diagnoses, association studies, ancestry inference and so forth. The development of DNA sequencing technologies makes it possible to obtain haplotypes from a set of aligned reads originated from both copies of a chromosome of a single individual. This approach is often known as haplotype assembly. Exact algorithms that can give optimal solutions to the haplotype assembly problem are highly demanded. Unfortunately, previous algorithms for this problem either fail to output optimal solutions or take too long time even executed on a PC cluster. We develop an approach to finding optimal solutions for the haplotype assembly problem under the minimum-error-correction (MEC) model. Most of the previous approaches assume that the columns in the input matrix correspond to (putative) heterozygous sites. This all-heterozygous assumption is correct for most columns, but it may be incorrect for a small number of columns. In this article, we consider the MEC model with or without the all-heterozygous assumption. In our approach, we first use new methods to decompose the input read matrix into small independent blocks and then model the problem for each block as an integer linear programming problem, which is then solved by an integer linear programming solver. We have tested our program on a single PC [a Linux (x64) desktop PC with i7-3960X CPU], using the filtered HuRef and the NA 12878 datasets (after applying some variant calling methods). With the all-heterozygous assumption, our approach can optimally solve the whole HuRef data set within a total time of 31 h (26 h for the most difficult block of the 15th chromosome and only 5 h for the other blocks). To our knowledge, this is the first time that MEC optimal solutions are completely obtained for the filtered HuRef dataset. Moreover, in the general case (without the all-heterozygous assumption), for the HuRef dataset our approach can optimally solve all the chromosomes except the most difficult block in chromosome 15 within a total time of 12 days. For both of the HuRef and NA12878 datasets, the optimal costs in the general case are sometimes much smaller than those in the all-heterozygous case. This implies that some columns in the input matrix (after applying certain variant calling methods) still correspond to false-heterozygous sites. Our program, the optimal solutions found for the HuRef dataset available at http://rnc.r.dendai.ac.jp/hapAssembly.html.

A mathematical model for maximizing the value of phase 3 drug development portfolios incorporating budget constraints and risk.

PubMed

Patel, Nitin R; Ankolekar, Suresh; Antonijevic, Zoran; Rajicic, Natasa

2013-05-10

We describe a value-driven approach to optimizing pharmaceutical portfolios. Our approach incorporates inputs from research and development and commercial functions by simultaneously addressing internal and external factors. This approach differentiates itself from current practices in that it recognizes the impact of study design parameters, sample size in particular, on the portfolio value. We develop an integer programming (IP) model as the basis for Bayesian decision analysis to optimize phase 3 development portfolios using expected net present value as the criterion. We show how this framework can be used to determine optimal sample sizes and trial schedules to maximize the value of a portfolio under budget constraints. We then illustrate the remarkable flexibility of the IP model to answer a variety of 'what-if' questions that reflect situations that arise in practice. We extend the IP model to a stochastic IP model to incorporate uncertainty in the availability of drugs from earlier development phases for phase 3 development in the future. We show how to use stochastic IP to re-optimize the portfolio development strategy over time as new information accumulates and budget changes occur. Copyright © 2013 John Wiley & Sons, Ltd.

Toward a More Efficient Implementation of Antifibrillation Pacing

PubMed Central

Wilson, Dan; Moehlis, Jeff

2016-01-01

We devise a methodology to determine an optimal pattern of inputs to synchronize firing patterns of cardiac cells which only requires the ability to measure action potential durations in individual cells. In numerical bidomain simulations, the resulting synchronizing inputs are shown to terminate spiral waves with a higher probability than comparable inputs that do not synchronize the cells as strongly. These results suggest that designing stimuli which promote synchronization in cardiac tissue could improve the success rate of defibrillation, and point towards novel strategies for optimizing antifibrillation pacing. PMID:27391010

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.

Capturing planar shapes by approximating their outlines

NASA Astrophysics Data System (ADS)

Sarfraz, M.; Riyazuddin, M.; Baig, M. H.

2006-05-01

A non-deterministic evolutionary approach for approximating the outlines of planar shapes has been developed. Non-uniform Rational B-splines (NURBS) have been utilized as an underlying approximation curve scheme. Simulated Annealing heuristic is used as an evolutionary methodology. In addition to independent studies of the optimization of weight and knot parameters of the NURBS, a separate scheme has also been developed for the optimization of weights and knots simultaneously. The optimized NURBS models have been fitted over the contour data of the planar shapes for the ultimate and automatic output. The output results are visually pleasing with respect to the threshold provided by the user. A web-based system has also been developed for the effective and worldwide utilization. The objective of this system is to provide the facility to visualize the output to the whole world through internet by providing the freedom to the user for various desired input parameters setting in the algorithm designed.

The Development of Models to Optimize Selection of Nuclear Fuels through Atomic-Level Simulation

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

Prof. Simon Phillpot; Prof. Susan B. Sinnott; Prof. Hans Seifert

2009-01-26

Demonstrated that FRAPCON can be modified to accept data generated from first principles studies, and that the result obtained from the modified FRAPCON make sense in terms of the inputs. Determined the temperature dependence of the thermal conductivity of single crystal UO2 from atomistic simulation.

Oil Formation Volume Factor Determination Through a Fused Intelligence

NASA Astrophysics Data System (ADS)

Gholami, Amin

2016-12-01

Volume change of oil between reservoir condition and standard surface condition is called oil formation volume factor (FVF), which is very time, cost and labor intensive to determine. This study proposes an accurate, rapid and cost-effective approach for determining FVF from reservoir temperature, dissolved gas oil ratio, and specific gravity of both oil and dissolved gas. Firstly, structural risk minimization (SRM) principle of support vector regression (SVR) was employed to construct a robust model for estimating FVF from the aforementioned inputs. Subsequently, an alternating conditional expectation (ACE) was used for approximating optimal transformations of input/output data to a higher correlated data and consequently developing a sophisticated model between transformed data. Eventually, a committee machine with SVR and ACE was constructed through the use of hybrid genetic algorithm-pattern search (GA-PS). Committee machine integrates ACE and SVR models in an optimal linear combination such that makes benefit of both methods. A group of 342 data points was used for model development and a group of 219 data points was used for blind testing the constructed model. Results indicated that the committee machine performed better than individual models.

Conceptual Design and Optimal Power Control Strategy for AN Eco-Friendly Hybrid Vehicle

NASA Astrophysics Data System (ADS)

Nasiri, N. Mir; Chieng, Frederick T. A.

2011-06-01

This paper presents a new concept for a hybrid vehicle using a torque and speed splitting technique. It is implemented by the newly developed controller in combination with a two degree of freedom epicyclic gear transmission. This approach enables optimization of the power split between the less powerful electrical motor and more powerful engine while driving a car load. The power split is fundamentally a dual-energy integration mechanism as it is implemented by using the epicyclic gear transmission that has two inputs and one output for a proper power distribution. The developed power split control system manages the operation of both the inputs to have a known output with the condition of maintaining optimum operating efficiency of the internal combustion engine and electrical motor. This system has a huge potential as it is possible to integrate all the features of hybrid vehicle known to-date such as the regenerative braking system, series hybrid, parallel hybrid, series/parallel hybrid, and even complex hybrid (bidirectional). By using the new power split system it is possible to further reduce fuel consumption and increase overall efficiency.

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

Surrogate-based optimization of hydraulic fracturing in pre-existing fracture networks

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Sun, Yunwei; Fu, Pengcheng; Carrigan, Charles R.; Lu, Zhiming; Tong, Charles H.; Buscheck, Thomas A.

2013-08-01

Hydraulic fracturing has been used widely to stimulate production of oil, natural gas, and geothermal energy in formations with low natural permeability. Numerical optimization of fracture stimulation often requires a large number of evaluations of objective functions and constraints from forward hydraulic fracturing models, which are computationally expensive and even prohibitive in some situations. Moreover, there are a variety of uncertainties associated with the pre-existing fracture distributions and rock mechanical properties, which affect the optimized decisions for hydraulic fracturing. In this study, a surrogate-based approach is developed for efficient optimization of hydraulic fracturing well design in the presence of natural-system uncertainties. The fractal dimension is derived from the simulated fracturing network as the objective for maximizing energy recovery sweep efficiency. The surrogate model, which is constructed using training data from high-fidelity fracturing models for mapping the relationship between uncertain input parameters and the fractal dimension, provides fast approximation of the objective functions and constraints. A suite of surrogate models constructed using different fitting methods is evaluated and validated for fast predictions. Global sensitivity analysis is conducted to gain insights into the impact of the input variables on the output of interest, and further used for parameter screening. The high efficiency of the surrogate-based approach is demonstrated for three optimization scenarios with different and uncertain ambient conditions. Our results suggest the critical importance of considering uncertain pre-existing fracture networks in optimization studies of hydraulic fracturing.

Simple Example of Backtest Overfitting (SEBO)

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

In the field of mathematical finance, a "backtest" is the usage of historical market data to assess the performance of a proposed trading strategy. It is a relatively simple matter for a present-day computer system to explore thousands, millions or even billions of variations of a proposed strategy, and pick the best performing variant as the "optimal" strategy "in sample" (i.e., on the input dataset). Unfortunately, such an "optimal" strategy often performs very poorly "out of sample" (i.e. on another dataset), because the parameters of the invest strategy have been oversit to the in-sample data, a situation known as "backtestmore » overfitting". While the mathematics of backtest overfitting has been examined in several recent theoretical studies, here we pursue a more tangible analysis of this problem, in the form of an online simulator tool. Given a input random walk time series, the tool develops an "optimal" variant of a simple strategy by exhaustively exploring all integer parameter values among a handful of parameters. That "optimal" strategy is overfit, since by definition a random walk is unpredictable. Then the tool tests the resulting "optimal" strategy on a second random walk time series. In most runs using our online tool, the "optimal" strategy derived from the first time series performs poorly on the second time series, demonstrating how hard it is not to overfit a backtest. We offer this online tool, "Simple Example of Backtest Overfitting (SEBO)", to facilitate further research in this area.« less

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering.

PubMed

Menolascina, Filippo; Bellomo, Domenico; Maiwald, Thomas; Bevilacqua, Vitoantonio; Ciminelli, Caterina; Paradiso, Angelo; Tommasi, Stefania

2009-10-15

Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments. We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification. We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich--data poor' paradox in Systems Biology.

Modelling and multi objective optimization of WEDM of commercially Monel super alloy using evolutionary algorithms

NASA Astrophysics Data System (ADS)

Varun, Sajja; Reddy, Kalakada Bhargav Bal; Vardhan Reddy, R. R. Vishnu

2016-09-01

In this research work, development of a multi response optimization technique has been undertaken, using traditional desirability analysis and non-traditional particle swarm optimization techniques (for different customer's priorities) in wire electrical discharge machining (WEDM). Monel 400 has been selected as work material for experimentation. The effect of key process parameters such as pulse on time (TON), pulse off time (TOFF), peak current (IP), wire feed (WF) were on material removal rate (MRR) and surface roughness(SR) in WEDM operation were investigated. Further, the responses such as MRR and SR were modelled empirically through regression analysis. The developed models can be used by the machinists to predict the MRR and SR over a wide range of input parameters. The optimization of multiple responses has been done for satisfying the priorities of multiple users by using Taguchi-desirability function method and particle swarm optimization technique. The analysis of variance (ANOVA) is also applied to investigate the effect of influential parameters. Finally, the confirmation experiments were conducted for the optimal set of machining parameters, and the betterment has been proved.

Maximally informative pairwise interactions in networks

PubMed Central

Fitzgerald, Jeffrey D.; Sharpee, Tatyana O.

2010-01-01

Several types of biological networks have recently been shown to be accurately described by a maximum entropy model with pairwise interactions, also known as the Ising model. Here we present an approach for finding the optimal mappings between input signals and network states that allow the network to convey the maximal information about input signals drawn from a given distribution. This mapping also produces a set of linear equations for calculating the optimal Ising-model coupling constants, as well as geometric properties that indicate the applicability of the pairwise Ising model. We show that the optimal pairwise interactions are on average zero for Gaussian and uniformly distributed inputs, whereas they are nonzero for inputs approximating those in natural environments. These nonzero network interactions are predicted to increase in strength as the noise in the response functions of each network node increases. This approach also suggests ways for how interactions with unmeasured parts of the network can be inferred from the parameters of response functions for the measured network nodes. PMID:19905153

Development of the disable software reporting system on the basis of the neural network

NASA Astrophysics Data System (ADS)

Gavrylenko, S.; Babenko, O.; Ignatova, E.

2018-04-01

The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

Adaptive Actor-Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong

2016-01-01

This paper is concerned with the problem of integral sliding-mode control for a class of nonlinear systems with input disturbances and unknown nonlinear terms through the adaptive actor-critic (AC) control method. The main objective is to design a sliding-mode control methodology based on the adaptive dynamic programming (ADP) method, so that the closed-loop system with time-varying disturbances is stable and the nearly optimal performance of the sliding-mode dynamics can be guaranteed. In the first step, a neural network (NN)-based observer and a disturbance observer are designed to approximate the unknown nonlinear terms and estimate the input disturbances, respectively. Based on the NN approximations and disturbance estimations, the discontinuous part of the sliding-mode control is constructed to eliminate the effect of the disturbances and attain the expected equivalent sliding-mode dynamics. Then, the ADP method with AC structure is presented to learn the optimal control for the sliding-mode dynamics online. Reconstructed tuning laws are developed to guarantee the stability of the sliding-mode dynamics and the convergence of the weights of critic and actor NNs. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.

A homotopy algorithm for digital optimal projection control GASD-HADOC

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G., Jr.; Richter, Stephen; Davis, Lawrence D.

1993-01-01

The linear-quadratic-gaussian (LQG) compensator was developed to facilitate the design of control laws for multi-input, multi-output (MIMO) systems. The compensator is computed by solving two algebraic equations for which standard closed-loop solutions exist. Unfortunately, the minimal dimension of an LQG compensator is almost always equal to the dimension of the plant and can thus often violate practical implementation constraints on controller order. This deficiency is especially highlighted when considering control-design for high-order systems such as flexible space structures. This deficiency motivated the development of techniques that enable the design of optimal controllers whose dimension is less than that of the design plant. A homotopy approach based on the optimal projection equations that characterize the necessary conditions for optimal reduced-order control. Homotopy algorithms have global convergence properties and hence do not require that the initializing reduced-order controller be close to the optimal reduced-order controller to guarantee convergence. However, the homotopy algorithm previously developed for solving the optimal projection equations has sublinear convergence properties and the convergence slows at higher authority levels and may fail. A new homotopy algorithm for synthesizing optimal reduced-order controllers for discrete-time systems is described. Unlike the previous homotopy approach, the new algorithm is a gradient-based, parameter optimization formulation and was implemented in MATLAB. The results reported may offer the foundation for a reliable approach to optimal, reduced-order controller design.

Optimal actuator location within a morphing wing scissor mechanism configuration

NASA Astrophysics Data System (ADS)

Joo, James J.; Sanders, Brian; Johnson, Terrence; Frecker, Mary I.

2006-03-01

In this paper, the optimal location of a distributed network of actuators within a scissor wing mechanism is investigated. The analysis begins by developing a mechanical understanding of a single cell representation of the mechanism. This cell contains four linkages connected by pin joints, a single actuator, two springs to represent the bidirectional behavior of a flexible skin, and an external load. Equilibrium equations are developed using static analysis and the principle of virtual work equations. An objective function is developed to maximize the efficiency of the unit cell model. It is defined as useful work over input work. There are two constraints imposed on this problem. The first is placed on force transferred from the external source to the actuator. It should be less than the blocked actuator force. The other is to require the ratio of output displacement over input displacement, i.e., geometrical advantage (GA), of the cell to be larger than a prescribed value. Sequential quadratic programming is used to solve the optimization problem. This process suggests a systematic approach to identify an optimum location of an actuator and to avoid the selection of location by trial and error. Preliminary results show that optimum locations of an actuator can be selected out of feasible regions according to the requirements of the problem such as a higher GA, a higher efficiency, or a smaller transferred force from external force. Results include analysis of single and multiple cell wing structures and some experimental comparisons.

Analysis and selection of optimal function implementations in massively parallel computer

DOEpatents

Archer, Charles Jens [Rochester, MN; Peters, Amanda [Rochester, MN; Ratterman, Joseph D [Rochester, MN

2011-05-31

An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.

Approximation of discrete-time LQG compensators for distributed systems with boundary input and unbounded measurement

NASA Technical Reports Server (NTRS)

Gibson, J. S.; Rosen, I. G.

1987-01-01

The approximation of optimal discrete-time linear quadratic Gaussian (LQG) compensators for distributed parameter control systems with boundary input and unbounded measurement is considered. The approach applies to a wide range of problems that can be formulated in a state space on which both the discrete-time input and output operators are continuous. Approximating compensators are obtained via application of the LQG theory and associated approximation results for infinite dimensional discrete-time control systems with bounded input and output. Numerical results for spline and modal based approximation schemes used to compute optimal compensators for a one dimensional heat equation with either Neumann or Dirichlet boundary control and pointwise measurement of temperature are presented and discussed.

The minimal amount of starting DNA for Agilent’s hybrid capture-based targeted massively parallel sequencing

PubMed Central

Chung, Jongsuk; Son, Dae-Soon; Jeon, Hyo-Jeong; Kim, Kyoung-Mee; Park, Gahee; Ryu, Gyu Ha; Park, Woong-Yang; Park, Donghyun

2016-01-01

Targeted capture massively parallel sequencing is increasingly being used in clinical settings, and as costs continue to decline, use of this technology may become routine in health care. However, a limited amount of tissue has often been a challenge in meeting quality requirements. To offer a practical guideline for the minimum amount of input DNA for targeted sequencing, we optimized and evaluated the performance of targeted sequencing depending on the input DNA amount. First, using various amounts of input DNA, we compared commercially available library construction kits and selected Agilent’s SureSelect-XT and KAPA Biosystems’ Hyper Prep kits as the kits most compatible with targeted deep sequencing using Agilent’s SureSelect custom capture. Then, we optimized the adapter ligation conditions of the Hyper Prep kit to improve library construction efficiency and adapted multiplexed hybrid selection to reduce the cost of sequencing. In this study, we systematically evaluated the performance of the optimized protocol depending on the amount of input DNA, ranging from 6.25 to 200 ng, suggesting the minimal input DNA amounts based on coverage depths required for specific applications. PMID:27220682

Program document for Energy Systems Optimization Program 2 (ESOP2). Volume 1: Engineering manual

NASA Technical Reports Server (NTRS)

Hamil, R. G.; Ferden, S. L.

1977-01-01

The Energy Systems Optimization Program, which is used to provide analyses of Modular Integrated Utility Systems (MIUS), is discussed. Modifications to the input format to allow modular inputs in specified blocks of data are described. An optimization feature which enables the program to search automatically for the minimum value of one parameter while varying the value of other parameters is reported. New program option flags for prime mover analyses and solar energy for space heating and domestic hot water are also covered.

Mass drivers. 3: Engineering

NASA Technical Reports Server (NTRS)

Arnold, W.; Bowen, S.; Cohen, S.; Fine, K.; Kaplan, D.; Kolm, M.; Kolm, H.; Newman, J.; Oneill, G. K.; Snow, W.

1979-01-01

The last of a series of three papers by the Mass-Driver Group of the 1977 Ames Summer Study is presented. It develops the engineering principles required to implement the basic mass-driver. Optimum component mass trade-offs are derived from a set of four input parameters, and the program used to design a lunar launcher. The mass optimization procedures is then incorporated into a more comprehensive mission optimization program called OPT-4, which evaluates an optimized mass-driver reaction engine and its performance in a range of specified missions. Finally, this paper discusses, to the extent that time permitted, certain peripheral problems: heating effects in buckets due to magnetic field ripple; an approximate derivation of guide force profiles; the mechanics of inserting and releasing payloads; the reaction mass orbits; and a proposed research and development plan for implementing mass drivers.

Investigation of 16 × 10 Gbps DWDM System Based on Optimized Semiconductor Optical Amplifier

NASA Astrophysics Data System (ADS)

Rani, Aruna; Dewra, Sanjeev

2017-08-01

This paper investigates the performance of an optical system based on optimized semiconductor optical amplifier (SOA) at 160 Gbps with 0.8 nm channel spacing. Transmission distances up to 280 km at -30 dBm input signal power and up to 247 km at -32 dBm input signal power with acceptable bit error rate (BER) and Q-factor are examined. It is also analyzed that the transmission distance up to 292 km has been covered at -28 dBm input signal power using Dispersion Shifted (DS)-Normal fiber without any power compensation methods.

Active Vibration Control for Helicopter Interior Noise Reduction Using Power Minimization

NASA Technical Reports Server (NTRS)

Mendoza, J.; Chevva, K.; Sun, F.; Blanc, A.; Kim, S. B.

2014-01-01

This report describes work performed by United Technologies Research Center (UTRC) for NASA Langley Research Center (LaRC) under Contract NNL11AA06C. The objective of this program is to develop technology to reduce helicopter interior noise resulting from multiple gear meshing frequencies. A novel active vibration control approach called Minimum Actuation Power (MAP) is developed. MAP is an optimal control strategy that minimizes the total input power into a structure by monitoring and varying the input power of controlling sources. MAP control was implemented without explicit knowledge of the phasing and magnitude of the excitation sources by driving the real part of the input power from the controlling sources to zero. It is shown that this occurs when the total mechanical input power from the excitation and controlling sources is a minimum. MAP theory is developed for multiple excitation sources with arbitrary relative phasing for single or multiple discrete frequencies and controlled by a single or multiple controlling sources. Simulations and experimental results demonstrate the feasibility of MAP for structural vibration reduction of a realistic rotorcraft interior structure. MAP control resulted in significant average global vibration reduction of a single frequency and multiple frequency excitations with one controlling actuator. Simulations also demonstrate the potential effectiveness of the observed vibration reductions on interior radiated noise.

Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction.

PubMed

Šiljić Tomić, Aleksandra; Antanasijević, Davor; Ristić, Mirjana; Perić-Grujić, Aleksandra; Pocajt, Viktor

2018-04-01

This paper presents an application of experimental design for the optimization of artificial neural network (ANN) for the prediction of dissolved oxygen (DO) content in the Danube River. The aim of this research was to obtain a more reliable ANN model that uses fewer monitoring records, by simultaneous optimization of the following model parameters: number of monitoring sites, number of historical monitoring data (expressed in years), and number of input water quality parameters used. Box-Behnken three-factor at three levels experimental design was applied for simultaneous spatial, temporal, and input variables optimization of the ANN model. The prediction of DO was performed using a feed-forward back-propagation neural network (BPNN), while the selection of most important inputs was done off-model using multi-filter approach that combines a chi-square ranking in the first step with a correlation-based elimination in the second step. The contour plots of absolute and relative error response surfaces were utilized to determine the optimal values of design factors. From the contour plots, two BPNN models that cover entire Danube flow through Serbia are proposed: an upstream model (BPNN-UP) that covers 8 monitoring sites prior to Belgrade and uses 12 inputs measured in the 7-year period and a downstream model (BPNN-DOWN) which covers 9 monitoring sites and uses 11 input parameters measured in the 6-year period. The main difference between the two models is that BPNN-UP utilizes inputs such as BOD, P, and PO 4 3- , which is in accordance with the fact that this model covers northern part of Serbia (Vojvodina Autonomous Province) which is well-known for agricultural production and extensive use of fertilizers. Both models have shown very good agreement between measured and predicted DO (with R 2  ≥ 0.86) and demonstrated that they can effectively forecast DO content in the Danube River.

QTL examination of a bi-parental mapping population segregating for “short-stature” in hop (Humulus lupulus L.)

USDA-ARS?s Scientific Manuscript database

Increasing labor costs and reduced labor pools for hop production have resulted in the necessity to develop strategies to improve efficiency and automate hop production and harvest. One solution for reducing labor inputs is the use and production of “low-trellis” hop varieties optimized for mechani...

Approach for Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Putko, Michele M.; Newman, Perry A.; Taylor, Arthur C., III; Green, Lawrence L.

2001-01-01

This paper presents an implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for a quasi 1-D Euler CFD (computational fluid dynamics) code. Given uncertainties in statistically independent, random, normally distributed input variables, a first- and second-order statistical moment matching procedure is performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, the moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH.

PubMed

Volk, Jochen; Herrmann, Torsten; Wüthrich, Kurt

2008-07-01

MATCH (Memetic Algorithm and Combinatorial Optimization Heuristics) is a new memetic algorithm for automated sequence-specific polypeptide backbone NMR assignment of proteins. MATCH employs local optimization for tracing partial sequence-specific assignments within a global, population-based search environment, where the simultaneous application of local and global optimization heuristics guarantees high efficiency and robustness. MATCH thus makes combined use of the two predominant concepts in use for automated NMR assignment of proteins. Dynamic transition and inherent mutation are new techniques that enable automatic adaptation to variable quality of the experimental input data. The concept of dynamic transition is incorporated in all major building blocks of the algorithm, where it enables switching between local and global optimization heuristics at any time during the assignment process. Inherent mutation restricts the intrinsically required randomness of the evolutionary algorithm to those regions of the conformation space that are compatible with the experimental input data. Using intact and artificially deteriorated APSY-NMR input data of proteins, MATCH performed sequence-specific resonance assignment with high efficiency and robustness.

Optimal nonlinear codes for the perception of natural colours.

PubMed

von der Twer, T; MacLeod, D I

2001-08-01

We discuss how visual nonlinearity can be optimized for the precise representation of environmental inputs. Such optimization leads to neural signals with a compressively nonlinear input-output function the gradient of which is matched to the cube root of the probability density function (PDF) of the environmental input values (and not to the PDF directly as in histogram equalization). Comparisons between theory and psychophysical and electrophysiological data are roughly consistent with the idea that parvocellular (P) cells are optimized for precision representation of colour: their contrast-response functions span a range appropriately matched to the environmental distribution of natural colours along each dimension of colour space. Thus P cell codes for colour may have been selected to minimize error in the perceptual estimation of stimulus parameters for natural colours. But magnocellular (M) cells have a much stronger than expected saturating nonlinearity; this supports the view that the function of M cells is mainly to detect boundaries rather than to specify contrast or lightness.

Consideration of Optimal Input on Semi-Active Shock Control System

NASA Astrophysics Data System (ADS)

Kawashima, Takeshi

In press working, unidirectional transmission of mechanical energy is expected in order to maximize the life of the dies. To realize this transmission, the author has developed a shock control system based on the sliding mode control technique. The controller makes a collision-receiving object effectively deform plastically by adjusting the force of the actuator inserted between the colliding objects, while the deformation of the colliding object is held at the necessity minimum. However, the actuator has to generate a large force corresponding to the impulsive force. Therefore, development of such an actuator is a formidable challenge. The author has proposed a semi-active shock control system in which the impulsive force is adjusted by a brake mechanism, although the system exhibits inferior performance. Thus, the author has also designed an actuator using a friction device for semi-active shock control, and proposed an active seatbelt system as an application. The effectiveness has been confirmed by a numerical simulation and model experiment. In this study, the optimal deformation change of the colliding object is theoretically examined in the case that the collision-receiving object has perfect plasticity and the colliding object has perfect elasticity. As a result, the optimal input condition is obtained so that the ratio of the maximum deformation of the collision-receiving object to the maximum deformation of the colliding object becomes the maximum. Additionally, the energy balance is examined.

Common Methodology for Efficient Airspace Operations

NASA Technical Reports Server (NTRS)

Sridhar, Banavar

2012-01-01

Topics include: a) Developing a common methodology to model and avoid disturbances affecting airspace. b) Integrated contrails and emission models to a national level airspace simulation. c) Developed capability to visualize, evaluate technology and alternate operational concepts and provide inputs for policy-analysis tools to reduce the impact of aviation on the environment. d) Collaborating with Volpe Research Center, NOAA and DLR to leverage expertise and tools in aircraft emissions and weather/climate modeling. Airspace operations is a trade-off balancing safety, capacity, efficiency and environmental considerations. Ideal flight: Unimpeded wind optimal route with optimal climb and descent. Operations degraded due to reduction in airport and airspace capacity caused by inefficient procedures and disturbances.

Event-Triggered Distributed Approximate Optimal State and Output Control of Affine Nonlinear Interconnected Systems.

PubMed

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-06-08

This paper presents an approximate optimal distributed control scheme for a known interconnected system composed of input affine nonlinear subsystems using event-triggered state and output feedback via a novel hybrid learning scheme. First, the cost function for the overall system is redefined as the sum of cost functions of individual subsystems. A distributed optimal control policy for the interconnected system is developed using the optimal value function of each subsystem. To generate the optimal control policy, forward-in-time, neural networks are employed to reconstruct the unknown optimal value function at each subsystem online. In order to retain the advantages of event-triggered feedback for an adaptive optimal controller, a novel hybrid learning scheme is proposed to reduce the convergence time for the learning algorithm. The development is based on the observation that, in the event-triggered feedback, the sampling instants are dynamic and results in variable interevent time. To relax the requirement of entire state measurements, an extended nonlinear observer is designed at each subsystem to recover the system internal states from the measurable feedback. Using a Lyapunov-based analysis, it is demonstrated that the system states and the observer errors remain locally uniformly ultimately bounded and the control policy converges to a neighborhood of the optimal policy. Simulation results are presented to demonstrate the performance of the developed controller.

Adaptive optimal stochastic state feedback control of resistive wall modes in tokamaks

NASA Astrophysics Data System (ADS)

Sun, Z.; Sen, A. K.; Longman, R. W.

2006-01-01

An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least-square method with exponential forgetting factor and covariance resetting is used to identify (experimentally determine) the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time-dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used.

Adaptive Optimal Stochastic State Feedback Control of Resistive Wall Modes in Tokamaks

NASA Astrophysics Data System (ADS)

Sun, Z.; Sen, A. K.; Longman, R. W.

2007-06-01

An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least square method with exponential forgetting factor and covariance resetting is used to identify the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used.

Robust optimal control of material flows in demand-driven supply networks

NASA Astrophysics Data System (ADS)

Laumanns, Marco; Lefeber, Erjen

2006-04-01

We develop a model based on stochastic discrete-time controlled dynamical systems in order to derive optimal policies for controlling the material flow in supply networks. Each node in the network is described as a transducer such that the dynamics of the material and information flows within the entire network can be expressed by a system of first-order difference equations, where some inputs to the system act as external disturbances. We apply methods from constrained robust optimal control to compute the explicit control law as a function of the current state. For the numerical examples considered, these control laws correspond to certain classes of optimal ordering policies from inventory management while avoiding, however, any a priori assumptions about the general form of the policy.

Projection of Maximum Software Maintenance Manning Levels.

DTIC Science & Technology

1982-06-01

mainte- nance team development and for outyear support resource estimation, and to provide an analysis of applications of the model in areas other...by General Research Corporation of Santa Barbara, Ca., indicated that the Planning and Resource Management Information System (PARRIS) at the Air Force...determined that when the optimal input effort is applied, steps in the development would be achieved at a rate proportional to V(t). Thus the work-rate could

Characterization of real-world vibration sources with a view toward optimal energy harvesting architectures

NASA Astrophysics Data System (ADS)

Rantz, Robert; Roundy, Shad

2016-04-01

A tremendous amount of research has been performed on the design and analysis of vibration energy harvester architectures with the goal of optimizing power output; most studies assume idealized input vibrations without paying much attention to whether such idealizations are broadly representative of real sources. These "idealized input signals" are typically derived from the expected nature of the vibrations produced from a given source. Little work has been done on corroborating these expectations by virtue of compiling a comprehensive list of vibration signals organized by detailed classifications. Vibration data representing 333 signals were collected from the NiPS Laboratory "Real Vibration" database, processed, and categorized according to the source of the signal (e.g. animal, machine, etc.), the number of dominant frequencies, the nature of the dominant frequencies (e.g. stationary, band-limited noise, etc.), and other metrics. By categorizing signals in this way, the set of idealized vibration inputs commonly assumed for harvester input can be corroborated and refined, and heretofore overlooked vibration input types have motivation for investigation. An initial qualitative analysis of vibration signals has been undertaken with the goal of determining how often a standard linear oscillator based harvester is likely the optimal architecture, and how often a nonlinear harvester with a cubic stiffness function might provide improvement. Although preliminary, the analysis indicates that in at least 23% of cases, a linear harvester is likely optimal and in no more than 53% of cases would a nonlinear cubic stiffness based harvester provide improvement.

Capabilities and applications of the Program to Optimize Simulated Trajectories (POST). Program summary document

NASA Technical Reports Server (NTRS)

Brauer, G. L.; Cornick, D. E.; Stevenson, R.

1977-01-01

The capabilities and applications of the three-degree-of-freedom (3DOF) version and the six-degree-of-freedom (6DOF) version of the Program to Optimize Simulated Trajectories (POST) are summarized. The document supplements the detailed program manuals by providing additional information that motivates and clarifies basic capabilities, input procedures, applications and computer requirements of these programs. The information will enable prospective users to evaluate the programs, and to determine if they are applicable to their problems. Enough information is given to enable managerial personnel to evaluate the capabilities of the programs and describes the POST structure, formulation, input and output procedures, sample cases, and computer requirements. The report also provides answers to basic questions concerning planet and vehicle modeling, simulation accuracy, optimization capabilities, and general input rules. Several sample cases are presented.

Parametric analysis of parameters for electrical-load forecasting using artificial neural networks

NASA Astrophysics Data System (ADS)

Gerber, William J.; Gonzalez, Avelino J.; Georgiopoulos, Michael

1997-04-01

Accurate total system electrical load forecasting is a necessary part of resource management for power generation companies. The better the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Automating this process is a profitable goal and neural networks should provide an excellent means of doing the automation. However, prior to developing such a system, the optimal set of input parameters must be determined. The approach of this research was to determine what those inputs should be through a parametric study of potentially good inputs. Input parameters tested were ambient temperature, total electrical load, the day of the week, humidity, dew point temperature, daylight savings time, length of daylight, season, forecast light index and forecast wind velocity. For testing, a limited number of temperatures and total electrical loads were used as a basic reference input parameter set. Most parameters showed some forecasting improvement when added individually to the basic parameter set. Significantly, major improvements were exhibited with the day of the week, dew point temperatures, additional temperatures and loads, forecast light index and forecast wind velocity.

Multilayer modal actuator-based piezoelectric transformers.

PubMed

Huang, Yao-Tien; Wu, Wen-Jong; Wang, Yen-Chieh; Lee, Chih-Kung

2007-02-01

An innovative, multilayer piezoelectric transformer equipped with a full modal filtering input electrode is reported herein. This modal-shaped electrode, based on the orthogonal property of structural vibration modes, is characterized by full modal filtering to ensure that only the desired vibration mode is excited during operation. The newly developed piezoelectric transformer is comprised of three layers: a multilayered input layer, an insulation layer, and a single output layer. The electrode shape of the input layer is derived from its structural vibration modal shape, which takes advantage of the orthogonal property of the vibration modes to achieve a full modal filtering effect. The insulation layer possesses two functions: first, to couple the mechanical vibration energy between the input and output, and second, to provide electrical insulation between the two layers. To meet the two functions, a low temperature, co-fired ceramic (LTCC) was used to provide the high mechanical rigidity and high electrical insulation. It can be shown that this newly developed piezoelectric transformer has the advantage of possessing a more efficient energy transfer and a wider optimal working frequency range when compared to traditional piezoelectric transformers. A multilayer piezoelectric, transformer-based inverter applicable for use in LCD monitors or portable displays is presented as well.

Development of a Stochastically-driven, Forward Predictive Performance Model for PEMFCs

NASA Astrophysics Data System (ADS)

Harvey, David Benjamin Paul

A one-dimensional multi-scale coupled, transient, and mechanistic performance model for a PEMFC membrane electrode assembly has been developed. The model explicitly includes each of the 5 layers within a membrane electrode assembly and solves for the transport of charge, heat, mass, species, dissolved water, and liquid water. Key features of the model include the use of a multi-step implementation of the HOR reaction on the anode, agglomerate catalyst sub-models for both the anode and cathode catalyst layers, a unique approach that links the composition of the catalyst layer to key properties within the agglomerate model and the implementation of a stochastic input-based approach for component material properties. The model employs a new methodology for validation using statistically varying input parameters and statistically-based experimental performance data; this model represents the first stochastic input driven unit cell performance model. The stochastic input driven performance model was used to identify optimal ionomer content within the cathode catalyst layer, demonstrate the role of material variation in potential low performing MEA materials, provide explanation for the performance of low-Pt loaded MEAs, and investigate the validity of transient-sweep experimental diagnostic methods.

Neural network diagnosis of avascular necrosis from magnetic resonance images

NASA Astrophysics Data System (ADS)

Manduca, Armando; Christy, Paul S.; Ehman, Richard L.

1993-09-01

We have explored the use of artificial neural networks to diagnose avascular necrosis (AVN) of the femoral head from magnetic resonance images. We have developed multi-layer perceptron networks, trained with conjugate gradient optimization, which diagnose AVN from single sagittal images of the femoral head with 100% accuracy on the training data and 97% accuracy on test data. These networks use only the raw image as input (with minimal preprocessing to average the images down to 32 X 32 size and to scale the input data values) and learn to extract their own features for the diagnosis decision. Various experiments with these networks are described.

Response of capacitive micromachined ultrasonic transducers

NASA Astrophysics Data System (ADS)

Ge, Lifeng

2008-10-01

Capacitive micromachined ultrasonic transducers (CMUTs) have been developed for airborne ultrasonic applications, acoustic imaging, and chemical and biological detections. Much attention is also paid how to optimize their performance, so that the accurate simulation of the transmitting response of the CMUTs becomes extremely significant. This paper focuses on determining the total input mechanical impedance accountings for damping, and its resistance part is obtained by the calculated natural frequency and equivalent lumped parameters, and the typical 3-dB bandwidth. Thus, the transmitting response can be calculated by using the input mechanical impedance. Moreover, the equivalent electrical circuit can be also established by the determined lumped parameters.

SCI model structure determination program (OSR) user's guide. [optimal subset regression

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program, OSR (Optimal Subset Regression) which estimates models for rotorcraft body and rotor force and moment coefficients is described. The technique used is based on the subset regression algorithm. Given time histories of aerodynamic coefficients, aerodynamic variables, and control inputs, the program computes correlation between various time histories. The model structure determination is based on these correlations. Inputs and outputs of the program are given.

Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells

PubMed Central

Dähne, Sven; Wilbert, Niko; Wiskott, Laurenz

2014-01-01

The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world. PMID:24810948

Maximum likelihood identification and optimal input design for identifying aircraft stability and control derivatives

NASA Technical Reports Server (NTRS)

Stepner, D. E.; Mehra, R. K.

1973-01-01

A new method of extracting aircraft stability and control derivatives from flight test data is developed based on the maximum likelihood cirterion. It is shown that this new method is capable of processing data from both linear and nonlinear models, both with and without process noise and includes output error and equation error methods as special cases. The first application of this method to flight test data is reported for lateral maneuvers of the HL-10 and M2/F3 lifting bodies, including the extraction of stability and control derivatives in the presence of wind gusts. All the problems encountered in this identification study are discussed. Several different methods (including a priori weighting, parameter fixing and constrained parameter values) for dealing with identifiability and uniqueness problems are introduced and the results given. The method for the design of optimal inputs for identifying the parameters of linear dynamic systems is also given. The criterion used for the optimization is the sensitivity of the system output to the unknown parameters. Several simple examples are first given and then the results of an extensive stability and control dervative identification simulation for a C-8 aircraft are detailed.

Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

PubMed

Šiljić Tomić, Aleksandra N; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

2016-05-01

This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.

Power and Efficiency Optimized in Traveling-Wave Tubes Over a Broad Frequency Bandwidth

NASA Technical Reports Server (NTRS)

Wilson, Jeffrey D.

2001-01-01

A traveling-wave tube (TWT) is an electron beam device that is used to amplify electromagnetic communication waves at radio and microwave frequencies. TWT's are critical components in deep space probes, communication satellites, and high-power radar systems. Power conversion efficiency is of paramount importance for TWT's employed in deep space probes and communication satellites. A previous effort was very successful in increasing efficiency and power at a single frequency (ref. 1). Such an algorithm is sufficient for narrow bandwidth designs, but for optimal designs in applications that require high radiofrequency power over a wide bandwidth, such as high-density communications or high-resolution radar, the variation of the circuit response with respect to frequency must be considered. This work at the NASA Glenn Research Center is the first to develop techniques for optimizing TWT efficiency and output power over a broad frequency bandwidth (ref. 2). The techniques are based on simulated annealing, which has the advantage over conventional optimization techniques in that it enables the best possible solution to be obtained (ref. 3). Two new broadband simulated annealing algorithms were developed that optimize (1) minimum saturated power efficiency over a frequency bandwidth and (2) simultaneous bandwidth and minimum power efficiency over the frequency band with constant input power. The algorithms were incorporated into the NASA coupled-cavity TWT computer model (ref. 4) and used to design optimal phase velocity tapers using the 59- to 64-GHz Hughes 961HA coupled-cavity TWT as a baseline model. In comparison to the baseline design, the computational results of the first broad-band design algorithm show an improvement of 73.9 percent in minimum saturated efficiency (see the top graph). The second broadband design algorithm (see the bottom graph) improves minimum radiofrequency efficiency with constant input power drive by a factor of 2.7 at the high band edge (64 GHz) and increases simultaneous bandwidth by 500 MHz.

Command Generation and Control of Momentum Exchange Electrodynamic Reboost Tethered Satellite

NASA Technical Reports Server (NTRS)

Robertson, Michael J.

2005-01-01

The research completed for this NASA Graduate Student Research Program Fellowship sought to enhance the current state-of-the-art dynamic models and control laws for Momentum Exchange Electrodynamic Reboost satellite systems by utilizing command generation, specifically Input Shaping. The precise control of tethered spacecraft with flexible appendages is extremely difficult. The complexity is magnified many times when the satellite must interact with other satellites as in a momentum exchange via a tether. The Momentum Exchange Electronic Reboost Tether (MXER) concept encapsulates all of these challenging tasks [l]. Input Shaping is a command generation technique that allows flexible spacecraft to move without inducing residual vibration [2], limit transient deflection [3] and utilize fuel-efficient actuation [4]. Input shaping is implemented by convolving a sequence of impulses, known as the input shaper, with a desired system command to produce a shaped input that is then used to drive the system. This process is demonstrated in Figure 1. The shaped command is then use to drive the system without residual vibration while meeting many other performance specifications. The completed work developed tether control algorithms for retrieval. A simple model of the tether response has been developed and command shaping was implemented to minimize unwanted dynamics. A model of a flexible electrodynamic tether has been developed to investigate the tether s response during reboost. Command shaping techniques have been developed to eliminate the tether oscillations and reduce the tether s deflection to pre-specified levels during reboost. Additionally, a model for the spin-up of a tethered system was developed. This model was used in determining the parameters for optimization the resulting angular velocity.

Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features.

PubMed

Stember, Joseph N; Deng, Fang-Ming; Taneja, Samir S; Rosenkrantz, Andrew B

2014-08-01

To present results of a pilot study to develop software that identifies regions suspicious for prostate transition zone (TZ) tumor, free of user input. Eight patients with TZ tumors were used to develop the model by training a Naïve Bayes classifier to detect tumors based on selection of most accurate predictors among various signal and textural features on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps. Features tested as inputs were: average signal, signal standard deviation, energy, contrast, correlation, homogeneity and entropy (all defined on T2WI); and average ADC. A forward selection scheme was used on the remaining 20% of training set supervoxels to identify important inputs. The trained model was tested on a different set of ten patients, half with TZ tumors. In training cases, the software tiled the TZ with 4 × 4-voxel "supervoxels," 80% of which were used to train the classifier. Each of 100 iterations selected T2WI energy and average ADC, which therefore were deemed the optimal model input. The two-feature model was applied blindly to the separate set of test patients, again without operator input of suspicious foci. The software correctly predicted presence or absence of TZ tumor in all test patients. Furthermore, locations of predicted tumors corresponded spatially with locations of biopsies that had confirmed their presence. Preliminary findings suggest that this tool has potential to accurately predict TZ tumor presence and location, without operator input. © 2013 Wiley Periodicals, Inc.

A Measure Approximation for Distributionally Robust PDE-Constrained Optimization Problems

DOE PAGES

Kouri, Drew Philip

2017-12-19

In numerous applications, scientists and engineers acquire varied forms of data that partially characterize the inputs to an underlying physical system. This data is then used to inform decisions such as controls and designs. Consequently, it is critical that the resulting control or design is robust to the inherent uncertainties associated with the unknown probabilistic characterization of the model inputs. Here in this work, we consider optimal control and design problems constrained by partial differential equations with uncertain inputs. We do not assume a known probabilistic model for the inputs, but rather we formulate the problem as a distributionally robustmore » optimization problem where the outer minimization problem determines the control or design, while the inner maximization problem determines the worst-case probability measure that matches desired characteristics of the data. We analyze the inner maximization problem in the space of measures and introduce a novel measure approximation technique, based on the approximation of continuous functions, to discretize the unknown probability measure. Finally, we prove consistency of our approximated min-max problem and conclude with numerical results.« less

An Explicit Linear Filtering Solution for the Optimization of Guidance Systems with Statistical Inputs

NASA Technical Reports Server (NTRS)

Stewart, Elwood C.

1961-01-01

The determination of optimum filtering characteristics for guidance system design is generally a tedious process which cannot usually be carried out in general terms. In this report a simple explicit solution is given which is applicable to many different types of problems. It is shown to be applicable to problems which involve optimization of constant-coefficient guidance systems and time-varying homing type systems for several stationary and nonstationary inputs. The solution is also applicable to off-design performance, that is, the evaluation of system performance for inputs for which the system was not specifically optimized. The solution is given in generalized form in terms of the minimum theoretical error, the optimum transfer functions, and the optimum transient response. The effects of input signal, contaminating noise, and limitations on the response are included. From the results given, it is possible in an interception problem, for example, to rapidly assess the effects on minimum theoretical error of such factors as target noise and missile acceleration. It is also possible to answer important questions regarding the effect of type of target maneuver on optimum performance.

Reliability of system for precise cold forging

NASA Astrophysics Data System (ADS)

Krušič, Vid; Rodič, Tomaž

2017-07-01

The influence of scatter of principal input parameters of the forging system on the dimensional accuracy of product and on the tool life for closed-die forging process is presented in this paper. Scatter of the essential input parameters for the closed-die upsetting process was adjusted to the maximal values that enabled the reliable production of a dimensionally accurate product at optimal tool life. An operating window was created in which exists the maximal scatter of principal input parameters for the closed-die upsetting process that still ensures the desired dimensional accuracy of the product and the optimal tool life. Application of the adjustment of the process input parameters is shown on the example of making an inner race of homokinetic joint from mass production. High productivity in manufacture of elements by cold massive extrusion is often achieved by multiple forming operations that are performed simultaneously on the same press. By redesigning the time sequences of forming operations at multistage forming process of starter barrel during the working stroke the course of the resultant force is optimized.

Sizing a rainwater harvesting cistern by minimizing costs

NASA Astrophysics Data System (ADS)

Pelak, Norman; Porporato, Amilcare

2016-10-01

Rainwater harvesting (RWH) has the potential to reduce water-related costs by providing an alternate source of water, in addition to relieving pressure on public water sources and reducing stormwater runoff. Existing methods for determining the optimal size of the cistern component of a RWH system have various drawbacks, such as specificity to a particular region, dependence on numerical optimization, and/or failure to consider the costs of the system. In this paper a formulation is developed for the optimal cistern volume which incorporates the fixed and distributed costs of a RWH system while also taking into account the random nature of the depth and timing of rainfall, with a focus on RWH to supply domestic, nonpotable uses. With rainfall inputs modeled as a marked Poisson process, and by comparing the costs associated with building a cistern with the costs of externally supplied water, an expression for the optimal cistern volume is found which minimizes the water-related costs. The volume is a function of the roof area, water use rate, climate parameters, and costs of the cistern and of the external water source. This analytically tractable expression makes clear the dependence of the optimal volume on the input parameters. An analysis of the rainfall partitioning also characterizes the efficiency of a particular RWH system configuration and its potential for runoff reduction. The results are compared to the RWH system at the Duke Smart Home in Durham, NC, USA to show how the method could be used in practice.

VISIR-I: small vessels, least-time nautical routes using wave forecasts

NASA Astrophysics Data System (ADS)

Mannarini, G.; Pinardi, N.; Coppini, G.; Oddo, P.; Iafrati, A.

2015-09-01

A new numerical model for the on-demand computation of optimal ship routes based on sea-state forecasts has been developed. The model, named VISIR (discoVerIng Safe and effIcient Routes) is designed to support decision-makers when planning a marine voyage. The first version of the system, VISIR-I, considers medium and small motor vessels with lengths of up to a few tens of meters and a displacement hull. The model is made up of three components: the route optimization algorithm, the mechanical model of the ship, and the environmental fields. The optimization algorithm is based on a graph-search method with time-dependent edge weights. The algorithm is also able to compute a voluntary ship speed reduction. The ship model accounts for calm water and added wave resistance by making use of just the principal particulars of the vessel as input parameters. The system also checks the optimal route for parametric roll, pure loss of stability, and surfriding/broaching-to hazard conditions. Significant wave height, wave spectrum peak period, and wave direction forecast fields are employed as an input. Examples of VISIR-I routes in the Mediterranean Sea are provided. The optimal route may be longer in terms of miles sailed and yet it is faster and safer than the geodetic route between the same departure and arrival locations. Route diversions result from the safety constraints and the fact that the algorithm takes into account the full temporal evolution and spatial variability of the environmental fields.

A novel experimental design method to optimize hydrophilic matrix formulations with drug release profiles and mechanical properties.

PubMed

Choi, Du Hyung; Lim, Jun Yeul; Shin, Sangmun; Choi, Won Jun; Jeong, Seong Hoon; Lee, Sangkil

2014-10-01

To investigate the effects of hydrophilic polymers on the matrix system, an experimental design method was developed to integrate response surface methodology and the time series modeling. Moreover, the relationships among polymers on the matrix system were studied with the evaluation of physical properties including water uptake, mass loss, diffusion, and gelling index. A mixture simplex lattice design was proposed while considering eight input control factors: Polyethylene glycol 6000 (x1 ), polyethylene oxide (PEO) N-10 (x2 ), PEO 301 (x3 ), PEO coagulant (x4 ), PEO 303 (x5 ), hydroxypropyl methylcellulose (HPMC) 100SR (x6 ), HPMC 4000SR (x7 ), and HPMC 10(5) SR (x8 ). With the modeling, optimal formulations were obtained depending on the four types of targets. The optimal formulations showed the four significant factors (x1 , x2 , x3 , and x8 ) and other four input factors (x4 , x5 , x6 , and x7 ) were not significant based on drug release profiles. Moreover, the optimization results were analyzed with estimated values, targets values, absolute biases, and relative biases based on observed times for the drug release rates with four different targets. The result showed that optimal solutions and target values had consistent patterns with small biases. On the basis of the physical properties of the optimal solutions, the type and ratio of the hydrophilic polymer and the relationships between polymers significantly influenced the physical properties of the system and drug release. This experimental design method is very useful in formulating a matrix system with optimal drug release. Moreover, it can distinctly confirm the relationships between excipients and the effects on the system with extensive and intensive evaluations. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

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.

A method to stabilize linear systems using eigenvalue gradient information

NASA Technical Reports Server (NTRS)

Wieseman, C. D.

1985-01-01

Formal optimization methods and eigenvalue gradient information are used to develop a stabilizing control law for a closed loop linear system that is initially unstable. The method was originally formulated by using direct, constrained optimization methods with the constraints being the real parts of the eigenvalues. However, because of problems in trying to achieve stabilizing control laws, the problem was reformulated to be solved differently. The method described uses the Davidon-Fletcher-Powell minimization technique to solve an indirect, constrained minimization problem in which the performance index is the Kreisselmeier-Steinhauser function of the real parts of all the eigenvalues. The method is applied successfully to solve two different problems: the determination of a fourth-order control law stabilizes a single-input single-output active flutter suppression system and the determination of a second-order control law for a multi-input multi-output lateral-directional flight control system. Various sets of design variables and initial starting points were chosen to show the robustness of the method.

Numerical simulation of multi-rifled tube drawing - finding proper feedstock dimensions and tool geometry

NASA Astrophysics Data System (ADS)

Bella, P.; Buček, P.; Ridzoň, M.; Mojžiš, M.; Parilák, L.'

2017-02-01

Production of multi-rifled seamless steel tubes is quite a new technology in Železiarne Podbrezová. Therefore, a lot of technological questions emerges (process technology, input feedstock dimensions, material flow during drawing, etc.) Pilot experiments to fine tune the process cost a lot of time and energy. For this, numerical simulation would be an alternative solution for achieving optimal parameters in production technology. This would reduce the number of experiments needed, lowering the overall costs of development. However, to claim the numerical results to be relevant it is necessary to verify them against the actual plant trials. Searching for optimal input feedstock dimension for drawing of multi-rifled tube with dimensions Ø28.6 mm × 6.3 mm is what makes the main topic of this paper. As a secondary task, effective position of the plug - die couple has been solved via numerical simulation. Comparing the calculated results with actual numbers from plant trials a good agreement was observed.

Gust alleviation for a STOL transport by using elevator, spoilers, and flaps

NASA Technical Reports Server (NTRS)

Lallman, F. J.

1974-01-01

Control laws were developed to investigate methods of alleviating the response of a STOL transport to gusty air. The transport considered in the study had triple-slotted, externally blown jet flaps and a large T-tail. The control devices used were the elevator, spoilers, and flaps. A hybrid computing system was used to simulate linearized longitudinal dynamics of the aircraft and to implement a conjugate gradient optimal search algorithm. The aircraft was simulated in the low-speed approach condition only. Feedback control matrices were found which minimized the average of a quadratic functional involving passenger compartment accelerations, pitch angle and rate, flight path angle and speed variations. The optimization was performed for artificially designed gust inputs in the form of predetermined rectangular waveforms. Results were obtained for elevator, spoilers, and flaps acting singly and in combination. Additional results were obtained for unit sinusoidal gust inputs by using the gain matrices computed for the artificial test gusts. Various sensor configurations were also investigated.

Robust optimization based energy dispatch in smart grids considering demand uncertainty

NASA Astrophysics Data System (ADS)

Nassourou, M.; Puig, V.; Blesa, J.

2017-01-01

In this study we discuss the application of robust optimization to the problem of economic energy dispatch in smart grids. Robust optimization based MPC strategies for tackling uncertain load demands are developed. Unexpected additive disturbances are modelled by defining an affine dependence between the control inputs and the uncertain load demands. The developed strategies were applied to a hybrid power system connected to an electrical power grid. Furthermore, to demonstrate the superiority of the standard Economic MPC over the MPC tracking, a comparison (e.g average daily cost) between the standard MPC tracking, the standard Economic MPC, and the integration of both in one-layer and two-layer approaches was carried out. The goal of this research is to design a controller based on Economic MPC strategies, that tackles uncertainties, in order to minimise economic costs and guarantee service reliability of the system.

The Optimal Level of Insulation in a Home Attic

ERIC Educational Resources Information Center

Martin, Paul; Premadasa, Kirthi

2012-01-01

The project models the conductive heat loss through the ceiling of a home. Students are led through a sequence of tasks from measuring the area and insulation status of a home to developing several functions leading to a net savings function where the depth of insulation is the input. At this point students use calculus or a graphing utility to…

Economic Planning for Multicounty Rural Areas: Application of a Linear Programming Model in Northwest Arkansas. Technical Bulletin No. 1653.

ERIC Educational Resources Information Center

Williams, Daniel G.

Planners in multicounty rural areas can use the Rural Development, Activity Analysis Planning (RDAAP) model to try to influence the optimal growth of their areas among different general economic goals. The model implies that best industries for rural areas have: high proportion of imported inputs; low transportation costs; high value added/output…

Simplified analytical model and balanced design approach for light-weight wood-based structural panel in bending

Treesearch

Jinghao Li; John F. Hunt; Shaoqin Gong; Zhiyong Cai

2016-01-01

This paper presents a simplified analytical model and balanced design approach for modeling lightweight wood-based structural panels in bending. Because many design parameters are required to input for the model of finite element analysis (FEA) during the preliminary design process and optimization, the equivalent method was developed to analyze the mechanical...

Optimal Guaranteed Cost Sliding Mode Control for Constrained-Input Nonlinear Systems With Matched and Unmatched Disturbances.

PubMed

Zhang, Huaguang; Qu, Qiuxia; Xiao, Geyang; Cui, Yang

2018-06-01

Based on integral sliding mode and approximate dynamic programming (ADP) theory, a novel optimal guaranteed cost sliding mode control is designed for constrained-input nonlinear systems with matched and unmatched disturbances. When the system moves on the sliding surface, the optimal guaranteed cost control problem of sliding mode dynamics is transformed into the optimal control problem of a reformulated auxiliary system with a modified cost function. The ADP algorithm based on single critic neural network (NN) is applied to obtain the approximate optimal control law for the auxiliary system. Lyapunov techniques are used to demonstrate the convergence of the NN weight errors. In addition, the derived approximate optimal control is verified to guarantee the sliding mode dynamics system to be stable in the sense of uniform ultimate boundedness. Some simulation results are presented to verify the feasibility of the proposed control scheme.

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

NASA Technical Reports Server (NTRS)

Orme, John S.; Gilyard, Glenn B.

1992-01-01

Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust.

Preform Characterization in VARTM Process Model Development

NASA Technical Reports Server (NTRS)

Grimsley, Brian W.; Cano, Roberto J.; Hubert, Pascal; Loos, Alfred C.; Kellen, Charles B.; Jensen, Brian J.

2004-01-01

Vacuum-Assisted Resin Transfer Molding (VARTM) is a Liquid Composite Molding (LCM) process where both resin injection and fiber compaction are achieved under pressures of 101.3 kPa or less. Originally developed over a decade ago for marine composite fabrication, VARTM is now considered a viable process for the fabrication of aerospace composites (1,2). In order to optimize and further improve the process, a finite element analysis (FEA) process model is being developed to include the coupled phenomenon of resin flow, preform compaction and resin cure. The model input parameters are obtained from resin and fiber-preform characterization tests. In this study, the compaction behavior and the Darcy permeability of a commercially available carbon fabric are characterized. The resulting empirical model equations are input to the 3- Dimensional Infiltration, version 5 (3DINFILv.5) process model to simulate infiltration of a composite panel.

Structural tailoring of engine blades (STAEBL) user's manual

NASA Technical Reports Server (NTRS)

Brown, K. W.

1985-01-01

This User's Manual contains instructions and demonstration case to prepare input data, run, and modify the Structural Tailoring of Engine Blades (STAEBL) computer code. STAEBL was developed to perform engine fan and compressor blade numerical optimizations. This blade optimization seeks a minimum weight or cost design that satisfies realistic blade design constraints, by tuning one to twenty design variables. The STAEBL constraint analyses include blade stresses, vibratory response, flutter, and foreign object damage. Blade design variables include airfoil thickness at several locations, blade chord, and construction variables: hole size for hollow blades, and composite material layup for composite blades.

The economics of project analysis: Optimal investment criteria and methods of study

NASA Technical Reports Server (NTRS)

Scriven, M. C.

1979-01-01

Insight is provided toward the development of an optimal program for investment analysis of project proposals offering commercial potential and its components. This involves a critique of economic investment criteria viewed in relation to requirements of engineering economy analysis. An outline for a systems approach to project analysis is given Application of the Leontief input-output methodology to analysis of projects involving multiple processes and products is investigated. Effective application of elements of neoclassical economic theory to investment analysis of project components is demonstrated. Patterns of both static and dynamic activity levels are incorporated.

X-48B Preliminary Flight Test Results

NASA Technical Reports Server (NTRS)

Taylor, Brian R.

2009-01-01

This slide presentation reviews the preliminary Flight tests of the X-48B development program. The X-48B is a blended wing body aircraft that is being used to test various features of the BWB concept. The research concerns the following: (1) Turbofan Development, (2) Intelligent Flight Control and Optimization, (3) Airdata Calibration (4) Parameter Identification (i.e., Determination of the parameters of a mathematical model of a system based on observation of the system inputs and response.)

Robust Controller for Turbulent and Convective Boundary Layers

DTIC Science & Technology

2006-08-01

filter and an optimal regulator. The Kalman filter equation and the optimal regulator equation corresponding to the state-space equations, (2.20), are...separate steady-state algebraic Riccati equations. The Kalman filter is used here as a state observer rather than as an estimator since no noises are...2001) which will not be repeated here. For robustness, in the design, the Kalman filter input matrix G has been set equal to the control input

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.

Optimized mode-field adapter for low-loss fused fiber bundle signal and pump combiners

NASA Astrophysics Data System (ADS)

Koška, Pavel; Baravets, Yauhen; Peterka, Pavel; Písařík, Michael; Bohata, Jan

2015-03-01

In our contribution we report novel mode field adapter incorporated inside bundled tapered pump and signal combiner. Pump and signal combiners are crucial component of contemporary double clad high power fiber lasers. Proposed combiner allows simultaneous matching to single mode core on input and output. We used advanced optimization techniques to match the combiner to a single mode core simultaneously on input and output and to minimalize losses of the combiner signal branch. We designed two arrangements of combiners' mode field adapters. Our numerical simulations estimates losses in signal branches of optimized combiners of 0.23 dB for the first design and 0.16 dB for the second design for SMF-28 input fiber and SMF-28 matched output double clad fiber for the wavelength of 2000 nm. The splice losses of the actual combiner are expected to be even lower thanks to dopant diffusion during the splicing process.

New concepts for HgI2 scintillator gamma ray spectroscopy

NASA Technical Reports Server (NTRS)

Iwanczyk, Jan S.

1994-01-01

The primary goals of this project are development of the technology for HgI2 photodetectors (PD's), development of a HgI2/scintillator gamma detector, development of electronics, and development of a prototype gamma spectrometer. Work on the HgI2 PD's involved HgI2 purification and crystal growth, detector surface and electrical contact studies, PD structure optimization, encapsulation and packaging, and testing. Work on the HgI2/scintillator gamma detector involved a study of the optical - mechanical coupling for the optimization of CsI(Tl)/HgI2 gamma ray detectors and determination of the relationship between resolution versus scintillator type and size. The development of the electronics focused on low noise amplification circuits using different preamp input FET's and the use of a coincidence technique to maximize the signal, minimize the noise contribution in the gamma spectra, and improve the overall system resolution.

PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization.

PubMed

Antanasijević, Davor Z; Pocajt, Viktor V; Povrenović, Dragan S; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A

2013-01-15

This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM(10) emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM(10) emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM(10) emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM(10) emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables. Copyright © 2012 Elsevier B.V. All rights reserved.

Robust on-off pulse control of flexible space vehicles

NASA Technical Reports Server (NTRS)

Wie, Bong; Sinha, Ravi

1993-01-01

The on-off reaction jet control system is often used for attitude and orbital maneuvering of various spacecraft. Future space vehicles such as the orbital transfer vehicles, orbital maneuvering vehicles, and space station will extensively use reaction jets for orbital maneuvering and attitude stabilization. The proposed robust fuel- and time-optimal control algorithm is used for a three-mass spacing model of flexible spacecraft. A fuel-efficient on-off control logic is developed for robust rest-to-rest maneuver of a flexible vehicle with minimum excitation of structural modes. The first part of this report is concerned with the problem of selecting a proper pair of jets for practical trade-offs among the maneuvering time, fuel consumption, structural mode excitation, and performance robustness. A time-optimal control problem subject to parameter robustness constraints is formulated and solved. The second part of this report deals with obtaining parameter insensitive fuel- and time- optimal control inputs by solving a constrained optimization problem subject to robustness constraints. It is shown that sensitivity to modeling errors can be significantly reduced by the proposed, robustified open-loop control approach. The final part of this report deals with sliding mode control design for uncertain flexible structures. The benchmark problem of a flexible structure is used as an example for the feedback sliding mode controller design with bounded control inputs and robustness to parameter variations is investigated.

Structural tailoring of advanced turboprops (STAT): User's manual

NASA Technical Reports Server (NTRS)

Brown, K. W.

1991-01-01

This user's manual describes the Structural Tailoring of Advanced Turboprops program. It contains instructions to prepare the input for optimization, blade geometry and analysis, geometry generation, and finite element program control. In addition, a sample input file is provided as well as a section describing special applications (i.e., non-standard input).

Growing a hypercubical output space in a self-organizing feature map.

PubMed

Bauer, H U; Villmann, T

1997-01-01

Neural maps project data from an input space onto a neuron position in a (often lower dimensional) output space grid in a neighborhood preserving way, with neighboring neurons in the output space responding to neighboring data points in the input space. A map-learning algorithm can achieve an optimal neighborhood preservation only, if the output space topology roughly matches the effective structure of the data in the input space. We here present a growth algorithm, called the GSOM or growing self-organizing map, which enhances a widespread map self-organization process, Kohonen's self-organizing feature map (SOFM), by an adaptation of the output space grid during learning. The GSOM restricts the output space structure to the shape of a general hypercubical shape, with the overall dimensionality of the grid and its extensions along the different directions being subject of the adaptation. This constraint meets the demands of many larger information processing systems, of which the neural map can be a part. We apply our GSOM-algorithm to three examples, two of which involve real world data. Using recently developed methods for measuring the degree of neighborhood preservation in neural maps, we find the GSOM-algorithm to produce maps which preserve neighborhoods in a nearly optimal fashion.

An online air pollution forecasting system using neural networks.

PubMed

Kurt, Atakan; Gulbagci, Betul; Karaca, Ferhat; Alagha, Omar

2008-07-01

In this work, an online air pollution forecasting system for Greater Istanbul Area is developed. The system predicts three air pollution indicator (SO(2), PM(10) and CO) levels for the next three days (+1, +2, and +3 days) using neural networks. AirPolTool, a user-friendly website (http://airpol.fatih.edu.tr), publishes +1, +2, and +3 days predictions of air pollutants updated twice a day. Experiments presented in this paper show that quite accurate predictions of air pollutant indicator levels are possible with a simple neural network. It is shown that further optimizations of the model can be achieved using different input parameters and different experimental setups. Firstly, +1, +2, and +3 days' pollution levels are predicted independently using same training data, then +2 and +3 days are predicted cumulatively using previously days predicted values. Better prediction results are obtained in the cumulative method. Secondly, the size of training data base used in the model is optimized. The best modeling performance with minimum error rate is achieved using 3-15 past days in the training data set. Finally, the effect of the day of week as an input parameter is investigated. Better forecasts with higher accuracy are observed using the day of week as an input parameter.

Optimized distributed computing environment for mask data preparation

NASA Astrophysics Data System (ADS)

Ahn, Byoung-Sup; Bang, Ju-Mi; Ji, Min-Kyu; Kang, Sun; Jang, Sung-Hoon; Choi, Yo-Han; Ki, Won-Tai; Choi, Seong-Woon; Han, Woo-Sung

2005-11-01

As the critical dimension (CD) becomes smaller, various resolution enhancement techniques (RET) are widely adopted. In developing sub-100nm devices, the complexity of optical proximity correction (OPC) is severely increased and applied OPC layers are expanded to non-critical layers. The transformation of designed pattern data by OPC operation causes complexity, which cause runtime overheads to following steps such as mask data preparation (MDP), and collapse of existing design hierarchy. Therefore, many mask shops exploit the distributed computing method in order to reduce the runtime of mask data preparation rather than exploit the design hierarchy. Distributed computing uses a cluster of computers that are connected to local network system. However, there are two things to limit the benefit of the distributing computing method in MDP. First, every sequential MDP job, which uses maximum number of available CPUs, is not efficient compared to parallel MDP job execution due to the input data characteristics. Second, the runtime enhancement over input cost is not sufficient enough since the scalability of fracturing tools is limited. In this paper, we will discuss optimum load balancing environment that is useful in increasing the uptime of distributed computing system by assigning appropriate number of CPUs for each input design data. We will also describe the distributed processing (DP) parameter optimization to obtain maximum throughput in MDP job processing.

Optimal Design of a Thermoelectric Cooling/Heating System for Car Seat Climate Control (CSCC)

NASA Astrophysics Data System (ADS)

Elarusi, Abdulmunaem; Attar, Alaa; Lee, Hosung

2017-04-01

In the present work, the optimum design of thermoelectric car seat climate control (CSCC) is studied analytically in an attempt to achieve high system efficiency. Optimal design of a thermoelectric device (element length, cross-section area and number of thermocouples) is carried out using our newly developed optimization method based on the ideal thermoelectric equations and dimensional analysis to improve the performance of the thermoelectric device in terms of the heating/cooling power and the coefficient of performance (COP). Then, a new innovative system design is introduced which also includes the optimum input current for the initial (transient) startup warming and cooling before the car heating ventilation and air conditioner (HVAC) is active in the cabin. The air-to-air heat exchanger's configuration was taken into account to investigate the optimal design of the CSCC.

Optimal modified tracking performance for MIMO networked control systems with communication constraints.

PubMed

Wu, Jie; Zhou, Zhu-Jun; Zhan, Xi-Sheng; Yan, Huai-Cheng; Ge, Ming-Feng

2017-05-01

This paper investigates the optimal modified tracking performance of multi-input multi-output (MIMO) networked control systems (NCSs) with packet dropouts and bandwidth constraints. Some explicit expressions are obtained by using co-prime factorization and the spectral decomposition technique. The obtained results show that the optimal modified tracking performance is related to the intrinsic properties of a given plant such as non-minimum phase (NMP) zeros, unstable poles, and their directions. Furthermore, the modified factor, packet dropouts probability and bandwidth also impact the optimal modified tracking performance of the NCSs. The optimal modified tracking performance with channel input power constraint is obtained by searching through all stabilizing two-parameter compensator. Finally, some typical examples are given to illustrate the effectiveness of the theoretical results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

OPDOT: A computer program for the optimum preliminary design of a transport airplane

NASA Technical Reports Server (NTRS)

Sliwa, S. M.; Arbuckle, P. D.

1980-01-01

A description of a computer program, OPDOT, for the optimal preliminary design of transport aircraft is given. OPDOT utilizes constrained parameter optimization to minimize a performance index (e.g., direct operating cost per block hour) while satisfying operating constraints. The approach in OPDOT uses geometric descriptors as independent design variables. The independent design variables are systematically iterated to find the optimum design. The technical development of the program is provided and a program listing with sample input and output are utilized to illustrate its use in preliminary design. It is not meant to be a user's guide, but rather a description of a useful design tool developed for studying the application of new technologies to transport airplanes.

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

PubMed

Chande, Ruchi D; Wayne, Jennifer S

2017-09-01

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

Multi-response optimization of process parameters for GTAW process in dissimilar welding of Incoloy 800HT and P91 steel by using grey relational analysis

NASA Astrophysics Data System (ADS)

vellaichamy, Lakshmanan; Paulraj, Sathiya

2018-02-01

The dissimilar welding of Incoloy 800HT and P91 steel using Gas Tungsten arc welding process (GTAW) This material is being used in the Nuclear Power Plant and Aerospace Industry based application because Incoloy 800HT possess good corrosion and oxidation resistance and P91 possess high temperature strength and creep resistance. This work discusses on multi-objective optimization using gray relational analysis (GRA) using 9CrMoV-N filler materials. The experiment conducted L9 orthogonal array. The input parameter are current, voltage, speed. The output response are Tensile strength, Hardness and Toughness. To optimize the input parameter and multiple output variable by using GRA. The optimal parameter is combination was determined as A2B1C1 so given input parameter welding current at 120 A, voltage at 16 V and welding speed at 0.94 mm/s. The output of the mechanical properties for best and least grey relational grade was validated by the metallurgical characteristics.

Design and multi-physics optimization of rotary MRF brakes

NASA Astrophysics Data System (ADS)

Topcu, Okan; Taşcıoğlu, Yiğit; Konukseven, Erhan İlhan

2018-03-01

Particle swarm optimization (PSO) is a popular method to solve the optimization problems. However, calculations for each particle will be excessive when the number of particles and complexity of the problem increases. As a result, the execution speed will be too slow to achieve the optimized solution. Thus, this paper proposes an automated design and optimization method for rotary MRF brakes and similar multi-physics problems. A modified PSO algorithm is developed for solving multi-physics engineering optimization problems. The difference between the proposed method and the conventional PSO is to split up the original single population into several subpopulations according to the division of labor. The distribution of tasks and the transfer of information to the next party have been inspired by behaviors of a hunting party. Simulation results show that the proposed modified PSO algorithm can overcome the problem of heavy computational burden of multi-physics problems while improving the accuracy. Wire type, MR fluid type, magnetic core material, and ideal current inputs have been determined by the optimization process. To the best of the authors' knowledge, this multi-physics approach is novel for optimizing rotary MRF brakes and the developed PSO algorithm is capable of solving other multi-physics engineering optimization problems. The proposed method has showed both better performance compared to the conventional PSO and also has provided small, lightweight, high impedance rotary MRF brake designs.

Workflow Optimization for Tuning Prostheses with High Input Channel

DTIC Science & Technology

2017-10-01

of Specific Aim 1 by driving a commercially available two DoF wrist and single DoF hand. The high -level control system will provide analog signals...AWARD NUMBER: W81XWH-16-1-0767 TITLE: Workflow Optimization for Tuning Prostheses with High Input Channel PRINCIPAL INVESTIGATOR: Daniel Merrill...Unlimited The views, opinions and/or findings contained in this report are those of the author(s) and should not be construed as an official Department

Application of optimization techniques to vehicle design: A review

NASA Technical Reports Server (NTRS)

Prasad, B.; Magee, C. L.

1984-01-01

The work that has been done in the last decade or so in the application of optimization techniques to vehicle design is discussed. Much of the work reviewed deals with the design of body or suspension (chassis) components for reduced weight. Also reviewed are studies dealing with system optimization problems for improved functional performance, such as ride or handling. In reviewing the work on the use of optimization techniques, one notes the transition from the rare mention of the methods in the 70's to an increased effort in the early 80's. Efficient and convenient optimization and analysis tools still need to be developed so that they can be regularly applied in the early design stage of the vehicle development cycle to be most effective. Based on the reported applications, an attempt is made to assess the potential for automotive application of optimization techniques. The major issue involved remains the creation of quantifiable means of analysis to be used in vehicle design. The conventional process of vehicle design still contains much experience-based input because it has not yet proven possible to quantify all important constraints. This restraint on the part of the analysis will continue to be a major limiting factor in application of optimization to vehicle design.

Analytical sizing methods for behind-the-meter battery storage

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

Wu, Di; Kintner-Meyer, Michael; Yang, Tao

In behind-the-meter application, battery storage system (BSS) is utilized to reduce a commercial or industrial customer’s payment for electricity use, including energy charge and demand charge. The potential value of BSS in payment reduction and the most economic size can be determined by formulating and solving standard mathematical programming problems. In this method, users input system information such as load profiles, energy/demand charge rates, and battery characteristics to construct a standard programming problem that typically involve a large number of constraints and decision variables. Such a large scale programming problem is then solved by optimization solvers to obtain numerical solutions.more » Such a method cannot directly link the obtained optimal battery sizes to input parameters and requires case-by-case analysis. In this paper, we present an objective quantitative analysis of costs and benefits of customer-side energy storage, and thereby identify key factors that affect battery sizing. Based on the analysis, we then develop simple but effective guidelines that can be used to determine the most cost-effective battery size or guide utility rate design for stimulating energy storage development. The proposed analytical sizing methods are innovative, and offer engineering insights on how the optimal battery size varies with system characteristics. We illustrate the proposed methods using practical building load profile and utility rate. The obtained results are compared with the ones using mathematical programming based methods for validation.« less

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two-input/two-output drone flight control system.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

PubMed

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Mathematical modelling of methanogenic reactor start-up: Importance of volatile fatty acids degrading population.

PubMed

Jabłoński, Sławomir J; Łukaszewicz, Marcin

2014-12-01

Development of balanced community of microorganisms is one of the obligatory for stable anaerobic digestion. Application of mathematical models might be helpful in development of reliable procedures during the process start-up period. Yet, the accuracy of forecast depends on the quality of input and parameters. In this study, the specific anaerobic activity (SAA) tests were applied in order to estimate microbial community structure. Obtained data was applied as input conditions for mathematical model of anaerobic digestion. The initial values of variables describing the amount of acetate and propionate utilizing microorganisms could be calculated on the basis of SAA results. The modelling based on those optimized variables could successfully reproduce the behavior of a real system during the continuous fermentation. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Teaching and Learning Activity Sequencing System using Distributed Genetic Algorithms

NASA Astrophysics Data System (ADS)

Matsui, Tatsunori; Ishikawa, Tomotake; Okamoto, Toshio

The purpose of this study is development of a supporting system for teacher's design of lesson plan. Especially design of lesson plan which relates to the new subject "Information Study" is supported. In this study, we developed a system which generates teaching and learning activity sequences by interlinking lesson's activities corresponding to the various conditions according to the user's input. Because user's input is multiple information, there will be caused contradiction which the system should solve. This multiobjective optimization problem is resolved by Distributed Genetic Algorithms, in which some fitness functions are defined with reference models on lesson, thinking and teaching style. From results of various experiments, effectivity and validity of the proposed methods and reference models were verified; on the other hand, some future works on reference models and evaluation functions were also pointed out.

Variational theory of the tapered impedance transformer

NASA Astrophysics Data System (ADS)

Erickson, Robert P.

2018-02-01

Superconducting amplifiers are key components of modern quantum information circuits. To minimize information loss and reduce oscillations, a tapered impedance transformer of new design is needed at the input/output for compliance with other 50 Ω components. We show that an optimal tapered transformer of length ℓ, joining the amplifier to the input line, can be constructed using a variational principle applied to the linearized Riccati equation describing the voltage reflection coefficient of the taper. For an incident signal of frequency ωo, the variational solution results in an infinite set of equivalent optimal transformers, each with the same form for the reflection coefficient, each able to eliminate input-line reflections. For the special case of optimal lossless transformers, the group velocity vg is shown to be constant, with characteristic impedance dependent on frequency ωc = πvg/ℓ. While these solutions inhibit input-line reflections only for frequency ωo, a subset of optimal lossless transformers with ωo significantly detuned from ωc does exhibit a wide bandpass. Specifically, by choosing ωo → 0 (ωo → ∞), we obtain a subset of optimal low-pass (high-pass) lossless tapers with bandwidth (0, ˜ ωc) [(˜ωc, ∞)]. From the subset of solutions, we derive both the wide-band low-pass and high-pass transformers, and we discuss the extent to which they can be realized given fabrication constraints. In particular, we demonstrate the superior reflection response of our high-pass transformer when compared to other taper designs. Our results have application to amplifiers, transceivers, and other components sensitive to impedance mismatch.

Round-Robin approach to data flow optimization

NASA Technical Reports Server (NTRS)

Witt, J.

1978-01-01

A large data base, circular in structure, was required (for the Voyager Mission to Jupiter/Saturn) with the capability to completely update the data every four hours during high activity periods. The data is stored in key ordered format for retrieval but is not input in key order. Existing access methods for large data bases with rapid data replacement by keys become inefficient as the volume of data being replaced grows. The Round-Robin method was developed to alleviate this problem. The Round-Robin access method allows rapid updating of the data with continuous self cleaning where the oldest data (by key) is deleted and the newest data (by key) is kept regardless of the order of input.

Mechanical System Analysis/Design Tool (MSAT) Quick Guide

NASA Technical Reports Server (NTRS)

Lee, HauHua; Kolb, Mark; Madelone, Jack

1998-01-01

MSAT is a unique multi-component multi-disciplinary tool that organizes design analysis tasks around object-oriented representations of configuration components, analysis programs and modules, and data transfer links between them. This creative modular architecture enables rapid generation of input stream for trade-off studies of various engine configurations. The data transfer links automatically transport output from one application as relevant input to the next application once the sequence is set up by the user. The computations are managed via constraint propagation - the constraints supplied by the user as part of any optimization module. The software can be used in the preliminary design stage as well as during the detail design of product development process.

Brief announcement: Hypergraph parititioning for parallel sparse matrix-matrix multiplication

DOE PAGES

Ballard, Grey; Druinsky, Alex; Knight, Nicholas; ...

2015-01-01

The performance of parallel algorithms for sparse matrix-matrix multiplication is typically determined by the amount of interprocessor communication performed, which in turn depends on the nonzero structure of the input matrices. In this paper, we characterize the communication cost of a sparse matrix-matrix multiplication algorithm in terms of the size of a cut of an associated hypergraph that encodes the computation for a given input nonzero structure. Obtaining an optimal algorithm corresponds to solving a hypergraph partitioning problem. Furthermore, our hypergraph model generalizes several existing models for sparse matrix-vector multiplication, and we can leverage hypergraph partitioners developed for that computationmore » to improve application-specific algorithms for multiplying sparse matrices.« less

Development of a Pattern Recognition Methodology for Determining Operationally Optimal Heat Balance Instrumentation Calibration Schedules

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

Kurt Beran; John Christenson; Dragos Nica

2002-12-15

The goal of the project is to enable plant operators to detect with high sensitivity and reliability the onset of decalibration drifts in all of the instrumentation used as input to the reactor heat balance calculations. To achieve this objective, the collaborators developed and implemented at DBNPS an extension of the Multivariate State Estimation Technique (MSET) pattern recognition methodology pioneered by ANAL. The extension was implemented during the second phase of the project and fully achieved the project goal.

Development of a software for quantitative evaluation radiotherapy target and organ-at-risk segmentation comparison.

PubMed

Kalpathy-Cramer, Jayashree; Awan, Musaddiq; Bedrick, Steven; Rasch, Coen R N; Rosenthal, David I; Fuller, Clifton D

2014-02-01

Modern radiotherapy requires accurate region of interest (ROI) inputs for plan optimization and delivery. Target delineation, however, remains operator-dependent and potentially serves as a major source of treatment delivery error. In order to optimize this critical, yet observer-driven process, a flexible web-based platform for individual and cooperative target delineation analysis and instruction was developed in order to meet the following unmet needs: (1) an open-source/open-access platform for automated/semiautomated quantitative interobserver and intraobserver ROI analysis and comparison, (2) a real-time interface for radiation oncology trainee online self-education in ROI definition, and (3) a source for pilot data to develop and validate quality metrics for institutional and cooperative group quality assurance efforts. The resultant software, Target Contour Testing/Instructional Computer Software (TaCTICS), developed using Ruby on Rails, has since been implemented and proven flexible, feasible, and useful in several distinct analytical and research applications.

Control of nonlinear systems represented in quasilinear form. Ph.D. Thesis, 1994 Final Report

NASA Technical Reports Server (NTRS)

Coetsee, Josef A.

1993-01-01

Methods to synthesize controllers for nonlinear systems are developed by exploiting the fact that under mild differentiability conditions, systems of the form: x-dot = f(x) + G(x)u can be represented in quasilinear form, viz: x-dot = A(x)x + B(x)u. Two classes of control methods are investigated. The first is zero-look-ahead control, where the control input depends only on the current values of A(x) and B(x). For this case the control input is computed by continuously solving a matrix Riccati equation as the system progresses along a trajectory. The second is controllers with look-ahead, where the control input depends on the future behavior of A(x) and B(x). These controllers use the similarity between quasilinear systems and linear time varying systems to find approximate solutions to optimal control type problems. The methods that are developed are not guaranteed to be globally stable. However in simulation studies they were found to be useful alternatives for synthesizing control laws for a general class of nonlinear systems.

Neonatal Restriction of Tactile Inputs Leads to Long-Lasting Impairments of Cross-Modal Processing

PubMed Central

Röder, Brigitte; Hanganu-Opatz, Ileana L.

2015-01-01

Optimal behavior relies on the combination of inputs from multiple senses through complex interactions within neocortical networks. The ontogeny of this multisensory interplay is still unknown. Here, we identify critical factors that control the development of visual-tactile processing by combining in vivo electrophysiology with anatomical/functional assessment of cortico-cortical communication and behavioral investigation of pigmented rats. We demonstrate that the transient reduction of unimodal (tactile) inputs during a short period of neonatal development prior to the first cross-modal experience affects feed-forward subcortico-cortical interactions by attenuating the cross-modal enhancement of evoked responses in the adult primary somatosensory cortex. Moreover, the neonatal manipulation alters cortico-cortical interactions by decreasing the cross-modal synchrony and directionality in line with the sparsification of direct projections between primary somatosensory and visual cortices. At the behavioral level, these functional and structural deficits resulted in lower cross-modal matching abilities. Thus, neonatal unimodal experience during defined developmental stages is necessary for setting up the neuronal networks of multisensory processing. PMID:26600123

Digital Imprinting of RNA Recognition and Processing on a Self-Assembled Nucleic Acid Matrix

NASA Astrophysics Data System (ADS)

Redhu, Shiv K.; Castronovo, Matteo; Nicholson, Allen W.

2013-08-01

The accelerating progress of research in nanomedicine and nanobiotechnology has included initiatives to develop highly-sensitive, high-throughput methods to detect biomarkers at the single-cell level. Current sensing approaches, however, typically involve integrative instrumentation that necessarily must balance sensitivity with rapidity in optimizing biomarker detection quality. We show here that laterally-confined, self-assembled monolayers of a short, double-stranded(ds)[RNA-DNA] chimera enable permanent digital detection of dsRNA-specific inputs. The action of ribonuclease III and the binding of an inactive, dsRNA-binding mutant can be permanently recorded by the input-responsive action of a restriction endonuclease that cleaves an ancillary reporter site within the dsDNA segment. The resulting irreversible height change of the arrayed ds[RNA-DNA], as measured by atomic force microscopy, provides a distinct digital output for each dsRNA-specific input. These findings provide the basis for developing imprinting-based bio-nanosensors, and reveal the versatility of AFM as a tool for characterizing the behaviour of highly-crowded biomolecules at the nanoscale.

Processing Oscillatory Signals by Incoherent Feedforward Loops

PubMed Central

Zhang, Carolyn; You, Lingchong

2016-01-01

From the timing of amoeba development to the maintenance of stem cell pluripotency, many biological signaling pathways exhibit the ability to differentiate between pulsatile and sustained signals in the regulation of downstream gene expression. While the networks underlying this signal decoding are diverse, many are built around a common motif, the incoherent feedforward loop (IFFL), where an input simultaneously activates an output and an inhibitor of the output. With appropriate parameters, this motif can exhibit temporal adaptation, where the system is desensitized to a sustained input. This property serves as the foundation for distinguishing input signals with varying temporal profiles. Here, we use quantitative modeling to examine another property of IFFLs—the ability to process oscillatory signals. Our results indicate that the system’s ability to translate pulsatile dynamics is limited by two constraints. The kinetics of the IFFL components dictate the input range for which the network is able to decode pulsatile dynamics. In addition, a match between the network parameters and input signal characteristics is required for optimal “counting”. We elucidate one potential mechanism by which information processing occurs in natural networks, and our work has implications in the design of synthetic gene circuits for this purpose. PMID:27623175

Implications of Preference and Problem Formulation on the Operating Policies of Complex Multi-Reservoir Systems

NASA Astrophysics Data System (ADS)

Quinn, J.; Reed, P. M.; Giuliani, M.; Castelletti, A.

2016-12-01

Optimizing the operations of multi-reservoir systems poses several challenges: 1) the high dimension of the problem's states and controls, 2) the need to balance conflicting multi-sector objectives, and 3) understanding how uncertainties impact system performance. These difficulties motivated the development of the Evolutionary Multi-Objective Direct Policy Search (EMODPS) framework, in which multi-reservoir operating policies are parameterized in a given family of functions and then optimized for multiple objectives through simulation over a set of stochastic inputs. However, properly framing these objectives remains a severe challenge and a neglected source of uncertainty. Here, we use EMODPS to optimize operating policies for a 4-reservoir system in the Red River Basin in Vietnam, exploring the consequences of optimizing to different sets of objectives related to 1) hydropower production, 2) meeting multi-sector water demands, and 3) providing flood protection to the capital city of Hanoi. We show how coordinated operation of the reservoirs can differ markedly depending on how decision makers weigh these concerns. Moreover, we illustrate how formulation choices that emphasize the mean, tail, or variability of performance across objective combinations must be evaluated carefully. Our results show that these choices can significantly improve attainable system performance, or yield severe unintended consequences. Finally, we show that satisfactory validation of the operating policies on a set of out-of-sample stochastic inputs depends as much or more on the formulation of the objectives as on effective optimization of the policies. These observations highlight the importance of carefully considering how we abstract stakeholders' objectives and of iteratively optimizing and visualizing multiple problem formulation hypotheses to ensure that we capture the most important tradeoffs that emerge from different stakeholder preferences.

Application of Interval Predictor Models to Space Radiation Shielding

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy,Daniel P.; Norman, Ryan B.; Blattnig, Steve R.

2016-01-01

This paper develops techniques for predicting the uncertainty range of an output variable given input-output data. These models are called Interval Predictor Models (IPM) because they yield an interval valued function of the input. This paper develops IPMs having a radial basis structure. This structure enables the formal description of (i) the uncertainty in the models parameters, (ii) the predicted output interval, and (iii) the probability that a future observation would fall in such an interval. In contrast to other metamodeling techniques, this probabilistic certi cate of correctness does not require making any assumptions on the structure of the mechanism from which data are drawn. Optimization-based strategies for calculating IPMs having minimal spread while containing all the data are developed. Constraints for bounding the minimum interval spread over the continuum of inputs, regulating the IPMs variation/oscillation, and centering its spread about a target point, are used to prevent data over tting. Furthermore, we develop an approach for using expert opinion during extrapolation. This metamodeling technique is illustrated using a radiation shielding application for space exploration. In this application, we use IPMs to describe the error incurred in predicting the ux of particles resulting from the interaction between a high-energy incident beam and a target.

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

PubMed

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-07-20

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

Statistics of optimal information flow in ensembles of regulatory motifs

NASA Astrophysics Data System (ADS)

Crisanti, Andrea; De Martino, Andrea; Fiorentino, Jonathan

2018-02-01

Genetic regulatory circuits universally cope with different sources of noise that limit their ability to coordinate input and output signals. In many cases, optimal regulatory performance can be thought to correspond to configurations of variables and parameters that maximize the mutual information between inputs and outputs. Since the mid-2000s, such optima have been well characterized in several biologically relevant cases. Here we use methods of statistical field theory to calculate the statistics of the maximal mutual information (the "capacity") achievable by tuning the input variable only in an ensemble of regulatory motifs, such that a single controller regulates N targets. Assuming (i) sufficiently large N , (ii) quenched random kinetic parameters, and (iii) small noise affecting the input-output channels, we can accurately reproduce numerical simulations both for the mean capacity and for the whole distribution. Our results provide insight into the inherent variability in effectiveness occurring in regulatory systems with heterogeneous kinetic parameters.

Computer simulation and design of a three degree-of-freedom shoulder module

NASA Technical Reports Server (NTRS)

Marco, David; Torfason, L.; Tesar, Delbert

1989-01-01

An in-depth kinematic analysis of a three degree of freedom fully-parallel robotic shoulder module is presented. The major goal of the analysis is to determine appropriate link dimensions which will provide a maximized workspace along with desirable input to output velocity and torque amplification. First order kinematic influence coefficients which describe the output velocity properties in terms of actuator motions provide a means to determine suitable geometric dimensions for the device. Through the use of computer simulation, optimal or near optimal link dimensions based on predetermined design criteria are provided for two different structural designs of the mechanism. The first uses three rotational inputs to control the output motion. The second design involves the use of four inputs, actuating any three inputs for a given position of the output link. Alternative actuator placements are examined to determine the most effective approach to control the output motion.

Noniterative computation of infimum in H(infinity) optimisation for plants with invariant zeros on the j(omega)-axis

NASA Technical Reports Server (NTRS)

Chen, B. M.; Saber, A.

1993-01-01

A simple and noniterative procedure for the computation of the exact value of the infimum in the singular H(infinity)-optimization problem is presented, as a continuation of our earlier work. Our problem formulation is general and we do not place any restrictions in the finite and infinite zero structures of the system, and the direct feedthrough terms between the control input and the controlled output variables and between the disturbance input and the measurement output variables. Our method is applicable to a class of singular H(infinity)-optimization problems for which the transfer functions from the control input to the controlled output and from the disturbance input to the measurement output satisfy certain geometric conditions. In particular, the paper extends the result of earlier work by allowing these two transfer functions to have invariant zeros on the j(omega) axis.

Directed Design of Experiments for Validating Probability of Detection Capability of a Testing System

NASA Technical Reports Server (NTRS)

Generazio, Edward R. (Inventor)

2012-01-01

A method of validating a probability of detection (POD) testing system using directed design of experiments (DOE) includes recording an input data set of observed hit and miss or analog data for sample components as a function of size of a flaw in the components. The method also includes processing the input data set to generate an output data set having an optimal class width, assigning a case number to the output data set, and generating validation instructions based on the assigned case number. An apparatus includes a host machine for receiving the input data set from the testing system and an algorithm for executing DOE to validate the test system. The algorithm applies DOE to the input data set to determine a data set having an optimal class width, assigns a case number to that data set, and generates validation instructions based on the case number.

Content-aware photo collage using circle packing.

PubMed

Yu, Zongqiao; Lu, Lin; Guo, Yanwen; Fan, Rongfei; Liu, Mingming; Wang, Wenping

2014-02-01

In this paper, we present a novel approach for automatically creating the photo collage that assembles the interest regions of a given group of images naturally. Previous methods on photo collage are generally built upon a well-defined optimization framework, which computes all the geometric parameters and layer indices for input photos on the given canvas by optimizing a unified objective function. The complex nonlinear form of optimization function limits their scalability and efficiency. From the geometric point of view, we recast the generation of collage as a region partition problem such that each image is displayed in its corresponding region partitioned from the canvas. The core of this is an efficient power-diagram-based circle packing algorithm that arranges a series of circles assigned to input photos compactly in the given canvas. To favor important photos, the circles are associated with image importances determined by an image ranking process. A heuristic search process is developed to ensure that salient information of each photo is displayed in the polygonal area resulting from circle packing. With our new formulation, each factor influencing the state of a photo is optimized in an independent stage, and computation of the optimal states for neighboring photos are completely decoupled. This improves the scalability of collage results and ensures their diversity. We also devise a saliency-based image fusion scheme to generate seamless compositive collage. Our approach can generate the collages on nonrectangular canvases and supports interactive collage that allows the user to refine collage results according to his/her personal preferences. We conduct extensive experiments and show the superiority of our algorithm by comparing against previous methods.

Graphical user interface to optimize image contrast parameters used in object segmentation - biomed 2009.

PubMed

Anderson, Jeffrey R; Barrett, Steven F

2009-01-01

Image segmentation is the process of isolating distinct objects within an image. Computer algorithms have been developed to aid in the process of object segmentation, but a completely autonomous segmentation algorithm has yet to be developed [1]. This is because computers do not have the capability to understand images and recognize complex objects within the image. However, computer segmentation methods [2], requiring user input, have been developed to quickly segment objects in serial sectioned images, such as magnetic resonance images (MRI) and confocal laser scanning microscope (CLSM) images. In these cases, the segmentation process becomes a powerful tool in visualizing the 3D nature of an object. The user input is an important part of improving the performance of many segmentation methods. A double threshold segmentation method has been investigated [3] to separate objects in gray scaled images, where the gray level of the object is among the gray levels of the background. In order to best determine the threshold values for this segmentation method the image must be manipulated for optimal contrast. The same is true of other segmentation and edge detection methods as well. Typically, the better the image contrast, the better the segmentation results. This paper describes a graphical user interface (GUI) that allows the user to easily change image contrast parameters that will optimize the performance of subsequent object segmentation. This approach makes use of the fact that the human brain is extremely effective in object recognition and understanding. The GUI provides the user with the ability to define the gray scale range of the object of interest. These lower and upper bounds of this range are used in a histogram stretching process to improve image contrast. Also, the user can interactively modify the gamma correction factor that provides a non-linear distribution of gray scale values, while observing the corresponding changes to the image. This interactive approach gives the user the power to make optimal choices in the contrast enhancement parameters.

Optimization of the transmission of observable expectation values and observable statistics in continuous-variable teleportation

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

Albano Farias, L.; Stephany, J.

2010-12-15

We analyze the statistics of observables in continuous-variable (CV) quantum teleportation in the formalism of the characteristic function. We derive expressions for average values of output-state observables, in particular, cumulants which are additive in terms of the input state and the resource of teleportation. Working with a general class of teleportation resources, the squeezed-bell-like states, which may be optimized in a free parameter for better teleportation performance, we discuss the relation between resources optimal for fidelity and those optimal for different observable averages. We obtain the values of the free parameter of the squeezed-bell-like states which optimize the central momentamore » and cumulants up to fourth order. For the cumulants the distortion between in and out states due to teleportation depends only on the resource. We obtain optimal parameters {Delta}{sub (2)}{sup opt} and {Delta}{sub (4)}{sup opt} for the second- and fourth-order cumulants, which do not depend on the squeezing of the resource. The second-order central momenta, which are equal to the second-order cumulants, and the photon number average are also optimized by the resource with {Delta}{sub (2)}{sup opt}. We show that the optimal fidelity resource, which has been found previously to depend on the characteristics of input, approaches for high squeezing to the resource that optimizes the second-order momenta. A similar behavior is obtained for the resource that optimizes the photon statistics, which is treated here using the sum of the squared differences in photon probabilities of input versus output states as the distortion measure. This is interpreted naturally to mean that the distortions associated with second-order momenta dominate the behavior of the output state for large squeezing of the resource. Optimal fidelity resources and optimal photon statistics resources are compared, and it is shown that for mixtures of Fock states both resources are equivalent.« less

Fume generation and content of total chromium and hexavalent chromium in flux-cored arc welding.

PubMed

Yoon, Chung Sik; Paik, Nam Won; Kim, Jeong Han

2003-11-01

This study was performed to investigate the fume generation rates (FGRs) and the concentrations of total chromium and hexavalent chromium when stainless steel was welded using flux-cored arc welding (FCAW) with CO2 gas. FGRs and concentrations of total chromium and hexavalent chromium were quantified using a method recommended by the American Welding Society, inductively coupled plasma-atomic emission spectroscopy (NIOSH Method 7300) and ion chromatography (modified NIOSH Method 7604), respectively. The amount of total fume generated was significantly related to the level of input power. The ranges of FGR were 189-344, 389-698 and 682-1157 mg/min at low, optimal and high input power, respectively. It was found that the FGRs increased with input power by an exponent of 1.19, and increased with current by an exponent of 1.75. The ranges of total chromium fume generation rate (FGRCr) were 3.83-8.27, 12.75-37.25 and 38.79-76.46 mg/min at low, optimal and high input power, respectively. The ranges of hexavalent chromium fume generation rate (FGRCr6+) were 0.46-2.89, 0.76-6.28 and 1.70-11.21 mg/min at low, optimal and high input power, respectively. Thus, hexavalent chromium, which is known to be a carcinogen, generated 1.9 (1.0-2.7) times and 3.7 (2.4-5.0) times as the input power increased from low to optimal and low to high, respectively. As a function of input power, the concentration of total chromium in the fume increased from 1.57-2.65 to 5.45-8.13% while the concentration of hexavalent chromium ranged from 0.15 to 1.08%. The soluble fraction of hexavalent chromium produced by FCAW was approximately 80-90% of total hexavalent chromium. The concentration of total chromium and the solubility of hexavalent chromium were similar to those reported from other studies of shielded metal arc welding fumes, and the concentration of hexavalent chromium was similar to that obtained for metal inert gas-welding fumes.

Uncertainty in BMP evaluation and optimization for watershed management

NASA Astrophysics Data System (ADS)

Chaubey, I.; Cibin, R.; Sudheer, K.; Her, Y.

2012-12-01

Use of computer simulation models have increased substantially to make watershed management decisions and to develop strategies for water quality improvements. These models are often used to evaluate potential benefits of various best management practices (BMPs) for reducing losses of pollutants from sources areas into receiving waterbodies. Similarly, use of simulation models in optimizing selection and placement of best management practices under single (maximization of crop production or minimization of pollutant transport) and multiple objective functions has increased recently. One of the limitations of the currently available assessment and optimization approaches is that the BMP strategies are considered deterministic. Uncertainties in input data (e.g. precipitation, streamflow, sediment, nutrient and pesticide losses measured, land use) and model parameters may result in considerable uncertainty in watershed response under various BMP options. We have developed and evaluated options to include uncertainty in BMP evaluation and optimization for watershed management. We have also applied these methods to evaluate uncertainty in ecosystem services from mixed land use watersheds. In this presentation, we will discuss methods to to quantify uncertainties in BMP assessment and optimization solutions due to uncertainties in model inputs and parameters. We have used a watershed model (Soil and Water Assessment Tool or SWAT) to simulate the hydrology and water quality in mixed land use watershed located in Midwest USA. The SWAT model was also used to represent various BMPs in the watershed needed to improve water quality. SWAT model parameters, land use change parameters, and climate change parameters were considered uncertain. It was observed that model parameters, land use and climate changes resulted in considerable uncertainties in BMP performance in reducing P, N, and sediment loads. In addition, climate change scenarios also affected uncertainties in SWAT simulated crop yields. Considerable uncertainties in the net cost and the water quality improvements resulted due to uncertainties in land use, climate change, and model parameter values.

iTOUGH2 Universal Optimization Using the PEST Protocol

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

Finsterle, S.A.

2010-07-01

iTOUGH2 (http://www-esd.lbl.gov/iTOUGH2) is a computer program for parameter estimation, sensitivity analysis, and uncertainty propagation analysis [Finsterle, 2007a, b, c]. iTOUGH2 contains a number of local and global minimization algorithms for automatic calibration of a model against measured data, or for the solution of other, more general optimization problems (see, for example, Finsterle [2005]). A detailed residual and estimation uncertainty analysis is conducted to assess the inversion results. Moreover, iTOUGH2 can be used to perform a formal sensitivity analysis, or to conduct Monte Carlo simulations for the examination for prediction uncertainties. iTOUGH2's capabilities are continually enhanced. As the name implies, iTOUGH2more » is developed for use in conjunction with the TOUGH2 forward simulator for nonisothermal multiphase flow in porous and fractured media [Pruess, 1991]. However, iTOUGH2 provides FORTRAN interfaces for the estimation of user-specified parameters (see subroutine USERPAR) based on user-specified observations (see subroutine USEROBS). These user interfaces can be invoked to add new parameter or observation types to the standard set provided in iTOUGH2. They can also be linked to non-TOUGH2 models, i.e., iTOUGH2 can be used as a universal optimization code, similar to other model-independent, nonlinear parameter estimation packages such as PEST [Doherty, 2008] or UCODE [Poeter and Hill, 1998]. However, to make iTOUGH2's optimization capabilities available for use with an external code, the user is required to write some FORTRAN code that provides the link between the iTOUGH2 parameter vector and the input parameters of the external code, and between the output variables of the external code and the iTOUGH2 observation vector. While allowing for maximum flexibility, the coding requirement of this approach limits its applicability to those users with FORTRAN coding knowledge. To make iTOUGH2 capabilities accessible to many application models, the PEST protocol [Doherty, 2007] has been implemented into iTOUGH2. This protocol enables communication between the application (which can be a single 'black-box' executable or a script or batch file that calls multiple codes) and iTOUGH2. The concept requires that for the application model: (1) Input is provided on one or more ASCII text input files; (2) Output is returned to one or more ASCII text output files; (3) The model is run using a system command (executable or script/batch file); and (4) The model runs to completion without any user intervention. For each forward run invoked by iTOUGH2, select parameters cited within the application model input files are then overwritten with values provided by iTOUGH2, and select variables cited within the output files are extracted and returned to iTOUGH2. It should be noted that the core of iTOUGH2, i.e., its optimization routines and related analysis tools, remains unchanged; it is only the communication format between input parameters, the application model, and output variables that are borrowed from PEST. The interface routines have been provided by Doherty [2007]. The iTOUGH2-PEST architecture is shown in Figure 1. This manual contains installation instructions for the iTOUGH2-PEST module, and describes the PEST protocol as well as the input formats needed in iTOUGH2. Examples are provided that demonstrate the use of model-independent optimization and analysis using iTOUGH2.« less

Infrared Retrievals of Ice Cloud Properties and Uncertainties with an Optimal Estimation Retrieval Method

NASA Astrophysics Data System (ADS)

Wang, C.; Platnick, S. E.; Meyer, K.; Zhang, Z.

2014-12-01

We developed an optimal estimation (OE)-based method using infrared (IR) observations to retrieve ice cloud optical thickness (COT), cloud effective radius (CER), and cloud top height (CTH) simultaneously. The OE-based retrieval is coupled with a fast IR radiative transfer model (RTM) that simulates observations of different sensors, and corresponding Jacobians in cloudy atmospheres. Ice cloud optical properties are calculated using the MODIS Collection 6 (C6) ice crystal habit (severely roughened hexagonal column aggregates). The OE-based method can be applied to various IR space-borne and airborne sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the enhanced MODIS Airborne Simulator (eMAS), by optimally selecting IR bands with high information content. Four major error sources (i.e., the measurement error, fast RTM error, model input error, and pre-assumed ice crystal habit error) are taken into account in our OE retrieval method. We show that measurement error and fast RTM error have little impact on cloud retrievals, whereas errors from the model input and pre-assumed ice crystal habit significantly increase retrieval uncertainties when the cloud is optically thin. Comparisons between the OE-retrieved ice cloud properties and other operational cloud products (e.g., the MODIS C6 and CALIOP cloud products) are shown.

Optimal time-domain technique for pulse width modulation in power electronics

NASA Astrophysics Data System (ADS)

Mayergoyz, I.; Tyagi, S.

2018-05-01

Optimal time-domain technique for pulse width modulation is presented. It is based on exact and explicit analytical solutions for inverter circuits, obtained for any sequence of input voltage rectangular pulses. Two optimal criteria are discussed and illustrated by numerical examples.

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study.

PubMed

Hashim, H A; Abido, M A

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

Harmony search optimization in dimensional accuracy of die sinking EDM process using SS316L stainless steel

NASA Astrophysics Data System (ADS)

Deris, A. M.; Zain, A. M.; Sallehuddin, R.; Sharif, S.

2017-09-01

Electric discharge machine (EDM) is one of the widely used nonconventional machining processes for hard and difficult to machine materials. Due to the large number of machining parameters in EDM and its complicated structural, the selection of the optimal solution of machining parameters for obtaining minimum machining performance is remain as a challenging task to the researchers. This paper proposed experimental investigation and optimization of machining parameters for EDM process on stainless steel 316L work piece using Harmony Search (HS) algorithm. The mathematical model was developed based on regression approach with four input parameters which are pulse on time, peak current, servo voltage and servo speed to the output response which is dimensional accuracy (DA). The optimal result of HS approach was compared with regression analysis and it was found HS gave better result y giving the most minimum DA value compared with regression approach.

Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study

PubMed Central

Hashim, H. A.; Abido, M. A.

2015-01-01

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed. PMID:25960738

A tool for efficient, model-independent management optimization under uncertainty

USGS Publications Warehouse

White, Jeremy; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.

2018-01-01

To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

Fitting Nonlinear Curves by use of Optimization Techniques

NASA Technical Reports Server (NTRS)

Hill, Scott A.

2005-01-01

MULTIVAR is a FORTRAN 77 computer program that fits one of the members of a set of six multivariable mathematical models (five of which are nonlinear) to a multivariable set of data. The inputs to MULTIVAR include the data for the independent and dependent variables plus the user s choice of one of the models, one of the three optimization engines, and convergence criteria. By use of the chosen optimization engine, MULTIVAR finds values for the parameters of the chosen model so as to minimize the sum of squares of the residuals. One of the optimization engines implements a routine, developed in 1982, that utilizes the Broydon-Fletcher-Goldfarb-Shanno (BFGS) variable-metric method for unconstrained minimization in conjunction with a one-dimensional search technique that finds the minimum of an unconstrained function by polynomial interpolation and extrapolation without first finding bounds on the solution. The second optimization engine is a faster and more robust commercially available code, denoted Design Optimization Tool, that also uses the BFGS method. The third optimization engine is a robust and relatively fast routine that implements the Levenberg-Marquardt algorithm.

Carbon Dioxide Emission Peak and Green Innovation-Driven — Research of Escaping Middle Income Trap for China

NASA Astrophysics Data System (ADS)

Liu, Jianya; Sun, Zhenqing; Lan, Zirui; Kou, Chunxiao

2018-06-01

This study explains and demonstrates whether China has the capability to avoid the Middle Income Trap. The 19th National Congress of CPC report points out: by 2035, China will become an international leader in innovation. At present, China is in the juncture of changing the mode of development, optimizing the economic structure and transforming the growth momentum. The juncture means that it is possible to be stagnation or retrogression of national economy as the national ability of innovation is insufficient, then sticking in the middle-income trap (MIC) for a long time. In this paper, we used the TFP to prove that the input-output ratio of different regions of China, with the per capita GDP and carbon emissions are fitting again, dividing China into optimized zones and non-optimized zones. It can be seen from the results that the optimized zones have achieved the peak of carbon emission and had relative advantages in green innovation. However, if China wants to achieve her overall carbon emissions and get out of the middle income trap, she must optimize the development zones to spillover technologies and talents.

CORSS: Cylinder Optimization of Rings, Skin, and Stringers

NASA Technical Reports Server (NTRS)

Finckenor, J.; Rogers, P.; Otte, N.

1994-01-01

Launch vehicle designs typically make extensive use of cylindrical skin stringer construction. Structural analysis methods are well developed for preliminary design of this type of construction. This report describes an automated, iterative method to obtain a minimum weight preliminary design. Structural optimization has been researched extensively, and various programs have been written for this purpose. Their complexity and ease of use depends on their generality, the failure modes considered, the methodology used, and the rigor of the analysis performed. This computer program employs closed-form solutions from a variety of well-known structural analysis references and joins them with a commercially available numerical optimizer called the 'Design Optimization Tool' (DOT). Any ring and stringer stiffened shell structure of isotropic materials that has beam type loading can be analyzed. Plasticity effects are not included. It performs a more limited analysis than programs such as PANDA, but it provides an easy and useful preliminary design tool for a large class of structures. This report briefly describes the optimization theory, outlines the development and use of the program, and describes the analysis techniques that are used. Examples of program input and output, as well as the listing of the analysis routines, are included.

Optimal distribution of borehole geophones for monitoring CO2-injection-induced seismicity

NASA Astrophysics Data System (ADS)

Huang, L.; Chen, T.; Foxall, W.; Wagoner, J. L.

2016-12-01

The U.S. DOE initiative, National Risk Assessment Partnership (NRAP), aims to develop quantitative risk assessment methodologies for carbon capture, utilization and storage (CCUS). As part of tasks of the Strategic Monitoring Group of NRAP, we develop a tool for optimal design of a borehole geophones distribution for monitoring CO2-injection-induced seismicity. The tool consists of a number of steps, including building a geophysical model for a given CO2 injection site, defining target monitoring regions within CO2-injection/migration zones, generating synthetic seismic data, giving acceptable uncertainties in input data, and determining the optimal distribution of borehole geophones. We use a synthetic geophysical model as an example to demonstrate the capability our new tool to design an optimal/cost-effective passive seismic monitoring network using borehole geophones. The model is built based on the geologic features found at the Kimberlina CCUS pilot site located in southern San Joaquin Valley, California. This tool can provide CCUS operators with a guideline for cost-effective microseismic monitoring of geologic carbon storage and utilization.

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

NASA Astrophysics Data System (ADS)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

Development of Watch Schedule Using Rules Approach

NASA Astrophysics Data System (ADS)

Jurkevicius, Darius; Vasilecas, Olegas

The software for schedule creation and optimization solves a difficult, important and practical problem. The proposed solution is an online employee portal where administrator users can create and manage watch schedules and employee requests. Each employee can login with his/her own account and see his/her assignments, manage requests, etc. Employees set as administrators can perform the employee scheduling online, manage requests, etc. This scheduling software allows users not only to see the initial and optimized watch schedule in a simple and understandable form, but also to create special rules and criteria and input their business. The system using rules automatically will generate watch schedule.

Modeling of frequency agile devices: development of PKI neuromodeling library based on hierarchical network structure

NASA Astrophysics Data System (ADS)

Sanchez, P.; Hinojosa, J.; Ruiz, R.

2005-06-01

Recently, neuromodeling methods of microwave devices have been developed. These methods are suitable for the model generation of novel devices. They allow fast and accurate simulations and optimizations. However, the development of libraries makes these methods to be a formidable task, since they require massive input-output data provided by an electromagnetic simulator or measurements and repeated artificial neural network (ANN) training. This paper presents a strategy reducing the cost of library development with the advantages of the neuromodeling methods: high accuracy, large range of geometrical and material parameters and reduced CPU time. The library models are developed from a set of base prior knowledge input (PKI) models, which take into account the characteristics common to all the models in the library, and high-level ANNs which give the library model outputs from base PKI models. This technique is illustrated for a microwave multiconductor tunable phase shifter using anisotropic substrates. Closed-form relationships have been developed and are presented in this paper. The results show good agreement with the expected ones.

Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment.

PubMed

Berkes, Pietro; Orbán, Gergo; Lengyel, Máté; Fiser, József

2011-01-07

The brain maintains internal models of its environment to interpret sensory inputs and to prepare actions. Although behavioral studies have demonstrated that these internal models are optimally adapted to the statistics of the environment, the neural underpinning of this adaptation is unknown. Using a Bayesian model of sensory cortical processing, we related stimulus-evoked and spontaneous neural activities to inferences and prior expectations in an internal model and predicted that they should match if the model is statistically optimal. To test this prediction, we analyzed visual cortical activity of awake ferrets during development. Similarity between spontaneous and evoked activities increased with age and was specific to responses evoked by natural scenes. This demonstrates the progressive adaptation of internal models to the statistics of natural stimuli at the neural level.

Extension of an Object-Oriented Optimization Tool: User's Reference Manual

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Truong, Samson S.

2015-01-01

The National Aeronautics and Space Administration Armstrong Flight Research Center has developed a cost-effective and flexible object-oriented optimization (O (sup 3)) tool that leverages existing tools and practices and allows easy integration and adoption of new state-of-the-art software. This object-oriented framework can integrate the analysis codes for multiple disciplines, as opposed to relying on one code to perform analysis for all disciplines. Optimization can thus take place within each discipline module, or in a loop between the O (sup 3) tool and the discipline modules, or both. Six different sample mathematical problems are presented to demonstrate the performance of the O (sup 3) tool. Instructions for preparing input data for the O (sup 3) tool are detailed in this user's manual.

On Fusing Recursive Traversals of K-d Trees

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

Rajbhandari, Samyam; Kim, Jinsung; Krishnamoorthy, Sriram

Loop fusion is a key program transformation for data locality optimization that is implemented in production compilers. But optimizing compilers currently cannot exploit fusion opportunities across a set of recursive tree traversal computations with producer-consumer relationships. In this paper, we develop a compile-time approach to dependence characterization and program transformation to enable fusion across recursively specified traversals over k-ary trees. We present the FuseT source-to-source code transformation framework to automatically generate fused composite recursive operators from an input program containing a sequence of primitive recursive operators. We use our framework to implement fused operators for MADNESS, Multiresolution Adaptive Numerical Environmentmore » for Scientific Simulation. We show that locality optimization through fusion can offer more than an order of magnitude performance improvement.« less

Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New Tile-based Method for Large Biological Datasets.

PubMed

Shrimankar, D D; Sathe, S R

2016-01-01

Sequence alignment is an important tool for describing the relationships between DNA sequences. Many sequence alignment algorithms exist, differing in efficiency, in their models of the sequences, and in the relationship between sequences. The focus of this study is to obtain an optimal alignment between two sequences of biological data, particularly DNA sequences. The algorithm is discussed with particular emphasis on time, speedup, and efficiency optimizations. Parallel programming presents a number of critical challenges to application developers. Today's supercomputer often consists of clusters of SMP nodes. Programming paradigms such as OpenMP and MPI are used to write parallel codes for such architectures. However, the OpenMP programs cannot be scaled for more than a single SMP node. However, programs written in MPI can have more than single SMP nodes. But such a programming paradigm has an overhead of internode communication. In this work, we explore the tradeoffs between using OpenMP and MPI. We demonstrate that the communication overhead incurs significantly even in OpenMP loop execution and increases with the number of cores participating. We also demonstrate a communication model to approximate the overhead from communication in OpenMP loops. Our results are astonishing and interesting to a large variety of input data files. We have developed our own load balancing and cache optimization technique for message passing model. Our experimental results show that our own developed techniques give optimum performance of our parallel algorithm for various sizes of input parameter, such as sequence size and tile size, on a wide variety of multicore architectures.

Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New Tile-based Method for Large Biological Datasets

PubMed Central

Shrimankar, D. D.; Sathe, S. R.

2016-01-01

Sequence alignment is an important tool for describing the relationships between DNA sequences. Many sequence alignment algorithms exist, differing in efficiency, in their models of the sequences, and in the relationship between sequences. The focus of this study is to obtain an optimal alignment between two sequences of biological data, particularly DNA sequences. The algorithm is discussed with particular emphasis on time, speedup, and efficiency optimizations. Parallel programming presents a number of critical challenges to application developers. Today’s supercomputer often consists of clusters of SMP nodes. Programming paradigms such as OpenMP and MPI are used to write parallel codes for such architectures. However, the OpenMP programs cannot be scaled for more than a single SMP node. However, programs written in MPI can have more than single SMP nodes. But such a programming paradigm has an overhead of internode communication. In this work, we explore the tradeoffs between using OpenMP and MPI. We demonstrate that the communication overhead incurs significantly even in OpenMP loop execution and increases with the number of cores participating. We also demonstrate a communication model to approximate the overhead from communication in OpenMP loops. Our results are astonishing and interesting to a large variety of input data files. We have developed our own load balancing and cache optimization technique for message passing model. Our experimental results show that our own developed techniques give optimum performance of our parallel algorithm for various sizes of input parameter, such as sequence size and tile size, on a wide variety of multicore architectures. PMID:27932868

Techniques for designing rotorcraft control systems

NASA Technical Reports Server (NTRS)

Levine, William S.; Barlow, Jewel

1993-01-01

This report summarizes the work that was done on the project from 1 Apr. 1992 to 31 Mar. 1993. The main goal of this research is to develop a practical tool for rotorcraft control system design based on interactive optimization tools (CONSOL-OPTCAD) and classical rotorcraft design considerations (ADOCS). This approach enables the designer to combine engineering intuition and experience with parametric optimization. The combination should make it possible to produce a better design faster than would be possible using either pure optimization or pure intuition and experience. We emphasize that the goal of this project is not to develop an algorithm. It is to develop a tool. We want to keep the human designer in the design process to take advantage of his or her experience and creativity. The role of the computer is to perform the calculation necessary to improve and to display the performance of the nominal design. Briefly, during the first year we have connected CONSOL-OPTCAD, an existing software package for optimizing parameters with respect to multiple performance criteria, to a simplified nonlinear simulation of the UH-60 rotorcraft. We have also created mathematical approximations to the Mil-specs for rotorcraft handling qualities and input them into CONSOL-OPTCAD. Finally, we have developed the additional software necessary to use CONSOL-OPTCAD for the design of rotorcraft controllers.

Modeling and sliding mode predictive control of the ultra-supercritical boiler-turbine system with uncertainties and input constraints.

PubMed

Tian, Zhen; Yuan, Jingqi; Zhang, Xiang; Kong, Lei; Wang, Jingcheng

2018-05-01

The coordinated control system (CCS) serves as an important role in load regulation, efficiency optimization and pollutant reduction for coal-fired power plants. The CCS faces with tough challenges, such as the wide-range load variation, various uncertainties and constraints. This paper aims to improve the load tacking ability and robustness for boiler-turbine units under wide-range operation. To capture the key dynamics of the ultra-supercritical boiler-turbine system, a nonlinear control-oriented model is developed based on mechanism analysis and model reduction techniques, which is validated with the history operation data of a real 1000 MW unit. To simultaneously address the issues of uncertainties and input constraints, a discrete-time sliding mode predictive controller (SMPC) is designed with the dual-mode control law. Moreover, the input-to-state stability and robustness of the closed-loop system are proved. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves good tracking performance, disturbance rejection ability and compatibility to input constraints. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Constraint programming based biomarker optimization.

PubMed

Zhou, Manli; Luo, Youxi; Sun, Guoquan; Mai, Guoqin; Zhou, Fengfeng

2015-01-01

Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study investigates this question as fixing a few user-input features in the finally selected feature subset and formulates these user-input features as constraints for a programming model. The proposed algorithm, fsCoP (feature selection based on constrained programming), performs well similar to or much better than the existing feature selection algorithms, even with the constraints from both literature and the existing algorithms. An fsCoP biomarker may be intriguing for further wet lab validation, since it satisfies both the classification optimization function and the biomedical knowledge. fsCoP may also be used for the interactive exploration of bio-OMIC big data by interactively adding user-defined constraints for modeling.

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.

A computational algorithm for spacecraft control and momentum management

NASA Technical Reports Server (NTRS)

Dzielski, John; Bergmann, Edward; Paradiso, Joseph

1990-01-01

Developments in the area of nonlinear control theory have shown how coordinate changes in the state and input spaces of a dynamical system can be used to transform certain nonlinear differential equations into equivalent linear equations. These techniques are applied to the control of a spacecraft equipped with momentum exchange devices. An optimal control problem is formulated that incorporates a nonlinear spacecraft model. An algorithm is developed for solving the optimization problem using feedback linearization to transform to an equivalent problem involving a linear dynamical constraint and a functional approximation technique to solve for the linear dynamics in terms of the control. The original problem is transformed into an unconstrained nonlinear quadratic program that yields an approximate solution to the original problem. Two examples are presented to illustrate the results.

Application of Adjoint Methodology in Various Aspects of Sonic Boom Design

NASA Technical Reports Server (NTRS)

Rallabhandi, Sriram K.

2014-01-01

One of the advances in computational design has been the development of adjoint methods allowing efficient calculation of sensitivities in gradient-based shape optimization. This paper discusses two new applications of adjoint methodology that have been developed to aid in sonic boom mitigation exercises. In the first, equivalent area targets are generated using adjoint sensitivities of selected boom metrics. These targets may then be used to drive the vehicle shape during optimization. The second application is the computation of adjoint sensitivities of boom metrics on the ground with respect to parameters such as flight conditions, propagation sampling rate, and selected inputs to the propagation algorithms. These sensitivities enable the designer to make more informed selections of flight conditions at which the chosen cost functionals are less sensitive.

Optimizing microwave photodetection: input-output theory

NASA Astrophysics Data System (ADS)

Schöndorf, M.; Govia, L. C. G.; Vavilov, M. G.; McDermott, R.; Wilhelm, F. K.

2018-04-01

High fidelity microwave photon counting is an important tool for various areas from background radiation analysis in astronomy to the implementation of circuit quantum electrodynamic architectures for the realization of a scalable quantum information processor. In this work we describe a microwave photon counter coupled to a semi-infinite transmission line. We employ input-output theory to examine a continuously driven transmission line as well as traveling photon wave packets. Using analytic and numerical methods, we calculate the conditions on the system parameters necessary to optimize measurement and achieve high detection efficiency. With this we can derive a general matching condition depending on the different system rates, under which the measurement process is optimal.

Experimental Optimization of a Free-to-Rotate Wing for Small UAS

NASA Technical Reports Server (NTRS)

Logan, Michael J.; DeLoach, Richard; Copeland, Tiwana; Vo, Steven

2014-01-01

This paper discusses an experimental investigation conducted to optimize a free-to-rotate wing for use on a small unmanned aircraft system (UAS). Although free-to-rotate wings have been used for decades on various small UAS and small manned aircraft, little is known about how to optimize these unusual wings for a specific application. The paper discusses some of the design rationale of the basic wing. In addition, three main parameters were selected for "optimization", wing camber, wing pivot location, and wing center of gravity (c.g.) location. A small apparatus was constructed to enable some simple experimental analysis of these parameters. A design-of-experiment series of tests were first conducted to discern which of the main optimization parameters were most likely to have the greatest impact on the outputs of interest, namely, some measure of "stability", some measure of the lift being generated at the neutral position, and how quickly the wing "recovers" from an upset. A second set of tests were conducted to develop a response-surface numerical representation of these outputs as functions of the three primary inputs. The response surface numerical representations are then used to develop an "optimum" within the trade space investigated. The results of the optimization are then tested experimentally to validate the predictions.

Simulation based optimized beam velocity in additive manufacturing

NASA Astrophysics Data System (ADS)

Vignat, Frédéric; Béraud, Nicolas; Villeneuve, François

2017-08-01

Manufacturing good parts with additive technologies rely on melt pool dimension and temperature and are controlled by manufacturing strategies often decided on machine side. Strategies are built on beam path and variable energy input. Beam path are often a mix of contour and hatching strategies filling the contours at each slice. Energy input depend on beam intensity and speed and is determined from simple thermal models to control melt pool dimensions and temperature and ensure porosity free material. These models take into account variation in thermal environment such as overhanging surfaces or back and forth hatching path. However not all the situations are correctly handled and precision is limited. This paper proposes new method to determine energy input from full built chamber 3D thermal simulation. Using the results of the simulation, energy is modified to keep melt pool temperature in a predetermined range. The paper present first an experimental method to determine the optimal range of temperature. In a second part the method to optimize the beam speed from the simulation results is presented. Finally, the optimized beam path is tested in the EBM machine and built part are compared with part built with ordinary beam path.

Beam-energy-spread minimization using cell-timing optimization

NASA Astrophysics Data System (ADS)

Rose, C. R.; Ekdahl, C.; Schulze, M.

2012-04-01

Beam energy spread, and related beam motion, increase the difficulty in tuning for multipulse radiographic experiments at the dual-axis radiographic hydrodynamic test facility’s axis-II linear induction accelerator (LIA). In this article, we describe an optimization method to reduce the energy spread by adjusting the timing of the cell voltages (both unloaded and loaded), either advancing or retarding, such that the injector voltage and summed cell voltages in the LIA result in a flatter energy profile. We developed a nonlinear optimization routine which accepts as inputs the 74 cell-voltage, injector voltage, and beam current waveforms. It optimizes cell timing per user-selected groups of cells and outputs timing adjustments, one for each of the selected groups. To verify the theory, we acquired and present data for both unloaded and loaded cell-timing optimizations. For the unloaded cells, the preoptimization baseline energy spread was reduced by 34% and 31% for two shots as compared to baseline. For the loaded-cell case, the measured energy spread was reduced by 49% compared to baseline.

Multi-objective optimization in the development of oil and water repellent cellulose fabric based on response surface methodology and the desirability function

NASA Astrophysics Data System (ADS)

Ahmad, Naseer; Kamal, Shahid; Raza, Zulfiqar Ali; Hussain, Tanveer

2017-03-01

The present study investigated multi-response optimization of certain input parameters viz. concentrations of oil and water repellent finish (Oleophobol CP-C®), dimethylol dihydroxy ethylene urea based cross linking agent (Knittex FEL) and curing temperature on some mechanical, (i.e. tear and tensile strengths), functional (i.e., water contact angle ‘WCA’, oil contact angle ‘OCA’) and comfort (i.e. crease recovery angle ‘CRA’, air permeability ‘AP’, and stiffness) properties of an oleo-hydrophobic finished fabric under response surface methodology and the desirability function. The results have been examined using analysis of variance (ANOVA) and desirability function for the identification of optimum levels of input variables. The ANOVA was employed also to identify the percentage contribution of process factors. Under the optimized conditions, which were obtained with a total desirability value of 0.7769, the experimental values of Oleophobol CP-C® (O-CPC), Knittex FEL (K-FEL) and curing temperature (C-Temp) agreed closely with the predicted values. The optimized process parameters for maximum WCA (135°), OCA (129°), AP (290 m s-1), CRA (214°), tear (1492 gf) and tensile (764 N) strengths and minimum stiffness (3.2928 cm) were found to be: concentration of OCP-C as 44.44 g l-1, concentration of cross linker K-FEL as 32.07 g l-1 and C-Temp as 161.81 °C.

Optimal implementation of green infrastructure practices to reduce adverse impacts of urban areas on hydrology and water quality

NASA Astrophysics Data System (ADS)

Liu, Y.; Collingsworth, P.; Pijanowski, B. C.; Engel, B.

2016-12-01

Nutrient loading from Maumee River watershed is a significant reason for the harmful algal blooms (HABs) problem in Lake Erie. Although studies have explored strategies to reduce nutrient loading from agricultural areas in the Maumee River watershed, the nutrient loading in urban areas also needs to be reduced. Green infrastructure practices are popular approaches for stormwater management and useful for improving hydrology and water quality. In this study, the Long-Term Hydrologic Impact Assessment-Low Impact Development 2.1 (L-THIA-LID 2.1) model was used to determine how different strategies for implementing green infrastructure practices can be optimized to reduce impacts on hydrology and water quality in an urban watershed in the upper Maumee River system. Community inputs, such as the types of green infrastructure practices of greatest interest and environmental concerns for the community, were also considered during the study. Based on community input, the following environmental concerns were considered: runoff volume, Total Suspended Solids (TSS), Total Phosphorous (TP), Total Kjeldahl Nitrogen (TKN), and Nitrate+Nitrite (NOx); green infrastructure practices of interest included rain barrel, cistern, green roof, permeable patio, porous pavement, grassed swale, bioretention system, grass strip, wetland channel, detention basin, retention pond, and wetland basin. Spatial optimization of green infrastructure practice implementation was conducted to maximize environmental benefits while minimizing the cost of implementation. The green infrastructure practice optimization results can be used by the community to solve hydrology and water quality problems.

Simulation on an optimal combustion control strategy for 3-D temperature distributions in tangentially pc-fired utility boiler furnaces.

PubMed

Wang, Xi-fen; Zhou, Huai-chun

2005-01-01

The control of 3-D temperature distribution in a utility boiler furnace is essential for the safe, economic and clean operation of pc-fired furnace with multi-burner system. The development of the visualization of 3-D temperature distributions in pc-fired furnaces makes it possible for a new combustion control strategy directly with the furnace temperature as its goal to improve the control quality for the combustion processes. Studied in this paper is such a new strategy that the whole furnace is divided into several parts in the vertical direction, and the average temperature and its bias from the center in every cross section can be extracted from the visualization results of the 3-D temperature distributions. In the simulation stage, a computational fluid dynamics (CFD) code served to calculate the 3-D temperature distributions in a furnace, then a linear model was set up to relate the features of the temperature distributions with the input of the combustion processes, such as the flow rates of fuel and air fed into the furnaces through all the burners. The adaptive genetic algorithm was adopted to find the optimal combination of the whole input parameters which ensure to form an optimal 3-D temperature field in the furnace desired for the operation of boiler. Simulation results showed that the strategy could soon find the factors making the temperature distribution apart from the optimal state and give correct adjusting suggestions.

Simulation of a 20-ton LiBr/H{sub 2}O absorption cooling system

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

Wardono, B.; Nelson, R.M.

The possibility of using solar energy as the main heat input for cooling systems has led to several studies of available cooling technologies that use solar energy. The results show that double-effect absorption cooling systems give relatively high performance. To further study absorption cooling systems, a computer code was developed for a double-effect lithium bromide/water (LiBr/H{sub 2}O) absorption system. To evaluate the performance, two objective functions were developed including the coefficient of performance (COP) and the system cost. Based on the system cost, an optimization to find the minimum cost was performed to determine the nominal heat transfer areas ofmore » each heat exchanger. The nominal values of other system variables, such as the mass flow rates and inlet temperatures of the hot water, cooling water, and chilled water, are specified as commonly used values for commercial machines. The results of the optimization show that there are optimum heat transfer areas. In this study, hot water is used as the main energy input. Using a constant load of 20 tons cooling capacity, the effects of various variables including the heat transfer ares, mass flow rates, and inlet temperatures of hot water, cooling water, and chilled water are presented.« less

Optimally growing boundary layer disturbances in a convergent nozzle preceded by a circular pipe

NASA Astrophysics Data System (ADS)

Uzun, Ali; Davis, Timothy B.; Alvi, Farrukh S.; Hussaini, M. Yousuff

2017-06-01

We report the findings from a theoretical analysis of optimally growing disturbances in an initially turbulent boundary layer. The motivation behind this study originates from the desire to generate organized structures in an initially turbulent boundary layer via excitation by disturbances that are tailored to be preferentially amplified. Such optimally growing disturbances are of interest for implementation in an active flow control strategy that is investigated for effective jet noise control. Details of the optimal perturbation theory implemented in this study are discussed. The relevant stability equations are derived using both the standard decomposition and the triple decomposition. The chosen test case geometry contains a convergent nozzle, which generates a Mach 0.9 round jet, preceded by a circular pipe. Optimally growing disturbances are introduced at various stations within the circular pipe section to facilitate disturbance energy amplification upstream of the favorable pressure gradient zone within the convergent nozzle, which has a stabilizing effect on disturbance growth. Effects of temporal frequency, disturbance input and output plane locations as well as separation distance between output and input planes are investigated. The results indicate that optimally growing disturbances appear in the form of longitudinal counter-rotating vortex pairs, whose size can be on the order of several times the input plane mean boundary layer thickness. The azimuthal wavenumber, which represents the number of counter-rotating vortex pairs, is found to generally decrease with increasing separation distance. Compared to the standard decomposition, the triple decomposition analysis generally predicts relatively lower azimuthal wavenumbers and significantly reduced energy amplification ratios for the optimal disturbances.

Spermatozoa input concentrations and RNA isolation methods on RNA yield and quality in bull (Bos taurus).

PubMed

Parthipan, Sivashanmugam; Selvaraju, Sellappan; Somashekar, Lakshminarayana; Kolte, Atul P; Arangasamy, Arunachalam; Ravindra, Janivara Parameswaraiah

2015-08-01

Sperm RNA can be used to understand the past spermatogenic process, future successful fertilization, and embryo development. To study the sperm RNA composition and function, isolation of good quality RNA with sufficient quantity is essential. The objective of this study was to assess the influence of sperm input concentrations and RNA isolation methods on RNA yield and quality in bull sperm. The fresh semen samples from bulls (n = 6) were snap-frozen in liquid nitrogen and stored at -80 °C. The sperm RNA was isolated using membrane-based methods combined with TRIzol (RNeasy+TRIzol and PureLink+TRIzol) and conventional methods (TRIzol, Double TRIzol, and RNAzol RT). Based on fluorometric quantification, combined methods resulted in significantly (P < 0.05) higher total RNA yields (800-900 ng/30-40 × 10(6)) as compared with other methods and yielded 20 to 30 fg of RNA/spermatozoon. The quality of RNA isolated by membrane-based methods was superior to that isolated by conventional methods. The sperm RNA was observed to be intact as well as fragmented (50-2000 bp). The study revealed that the membrane-based methods with a cocktail of lysis solution and an optimal input concentration of 30 to 40 million sperm were optimal for maximum recovery of RNA from bull spermatozoa. Copyright © 2015 Elsevier Inc. All rights reserved.

Surrogates for numerical simulations; optimization of eddy-promoter heat exchangers

NASA Technical Reports Server (NTRS)

Patera, Anthony T.; Patera, Anthony

1993-01-01

Although the advent of fast and inexpensive parallel computers has rendered numerous previously intractable calculations feasible, many numerical simulations remain too resource-intensive to be directly inserted in engineering optimization efforts. An attractive alternative to direct insertion considers models for computational systems: the expensive simulation is evoked only to construct and validate a simplified, input-output model; this simplified input-output model then serves as a simulation surrogate in subsequent engineering optimization studies. A simple 'Bayesian-validated' statistical framework for the construction, validation, and purposive application of static computer simulation surrogates is presented. As an example, dissipation-transport optimization of laminar-flow eddy-promoter heat exchangers are considered: parallel spectral element Navier-Stokes calculations serve to construct and validate surrogates for the flowrate and Nusselt number; these surrogates then represent the originating Navier-Stokes equations in the ensuing design process.

Digital multi-channel stabilization of four-mode phase-sensitive parametric multicasting.

PubMed

Liu, Lan; Tong, Zhi; Wiberg, Andreas O J; Kuo, Bill P P; Myslivets, Evgeny; Alic, Nikola; Radic, Stojan

2014-07-28

Stable four-mode phase-sensitive (4MPS) process was investigated as a means to enhance two-pump driven parametric multicasting conversion efficiency (CE) and signal to noise ratio (SNR). Instability of multi-beam, phase sensitive (PS) device that inherently behaves as an interferometer, with output subject to ambient induced fluctuations, was addressed theoretically and experimentally. A new stabilization technique that controls phases of three input waves of the 4MPS multicaster and maximizes CE was developed and described. Stabilization relies on digital phase-locked loop (DPLL) specifically was developed to control pump phases to guarantee stable 4MPS operation that is independent of environmental fluctuations. The technique also controls a single (signal) input phase to optimize the PS-induced improvement of the CE and SNR. The new, continuous-operation DPLL has allowed for fully stabilized PS parametric broadband multicasting, demonstrating CE improvement over 20 signal copies in excess of 10 dB.

Developments in Human Centered Cueing Algorithms for Control of Flight Simulator Motion Systems

NASA Technical Reports Server (NTRS)

Houck, Jacob A.; Telban, Robert J.; Cardullo, Frank M.

1997-01-01

The authors conducted further research with cueing algorithms for control of flight simulator motion systems. A variation of the so-called optimal algorithm was formulated using simulated aircraft angular velocity input as a basis. Models of the human vestibular sensation system, i.e. the semicircular canals and otoliths, are incorporated within the algorithm. Comparisons of angular velocity cueing responses showed a significant improvement over a formulation using angular acceleration input. Results also compared favorably with the coordinated adaptive washout algorithm, yielding similar results for angular velocity cues while eliminating false cues and reducing the tilt rate for longitudinal cues. These results were confirmed in piloted tests on the current motion system at NASA-Langley, the Visual Motion Simulator (VMS). Proposed future developments by the authors in cueing algorithms are revealed. The new motion system, the Cockpit Motion Facility (CMF), where the final evaluation of the cueing algorithms will be conducted, is also described.

Development of cryogenic CMOS Readout ASICs for the Point-Contact HPGe Detectors for Dark Matter Search and Neutrino Experiments

NASA Astrophysics Data System (ADS)

Deng, Zhi; He, Li; Liu, Feng; Liu, Yinong; Xue, Tao; Li, Yulan; Yue, Qian

2017-05-01

The paper presents the developments of two cryogenic readout ASICs for the point-contact HPGe detectors for dark matter search and neutrino experiments. Extremely low noise readout electronics were demanded and the capability of working at cryogenic temperatures may bring great advantages. The first ASIC was a monolithic CMOS charge sensitive preamplifier with its noise optimized for ∼1 pF input capacitance. The second ASIC was a waveform recorder based on switched capacitor array. These two ASICs were fabricated in CMOS 350 nm and 180 nm processes respectively. The prototype chips were tested and showed promising results. Both ASICs worked well at low temperature. The preamplifier had achieved ENC of 10.3 electrons with 0.7 pF input capacitance and the SCA chip could run at 9 bit effective resolution and 25 MSPS sampling rate.

Design and Development of Turbodrill Blade Used in Crystallized Section

PubMed Central

Yu, Wang; Jianyi, Yao; Zhijun, Li

2014-01-01

Turbodrill is a type of hydraulic axial turbomachinery which has a multistage blade consisting of stators and rotors. In this paper, a turbodrill blade that can be applied in crystallized section under high temperature and pressure conditions is developed. On the basis of Euler equations, the law of energy transfer is analyzed and the output characteristics of turbodrill blade are proposed. Moreover, considering the properties of the layer and the bole-hole conditions, the radical size, the geometrical dimension, and the blade profile are optimized. A computational model of a single-stage blade is built on the ANSYS CFD into which the three-dimensional model of turbodrill is input. In light of the distribution law of the pressure and flow field, the functions of the turbodrill blade are improved and optimized. The turbodrill blade optimization model was verified based on laboratory experiments. The results show that the design meets the deep hard rock mineral exploration application and provides good references for further study. PMID:25276857

Analysis of EnergyPlus for use in residential building energy optimization

NASA Astrophysics Data System (ADS)

Spencer, Justin S.

This work explored the utility of EnergyPlus as a simulation engine for doing residential building energy optimization, with the objective of finding the modeling areas that require further development in EnergyPlus for residential optimization applications. This work was conducted primarily during 2006-2007, with publication occurring later in 2010. The assessments and recommendations apply to the simulation tool versions available in 2007. During this work, an EnergyPlus v2.0 (2007) input file generator was developed for use in BEopt 0.8.0.4 (2007). BEopt 0.8.0.4 is a residential Building Energy optimization program developed at the National Renewable Energy Laboratory in Golden, Colorado. Residential modeling capabilities of EnergyPlus v2.0 were scrutinized and tested. Modeling deficiencies were identified in a number of areas. These deficiencies were compared to deficiencies in the DOE2.2 V44E4(2007)/TRNSYS simulation engines. The highest priority gaps in EnergyPlus v2.0's residential modeling capability are in infiltration, duct leakage, and foundation modeling. Optimization results from DOE2.2 V44E4 and EnergyPlus v2.0 were analyzed to search for modeling differences that have a significant impact on optimization results. Optimal buildings at different energy savings levels were compared to look for biases. It was discovered that the EnergyPlus v2.0 optimizations consistently chose higher wall insulation levels than the DOE2.2 V44E4 optimizations. The points composing the optimal paths chosen by DOE2.2 V44E4 and EnergyPlus v2.0 were compared to look for points chosen by one optimization that were significantly different from the other optimal path. These outliers were compared to consensus optimal points to determine the simulation differences that cause disparities in the optimization results. The differences were primarily caused by modeling of window radiation exchange and HVAC autosizing.

Dynamic PET of human liver inflammation: impact of kinetic modeling with optimization-derived dual-blood input function.

PubMed

Wang, Guobao; Corwin, Michael T; Olson, Kristin A; Badawi, Ramsey D; Sarkar, Souvik

2018-05-30

The hallmark of nonalcoholic steatohepatitis is hepatocellular inflammation and injury in the setting of hepatic steatosis. Recent work has indicated that dynamic 18F-FDG PET with kinetic modeling has the potential to assess hepatic inflammation noninvasively, while static FDG-PET did not show a promise. Because the liver has dual blood supplies, kinetic modeling of dynamic liver PET data is challenging in human studies. The objective of this study is to evaluate and identify a dual-input kinetic modeling approach for dynamic FDG-PET of human liver inflammation. Fourteen human patients with nonalcoholic fatty liver disease were included in the study. Each patient underwent one-hour dynamic FDG-PET/CT scan and had liver biopsy within six weeks. Three models were tested for kinetic analysis: traditional two-tissue compartmental model with an image-derived single-blood input function (SBIF), model with population-based dual-blood input function (DBIF), and modified model with optimization-derived DBIF through a joint estimation framework. The three models were compared using Akaike information criterion (AIC), F test and histopathologic inflammation reference. The results showed that the optimization-derived DBIF model improved the fitting of liver time activity curves and achieved lower AIC values and higher F values than the SBIF and population-based DBIF models in all patients. The optimization-derived model significantly increased FDG K1 estimates by 101% and 27% as compared with traditional SBIF and population-based DBIF. K1 by the optimization-derived model was significantly associated with histopathologic grades of liver inflammation while the other two models did not provide a statistical significance. In conclusion, modeling of DBIF is critical for kinetic analysis of dynamic liver FDG-PET data in human studies. The optimization-derived DBIF model is more appropriate than SBIF and population-based DBIF for dynamic FDG-PET of liver inflammation. © 2018 Institute of Physics and Engineering in Medicine.

An input-to-state stability approach to verify almost global stability of a synchronous-machine-infinite-bus system.

PubMed

Schiffer, Johannes; Efimov, Denis; Ortega, Romeo; Barabanov, Nikita

2017-08-13

Conditions for almost global stability of an operating point of a realistic model of a synchronous generator with constant field current connected to an infinite bus are derived. The analysis is conducted by employing the recently proposed concept of input-to-state stability (ISS)-Leonov functions, which is an extension of the powerful cell structure principle developed by Leonov and Noldus to the ISS framework. Compared with the original ideas of Leonov and Noldus, the ISS-Leonov approach has the advantage of providing additional robustness guarantees. The efficiency of the derived sufficient conditions is illustrated via numerical experiments.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).

Multi-mode horn

NASA Technical Reports Server (NTRS)

Neilson, Jeffrey M. (Inventor)

2002-01-01

A horn has an input aperture and an output aperture, and comprises a conductive inner surface formed by rotating a curve about a central axis. The curve comprises a first arc having an input aperture end and a transition end, and a second arc having a transition end and an output aperture end. When rotated about the central axis, the first arc input aperture end forms an input aperture, and the second arc output aperture end forms an output aperture. The curve is then optimized to provide a mode conversion which maximizes the power transfer of input energy to the Gaussian mode at the output aperture.

Artificial neural network intelligent method for prediction

NASA Astrophysics Data System (ADS)

Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

2017-09-01

Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

Application and optimization of input parameter spaces in mass flow modelling: a case study with r.randomwalk and r.ranger

NASA Astrophysics Data System (ADS)

Krenn, Julia; Zangerl, Christian; Mergili, Martin

2017-04-01

r.randomwalk is a GIS-based, multi-functional, conceptual open source model application for forward and backward analyses of the propagation of mass flows. It relies on a set of empirically derived, uncertain input parameters. In contrast to many other tools, r.randomwalk accepts input parameter ranges (or, in case of two or more parameters, spaces) in order to directly account for these uncertainties. Parameter spaces represent a possibility to withdraw from discrete input values which in most cases are likely to be off target. r.randomwalk automatically performs multiple calculations with various parameter combinations in a given parameter space, resulting in the impact indicator index (III) which denotes the fraction of parameter value combinations predicting an impact on a given pixel. Still, there is a need to constrain the parameter space used for a certain process type or magnitude prior to performing forward calculations. This can be done by optimizing the parameter space in terms of bringing the model results in line with well-documented past events. As most existing parameter optimization algorithms are designed for discrete values rather than for ranges or spaces, the necessity for a new and innovative technique arises. The present study aims at developing such a technique and at applying it to derive guiding parameter spaces for the forward calculation of rock avalanches through back-calculation of multiple events. In order to automatize the work flow we have designed r.ranger, an optimization and sensitivity analysis tool for parameter spaces which can be directly coupled to r.randomwalk. With r.ranger we apply a nested approach where the total value range of each parameter is divided into various levels of subranges. All possible combinations of subranges of all parameters are tested for the performance of the associated pattern of III. Performance indicators are the area under the ROC curve (AUROC) and the factor of conservativeness (FoC). This strategy is best demonstrated for two input parameters, but can be extended arbitrarily. We use a set of small rock avalanches from western Austria, and some larger ones from Canada and New Zealand, to optimize the basal friction coefficient and the mass-to-drag ratio of the two-parameter friction model implemented with r.randomwalk. Thereby we repeat the optimization procedure with conservative and non-conservative assumptions of a set of complementary parameters and with different raster cell sizes. Our preliminary results indicate that the model performance in terms of AUROC achieved with broad parameter spaces is hardly surpassed by the performance achieved with narrow parameter spaces. However, broad spaces may result in very conservative or very non-conservative predictions. Therefore, guiding parameter spaces have to be (i) broad enough to avoid the risk of being off target; and (ii) narrow enough to ensure a reasonable level of conservativeness of the results. The next steps will consist in (i) extending the study to other types of mass flow processes in order to support forward calculations using r.randomwalk; and (ii) in applying the same strategy to the more complex, dynamic model r.avaflow.

Wireless Sensor Network Optimization: Multi-Objective Paradigm

PubMed Central

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-01-01

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

SciDAC-Data, A Project to Enabling Data Driven Modeling of Exascale Computing

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

Mubarak, M.; Ding, P.; Aliaga, L.

The SciDAC-Data project is a DOE funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab Data Center on the organization, movement, and consumption of High Energy Physics data. The project will analyze the analysis patterns and data organization that have been used by the NOvA, MicroBooNE, MINERvA and other experiments, to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulations aremore » designed to address questions of data handling, cache optimization and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership class exascale computing facilities. We will address the use of the SciDAC-Data distributions acquired from Fermilab Data Center’s analysis workflows and corresponding to around 71,000 HEP jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in HPC environments. In particular we describe in detail how the Sequential Access via Metadata (SAM) data handling system in combination with the dCache/Enstore based data archive facilities have been analyzed to develop the radically different models of the analysis of HEP data. We present how the simulation may be used to analyze the impact of design choices in archive facilities.« less

A study on optimization of hybrid drive train using Advanced Vehicle Simulator (ADVISOR)

NASA Astrophysics Data System (ADS)

Same, Adam; Stipe, Alex; Grossman, David; Park, Jae Wan

This study investigates the advantages and disadvantages of three hybrid drive train configurations: series, parallel, and "through-the-ground" parallel. Power flow simulations are conducted with the MATLAB/Simulink-based software ADVISOR. These simulations are then applied in an application for the UC Davis SAE Formula Hybrid vehicle. ADVISOR performs simulation calculations for vehicle position using a combined backward/forward method. These simulations are used to study how efficiency and agility are affected by the motor, fuel converter, and hybrid configuration. Three different vehicle models are developed to optimize the drive train of a vehicle for three stages of the SAE Formula Hybrid competition: autocross, endurance, and acceleration. Input cycles are created based on rough estimates of track geometry. The output from these ADVISOR simulations is a series of plots of velocity profile and energy storage State of Charge that provide a good estimate of how the Formula Hybrid vehicle will perform on the given course. The most noticeable discrepancy between the input cycle and the actual velocity profile of the vehicle occurs during deceleration. A weighted ranking system is developed to organize the simulation results and to determine the best drive train configuration for the Formula Hybrid vehicle. Results show that the through-the-ground parallel configuration with front-mounted motors achieves an optimal balance of efficiency, simplicity, and cost. ADVISOR is proven to be a useful tool for vehicle power train design for the SAE Formula Hybrid competition. This vehicle model based on ADVISOR simulation is applicable to various studies concerning performance and efficiency of hybrid drive trains.

Computational problems in autoregressive moving average (ARMA) models

NASA Technical Reports Server (NTRS)

Agarwal, G. C.; Goodarzi, S. M.; Oneill, W. D.; Gottlieb, G. L.

1981-01-01

The choice of the sampling interval and the selection of the order of the model in time series analysis are considered. Band limited (up to 15 Hz) random torque perturbations are applied to the human ankle joint. The applied torque input, the angular rotation output, and the electromyographic activity using surface electrodes from the extensor and flexor muscles of the ankle joint are recorded. Autoregressive moving average models are developed. A parameter constraining technique is applied to develop more reliable models. The asymptotic behavior of the system must be taken into account during parameter optimization to develop predictive models.

Real-time PCR probe optimization using design of experiments approach.

PubMed

Wadle, S; Lehnert, M; Rubenwolf, S; Zengerle, R; von Stetten, F

2016-03-01

Primer and probe sequence designs are among the most critical input factors in real-time polymerase chain reaction (PCR) assay optimization. In this study, we present the use of statistical design of experiments (DOE) approach as a general guideline for probe optimization and more specifically focus on design optimization of label-free hydrolysis probes that are designated as mediator probes (MPs), which are used in reverse transcription MP PCR (RT-MP PCR). The effect of three input factors on assay performance was investigated: distance between primer and mediator probe cleavage site; dimer stability of MP and target sequence (influenza B virus); and dimer stability of the mediator and universal reporter (UR). The results indicated that the latter dimer stability had the greatest influence on assay performance, with RT-MP PCR efficiency increased by up to 10% with changes to this input factor. With an optimal design configuration, a detection limit of 3-14 target copies/10 μl reaction could be achieved. This improved detection limit was confirmed for another UR design and for a second target sequence, human metapneumovirus, with 7-11 copies/10 μl reaction detected in an optimum case. The DOE approach for improving oligonucleotide designs for real-time PCR not only produces excellent results but may also reduce the number of experiments that need to be performed, thus reducing costs and experimental times.

A new high dynamic range ROIC with smart light intensity control unit

NASA Astrophysics Data System (ADS)

Yazici, Melik; Ceylan, Omer; Shafique, Atia; Abbasi, Shahbaz; Galioglu, Arman; Gurbuz, Yasar

2017-05-01

This journal presents a new high dynamic range ROIC with smart pixel which consists of two pre-amplifiers that are controlled by a circuit inside the pixel. Each pixel automatically decides which pre-amplifier is used according to the incoming illumination level. Instead of using single pre-amplifier, two input pre-amplifiers, which are optimized for different signal levels, are placed inside each pixel. The smart circuit mechanism, which decides the best input circuit according to the incoming light level, is also designed for each pixel. In short, an individual pixel has the ability to select the best input amplifier circuit that performs the best/highest SNR for the incoming signal level. A 32 × 32 ROIC prototype chip is designed to demonstrate the concept in 0.18 μ m CMOS technology. The prototype is optimized for NIR and SWIR bands. Instead of a detector, process variation optimized current sources are placed inside the ROIC. The chip achieves minimum 8.6 e- input referred noise and 98.9 dB dynamic range. It has the highest dynamic range in the literature in terms of analog ROICs for SWIR band. It is operating in room temperature and power consumption is 2.8 μ W per pixel.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 2

NASA Technical Reports Server (NTRS)

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

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 1

NASA Technical Reports Server (NTRS)

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

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean and the variance of the model's parameters, thus of the predicted output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, can be bounded tightly and rigorously.

Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays

PubMed Central

Salt, Julián; Guinaldo, María; Chacón, Jesús

2018-01-01

In this work, we consider a dual-rate scenario with slow input and fast output. Our objective is the maximization of the decay rate of the system through the suitable choice of the n-input signals between two measures (periodic sampling) and their times of application. The optimization algorithm is extended for time-varying delays in order to make possible its implementation in networked control systems. We provide experimental results in an air levitation system to verify the validity of the algorithm in a real plant. PMID:29747441

Control Systems with Normalized and Covariance Adaptation by Optimal Control Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T. (Inventor); Burken, John J. (Inventor); Hanson, Curtis E. (Inventor)

2016-01-01

Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.

On the use of ANN interconnection weights in optimal structural design

NASA Technical Reports Server (NTRS)

Hajela, P.; Szewczyk, Z.

1992-01-01

The present paper describes the use of interconnection weights of a multilayer, feedforward network, to extract information pertinent to the mapping space that the network is assumed to represent. In particular, these weights can be used to determine an appropriate network architecture, and an adequate number of training patterns (input-output pairs) have been used for network training. The weight analysis also provides an approach to assess the influence of each input parameter on a selected output component. The paper shows the significance of this information in decomposition driven optimal design.

Real-time edge-enhanced optical correlator

NASA Technical Reports Server (NTRS)

Liu, Tsuen-Hsi (Inventor); Cheng, Li-Jen (Inventor)

1992-01-01

Edge enhancement of an input image by four-wave mixing a first write beam with a second write beam in a photorefractive crystal, GaAs, was achieved for VanderLugt optical correlation with an edge enhanced reference image by optimizing the power ratio of a second write beam to the first write beam (70:1) and optimizing the power ratio of a read beam, which carries the reference image to the first write beam (100:701). Liquid crystal TV panels are employed as spatial light modulators to change the input and reference images in real time.

Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays.

PubMed

Aranda-Escolástico, Ernesto; Salt, Julián; Guinaldo, María; Chacón, Jesús; Dormido, Sebastián

2018-05-09

In this work, we consider a dual-rate scenario with slow input and fast output. Our objective is the maximization of the decay rate of the system through the suitable choice of the n -input signals between two measures (periodic sampling) and their times of application. The optimization algorithm is extended for time-varying delays in order to make possible its implementation in networked control systems. We provide experimental results in an air levitation system to verify the validity of the algorithm in a real plant.

Realization of optimized quantum controlled-logic gate based on the orbital angular momentum of light.

PubMed

Zeng, Qiang; Li, Tao; Song, Xinbing; Zhang, Xiangdong

2016-04-18

We propose and experimentally demonstrate an optimized setup to implement quantum controlled-NOT operation using polarization and orbital angular momentum qubits. This device is more adaptive to inputs with various polarizations, and can work both in classical and quantum single-photon regime. The logic operations performed by such a setup not only possess high stability and polarization-free character, they can also be easily extended to deal with multi-qubit input states. As an example, the experimental implementation of generalized three-qubit Toffoli gate has been presented.

Systematic Propulsion Optimization Tools (SPOT)

NASA Technical Reports Server (NTRS)

Bower, Mark; Celestian, John

1992-01-01

This paper describes a computer program written by senior-level Mechanical Engineering students at the University of Alabama in Huntsville which is capable of optimizing user-defined delivery systems for carrying payloads into orbit. The custom propulsion system is designed by the user through the input of configuration, payload, and orbital parameters. The primary advantages of the software, called Systematic Propulsion Optimization Tools (SPOT), are a user-friendly interface and a modular FORTRAN 77 code designed for ease of modification. The optimization of variables in an orbital delivery system is of critical concern in the propulsion environment. The mass of the overall system must be minimized within the maximum stress, force, and pressure constraints. SPOT utilizes the Design Optimization Tools (DOT) program for the optimization techniques. The SPOT program is divided into a main program and five modules: aerodynamic losses, orbital parameters, liquid engines, solid engines, and nozzles. The program is designed to be upgraded easily and expanded to meet specific user needs. A user's manual and a programmer's manual are currently being developed to facilitate implementation and modification.

Development of cubic Bezier curve and curve-plane intersection method for parametric submarine hull form design to optimize hull resistance using CFD

NASA Astrophysics Data System (ADS)

Chrismianto, Deddy; Zakki, Ahmad Fauzan; Arswendo, Berlian; Kim, Dong Joon

2015-12-01

Optimization analysis and computational fluid dynamics (CFDs) have been applied simultaneously, in which a parametric model plays an important role in finding the optimal solution. However, it is difficult to create a parametric model for a complex shape with irregular curves, such as a submarine hull form. In this study, the cubic Bezier curve and curve-plane intersection method are used to generate a solid model of a parametric submarine hull form taking three input parameters into account: nose radius, tail radius, and length-height hull ratio ( L/ H). Application program interface (API) scripting is also used to write code in the ANSYS design modeler. The results show that the submarine shape can be generated with some variation of the input parameters. An example is given that shows how the proposed method can be applied successfully to a hull resistance optimization case. The parametric design of the middle submarine type was chosen to be modified. First, the original submarine model was analyzed, in advance, using CFD. Then, using the response surface graph, some candidate optimal designs with a minimum hull resistance coefficient were obtained. Further, the optimization method in goal-driven optimization (GDO) was implemented to find the submarine hull form with the minimum hull resistance coefficient ( C t ). The minimum C t was obtained. The calculated difference in C t values between the initial submarine and the optimum submarine is around 0.26%, with the C t of the initial submarine and the optimum submarine being 0.001 508 26 and 0.001 504 29, respectively. The results show that the optimum submarine hull form shows a higher nose radius ( r n ) and higher L/ H than those of the initial submarine shape, while the radius of the tail ( r t ) is smaller than that of the initial shape.

Multi Objective Controller Design for Linear System via Optimal Interpolation

NASA Technical Reports Server (NTRS)

Ozbay, Hitay

1996-01-01

We propose a methodology for the design of a controller which satisfies a set of closed-loop objectives simultaneously. The set of objectives consists of: (1) pole placement, (2) decoupled command tracking of step inputs at steady-state, and (3) minimization of step response transients with respect to envelope specifications. We first obtain a characterization of all controllers placing the closed-loop poles in a prescribed region of the complex plane. In this characterization, the free parameter matrix Q(s) is to be determined to attain objectives (2) and (3). Objective (2) is expressed as determining a Pareto optimal solution to a vector valued optimization problem. The solution of this problem is obtained by transforming it to a scalar convex optimization problem. This solution determines Q(O) and the remaining freedom in choosing Q(s) is used to satisfy objective (3). We write Q(s) = (l/v(s))bar-Q(s) for a prescribed polynomial v(s). Bar-Q(s) is a polynomial matrix which is arbitrary except that Q(O) and the order of bar-Q(s) are fixed. Obeying these constraints bar-Q(s) is now to be 'shaped' to minimize the step response characteristics of specific input/output pairs according to the maximum envelope violations. This problem is expressed as a vector valued optimization problem using the concept of Pareto optimality. We then investigate a scalar optimization problem associated with this vector valued problem and show that it is convex. The organization of the report is as follows. The next section includes some definitions and preliminary lemmas. We then give the problem statement which is followed by a section including a detailed development of the design procedure. We then consider an aircraft control example. The last section gives some concluding remarks. The Appendix includes the proofs of technical lemmas, printouts of computer programs, and figures.

Estimation and impact assessment of input and parameter uncertainty in predicting groundwater flow with a fully distributed model

NASA Astrophysics Data System (ADS)

Touhidul Mustafa, Syed Md.; Nossent, Jiri; Ghysels, Gert; Huysmans, Marijke

2017-04-01

Transient numerical groundwater flow models have been used to understand and forecast groundwater flow systems under anthropogenic and climatic effects, but the reliability of the predictions is strongly influenced by different sources of uncertainty. Hence, researchers in hydrological sciences are developing and applying methods for uncertainty quantification. Nevertheless, spatially distributed flow models pose significant challenges for parameter and spatially distributed input estimation and uncertainty quantification. In this study, we present a general and flexible approach for input and parameter estimation and uncertainty analysis of groundwater models. The proposed approach combines a fully distributed groundwater flow model (MODFLOW) with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. To avoid over-parameterization, the uncertainty of the spatially distributed model input has been represented by multipliers. The posterior distributions of these multipliers and the regular model parameters were estimated using DREAM. The proposed methodology has been applied in an overexploited aquifer in Bangladesh where groundwater pumping and recharge data are highly uncertain. The results confirm that input uncertainty does have a considerable effect on the model predictions and parameter distributions. Additionally, our approach also provides a new way to optimize the spatially distributed recharge and pumping data along with the parameter values under uncertain input conditions. It can be concluded from our approach that considering model input uncertainty along with parameter uncertainty is important for obtaining realistic model predictions and a correct estimation of the uncertainty bounds.

Bionic models for identification of biological systems

NASA Astrophysics Data System (ADS)

Gerget, O. M.

2017-01-01

This article proposes a clinical decision support system that processes biomedical data. For this purpose a bionic model has been designed based on neural networks, genetic algorithms and immune systems. The developed system has been tested on data from pregnant women. The paper focuses on the approach to enable selection of control actions that can minimize the risk of adverse outcome. The control actions (hyperparameters of a new type) are further used as an additional input signal. Its values are defined by a hyperparameter optimization method. A software developed with Python is briefly described.

Aircraft Dynamic Modeling in Turbulence

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; Cunninham, Kevin

2012-01-01

A method for accurately identifying aircraft dynamic models in turbulence was developed and demonstrated. The method uses orthogonal optimized multisine excitation inputs and an analytic method for enhancing signal-to-noise ratio for dynamic modeling in turbulence. A turbulence metric was developed to accurately characterize the turbulence level using flight measurements. The modeling technique was demonstrated in simulation, then applied to a subscale twin-engine jet transport aircraft in flight. Comparisons of modeling results obtained in turbulent air to results obtained in smooth air were used to demonstrate the effectiveness of the approach.

Design, Fabrication, and Modeling of a Novel Dual-Axis Control Input PZT Gyroscope.

PubMed

Chang, Cheng-Yang; Chen, Tsung-Lin

2017-10-31

Conventional gyroscopes are equipped with a single-axis control input, limiting their performance. Although researchers have proposed control algorithms with dual-axis control inputs to improve gyroscope performance, most have verified the control algorithms through numerical simulations because they lacked practical devices with dual-axis control inputs. The aim of this study was to design a piezoelectric gyroscope equipped with a dual-axis control input so that researchers may experimentally verify those control algorithms in future. Designing a piezoelectric gyroscope with a dual-axis control input is more difficult than designing a conventional gyroscope because the control input must be effective over a broad frequency range to compensate for imperfections, and the multiple mode shapes in flexural deformations complicate the relation between flexural deformation and the proof mass position. This study solved these problems by using a lead zirconate titanate (PZT) material, introducing additional electrodes for shielding, developing an optimal electrode pattern, and performing calibrations of undesired couplings. The results indicated that the fabricated device could be operated at 5.5±1 kHz to perform dual-axis actuations and position measurements. The calibration of the fabricated device was completed by system identifications of a new dynamic model including gyroscopic motions, electromechanical coupling, mechanical coupling, electrostatic coupling, and capacitive output impedance. Finally, without the assistance of control algorithms, the "open loop sensitivity" of the fabricated gyroscope was 1.82 μV/deg/s with a nonlinearity of 9.5% full-scale output. This sensitivity is comparable with those of other PZT gyroscopes with single-axis control inputs.

Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion

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

He, Xu; Tuo, Rui; Jeff Wu, C. F.

Computer experiments based on mathematical models are powerful tools for understanding physical processes. This article addresses the problem of kriging-based optimization for deterministic computer experiments with tunable accuracy. Our approach is to use multi- delity computer experiments with increasing accuracy levels and a nonstationary Gaussian process model. We propose an optimization scheme that sequentially adds new computer runs by following two criteria. The first criterion, called EQI, scores candidate inputs with given level of accuracy, and the second criterion, called EQIE, scores candidate combinations of inputs and accuracy. Here, from simulation results and a real example using finite element analysis,more » our method out-performs the expected improvement (EI) criterion which works for single-accuracy experiments.« less

The DOPEX code: An application of the method of steepest descent to laminated-shield-weight optimization with several constraints

NASA Technical Reports Server (NTRS)

Lahti, G. P.

1972-01-01

A two- or three-constraint, two-dimensional radiation shield weight optimization procedure and a computer program, DOPEX, is described. The DOPEX code uses the steepest descent method to alter a set of initial (input) thicknesses for a shield configuration to achieve a minimum weight while simultaneously satisfying dose constaints. The code assumes an exponential dose-shield thickness relation with parameters specified by the user. The code also assumes that dose rates in each principal direction are dependent only on thicknesses in that direction. Code input instructions, FORTRAN 4 listing, and a sample problem are given. Typical computer time required to optimize a seven-layer shield is about 0.1 minute on an IBM 7094-2.

Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion

DOE PAGES

He, Xu; Tuo, Rui; Jeff Wu, C. F.

2017-01-31

Computer experiments based on mathematical models are powerful tools for understanding physical processes. This article addresses the problem of kriging-based optimization for deterministic computer experiments with tunable accuracy. Our approach is to use multi- delity computer experiments with increasing accuracy levels and a nonstationary Gaussian process model. We propose an optimization scheme that sequentially adds new computer runs by following two criteria. The first criterion, called EQI, scores candidate inputs with given level of accuracy, and the second criterion, called EQIE, scores candidate combinations of inputs and accuracy. Here, from simulation results and a real example using finite element analysis,more » our method out-performs the expected improvement (EI) criterion which works for single-accuracy experiments.« less

Optimal guidance law development for an advanced launch system

NASA Technical Reports Server (NTRS)

Calise, Anthony J.; Hodges, Dewey H.

1990-01-01

A regular perturbation analysis is presented. Closed-loop simulations were performed with a first order correction including all of the atmospheric terms. In addition, a method was developed for independently checking the accuracy of the analysis and the rather extensive programming required to implement the complete first order correction with all of the aerodynamic effects included. This amounted to developing an equivalent Hamiltonian computed from the first order analysis. A second order correction was also completed for the neglected spherical Earth and back-pressure effects. Finally, an analysis was begun on a method for dealing with control inequality constraints. The results on including higher order corrections do show some improvement for this application; however, it is not known at this stage if significant improvement will result when the aerodynamic forces are included. The weak formulation for solving optimal problems was extended in order to account for state inequality constraints. The formulation was tested on three example problems and numerical results were compared to the exact solutions. Development of a general purpose computational environment for the solution of a large class of optimal control problems is under way. An example, along with the necessary input and the output, is given.

Optimal design and uncertainty quantification in blood flow simulations for congenital heart disease

NASA Astrophysics Data System (ADS)

Marsden, Alison

2009-11-01

Recent work has demonstrated substantial progress in capabilities for patient-specific cardiovascular flow simulations. Recent advances include increasingly complex geometries, physiological flow conditions, and fluid structure interaction. However inputs to these simulations, including medical image data, catheter-derived pressures and material properties, can have significant uncertainties associated with them. For simulations to predict clinically useful and reliable output information, it is necessary to quantify the effects of input uncertainties on outputs of interest. In addition, blood flow simulation tools can now be efficiently coupled to shape optimization algorithms for surgery design applications, and these tools should incorporate uncertainty information. We present a unified framework to systematically and efficient account for uncertainties in simulations using adaptive stochastic collocation. In addition, we present a framework for derivative-free optimization of cardiovascular geometries, and layer these tools to perform optimization under uncertainty. These methods are demonstrated using simulations and surgery optimization to improve hemodynamics in pediatric cardiology applications.

Consideration of plant behaviour in optimal servo-compensator design

NASA Astrophysics Data System (ADS)

Moase, W. H.; Manzie, C.

2016-07-01

Where the most prevalent optimal servo-compensator formulations penalise the behaviour of an error system, this paper considers the problem of additionally penalising the actual states and inputs of the plant. Doing so has the advantage of enabling the penalty function to better resemble an economic cost. This is especially true of problems where control effort needs to be sensibly allocated across weakly redundant inputs or where one wishes to use penalties to soft-constrain certain states or inputs. It is shown that, although the resulting cost function grows unbounded as its horizon approaches infinity, it is possible to formulate an equivalent optimisation problem with a bounded cost. The resulting optimisation problem is similar to those in earlier studies but has an additional 'correction term' in the cost function, and a set of equality constraints that arise when there are redundant inputs. A numerical approach to solve the resulting optimisation problem is presented, followed by simulations on a micro-macro positioner that illustrate the benefits of the proposed servo-compensator design approach.

Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

NASA Astrophysics Data System (ADS)

Karimi, Sepideh; Kisi, Ozgur; Shiri, Jalal; Makarynskyy, Oleg

2013-03-01

Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1 h, 24 h, 48 h and 72 h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.

Adjoint Method and Predictive Control for 1-D Flow in NASA Ames 11-Foot Transonic Wind Tunnel

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Ardema, Mark

2006-01-01

This paper describes a modeling method and a new optimal control approach to investigate a Mach number control problem for the NASA Ames 11-Foot Transonic Wind Tunnel. The flow in the wind tunnel is modeled by the 1-D unsteady Euler equations whose boundary conditions prescribe a controlling action by a compressor. The boundary control inputs to the compressor are in turn controlled by a drive motor system and an inlet guide vane system whose dynamics are modeled by ordinary differential equations. The resulting Euler equations are thus coupled to the ordinary differential equations via the boundary conditions. Optimality conditions are established by an adjoint method and are used to develop a model predictive linear-quadratic optimal control for regulating the Mach number due to a test model disturbance during a continuous pitch

Research on torsional vibration modelling and control of printing cylinder based on particle swarm optimization

NASA Astrophysics Data System (ADS)

Wang, Y. M.; Xu, W. C.; Wu, S. Q.; Chai, C. W.; Liu, X.; Wang, S. H.

2018-03-01

The torsional oscillation is the dominant vibration form for the impression cylinder of printing machine (printing cylinder for short), directly restricting the printing speed up and reducing the quality of the prints. In order to reduce torsional vibration, the active control method for the printing cylinder is obtained. Taking the excitation force and moment from the cylinder gap and gripper teeth open & closing cam mechanism as variable parameters, authors establish the dynamic mathematical model of torsional vibration for the printing cylinder. The torsional active control method is based on Particle Swarm Optimization(PSO) algorithm to optimize input parameters for the serve motor. Furthermore, the input torque of the printing cylinder is optimized, and then compared with the numerical simulation results. The conclusions are that torsional vibration active control based on PSO is an availability method to the torsional vibration of printing cylinder.

Improving stability margins in discrete-time LQG controllers

NASA Technical Reports Server (NTRS)

Oranc, B. Tarik; Phillips, Charles L.

1987-01-01

Some of the problems are discussed which are encountered in the design of discrete-time stochastic controllers for problems that may adequately be described by the Linear Quadratic Gaussian (LQG) assumptions; namely, the problems of obtaining acceptable relative stability, robustness, and disturbance rejection properties. A dynamic compensator is proposed to replace the optimal full state feedback regulator gains at steady state, provided that all states are measurable. The compensator increases the stability margins at the plant input, which may possibly be inadequate in practical applications. Though the optimal regulator has desirable properties the observer based controller as implemented with a Kalman filter, in a noisy environment, has inadequate stability margins. The proposed compensator is designed to match the return difference matrix at the plant input to that of the optimal regulator while maintaining the optimality of the state estimates as directed by the measurement noise characteristics.

Prediction and optimization of the laccase-mediated synthesis of the antimicrobial compound iodine (I2).

PubMed

Schubert, M; Fey, A; Ihssen, J; Civardi, C; Schwarze, F W M R; Mourad, S

2015-01-10

An artificial neural network (ANN) and genetic algorithm (GA) were applied to improve the laccase-mediated oxidation of iodide (I(-)) to elemental iodine (I2). Biosynthesis of iodine (I2) was studied with a 5-level-4-factor central composite design (CCD). The generated ANN network was mathematically evaluated by several statistical indices and revealed better results than a classical quadratic response surface (RS) model. Determination of the relative significance of model input parameters, ranking the process parameters in order of importance (pH>laccase>mediator>iodide), was performed by sensitivity analysis. ANN-GA methodology was used to optimize the input space of the neural network model to find optimal settings for the laccase-mediated synthesis of iodine. ANN-GA optimized parameters resulted in a 9.9% increase in the conversion rate. Copyright © 2014 Elsevier B.V. All rights reserved.

Surrogate modeling of deformable joint contact using artificial neural networks.

PubMed

Eskinazi, Ilan; Fregly, Benjamin J

2015-09-01

Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Surrogate Modeling of Deformable Joint Contact using Artificial Neural Networks

PubMed Central

Eskinazi, Ilan; Fregly, Benjamin J.

2016-01-01

Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

Depth optimal sorting networks resistant to k passive faults

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

Piotrow, M.

In this paper, we study the problem of constructing a sorting network that is tolerant to faults and whose running time (i.e. depth) is as small as possible. We consider the scenario of worst-case comparator faults and follow the model of passive comparator failure proposed by Yao and Yao, in which a faulty comparator outputs directly its inputs without comparison. Our main result is the first construction of an N-input, k-fault-tolerant sorting network that is of an asymptotically optimal depth {theta}(log N+k). That improves over the recent result of Leighton and Ma, whose network is of depth O(log N +more » k log log N/log k). Actually, we present a fault-tolerant correction network that can be added after any N-input sorting network to correct its output in the presence of at most k faulty comparators. Since the depth of the network is O(log N + k) and the constants hidden behind the {open_quotes}O{close_quotes} notation are not big, the construction can be of practical use. Developing the techniques necessary to show the main result, we construct a fault-tolerant network for the insertion problem. As a by-product, we get an N-input, O(log N)-depth INSERT-network that is tolerant to random faults, thereby answering a question posed by Ma in his PhD thesis. The results are based on a new notion of constant delay comparator networks, that is, networks in which each register is used (compared) only in a period of time of a constant length. Copies of such networks can be put one after another with only a constant increase in depth per copy.« less

Tribological behaviour predictions of r-GO reinforced Mg composite using ANN coupled Taguchi approach

NASA Astrophysics Data System (ADS)

Kavimani, V.; Prakash, K. Soorya

2017-11-01

This paper deals with the fabrication of reduced graphene oxide (r-GO) reinforced Magnesium Metal Matrix Composite (MMC) through a novel solvent based powder metallurgy route. Investigations over basic and functional properties of developed MMC reveals that addition of r-GO improvises the microhardness upto 64 HV but however decrement in specific wear rate is also notified. Visualization of worn out surfaces through SEM images clearly explains for the occurrence of plastic deformation and the presence of wear debris because of ploughing out action. Taguchi coupled Artificial Neural Network (ANN) technique is adopted to arrive at optimal values of the input parameters such as load, reinforcement weight percentage, sliding distance and sliding velocity and thereby achieve minimal target output value viz. specific wear rate. Influence of any of the input parameter over specific wear rate studied through ANOVA reveals that load acting on pin has a major influence with 38.85% followed by r-GO wt. % of 25.82%. ANN model developed to predict specific wear rate value based on the variation of input parameter facilitates better predictability with R-value of 98.4% when compared with the outcomes of regression model.

Gaussian-input Gaussian mixture model for representing density maps and atomic models.

PubMed

Kawabata, Takeshi

2018-07-01

A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the parameters. It accepted a set of 3D points with weights, corresponding to voxel or atomic centers. Although the standard algorithm worked reasonably well; however, it had three problems. First, it ignored the size (voxel width or atomic radius) of the input, and thus it could lead to a GMM with a smaller spread than the input. Second, the algorithm had a singularity problem, as it sometimes stopped the iterative procedure due to a Gaussian function with almost zero variance. Third, a map with a large number of voxels required a long computation time for conversion to a GMM. To solve these problems, we have introduced a Gaussian-input GMM algorithm, which considers the input atoms or voxels as a set of Gaussian functions. The standard EM algorithm of GMM was extended to optimize the new GMM. The new GMM has identical radius of gyration to the input, and does not suddenly stop due to the singularity problem. For fast computation, we have introduced a down-sampled Gaussian functions (DSG) by merging neighboring voxels into an anisotropic Gaussian function. It provides a GMM with thousands of Gaussian functions in a short computation time. We also have introduced a DSG-input GMM: the Gaussian-input GMM with the DSG as the input. This new algorithm is much faster than the standard algorithm. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Dynamic optimization of CELSS crop photosynthetic rate by computer-assisted feedback control

NASA Astrophysics Data System (ADS)

Chun, C.; Mitchell, C. A.

1997-01-01

A procedure for dynamic optimization of net photosynthetic rate (Pn) for crop production in Controlled Ecological Life-Support Systems (CELSS) was developed using leaf lettuce as a model crop. Canopy Pn was measured in real time and fed back for environmental control. Setpoints of photosynthetic photon flux (PPF) and CO_2 concentration for each hour of the crop-growth cycle were decided by computer to reach a targeted Pn each day. Decision making was based on empirical mathematical models combined with rule sets developed from recent experimental data. Comparisons showed that dynamic control resulted in better yield per unit energy input to the growth system than did static control. With comparable productivity parameters and potential for significant energy savings, dynamic control strategies will contribute greatly to the sustainability of space-deployed CELSS.

Integration of Dakota into the NEAMS Workbench

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

Swiler, Laura Painton; Lefebvre, Robert A.; Langley, Brandon R.

2017-07-01

This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on integrating Dakota into the NEAMS Workbench. The NEAMS Workbench, developed at Oak Ridge National Laboratory, is a new software framework that provides a graphical user interface, input file creation, parsing, validation, job execution, workflow management, and output processing for a variety of nuclear codes. Dakota is a tool developed at Sandia National Laboratories that provides a suite of uncertainty quantification and optimization algorithms. Providing Dakota within the NEAMS Workbench allows users of nuclear simulation codes to perform uncertainty and optimization studies on their nuclear codes frommore » within a common, integrated environment. Details of the integration and parsing are provided, along with an example of Dakota running a sampling study on the fuels performance code, BISON, from within the NEAMS Workbench.« less

Aeroelastic Optimization of Generalized Tube and Wing Aircraft Concepts Using HCDstruct Version 2.0

NASA Technical Reports Server (NTRS)

Quinlan, Jesse R.; Gern, Frank H.

2017-01-01

Major enhancements were made to the Higher-fidelity Conceptual Design and structural optimization (HCDstruct) tool developed at NASA Langley Research Center (LaRC). Whereas previous versions were limited to hybrid wing body (HWB) configurations, the current version of HCDstruct now supports the analysis of generalized tube and wing (TW) aircraft concepts. Along with significantly enhanced user input options for all air- craft configurations, these enhancements represent HCDstruct version 2.0. Validation was performed using a Boeing 737-200 aircraft model, for which primary structure weight estimates agreed well with available data. Additionally, preliminary analysis of the NASA D8 (ND8) aircraft concept was performed, highlighting several new features of the tool.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits

PubMed Central

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-01-01

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits. DOI: http://dx.doi.org/10.7554/eLife.10056.001 PMID:26705334

Training feed-forward neural networks with gain constraints

PubMed

Hartman

2000-04-01

Inaccurate input-output gains (partial derivatives of outputs with respect to inputs) are common in neural network models when input variables are correlated or when data are incomplete or inaccurate. Accurate gains are essential for optimization, control, and other purposes. We develop and explore a method for training feedforward neural networks subject to inequality or equality-bound constraints on the gains of the learned mapping. Gain constraints are implemented as penalty terms added to the objective function, and training is done using gradient descent. Adaptive and robust procedures are devised for balancing the relative strengths of the various terms in the objective function, which is essential when the constraints are inconsistent with the data. The approach has the virtue that the model domain of validity can be extended via extrapolation training, which can dramatically improve generalization. The algorithm is demonstrated here on artificial and real-world problems with very good results and has been advantageously applied to dozens of models currently in commercial use.

The Use of Input-Output Control System Analysis for Sustainable Development of Multivariable Environmental Systems

NASA Astrophysics Data System (ADS)

Koliopoulos, T. C.; Koliopoulou, G.

2007-10-01

We present an input-output solution for simulating the associated behavior and optimized physical needs of an environmental system. The simulations and numerical analysis determined the accurate boundary loads and areas that were required to interact for the proper physical operation of a complicated environmental system. A case study was conducted to simulate the optimum balance of an environmental system based on an artificial intelligent multi-interacting input-output numerical scheme. The numerical results were focused on probable further environmental management techniques, with the objective of minimizing any risks and associated environmental impact to protect the quality of public health and the environment. Our conclusions allowed us to minimize the associated risks, focusing on probable cases in an emergency to protect the surrounded anthropogenic or natural environment. Therefore, the lining magnitude could be determined for any useful associated technical works to support the environmental system under examination, taking into account its particular boundary necessities and constraints.

Full-order optimal compensators for flow control: the multiple inputs case

NASA Astrophysics Data System (ADS)

Semeraro, Onofrio; Pralits, Jan O.

2018-03-01

Flow control has been the subject of numerous experimental and theoretical works. We analyze full-order, optimal controllers for large dynamical systems in the presence of multiple actuators and sensors. The full-order controllers do not require any preliminary model reduction or low-order approximation: this feature allows us to assess the optimal performance of an actuated flow without relying on any estimation process or further hypothesis on the disturbances. We start from the original technique proposed by Bewley et al. (Meccanica 51(12):2997-3014, 2016. https://doi.org/10.1007/s11012-016-0547-3), the adjoint of the direct-adjoint (ADA) algorithm. The algorithm is iterative and allows bypassing the solution of the algebraic Riccati equation associated with the optimal control problem, typically infeasible for large systems. In this numerical work, we extend the ADA iteration into a more general framework that includes the design of controllers with multiple, coupled inputs and robust controllers (H_{∞} methods). First, we demonstrate our results by showing the analytical equivalence between the full Riccati solutions and the ADA approximations in the multiple inputs case. In the second part of the article, we analyze the performance of the algorithm in terms of convergence of the solution, by comparing it with analogous techniques. We find an excellent scalability with the number of inputs (actuators), making the method a viable way for full-order control design in complex settings. Finally, the applicability of the algorithm to fluid mechanics problems is shown using the linearized Kuramoto-Sivashinsky equation and the Kármán vortex street past a two-dimensional cylinder.

A study of remote sensing as applied to regional and small watersheds. Volume 1: Summary report

NASA Technical Reports Server (NTRS)

Ambaruch, R.

1974-01-01

The accuracy of remotely sensed measurements to provide inputs to hydrologic models of watersheds is studied. A series of sensitivity analyses on continuous simulation models of three watersheds determined: (1)Optimal values and permissible tolerances of inputs to achieve accurate simulation of streamflow from the watersheds; (2) Which model inputs can be quantified from remote sensing, directly, indirectly or by inference; and (3) How accurate remotely sensed measurements (from spacecraft or aircraft) must be to provide a basis for quantifying model inputs within permissible tolerances.

Optimization of DSC MRI Echo Times for CBV Measurements Using Error Analysis in a Pilot Study of High-Grade Gliomas.

PubMed

Bell, L C; Does, M D; Stokes, A M; Baxter, L C; Schmainda, K M; Dueck, A C; Quarles, C C

2017-09-01

The optimal TE must be calculated to minimize the variance in CBV measurements made with DSC MR imaging. Simulations can be used to determine the influence of the TE on CBV, but they may not adequately recapitulate the in vivo heterogeneity of precontrast T2*, contrast agent kinetics, and the biophysical basis of contrast agent-induced T2* changes. The purpose of this study was to combine quantitative multiecho DSC MRI T2* time curves with error analysis in order to compute the optimal TE for a traditional single-echo acquisition. Eleven subjects with high-grade gliomas were scanned at 3T with a dual-echo DSC MR imaging sequence to quantify contrast agent-induced T2* changes in this retrospective study. Optimized TEs were calculated with propagation of error analysis for high-grade glial tumors, normal-appearing white matter, and arterial input function estimation. The optimal TE is a weighted average of the T2* values that occur as a contrast agent bolus transverses a voxel. The mean optimal TEs were 30.0 ± 7.4 ms for high-grade glial tumors, 36.3 ± 4.6 ms for normal-appearing white matter, and 11.8 ± 1.4 ms for arterial input function estimation (repeated-measures ANOVA, P < .001). Greater heterogeneity was observed in the optimal TE values for high-grade gliomas, and mean values of all 3 ROIs were statistically significant. The optimal TE for the arterial input function estimation is much shorter; this finding implies that quantitative DSC MR imaging acquisitions would benefit from multiecho acquisitions. In the case of a single-echo acquisition, the optimal TE prescribed should be 30-35 ms (without a preload) and 20-30 ms (with a standard full-dose preload). © 2017 by American Journal of Neuroradiology.

Teleportation of squeezing: Optimization using non-Gaussian resources

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

Dell'Anno, Fabio; De Siena, Silvio; Illuminati, Fabrizio

2010-12-15

We study the continuous-variable quantum teleportation of states, statistical moments of observables, and scale parameters such as squeezing. We investigate the problem both in ideal and imperfect Vaidman-Braunstein-Kimble protocol setups. We show how the teleportation fidelity is maximized and the difference between output and input variances is minimized by using suitably optimized entangled resources. Specifically, we consider the teleportation of coherent squeezed states, exploiting squeezed Bell states as entangled resources. This class of non-Gaussian states, introduced by Illuminati and co-workers [F. Dell'Anno, S. De Siena, L. Albano, and F. Illuminati, Phys. Rev. A 76, 022301 (2007); F. Dell'Anno, S. Demore » Siena, and F. Illuminati, ibid. 81, 012333 (2010)], includes photon-added and photon-subtracted squeezed states as special cases. At variance with the case of entangled Gaussian resources, the use of entangled non-Gaussian squeezed Bell resources allows one to choose different optimization procedures that lead to inequivalent results. Performing two independent optimization procedures, one can either maximize the state teleportation fidelity, or minimize the difference between input and output quadrature variances. The two different procedures are compared depending on the degrees of displacement and squeezing of the input states and on the working conditions in ideal and nonideal setups.« less

Comprehensive optimization of friction stir weld parameters of lap joint AA1100 plates using artificial neural networks and modified NSGA-II

NASA Astrophysics Data System (ADS)

Khalkhali, Abolfazl; Ebrahimi-Nejad, Salman; Geran Malek, Nima

2018-06-01

Friction stir welding (FSW) process overcomes many difficulties arising in conventional fusion welding processes of aluminum alloys. The current paper presents a comprehensive investigation on the effects of rotational speed, traverse speed, tool tilt angle and tool pin profile on the longitudinal force, axial force, maximum temperature, tensile strength, percent elongation, grain size, micro-hardness of welded zone and welded zone thickness of AA1100 aluminum alloy sheets. Design of experiments (DOE) was applied using the Taguchi approach and subsequently, effects of the input parameter on process outputs were investigated using analysis of variance (ANOVA). A perceptron neural network model was developed to find a correlation between the inputs and outputs. Multi-objective optimization using modified NSGA-II was implemented followed by NIP and TOPSIS approaches to propose optimum points for each of the square, pentagon, hexagon, and circular pin profiles. Results indicate that the optimization process can reach horizontal and vertical forces as low as 1452 N and 2913 N, respectively and a grain size as low as 2 μm. This results in hardness values of up to 57.2 and tensile strength, elongation and joint thickness of 2126 N, 5.9% and 3.7 mm, respectively. The maximum operating temperature can also reach a sufficiently high value of 374 °C to provide adequate material flow.

Hybrid feedforward and feedback controller design for nuclear steam generators over wide range operation using genetic algorithm

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

Zhao, Y.; Edwards, R.M.; Lee, K.Y.

1997-03-01

In this paper, a simplified model with a lower order is first developed for a nuclear steam generator system and verified against some realistic environments. Based on this simplified model, a hybrid multi-input and multi-out (MIMO) control system, consisting of feedforward control (FFC) and feedback control (FBC), is designed for wide range conditions by using the genetic algorithm (GA) technique. The FFC control, obtained by the GA optimization method, injects an a priori command input into the system to achieve an optimal performance for the designed system, while the GA-based FBC control provides the necessary compensation for any disturbances ormore » uncertainties in a real steam generator. The FBC control is an optimal design of a PI-based control system which would be more acceptable for industrial practices and power plant control system upgrades. The designed hybrid MIMO FFC/FBC control system is first applied to the simplified model and then to a more complicated model with a higher order which is used as a substitute of the real system to test the efficacy of the designed control system. Results from computer simulations show that the designed GA-based hybrid MIMO FFC/FBC control can achieve good responses and robust performances. Hence, it can be considered as a viable alternative to the current control system upgrade.« less

An accelerated training method for back propagation networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O. (Inventor)

1993-01-01

The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

PubMed Central

Qazi, Abroon Jamal; de Silva, Clarence W.

2014-01-01

This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control. PMID:24574868

Illumination system development using design and analysis of computer experiments

NASA Astrophysics Data System (ADS)

Keresztes, Janos C.; De Ketelaere, Bart; Audenaert, Jan; Koshel, R. J.; Saeys, Wouter

2015-09-01

Computer assisted optimal illumination design is crucial when developing cost-effective machine vision systems. Standard local optimization methods, such as downhill simplex optimization (DHSO), often result in an optimal solution that is influenced by the starting point by converging to a local minimum, especially when dealing with high dimensional illumination designs or nonlinear merit spaces. This work presents a novel nonlinear optimization approach, based on design and analysis of computer experiments (DACE). The methodology is first illustrated with a 2D case study of four light sources symmetrically positioned along a fixed arc in order to obtain optimal irradiance uniformity on a flat Lambertian reflecting target at the arc center. The first step consists of choosing angular positions with no overlap between sources using a fast, flexible space filling design. Ray-tracing simulations are then performed at the design points and a merit function is used for each configuration to quantify the homogeneity of the irradiance at the target. The obtained homogeneities at the design points are further used as input to a Gaussian Process (GP), which develops a preliminary distribution for the expected merit space. Global optimization is then performed on the GP more likely providing optimal parameters. Next, the light positioning case study is further investigated by varying the radius of the arc, and by adding two spots symmetrically positioned along an arc diametrically opposed to the first one. The added value of using DACE with regard to the performance in convergence is 6 times faster than the standard simplex method for equal uniformity of 97%. The obtained results were successfully validated experimentally using a short-wavelength infrared (SWIR) hyperspectral imager monitoring a Spectralon panel illuminated by tungsten halogen sources with 10% of relative error.

Framework GRASP: routine library for optimize processing of aerosol remote sensing observation

NASA Astrophysics Data System (ADS)

Fuertes, David; Torres, Benjamin; Dubovik, Oleg; Litvinov, Pavel; Lapyonok, Tatyana; Ducos, Fabrice; Aspetsberger, Michael; Federspiel, Christian

The present the development of a Framework for the Generalized Retrieval of Aerosol and Surface Properties (GRASP) developed by Dubovik et al., (2011). The framework is a source code project that attempts to strengthen the value of the GRASP inversion algorithm by transforming it into a library that will be used later for a group of customized application modules. The functions of the independent modules include the managing of the configuration of the code execution, as well as preparation of the input and output. The framework provides a number of advantages in utilization of the code. First, it implements loading data to the core of the scientific code directly from memory without passing through intermediary files on disk. Second, the framework allows consecutive use of the inversion code without the re-initiation of the core routine when new input is received. These features are essential for optimizing performance of the data production in processing of large observation sets, such as satellite images by the GRASP. Furthermore, the framework is a very convenient tool for further development, because this open-source platform is easily extended for implementing new features. For example, it could accommodate loading of raw data directly onto the inversion code from a specific instrument not included in default settings of the software. Finally, it will be demonstrated that from the user point of view, the framework provides a flexible, powerful and informative configuration system.

Least-cost input mixtures of water and nitrogen for photosynthesis.

PubMed

Wright, Ian J; Reich, Peter B; Westoby, Mark

2003-01-01

In microeconomics, a standard framework is used for determining the optimal input mix for a two-input production process. Here we adapt this framework for understanding the way plants use water and nitrogen (N) in photosynthesis. The least-cost input mixture for generating a given output depends on the relative cost of procuring and using nitrogen versus water. This way of considering the issue integrates concepts such as water-use efficiency and photosynthetic nitrogen-use efficiency into the more inclusive objective of optimizing the input mix for a given situation. We explore the implications of deploying alternative combinations of leaf nitrogen concentration and stomatal conductance to water, focusing on comparing hypothetical species occurring in low- versus high-humidity habitats. We then present data from sites in both the United States and Australia and show that low-rainfall species operate with substantially higher leaf N concentration per unit leaf area. The extra protein reflected in higher leaf N concentration is associated with a greater drawdown of internal CO2, such that low-rainfall species achieve higher photosynthetic rates at a given stomatal conductance. This restraint of transpirational water use apparently counterbalances the multiple costs of deploying high-nitrogen leaves.

Interdicting an Adversary’s Economy Viewed As a Trade Sanction Inoperability Input Output Model

DTIC Science & Technology

2017-03-01

set of sectors. The design of an economic sanction, in the context of this thesis, is the selection of the sector or set of sectors to sanction...We propose two optimization models. The first, the Trade Sanction Inoperability Input-output Model (TS-IIM), selects the sector or set of sectors that...Interdependency analysis: Extensions to demand reduction inoperability input-output modeling and portfolio selection . Unpublished doctoral dissertation

Design Optimization Tool for Synthetic Jet Actuators Using Lumped Element Modeling

NASA Technical Reports Server (NTRS)

Gallas, Quentin; Sheplak, Mark; Cattafesta, Louis N., III; Gorton, Susan A. (Technical Monitor)

2005-01-01

The performance specifications of any actuator are quantified in terms of an exhaustive list of parameters such as bandwidth, output control authority, etc. Flow-control applications benefit from a known actuator frequency response function that relates the input voltage to the output property of interest (e.g., maximum velocity, volumetric flow rate, momentum flux, etc.). Clearly, the required performance metrics are application specific, and methods are needed to achieve the optimal design of these devices. Design and optimization studies have been conducted for piezoelectric cantilever-type flow control actuators, but the modeling issues are simpler compared to synthetic jets. Here, lumped element modeling (LEM) is combined with equivalent circuit representations to estimate the nonlinear dynamic response of a synthetic jet as a function of device dimensions, material properties, and external flow conditions. These models provide reasonable agreement between predicted and measured frequency response functions and thus are suitable for use as design tools. In this work, we have developed a Matlab-based design optimization tool for piezoelectric synthetic jet actuators based on the lumped element models mentioned above. Significant improvements were achieved by optimizing the piezoceramic diaphragm dimensions. Synthetic-jet actuators were fabricated and benchtop tested to fully document their behavior and validate a companion optimization effort. It is hoped that the tool developed from this investigation will assist in the design and deployment of these actuators.

Optimizing structure of complex technical system by heterogeneous vector criterion in interval form

NASA Astrophysics Data System (ADS)

Lysenko, A. V.; Kochegarov, I. I.; Yurkov, N. K.; Grishko, A. K.

2018-05-01

The article examines the methods of development and multi-criteria choice of the preferred structural variant of the complex technical system at the early stages of its life cycle in the absence of sufficient knowledge of parameters and variables for optimizing this structure. The suggested methods takes into consideration the various fuzzy input data connected with the heterogeneous quality criteria of the designed system and the parameters set by their variation range. The suggested approach is based on the complex use of methods of interval analysis, fuzzy sets theory, and the decision-making theory. As a result, the method for normalizing heterogeneous quality criteria has been developed on the basis of establishing preference relations in the interval form. The method of building preferential relations in the interval form on the basis of the vector of heterogeneous quality criteria suggest the use of membership functions instead of the coefficients considering the criteria value. The former show the degree of proximity of the realization of the designed system to the efficient or Pareto optimal variants. The study analyzes the example of choosing the optimal variant for the complex system using heterogeneous quality criteria.

Forecasting of Storm-Surge Floods Using ADCIRC and Optimized DEMs

NASA Technical Reports Server (NTRS)

Valenti, Elizabeth; Fitzpatrick, Patrick

2006-01-01

Increasing the accuracy of storm-surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm-surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm-surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and, most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite-element-based codes. It affords a capability for simulating tidal circulation and storm-surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm-surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate. To keep the computational load manageable, this work was conducted using only the wind stress, calculated by using historical data from Hurricane Camille, as the input condition for the model. Hurricane storm-surge simulations were performed on an eight-node Linux computer cluster. Each node contained dual 2-GHz processors, 2GB of memory, and a 40GB hard drive. The digital elevation model (DEM) for this region was specified using a combination of Navy data (over water), NOAA data (for the coastline), and optimized Interferometric Synthetic Aperture Radar data (over land). This high-resolution topographical data of the Mississippi coastal region provided the ADCIRC model with improved input with which to calculate improved storm-surge forecasts.

MEDOF - MINIMUM EUCLIDEAN DISTANCE OPTIMAL FILTER

NASA Technical Reports Server (NTRS)

Barton, R. S.

1994-01-01

The Minimum Euclidean Distance Optimal Filter program, MEDOF, generates filters for use in optical correlators. The algorithm implemented in MEDOF follows theory put forth by Richard D. Juday of NASA/JSC. This program analytically optimizes filters on arbitrary spatial light modulators such as coupled, binary, full complex, and fractional 2pi phase. MEDOF optimizes these modulators on a number of metrics including: correlation peak intensity at the origin for the centered appearance of the reference image in the input plane, signal to noise ratio including the correlation detector noise as well as the colored additive input noise, peak to correlation energy defined as the fraction of the signal energy passed by the filter that shows up in the correlation spot, and the peak to total energy which is a generalization of PCE that adds the passed colored input noise to the input image's passed energy. The user of MEDOF supplies the functions that describe the following quantities: 1) the reference signal, 2) the realizable complex encodings of both the input and filter SLM, 3) the noise model, possibly colored, as it adds at the reference image and at the correlation detection plane, and 4) the metric to analyze, here taken to be one of the analytical ones like SNR (signal to noise ratio) or PCE (peak to correlation energy) rather than peak to secondary ratio. MEDOF calculates filters for arbitrary modulators and a wide range of metrics as described above. MEDOF examines the statistics of the encoded input image's noise (if SNR or PCE is selected) and the filter SLM's (Spatial Light Modulator) available values. These statistics are used as the basis of a range for searching for the magnitude and phase of k, a pragmatically based complex constant for computing the filter transmittance from the electric field. The filter is produced for the mesh points in those ranges and the value of the metric that results from these points is computed. When the search is concluded, the values of amplitude and phase for the k whose metric was largest, as well as consistency checks, are reported. A finer search can be done in the neighborhood of the optimal k if desired. The filter finally selected is written to disk in terms of drive values, not in terms of the filter's complex transmittance. Optionally, the impulse response of the filter may be created to permit users to examine the response for the features the algorithm deems important to the recognition process under the selected metric, limitations of the filter SLM, etc. MEDOF uses the filter SLM to its greatest potential, therefore filter competence is not compromised for simplicity of computation. MEDOF is written in C-language for Sun series computers running SunOS. With slight modifications, it has been implemented on DEC VAX series computers using the DEC-C v3.30 compiler, although the documentation does not currently support this platform. MEDOF can also be compiled using Borland International Inc.'s Turbo C++ v1.0, but IBM PC memory restrictions greatly reduce the maximum size of the reference images from which the filters can be calculated. MEDOF requires a two dimensional Fast Fourier Transform (2DFFT). One 2DFFT routine which has been used successfully with MEDOF is a routine found in "Numerical Recipes in C: The Art of Scientific Programming," which is available from Cambridge University Press, New Rochelle, NY 10801. The standard distribution medium for MEDOF is a .25 inch streaming magnetic tape cartridge (Sun QIC-24) in UNIX tar format. MEDOF was developed in 1992-1993.

Functional Differences between Statistical Learning with and without Explicit Training

ERIC Educational Resources Information Center

Batterink, Laura J.; Reber, Paul J.; Paller, Ken A.

2015-01-01

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and…

Fabrication of Scalable Indoor Light Energy Harvester and Study for Agricultural IoT Applications

NASA Astrophysics Data System (ADS)

Watanabe, M.; Nakamura, A.; Kunii, A.; Kusano, K.; Futagawa, M.

2015-12-01

A scalable indoor light energy harvester was fabricated by microelectromechanical system (MEMS) and printing hybrid technology and evaluated for agricultural IoT applications under different environmental input power density conditions, such as outdoor farming under the sun, greenhouse farming under scattered lighting, and a plant factory under LEDs. We fabricated and evaluated a dye- sensitized-type solar cell (DSC) as a low cost and “scalable” optical harvester device. We developed a transparent conductive oxide (TCO)-less process with a honeycomb metal mesh substrate fabricated by MEMS technology. In terms of the electrical and optical properties, we achieved scalable harvester output power by cell area sizing. Second, we evaluated the dependence of the input power scalable characteristics on the input light intensity, spectrum distribution, and light inlet direction angle, because harvested environmental input power is unstable. The TiO2 fabrication relied on nanoimprint technology, which was designed for optical optimization and fabrication, and we confirmed that the harvesters are robust to a variety of environments. Finally, we studied optical energy harvesting applications for agricultural IoT systems. These scalable indoor light harvesters could be used in many applications and situations in smart agriculture.

Variable input observer for structural health monitoring of high-rate systems

NASA Astrophysics Data System (ADS)

Hong, Jonathan; Laflamme, Simon; Cao, Liang; Dodson, Jacob

2017-02-01

The development of high-rate structural health monitoring methods is intended to provide damage detection on timescales of 10 µs -10ms where speed of detection is critical to maintain structural integrity. Here, a novel Variable Input Observer (VIO) coupled with an adaptive observer is proposed as a potential solution for complex high-rate problems. The VIO is designed to adapt its input space based on real-time identification of the system's essential dynamics. By selecting appropriate time-delayed coordinates defined by both a time delay and an embedding dimension, the proper input space is chosen which allows more accurate estimations of the current state and a reduction of the convergence rate. The optimal time-delay is estimated based on mutual information, and the embedding dimension is based on false nearest neighbors. A simulation of the VIO is conducted on a two degree-of-freedom system with simulated damage. Results are compared with an adaptive Luenberger observer, a fixed time-delay observer, and a Kalman Filter. Under its preliminary design, the VIO converges significantly faster than the Luenberger and fixed observer. It performed similarly to the Kalman Filter in terms of convergence, but with greater accuracy.

Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method—A Case Study of Western Jilin Province

PubMed Central

An, Yongkai; Lu, Wenxi; Cheng, Weiguo

2015-01-01

This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately. PMID:26264008

Smooth function approximation using neural networks.

PubMed

Ferrari, Silvia; Stengel, Robert F

2005-01-01

An algebraic approach for representing multidimensional nonlinear functions by feedforward neural networks is presented. In this paper, the approach is implemented for the approximation of smooth batch data containing the function's input, output, and possibly, gradient information. The training set is associated to the network adjustable parameters by nonlinear weight equations. The cascade structure of these equations reveals that they can be treated as sets of linear systems. Hence, the training process and the network approximation properties can be investigated via linear algebra. Four algorithms are developed to achieve exact or approximate matching of input-output and/or gradient-based training sets. Their application to the design of forward and feedback neurocontrollers shows that algebraic training is characterized by faster execution speeds and better generalization properties than contemporary optimization techniques.

RBT-GA: a novel metaheuristic for solving the Multiple Sequence Alignment problem.

PubMed

Taheri, Javid; Zomaya, Albert Y

2009-07-07

Multiple Sequence Alignment (MSA) has always been an active area of research in Bioinformatics. MSA is mainly focused on discovering biologically meaningful relationships among different sequences or proteins in order to investigate the underlying main characteristics/functions. This information is also used to generate phylogenetic trees. This paper presents a novel approach, namely RBT-GA, to solve the MSA problem using a hybrid solution methodology combining the Rubber Band Technique (RBT) and the Genetic Algorithm (GA) metaheuristic. RBT is inspired by the behavior of an elastic Rubber Band (RB) on a plate with several poles, which is analogues to locations in the input sequences that could potentially be biologically related. A GA attempts to mimic the evolutionary processes of life in order to locate optimal solutions in an often very complex landscape. RBT-GA is a population based optimization algorithm designed to find the optimal alignment for a set of input protein sequences. In this novel technique, each alignment answer is modeled as a chromosome consisting of several poles in the RBT framework. These poles resemble locations in the input sequences that are most likely to be correlated and/or biologically related. A GA-based optimization process improves these chromosomes gradually yielding a set of mostly optimal answers for the MSA problem. RBT-GA is tested with one of the well-known benchmarks suites (BALiBASE 2.0) in this area. The obtained results show that the superiority of the proposed technique even in the case of formidable sequences.

Design and development of cell queuing, processing, and scheduling modules for the iPOINT input-buffered ATM testbed

NASA Astrophysics Data System (ADS)

Duan, Haoran

1997-12-01

This dissertation presents the concepts, principles, performance, and implementation of input queuing and cell-scheduling modules for the Illinois Pulsar-based Optical INTerconnect (iPOINT) input-buffered Asynchronous Transfer Mode (ATM) testbed. Input queuing (IQ) ATM switches are well suited to meet the requirements of current and future ultra-broadband ATM networks. The IQ structure imposes minimum memory bandwidth requirements for cell buffering, tolerates bursty traffic, and utilizes memory efficiently for multicast traffic. The lack of efficient cell queuing and scheduling solutions has been a major barrier to build high-performance, scalable IQ-based ATM switches. This dissertation proposes a new Three-Dimensional Queue (3DQ) and a novel Matrix Unit Cell Scheduler (MUCS) to remove this barrier. 3DQ uses a linked-list architecture based on Synchronous Random Access Memory (SRAM) to combine the individual advantages of per-virtual-circuit (per-VC) queuing, priority queuing, and N-destination queuing. It avoids Head of Line (HOL) blocking and provides per-VC Quality of Service (QoS) enforcement mechanisms. Computer simulation results verify the QoS capabilities of 3DQ. For multicast traffic, 3DQ provides efficient usage of cell buffering memory by storing multicast cells only once. Further, the multicast mechanism of 3DQ prevents a congested destination port from blocking other less- loaded ports. The 3DQ principle has been prototyped in the Illinois Input Queue (iiQueue) module. Using Field Programmable Gate Array (FPGA) devices, SRAM modules, and integrated on a Printed Circuit Board (PCB), iiQueue can process incoming traffic at 800 Mb/s. Using faster circuit technology, the same design is expected to operate at the OC-48 rate (2.5 Gb/s). MUCS resolves the output contention by evaluating the weight index of each candidate and selecting the heaviest. It achieves near-optimal scheduling and has a very short response time. The algorithm originates from a heuristic strategy that leads to 'socially optimal' solutions, yielding a maximum number of contention-free cells being scheduled. A novel mixed digital-analog circuit has been designed to implement the MUCS core functionality. The MUCS circuit maps the cell scheduling computation to the capacitor charging and discharging procedures that are conducted fully in parallel. The design has a uniform circuit structure, low interconnect counts, and low chip I/O counts. Using 2 μm CMOS technology, the design operates on a 100 MHz clock and finds a near-optimal solution within a linear processing time. The circuit has been verified at the transistor level by HSPICE simulation. During this research, a five-port IQ-based optoelectronic iPOINT ATM switch has been developed and demonstrated. It has been fully functional with an aggregate throughput of 800 Mb/s. The second-generation IQ-based switch is currently under development. Equipped with iiQueue modules and MUCS module, the new switch system will deliver a multi-gigabit aggregate throughput, eliminate HOL blocking, provide per-VC QoS, and achieve near-100% link bandwidth utilization. Complete documentation of input modules and trunk module for the existing testbed, and complete documentation of 3DQ, iiQueue, and MUCS for the second-generation testbed are given in this dissertation.

Using Optimization to Improve Test Planning

DTIC Science & Technology

2017-09-01

friendly and to display the output differently, the test and evaluation test schedule optimization model would be a good tool for the test and... evaluation schedulers. 14. SUBJECT TERMS schedule optimization, test planning 15. NUMBER OF PAGES 223 16. PRICE CODE 17. SECURITY CLASSIFICATION OF...make the input more user-friendly and to display the output differently, the test and evaluation test schedule optimization model would be a good tool

Multi-objective optimization of hole characteristics during pulsed Nd:YAG laser microdrilling of gamma-titanium aluminide alloy sheet

NASA Astrophysics Data System (ADS)

Biswas, R.; Kuar, A. S.; Mitra, S.

2014-09-01

Nd:YAG laser microdrilled holes on gamma-titanium aluminide, a newly developed alloy having wide applications in turbine blades, engine valves, cases, metal cutting tools, missile components, nuclear fuel and biomedical engineering, are important from the dimensional accuracy and quality of hole point of view. Keeping this in mind, a central composite design (CCD) based on response surface methodology (RSM) is employed for multi-objective optimization of pulsed Nd:YAG laser microdrilling operation on gamma-titanium aluminide alloy sheet to achieve optimum hole characteristics within existing resources. The three characteristics such as hole diameter at entry, hole diameter at exit and hole taper have been considered for simultaneous optimization. The individual optimization of all three responses has also been carried out. The input parameters considered are lamp current, pulse frequency, assist air pressure and thickness of the job. The responses at predicted optimum parameter level are in good agreement with the results of confirmation experiments conducted for verification tests.

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

Bartkiewicz, Karol; Miranowicz, Adam

We find an optimal quantum cloning machine, which clones qubits of arbitrary symmetrical distribution around the Bloch vector with the highest fidelity. The process is referred to as phase-independent cloning in contrast to the standard phase-covariant cloning for which an input qubit state is a priori better known. We assume that the information about the input state is encoded in an arbitrary axisymmetric distribution (phase function) on the Bloch sphere of the cloned qubits. We find analytical expressions describing the optimal cloning transformation and fidelity of the clones. As an illustration, we analyze cloning of qubit state described by themore » von Mises-Fisher and Brosseau distributions. Moreover, we show that the optimal phase-independent cloning machine can be implemented by modifying the mirror phase-covariant cloning machine for which quantum circuits are known.« less

A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

NASA Astrophysics Data System (ADS)

Luk, K. C.; Ball, J. E.; Sharma, A.

2000-01-01

Artificial neural networks (ANNs), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Due to their powerful capability and functionality, ANNs provide an alternative approach for many engineering problems that are difficult to solve by conventional approaches. Rainfall forecasting has been a difficult subject in hydrology due to the complexity of the physical processes involved and the variability of rainfall in space and time. In this study, ANNs were adopted to forecast short-term rainfall for an urban catchment. The ANNs were trained to recognise historical rainfall patterns as recorded from a number of gauges in the study catchment for reproduction of relevant patterns for new rainstorm events. The primary objective of this paper is to investigate the effect of temporal and spatial information on short-term rainfall forecasting. To achieve this aim, a comparison test on the forecast accuracy was made among the ANNs configured with different orders of lag and different numbers of spatial inputs. In developing the ANNs with alternative configurations, the ANNs were trained to an optimal level to achieve good generalisation of data. It was found in this study that the ANNs provided the most accurate predictions when an optimum number of spatial inputs was included into the network, and that the network with lower lag consistently produced better performance.

Thales Cryogenics rotary cryocoolers for HOT applications

NASA Astrophysics Data System (ADS)

Martin, Jean-Yves; Cauquil, Jean-Marc; Benschop, Tonny; Freche, Sébastien

2012-06-01

Thales Cryogenics has an extensive background in delivering reliable linear and rotary coolers for military, civil and space programs. Recent work carried out at detector level enable to consider a higher operation temperature for the cooled detectors. This has a direct impact on the cooling power required to the cryocooler. In continuation of the work presented last year, Thales cryogenics has studied the operation and optimization of the rotary cryocoolers at high cold regulation temperature. In this paper, the performances of the Thales Cryogenics rotary cryocoolers at elevated cold regulation temperature will be presented. From these results, some trade-offs can be made to combine correct operation of the cryocooler on all the ambient operational range and maximum efficiency of the cryocooler. These trade-offs and the impact on MTTF of elevated cold regulation temperature will be presented and discussed. In correlation with the increase of the cold operation temperature, the cryocooler input power is significantly decreased. As a consequence, the cooler drive electronics own consumption becomes relatively important and must be reduced in order to minimize global input power to the cooling function (cryocooler and cooler drive electronics). Thales Cryogenics has developed a new drive electronics optimized for low input power requirements. In parallel, improvements on RM1 and RM2 cryocoolers have been defined and implemented. The main impacts on performances of these new designs will be presented. Thales cryogenics is now able to propose an efficient cooling function for application requiring a high cold regulation temperature including a range of tuned rotary coolers.

Daily River Flow Forecasting with Hybrid Support Vector Machine – Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Zaini, N.; Malek, M. A.; Yusoff, M.; Mardi, N. H.; Norhisham, S.

2018-04-01

The application of artificial intelligence techniques for river flow forecasting can further improve the management of water resources and flood prevention. This study concerns the development of support vector machine (SVM) based model and its hybridization with particle swarm optimization (PSO) to forecast short term daily river flow at Upper Bertam Catchment located in Cameron Highland, Malaysia. Ten years duration of historical rainfall, antecedent river flow data and various meteorology parameters data from 2003 to 2012 are used in this study. Four SVM based models are proposed which are SVM1, SVM2, SVM-PSO1 and SVM-PSO2 to forecast 1 to 7 day ahead of river flow. SVM1 and SVM-PSO1 are the models with historical rainfall and antecedent river flow as its input, while SVM2 and SVM-PSO2 are the models with historical rainfall, antecedent river flow data and additional meteorological parameters as input. The performances of the proposed model are measured in term of RMSE and R2 . It is found that, SVM2 outperformed SVM1 and SVM-PSO2 outperformed SVM-PSO1 which meant the additional meteorology parameters used as input to the proposed models significantly affect the model performances. Hybrid models SVM-PSO1 and SVM-PSO2 yield higher performances as compared to SVM1 and SVM2. It is found that hybrid models are more effective in forecasting river flow at 1 to 7 day ahead at the study area.

U.S. EPA CSO CAPSTONE REPORT: CONTROL SYSTEM OPTIMIZATION

EPA Science Inventory

An optimized combined sewer overflow (CSO) requires a storage treatment system because storm flow in the combined sewer system is intermittent and highly variable in both pollutant concentration and flow rate. Storage and treatment alternatives are strongly influenced by input...

Prediction of sonic boom from experimental near-field overpressure data. Volume 1: Method and results

NASA Technical Reports Server (NTRS)

Glatt, C. R.; Hague, D. S.; Reiners, S. J.

1975-01-01

A computerized procedure for predicting sonic boom from experimental near-field overpressure data has been developed. The procedure extrapolates near-field pressure signatures for a specified flight condition to the ground by the Thomas method. Near-field pressure signatures are interpolated from a data base of experimental pressure signatures. The program is an independently operated ODIN (Optimal Design Integration) program which obtains flight path information from other ODIN programs or from input.

Develop Measures of Effectiveness and Deployment Optimization Rules for Networked Ground Micro-Sensors

DTIC Science & Technology

2001-05-01

types and total #) Ø Control of Sensors ( Scheduling ) Ø Coverage (Time & Area) Uncontrollable Inputs ØWeather Ø Atmospheric Effects Ø Equipment...are widely scattered and used to cue or wakeup other higher-level sensors. Trip line sensors consist of some combination of acoustic, seismic and...Employ a mix if different sensor types in order to increase detection probability 4.4.4.2 Minimize Battery Power • Set schedule turn on and off

A multi-factor GIS method to identify optimal geographic locations for electric vehicle (EV) charging stations

NASA Astrophysics Data System (ADS)

Zhang, Yongqin; Iman, Kory

2018-05-01

Fuel-based transportation is one of the major contributors to poor air quality in the United States. Electric Vehicle (EV) is potentially the cleanest transportation technology to our environment. This research developed a spatial suitability model to identify optimal geographic locations for installing EV charging stations for travelling public. The model takes into account a variety of positive and negative factors to identify prime locations for installing EV charging stations in Wasatch Front, Utah, where automobile emission causes severe air pollution due to atmospheric inversion condition near the valley floor. A walkable factor grid was created to store index scores from input factor layers to determine prime locations. 27 input factors including land use, demographics, employment centers etc. were analyzed. Each factor layer was analyzed to produce a summary statistic table to determine the site suitability. Potential locations that exhibit high EV charging usage were identified and scored. A hot spot map was created to demonstrate high, moderate, and low suitability areas for installing EV charging stations. A spatially well distributed EV charging system was then developed, aiming to reduce "range anxiety" from traveling public. This spatial methodology addresses the complex problem of locating and establishing a robust EV charging station infrastructure for decision makers to build a clean transportation infrastructure, and eventually improve environment pollution.

Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression.

PubMed

Aksu, Buket; Paradkar, Anant; de Matas, Marcel; Ozer, Ozgen; Güneri, Tamer; York, Peter

2012-12-01

The publication of the International Conference of Harmonization (ICH) Q8, Q9, and Q10 guidelines paved the way for the standardization of quality after the Food and Drug Administration issued current Good Manufacturing Practices guidelines in 2003. "Quality by Design", mentioned in the ICH Q8 guideline, offers a better scientific understanding of critical process and product qualities using knowledge obtained during the life cycle of a product. In this scope, the "knowledge space" is a summary of all process knowledge obtained during product development, and the "design space" is the area in which a product can be manufactured within acceptable limits. To create the spaces, artificial neural networks (ANNs) can be used to emphasize the multidimensional interactions of input variables and to closely bind these variables to a design space. This helps guide the experimental design process to include interactions among the input variables, along with modeling and optimization of pharmaceutical formulations. The objective of this study was to develop an integrated multivariate approach to obtain a quality product based on an understanding of the cause-effect relationships between formulation ingredients and product properties with ANNs and genetic programming on the ramipril tablets prepared by the direct compression method. In this study, the data are generated through the systematic application of the design of experiments (DoE) principles and optimization studies using artificial neural networks and neurofuzzy logic programs.

Development of a Stiffness-Based Chemistry Load Balancing Scheme, and Optimization of Input/Output and Communication, to Enable Massively Parallel High-Fidelity Internal Combustion Engine Simulations

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

Kodavasal, Janardhan; Harms, Kevin; Srivastava, Priyesh

A closed-cycle gasoline compression ignition engine simulation near top dead center (TDC) was used to profile the performance of a parallel commercial engine computational fluid dynamics code, as it was scaled on up to 4096 cores of an IBM Blue Gene/Q supercomputer. The test case has 9 million cells near TDC, with a fixed mesh size of 0.15 mm, and was run on configurations ranging from 128 to 4096 cores. Profiling was done for a small duration of 0.11 crank angle degrees near TDC during ignition. Optimization of input/output performance resulted in a significant speedup in reading restart files, andmore » in an over 100-times speedup in writing restart files and files for post-processing. Improvements to communication resulted in a 1400-times speedup in the mesh load balancing operation during initialization, on 4096 cores. An improved, “stiffness-based” algorithm for load balancing chemical kinetics calculations was developed, which results in an over 3-times faster run-time near ignition on 4096 cores relative to the original load balancing scheme. With this improvement to load balancing, the code achieves over 78% scaling efficiency on 2048 cores, and over 65% scaling efficiency on 4096 cores, relative to 256 cores.« less

Finite-horizon control-constrained nonlinear optimal control using single network adaptive critics.

PubMed

Heydari, Ali; Balakrishnan, Sivasubramanya N

2013-01-01

To synthesize fixed-final-time control-constrained optimal controllers for discrete-time nonlinear control-affine systems, a single neural network (NN)-based controller called the Finite-horizon Single Network Adaptive Critic is developed in this paper. Inputs to the NN are the current system states and the time-to-go, and the network outputs are the costates that are used to compute optimal feedback control. Control constraints are handled through a nonquadratic cost function. Convergence proofs of: 1) the reinforcement learning-based training method to the optimal solution; 2) the training error; and 3) the network weights are provided. The resulting controller is shown to solve the associated time-varying Hamilton-Jacobi-Bellman equation and provide the fixed-final-time optimal solution. Performance of the new synthesis technique is demonstrated through different examples including an attitude control problem wherein a rigid spacecraft performs a finite-time attitude maneuver subject to control bounds. The new formulation has great potential for implementation since it consists of only one NN with single set of weights and it provides comprehensive feedback solutions online, though it is trained offline.

Optimization of Residual Stresses in MMC's Using Compensating/Compliant Interfacial Layers. Part 2: OPTCOMP User's Guide

NASA Technical Reports Server (NTRS)

Pindera, Marek-Jerzy; Salzar, Robert S.; Williams, Todd O.

1994-01-01

A user's guide for the computer program OPTCOMP is presented in this report. This program provides a capability to optimize the fabrication or service-induced residual stresses in uni-directional metal matrix composites subjected to combined thermo-mechanical axisymmetric loading using compensating or compliant layers at the fiber/matrix interface. The user specifies the architecture and the initial material parameters of the interfacial region, which can be either elastic or elastoplastic, and defines the design variables, together with the objective function, the associated constraints and the loading history through a user-friendly data input interface. The optimization procedure is based on an efficient solution methodology for the elastoplastic response of an arbitrarily layered multiple concentric cylinder model that is coupled to the commercial optimization package DOT. The solution methodology for the arbitrarily layered cylinder is based on the local-global stiffness matrix formulation and Mendelson's iterative technique of successive elastic solutions developed for elastoplastic boundary-value problems. The optimization algorithm employed in DOT is based on the method of feasible directions.

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

Li, Dengwang; Wang, Jie; Kapp, Daniel S.

Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data weremore » segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is supported by NIH/NIBIB (1R01-EB016777), National Natural Science Foundation of China (No.61471226 and No.61201441), Research funding from Shandong Province (No.BS2012DX038 and No.J12LN23), and Research funding from Jinan City (No.201401221 and No.20120109)« less

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

PubMed

Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin

2015-02-01

To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach

PubMed Central

Enns, Eva A.; Cipriano, Lauren E.; Simons, Cyrena T.; Kong, Chung Yin

2014-01-01

Background To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single “goodness-of-fit” (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. Methods We demonstrate the Pareto frontier approach in the calibration of two models: a simple, illustrative Markov model and a previously-published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to two possible weighted-sum GOF scoring systems, and compare the health economic conclusions arising from these different definitions of best-fitting. Results For the simple model, outcomes evaluated over the best-fitting input sets according to the two weighted-sum GOF schemes were virtually non-overlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95%CI: 72,500 – 87,600] vs. $139,700 [95%CI: 79,900 - 182,800] per QALY gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95%CI: 64,900 – 156,200] per QALY gained). The TAVR model yielded similar results. Conclusions Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. PMID:24799456

Scidac-Data: Enabling Data Driven Modeling of Exascale Computing

DOE PAGES

Mubarak, Misbah; Ding, Pengfei; Aliaga, Leo; ...

2017-11-23

Here, the SciDAC-Data project is a DOE-funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab data center on the organization, movement, and consumption of high energy physics (HEP) data. The project analyzes the analysis patterns and data organization that have been used by NOvA, MicroBooNE, MINERvA, CDF, D0, and other experiments to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulationsmore » are designed to address questions of data handling, cache optimization, and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership-class exascale computing facilities. We present the use of a subset of the SciDAC-Data distributions, acquired from analysis of approximately 71,000 HEP workflows run on the Fermilab data center and corresponding to over 9 million individual analysis jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in high performance computing (HPC) and high throughput computing (HTC) environments. In particular we describe how the Sequential Access via Metadata (SAM) data-handling system in combination with the dCache/Enstore-based data archive facilities has been used to develop radically different models for analyzing the HEP data. We also show how the simulations may be used to assess the impact of design choices in archive facilities.« less

Scidac-Data: Enabling Data Driven Modeling of Exascale Computing

NASA Astrophysics Data System (ADS)

Mubarak, Misbah; Ding, Pengfei; Aliaga, Leo; Tsaris, Aristeidis; Norman, Andrew; Lyon, Adam; Ross, Robert

2017-10-01

The SciDAC-Data project is a DOE-funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab data center on the organization, movement, and consumption of high energy physics (HEP) data. The project analyzes the analysis patterns and data organization that have been used by NOvA, MicroBooNE, MINERvA, CDF, D0, and other experiments to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulations are designed to address questions of data handling, cache optimization, and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership-class exascale computing facilities. We present the use of a subset of the SciDAC-Data distributions, acquired from analysis of approximately 71,000 HEP workflows run on the Fermilab data center and corresponding to over 9 million individual analysis jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in high performance computing (HPC) and high throughput computing (HTC) environments. In particular we describe how the Sequential Access via Metadata (SAM) data-handling system in combination with the dCache/Enstore-based data archive facilities has been used to develop radically different models for analyzing the HEP data. We also show how the simulations may be used to assess the impact of design choices in archive facilities.

Evaluation of Building Energy Saving Through the Development of Venetian Blinds' Optimal Control Algorithm According to the Orientation and Window-to-Wall Ratio

NASA Astrophysics Data System (ADS)

Kwon, Hyuk Ju; Yeon, Sang Hun; Lee, Keum Ho; Lee, Kwang Ho

2018-02-01

As various studies focusing on building energy saving have been continuously conducted, studies utilizing renewable energy sources, instead of fossil fuel, are needed. In particular, studies regarding solar energy are being carried out in the field of building science; in order to utilize such solar energy effectively, solar radiation being brought into the indoors should be acquired and blocked properly. Blinds are a typical solar radiation control device that is capable of controlling indoor thermal and light environments. However, slat-type blinds are manually controlled, giving a negative effect on building energy saving. In this regard, studies regarding the automatic control of slat-type blinds have been carried out for the last couple of decades. Therefore, this study aims to provide preliminary data for optimal control research through the controlling of slat angle in slat-type blinds by comprehensively considering various input variables. The window area ratio and orientation were selected as input variables. It was found that an optimal control algorithm was different among each window-to-wall ratio and window orientation. In addition, through comparing and analyzing the building energy saving performance for each condition by applying the developed algorithms to simulations, up to 20.7 % energy saving was shown in the cooling period and up to 12.3 % energy saving was shown in the heating period. In addition, building energy saving effect was greater as the window area ratio increased given the same orientation, and the effects of window-to-wall ratio in the cooling period were higher than those of window-to-wall ratio in the heating period.

Scidac-Data: Enabling Data Driven Modeling of Exascale Computing

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

Mubarak, Misbah; Ding, Pengfei; Aliaga, Leo

Here, the SciDAC-Data project is a DOE-funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab data center on the organization, movement, and consumption of high energy physics (HEP) data. The project analyzes the analysis patterns and data organization that have been used by NOvA, MicroBooNE, MINERvA, CDF, D0, and other experiments to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulationsmore » are designed to address questions of data handling, cache optimization, and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership-class exascale computing facilities. We present the use of a subset of the SciDAC-Data distributions, acquired from analysis of approximately 71,000 HEP workflows run on the Fermilab data center and corresponding to over 9 million individual analysis jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in high performance computing (HPC) and high throughput computing (HTC) environments. In particular we describe how the Sequential Access via Metadata (SAM) data-handling system in combination with the dCache/Enstore-based data archive facilities has been used to develop radically different models for analyzing the HEP data. We also show how the simulations may be used to assess the impact of design choices in archive facilities.« less

A Combined Structural and Electromechanical FE Approach for Industrial Ultrasonic Devices Design

NASA Astrophysics Data System (ADS)

Schorderet, Alain; Prenleloup, Alain; Colla, Enrico

2011-05-01

Ultrasonic assistance is widely used in manufacturing, both for conventional (e.g. grinding, drilling) and non-conventional (e.g. EDM) processes. Ultrasonic machining is also used as a stand alone process for instance for micro-drilling. Industrial application of these processes requires increasingly efficient and accurate development tools to predict the performance of the ultrasonic device: the so-called sonotrode and the piezo-transducer. This electromechanical system consists of a structural part and of a piezo-electrical part (actuator). In this paper, we show how to combine two simulation softwares—for stuctures and electromechanical devices—to perform a complete design analysis and optimization of a sonotrode for ultrasonic drilling applications. The usual design criteria are the eigenfrequencies of the desired vibrational modes. In addition, during the optimization phase, one also needs to consider the maximum achievable displacement for a given applied voltage. Therefore, one must be able to predict the electromechanical behavior of the integrated piezo-structure system, in order to define, adapt and optimize the electric power supply as well as the control strategy (search, tracking of the eigenfrequency). In this procedure, numerical modelling follows a two-step approach, by means of a solid mechanics FE code (ABAQUS) and of an electromechanical simulation software (ATILA). The example presented illustrates the approach and describes the obtained results for the development of an industrial sonotrode system dedicated to ultrasonic micro-drilling of ceramics. The 3D model of the sonotrode serves as input for generating the FE mesh in ABAQUS and this mesh is then translated into an input file for ATILA. ABAQUS results are used to perform the first optimization step in order to obtain a sonotrode design leading to the requested modal behaviour—eigen-frequency and corresponding dynamic amplification. The second step aims at evaluating the dynamic mechanical response of the complete sonotrode subjected to an ultrasonic voltage excitation. Piezoelectric properties as well as damping properties are requested to fulfill this step. The obtained electrical results—complex system's impedance and electric current- are used to optimize the sonotrode-power supply complete system.

Energy extraction from atmospheric turbulence to improve flight vehicle performance

NASA Astrophysics Data System (ADS)

Patel, Chinmay Karsandas

Small 'bird-sized' Unmanned Aerial Vehicles (UAVs) have now become practical due to technological advances in embedded electronics, miniature sensors and actuators, and propulsion systems. Birds are known to take advantage of wind currents to conserve energy and fly long distances without flapping their wings. This dissertation explores the possibility of improving the performance of small UAVs by extracting the energy available in atmospheric turbulence. An aircraft can gain energy from vertical gusts by increasing its lift in regions of updraft and reducing its lift in downdrafts - a concept that has been known for decades. Starting with a simple model of a glider flying through a sinusoidal gust, a parametric optimization approach is used to compute the minimum gust amplitude and optimal control input required for the glider to sustain flight without losing energy. For small UAVs using optimal control inputs, sinusoidal gusts with amplitude of 10--15% of the cruise speed are sufficient to keep the aircraft aloft. The method is then modified and extended to include random gusts that are representative of natural turbulence. A procedure to design optimal control laws for energy extraction from realistic gust profiles is developed using a Genetic Algorithm (GA). A feedback control law is designed to perform well over a variety of random gusts, and not be tailored for one particular gust. A small UAV flying in vertical turbulence is shown to obtain average energy savings of 35--40% with the use of a simple control law. The design procedure is also extended to determine optimal control laws for sinusoidal as well as turbulent lateral gusts. The theoretical work is complemented by experimental validation using a small autonomous UAV. The development of a lightweight autopilot and UAV platform is presented. Flight test results show that active control of the lift of an autonomous glider resulted in approximately 46% average energy savings compared to glides with fixed control surfaces. Statistical analysis of test samples shows that 19% of the active control test runs resulted in no energy loss, thus demonstrating the potential of the 'gust soaring' concept to dramatically improve the performance of small UAVs.

A simulation model to estimate the cost and effectiveness of alternative dialysis initiation strategies.

PubMed

Lee, Chris P; Chertow, Glenn M; Zenios, Stefanos A

2006-01-01

Patients with end-stage renal disease (ESRD) require dialysis to maintain survival. The optimal timing of dialysis initiation in terms of cost-effectiveness has not been established. We developed a simulation model of individuals progressing towards ESRD and requiring dialysis. It can be used to analyze dialysis strategies and scenarios. It was embedded in an optimization frame worked to derive improved strategies. Actual (historical) and simulated survival curves and hospitalization rates were virtually indistinguishable. The model overestimated transplantation costs (10%) but it was related to confounding by Medicare coverage. To assess the model's robustness, we examined several dialysis strategies while input parameters were perturbed. Under all 38 scenarios, relative rankings remained unchanged. An improved policy for a hypothetical patient was derived using an optimization algorithm. The model produces reliable results and is robust. It enables the cost-effectiveness analysis of dialysis strategies.

Computational Process Modeling for Additive Manufacturing

NASA Technical Reports Server (NTRS)

Bagg, Stacey; Zhang, Wei

2014-01-01

Computational Process and Material Modeling of Powder Bed additive manufacturing of IN 718. Optimize material build parameters with reduced time and cost through modeling. Increase understanding of build properties. Increase reliability of builds. Decrease time to adoption of process for critical hardware. Potential to decrease post-build heat treatments. Conduct single-track and coupon builds at various build parameters. Record build parameter information and QM Meltpool data. Refine Applied Optimization powder bed AM process model using data. Report thermal modeling results. Conduct metallography of build samples. Calibrate STK models using metallography findings. Run STK models using AO thermal profiles and report STK modeling results. Validate modeling with additional build. Photodiode Intensity measurements highly linear with power input. Melt Pool Intensity highly correlated to Melt Pool Size. Melt Pool size and intensity increase with power. Applied Optimization will use data to develop powder bed additive manufacturing process model.

A single-loop optimization method for reliability analysis with second order uncertainty

NASA Astrophysics Data System (ADS)

Xie, Shaojun; Pan, Baisong; Du, Xiaoping

2015-08-01

Reliability analysis may involve random variables and interval variables. In addition, some of the random variables may have interval distribution parameters owing to limited information. This kind of uncertainty is called second order uncertainty. This article develops an efficient reliability method for problems involving the three aforementioned types of uncertain input variables. The analysis produces the maximum and minimum reliability and is computationally demanding because two loops are needed: a reliability analysis loop with respect to random variables and an interval analysis loop for extreme responses with respect to interval variables. The first order reliability method and nonlinear optimization are used for the two loops, respectively. For computational efficiency, the two loops are combined into a single loop by treating the Karush-Kuhn-Tucker (KKT) optimal conditions of the interval analysis as constraints. Three examples are presented to demonstrate the proposed method.

A trajectory planning scheme for spacecraft in the space station environment. M.S. Thesis - University of California

NASA Technical Reports Server (NTRS)

Soller, Jeffrey Alan; Grunwald, Arthur J.; Ellis, Stephen R.

1991-01-01

Simulated annealing is used to solve a minimum fuel trajectory problem in the space station environment. The environment is special because the space station will define a multivehicle environment in space. The optimization surface is a complex nonlinear function of the initial conditions of the chase and target crafts. Small permutations in the input conditions can result in abrupt changes to the optimization surface. Since no prior knowledge about the number or location of local minima on the surface is available, the optimization must be capable of functioning on a multimodal surface. It was reported in the literature that the simulated annealing algorithm is more effective on such surfaces than descent techniques using random starting points. The simulated annealing optimization was found to be capable of identifying a minimum fuel, two-burn trajectory subject to four constraints which are integrated into the optimization using a barrier method. The computations required to solve the optimization are fast enough that missions could be planned on board the space station. Potential applications for on board planning of missions are numerous. Future research topics may include optimal planning of multi-waypoint maneuvers using a knowledge base to guide the optimization, and a study aimed at developing robust annealing schedules for potential on board missions.

Topology and boundary shape optimization as an integrated design tool

NASA Technical Reports Server (NTRS)

Bendsoe, Martin Philip; Rodrigues, Helder Carrico

1990-01-01

The optimal topology of a two dimensional linear elastic body can be computed by regarding the body as a domain of the plane with a high density of material. Such an optimal topology can then be used as the basis for a shape optimization method that computes the optimal form of the boundary curves of the body. This results in an efficient and reliable design tool, which can be implemented via common FEM mesh generator and CAD type input-output facilities.

Efficient dynamic optimization of logic programs

NASA Technical Reports Server (NTRS)

Laird, Phil

1992-01-01

A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

Primal-dual techniques for online algorithms and mechanisms

NASA Astrophysics Data System (ADS)

Liaghat, Vahid

An offline algorithm is one that knows the entire input in advance. An online algorithm, however, processes its input in a serial fashion. In contrast to offline algorithms, an online algorithm works in a local fashion and has to make irrevocable decisions without having the entire input. Online algorithms are often not optimal since their irrevocable decisions may turn out to be inefficient after receiving the rest of the input. For a given online problem, the goal is to design algorithms which are competitive against the offline optimal solutions. In a classical offline scenario, it is often common to see a dual analysis of problems that can be formulated as a linear or convex program. Primal-dual and dual-fitting techniques have been successfully applied to many such problems. Unfortunately, the usual tricks come short in an online setting since an online algorithm should make decisions without knowing even the whole program. In this thesis, we study the competitive analysis of fundamental problems in the literature such as different variants of online matching and online Steiner connectivity, via online dual techniques. Although there are many generic tools for solving an optimization problem in the offline paradigm, in comparison, much less is known for tackling online problems. The main focus of this work is to design generic techniques for solving integral linear optimization problems where the solution space is restricted via a set of linear constraints. A general family of these problems are online packing/covering problems. Our work shows that for several seemingly unrelated problems, primal-dual techniques can be successfully applied as a unifying approach for analyzing these problems. We believe this leads to generic algorithmic frameworks for solving online problems. In the first part of the thesis, we show the effectiveness of our techniques in the stochastic settings and their applications in Bayesian mechanism design. In particular, we introduce new techniques for solving a fundamental linear optimization problem, namely, the stochastic generalized assignment problem (GAP). This packing problem generalizes various problems such as online matching, ad allocation, bin packing, etc. We furthermore show applications of such results in the mechanism design by introducing Prophet Secretary, a novel Bayesian model for online auctions. In the second part of the thesis, we focus on the covering problems. We develop the framework of "Disk Painting" for a general class of network design problems that can be characterized by proper functions. This class generalizes the node-weighted and edge-weighted variants of several well-known Steiner connectivity problems. We furthermore design a generic technique for solving the prize-collecting variants of these problems when there exists a dual analysis for the non-prize-collecting counterparts. Hence, we solve the online prize-collecting variants of several network design problems for the first time. Finally we focus on designing techniques for online problems with mixed packing/covering constraints. We initiate the study of degree-bounded graph optimization problems in the online setting by designing an online algorithm with a tight competitive ratio for the degree-bounded Steiner forest problem. We hope these techniques establishes a starting point for the analysis of the important class of online degree-bounded optimization on graphs.

Optimizing model: insemination, replacement, seasonal production, and cash flow.

PubMed

DeLorenzo, M A; Spreen, T H; Bryan, G R; Beede, D K; Van Arendonk, J A

1992-03-01

Dynamic programming to solve the Markov decision process problem of optimal insemination and replacement decisions was adapted to address large dairy herd management decision problems in the US. Expected net present values of cow states (151,200) were used to determine the optimal policy. States were specified by class of parity (n = 12), production level (n = 15), month of calving (n = 12), month of lactation (n = 16), and days open (n = 7). Methodology optimized decisions based on net present value of an individual cow and all replacements over a 20-yr decision horizon. Length of decision horizon was chosen to ensure that optimal policies were determined for an infinite planning horizon. Optimization took 286 s of central processing unit time. The final probability transition matrix was determined, in part, by the optimal policy. It was estimated iteratively to determine post-optimization steady state herd structure, milk production, replacement, feed inputs and costs, and resulting cash flow on a calendar month and annual basis if optimal policies were implemented. Implementation of the model included seasonal effects on lactation curve shapes, estrus detection rates, pregnancy rates, milk prices, replacement costs, cull prices, and genetic progress. Other inputs included calf values, values of dietary TDN and CP per kilogram, and discount rate. Stochastic elements included conception (and, thus, subsequent freshening), cow milk production level within herd, and survival. Validation of optimized solutions was by separate simulation model, which implemented policies on a simulated herd and also described herd dynamics during transition to optimized structure.

Fundamental Principles of Tremor Propagation in the Upper Limb.

PubMed

Davidson, Andrew D; Charles, Steven K

2017-04-01

Although tremor is the most common movement disorder, there exist few effective tremor-suppressing devices, in part because the characteristics of tremor throughout the upper limb are unknown. To clarify, optimally suppressing tremor requires a knowledge of the mechanical origin, propagation, and distribution of tremor throughout the upper limb. Here we present the first systematic investigation of how tremor propagates between the shoulder, elbow, forearm, and wrist. We simulated tremor propagation using a linear, time-invariant, lumped-parameter model relating joint torques and the resulting joint displacements. The model focused on the seven main degrees of freedom from the shoulder to the wrist and included coupled joint inertia, damping, and stiffness. We deliberately implemented a simple model to focus first on the most basic effects. Simulating tremorogenic joint torque as a sinusoidal input, we used the model to establish fundamental principles describing how input parameters (torque location and frequency) and joint impedance (inertia, damping, and stiffness) affect tremor propagation. We expect that the methods and principles presented here will serve as the groundwork for future refining studies to understand the origin, propagation, and distribution of tremor throughout the upper limb in order to enable the future development of optimal tremor-suppressing devices.

Fundamental Principles of Tremor Propagation in the Upper Limb

PubMed Central

Davidson, Andrew D.; Charles, Steven K.

2017-01-01

Although tremor is the most common movement disorder, there exist few effective tremor-suppressing devices, in part because the characteristics of tremor throughout the upper limb are unknown. To clarify, optimally suppressing tremor requires a knowledge of the mechanical origin, propagation, and distribution of tremor throughout the upper limb. Here we present the first systematic investigation of how tremor propagates between the shoulder, elbow, forearm, and wrist. We simulated tremor propagation using a linear, time-invariant, lumped-parameter model relating joint torques and the resulting joint displacements. The model focused on the seven main degrees of freedom from the shoulder to the wrist and included coupled joint inertia, damping, and stiffness. We deliberately implemented a simple model to focus first on the most basic effects. Simulating tremorogenic joint torque as a sinusoidal input, we used the model to establish fundamental principles describing how input parameters (torque location and frequency) and joint impedance (inertia, damping, and stiffness) affect tremor propagation. We expect that the methods and principles presented here will serve as the groundwork for future refining studies to understand the origin, propagation, and distribution of tremor throughout the upper limb in order to enable the future development of optimal tremor-suppressing devices. PMID:27957608

A novel technique for optimal integration of active steering and differential braking with estimation to improve vehicle directional stability.

PubMed

Mirzaeinejad, Hossein; Mirzaei, Mehdi; Rafatnia, Sadra

2018-06-11

This study deals with the enhancement of directional stability of vehicle which turns with high speeds on various road conditions using integrated active steering and differential braking systems. In this respect, the minimum usage of intentional asymmetric braking force to compensate the drawbacks of active steering control with small reduction of vehicle longitudinal speed is desired. To this aim, a new optimal multivariable controller is analytically developed for integrated steering and braking systems based on the prediction of vehicle nonlinear responses. A fuzzy programming extracted from the nonlinear phase plane analysis is also used for managing the two control inputs in various driving conditions. With the proposed fuzzy programming, the weight factors of the control inputs are automatically tuned and softly changed. In order to simulate a real-world control system, some required information about the system states and parameters which cannot be directly measured, are estimated using the Unscented Kalman Filter (UKF). Finally, simulations studies are carried out using a validated vehicle model to show the effectiveness of the proposed integrated control system in the presence of model uncertainties and estimation errors. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Real-time skeleton tracking for embedded systems

NASA Astrophysics Data System (ADS)

Coleca, Foti; Klement, Sascha; Martinetz, Thomas; Barth, Erhardt

2013-03-01

Touch-free gesture technology is beginning to become more popular with consumers and may have a significant future impact on interfaces for digital photography. However, almost every commercial software framework for gesture and pose detection is aimed at either desktop PCs or high-powered GPUs, making mobile implementations for gesture recognition an attractive area for research and development. In this paper we present an algorithm for hand skeleton tracking and gesture recognition that runs on an ARM-based platform (Pandaboard ES, OMAP 4460 architecture). The algorithm uses self-organizing maps to fit a given topology (skeleton) into a 3D point cloud. This is a novel way of approaching the problem of pose recognition as it does not employ complex optimization techniques or data-based learning. After an initial background segmentation step, the algorithm is ran in parallel with heuristics, which detect and correct artifacts arising from insufficient or erroneous input data. We then optimize the algorithm for the ARM platform using fixed-point computation and the NEON SIMD architecture the OMAP4460 provides. We tested the algorithm with two different depth-sensing devices (Microsoft Kinect, PMD Camboard). For both input devices we were able to accurately track the skeleton at the native framerate of the cameras.

PopED lite: An optimal design software for preclinical pharmacokinetic and pharmacodynamic studies.

PubMed

Aoki, Yasunori; Sundqvist, Monika; Hooker, Andrew C; Gennemark, Peter

2016-04-01

Optimal experimental design approaches are seldom used in preclinical drug discovery. The objective is to develop an optimal design software tool specifically designed for preclinical applications in order to increase the efficiency of drug discovery in vivo studies. Several realistic experimental design case studies were collected and many preclinical experimental teams were consulted to determine the design goal of the software tool. The tool obtains an optimized experimental design by solving a constrained optimization problem, where each experimental design is evaluated using some function of the Fisher Information Matrix. The software was implemented in C++ using the Qt framework to assure a responsive user-software interaction through a rich graphical user interface, and at the same time, achieving the desired computational speed. In addition, a discrete global optimization algorithm was developed and implemented. The software design goals were simplicity, speed and intuition. Based on these design goals, we have developed the publicly available software PopED lite (http://www.bluetree.me/PopED_lite). Optimization computation was on average, over 14 test problems, 30 times faster in PopED lite compared to an already existing optimal design software tool. PopED lite is now used in real drug discovery projects and a few of these case studies are presented in this paper. PopED lite is designed to be simple, fast and intuitive. Simple, to give many users access to basic optimal design calculations. Fast, to fit a short design-execution cycle and allow interactive experimental design (test one design, discuss proposed design, test another design, etc). Intuitive, so that the input to and output from the software tool can easily be understood by users without knowledge of the theory of optimal design. In this way, PopED lite is highly useful in practice and complements existing tools. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A multiple objective optimization approach to quality control

NASA Technical Reports Server (NTRS)

Seaman, Christopher Michael

1991-01-01

The use of product quality as the performance criteria for manufacturing system control is explored. The goal in manufacturing, for economic reasons, is to optimize product quality. The problem is that since quality is a rather nebulous product characteristic, there is seldom an analytic function that can be used as a measure. Therefore standard control approaches, such as optimal control, cannot readily be applied. A second problem with optimizing product quality is that it is typically measured along many dimensions: there are many apsects of quality which must be optimized simultaneously. Very often these different aspects are incommensurate and competing. The concept of optimality must now include accepting tradeoffs among the different quality characteristics. These problems are addressed using multiple objective optimization. It is shown that the quality control problem can be defined as a multiple objective optimization problem. A controller structure is defined using this as the basis. Then, an algorithm is presented which can be used by an operator to interactively find the best operating point. Essentially, the algorithm uses process data to provide the operator with two pieces of information: (1) if it is possible to simultaneously improve all quality criteria, then determine what changes to the process input or controller parameters should be made to do this; and (2) if it is not possible to improve all criteria, and the current operating point is not a desirable one, select a criteria in which a tradeoff should be made, and make input changes to improve all other criteria. The process is not operating at an optimal point in any sense if no tradeoff has to be made to move to a new operating point. This algorithm ensures that operating points are optimal in some sense and provides the operator with information about tradeoffs when seeking the best operating point. The multiobjective algorithm was implemented in two different injection molding scenarios: tuning of process controllers to meet specified performance objectives and tuning of process inputs to meet specified quality objectives. Five case studies are presented.

Optimal allocation of bulk water supplies to competing use sectors based on economic criterion - An application to the Chao Phraya River Basin, Thailand

NASA Astrophysics Data System (ADS)

Divakar, L.; Babel, M. S.; Perret, S. R.; Gupta, A. Das

2011-04-01

SummaryThe study develops a model for optimal bulk allocations of limited available water based on an economic criterion to competing use sectors such as agriculture, domestic, industry and hydropower. The model comprises a reservoir operation module (ROM) and a water allocation module (WAM). ROM determines the amount of water available for allocation, which is used as an input to WAM with an objective function to maximize the net economic benefits of bulk allocations to different use sectors. The total net benefit functions for agriculture and hydropower sectors and the marginal net benefit from domestic and industrial sectors are established and are categorically taken as fixed in the present study. The developed model is applied to the Chao Phraya basin in Thailand. The case study results indicate that the WAM can improve net economic returns compared to the current water allocation practices.

Difficult Decisions Made Easier

NASA Technical Reports Server (NTRS)

2006-01-01

NASA missions are extremely complex and prone to sudden, catastrophic failure if equipment falters or if an unforeseen event occurs. For these reasons, NASA trains to expect the unexpected. It tests its equipment and systems in extreme conditions, and it develops risk-analysis tests to foresee any possible problems. The Space Agency recently worked with an industry partner to develop reliability analysis software capable of modeling complex, highly dynamic systems, taking into account variations in input parameters and the evolution of the system over the course of a mission. The goal of this research was multifold. It included performance and risk analyses of complex, multiphase missions, like the insertion of the Mars Reconnaissance Orbiter; reliability analyses of systems with redundant and/or repairable components; optimization analyses of system configurations with respect to cost and reliability; and sensitivity analyses to identify optimal areas for uncertainty reduction or performance enhancement.

Engineering model 8-cm thruster subsystem

NASA Technical Reports Server (NTRS)

Herron, B. G.; Hyman, J.; Hopper, D. J.; Williamson, W. S.; Dulgeroff, C. R.; Collett, C. R.

1978-01-01

An Engineering Model (EM) 8 cm Ion Thruster Propulsion Subsystem was developed for operation at a thrust level 5 mN (1.1 mlb) at a specific impulse 1 sub sp = 2667 sec with a total system input power P sub in = 165 W. The system dry mass is 15 kg with a mercury-propellant-reservoir capacity of 8.75 kg permitting uninterrupted operation for about 12,500 hr. The subsystem can be started from a dormant condition in a time less than or equal to 15 min. The thruster has a design lifetime of 20,000 hr with 10,000 startup cycles. A gimbal unit is included to provide a thrust vector deflection capability of + or - 10 degrees in any direction from the zero position. The EM subsystem development program included thruster optimization, power-supply circuit optimization and flight packaging, subsystem integration, and subsystem acceptance testing including a cyclic test of the total propulsion package.

Development of an Algorithm for Automatic Analysis of the Impedance Spectrum Based on a Measurement Model

NASA Astrophysics Data System (ADS)

Kobayashi, Kiyoshi; Suzuki, Tohru S.

2018-03-01

A new algorithm for the automatic estimation of an equivalent circuit and the subsequent parameter optimization is developed by combining the data-mining concept and complex least-squares method. In this algorithm, the program generates an initial equivalent-circuit model based on the sampling data and then attempts to optimize the parameters. The basic hypothesis is that the measured impedance spectrum can be reproduced by the sum of the partial-impedance spectra presented by the resistor, inductor, resistor connected in parallel to a capacitor, and resistor connected in parallel to an inductor. The adequacy of the model is determined by using a simple artificial-intelligence function, which is applied to the output function of the Levenberg-Marquardt module. From the iteration of model modifications, the program finds an adequate equivalent-circuit model without any user input to the equivalent-circuit model.

Optimization Strategies for Hardware-Based Cofactorization

NASA Astrophysics Data System (ADS)

Loebenberger, Daniel; Putzka, Jens

We use the specific structure of the inputs to the cofactorization step in the general number field sieve (GNFS) in order to optimize the runtime for the cofactorization step on a hardware cluster. An optimal distribution of bitlength-specific ECM modules is proposed and compared to existing ones. With our optimizations we obtain a speedup between 17% and 33% of the cofactorization step of the GNFS when compared to the runtime of an unoptimized cluster.

C-SWAT: The Soil and Water Assessment Tool with consolidated input files in alleviating computational burden of recursive simulations

USDA-ARS?s Scientific Manuscript database

The temptation to include model parameters and high resolution input data together with the availability of powerful optimization and uncertainty analysis algorithms has significantly enhanced the complexity of hydrologic and water quality modeling. However, the ability to take advantage of sophist...

Input and output constraints affecting irrigation development

NASA Astrophysics Data System (ADS)

Schramm, G.

1981-05-01

In many of the developing countries the expansion of irrigated agriculture is used as a major development tool for bringing about increases in agricultural output, rural economic growth and income distribution. Apart from constraints imposed by water availability, the major limitations considered to any acceleration of such programs are usually thought to be those of costs and financial resources. However, as is shown on the basis of empirical data drawn from Mexico, in reality the feasibility and effectiveness of such development programs is even more constrained by the lack of specialized physical and human factors on the input and market limitations on the output side. On the input side, the limited availability of complementary factors such as, for example, truly functioning credit systems for small-scale farmers or effective agricultural extension services impose long-term constraints on development. On the output side the limited availability, high risk, and relatively slow growth of markets for high-value crops sharply reduce the usually hoped-for and projected profitable crop mix that would warrant the frequently high costs of irrigation investments. Three conclusions are drawn: (1) Factors in limited supply have to be shadow-priced to reflect their high opportunity costs in alternative uses. (2) Re-allocation of financial resources from immediate construction of projects to longer-term increase in the supply of scarce, highly-trained manpower resources are necessary in order to optimize development over time. (3) Inclusion of high-value, high-income producing crops in the benefit-cost analysis of new projects is inappropriate if these crops could potentially be grown in already existing projects.

Multi-Response Optimization of WEDM Process Parameters Using Taguchi Based Desirability Function Analysis

NASA Astrophysics Data System (ADS)

Majumder, Himadri; Maity, Kalipada

2018-03-01

Shape memory alloy has a unique capability to return to its original shape after physical deformation by applying heat or thermo-mechanical or magnetic load. In this experimental investigation, desirability function analysis (DFA), a multi-attribute decision making was utilized to find out the optimum input parameter setting during wire electrical discharge machining (WEDM) of Ni-Ti shape memory alloy. Four critical machining parameters, namely pulse on time (TON), pulse off time (TOFF), wire feed (WF) and wire tension (WT) were taken as machining inputs for the experiments to optimize three interconnected responses like cutting speed, kerf width, and surface roughness. Input parameter combination TON = 120 μs., TOFF = 55 μs., WF = 3 m/min. and WT = 8 kg-F were found to produce the optimum results. The optimum process parameters for each desired response were also attained using Taguchi’s signal-to-noise ratio. Confirmation test has been done to validate the optimum machining parameter combination which affirmed DFA was a competent approach to select optimum input parameters for the ideal response quality for WEDM of Ni-Ti shape memory alloy.

Method of generating features optimal to a dataset and classifier

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

Bruillard, Paul J.; Gosink, Luke J.; Jarman, Kenneth D.

A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.

Subsystem design in aircraft power distribution systems using optimization

NASA Astrophysics Data System (ADS)

Chandrasekaran, Sriram

2000-10-01

The research reported in this dissertation focuses on the development of optimization tools for the design of subsystems in a modern aircraft power distribution system. The baseline power distribution system is built around a 270V DC bus. One of the distinguishing features of this power distribution system is the presence of regenerative power from the electrically driven flight control actuators and structurally integrated smart actuators back to the DC bus. The key electrical components of the power distribution system are bidirectional switching power converters, which convert, control and condition electrical power between the sources and the loads. The dissertation is divided into three parts. Part I deals with the formulation of an optimization problem for a sample system consisting of a regulated DC-DC buck converter preceded by an input filter. The individual subsystems are optimized first followed by the integrated optimization of the sample system. It is shown that the integrated optimization provides better results than that obtained by integrating the individually optimized systems. Part II presents a detailed study of piezoelectric actuators. This study includes modeling, optimization of the drive amplifier and the development of a current control law for piezoelectric actuators coupled to a simple mechanical structure. Linear and nonlinear methods to study subsystem interaction and stability are studied in Part III. A multivariable impedance ratio criterion applicable to three phase systems is proposed. Bifurcation methods are used to obtain global stability characteristics of interconnected systems. The application of a nonlinear design methodology, widely used in power systems, to incrementally improve the robustness of a system to Hopf bifurcation instability is discussed.

Application of artificial neural networks to the design optimization of aerospace structural components

NASA Technical Reports Server (NTRS)

Berke, Laszlo; Patnaik, Surya N.; Murthy, Pappu L. N.

1993-01-01

The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

Runway Exit Designs for Capacity Improvement Demonstrations. Phase 1: Algorithm Development

NASA Technical Reports Server (NTRS)

Trani, A. A.; Hobeika, A. G.; Sherali, H.; Kim, B. J.; Sadam, C. K.

1990-01-01

A description and results are presented of a study to locate and design rapid runway exits under realistic airport conditions. The study developed a PC-based computer simulation-optimization program called REDIM (runway exit design interactive model) to help future airport designers and planners to locate optimal exits under various airport conditions. The model addresses three sets of problems typically arising during runway exit design evaluations. These are the evaluations of existing runway configurations, addition of new rapid runway turnoffs, and the design of new runway facilities. The model is highly interactive and allows a quick estimation of the expected value of runway occupancy time. Aircraft populations and airport environmental conditions are among the multiple inputs to the model to execute a viable runway location and geometric design solution. The results presented suggest that possible reductions on runway occupancy time (ROT) can be achieved with the use of optimally tailored rapid runway designs for a given aircraft population. Reductions of up to 9 to 6 seconds are possible with the implementation of 30 m/sec variable geometry exits.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

PubMed

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Modeling Reservoir-River Networks in Support of Optimizing Seasonal-Scale Reservoir Operations

NASA Astrophysics Data System (ADS)

Villa, D. L.; Lowry, T. S.; Bier, A.; Barco, J.; Sun, A.

2011-12-01

HydroSCOPE (Hydropower Seasonal Concurrent Optimization of Power and the Environment) is a seasonal time-scale tool for scenario analysis and optimization of reservoir-river networks. Developed in MATLAB, HydroSCOPE is an object-oriented model that simulates basin-scale dynamics with an objective of optimizing reservoir operations to maximize revenue from power generation, reliability in the water supply, environmental performance, and flood control. HydroSCOPE is part of a larger toolset that is being developed through a Department of Energy multi-laboratory project. This project's goal is to provide conventional hydropower decision makers with better information to execute their day-ahead and seasonal operations and planning activities by integrating water balance and operational dynamics across a wide range of spatial and temporal scales. This presentation details the modeling approach and functionality of HydroSCOPE. HydroSCOPE consists of a river-reservoir network model and an optimization routine. The river-reservoir network model simulates the heat and water balance of river-reservoir networks for time-scales up to one year. The optimization routine software, DAKOTA (Design Analysis Kit for Optimization and Terascale Applications - dakota.sandia.gov), is seamlessly linked to the network model and is used to optimize daily volumetric releases from the reservoirs to best meet a set of user-defined constraints, such as maximizing revenue while minimizing environmental violations. The network model uses 1-D approximations for both the reservoirs and river reaches and is able to account for surface and sediment heat exchange as well as ice dynamics for both models. The reservoir model also accounts for inflow, density, and withdrawal zone mixing, and diffusive heat exchange. Routing for the river reaches is accomplished using a modified Muskingum-Cunge approach that automatically calculates the internal timestep and sub-reach lengths to match the conditions of each timestep and minimize computational overhead. Power generation for each reservoir is estimated using a 2-dimensional regression that accounts for both the available head and turbine efficiency. The object-oriented architecture makes run configuration easy to update. The dynamic model inputs include inflow and meteorological forecasts while static inputs include bathymetry data, reservoir and power generation characteristics, and topological descriptors. Ensemble forecasts of hydrological and meteorological conditions are supplied in real-time by Pacific Northwest National Laboratory and are used as a proxy for uncertainty, which is carried through the simulation and optimization process to produce output that describes the probability that different operational scenario's will be optimal. The full toolset, which includes HydroSCOPE, is currently being tested on the Feather River system in Northern California and the Upper Colorado Storage Project.

Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus

NASA Technical Reports Server (NTRS)

Momoh, James; Chattopadhyay, Deb; Elfayoumy, Mahmoud

1996-01-01

The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the GP model which in turn produces the pre-schedule of the optimal control model. Some preliminary results based on a hypothetical test case will be presented for the load forecasting module. The computer codes for the three modules will be made available fe adoption by bus operating agencies. Sample results will be provided using these models. The software will be a useful tool for supporting the control systems for the Electric Bus project of NASA.

A single-layer platform for Boolean logic and arithmetic through DNA excision in mammalian cells

PubMed Central

Weinberg, Benjamin H.; Hang Pham, N. T.; Caraballo, Leidy D.; Lozanoski, Thomas; Engel, Adrien; Bhatia, Swapnil; Wong, Wilson W.

2017-01-01

Genetic circuits engineered for mammalian cells often require extensive fine-tuning to perform their intended functions. To overcome this problem, we present a generalizable biocomputing platform that can engineer genetic circuits which function in human cells with minimal optimization. We used our Boolean Logic and Arithmetic through DNA Excision (BLADE) platform to build more than 100 multi-input-multi-output circuits. We devised a quantitative metric to evaluate the performance of the circuits in human embryonic kidney and Jurkat T cells. Of 113 circuits analysed, 109 functioned (96.5%) with the correct specified behavior without any optimization. We used our platform to build a three-input, two-output Full Adder and six-input, one-output Boolean Logic Look Up Table. We also used BLADE to design circuits with temporal small molecule-mediated inducible control and circuits that incorporate CRISPR/Cas9 to regulate endogenous mammalian genes. PMID:28346402

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.

An exact algebraic solution of the infimum in H-infinity optimization with output feedback

NASA Technical Reports Server (NTRS)

Chen, Ben M.; Saberi, Ali; Ly, Uy-Loi

1991-01-01

This paper presents a simple and noniterative procedure for the computation of the exact value of the infimum in the standard H-infinity-optimal control with output feedback. The problem formulation is general and does not place any restrictions on the direct feedthrough terms between the control input and the controlled output variables, and between the disturbance input and the measurement output variables. The method is applicable to systems that satisfy (1) the transfer function from the control input to the controlled output is right-invertible and has no invariant zeros on the j(w) axis and, (2) the transfer function from the disturbance to the measurement output is left-invertible and has no invariant zeros on the j(w) axis. A set of necessary and sufficient conditions for the solvability of H-infinity-almost disturbance decoupling problem via measurement feedback with internal stability is also given.

Optimizing information flow in small genetic networks. IV. Spatial coupling

NASA Astrophysics Data System (ADS)

Sokolowski, Thomas R.; Tkačik, Gašper

2015-06-01

We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the input) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively different regulatory strategy emerges where individual cells respond to the input in a nearly steplike fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.

Enabling Real-time Water Decision Support Services Using Model as a Service

NASA Astrophysics Data System (ADS)

Zhao, T.; Minsker, B. S.; Lee, J. S.; Salas, F. R.; Maidment, D. R.; David, C. H.

2014-12-01

Through application of computational methods and an integrated information system, data and river modeling services can help researchers and decision makers more rapidly understand river conditions under alternative scenarios. To enable this capability, workflows (i.e., analysis and model steps) are created and published as Web services delivered through an internet browser, including model inputs, a published workflow service, and visualized outputs. The RAPID model, which is a river routing model developed at University of Texas Austin for parallel computation of river discharge, has been implemented as a workflow and published as a Web application. This allows non-technical users to remotely execute the model and visualize results as a service through a simple Web interface. The model service and Web application has been prototyped in the San Antonio and Guadalupe River Basin in Texas, with input from university and agency partners. In the future, optimization model workflows will be developed to link with the RAPID model workflow to provide real-time water allocation decision support services.

Cognitive/emotional models for human behavior representation in 3D avatar simulations

NASA Astrophysics Data System (ADS)

Peterson, James K.

2004-08-01

Simplified models of human cognition and emotional response are presented which are based on models of auditory/ visual polymodal fusion. At the core of these models is a computational model of Area 37 of the temporal cortex which is based on new isocortex models presented recently by Grossberg. These models are trained using carefully chosen auditory (musical sequences), visual (paintings) and higher level abstract (meta level) data obtained from studies of how optimization strategies are chosen in response to outside managerial inputs. The software modules developed are then used as inputs to character generation codes in standard 3D virtual world simulations. The auditory and visual training data also enable the development of simple music and painting composition generators which significantly enhance one's ability to validate the cognitive model. The cognitive models are handled as interacting software agents implemented as CORBA objects to allow the use of multiple language coding choices (C++, Java, Python etc) and efficient use of legacy code.

AutoBayes Program Synthesis System Users Manual

NASA Technical Reports Server (NTRS)

Schumann, Johann; Jafari, Hamed; Pressburger, Tom; Denney, Ewen; Buntine, Wray; Fischer, Bernd

2008-01-01

Program synthesis is the systematic, automatic construction of efficient executable code from high-level declarative specifications. AutoBayes is a fully automatic program synthesis system for the statistical data analysis domain; in particular, it solves parameter estimation problems. It has seen many successful applications at NASA and is currently being used, for example, to analyze simulation results for Orion. The input to AutoBayes is a concise description of a data analysis problem composed of a parameterized statistical model and a goal that is a probability term involving parameters and input data. The output is optimized and fully documented C/C++ code computing the values for those parameters that maximize the probability term. AutoBayes can solve many subproblems symbolically rather than having to rely on numeric approximation algorithms, thus yielding effective, efficient, and compact code. Statistical analysis is faster and more reliable, because effort can be focused on model development and validation rather than manual development of solution algorithms and code.

Optimal space communications techniques. [all digital phase locked loop for FM demodulation

NASA Technical Reports Server (NTRS)

Schilling, D. L.

1973-01-01

The design, development, and analysis are reported of a digital phase-locked loop (DPLL) for FM demodulation and threshold extension. One of the features of the developed DPLL is its synchronous, real time operation. The sampling frequency is constant and all the required arithmetic and logic operations are performed within one sampling period, generating an output sequence which is converted to analog form and filtered. An equation relating the sampling frequency to the carrier frequency must be satisfied to guarantee proper DPLL operation. The synchronous operation enables a time-shared operation of one DPLL to demodulate several FM signals simultaneously. In order to obtain information about the DPLL performance at low input signal-to-noise ratios, a model of an input noise spike was introduced, and the DPLL equation was solved using a digital computer. The spike model was successful in finding a second order DPLL which yielded a five db threshold extension beyond that of a first order DPLL.

Specificity and timescales of cortical adaptation as inferences about natural movie statistics.

PubMed

Snow, Michoel; Coen-Cagli, Ruben; Schwartz, Odelia

2016-10-01

Adaptation is a phenomenological umbrella term under which a variety of temporal contextual effects are grouped. Previous models have shown that some aspects of visual adaptation reflect optimal processing of dynamic visual inputs, suggesting that adaptation should be tuned to the properties of natural visual inputs. However, the link between natural dynamic inputs and adaptation is poorly understood. Here, we extend a previously developed Bayesian modeling framework for spatial contextual effects to the temporal domain. The model learns temporal statistical regularities of natural movies and links these statistics to adaptation in primary visual cortex via divisive normalization, a ubiquitous neural computation. In particular, the model divisively normalizes the present visual input by the past visual inputs only to the degree that these are inferred to be statistically dependent. We show that this flexible form of normalization reproduces classical findings on how brief adaptation affects neuronal selectivity. Furthermore, prior knowledge acquired by the Bayesian model from natural movies can be modified by prolonged exposure to novel visual stimuli. We show that this updating can explain classical results on contrast adaptation. We also simulate the recent finding that adaptation maintains population homeostasis, namely, a balanced level of activity across a population of neurons with different orientation preferences. Consistent with previous disparate observations, our work further clarifies the influence of stimulus-specific and neuronal-specific normalization signals in adaptation.

Specificity and timescales of cortical adaptation as inferences about natural movie statistics

PubMed Central

Snow, Michoel; Coen-Cagli, Ruben; Schwartz, Odelia

2016-01-01

Adaptation is a phenomenological umbrella term under which a variety of temporal contextual effects are grouped. Previous models have shown that some aspects of visual adaptation reflect optimal processing of dynamic visual inputs, suggesting that adaptation should be tuned to the properties of natural visual inputs. However, the link between natural dynamic inputs and adaptation is poorly understood. Here, we extend a previously developed Bayesian modeling framework for spatial contextual effects to the temporal domain. The model learns temporal statistical regularities of natural movies and links these statistics to adaptation in primary visual cortex via divisive normalization, a ubiquitous neural computation. In particular, the model divisively normalizes the present visual input by the past visual inputs only to the degree that these are inferred to be statistically dependent. We show that this flexible form of normalization reproduces classical findings on how brief adaptation affects neuronal selectivity. Furthermore, prior knowledge acquired by the Bayesian model from natural movies can be modified by prolonged exposure to novel visual stimuli. We show that this updating can explain classical results on contrast adaptation. We also simulate the recent finding that adaptation maintains population homeostasis, namely, a balanced level of activity across a population of neurons with different orientation preferences. Consistent with previous disparate observations, our work further clarifies the influence of stimulus-specific and neuronal-specific normalization signals in adaptation. PMID:27699416

User's guide for mapIMG 3--Map image re-projection software package

USGS Publications Warehouse

Finn, Michael P.; Mattli, David M.

2012-01-01

Version 0.0 (1995), Dan Steinwand, U.S. Geological Survey (USGS)/Earth Resources Observation Systems (EROS) Data Center (EDC)--Version 0.0 was a command line version for UNIX that required four arguments: the input metadata, the output metadata, the input data file, and the output destination path. Version 1.0 (2003), Stephen Posch and Michael P. Finn, USGS/Mid-Continent Mapping Center (MCMC--Version 1.0 added a GUI interface that was built using the Qt library for cross platform development. Version 1.01 (2004), Jason Trent and Michael P. Finn, USGS/MCMC--Version 1.01 suggested bounds for the parameters of each projection. Support was added for larger input files, storage of the last used input and output folders, and for TIFF/ GeoTIFF input images. Version 2.0 (2005), Robert Buehler, Jason Trent, and Michael P. Finn, USGS/National Geospatial Technical Operations Center (NGTOC)--Version 2.0 added Resampling Methods (Mean, Mode, Min, Max, and Sum), updated the GUI design, and added the viewer/pre-viewer. The metadata style was changed to XML and was switched to a new naming convention. Version 3.0 (2009), David Mattli and Michael P. Finn, USGS/Center of Excellence for Geospatial Information Science (CEGIS)--Version 3.0 brings optimized resampling methods, an updated GUI, support for less than global datasets, UTM support and the whole codebase was ported to Qt4.

A multi-purpose readout electronics for CdTe and CZT detectors for x-ray imaging applications

NASA Astrophysics Data System (ADS)

Yue, X. B.; Deng, Z.; Xing, Y. X.; Liu, Y. N.

2017-09-01

A multi-purpose readout electronics based on the DPLMS digital filter has been developed for CdTe and CZT detectors for X-ray imaging applications. Different filter coefficients can be synthesized optimized either for high energy resolution at relatively low counting rate or for high rate photon-counting with reduced energy resolution. The effects of signal width constraints, sampling rate and length were numerical studied by Mento Carlo simulation with simple CRRC shaper input signals. The signal width constraint had minor effect and the ENC was only increased by 6.5% when the signal width was shortened down to 2 τc. The sampling rate and length depended on the characteristic time constants of both input and output signals. For simple CR-RC input signals, the minimum number of the filter coefficients was 12 with 10% increase in ENC when the output time constant was close to the input shaping time. A prototype readout electronics was developed for demonstration, using a previously designed analog front ASIC and a commercial ADC card. Two different DPLMS filters were successfully synthesized and applied for high resolution and high counting rate applications respectively. The readout electronics was also tested with a linear array CdTe detector. The energy resolutions of Am-241 59.5 keV peak were measured to be 6.41% in FWHM for the high resolution filter and to be 13.58% in FWHM for the high counting rate filter with 160 ns signal width constraint.

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

PubMed Central

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-01-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. PMID:29194393

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection.

PubMed

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-12-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.

Teleportation of squeezing: Optimization using non-Gaussian resources

NASA Astrophysics Data System (ADS)

Dell'Anno, Fabio; de Siena, Silvio; Adesso, Gerardo; Illuminati, Fabrizio

2010-12-01

We study the continuous-variable quantum teleportation of states, statistical moments of observables, and scale parameters such as squeezing. We investigate the problem both in ideal and imperfect Vaidman-Braunstein-Kimble protocol setups. We show how the teleportation fidelity is maximized and the difference between output and input variances is minimized by using suitably optimized entangled resources. Specifically, we consider the teleportation of coherent squeezed states, exploiting squeezed Bell states as entangled resources. This class of non-Gaussian states, introduced by Illuminati and co-workers [F. Dell’Anno, S. De Siena, L. Albano, and F. Illuminati, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.76.022301 76, 022301 (2007); F. Dell’Anno, S. De Siena, and F. Illuminati, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.81.012333 81, 012333 (2010)], includes photon-added and photon-subtracted squeezed states as special cases. At variance with the case of entangled Gaussian resources, the use of entangled non-Gaussian squeezed Bell resources allows one to choose different optimization procedures that lead to inequivalent results. Performing two independent optimization procedures, one can either maximize the state teleportation fidelity, or minimize the difference between input and output quadrature variances. The two different procedures are compared depending on the degrees of displacement and squeezing of the input states and on the working conditions in ideal and nonideal setups.

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

PubMed

Qiu, Mingyue; Song, Yu

2016-01-01

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

PubMed Central

Qiu, Mingyue; Song, Yu

2016-01-01

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. PMID:27196055

Efficient design of gain-flattened multi-pump Raman fiber amplifiers using least squares support vector regression

NASA Astrophysics Data System (ADS)

Chen, Jing; Qiu, Xiaojie; Yin, Cunyi; Jiang, Hao

2018-02-01

An efficient method to design the broadband gain-flattened Raman fiber amplifier with multiple pumps is proposed based on least squares support vector regression (LS-SVR). A multi-input multi-output LS-SVR model is introduced to replace the complicated solving process of the nonlinear coupled Raman amplification equation. The proposed approach contains two stages: offline training stage and online optimization stage. During the offline stage, the LS-SVR model is trained. Owing to the good generalization capability of LS-SVR, the net gain spectrum can be directly and accurately obtained when inputting any combination of the pump wavelength and power to the well-trained model. During the online stage, we incorporate the LS-SVR model into the particle swarm optimization algorithm to find the optimal pump configuration. The design results demonstrate that the proposed method greatly shortens the computation time and enhances the efficiency of the pump parameter optimization for Raman fiber amplifier design.

Multi-input multioutput orthogonal frequency division multiplexing radar waveform design for improving the detection performance of space-time adaptive processing

NASA Astrophysics Data System (ADS)

Wang, Hongyan

2017-04-01

This paper addresses the waveform optimization problem for improving the detection performance of multi-input multioutput (MIMO) orthogonal frequency division multiplexing (OFDM) radar-based space-time adaptive processing (STAP) in the complex environment. By maximizing the output signal-to-interference-and-noise-ratio (SINR) criterion, the waveform optimization problem for improving the detection performance of STAP, which is subjected to the constant modulus constraint, is derived. To tackle the resultant nonlinear and complicated optimization issue, a diagonal loading-based method is proposed to reformulate the issue as a semidefinite programming one; thereby, this problem can be solved very efficiently. In what follows, the optimized waveform can be obtained to maximize the output SINR of MIMO-OFDM such that the detection performance of STAP can be improved. The simulation results show that the proposed method can improve the output SINR detection performance considerably as compared with that of uncorrelated waveforms and the existing MIMO-based STAP method.

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.

Chaos control in solar fed DC-DC boost converter by optimal parameters using nelder-mead algorithm powered enhanced BFOA

NASA Astrophysics Data System (ADS)

Sudhakar, N.; Rajasekar, N.; Akhil, Saya; Jyotheeswara Reddy, K.

2017-11-01

The boost converter is the most desirable DC-DC power converter for renewable energy applications for its favorable continuous input current characteristics. In other hand, these DC-DC converters known as practical nonlinear systems are prone to several types of nonlinear phenomena including bifurcation, quasiperiodicity, intermittency and chaos. These undesirable effects has to be controlled for maintaining normal periodic operation of the converter and to ensure the stability. This paper presents an effective solution to control the chaos in solar fed DC-DC boost converter since the converter experiences wide range of input power variation which leads to chaotic phenomena. Controlling of chaos is significantly achieved using optimal circuit parameters obtained through Nelder-Mead Enhanced Bacterial Foraging Optimization Algorithm. The optimization renders the suitable parameters in minimum computational time. The results are compared with the traditional methods. The obtained results of the proposed system ensures the operation of the converter within the controllable region.

The Effects of Manure and Nitrogen Fertilizer Applications on Soil Organic Carbon and Nitrogen in a High-Input Cropping System

PubMed Central

Ren, Tao; Wang, Jingguo; Chen, Qing; Zhang, Fusuo; Lu, Shuchang

2014-01-01

With the goal of improving N fertilizer management to maximize soil organic carbon (SOC) storage and minimize N losses in high-intensity cropping system, a 6-years greenhouse vegetable experiment was conducted from 2004 to 2010 in Shouguang, northern China. Treatment tested the effects of organic manure and N fertilizer on SOC, total N (TN) pool and annual apparent N losses. The results demonstrated that SOC and TN concentrations in the 0-10cm soil layer decreased significantly without organic manure and mineral N applications, primarily because of the decomposition of stable C. Increasing C inputs through wheat straw and chicken manure incorporation couldn't increase SOC pools over the 4 year duration of the experiment. In contrast to the organic manure treatment, the SOC and TN pools were not increased with the combination of organic manure and N fertilizer. However, the soil labile carbon fractions increased significantly when both chicken manure and N fertilizer were applied together. Additionally, lower optimized N fertilizer inputs did not decrease SOC and TN accumulation compared with conventional N applications. Despite the annual apparent N losses for the optimized N treatment were significantly lower than that for the conventional N treatment, the unchanged SOC over the past 6 years might limit N storage in the soil and more surplus N were lost to the environment. Consequently, optimized N fertilizer inputs according to root-zone N management did not influence the accumulation of SOC and TN in soil; but beneficial in reducing apparent N losses. N fertilizer management in a greenhouse cropping system should not only identify how to reduce N fertilizer input but should also be more attentive to improving soil fertility with better management of organic manure. PMID:24830463

A Neural Network Aero Design System for Advanced Turbo-Engines

NASA Technical Reports Server (NTRS)

Sanz, Jose M.

1999-01-01

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

NASA Technical Reports Server (NTRS)

Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

2015-01-01

Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

Nonlinear optimal control for the synchronization of chaotic and hyperchaotic finance systems

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Loia, V.; Ademi, S.; Ghosh, T.

2017-11-01

It is possible to make specific finance systems get synchronized to other finance systems exhibiting chaotic and hyperchaotic dynamics, by applying nonlinear optimal (H-infinity) control. This signifies that chaotic behavior can be generated in finance systems by exerting a suitable control input. Actually, a lead financial system is considered which exhibits inherently chaotic dynamics. Moreover, a follower finance system is introduced having parameters in its model that inherently prohibit the appearance of chaotic dynamics. Through the application of a suitable nonlinear optimal (H-infinity) control input it is proven that the follower finance system can replicate the chaotic dynamics of the lead finance system. By applying Lyapunov analysis it is proven that asymptotically the follower finance system gets synchronized with the lead system and that the tracking error between the state variables of the two systems vanishes.

African crop yield reductions due to increasingly unbalanced Nitrogen and Phosphorus consumption

NASA Astrophysics Data System (ADS)

van der Velde, Marijn; Folberth, Christian; Balkovič, Juraj; Ciais, Philippe; Fritz, Steffen; Janssens, Ivan A.; Obersteiner, Michael; See, Linda; Skalský, Rastislav; Xiong, Wei; Peñuealas, Josep

2014-05-01

The impact of soil nutrient depletion on crop production has been known for decades, but robust assessments of the impact of increasingly unbalanced nitrogen (N) and phosphorus (P) application rates on crop production are lacking. Here, we use crop response functions based on 741 FAO maize crop trials and EPIC crop modeling across Africa to examine maize yield deficits resulting from unbalanced N:P applications under low, medium, and high input scenarios, for past (1975), current, and future N:P mass ratios of respectively, 1:0.29, 1:0.15, and 1:0.05. At low N inputs (10 kg/ha), current yield deficits amount to 10% but will increase up to 27% under the assumed future N:P ratio, while at medium N inputs (50 kg N/ha), future yield losses could amount to over 40%. The EPIC crop model was then used to simulate maize yields across Africa. The model results showed relative median future yield reductions at low N inputs of 40%, and 50% at medium and high inputs, albeit with large spatial variability. Dominant low-quality soils such as Ferralsols, which are strongly adsorbing P, and Arenosols with a low nutrient retention capacity, are associated with a strong yield decline, although Arenosols show very variable crop yield losses at low inputs. Optimal N:P ratios, i.e. those where the lowest amount of applied P produces the highest yield (given N input) where calculated with EPIC to be as low as 1:0.5. Finally, we estimated the additional P required given current N inputs, and given N inputs that would allow Africa to close yield gaps (ca. 70%). At current N inputs, P consumption would have to increase 2.3-fold to be optimal, and to increase 11.7-fold to close yield gaps. The P demand to overcome these yield deficits would provide a significant additional pressure on current global extraction of P resources.

Decision & Management Tools for DNAPL Sites: Optimization of Chlorinated Solvent Source and Plume Remediation Considering Uncertainty

DTIC Science & Technology

2010-09-01

differentiated between source codes and input/output files. The text makes references to a REMChlor-GoldSim model. The text also refers to the REMChlor...To the extent possible, the instructions should be accurate and precise. The documentation should differentiate between describing what is actually...Windows XP operating system Model Input Paran1eters. · n1e input parameters were identical to those utilized and reported by CDM (See Table .I .from

Inverse Diffusion Curves Using Shape Optimization.

PubMed

Zhao, Shuang; Durand, Fredo; Zheng, Changxi

2018-07-01

The inverse diffusion curve problem focuses on automatic creation of diffusion curve images that resemble user provided color fields. This problem is challenging since the 1D curves have a nonlinear and global impact on resulting color fields via a partial differential equation (PDE). We introduce a new approach complementary to previous methods by optimizing curve geometry. In particular, we propose a novel iterative algorithm based on the theory of shape derivatives. The resulting diffusion curves are clean and well-shaped, and the final image closely approximates the input. Our method provides a user-controlled parameter to regularize curve complexity, and generalizes to handle input color fields represented in a variety of formats.

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.

Challenges to overcome: energy supply for remote consumers in the Russian Arctic

NASA Astrophysics Data System (ADS)

Morgunova, M. O.; Solovyev, D. A.

2017-11-01

The paper explores challenges of power supply for remote users through the case of the Northern Sea Route (NSR) supportive infrastructure development and specially nature protected areas (NPA) of the Russian Arctic. The study is based on a comprehensive analysis of relevant data of the state of renewable energy in the Russian Arctic. The paper gives policy recommendations on how to extend the use of renewable energy power plants in the region, optimize their input and increase cost-effectiveness and safety.

Experimental and Theoretical Results in Output Trajectory Redesign for Flexible Structures

NASA Technical Reports Server (NTRS)

Dewey, J. S.; Leang, K.; Devasia, S.

1998-01-01

In this paper we study the optimal redesign of output trajectories for linear invertible systems. This is particularly important for tracking control of flexible structures because the input-state trajectores, that achieve tracking of the required output may cause excessive vibrations in the structure. We pose and solve this problem, in the context of linear systems, as the minimization of a quadratic cost function. The theory is developed and applied to the output tracking of a flexible structure and experimental results are presented.

Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

NASA Astrophysics Data System (ADS)

Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad

2016-11-01

In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.

Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?

PubMed Central

Lücke, Jörg

2012-01-01

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing. PMID:22457610

Multiple response optimization for higher dimensions in factors and responses

DOE PAGES

Lu, Lu; Chapman, Jessica L.; Anderson-Cook, Christine M.

2016-07-19

When optimizing a product or process with multiple responses, a two-stage Pareto front approach is a useful strategy to evaluate and balance trade-offs between different estimated responses to seek optimum input locations for achieving the best outcomes. After objectively eliminating non-contenders in the first stage by looking for a Pareto front of superior solutions, graphical tools can be used to identify a final solution in the second subjective stage to compare options and match with user priorities. Until now, there have been limitations on the number of response variables and input factors that could effectively be visualized with existing graphicalmore » summaries. We present novel graphical tools that can be more easily scaled to higher dimensions, in both the input and response spaces, to facilitate informed decision making when simultaneously optimizing multiple responses. A key aspect of these graphics is that the potential solutions can be flexibly sorted to investigate specific queries, and that multiple aspects of the solutions can be simultaneously considered. As a result, recommendations are made about how to evaluate the impact of the uncertainty associated with the estimated response surfaces on decision making with higher dimensions.« less

Adjoint-Baed Optimal Control on the Pitch Angle of a Single-Bladed Vertical-Axis Wind Turbine

NASA Astrophysics Data System (ADS)

Tsai, Hsieh-Chen; Colonius, Tim

2017-11-01

Optimal control on the pitch angle of a NACA0018 single-bladed vertical-axis wind turbine (VAWT) is numerically investigated at a low Reynolds number of 1500. With fixed tip-speed ratio, the input power is minimized and mean tangential force is maximized over a specific time horizon. The immersed boundary method is used to simulate the two-dimensional, incompressible flow around a horizontal cross section of the VAWT. The problem is formulated as a PDE constrained optimization problem and an iterative solution is obtained using adjoint-based conjugate gradient methods. By the end of the longest control horizon examined, two controls end up with time-invariant pitch angles of about the same magnitude but with the opposite signs. The results show that both cases lead to a reduction in the input power but not necessarily an enhancement in the mean tangential force. These reductions in input power are due to the removal of a power-damaging phenomenon that occurs when a vortex pair is captured by the blade in the upwind-half region of a cycle. This project was supported by Caltech FLOWE center/Gordon and Betty Moore Foundation.

Example-based human motion denoising.

PubMed

Lou, Hui; Chai, Jinxiang

2010-01-01

With the proliferation of motion capture data, interest in removing noise and outliers from motion capture data has increased. In this paper, we introduce an efficient human motion denoising technique for the simultaneous removal of noise and outliers from input human motion data. The key idea of our approach is to learn a series of filter bases from precaptured motion data and use them along with robust statistics techniques to filter noisy motion data. Mathematically, we formulate the motion denoising process in a nonlinear optimization framework. The objective function measures the distance between the noisy input and the filtered motion in addition to how well the filtered motion preserves spatial-temporal patterns embedded in captured human motion data. Optimizing the objective function produces an optimal filtered motion that keeps spatial-temporal patterns in captured motion data. We also extend the algorithm to fill in the missing values in input motion data. We demonstrate the effectiveness of our system by experimenting with both real and simulated motion data. We also show the superior performance of our algorithm by comparing it with three baseline algorithms and to those in state-of-art motion capture data processing software such as Vicon Blade.

Optimal intravenous infusion to decrease the haematocrit level in patient of DHF infection

NASA Astrophysics Data System (ADS)

Handayani, D.; Nuraini, N.; Saragih, R.; Wijaya, K. P.; Naiborhu, J.

2014-02-01

The optimal control of infusion model for Dengue Hemorrhagic Fever (DHF) infection is formulated here. The infusion model will be presented in form of haematocrit level. The input control aim to normalize the haematocrit level and is expressed as infusion volume on mL/day. The stability near the equilibrium points will be analyzed. Numerical simulation shows the dynamic of each infection compartments which gives a description of within-host dynamic of dengue virus. These results show particularly that infected compartments tend to be vanished in ±15days after the onset of the virus. In fact, without any control added, the haematocrit level will decrease but not up to the normal level. Therefore the effective haematocrit normalization should be done with the treatment control. Control treatment for a fixed time using a control input can bring haematocrit level to normal range 42-47%. The optimal control in this paper is divided into three cases, i.e. fixed end point, constrained input, and tracking haematocrit state. Each case shows different infection condition in human body. However, all cases require that the haematocrit level to be in normal range in fixed final time.

Feedforward inhibition and synaptic scaling--two sides of the same coin?

PubMed

Keck, Christian; Savin, Cristina; Lücke, Jörg

2012-01-01

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

RBT-GA: a novel metaheuristic for solving the multiple sequence alignment problem

PubMed Central

Taheri, Javid; Zomaya, Albert Y

2009-01-01

Background Multiple Sequence Alignment (MSA) has always been an active area of research in Bioinformatics. MSA is mainly focused on discovering biologically meaningful relationships among different sequences or proteins in order to investigate the underlying main characteristics/functions. This information is also used to generate phylogenetic trees. Results This paper presents a novel approach, namely RBT-GA, to solve the MSA problem using a hybrid solution methodology combining the Rubber Band Technique (RBT) and the Genetic Algorithm (GA) metaheuristic. RBT is inspired by the behavior of an elastic Rubber Band (RB) on a plate with several poles, which is analogues to locations in the input sequences that could potentially be biologically related. A GA attempts to mimic the evolutionary processes of life in order to locate optimal solutions in an often very complex landscape. RBT-GA is a population based optimization algorithm designed to find the optimal alignment for a set of input protein sequences. In this novel technique, each alignment answer is modeled as a chromosome consisting of several poles in the RBT framework. These poles resemble locations in the input sequences that are most likely to be correlated and/or biologically related. A GA-based optimization process improves these chromosomes gradually yielding a set of mostly optimal answers for the MSA problem. Conclusion RBT-GA is tested with one of the well-known benchmarks suites (BALiBASE 2.0) in this area. The obtained results show that the superiority of the proposed technique even in the case of formidable sequences. PMID:19594869

Automation of POST Cases via External Optimizer and "Artificial p2" Calculation

NASA Technical Reports Server (NTRS)

Dees, Patrick D.; Zwack, Mathew R.

2017-01-01

During early conceptual design of complex systems, speed and accuracy are often at odds with one another. While many characteristics of the design are fluctuating rapidly during this phase there is nonetheless a need to acquire accurate data from which to down-select designs as these decisions will have a large impact upon program life-cycle cost. Therefore enabling the conceptual designer to produce accurate data in a timely manner is tantamount to program viability. For conceptual design of launch vehicles, trajectory analysis and optimization is a large hurdle. Tools such as the industry standard Program to Optimize Simulated Trajectories (POST) have traditionally required an expert in the loop for setting up inputs, running the program, and analyzing the output. The solution space for trajectory analysis is in general non-linear and multi-modal requiring an experienced analyst to weed out sub-optimal designs in pursuit of the global optimum. While an experienced analyst presented with a vehicle similar to one which they have already worked on can likely produce optimal performance figures in a timely manner, as soon as the "experienced" or "similar" adjectives are invalid the process can become lengthy. In addition, an experienced analyst working on a similar vehicle may go into the analysis with preconceived ideas about what the vehicle's trajectory should look like which can result in sub-optimal performance being recorded. Thus, in any case but the ideal either time or accuracy can be sacrificed. In the authors' previous work a tool called multiPOST was created which captures the heuristics of a human analyst over the process of executing trajectory analysis with POST. However without the instincts of a human in the loop, this method relied upon Monte Carlo simulation to find successful trajectories. Overall the method has mixed results, and in the context of optimizing multiple vehicles it is inefficient in comparison to the method presented POST's internal optimizer functions like any other gradient-based optimizer. It has a specified variable to optimize whose value is represented as optval, a set of dependent constraints to meet with associated forms and tolerances whose value is represented as p2, and a set of independent variables known as the u-vector to modify in pursuit of optimality. Each of these quantities are calculated or manipulated at a certain phase within the trajectory. The optimizer is further constrained by the requirement that the input u-vector must result in a trajectory which proceeds through each of the prescribed events in the input file. For example, if the input u-vector causes the vehicle to crash before it can achieve the orbital parameters required for a parking orbit, then the run will fail without engaging the optimizer, and a p2 value of exactly zero is returned. This poses a problem, as this "non-connecting" region of the u-vector space is far larger than the "connecting" region which returns a non-zero value of p2 and can be worked on by the internal optimizer. Finding this connecting region and more specifically the global optimum within this region has traditionally required the use of an expert analyst.

Model's sparse representation based on reduced mixed GMsFE basis methods

NASA Astrophysics Data System (ADS)

Jiang, Lijian; Li, Qiuqi

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random porous media is simulated by the proposed sparse representation method.

Model's sparse representation based on reduced mixed GMsFE basis methods

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

Jiang, Lijian, E-mail: ljjiang@hnu.edu.cn; Li, Qiuqi, E-mail: qiuqili@hnu.edu.cn

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a largemore » number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random porous media is simulated by the proposed sparse representation method.« less

Robust fuel- and time-optimal control of uncertain flexible space structures

NASA Technical Reports Server (NTRS)

Wie, Bong; Sinha, Ravi; Sunkel, John; Cox, Ken

1993-01-01

The problem of computing open-loop, fuel- and time-optimal control inputs for flexible space structures in the face of modeling uncertainty is investigated. Robustified, fuel- and time-optimal pulse sequences are obtained by solving a constrained optimization problem subject to robustness constraints. It is shown that 'bang-off-bang' pulse sequences with a finite number of switchings provide a practical tradeoff among the maneuvering time, fuel consumption, and performance robustness of uncertain flexible space structures.

Active control of combustion instabilities

NASA Astrophysics Data System (ADS)

Al-Masoud, Nidal A.

A theoretical analysis of active control of combustion thermo-acoustic instabilities is developed in this dissertation. The theoretical combustion model is based on the dynamics of a two-phase flow in a liquid-fueled propulsion system. The formulation is based on a generalized wave equation with pressure as the dependent variable, and accommodates all influences of combustion, mean flow, unsteady motions and control inputs. The governing partial differential equations are converted to an equivalent set of ordinary differential equations using Galerkin's method by expressing the unsteady pressure and velocity fields as functions of normal mode shapes of the chamber. This procedure yields a representation of the unsteady flow field as a system of coupled nonlinear oscillators that is used as a basis for controllers design. Major research attention is focused on the control of longitudinal oscillations with both linear and nonlinear processes being considered. Starting with a linear model using point actuators, the optimal locations of actuators and sensors are developed. The approach relies on the quantitative measures of the degree of controllability and component cost. These criterion are arrived at by considering the energies of the system's inputs and outputs. The optimality criteria for sensor and actuator locations provide a balance between the importance of the lower order (controlled) and the higher (residual) order modes. To address the issue of uncertainties in system's parameter, the minimax principles based controller is used. The minimax corresponds to finding the best controller for the worst parameter deviation. In other words, choosing controller parameters to minimize, and parameter deviation to maximize some quadratic performance metric. Using the minimax-based controller, a remarkable improvement in the control system's ability to handle parameter uncertainties is achieved when compared to the robustness of the regular control schemes such as LQR and LQG. Since the observed instabilities are harmonic, the concept of "harmonic input" is successfully implemented using a parametric controller to eliminate the thermo-acoustic instability. This control scheme relies on the determination of a phase-shift to maximize the energy dissipation and a controller gain to assure stability and minimize a pre-specified performance index. The closed loop control law design is based on finding an optimal phase angle such that the heat release produced by secondary oscillatory fuel injection is out of phase with the mode's pressure oscillations, thus maximizing energy dissipation, and on finding the limits on the controller gain that ensures system stability. The optimal gains are determined using ITA, ISE, ITAE performance indices. Simulations show successful implementation of the proposed technique.

User's manual for the BNW-II optimization code for dry/wet-cooled power plants

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

Braun, D.J.; Bamberger, J.A.; Braun, D.J.

1978-05-01

This volume provides a listing of the BNW-II dry/wet ammonia heat rejection optimization code and is an appendix to Volume I which gives a narrative description of the code's algorithms as well as logic, input and output information.

Optimal spacecraft formation establishment and reconfiguration propelled by the geomagnetic Lorentz force

NASA Astrophysics Data System (ADS)

Huang, Xu; Yan, Ye; Zhou, Yang

2014-12-01

The Lorentz force acting on an electrostatically charged spacecraft as it moves through the planetary magnetic field could be utilized as propellantless electromagnetic propulsion for orbital maneuvering, such as spacecraft formation establishment and formation reconfiguration. By assuming that the Earth's magnetic field could be modeled as a tilted dipole located at the center of Earth that corotates with Earth, a dynamical model that describes the relative orbital motion of Lorentz spacecraft is developed. Based on the proposed dynamical model, the energy-optimal open-loop trajectories of control inputs, namely, the required specific charges of Lorentz spacecraft, for Lorentz-propelled spacecraft formation establishment or reconfiguration problems with both fixed and free final conditions constraints are derived via Gauss pseudospectral method. The effect of the magnetic dipole tilt angle on the optimal control inputs and the relative transfer trajectories for formation establishment or reconfiguration is also investigated by comparisons with the results derived from a nontilted dipole model. Furthermore, a closed-loop integral sliding mode controller is designed to guarantee the trajectory tracking in the presence of external disturbances and modeling errors. The stability of the closed-loop system is proved by a Lyapunov-based approach. Numerical simulations are presented to verify the validity of the proposed open-loop control methods and demonstrate the performance of the closed-loop controller. Also, the results indicate the dipole tilt angle should be considered when designing control strategies for Lorentz-propelled spacecraft formation establishment or reconfiguration.

Feasibility of Stochastic Voltage/VAr Optimization Considering Renewable Energy Resources for Smart Grid

NASA Astrophysics Data System (ADS)

Momoh, James A.; Salkuti, Surender Reddy

2016-06-01

This paper proposes a stochastic optimization technique for solving the Voltage/VAr control problem including the load demand and Renewable Energy Resources (RERs) variation. The RERs often take along some inputs like stochastic behavior. One of the important challenges i. e., Voltage/VAr control is a prime source for handling power system complexity and reliability, hence it is the fundamental requirement for all the utility companies. There is a need for the robust and efficient Voltage/VAr optimization technique to meet the peak demand and reduction of system losses. The voltages beyond the limit may damage costly sub-station devices and equipments at consumer end as well. Especially, the RERs introduces more disturbances and some of the RERs are not even capable enough to meet the VAr demand. Therefore, there is a strong need for the Voltage/VAr control in RERs environment. This paper aims at the development of optimal scheme for Voltage/VAr control involving RERs. In this paper, Latin Hypercube Sampling (LHS) method is used to cover full range of variables by maximally satisfying the marginal distribution. Here, backward scenario reduction technique is used to reduce the number of scenarios effectively and maximally retain the fitting accuracy of samples. The developed optimization scheme is tested on IEEE 24 bus Reliability Test System (RTS) considering the load demand and RERs variation.

Three-input majority function as the unique optimal function for the bias amplification using nonlocal boxes

NASA Astrophysics Data System (ADS)

Mori, Ryuhei

2016-11-01

Brassard et al. [Phys. Rev. Lett. 96, 250401 (2006), 10.1103/PhysRevLett.96.250401] showed that shared nonlocal boxes with a CHSH (Clauser, Horne, Shimony, and Holt) probability greater than 3/+√{6 } 6 yield trivial communication complexity. There still exists a gap with the maximum CHSH probability 2/+√{2 } 4 achievable by quantum mechanics. It is an interesting open question to determine the exact threshold for the trivial communication complexity. Brassard et al.'s idea is based on recursive bias amplification by the three-input majority function. It was not obvious if another choice of function exhibits stronger bias amplification. We show that the three-input majority function is the unique optimal function, so that one cannot improve the threshold 3/+√{6 } 6 by Brassard et al.'s bias amplification. In this work, protocols for computing the function used for the bias amplification are restricted to be nonadaptive protocols or a particular adaptive protocol inspired by Pawłowski et al.'s protocol for information causality [Nature (London) 461, 1101 (2009), 10.1038/nature08400]. We first show an adaptive protocol inspired by Pawłowski et al.'s protocol, and then show that the adaptive protocol improves upon nonadaptive protocols. Finally, we show that the three-input majority function is the unique optimal function for the bias amplification if we apply the adaptive protocol to each step of the bias amplification.

Assessment of economically optimal water management and geospatial potential for large-scale water storage

NASA Astrophysics Data System (ADS)

Weerasinghe, Harshi; Schneider, Uwe A.

2010-05-01

Assessment of economically optimal water management and geospatial potential for large-scale water storage Weerasinghe, Harshi; Schneider, Uwe A Water is an essential but limited and vulnerable resource for all socio-economic development and for maintaining healthy ecosystems. Water scarcity accelerated due to population expansion, improved living standards, and rapid growth in economic activities, has profound environmental and social implications. These include severe environmental degradation, declining groundwater levels, and increasing problems of water conflicts. Water scarcity is predicted to be one of the key factors limiting development in the 21st century. Climate scientists have projected spatial and temporal changes in precipitation and changes in the probability of intense floods and droughts in the future. As scarcity of accessible and usable water increases, demand for efficient water management and adaptation strategies increases as well. Addressing water scarcity requires an intersectoral and multidisciplinary approach in managing water resources. This would in return safeguard the social welfare and the economical benefit to be at their optimal balance without compromising the sustainability of ecosystems. This paper presents a geographically explicit method to assess the potential for water storage with reservoirs and a dynamic model that identifies the dimensions and material requirements under an economically optimal water management plan. The methodology is applied to the Elbe and Nile river basins. Input data for geospatial analysis at watershed level are taken from global data repositories and include data on elevation, rainfall, soil texture, soil depth, drainage, land use and land cover; which are then downscaled to 1km spatial resolution. Runoff potential for different combinations of land use and hydraulic soil groups and for mean annual precipitation levels are derived by the SCS-CN method. Using the overlay and decision tree algorithms in GIS, potential water storage sites are identified for constructing regional reservoirs. Subsequently, sites are prioritized based on runoff generation potential (m3 per unit area), and geographical suitability for constructing storage structures. The results from the spatial analysis are used as input for the optimization model. Allocation of resources and appropriate dimension for dams and associated structures are identified using the optimization model. The model evaluates the capability of alternative reservoirs for cost-efficient water management. The Geographic Information System is used to store, analyze, and integrate spatially explicit and non-spatial attribute information whereas the algebraic modeling platform is used to develop the dynamic optimization model. The results of this methodology are validated over space against satellite remote sensing data and existing data on reservoir capacities and runoff. The method is suitable for application of on-farm water storage structures, water distribution networks, and moisture conservation structures in a global context.

Optimization of a GO2/GH2 Swirl Coaxial Injector Element

NASA Technical Reports Server (NTRS)

Tucker, P. Kevin; Shyy, Wei; Vaidyanathan, Rajkumar

1999-01-01

An injector optimization methodology, method i, is used to investigate optimal design points for a gaseous oxygen/gaseous hydrogen (GO2/GH2) swirl coaxial injector element. The element is optimized in terms of design variables such as fuel pressure drop, DELTA P(sub f), oxidizer pressure drop, DELTA P(sub 0) combustor length, L(sub comb), and full cone swirl angle, theta, for a given mixture ratio and chamber pressure. Dependent variables such as energy release efficiency, ERE, wall heat flux, Q(sub w) injector heat flux, Q(sub inj), relative combustor weight, W(sub rel), and relative injector cost, C(sub rel), are calculated and then correlated with the design variables. An empirical design methodology is used to generate these responses for 180 combinations of input variables. Method i is then used to generate response surfaces for each dependent variable. Desirability functions based on dependent variable constraints are created and used to facilitate development of composite response surfaces representing some, or all, of the five dependent variables in terms of the input variables. Two examples illustrating the utility and flexibility of method i are discussed in detail. First, joint response surfaces are constructed by sequentially adding dependent variables. Optimum designs are identified after addition of each variable and the effect each variable has on the design is shown. This stepwise demonstration also highlights the importance of including variables such as weight and cost early in the design process. Secondly, using the composite response surface that includes all five dependent variables, unequal weights are assigned to emphasize certain variables relative to others. Here, method i is used to enable objective trade studies on design issues such as component life and thrust to weight ratio.

Strategies of experiment standardization and response optimization in a rat model of hemorrhagic shock and chronic hypertension.

PubMed

Reynolds, Penny S; Tamariz, Francisco J; Barbee, Robert Wayne

2010-04-01

Exploratory pilot studies are crucial to best practice in research but are frequently conducted without a systematic method for maximizing the amount and quality of information obtained. We describe the use of response surface regression models and simultaneous optimization methods to develop a rat model of hemorrhagic shock in the context of chronic hypertension, a clinically relevant comorbidity. Response surface regression model was applied to determine optimal levels of two inputs--dietary NaCl concentration (0.49%, 4%, and 8%) and time on the diet (4, 6, 8 weeks)--to achieve clinically realistic and stable target measures of systolic blood pressure while simultaneously maximizing critical oxygen delivery (a measure of vulnerability to hemorrhagic shock) and body mass M. Simultaneous optimization of the three response variables was performed though a dimensionality reduction strategy involving calculation of a single aggregate measure, the "desirability" function. Optimal conditions for inducing systolic blood pressure of 208 mmHg, critical oxygen delivery of 4.03 mL/min, and M of 290 g were determined to be 4% [NaCl] for 5 weeks. Rats on the 8% diet did not survive past 7 weeks. Response surface regression model and simultaneous optimization method techniques are commonly used in process engineering but have found little application to date in animal pilot studies. These methods will ensure both the scientific and ethical integrity of experimental trials involving animals and provide powerful tools for the development of novel models of clinically interacting comorbidities with shock.

Flight instrument and telemetry response and its inversion

NASA Technical Reports Server (NTRS)

Weinberger, M. R.

1971-01-01

Mathematical models of rate gyros, servo accelerometers, pressure transducers, and telemetry systems were derived and their parameters were obtained from laboratory tests. Analog computer simulations were used extensively for verification of the validity for fast and large input signals. An optimal inversion method was derived to reconstruct input signals from noisy output signals and a computer program was prepared.

Measuring circuit

DOEpatents

Sun, Shan C.; Chaprnka, Anthony G.

1977-01-11

An automatic gain control circuit functions to adjust the magnitude of an input signal supplied to a measuring circuit to a level within the dynamic range of the measuring circuit while a log-ratio circuit adjusts the magnitude of the output signal from the measuring circuit to the level of the input signal and optimizes the signal-to-noise ratio performance of the measuring circuit.

Statistical linearization for multi-input/multi-output nonlinearities

NASA Technical Reports Server (NTRS)

Lin, Ching-An; Cheng, Victor H. L.

1991-01-01

Formulas are derived for the computation of the random input-describing functions for MIMO nonlinearities; these straightforward and rigorous derivations are based on the optimal mean square linear approximation. The computations involve evaluations of multiple integrals. It is shown that, for certain classes of nonlinearities, multiple-integral evaluations are obviated and the computations are significantly simplified.

TRANDESNF: A computer program for transonic airfoil design and analysis in nonuniform flow

NASA Technical Reports Server (NTRS)

Chang, J. F.; Lan, C. Edward

1987-01-01

The use of a transonic airfoil code for analysis, inverse design, and direct optimization of an airfoil immersed in propfan slipstream is described. A summary of the theoretical method, program capabilities, input format, output variables, and program execution are described. Input data of sample test cases and the corresponding output are given.

Regional distribution of nitrogen fertilizer use and N-saving potential for improvement of food production and nitrogen use efficiency in China.

PubMed

Wang, Xiaobin; Cai, Dianxiong; Hoogmoed, Willem B; Oenema, Oene

2011-08-30

An apparently large disparity still exists between developed and developing countries in historical trends of the amounts of nitrogen (N) fertilizers consumed, and the same situation holds true in China. The situation of either N overuse or underuse has become one of the major limiting factors in agricultural production and economic development in China. The issue of food security in N-poor regions has been given the greatest attention internationally. Balanced and appropriate use of N fertilizer for enriching soil fertility is an effective step in preventing soil degradation, ensuring food security, and further contributing to poverty alleviation and rural economic development in the N-poor regions. Based on the China Statistical Yearbook (2007), there could be scope for improvement of N use efficiency (NUE) in N-rich regions by reducing N fertilizer input to an optimal level (≤180 kg N ha(-1)), and also potential for increasing yield in the N-poor regions by further increasing N fertilizer supply (up to 116 kg N ha(-1)). For the N-rich regions, the average estimated potential of N saving and NUE increase could be about 15% and 23%, respectively, while for the N-poor regions the average estimated potential for yield increase could be 21% on a regional scale, when N input is increased by 13%. The study suggests that to achieve the goals of regional yield improvement, it is necessary to readjust and optimize regional distribution of N fertilizer use between the N-poor and N-rich regions in China, in combination with other nutrient management practices. Copyright © 2011 Society of Chemical Industry.

Data-Driven Modeling and Rendering of Force Responses from Elastic Tool Deformation

PubMed Central

Rakhmatov, Ruslan; Ogay, Tatyana; Jeon, Seokhee

2018-01-01

This article presents a new data-driven model design for rendering force responses from elastic tool deformation. The new design incorporates a six-dimensional input describing the initial position of the contact, as well as the state of the tool deformation. The input-output relationship of the model was represented by a radial basis functions network, which was optimized based on training data collected from real tool-surface contact. Since the input space of the model is represented in the local coordinate system of a tool, the model is independent of recording and rendering devices and can be easily deployed to an existing simulator. The model also supports complex interactions, such as self and multi-contact collisions. In order to assess the proposed data-driven model, we built a custom data acquisition setup and developed a proof-of-concept rendering simulator. The simulator was evaluated through numerical and psychophysical experiments with four different real tools. The numerical evaluation demonstrated the perceptual soundness of the proposed model, meanwhile the user study revealed the force feedback of the proposed simulator to be realistic. PMID:29342964

Minimization of the hole overcut and cylindricity errors during rotary ultrasonic drilling of Ti-6Al-4V

NASA Astrophysics Data System (ADS)

Nasr, M.; Anwar, S.; El-Tamimi, A.; Pervaiz, S.

2018-04-01

Titanium and its alloys e.g. Ti6Al4V have widespread applications in aerospace, automotive and medical industry. At the same time titanium and its alloys are regarded as difficult to machine materials due to their high strength and low thermal conductivity. Significant efforts have been dispensed to improve the accuracy of the machining processes for Ti6Al4V. The current study present the use of the rotary ultrasonic drilling (RUD) process for machining high quality holes in Ti6Al4V. The study takes into account the effects of the main RUD input parameters including spindle speed, ultrasonic power, feed rate and tool diameter on the key output responses related to the accuracy of the drilled holes including cylindricity and overcut errors. Analysis of variance (ANOVA) was employed to study the influence of the input parameters on cylindricity and overcut error. Later, regression models were developed to find the optimal set of input parameters to minimize the cylindricity and overcut errors.

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

Na, Man Gyun; Oh, Seungrohk

A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce themore » time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.« less

Can Structural Optimization Explain Slow Dynamics of Rocks?

NASA Astrophysics Data System (ADS)

Kim, H.; Vistisen, O.; Tencate, J. A.

2009-12-01

Slow dynamics is a recovery process that describes the return to an equilibrium state after some external energy input is applied and then removed. Experimental studies on many rocks have shown that a modest acoustic energy input results in slow dynamics. The recovery process of the stiffness has consistently been found to be linear to log(time) for a wide range of geomaterials and the time constants appear to be unique to the material [TenCate JA, Shankland TJ (1996), Geophys Res Lett 23, 3019-3022]. Measurements of this nonequilibrium effect in rocks (e.g. sandstones and limestones) have been linked directly to the cement holding the individual grains together [Darling TW, TenCate JA, Brown DW, Clausen B, Vogel SC (2004), Geophys Res Lett 31, L16604], also suggesting a potential link to porosity and permeability. Noting that slow dynamics consistently returns the overall stiffness of rocks to its maximum (original) state, it is hypothesized that the original state represents the global minimum strain energy state. Consequently the slow dynamics process represents the global minimization or optimization process. Structural optimization, which has been developed for engineering design, minimises the total strain energy by rearranging the material distribution [Kim H, Querin OM, Steven GP, Xie YM (2002), Struct Multidiscip Optim 24, 441-448]. The optimization process effectively rearranges the way the material is cemented. One of the established global optimization methods is simulated annealing (SA). Derived from cooling of metal to a thermal equilibrium, SA finds an optimum solution by iteratively moving the system towards the minimum energy state with a probability of 'uphill' moves. It has been established that the global optimum can be guaranteed by applying a log(time) linear cooling schedule [Hajek B (1988, Math Ops Res, 15, 311-329]. This work presents the original study of applying SA to the maximum stiffness optimization problem. Preliminary results indicate that the maximum stiffness solutions are achieved when using log(time) linear cooling schedule. The optimization history reveals that the overall stiffness of the structure is increased linearly to log(time). The results closely resemble the slow dynamics stiffness recovery of geomaterials and support the hypothesis that the slow dynamics is an optimization process for strain energy. [Work supported by the Department of Energy through the LANL/LDRD Program].

CBM First-level Event Selector Input Interface Demonstrator

NASA Astrophysics Data System (ADS)

Hutter, Dirk; de Cuveland, Jan; Lindenstruth, Volker

2017-10-01

CBM is a heavy-ion experiment at the future FAIR facility in Darmstadt, Germany. Featuring self-triggered front-end electronics and free-streaming read-out, event selection will exclusively be done by the First Level Event Selector (FLES). Designed as an HPC cluster with several hundred nodes its task is an online analysis and selection of the physics data at a total input data rate exceeding 1 TByte/s. To allow efficient event selection, the FLES performs timeslice building, which combines the data from all given input links to self-contained, potentially overlapping processing intervals and distributes them to compute nodes. Partitioning the input data streams into specialized containers allows performing this task very efficiently. The FLES Input Interface defines the linkage between the FEE and the FLES data transport framework. A custom FPGA PCIe board, the FLES Interface Board (FLIB), is used to receive data via optical links and transfer them via DMA to the host’s memory. The current prototype of the FLIB features a Kintex-7 FPGA and provides up to eight 10 GBit/s optical links. A custom FPGA design has been developed for this board. DMA transfers and data structures are optimized for subsequent timeslice building. Index tables generated by the FPGA enable fast random access to the written data containers. In addition the DMA target buffers can directly serve as InfiniBand RDMA source buffers without copying the data. The usage of POSIX shared memory for these buffers allows data access from multiple processes. An accompanying HDL module has been developed to integrate the FLES link into the front-end FPGA designs. It implements the front-end logic interface as well as the link protocol. Prototypes of all Input Interface components have been implemented and integrated into the FLES test framework. This allows the implementation and evaluation of the foreseen CBM read-out chain.